diff --git a/store/neurostore/data/ace/sample_coordinates.csv b/store/neurostore/data/ace/sample_coordinates.csv
index f021518ec..54b3fa658 100644
--- a/store/neurostore/data/ace/sample_coordinates.csv
+++ b/store/neurostore/data/ace/sample_coordinates.csv
@@ -1,133 +1,124 @@
-groups,p_value,y,region,table_number,table_id,activation_id,table_caption,statistic,table_label,x,pmid,z,size
-,,16.0,,2,5576,83479,. Comparison of measurement points with activation,,Table 2,15.0,10585521,19.0,
-,,16.0,,2,5576,83480,. Comparison of measurement points with activation,,Table 2,15.0,10585521,19.0,
-,,19.0,,2,5576,83481,. Comparison of measurement points with activation,,Table 2,15.0,10585521,20.0,
-,,-28.0,Superior temporal gyrus or Wernicke's area,1,7680,110059,. Main brain regions differentially activated by theory of mind task between control (C) and autism (A) groups. BA=Brodmann area,,Table 1,-55.0,10781695,15.0,12
-,,-28.0,Superior temporal gyrus,1,7680,110060,. Main brain regions differentially activated by theory of mind task between control (C) and autism (A) groups. BA=Brodmann area,,Table 1,40.0,10781695,15.0,8
-,,22.0,Inferior frontal gyrus or Broca's area,1,7680,110061,. Main brain regions differentially activated by theory of mind task between control (C) and autism (A) groups. BA=Brodmann area,,Table 1,-46.0,10781695,9.0,5
-fMRI experiment 1: self versus novel,,-3.0,-,2,5575,83461,. Areas of significant activation during recognition of own (minus unknown) and partner's (minus unknown) face,,Table 2,49.0,11062324,4.0,50
-fMRI experiment 1: self versus novel,,-3.0,-,2,5575,83462,. Areas of significant activation during recognition of own (minus unknown) and partner's (minus unknown) face,,Table 2,46.0,11062324,-2.0,23
-fMRI experiment 1: self versus novel,,-45.0,27/30,2,5575,83463,. Areas of significant activation during recognition of own (minus unknown) and partner's (minus unknown) face,,Table 2,11.0,11062324,4.0,7
-fMRI experiment 1: self versus novel,,-36.0,,2,5575,83464,. Areas of significant activation during recognition of own (minus unknown) and partner's (minus unknown) face,,Table 2,12.0,11062324,-2.0,6
-fMRI experiment 1: self versus novel,,36.0,24/32,2,5575,83465,. Areas of significant activation during recognition of own (minus unknown) and partner's (minus unknown) face,,Table 2,3.0,11062324,4.0,12
-fMRI experiment 1: self versus novel,,42.0,,2,5575,83466,. Areas of significant activation during recognition of own (minus unknown) and partner's (minus unknown) face,,Table 2,6.0,11062324,-2.0,8
-fMRI experiment 1: self versus novel,,6.0,,2,5575,83467,. Areas of significant activation during recognition of own (minus unknown) and partner's (minus unknown) face,,Table 2,0.0,11062324,37.0,14
-fMRI experiment 1: self versus novel,,-64.0,31,2,5575,83468,. Areas of significant activation during recognition of own (minus unknown) and partner's (minus unknown) face,,Table 2,6.0,11062324,20.0,9
-fMRI experiment 1: self versus novel,,-61.0,,2,5575,83469,. Areas of significant activation during recognition of own (minus unknown) and partner's (minus unknown) face,,Table 2,9.0,11062324,26.0,8
-fMRI experiment 1: self versus novel,,31.0,45/46,2,5575,83470,. Areas of significant activation during recognition of own (minus unknown) and partner's (minus unknown) face,,Table 2,-38.0,11062324,4.0,11
-fMRI experiment 1: self versus novel,,31.0,,2,5575,83471,. Areas of significant activation during recognition of own (minus unknown) and partner's (minus unknown) face,,Table 2,-32.0,11062324,20.0,18
-fMRI experiment 1: self versus novel,,31.0,8/9,2,5575,83472,. Areas of significant activation during recognition of own (minus unknown) and partner's (minus unknown) face,,Table 2,-26.0,11062324,37.0,9
-fMRI experiment 1: self versus novel,,-14.0,42,2,5575,83473,. Areas of significant activation during recognition of own (minus unknown) and partner's (minus unknown) face,,Table 2,-43.0,11062324,9.0,16
-fMRI experiment 1: self versus novel,,-42.0,40,2,5575,83474,. Areas of significant activation during recognition of own (minus unknown) and partner's (minus unknown) face,,Table 2,-49.0,11062324,31.0,11
-fMRI experiment 1: self versus novel,,-11.0,-,2,5575,83475,. Areas of significant activation during recognition of own (minus unknown) and partner's (minus unknown) face,,Table 2,12.0,11062324,-2.0,23
-fMRI experiment 1: self versus novel,,-47.0,-,2,5575,83476,. Areas of significant activation during recognition of own (minus unknown) and partner's (minus unknown) face,,Table 2,9.0,11062324,-18.0,18
-fMRI experiment 1: self versus novel,,-83.0,,2,5575,83477,. Areas of significant activation during recognition of own (minus unknown) and partner's (minus unknown) face,,Table 2,-20.0,11062324,-13.0,34
-fMRI experiment 2: partner versus novel,,14.0,-,2,5575,83478,. Areas of significant activation during recognition of own (minus unknown) and partner's (minus unknown) face,,Table 2,26.0,11062324,15.0,5
-,,10.0,Authors,I,682,10051,. Normal subjects (Nociception - Capsaicin allodynia).,,Table I,9.0,11126640,32.0,
-,,10.0,Disease / Authors,III,683,10052,. Patients with chronic pain.,,Table III,9.0,11126640,32.0,
-,,10.0,Disease / Authors,IV,684,10053,. Analgesic procedures studied with PET.,,Table IV,9.0,11126640,32.0,
-,,11.0,,1,681,10050,. Observations,,Tableau 1,5.0,12909390,13.0,
-Main effect of reasoning,,-93.0,,1,5574,83422,.,3.95,Table 1,12.0,15178381,-9.0,
-Main effect of reasoning,,-90.0,,1,5574,83423,.,3.52,Table 1,-6.0,15178381,-9.0,
-Main effect of reasoning,,-81.0,,1,5574,83424,.,4.72,Table 1,33.0,15178381,12.0,
-Main effect of reasoning,,-93.0,,1,5574,83425,.,4.57,Table 1,36.0,15178381,-3.0,
-Main effect of reasoning,,-66.0,,1,5574,83426,.,4.43,Table 1,-48.0,15178381,-12.0,
-Main effect of reasoning,,-57.0,,1,5574,83427,.,4.48,Table 1,-24.0,15178381,60.0,
-Main effect of reasoning,,-60.0,,1,5574,83428,.,4.45,Table 1,21.0,15178381,60.0,
-Main effect of reasoning,,-36.0,,1,5574,83429,.,4.20,Table 1,-45.0,15178381,54.0,
-Main effect of reasoning,,0.0,,1,5574,83430,.,5.36,Table 1,36.0,15178381,60.0,
-Main effect of reasoning,,6.0,,1,5574,83431,.,3.59,Table 1,9.0,15178381,-12.0,
-Main effect of reasoning,,18.0,,1,5574,83432,.,3.70,Table 1,-15.0,15178381,-9.0,
-Main effect of reasoning,,15.0,,1,5574,83433,.,3.79,Table 1,0.0,15178381,66.0,
-Main effect of reasoning,,24.0,,1,5574,83434,.,4.35,Table 1,-54.0,15178381,12.0,
-Deductive reasoning−baseline,,-54.0,,1,5574,83435,.,4.43,Table 1,-45.0,15178381,-36.0,
-Deductive reasoning−baseline,,-90.0,,1,5574,83436,.,4.45,Table 1,36.0,15178381,-6.0,
-Deductive reasoning−baseline,,-96.0,,1,5574,83437,.,3.93,Table 1,-33.0,15178381,-3.0,
-Deductive reasoning−baseline,,-69.0,,1,5574,83438,.,4.08,Table 1,-54.0,15178381,-9.0,
-Deductive reasoning−baseline,,-63.0,,1,5574,83439,.,4.22,Table 1,-48.0,15178381,27.0,
-Deductive reasoning−baseline,,-60.0,,1,5574,83440,.,3.57,Table 1,24.0,15178381,57.0,
-Deductive reasoning−baseline,,3.0,,1,5574,83441,.,4.81,Table 1,36.0,15178381,63.0,
-Deductive reasoning−baseline,,12.0,,1,5574,83442,.,"3,64",Table 1,-27.0,15178381,63.0,
-Deductive reasoning−baseline,,6.0,,1,5574,83443,.,3.68,Table 1,-42.0,15178381,45.0,
-Deductive reasoning−baseline,,6.0,,1,5574,83444,.,3.60,Table 1,12.0,15178381,-12.0,
-Deductive reasoning−baseline,,3.0,,1,5574,83445,.,3.46,Table 1,-15.0,15178381,3.0,
-Deductive reasoning−baseline,,21.0,,1,5574,83446,.,4.79,Table 1,-54.0,15178381,6.0,
-Inductive reasoning−baseline,,-84.0,,1,5574,83447,.,4.61,Table 1,27.0,15178381,-18.0,
-Inductive reasoning−baseline,,-90.0,,1,5574,83448,.,4.44,Table 1,9.0,15178381,-9.0,
-Inductive reasoning−baseline,,-87.0,,1,5574,83449,.,3.95,Table 1,30.0,15178381,12.0,
-Inductive reasoning−baseline,,-66.0,,1,5574,83450,.,4.12,Table 1,-27.0,15178381,42.0,
-Inductive reasoning−baseline,,-60.0,,1,5574,83451,.,3.87,Table 1,30.0,15178381,60.0,
-Inductive reasoning−baseline,,-33.0,,1,5574,83452,.,4.37,Table 1,-45.0,15178381,51.0,
-Inductive reasoning−baseline,,0.0,,1,5574,83453,.,4.59,Table 1,33.0,15178381,60.0,
-Inductive reasoning−baseline,,6.0,,1,5574,83454,.,3.77,Table 1,-6.0,15178381,48.0,
-Inductive reasoning−baseline,,6.0,,1,5574,83455,.,3.71,Table 1,-6.0,15178381,-12.0,
-Inductive reasoning−baseline,,24.0,,1,5574,83456,.,4.00,Table 1,-48.0,15178381,27.0,
-Inductive reasoning−baseline,,12.0,,1,5574,83457,.,3.31,Table 1,-42.0,15178381,36.0,
-Interaction (modulating to deduction; masked inclusively by main effect of reasoning),,12.0,,1,5574,83458,.,3.79,Table 1,-54.0,15178381,12.0,
-Interaction (modulating to induction masked inclusively by main effect of reasoning),,-84.0,,1,5574,83459,.,3.58,Table 1,27.0,15178381,12.0,
-Interaction (modulating to induction masked inclusively by main effect of reasoning),,21.0,,1,5574,83460,.,3.08,Table 1,-36.0,15178381,30.0,
-PTSD vs. healthy controls (no trauma),,3.0,,2,7570,108212,. Meta-analysis of studies comparing hippocampal volume (PTSD vs. controls): homogenuos clusters and sociodemographical and clinical moderators,,Table 2,1.0,16730374,15.0,2
-,,10.0,,3,7571,108213,. Metaanalyses volume of other brain structures,,Table 3,0.7,16730374,24.0,3
-Cingulate Gyrus,,29.0,Anterior,2,115,1524,. Interaction Effect of Group by Time in Mean Overall Activity,,Table 2,-12.0,18550030,8.0,439
-Cingulate Gyrus,,26.0,,2,115,1525,. Interaction Effect of Group by Time in Mean Overall Activity,,Table 2,-11.0,18550030,40.0,
-Cingulate Gyrus,,-2.0,Middle,2,115,1526,. Interaction Effect of Group by Time in Mean Overall Activity,,Table 2,-14.0,18550030,24.0,125
-Cingulate Gyrus,,-1.0,,2,115,1527,. Interaction Effect of Group by Time in Mean Overall Activity,,Table 2,-13.0,18550030,28.0,
-Cingulate Gyrus,,-9.0,,2,115,1528,. Interaction Effect of Group by Time in Mean Overall Activity,,Table 2,22.0,18550030,35.0,22
-Cingulate Gyrus,,-13.0,,2,115,1529,. Interaction Effect of Group by Time in Mean Overall Activity,,Table 2,21.0,18550030,45.0,
-Cingulate Gyrus,,-34.0,Posterior,2,115,1530,. Interaction Effect of Group by Time in Mean Overall Activity,,Table 2,17.0,18550030,40.0,33
-Cingulate Gyrus,,26.0,Middle Frontal Gyrus,2,115,1531,. Interaction Effect of Group by Time in Mean Overall Activity,,Table 2,-25.0,18550030,40.0,13
-Cingulate Gyrus,,26.0,,2,115,1532,. Interaction Effect of Group by Time in Mean Overall Activity,,Table 2,-25.0,18550030,45.0,
-Cingulate Gyrus,,30.0,Superior Frontal Gyrus,2,115,1533,. Interaction Effect of Group by Time in Mean Overall Activity,,Table 2,3.0,18550030,45.0,37
-Cingulate Gyrus,,29.0,,2,115,1534,. Interaction Effect of Group by Time in Mean Overall Activity,,Table 2,0.0,18550030,50.0,
-Cingulate Gyrus,,28.0,,2,115,1535,. Interaction Effect of Group by Time in Mean Overall Activity,,Table 2,-11.0,18550030,45.0,46
-Cingulate Gyrus,,28.0,,2,115,1536,. Interaction Effect of Group by Time in Mean Overall Activity,,Table 2,-16.0,18550030,50.0,
-Cingulate Gyrus,,-48.0,Inferior Parietal Cortex,2,115,1537,. Interaction Effect of Group by Time in Mean Overall Activity,,Table 2,29.0,18550030,40.0,116
-Cingulate Gyrus,,-39.0,,2,115,1538,. Interaction Effect of Group by Time in Mean Overall Activity,,Table 2,23.0,18550030,45.0,
-Cingulate Gyrus,,-53.0,Precuneus,2,115,1539,. Interaction Effect of Group by Time in Mean Overall Activity,,Table 2,10.0,18550030,40.0,171
-Cingulate Gyrus,,-36.0,,2,115,1540,. Interaction Effect of Group by Time in Mean Overall Activity,,Table 2,17.0,18550030,50.0,
-,,-59.0,Posterior Cingulate Gyrus,3,116,1541,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,-9.0,18550030,8.0,956
-,,-63.0,,3,116,1542,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,-2.0,18550030,28.0,
-,,-27.0,,3,116,1543,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,-4.0,18550030,24.0,31
-,,29.0,,3,116,1544,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,-5.0,18550030,28.0,
-,,-54.0,Middle Temporal Gyrus,3,116,1545,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,-56.0,18550030,-4.0,30
-,,-55.0,,3,116,1546,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,-56.0,18550030,1.0,
-,,-54.0,,3,116,1547,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,-48.0,18550030,8.0,71
-,,-25.0,Superior Temporal Gyrus,3,116,1548,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,-49.0,18550030,8.0,177
-,,-38.0,,3,116,1549,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,-50.0,18550030,20.0,
-,,-37.0,Inferior Parietal Cortex,3,116,1550,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,-44.0,18550030,24.0,938
-,,-45.0,,3,116,1551,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,-37.0,18550030,50.0,
-,,-55.0,Precuneus,3,116,1552,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,-1.0,18550030,32.0,983
-,,-55.0,,3,116,1553,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,5.0,18550030,45.0,
-,,-84.0,Fusiform Gyrus,3,116,1554,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,-29.0,18550030,-16.0,235
-,,-85.0,,3,116,1555,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,-31.0,18550030,-12.0,
-,,-82.0,,3,116,1556,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,32.0,18550030,-16.0,134
-,,-87.0,,3,116,1557,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,33.0,18550030,-12.0,
-,,-90.0,Inferior Occipital Gyrus,3,116,1558,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,-26.0,18550030,-8.0,98
-,,-89.0,,3,116,1559,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,-36.0,18550030,-1.0,
-,,-92.0,,3,116,1560,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,27.0,18550030,-8.0,139
-,,-89.0,,3,116,1561,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,27.0,18550030,4.0,
-,,-58.0,Lingual Gyrus,3,116,1562,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,-15.0,18550030,1.0,128
-,,-57.0,,3,116,1563,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,-10.0,18550030,4.0,
-,,-57.0,Cerebellum,3,116,1564,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,-33.0,18550030,-37.0,506
-,,-77.0,,3,116,1565,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,-37.0,18550030,-20.0,
-,,-52.0,,3,116,1566,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,34.0,18550030,-36.0,855
-,,-59.0,,3,116,1567,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,35.0,18550030,-16.0,
-,,-91.0,,3,116,1568,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,-8.0,18550030,-28.0,158
-,,-92.0,,3,116,1569,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,-10.0,18550030,-20.0,
-,,-59.0,,3,116,1570,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,12.0,18550030,-16.0,102
-,,-63.0,,3,116,1571,. Interaction Effect of Group by Time in Linear Load-Response,,Table 3,14.0,18550030,-12.0,
-RGD < Control Subjects,,-19.0,Right superior frontal gyrus,2,128,1769,. Results of Optimized VBM Analysis Comparing RGD Subjects with Healthy Control Subjects,3.62,Table 2,8.0,18550031,70.0,396
-RGD < Control Subjects,,-24.0,Left postcentral gyrus,2,128,1770,. Results of Optimized VBM Analysis Comparing RGD Subjects with Healthy Control Subjects,3.38,Table 2,-32.0,18550031,49.0,274
-RGD < Control Subjects,,-12.0,Right middle temporal gyrus,2,128,1771,. Results of Optimized VBM Analysis Comparing RGD Subjects with Healthy Control Subjects,3.06,Table 2,41.0,18550031,-12.0,150
-RGD > Control Subjects,,15.0,Left cingulate gyrus,2,128,1772,. Results of Optimized VBM Analysis Comparing RGD Subjects with Healthy Control Subjects,3.26,Table 2,-3.0,18550031,29.0,191
-,,-9.0,Amygdala,2,3054,45868,,,Table 2,-24.0,18550597,-13.0,
-,,-33.0,Hippocampus,2,3054,45869,,,Table 2,13.0,18550597,9.0,
-,,-34.0,Hippocampus,2,3054,45870,,,Table 2,-12.0,18550597,8.0,
-,,-13.0,Pallidum,2,3054,45871,,,Table 2,28.0,18550597,-1.0,
-,,24.0,Caudate,2,3054,45872,,,Table 2,15.0,18550597,-9.0,
-,,-16.0,Putamen,2,3054,45873,,,Table 2,30.0,18550597,-1.0,
-,,0.0,"Uncal cortex, parahippocampal gyrus",3,3055,45874,,4.18,Table 3,12.0,18550597,-30.0,1114
-,,-6.0,,3,3055,45875,,4.14,Table 3,-18.0,18550597,-36.0,653
-,,-7.0,,3,3055,45876,,3.78,Table 3,-18.0,18550597,-30.0,1714
-,,-11.0,Diencephalon,3,3055,45877,,4.57,Table 3,6.0,18550597,-4.0,5980
-,,-53.0,"Precuneus, superior parietal gyrus",3,3055,45878,,5.05,Table 3,-9.0,18550597,47.0,37727
+pmcid,table_id,table_label,x,y,z,pmid,table_caption,table_number,p_value,region,size,statistic,groups,source
+9001100.0,tab2,Table 2,21.0,39.0,51.0,35419051.0,,,,,,,,pubget
+9001100.0,tab2,Table 2,30.0,42.0,-18.0,35419051.0,,,,,,,,pubget
+5789340.0,brainsci-08-00009-t002,Table 2,-40.1,25.8,21.3,29300304.0,,,,,,,,pubget
+5789340.0,brainsci-08-00009-t002,Table 2,-11.6,20.6,5.2,29300304.0,,,,,,,,pubget
+5789340.0,brainsci-08-00009-t002,Table 2,-5.5,4.7,49.2,29300304.0,,,,,,,,pubget
+5789340.0,brainsci-08-00009-t002,Table 2,-50.6,0.6,31.0,29300304.0,,,,,,,,pubget
+5789340.0,brainsci-08-00009-t002,Table 2,-37.5,-49.3,45.5,29300304.0,,,,,,,,pubget
+5789340.0,brainsci-08-00009-t002,Table 2,-28.7,16.6,9.9,29300304.0,,,,,,,,pubget
+5789340.0,brainsci-08-00009-t002,Table 2,42.1,28.7,19.4,29300304.0,,,,,,,,pubget
+5789340.0,brainsci-08-00009-t002,Table 2,12.6,-22.2,6.1,29300304.0,,,,,,,,pubget
+5789340.0,brainsci-08-00009-t002,Table 2,40.0,5.4,11.2,29300304.0,,,,,,,,pubget
+5789340.0,brainsci-08-00009-t002,Table 2,37.3,-26.4,54.5,29300304.0,,,,,,,,pubget
+5789340.0,brainsci-08-00009-t002,Table 2,37.1,-21.0,56.1,29300304.0,,,,,,,,pubget
+3242169.0,T1,Table 1,4.0,30.0,36.0,22194728.0,,,,,,,,pubget
+3242169.0,T1,Table 1,-4.0,18.0,44.0,22194728.0,,,,,,,,pubget
+3242169.0,T1,Table 1,30.0,6.0,60.0,22194728.0,,,,,,,,pubget
+3242169.0,T1,Table 1,-42.0,28.0,32.0,22194728.0,,,,,,,,pubget
+3242169.0,T1,Table 1,-44.0,26.0,26.0,22194728.0,,,,,,,,pubget
+3242169.0,T1,Table 1,30.0,10.0,58.0,22194728.0,,,,,,,,pubget
+3242169.0,T1,Table 1,-44.0,-64.0,44.0,22194728.0,,,,,,,,pubget
+3242169.0,T1,Table 1,-46.0,-42.0,50.0,22194728.0,,,,,,,,pubget
+3242169.0,T1,Table 1,-38.0,-58.0,46.0,22194728.0,,,,,,,,pubget
+3242169.0,T1,Table 1,-46.0,-44.0,54.0,22194728.0,,,,,,,,pubget
+3242169.0,T1,Table 1,-38.0,-60.0,54.0,22194728.0,,,,,,,,pubget
+3242169.0,T1,Table 1,-36.0,-50.0,46.0,22194728.0,,,,,,,,pubget
+3242169.0,T1,Table 1,50.0,-56.0,46.0,22194728.0,,,,,,,,pubget
+3242169.0,T1,Table 1,46.0,-50.0,54.0,22194728.0,,,,,,,,pubget
+3242169.0,T1,Table 1,44.0,-54.0,48.0,22194728.0,,,,,,,,pubget
+3242169.0,T1,Table 1,44.0,-60.0,44.0,22194728.0,,,,,,,,pubget
+3242169.0,T1,Table 1,42.0,-48.0,46.0,22194728.0,,,,,,,,pubget
+3242169.0,T1,Table 1,42.0,-38.0,44.0,22194728.0,,,,,,,,pubget
+3242169.0,T1,Table 1,-10.0,-68.0,38.0,22194728.0,,,,,,,,pubget
+3242169.0,T1,Table 1,2.0,-64.0,48.0,22194728.0,,,,,,,,pubget
+3242169.0,T1,Table 1,-2.0,-62.0,48.0,22194728.0,,,,,,,,pubget
+3242169.0,T1,Table 1,-8.0,-64.0,46.0,22194728.0,,,,,,,,pubget
+3242169.0,T1,Table 1,14.0,-64.0,58.0,22194728.0,,,,,,,,pubget
+3242169.0,T1,Table 1,-38.0,68.0,-4.0,22194728.0,,,,,,,,pubget
+3242169.0,T1,Table 1,-32.0,64.0,6.0,22194728.0,,,,,,,,pubget
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-" Temporal cortex activation during speech recognition: an optical topography study - ScienceDirect JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main contentSkip to article ScienceDirectJournals & BooksSearchRegisterSign inView PDFDownload full issueSearch ScienceDirectOutlineAbstractKeywords1. Introduction2. Methods3. Results and discussion4. General discussionAcknowledgementsReferencesShow full outlineCited By (113)Figures (4)Tables (2)Table 1Table 2CognitionVolume 73, Issue 3, 17 December 1999, Pages B55-B66Temporal cortex activation during speech recognition: an optical topography studyAuthor links open overlay panelHiroki Sato a, Tatsuya Takeuchi a, Kuniyoshi L Sakai a bShow moreOutlineAdd to MendeleyShareCitehttps://doi.org/10.1016/S0010-0277(99)00060-8Get rights and contentAbstractCortical activity during speech recognition was examined using optical topography (OT), a recently developed non-invasive technique. To assess relative changes in hemoglobin oxygenation, local changes in near-infrared light absorption were measured simultaneously from 44 points in both hemispheres. A dichotic listening paradigm was used in this experiment, in which target stimuli and non-target stimuli were presented to different ears. Subjects were asked to track targets and to press a button when targets shifted from one ear to the other. We compared three tasks: (i) a control task, in which a tone was used as the target; (ii) a repeat task, in which the target was one repeated sentence; (iii) a story task, in which the targets were continuous sentences of a story. The activity for the story task, compared with the repeat task, was localized in the left superior temporal cortex. Relative to the control task, we observed in this region a larger increase in oxyhemoglobin concentration and a decrease in deoxyhemoglobin concentration in the story task than those in the repeat task. These results suggest that the activity in the left temporal association area reflects the load of auditory, memory, and language information processing.Previous article in issueNext article in issueKeywordsSpeech recognitionTemporal association areaHemoglobin oxygenation1. IntroductionOptical topography (OT) is a recently developed non-invasive technique of functional mapping, which measures temporal changes in hemoglobin oxygenation simultaneously from multiple regions (Maki et al., 1995, Yamashita et al., 1996, Koizumi et al., 1999). OT is a new extension of near-infrared spectroscopy (NIRS) used to acquire a topographical image, whereas NIRS measures spectroscopic reflection and scattering from a single region with a light emitter and a detector (Chance et al., 1993, Hoshi and Tamura, 1993, Kato et al., 1993, Villringer et al., 1993). The technological development from NIRS to OT is conceptually similar to that from nuclear magnetic resonance (NMR) spectroscopy to magnetic resonance imaging (MRI). Measurement from multiple regions is achieved by independently modulating the intensities of each radiated laser beam, that is, by frequency encoding of spatial information (Yamashita, Maki & Koizumi, 1999). The minimum distance between measurement points of the current OT system is 21 mm, and minimum sampling interval is 100 ms.There are several advantages of using OT over other functional mapping techniques. First, it is possible to independently measure the temporal changes in the concentrations of oxyhemoglobin and deoxyhemoglobin. Secondly, there is no scanning noise interfering with any auditory stimuli. Thirdly, its signal-to-noise ratio is relatively high, allowing for the observation of cortical activity even with a single trial. Finally, OT does not require a head constraint; thus its safe application to infants may make developmental mapping studies possible. One major disadvantage of OT is that its measurement is restricted to the cortical surface. Nevertheless, OT has a potential to open a new dimension for mapping human cognitive functions. Here we report the first successful application of this technique to the study of cortical responses during speech recognition. We have devised a dichotic listening task that requires intensive tracking of speech sounds and have compared a task with successive sentences of a story and that with repeated sentences. A portion of this study has been reported in abstract form (Sakai, Sato & Takeuchi, 1999).2. Methods2.1. SubjectsSeven male native Japanese speakers (ages: 21–32 years) participated in the present study. They showed right-handedness (laterality quotients: 81–100) by the Edinburgh inventory (Oldfield, 1971). The subjects’ consent was obtained according to the declaration of Helsinki. Approval for the human experiments was obtained from the institutional review board of the University of Tokyo, Graduate School of Arts and Sciences.2.2. Auditory stimuliThe auditory stimuli used in this study were speech sounds and non-speech sounds that were presented in speech-recognition tasks and a control task, respectively. All speech sounds were digitized (16 bit, 11 025 Hz) using speech synthesis software (Oshaberi-mate, Fujitsu, Tokyo, Japan) that converts Japanese written text into sound waveforms. Sine-wave tone and white noise used in the control task were synthesized by sound-editing software (Sound Forge XP, Sonic Foundry Inc.). Speech sounds and non-speech sounds were presented with a stereophonic headphone at the peak of 60- and 58-dB sound pressure levels, respectively.2.3. Task paradigmA dichotic listening paradigm was used for all tasks in this study. Target stimuli and non-target stimuli were simultaneously presented to different ears every 2 s, and a target was alternatively presented to either the left ear or the right ear at random intervals. The frequency of presentation of targets was balanced between the left and right ears. Subjects were asked to track targets and to press a button when a target was shifted to the other side.In the control task, a tone (sine wave: 1000 Hz) and white noise (low-pass cut-off frequency: 1000 or 10 000 Hz) were presented as targets and non-targets, respectively (duration: 1000 ms). In order to confirm that subjects performed tasks by recognizing targets, a tone of different pitch (sine wave: 300 Hz) was presented as a non-target at a lower rate. These probe stimuli prevented subjects from performing the tasks by tracking non-targets only.We used two speech-recognition tasks: (1) a repeat task, in which the targets consisted of one repeated sentence, and (2) a story task, in which the targets were successive different sentences of a continuous story. In the repeat task, one sentence (duration: 1000–1530 ms, mean: 1270 ms) was repeated for a 36-s period, and different sentences were used in each period. Successive sentences for the story task were divided into phrases at natural break-points (duration: 590–1650 ms, mean: 1150 ms) for the presentation. In both tasks, a non-target was obtained by scrambling the sequence of syllables of the correspondent target. These jumbled stimuli conformed to the rules of Japanese phonotactics but had no meaning. A sentence different from the target for the repeat task and contextually anomalous phrases for the story task were used as probe stimuli. These tasks, therefore, cannot be completed appropriately by identifying speech sounds without paying attention to their meanings.2.4. Experimental proceduresDuring the experiments, the subject sat in a chair with his eyes closed in a dark room. A pair of head shells with probe sockets was attached on both sides of the subject's head. In a single run with the repeat tasks (R), a 36-s period for the control task and the repeat task alternated three times, with one more control period at the end of a run. A single run with the story tasks (S) had the same alternation. Twelve runs were performed in the order of alternating S-R-R-S and R-S-S-R in one imaging session. Each subject was tested in at least two sessions. The first run of either S or R was counterbalanced by subsequent sessions.After the experiment, a three-dimensional (3D) magnetic resonance (MR) image was taken of the subject to reconstruct a cortical surface image. Alfacalcidol beads (0.25 μg) buried in a head shell were used as MR markers, which can be identified on the MR image as spheres (diameter: 3 mm).2.5. Optical topography proceduresWe used two OT systems with the same calibration (ETG-100 and ETG-A1; Hitachi Medical Corporation, Tokyo, Japan), one for each hemisphere. Near-infrared laser diodes with two wavelengths (ranges: 782–793 and 823–832 nm) were used as the light sources (maximum intensity: 2 mW/mm2: intensity modulation: 1–10 kHz). The reflected lights were detected with avalanche photodiodes located 30 mm from an incident position. Using lock-in amplifiers, the detected signal was separated into individual light sources with each wavelength (Yamashita et al., 1999). The transmittance data ln T(λ,t) for a wavelength (λ) at measurement time (t) were obtained for each run.The position of each MR marker was a midpoint between an incident point and a detection point. Each measurement point was defined as an intersection of the cortical surface and a perpendicular line from a marker point (Maki et al., 1996). Twenty-two points in each hemisphere were simultaneously measured at minimum spatial intervals of 21 mm, and each point was sampled every 500 ms. The measured region in each hemisphere centered on the Sylvian fissure and covered an area of 6×12 cm2 (Fig. 1a).Download : Download full-size imageFig. 1. Superior temporal cortex activation in the story task. (a) The measurement points in the left hemisphere. The numbers represent channel numbers (CH) for the measurement points. The right hemisphere has the same arrangement of measurement points (CH 1 at the superior-anterior corner). Cortical surface images were reconstructed from 3D MR images of a representative subject. (b) The r-map of Coxy in the story task relative to the repeat task. Circles at CH 16 (r=0.73) and CH 21 (r=0.84) are shown as activation points. The relative size of each circle represents the r-value. (c) The r-map of Cdeoxy in the story task relative to the repeat task. Activation at CH 21 (r=−0.73) is shown. The r-maps for the right hemisphere are not shown here because there was no significant activation in this comparison (r0.05), or in the probe performance (F(2,18)=3.3, P>0.05). On the other hand, there was a main effect of tasks in the reaction time (F(2,18)=7.5, P0.1). These results suggest that the repeat and story tasks were equally balanced in terms of behavioral control for task difficulty.Table 1. Behavioral performance for tasksaTaskTotal accuracy (%)Probe accuracy (%)Reaction time (ms)Control97±1.098±0.8660±59Repeat97±1.296±1.3870±73Story93±1.694±1.1990±49aMean and standard errors are shown (N=7).3.2. Mapping the difference between the story and repeat tasksThe r-map for the hemodynamics in the story run relative to that in the repeat run shows a focal activation in the left superior temporal cortex (Fig. 1b,c). In these figures, r-maps for averaged data among subjects were superimposed on a representative cortical surface image of one subject. Two measurement points at channel 16 (CH 16) and channel 21 (CH 21) showed significant activation in Coxy (Fig. 1b). These two measurement points were on the superior and middle temporal gyri. Comparing the individual mean hemoglobin changes during the story and repeat tasks (at a plateau level, from 10 s after the onset to the end of the task period) of CH 16 and those of CH 21, ANOVA (subjects × channels × periods) indicated the main effect of channels (F(1,32)=4.2, P0.1, CH 16 and CH 21 averaged). These results indicate that the temporal changes of Coxy and Cdeoxy may reflect different physiological processes whose temporal dynamics are dependent over the long term (∼30 s) but are different in the short term (0.1), but there was a significant interaction between hemispheres and tasks (F(1,44)=5.5, P0.1). This result is consistent with the r-map of direct comparison between the story and repeat runs (Fig. 1).4. General discussionThere are two major findings in the present OT studies. First, focal activation was found in the left superior temporal cortex, preferentially for the story task over the repeat task. Compared with the baseline, we confirmed that an increase in Coxy changes and a decrease in Cdeoxy changes synchronized with each period of the story run. Although similar changes were observed in the repeat run, the signal changes in the repeat run were half those in the story run. Secondly, we found a correspondence and dissociation between oxyhemoglobin and deoxyhemoglobin dynamics. The mean changes of Coxy and Cdeoxy in each activation period were negatively correlated, while the exact temporal dynamics of Coxy and Cdeoxy did not mirror each other.There are some possible cognitive factors that are differentially involved in the story and repeat tasks. Repeating one sentence in the repeat task may cause a habituation of cortical responses. However, Cdeoxy in the repeat task did not show an apparent decrease in signals after reaching the plateau level. A sustained habituation effect in the repeat task, if present, might result in a lack of Cdeoxy change during the activation period, in contrast to Cdeoxy in the story task (Fig. 3). But Coxy in the repeat task showed signal changes parallel to those of Coxy in the story task. Although we cannot exclude the involvement of transient habituation before reaching the plateau level, such a general repetition effect would have influenced many cortical regions, which was not the case in our observations.Critical differences in cognitive factors between the story and repeat tasks would be the load of processing speech stimuli. Recognition of successive different sentences of a story demands more auditory, memory, and language information processing than the recognition of repeated sentences. The selective activation in the superior and middle temporal gyri reported here is consistent with the role of the primate temporal association area in memory storage and memory retrieval (Sakai & Miyashita, 1993). Further, our finding is consistent with a previous PET study that clearly showed an activation of the left superior and middle temporal gyri when subjects listened to continuous speech in their native language (Mazoyer et al., 1993). Moreover, the left middle temporal gyrus was activated only in that story condition among other conditions such as distorted stories, matching with the prominent activation of CH 21 in the present OT study.From the current study, it is clear that the OT technique can be successfully applied to map the functional localization of cognitive factors, as well as to measure the temporal dynamics of cognitive activity. Because we found a correspondence and dissociation between oxyhemoglobin and deoxyhemoglobin dynamics, OT has the potential to provide novel information not previously obtained with other imaging techniques. Thus OT will open up new possibilities for studying cognitive function in the human cerebral cortex.AcknowledgementsWe would like to thank Dr Juro Kawachi for his encouragement; Dr Hideaki Koizumi, Dr Atsushi Maki and Mr Yuichi Yamashita for their technical advice; Dr Eiju Watanabe and Dr David Embick for their helpful discussion; Mr Ryuichiro Hashimoto, Mr Fumitaka Homae and Dr Kyoichi Nakajima for their technical assistance; and Ms Hiromi Matsuda for her administrative assistance. This research was supported by a CREST grant from JST to KLS.Recommended articlesReferencesBandettini et al., 1993P.A. Bandettini, A. Jesmanowicz, E.C. Wong, J.S. HydeProcessing strategies for time-course data sets in functional MRI of the human brainMagnetic Resonance in Medicine, 30 (1993), pp. 161-173CrossRefView in ScopusGoogle ScholarChance et al., 1993B. Chance, Z. Zhuang, C. Unah, C. Alter, L. 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Villringer, J. Planck, C. Hock, L. Schleinkofer, U. DirnaglNear infrared spectroscopy (NIRS): a new tool to study hemodynamic changes during activation of brain function in human adultsNeuroscience Letters, 154 (1993), pp. 101-104View PDFView articleView in ScopusGoogle ScholarWatanabe et al., 1998E. Watanabe, A. Maki, F. Kawaguchi, K. Takashiro, Y. Yamashita, H. Koizumi, Y. MayanagiNon-invasive assessment of language dominance with near-infrared spectroscopic mappingNeuroscience Letters, 256 (1998), pp. 49-52View PDFView articleView in ScopusGoogle ScholarYamashita et al., 1996Y. Yamashita, A. Maki, H. KoizumiNear-infrared topographic measurement system: imaging of absorbers localized in a scattering mediumReview of Scientific Instruments, 67 (1996), pp. 730-732View in ScopusGoogle ScholarYamashita et al., 1999Y. Yamashita, A. Maki, H. KoizumiA measurement system for non-invasive dynamic optical topographyJournal of Biomedical Optics (1999)Google ScholarCited by (113)Positive emotion of self-referential contexts could facilitate adult's novel word learning: An fNIRS study2021, Brain and LanguageShow abstractLearning words through contextual inference is a key way to enlarge one’s vocabulary especially for adults. However, few studies focused on the effects of different information contained in contexts on novel word learning. The present study used behavioral and fNIRS techniques to investigate the influences of positive, neutral and negative emotions inherent in self-related or other-related referential contexts. Participants were asked to perform a semantic consistency and a source judgment task after learning the relations between novel words and concepts in different contexts. The results showed that self-reference during lexical encoding could promote word learning generally. More importantly, there existed a self-positivity bias which is manifested in the significant interactions between contextual emotions and referential value. These interactions are related to the neural activities of the DLPFC and IFG. These results revealed the contextual information’s integrative contributions to semantic meaning acquisition and episodic source memory related with novel word learning.Investigating the role of temporal lobe activation in speech perception accuracy with normal hearing adults: An event-related fNIRS study2017, NeuropsychologiaCitation Excerpt :Foundational fMRI research demonstrated activation in the superior temporal gyrus (STG) to speech sounds (as opposed to no sounds) (Binder et al., 1994; Jancke et al., 1998; Mazoyer et al., 1993) while later studies revealed hemispheric and/or area-specific responses in the temporal lobe to various auditory stimuli (i.e. words, sentences, pseudo-words, reversed speech, tones, noise, etc.) (Binder et al., 2000; McGettigan et al., 2012; Specht et al., 2009). Results of fNIRS studies are analogous to these findings, reporting robust increases of HbO in the temporal lobe in response to various types of auditory & speech stimuli with both adults and children (Chen et al., 2015; Gallagher et al., 2012; Minagawa-Kawai et al., 2002; Sato et al., 1999; Sevy et al., 2010; Wiggins et al., 2016). Recently, four conditions (natural speech, vocoded speech, scrambled speech, environmental sounds) were designed to assess whether fNIRS could detect possible variations in temporal lobe activation during a passive-listening task.Show abstractFunctional near infrared spectroscopy (fNIRS) is a safe, non-invasive, relatively quiet imaging technique that is tolerant of movement artifact making it uniquely ideal for the assessment of hearing mechanisms. Previous research demonstrates the capacity for fNIRS to detect cortical changes to varying speech intelligibility, revealing a positive relationship between cortical activation amplitude and speech perception score. In the present study, we use an event-related design to investigate the hemodynamic response in the temporal lobe across different listening conditions. We presented participants with a speech recognition task using sentences in quiet, sentences in noise, and vocoded sentences. Hemodynamic responses were examined across conditions and then compared when speech perception was accurate compared to when speech perception was inaccurate in the context of noisy speech. Repeated measures, two-way ANOVAs revealed that the speech in noise condition (−2.8 dB signal-to-noise ratio/SNR) demonstrated significantly greater activation than the easier listening conditions on multiple channels bilaterally. Further analyses comparing correct recognition trials to incorrect recognition trials (during the presentation phase of the trial) revealed that activation was significantly greater during correct trials. Lastly, during the repetition phase of the trial, where participants correctly repeated the sentence, the hemodynamic response demonstrated significantly higher deoxyhemoglobin than oxyhemoglobin, indicating a difference between the effects of perception and production on the cortical response. Using fNIRS, the present study adds meaningful evidence to the body of knowledge that describes the brain/behavior relationship related to speech perception.Beyond the N400: Complementary access to early neural correlates of novel metaphor comprehension using combined electrophysiological and haemodynamic measurements2014, CortexCitation Excerpt :Although, up to now, no studies have used NIRS to investigate cortical haemodynamic responses specifically related to metaphoric language, the method has been repeatedly applied in various language perception studies in infants and adults and has been proven to be a useful and reliable tool in determining the neural basis of language comprehension during several task types (see Dieler et al., 2012, for an overview). Distinct frontal, temporal, and temporo-parietal activation patterns could be identified using NIRS in studies during auditory (Homae, Watanabe, Nakano, & Taga, 2007; Noguchi, Takeuchi, & Sakai, 2002; Saito et al., 2007; Sato, Sogabe, & Mazuka, 2007; Sato, Takeuchi, & Sakai, 1999), audiovisual (Bortfeld, Wruck, & Boas, 2007), and visual (Fallgatter, Mueller, & Strik, 1998; Hofmann et al., 2008; Liu, Borrett, Cheng, Gasparro, & Kwan, 2008) language presentation. Examining the functional correlates of nonliteral language processing in particular, a wide range of imaging studies has been conducted using fMRI.Show abstractThe simultaneous application of different neuroimaging methods combining high temporal and spatial resolution can uniquely contribute to current issues and open questions in the field of pragmatic language perception. In the present study, comprehension of novel metaphors was investigated using near-infrared spectroscopy (NIRS) combined with the simultaneous acquisition of electroencephalography (EEG)/event-related potentials (ERPs). For the first time, we investigated the effects of figurative language on early electrophysiological markers (P200, N400) and their functional relationship to cortical haemodynamic responses within the language network (Broca's area, Wernicke's area). To this end, 20 healthy subjects judged 120 sentences with respect to their meaningfulness, whereby phrases were either literal, metaphoric, or meaningless. Our results indicated a metaphor-specific P200 reduction and a linear increase of N400 amplitudes from literal over metaphoric to meaningless sentences. Moreover, there were metaphor related effects on haemodynamic responses accessed with NIRS, especially within the left lateral frontal cortex (Broca's area). Significant correlations between electrophysiological and haemodynamic responses indicated that P200 reductions during metaphor comprehension were associated with an increased recruitment of neural activity within left Wernicke's area, indicating a link between variations in neural activity and haemodynamic changes within Wernicke's area. This link may reflect processes related to interindividual differences regarding the ability to classify novel metaphors. The present study underlines the usefulness of simultaneous NIRS measurements in language paradigms – especially for investigating the functional significance of neurophysiological markers that have so far been rarely examined – as these measurements are easily and efficiently realizable and allow for a complementary examination of neural activity and associated metabolic changes in cortical areas.Quantitative evaluation of deep and shallow tissue layers' contribution to fNIRS signal using multi-distance optodes and independent component analysis2014, NeuroImageShow abstractTo quantify the effect of absorption changes in the deep tissue (cerebral) and shallow tissue (scalp, skin) layers on functional near-infrared spectroscopy (fNIRS) signals, a method using multi-distance (MD) optodes and independent component analysis (ICA), referred to as the MD-ICA method, is proposed. In previous studies, when the signal from the shallow tissue layer (shallow signal) needs to be eliminated, it was often assumed that the shallow signal had no correlation with the signal from the deep tissue layer (deep signal). In this study, no relationship between the waveforms of deep and shallow signals is assumed, and instead, it is assumed that both signals are linear combinations of multiple signal sources, which allows the inclusion of a “shared component” (such as systemic signals) that is contained in both layers. The method also assumes that the partial optical path length of the shallow layer does not change, whereas that of the deep layer linearly increases along with the increase of the source–detector (S–D) distance. Deep- and shallow-layer contribution ratios of each independent component (IC) are calculated using the dependence of the weight of each IC on the S–D distance. Reconstruction of deep- and shallow-layer signals are performed by the sum of ICs weighted by the deep and shallow contribution ratio. Experimental validation of the principle of this technique was conducted using a dynamic phantom with two absorbing layers. Results showed that our method is effective for evaluating deep-layer contributions even if there are high correlations between deep and shallow signals. Next, we applied the method to fNIRS signals obtained on a human head with 5-, 15-, and 30-mm S–D distances during a verbal fluency task, a verbal working memory task (prefrontal area), a finger tapping task (motor area), and a tetrametric visual checker-board task (occipital area) and then estimated the deep-layer contribution ratio. To evaluate the signal separation performance of our method, we used the correlation coefficients of a laser-Doppler flowmetry (LDF) signal and a nearest 5-mm S–D distance channel signal with the shallow signal. We demonstrated that the shallow signals have a higher temporal correlation with the LDF signals and with the 5-mm S–D distance channel than the deep signals. These results show the MD-ICA method can discriminate between deep and shallow signals.A brain of two halves: Insights into interhemispheric organization provided by near-infrared spectroscopy2014, NeuroImageShow abstractThe discovery of functional lateralization and localization of the brain marked the beginning of a new era in neuroscience. While the past 150 years of research have provided a great deal of knowledge of hemispheric differences and functional relationships, the precise organization of functional laterality remains a topic of intense debate. Here I will shed light on the functional organization of the two hemispheres by reviewing some of the most recent functional near-infrared spectroscopy (NIRS) studies that have reported hemispheric differences in activation patterns. Most NIRS studies using visual stimuli, which revealed functional differentiation between the hemispheres, have reported unilateral activation, i.e., significant levels of activation in only one hemisphere. Auditory stimuli, including speech sounds, elicited bilateral activation, while the limited number of studies on young infants revealed primarily unilateral activation. The stimulus modality and the age of the participants therefore determine whether the resulting cortical activation is unilateral or bilateral. By combining a review of the existing literature with NIRS results regarding homologous connectivity across hemispheres, I hypothesized that the origin of functional lateralization changes from the independence of each hemispheric region, to mutual inhibition between homologous regions during development. Future studies applying multi-modal measurements along with NIRS and spatiotemporal analyses will further deepen our understanding of the interhemispheric organization of brain function.Neural correlates of the DemTect in Alzheimer's disease and frontotemporal lobar degeneration - A combined MRI &amp; FDG-PET study2013, NeuroImage: ClinicalCitation Excerpt :Concerning the first trial of this subtest (not displayed), these areas spread slightly to the angular gyrus and the temporal pole. These regions have been related to auditory, memory and language information processing (Sato et al., 1999) and our results support prior findings about the neural correlates of verbal span in short-term memory in AD subjects (Collette et al., 1997; Desgranges et al., 1998). Posterior parts of the predominantly left temporal lobe have been discussed to play a role in recoding visual into phonological verbal material (Henson et al., 2000), the left temporal pole, the posterior temporal lobe and the angular gyrus are mentioned in the context of semantic processing of verbal items (Price et al., 1997).Show abstractValid screening devices are critical for an early diagnosis of dementia. The DemTect is such an internationally accepted tool. We aimed to characterize the neural networks associated with performance on the DemTect's subtests in two frequent dementia syndromes: early Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD). Voxel-based group comparisons of cerebral glucose utilization (as measured by F-18-fluorodeoxyglucose positron emission tomography) and gray matter atrophy (as measured by structural magnetic resonance imaging) were performed on data from 48 subjects with AD (n = 21), FTLD (n = 14) or subjective cognitive impairment (n = 13) as a control group. We performed group comparisons and correlation analyses between multimodal imaging data and performance on the DemTect's subtests. Group comparisons showed regional patterns consistent with previous findings for AD and FTLD. Interestingly, atrophy dominated in FTLD, whereas hypometabolism in AD. Across diagnostic groups performance on the “wordlist” subtest was positively correlated with glucose metabolism in the left temporal lobe. The “number transcoding” subtest was significantly associated with glucose metabolism in both a predominantly left lateralized frontotemporal network and a parietooccipital network including parts of the basal ganglia. Moreover, this subtest was associated with gray matter density in an extensive network including frontal, temporal, parietal and occipital areas. No significant correlates were observed for the “supermarket task” subtest. Scores on the “digit span reverse” subtest correlated with glucose metabolism in the left frontal cortex, the bilateral putamen, the head of caudate nucleus and the anterior insula. Disease-specific correlation analyses could partly verify or extend the correlates shown in the analyses across diagnostic groups. Correlates of gray matter density were found in FTLD for the “number transcoding” subtest and the “digit span reverse” subtest. Correlates of glucose metabolism were found in AD for the “wordlist” subtest and in FTLD for the “digit span reverse” subtest. Our study contributes to the understanding of the neural correlates of cognitive deficits in AD and FTLD and supports an external validation of the DemTect providing preliminary conclusions about disease-specific correlates.View all citing articles on ScopusView AbstractCopyright © 1999 Elsevier Science B.V. 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",10585521,"Cortical activity during speech recognition was examined using optical topography (OT), a recently developed non-invasive technique. To assess relative changes in hemoglobin oxygenation, local changes in near-infrared light absorption were measured simultaneously from 44 points in both hemispheres. A dichotic listening paradigm was used in this experiment, in which target stimuli and non-target stimuli were presented to different ears. Subjects were asked to track targets and to press a button when targets shifted from one ear to the other. We compared three tasks: (i) a control task, in which a tone was used as the target; (ii) a repeat task, in which the target was one repeated sentence; (iii) a story task, in which the targets were continuous sentences of a story. The activity for the story task, compared with the repeat task, was localized in the left superior temporal cortex. Relative to the control task, we observed in this region a larger increase in oxyhemoglobin concentration and a decrease in deoxyhemoglobin concentration in the story task than those in the repeat task. These results suggest that the activity in the left temporal association area reflects the load of auditory, memory, and language information processing.",Temporal cortex activation during speech recognition: an optical topography study.
-" The amygdala theory of autism - ScienceDirect JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main contentSkip to article ScienceDirectJournals & BooksSearchRegisterSign inView PDFDownload full issueSearch ScienceDirectOutlineAbstractKeywords1. The amygdala2. The amygdala nuclei3. Amygdalar function4. Evidence for the importance of the amygdala in primate social behaviour5. Evidence for an amygdala abnormality in autism6. fMRI Study of high functioning autism/asperger syndrome7. Other brain areas that might be abnormal in autism8. Future workAcknowledgementsReferencesShow full outlineCited By (837)Figures (2)Tables (1)Table 1Neuroscience & Biobehavioral ReviewsVolume 24, Issue 3, May 2000, Pages 355-364The amygdala theory of autismAuthor links open overlay panelS. Baron-Cohen a, H.A. Ring b, E.T. Bullmore a, S. Wheelwright a, C. Ashwin a, S.C.R. Williams cShow moreOutlineAdd to MendeleyShareCitehttps://doi.org/10.1016/S0149-7634(00)00011-7Get rights and contentAbstractBrothers (Brothers L. Concepts in Neuroscience 1990;1:27–51) proposed a network of neural regions that comprise the “social brain”, which includes the amygdala. Since the childhood psychiatric condition of autism involves deficits in “social intelligence”, it is plausible that autism may be caused by an amygdala abnormality. In this paper we review the evidence for a social function of the amygdala. This includes reference to the Kluver–Bucy syndrome (which Hetzler and Griffin suggested may serve as an animal model of autism). We then review evidence for an amygdala deficit in people with autism, who are well known to have deficits in social behaviour. This includes a detailed summary of our recent functional magnetic resonance imaging (fMRI) study involving judging from the expressions of another person's eyes what that other person might be thinking or feeling. In this study, patients with autism or AS did not activate the amygdala when making mentalistic inferences from the eyes, whilst people without autism did show amygdala activity. The amygdala is therefore proposed to be one of several neural regions that are abnormal in autism. We conclude that the amygdala theory of autism contains promise and suggest some new lines of research.Previous article in issueNext article in issueKeywordsAutismKluver–Bucy syndromeFunctional magnetic resonance imagingAmygdalaSocial intelligence is defined here as our ability to interpret others’ behaviour in terms of mental states (thoughts, intentions, desires, and beliefs), to interact both in complex social groups and in close relationships, to empathize with others’ states of mind, and to predict how others will feel, think, and act. We will use the term social intelligence synonymous with theory of mind [1].1 Autism is a neuropsychiatric condition that disrupts the development of social intelligence. Studies of autism can therefore allow us to study the neural basis of social intelligence.The idea that social intelligence might be independent of general intelligence comes from four sources.•There are individuals who are capable of considerable understanding of the non-social world (e.g. physics, maths, engineering) yet who readily admit to finding the social world confusing [2], [3].•The opposite type of individual also exists: people who have no difficulty interacting with the social world but who find non-social problem-solving confusing [4].•Certain kinds of brain damage (e.g. to the amygdala) can cause selective impairment in social judgement [5] without any necessary loss to general problem-solving ability. Loss of social judgement can of course co-occur with memory and executive dysfunction [6], but the functional double dissociation between social and non-social intelligence suggests their neural independence.•Many primatologists now believe that social problem-solving (independently of other factors such as tool-use or other non-social problem-solving) was a key driving force behind the evolution of primate intelligence [7].A neural basis of social intelligence was first proposed by Brothers [8]. She suggested from both animal lesion studies [9], single cell recording studies [10], and neurological studies (cited above) that social intelligence was a function of three regions: the amygdala, the orbito-frontal cortex (OFC), and the superior temporal sulcus and gyrus (STG). Together, she called these the “social brain”. Elsewhere, we have considered the contributions of the OFC and STG to autism [11], [12]. In this paper, we focus on the role of the amygdala in social intelligence, and develop an amygdala theory of autism. The theory proposes that the amygdala is one of several neural regions that are necessarily abnormal in autism.1. The amygdala2The amygdala is a collection of nuclei. It lies beneath the uncus of the temporal lobe at the anterior end of the hippocampal formation and the inferior horn of the lateral ventricle. It develops relatively early in gestation (embryonic day 30–50), but the separate nuclei do not differentiate until postnatal life, suggesting plasticity in the cues to which the amygdala responds [13]. The old view of the amygdala was that it was mainly only interconnected with the hypothalamus, but evidence over the last two decades reveals the amygdala is intricately interconnected with many brain regions, including neocortex, basal forebrain, the “limbic striatum” (nucleus accumbens and ventral pallidum), the neostriatal structures (the caudate nucleus, and the putamen), the hippocampal formation, and the claustrum [14], [15]. The amygdala blends in with the periamygdaloid cortex, a part of the uncus. It is also adjacent to the tail of the caudate nucleus. The amygdala does have some connections with the striatum, but the overall pattern of its connections is described next.1.1. Afferents to the amygdalaThe amygdala receives a great deal of sensory input in a highly processed form. Single amygdalar cells may respond to somatosensory, visual, auditory, and all types of visceral inputs. The afferents carrying this information reach the amygdala by travelling in the reverse direction along the paths followed by amygdalar efferents. Visceral inputs, particularly olfactory inputs, are especially prominent. Additional visceral information reaches the amygdala indirectly from the hypothalamus, setal area, orbital and insular-cortex, and also by more direct routes; for example, the parabrochial nucleus projects to the amygdala. The temporal and anterior cingulate cortices also project to the amygdala.1.2. Efferents from the amygdalaFibres leave the amygdala through two major pathways to reach many of the same areas that send efferents to it. The first pathway is the stria terminalis, which travels around from the temporal lobe toward the interventricular foramen, together with the caudate nucleus and the thalamostriate (or terminal) vein. The second efferent is the ventral amygdalofugal pathway. These fibres pass underneath the lenticular nucleus and spread out to the base of the brain, ending in the septal area and the hypothalamus, in olfactory regions like the anterior olfactory nucleus, the anterior perforated substance, the piriform cortex, and in the orbital and anterior cingulate cortices. Some reach the ventral striatum, which includes the area where the putamen and the caudate nucleus (the nucleus accumbens) fuse, as well as portions of the striatum. The ventral striatum in turn projects to an extension of the globus pallidus, the ventral pallidum, beneath the anterior commissure. The ventral striatum and pallidum are links in a basal ganglia circuit similar to that involved in motor functions. Many ventral amygdalofugal fibres reach the dorsomedial nucleus of the thalamus. Finally, some amygdalar efferents pass directly to entorhinal cortex and other cortical areas in the temporal lobe and beyond.2. The amygdala nucleiThe amygdala is not a single entity, but comprises a collection of 13 nuclei, located in the medial temporal lobe [16]. For this reason, the amygdala is sometimes called the amygdaloid complex. Traditional classification of the 13 nuclei are into three clusters:•The deep nuclei (lateral, basal, accessory, basal, and paralaminar), which have the greatest interaction with the neocortex and hippocampal formation, and the most connectivity with sensory processing.•The superficial regions (medial, anterior and posterior cortical nuclei), which are more closely associated with olfactory regions and with the hypothalamus. These are thought to play a role in maternal and sexual behaviour.•Other nuclei (central, anterior amygdaloid area, amygdalohippocampal area, and intercalated nuclei). Of these, only the central nucleus has been studied, and it appears to influence the brainstem (e.g. by mediating the cardiovascular and respiratory responses during fear [17]).Emery, using non-metric multidimensional scaling analysis based on macaque genus brains where the anatomical connections are already defined, suggests slightly different terminology for grouping these 13 nuclei into three clusters [18]. (1) The basolateral (BL) group (the lateral, lateral basal, mesial basal, and accessory basal nuclei). The BL group appears to be functionally distinct too, containing neurons responsive to faces and actions of others (Rolls, 1984; [113], 1992; [114]; Leonard et al., 1985; [10]; Brothers and Ring, 1992). These are not found in the next two clusters of amygdala nuclei. (2) The centromedial (CM) group (the central, medial, and cortical nuclei, and the perimamygdaloid complex). The CM group innervates many of the visceral and autonomic effector regions of the brainstem, such as the parabrachial nuclei (involved in respiratory control) and the dorsal motor nucleus (involved in cardiovascular control). (3) The peripheral nuclei (PN) group (cortical transition area, anterior amygdaloid area, and amygdalo-hippocampal area).Finally, in terms of neurochemistry, the amygdala has the highest density of benzodiazepine/GABAa receptors in the brain, and also has a substantial set of opiate receptors. It contains serotinergic, dopaminergic, cholinergic and noradrenergic cell bodies and pathways [19]. For a thorough review of the neuroanatomy of the amygdala, the reader is directed elsewhere [19], [20].3. Amygdalar functionAs the amygdala has extensive connections with the septal area and hypothalamus and with prefrontal cortex, it influences both drive-related behaviour and the related emotions. In the first of these two roles, the amygdala modulates the hypothalamus. Visceral or somatic activity that can be elicited by stimulating the hypothalamus (such as feeding, or cardiovascular and respiratory changes) can also be elicited by stimulating the amygdala. The role of the amygdala in emotions has also been revealed via electrical studies. When the animal's amygdala is stimulated, the animal typically stops whatever it was doing and becomes attentive. This may be followed by defensiveness, fight, or flight. In humans the most common emotion following amygdalar stimulation is fear, accompanied by its autonomic manifestations (dilation of the pupils, release of adrenalin, and increased heart rate). Conversely, bilateral destruction of the amygdala causes a decrease in aggression, with the result that the animals are described as tame and placid.4. Evidence for the importance of the amygdala in primate social behaviourThere are several important lines of evidence implicating the amygdala in primate social behaviour. Extensive reviews exist elsewhere [9]. Here we summarise the main lines of evidence.4.1. Lesions of the primate amygdala affect social behaviourIbotenic acid lesions of the amygdala affect the social behaviour of adult rhesus macaques [21]. In addition, amygdala-lesioned monkeys become socially isolated. They fail to initiate social interactions, and they fail to respond appropriately to social gestures [9], [22]. Kling and colleagues have shown this pattern of effects in rhesus monkeys in semi-natural settings (the Caribbean Regional Primate Center on Cayo Santiago) [23], in caged vervets [24], in free-ranging vervets [25], and in stumptailed macaques in different sized social groups [26]. The vervet study above showed that when the amygdala-lesioned monkeys were released into the wild they were unresponsive to group members, failed to display appropriate social signals (neither affiliative nor aggressive), they withdrew from other animals, and frequently they were killed. Those who were not killed never re-entered their original social groups. The socio-emotional deficits in amygdala lesions in infant rhesus monkeys produce last into adulthood [27], [28].In one of the earliest studies, Brown and Shafer lesioned the temporal cortex of a rhesus monkey and documented significant social and emotional deficits as a result [29]. This result was extended by Kluver and Bucy who showed that large lesions of the anterior temporal lobe (including amygdala, hippocampal formation, and temporal cortex) produced a syndrome which included the following symptoms: a tendency to over-react to all objects, hypoemotionality and loss of fear, hypersexuality3 (excessive masturbation, copulation with any object, and fellatio with both same sex and opposite sex monkeys), hyperorality (a tendency to investigate objects with their mouths, not their hands, even if the object was inedible), and in some (but not all) cases, an inability to recognize objects (visual agnosia) [30]. They called this new syndrome “psychic blindness”4 because the monkeys would approach animate and inanimate objects indiscriminately.5 Most striking was the loss of fear towards the experimenters, and a blunting of aggression.Subsequent work showed that the amygdala was found to be responsible for the emotional, oral, and sexual deficits [31], [32]; the temporal cortex was responsible for the visual deficits [31], [33]; and the dual lesions produced the combined and full syndrome [34]. Aggleton and Passingham [35] made selective radio frequency lesions of the whole amygdala, the basal and lateral nuclei, the lateral nucleus alone, the dorsal nuclei, and the white matter that borders the amygdala laterally and dorsally (the temporal stem). Their results showed that only lesions of the whole amygdala caused the complete Kluver–Bucy syndrome. Note that the sexual aberrations are not always replicated in juvenile monkeys with Kluver–Bucy syndrome [33].Bachevalier lesioned either the medial temporal lobe (including the amygdala, periamygdaloid cortex, hippocampus, entorhinal and perirhinal cortices), or the hippocampal formation and amygdala separately [36], [37]. The lesioned animal infants were raised and paired with an age-matched control animal. At two months, the infants with medial temporal lobe lesions were more passive, displayed increased temper tantrums, and initiated fewer social contacts. At six months they interacted very little with the control animal, and actively withdrew from all approaches by the normal animals. The animals with medial temporal lobe lesions also displayed emotionally expressionless faces and showed more self-directed behaviour and motor stereotypies. Such abnormalities were still evident in adulthood. Amygdala lesions alone produced a similar pattern of social abnormalities, but to a lesser extent.Rosvold et al. showed that amygdala lesions in monkeys had a direct effect on the animal's social status: social hierarchies were disrupted, this being due to the most dominant animal falling in dominance following the amygdala lesion [38]. Lesions in the amygdala of monkey mothers lead to the mothers showing a reduction in maternal behaviours towards her infant (suckling, cuddling, or protecting them), with the result that amygdala-lesioned mother monkeys are more likely to physically abuse or neglect their infants [39], [40], [41], [42], [43]. Note that amygdala lesions in infant monkeys do not disrupt the drive for attachment [44] but they do have major effects on initiating and responding to peer social interaction [37]. The data from non-human primates is largely consistent with the data from human lesion studies [45], [46], [47].4.2. Amygdala volume and group sizeThere is a significant correlation between amygdala volume and social group size (a positive correlation with the BL group, and a negative correlation with the CM group) [18]. This correlation remains significant even after removing the effects of overall brain size and the rest of the amygdala, as well as the effect of body size. This was computed for 44 primate species, excluding humans. In the Emery et al. study, group size is taken as a proxy measure of social complexity, and therefore an indicator of the likely evolutionary selection pressure on ‘social intelligence’. (It is acknowledged that this is an imperfect proxy measure, since species such as ants, termites, and bees live in large social groups but do not have the social intelligence of any primate). A similar correlation has been reported between social group size and neocortex size in primates [48]. On the basis of the correlation with the amygdala and social group size, Emery and Perrett emphasize the BL group as the cluster of amygdala nuclei with the clearest role in social cognition, relative to the other two amygdala clusters of nuclei (Emery and Perrett, in press). This is also based on the connections between the BL group and superior temporal sulcus and gyros, orbito-frontal and medial frontal cortex (STG, OFC, and MFC), which have all been demonstrated to play a role in social cognition.6, 74.3. Neuroimaging studies in humansThe human amygdala is activated in humans when decoding signals of social importance, such as gaze, expression–recognition (especially of fearful faces), and body movements) [49], [50], [51], [52], [53], [54].4.4. The amygdala, opiate system, and social groomingThe amygdala plays a major role in affiliative behaviours in primates, via grooming. Grooming when it is self-directed (autogrooming) is probably mainly aimed at cleaning the body surface, but when it is social (allogrooming) it is though to be primarily related to the formation and maintenance of social relationships and coalitions [55], [56]. Allogrooming reduces tension [57] via a decrease in heart rate, which is thought to be controlled by the central nucleus of the amygdala [58]. One mechanism for this is via the opiate system, since blocking opiate receptors with the opiod antagonist, naltrexone, increases allogrooming in talapoin monkeys [59], [60], [61]. Following social contact a measurable increase in opioid levels is also seen [62]. The link between opioid level, allogrooming/affiliative behaviour, and the amygdala is postulated because the amygdala contains a large number of opiate receptors [63].Since this paper focuses primarily on the amygdala, we do not discuss the other postulated regions in the social brain, the superior temporal gyrus6 or the orbito and medial–frontal cortex.75. Evidence for an amygdala abnormality in autismWe turn now to consider six lines of evidence for an amygdala deficit in autism.5.1. Post-mortem evidenceA neuroanatomical study of autism at post-mortem found microscopic pathology (in the form of increased cell density) in the amygdala, in the presence of normal amygdala volume [64], [65].5.2. An animal model of autismThe only animal model of autism involves ablation of the amygdala (in rhesus monkeys) [36]. (See above). There are obviously limits to any animal model of autism, given that the syndrome involves deficits in higher-order cognition, but Bachevalier makes the case that the effects of amygdala lesions in monkeys resemble some of the symptoms of autism. In particular, the Kluver–Bucy syndrome seems a fairly good animal model of autism [66].5.3. Similarities between autism and patients following amygdalotomyPatients with amygdala lesions show impairments in social judgement [45], [47] which have been likened to “acquired autism” [67]. The age of onset of deficits in acquired vs idiopathic cases is likely to mean that the two syndromes also differ in many ways, too. Similarly, patients with autism tend to show a similar pattern of deficits to those seen in patients with amygdala lesions [68].5.4. The effects of temporal lobe tubersIn cases of tuberous sclerosis, autistic comorbidity is determined by hamartomata in the temporal lobe [69].85.5. Structural neuroimagingA recent structural magnetic resonance imaging study of autism reported reduced amygdala volume [70].5.6. Functional neuroimagingUsing single photon emission computed tomography (SPECT), patients with autism spectrum conditions show significant reductions in temporal lobe blood flow. This is not simply an effect of temporal lobe epilepsy [71]. In our recent functional magnetic resonance imaging (fMRI) study, we found that adults with HFA or AS showed significantly less amygdala activation during a mentalizing task (Judging the mind in the Eyes task), compared to normal [49]. Because it constitutes the first direct in vivo evidence for a functional amygdala deficit in autism, we describe this study in detail next.6. fMRI Study of high functioning autism/asperger syndromeThe following is a summary of the above fMRI study [49]. Six subjects with autism (4m, 2f) were matched for mean age, handedness, IQ, socioeconomic status, and educational level, with 12 subjects in the normal group (6m, 6f). IQ was assessed with the full Wechsler Adult Intelligence Scale (WAIS-R). Subjects were only included if their IQ was in the normal range (i.e. above 85 both in terms of full-scale IQ, and in terms of performance and verbal IQ). Individuals in the clinical group all had a diagnosis of autism or Asperger Syndrome, using established criteria [72], [73].In the fMRI scanner, a blocked periodic ABA design was employed. Each epoch (A or B) was presented for 30 s, and there were five cycles of AB alternation in total. Images were acquired from each subject during visual presentation of two tasks, both of which involved deriving socially relevant information from facial stimuli. This periodically designed (ABA) experiment was expected to induce periodic MR signal change with signal maximum during task A in brain regions relatively specialized for gender recognition from facial stimuli; and periodic MR signal change with signal maximum during task B in brain regions relatively specialized for mental state recognition from facial stimuli. The response involved a forced choice between the two words offered (pressing one of two buttons with the right hand to select the right or left word). Correct words were counterbalanced to left and right side.Task A. Subjects were visually presented with a series of photographs of eyes and asked to indicate by right handed button press whether each stimulus was a man or a woman. In this first task (A: gender recognition), instructions to subjects were to decide for each stimulus which of two simultaneously presented words (“male” or “female”) best described the face. Each stimulus was presented for 5 s and was followed by a 0.75 s interval in which the screen was blank. Stimuli were drawn from 30 faces of women or men. Stimuli were presented 3.5 m from the subject, subtending visual angles of 10° horizontally and 8° vertically.Task B. Subjects were presented with exactly the same stimuli but were asked to indicate by button press which of two simultaneously presented words best described the mental state of the photographed person. Thus, the key difference between the two tasks was the type of judgement the subject had to make when viewing the eyes. Subjects were presented with an example of the stimuli before scanning. For this second task (B: theory of mind), instructions to subjects was to decide for each stimulus which of two simultaneously presented words best described what the person in the photograph was feeling or thinking. Task B is an “advanced” theory of mind test, in that it is used with adults.Adults with high-functioning autism or AS, with intelligence in the normal range, show deficits on this task [74], as do parents of children with autism/AS [75]. Children with William's Syndrome are not impaired on this test, despite their general retardation [76]. Examples of the eyes used in the experimental condition, together with the forced choice words that appeared underneath each face, are shown in Fig. 1.Download : Download high-res image (1MB)Download : Download full-size imageFig. 1. Examples of the stimuli used. During Task B photographs of eyes were presented with a choice of mental state words (examples as shown); during Task A the eyes were presented with a choice of the words “male” and “female”. (Top example: correct word in Task B=Concerned; correct word in Task A=Female. Both example: correct word in Task B=Sympathetic; correct word in Task A=Female).Functional MRI data were analysed in two stages: first, generic brain activation maps were constructed separately for the control and autism groups. These maps identified voxels demonstrating significant power of periodic signal change over all subjects in each group; they also represented differences between generically activated voxels in terms of phase of response to the experimental input function. Thus it was possible to determine which voxels were activated in each group by each of the two tasks. Second, we used ANOVA to identify voxels that demonstrated a significant difference between groups in mean power of response to each task.Fig. 2shows the functional system activated by presentation of the theory of mind task in the control and autism groups. This system can be anatomically subdivided into two main components: (i) a set of fronto-temporal neocortical regions, comprising left dorsolateral prefrontal cortex (DLPFC) approximately Brodmann area (BA) 44, 45, 46; the left medial frontal cortex MFC (BA 9); supplementary motor area (SMA) (medial BA 6); and bilateral temporo-parietal regions, including middle and superior temporal, angular and supramarginal gyri (BA 21, 22, 39, and 40); and (ii) a number of non-neocortical areas, including the left amygdala, the left hippocampal gyrus (BA 27 and 30), bilateral insulae, and left striatum.Download : Download high-res image (346KB)Download : Download full-size imageFig. 2. Generic brain activation maps separately computed from the control and autistic group data are superimposed in standard space. Only those voxels with maximum signal during the theory of mind task are shown. Voxel-wise probability of Type I error alpha=0.008 for both maps. Voxels activated in the control group only are coloured yellow; voxels activated in the autism group only are coloured red; voxels activated coincidentally in both groups are coloured blue. The right side of each map represents the left side of the brain. The z coordinates (mm) of each slice relative to the intercommissural line in the standard space [94] is shown above or below each slice. At −7 mm, the control group activated regions including bilateral insulae and left amygdala; at −2 mm, the main focus of activation in the control group is located in left parahippocampal gyrus; at +10 mm, the control group demonstrates activation of bilateral superior temporal gyrus (STG) and left prefrontal cortex, while the autism group demonstrates less extensive activation of predominantly left sided STG; at +26 and +32 mm, both groups activate left prefrontal cortex.The autism group activated the frontal components less extensively than the control group; and did not activate the amygdala at all. As shown in Table 1, the control group demonstrated significantly greater power of response in the left amygdala, right insula, and left inferior frontal gyrus. The autism group demonstrated significantly greater power of response in bilateral superior temporal gyrus (STG).Table 1. Main brain regions differentially activated by theory of mind task between control (C) and autism (A) groups. BA=Brodmann areaCerebral regionBASideN (voxels)xyzDifferencePSuperior temporal gyrus or Wernicke's area22L12−55−2815A>C0.004Superior temporal gyrus22R840−2815A>C0.002Inferior frontal gyrus or Broca's area44/45L5−46229C>A0.001InsulaR54011−7C>A0.001AmygdalaL4−23−11−7C>A0.001Regarding the left amygdala, this area may be critically involved in identifying mental state/emotional information from complex visual stimuli such as the eye region. This laterality effect is consistent with previous studies: the left amygdala appears to be specifically activated in emotion processing [52], [77] (but see [78], [79]). The autism group appears not to perform the task using the amygdala, but instead place a greater processing load on temporal lobe structures, specialized for verbally labelling complex visual stimuli and processing faces and eyes. This may arise as a compensation for an amygdala abnormality.This study suggests that mental state concepts are processed in the amygdala, both when the task involves inferring mental states from eyes, or other animate actions [50]. The fMRI study provides strong evidence of the role of the amygdala in normal social intelligence, and abnormality of the amygdala in autism. Although some structural imaging studies of the amygdala in autism suggests this is normal [80], [81], others have documented anomalies [70] and the fMRI study described above suggests functional anomalies exist.7. Other brain areas that might be abnormal in autismWhilst this paper highlights the necessary role on amygdala abnormality might play in autism, we do not suggest that this is the only abnormal neural region. For example, the case has been made for anomalous functioning in the cerebellum [82], hippocampal formation [83], medial frontal cortex [84], and fronto-limbic connections [85] in autism. Reduced neuron size and increased cell-packing density has also been found in the limbic system, specifically the hippocampus, subiculum, entorhinal cortex, amygdala, mammillary bodies, anterior cingulate, and septum in autism [64], [86], [87], [88], [89]. A full review of neuroimaging of autism may be found elsewhere [80]. Here, we instead follow a line of argument begun by other authors emphasising an amygdala theory of autism [37], [66], [86]. This is consistent with studies showing temporal lobe and limbic epilepsy in a proportion of children with autism [90]; for an excellent review see Ref. [37].8. Future workThe literature reviewed in this paper hints at the validity of an amygdala theory of autism, but future studies will be needed to test this more extensively. For example, it will be important to test if the amygdala in autism can be activated to normal levels using other cognitive tasks, or if the deficit associated with the Eyes Task extends to other tests of social intelligence.Secondly, it is known that the amygdala plays a role in the recognition of fear [45], [46], [91]. Related to this, the amygdala is implicated in the formation of conditioned fear responses to auditory stimuli [47], [92]. If there is an amygdala deficit in autism this might be expected to lead to abnormal fear responses in such children (either showing too little or too much fear, compared to non-autistic controls). Studies of fear in autism might be an indirect method to test predictions from the amygdala theory. Finally, future research will need to specify in greater detail, which of the 13 nuclei in the amygdala are intact in autism, and which are impaired.AcknowledgementsThis work was funded by a grant to SBC, HR, and SCW from the Wellcome Trust, and by a grant to the first author from the Gatsby Foundation. EB is also supported by the Wellcome Trust. We are grateful to Barry Everitt for comments on the first draft of this paper. We have also benefited from review papers by Nathan Emery and colleagues (Emery and Perrett, in press; [93].Recommended articlesReferences[1]H. WellmanChildren's theories of mind, MIT Press, Bradford (1990)Google Scholar[2]S. Baron-Cohen, S. Wheelwright, V. Stone, M. RutherfordA mathematician, a physicist, and a computer scientist with Asperger Syndrome: performance on folk psychology and folk physics testNeurocase (2000)(in press)Google Scholar[3]Sacks O. An anthropologist on Mars, 1994.Google Scholar[4]A. Karmiloff-Smith, J. Grant, U. Bellugi, S. 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Here, we review studies that investigate the relationship between amygdala function and ASD. We focus on studies that employ the same task and stimuli to directly compare people with ASD and patients with focal amygdala lesions, and we also discuss functional data associated with these studies. We show that the amygdala can only account for a limited number of deficits in ASD (primarily face perception tasks but not social attention tasks), a network view is, therefore, more appropriate. We next discuss atypical brain connectivity in ASD, factors that can explain such atypical brain connectivity, and novel tools to analyze brain connectivity. Lastly, we discuss new opportunities from multimodal neuroimaging with data fusion and human single-neuron recordings that can enable us to better understand the neural underpinnings of social dysfunctions in ASD. Together, the influential amygdala theory of autism should be extended with emerging data-driven scientific discoveries such as machine learning-based surrogate models to a broader framework that considers brain connectivity at the global scale.The basolateral amygdala to posterior insular cortex tract is necessary for social interaction with stressed juvenile rats2022, Behavioural Brain ResearchShow abstractVocalizations, chemosignals, and behaviors are influenced by one’s internal affective state and are used by others to shape social behaviors. A network of interconnected brain structures, often called the social behavior network or social decision-making network, integrates these stimuli and coordinates social behaviors, and in-network connectivity deficits underlie several psychiatric disorders such as schizophrenia and autism spectrum disorders. Here, we investigated the role of the basolateral amygdala (BLA) and its projections to the posterior insular cortex, regions independently implicated in a range of sociocognitive processes, in a social affective preference (SAP) test. Viral vectors containing the gene coding for inhibitory chemogenetic receptors (AAV5-hSyn-hM4Di-mCherry) were injected into the BLA. SAP tests, which allow for the observation of unconditioned behavioral responses to the affective states of others, were conducted after inhibition of the BLA by systemic administration of the hM4Di agonist clozapine-n-oxide (CNO), or inhibition of BLA-insula terminals by direct infusion of CNO to the insula. After vehicle infusions, rats displayed preference for interactions with stressed juvenile conspecifics. However, CNO treatment eliminated preference behavior. The current results suggest that social decision making involves the transfer of emotional information from the BLA to the insula which represents a previously unrecognized anatomical substrate for social cognition.Brain Dp140 alters glutamatergic transmission and social behaviour in the mdx52 mouse model of Duchenne muscular dystrophy2022, Progress in NeurobiologyCitation Excerpt :Furthermore, we observed that ASD-like behaviours in mdx52 mice were improved by the partial restoration of Dp140 following both exon 53 skipping and the mRNA therapeutics-mediated Dp140 overexpression. In human, Dp427 and Dp140 are expressed in the amygdala (Doorenweerd et al., 2017), and their deficiency is associated with emotional and social behaviours linked to ASD symptoms (Baron-Cohen et al., 2000a, 2000b). E-I imbalance in the BLA and mPFC-BLA pathway is involved in social deficits in ASD model mice (Chao et al., 2010; Huang et al., 2016; Yizhar et al., 2011).Show abstractDuchenne muscular dystrophy (DMD) is a muscle disorder caused by DMD mutations and is characterized by neurobehavioural comorbidities due to dystrophin deficiency in the brain. The lack of Dp140, a dystrophin short isoform, is clinically associated with intellectual disability and autism spectrum disorders (ASDs), but its postnatal functional role is not well understood. To investigate synaptic function in the presence or absence of brain Dp140, we utilized two DMD mouse models, mdx23 and mdx52 mice, in which Dp140 is preserved or lacking, respectively. ASD-like behaviours were observed in pups and 8-week-old mdx52 mice lacking Dp140. Paired-pulse ratio of excitatory postsynaptic currents, glutamatergic vesicle number in basolateral amygdala neurons, and glutamatergic transmission in medial prefrontal cortex-basolateral amygdala projections were significantly reduced in mdx52 mice compared to those in wild-type and mdx23 mice. ASD-like behaviour and electrophysiological findings in mdx52 mice were ameliorated by restoration of Dp140 following intra-cerebroventricular injection of antisense oligonucleotide drug-induced exon 53 skipping or intra-basolateral amygdala administration of Dp140 mRNA-based drug. Our results implicate Dp140 in ASD-like behaviour via altered glutamatergic transmission in the basolateral amygdala of mdx52 mice.Ketamine administration in early postnatal life as a tool for mimicking Autism Spectrum Disorders core symptoms2022, Progress in Neuro-Psychopharmacology and Biological PsychiatryShow abstractAutism Spectrum Disorders (ASD) core symptoms include deficits of social interaction, stereotyped behaviours, dysfunction in language and communication. Beyond them, several additional symptoms, such as cognitive impairment, anxiety-like states and hyperactivity are often occurring, mainly overlapping with other neuropsychiatric diseases. To untangle mechanisms underlying ASD etiology, and to identify possible pharmacological approaches, different factors, such as environmental, immunological and genetic ones, need to be considered. In this context, ASD animal models, aiming to reproduce the wide range of behavioural phenotypes of this uniquely human disorder, represent a very useful tool. Ketamine administration in early postnatal life of mice has already been studied as a suitable animal model resembling psychotic-like symptoms.Here, we investigated whether ketamine administration, at postnatal days 7, 9 and 11, might induce behavioural features able to mimic ASD typical symptoms in adult mice. To this aim, we developed a 4-days behavioural tests battery, including Marble Burying, Hole Board, Olfactory and Social tests, to assess repetitive and stereotyped behaviour, social deficits and anxiety-like symptoms. Moreover, by using this mouse model, we performed neurochemical and biomolecular analyses, quantifying neurotransmitters belonging to excitatory-inhibitory pathways, such as glutamate, glutamine and gamma-aminobutyric acid (GABA), as well as immune activation biomarkers related to ASD, such as CD11b and glial fibrillary acidic protein (GFAP), in the hippocampus and amygdala. Possible alterations in levels of brain-derived neurotrophic factor (BDNF) expression in the hippocampus and amygdala were also evaluated.Our results showed an increase in stereotyped behaviours, together with social impairments and anxiety-like behaviour in adult mice, receiving ketamine administration in early postnatal life. In addition, we found decreased BDNF and enhanced GFAP hippocampal expression levels, accompanied by elevations in glutamate amount, as well as reduction in GABA content in amygdala and hippocampus.In conclusion, early ketamine administration may represent a suitable animal model of ASD, exhibiting face validity to mimic specific ASD symptoms, such as social deficits, repetitive repertoire and anxiety-like behaviour.The amygdala connectivity with depression and suicide ideation with suicide behavior: A meta-analysis of structural MRI, resting-state fMRI and task fMRI2022, Progress in Neuro-Psychopharmacology and Biological PsychiatryShow abstractIn recent decades, the primary intention of neuroscientists and psychiatrics is to evaluate the connectivity between brain regions and psychiatric disorders. The amygdala has central immersion in memory alliance, stress response, emotional perception, and automatic responses to emotional stimuli. This paper uses a meta-analysis approach to establish the relationship between structural resting state and functional amygdala connectivity with depression and suicide ideation with suicide behavior. In addition, this study explores the moderating effect of patients' demographic characteristics (gender and age) based on 30 studies. The results show that structural amygdala connectivity is positively related to the instability of depression, while for resting and task functional connectivity amygdala shows a significant negative connection with depression. Furthermore, the amygdala showed a partial activation for non-suicide self-injuries and suicide ideation. From structural and functional magnetic imaging, the current findings also support the moderating effect of the age of the participants on the amygdala connectivity with psychiatric conditions. Generally, amygdala connectivity with psychiatric disorders was not significantly moderate with the role of gender, however, this study enhances the existing hypothetical review articles and confirms the connectivity of the psychological condition with the amygdala region. It concludes that the amygdala plays a vital role in regulating and responding to emotions.The mirror neuron system compensates for amygdala dysfunction - associated social deficits in individuals with higher autistic traits2022, NeuroImageShow abstractThe amygdala is a core node in the social brain which exhibits structural and functional abnormalities in Autism spectrum disorder and there is evidence that the mirror neuron system (MNS) can functionally compensate for impaired emotion processing following amygdala lesions. In the current study, we employed an fMRI paradigm in 241 subjects investigating MNS and amygdala responses to observation, imagination and imitation of dynamic facial expressions and whether these differed in individuals with higher (n = 77) as opposed to lower (n = 79) autistic traits. Results indicated that individuals with higher compared to lower autistic traits showed worse recognition memory for fearful faces, smaller real-life social networks, and decreased left basolateral amygdala (BLA) responses to imitation. Additionally, functional connectivity between the left BLA and the left inferior frontal gyrus (IFG) as well as some other MNS regions was increased in individuals with higher autistic traits, especially during imitation of fearful expressions. The left BLA-IFG connectivity significantly moderated the autistic group differences on recognition memory for fearful faces, indicating that increased amygdala-MNS connectivity could diminish the social behavioral differences between higher and lower autistic trait groups. Overall, findings demonstrate decreased imitation-related amygdala activity in individuals with higher autistic traits in the context of increased amygdala-MNS connectivity which may functionally compensate for amygdala dysfunction and social deficits. Training targeting the MNS may capitalize on this compensatory mechanism for therapeutic benefits in Autism spectrum disorder.View all citing articles on Scopus1We recognise that some social judgments do not require mentalistic inferences; hence emphasising that a specific definition of the term ‘social intelligence’ is being used here.2Information in this section is based on excellent reviews elsewhere [19,95,96].3Emery and Amaral (in press) [115], note that the projection from the amygdala to the hypothalamus may be involved in the initiation of penile erection and ejaculation, as electrical stimulation of the amygdala can cause these (Robinson and Mishkin, 1966; [116]; 1968; [117]).4”Psychic blindness” may approximate as a non-human animal equivalent of “mindblindness” [97].5It is notworthy for the amygdala theory of autism outlined later in this paper that the original description of young children with autism referred to this lack of a differential response to people (animate objects) and things (inanimate objects) [98]. The similarity between this aspect of the behaviour of the monkeys with Kluver–Bucy syndrome and children with autism may reflect a common aetiological factor: amygdala abnormality.6In the anterior superior temporal polysensory area, STPa [99] in the macaque monkey, there are cells which are relevant to understanding others’ actions (Emery and Perrett, in press). (a) One type of cell encodes the visual appearance of the face and body [100], [101], [102]. These include cells responsive to certain facial expressions (anger, fear). (b) A second type of cell codes facial and body movements but not still images of these [103], [104]. (c) A third type of cell codes facial and bodily movements as goal directed actions—for example, it responds to hands reaching for an object, but not to a hand movement alone). This cell type is found throughout the STG, and particularly frequently in area TEa [105], [106]. Finally, (d) there is a cell type which codes any movement which is not a predictable consequence of the monkey's own actions [107], [108].7The orbito-frontal and medial frontal cortex are also important for social intelligence, and are connected to the amygdala. For example, damage to the OFC impairs judgement of what is socially appropriate [109], and recent PET and SPECT studies of “theory of mind” (or the ability to impute mental states) also implicate areas of prefrontal cortex, specifically the medial frontal cortex (MFC) [110], [111] and the OFC [12]. We consider the OFC in our earlier papers [11], [112], to which the reader is referred.8We emphasize the amygdala theory of autism, though some of the lines of evidence cited here implicate temporal lobe structures, which include the amygdala but also include other adjacent mesiotemporal areas. It remains for future work to establish the specificity of an amygdala deficit in autism.View AbstractCopyright © 2000 Elsevier Science Ltd. All rights reserved.Recommended articlesStress Enables Reinforcement-Elicited Serotonergic Consolidation of Fear MemoryBiological Psychiatry, Volume 79, Issue 10, 2016, pp. 814-822Michael V. Baratta, …, Ki A. GoosensView PDFAtypical Amygdala Response to Fear Conditioning in Autism Spectrum DisorderBiological Psychiatry: Cognitive Neuroscience and Neuroimaging, Volume 1, Issue 4, 2016, pp. 308-315D. Nicholas Top Jr., …, Mikle SouthView PDFExposure to acute stress affects the retrieval of out-group related bias in healthy menBiological Psychology, Volume 166, 2021, Article 108210Dong-ni Pan, …, Christian J. MerzView PDFFace processing in autism spectrum disorders: From brain regions to brain networksNeuropsychologia, Volume 71, 2015, pp. 201-216Jason S. Nomi, Lucina Q. UddinView PDFOptogenetic activation of amygdala projections to nucleus accumbens can arrest conditioned and unconditioned alcohol consummatory behaviorNeuroscience, Volume 360, 2017, pp. 106-117E. Zayra Millan, …, Patricia H. JanakView PDFA preliminary longitudinal volumetric MRI study of amygdala and hippocampal volumes in autismProgress in Neuro-Psychopharmacology and Biological Psychiatry, Volume 48, 2014, pp. 124-128Naama Barnea-Goraly, …, Antonio Y. HardanView PDFShow 3 more articlesArticle MetricsCitationsCitation Indexes: 828Policy Citations: 2CapturesReaders: 1146Exports-Saves: 3MentionsReferences: 6Social MediaShares, Likes & Comments: 16Tweets: 4View detailsAbout ScienceDirectRemote accessShopping cartAdvertiseContact and supportTerms and conditionsPrivacy policyWe use cookies to help provide and enhance our service and tailor content and ads. By continuing you agree to the use of cookies.Copyright © 2023 Elsevier B.V. or its licensors or contributors. ScienceDirect® is a registered trademark of Elsevier B.V.ScienceDirect® is a registered trademark of Elsevier B.V. ",10781695,"Brothers (Brothers L. Concepts in Neuroscience 1990;1:27-51) proposed a network of neural regions that comprise the ""social brain"", which includes the amygdala. Since the childhood psychiatric condition of autism involves deficits in ""social intelligence"", it is plausible that autism may be caused by an amygdala abnormality. In this paper we review the evidence for a social function of the amygdala. This includes reference to the Kluver-Bucy syndrome (which Hetzler and Griffin suggested may serve as an animal model of autism). We then review evidence for an amygdala deficit in people with autism, who are well known to have deficits in social behaviour. This includes a detailed summary of our recent functional magnetic resonance imaging (fMRI) study involving judging from the expressions of another person's eyes what that other person might be thinking or feeling. In this study, patients with autism or AS did not activate the amygdala when making mentalistic inferences from the eyes, whilst people without autism did show amygdala activity. The amygdala is therefore proposed to be one of several neural regions that are abnormal in autism. We conclude that the amygdala theory of autism contains promise and suggest some new lines of research.",The amygdala theory of autism.
-" Recognizing one's own face - ScienceDirect JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main contentSkip to article ScienceDirectJournals & BooksSearchRegisterSign inView PDFDownload full issueSearch ScienceDirectOutlineAbstractKeywords1. Introduction2. Materials and methods3. Results4. Discussion5. ConclusionAcknowledgementsReferencesShow full outlineCited By (241)Figures (2)Tables (3)Table 1Table 2Table 3CognitionVolume 78, Issue 1, January 2001, Pages B1-B15Brief articleRecognizing one's own faceAuthor links open overlay panelTilo T.J Kircher a b, Carl Senior b, Mary L Phillips b, Sophia Rabe-Hesketh b, Philip J Benson c, Edward T Bullmore d, Mick Brammer b, Andrew Simmons b, Mathias Bartels a, Anthony S David bShow moreOutlineAdd to MendeleyShareCitehttps://doi.org/10.1016/S0010-0277(00)00104-9Get rights and contentAbstractWe report two studies of facial self-perception using individually tailored, standardized facial photographs of a group of volunteers and their partners. A computerized morphing procedure was used to merge each target face with an unknown control face. In the first set of experiments, a discrimination task revealed a delayed response time for the more extensively morphed self-face stimuli. In a second set of experiments, functional magnetic resonance imaging (fMRI) was used to measure brain activation while subjects viewed morphed versions of either their own or their partner's face, alternating in blocks with presentation of an unknown face. When subjects viewed themselves (minus activation for viewing an unknown face), increased blood oxygenation was detected in right limbic (hippocampal formation, insula, anterior cingulate), left prefrontal cortex and superior temporal cortex. In the partner (versus unknown) experiment, only the right insula was activated. We suggest that a neural network involving the right hemisphere in conjunction with left-sided associative and executive regions underlies the process of visual self-recognition. Together, this combination produces the unique experience of self-awareness.Previous article in issueNext article in issueKeywordsFace recognitionSelf-conceptSelf-perceptionReaction timeFunctional imaging1. IntroductionThe face is our most characteristic external feature. Mirror recognition does not occur in humans before 18 months or in other primates, except adult great apes (Gallup, 1970, Parker et al., 1994). Several investigations have shown that the processing of self-relevant information differs from processing objective information. For example, in word recognition studies, self-referential encoding facilitates recall better than any other mnemonic strategy (Kihlstrom and Klein, 1997, Schacter, 1989). Listening to autobiographical episodes (Fink et al., 1996) or judging one's own personality traits (Kircher, Brammer, Simmons, Bartels, & David, 2000) has been shown to activate distinct cerebral areas. Specific operations underlying self-processing have been proposed (Schacter, 1989, Snodgrass and Thompson, 1997). In previous imaging studies, verbal material has been used to investigate semantic self-referential knowledge (Craik et al., 1999). However, a stronger and more direct cue for investigating self-information processing is one's own face, with which we are very familiar from mirror reflections and photographs. Understanding of how we recognize a familiar face has grown through experiments in cognitive psychology (Bruce & Young, 1986), neurophysiology (Seeck et al., 1993), neuroimaging (Haxby et al., 1996, Kapur et al., 1995), and neuropsychological studies of patients with face recognition deficits (prosopagnosia) (Sergent & Poncet, 1990). Surprisingly, little research has been devoted to facial self-recognition, presumably because it is assumed that this cannot be separated from familiar face recognition. Some patients with severe prosopagnosia (Sergent & Poncet, 1990) and Alzheimer's disease (Bologna & Camp, 1997) fail to recognize themselves in the mirror yet no pure cases of ‘autoprosopagnosia’ have been described. In an event-related potential (ERP) study by Ninomiya, Onitsuka, Chen, Sato, and Tashiro (1998) a larger P300 response to the subject's own face compared to familiar faces has been reported.One major problem when studying self-face processing is to control for emotional salience and overlearnedness, since both are known to influence processing (Klatzky and Forrest, 1984, Phillips et al., 1997, Valentine and Bruce, 1986, Young et al., 1985). In our study, we tried to overcome this by using the face of each subject's partner for comparison. We used morphed versions of the subject's own face and their partner's face, each blended with an unknown, same sex identity, and conducted two sets of experiments. In the first set, we measured reaction time and categorical boundaries for recognition of the subject's own face and their partner's face. In the second set, we measured cerebral activation with fMRI while subjects viewed the same stimuli. We predicted a specific behavioural and neural response when subjects viewed their own face because of its outstanding subjective importance.2. Materials and methods2.1. Reaction time experiments2.1.1. SubjectsTwenty healthy, male volunteers participated in the behavioural study (mean age 31 years, mean IQ estimate 113; National Adult Reading Test, Nelson & Willison, 1991). Exclusion criteria were history of brain injury, and past and current psychiatric or neurological illness. No subject was taking regular medication. Neither the subjects nor their female partners wore spectacles and none had facial hair. Subjects had known their partners for between 1 and 16 years (median 2.7).2.1.2. StimuliColoured, full face, frontal view photographs were taken of the 20 participants and their partners in a photo studio under standardized conditions. The partner's face was chosen as an emotionally salient and highly overlearned non-self face. A similar-looking novel face (identity unknown to participants) matched for age and sex was paired with each of the self and partner faces. All the photos were digitized; the subjects' faces were then mirror transposed and a black template was applied semi-automatically to remove non-facial attributes such as background, hair and ears. The individual faces were manipulated (‘morphed’) using a computer warping package with their assigned identities in 5% steps (Benson, 1994), resulting in graded blending of facial features between two same sex identities. The purpose of this was to have a number of representations of an individual's face which were sufficiently novel to prevent habituation, yet were easily recognizable.2.1.3. Experimental designSubjects were initially familiarized with the endpoint of the morph series, i.e. the real identities, once for 15 s. Subjects were then presented with the morphed images along the dimensions self versus unknown and partner versus unknown. The 21 faces of one morph series were presented on a computer screen, one image at a time, in a randomized order and again serially, starting from each endpoint (presentation order: ‘known’ first and ‘unknown’ first). The repeated use of versions of the same novel face controlled for familiarity. Each series (presentation order: ‘random’, ‘known’, ‘unknown’) was presented with a short pause between each run, twice per subject in a pseudorandom order. The subjects had to indicate whether or not the face was known to them (self, partner) by pressing one of two buttons with the right or left index finger. The subjects were given the instruction: ‘If the face on the screen looks more like you (partner), press the right button with your right index finger, if the face looks more like the other person, press the left button with the left index finger.’ The image was displayed until a button was pressed; after a 1 s interval with the screen blank, a fixation point appeared in the centre of the screen and the next face was shown.2.2. fMRI experimentsSix weeks after the behavioural study, a subgroup of six subjects (mean age 31 years, mean IQ estimate 115) participated in two 5 min fMRI experiments for presentation of the self versus novel and partner versus novel face series. The seven faces closest to the overlearned endpoint (100–70% self or partner) in each series were presented one at a time on a computer screen in a randomized order for 2 s, each followed by a 1 s blank screen. This was followed by presentation of seven faces from the unknown endpoint (non-self, non-partner) in a similar manner. As distracters, two faces of the opposite end of the morph spectrum were randomly intermingled into each block to ensure that subjects attended. Stimuli were presented 3.5 m from the subject, subtending visual angles of 10° horizontally and 8° vertically. Each experiment consisted of ten separate 30 s presentation phases, alternating between overlearned (phase A) and novel (phase B) stimuli, with the first presentation being ‘overlearned’. In the ‘B’ phase of both experiments repeated morphed versions of a novel identity's face were employed. Therefore, this face soon became familiar and hence, in addition to processing of facial configurations per se, familiarity was controlled for across both phases of both experiments. The presentation order of the two experiments was counterbalanced across subjects. Subjects indicated whether the identity was overlearned (self or partner) or novel by pressing one of two buttons with the right thumb as quickly and accurately as possible. To familiarize subjects with the stimuli, they viewed the endpoints of each morphed series for 15 s in the scanner. Prior to MRI data acquisition, subjects were presented with six faces as a practice block.2.2.1. Image acquisition and analysisFunctional MRI data were acquired using a GE Signa 1.5 T system (General Electric, Milwaukee, WI, USA) with an ANMR operating console and hardware (Advanced Nuclear Magnetic Resonance, Woburn, MA, USA) for gradient echoplanar imaging (EPI) at the Maudsley Hospital, London. One hundred T2*-weighted images depicting BOLD contrast were acquired at each of 14 non-contiguous near axial planes (7 mm thick with 0.7 mm slice skip; in-plane resolution 3 mm) parallel to the intercommissural (AC-PC) line: TE=40 ms, TR=3 s, flip angle 90°, number of signal averages 1. At the same session, a 43 slice, high resolution inversion recovery echoplanar image of the whole brain was acquired in the AC-PC plane with TE=73 ms, TI=180 ms, TR=16 000 ms, in-plane resolution 1.5 mm, slice thickness 3 mm, slice gap 0.3 mm. Rigid body motion in 3D was estimated and corrected by realignment and regression (Brammer et al., 1997). Periodic change in MR signal intensity at the frequency of alternation between A and B tasks was estimated by fitting a sinusoidal regression model to the fMRI time series observed at each voxel. The model included sine and cosine waves at the frequency of the experimental input function, with amplitudes γ and δ, respectively. The standardized power of response at experimentally determined frequency was estimated by P=(γ2+δ2) divided by its standard error. The sign of γ identified the timing of maximum MR signal with respect to the input function: if γ>0, the maximum signal was observed in the first condition; if γ0 were coloured red and superimposed on a grey scale EPI template image to form a generic brain activation map (GBAM) (Brammer et al., 1997).We used repeated measures analysis of variance to estimate task-related differences in the power of functional response at each voxel. The main effect of task was tested for significance by permutation at voxels which demonstrated significant activation by one task or both (Bullmore et al., 1999, Edington, 1980).3. Results3.1. Behavioural experimentsWe performed an analysis of the perceived categorical boundaries for each subject in each trial. When the responses for each trial are sorted from image 1 to 21, the categorical boundary is defined as the mean between the first image judged as ‘unknown’ and the image after the last image judged as ‘known’. In both the self/unknown and the partner/unknown conditions, subjects judged stimuli as belonging to distinct categories with a sharp boundary between them (see Fig. 1). A regression of categorical boundary was performed on the ‘order of presentation’ and ‘identity’ again with a random effect for subject. There was a significant effect for ‘order of presentation’ (Ppartner's face)Cerebral regionBASideTal. xaTal. yTal. zNo. of activated voxelsbFusiform gyrus–L−14−81−1329Lenticular/subthalamic nucleus–R12−11−217Middle temporal gyrus21R46−3−716Anterior and mid-posterior insula–R40−3410–R52−6−28Inferior frontal gyrus45L−3825206Supramarginal gyrus/inferior parietal lobe40L−49−39316Hippocampal formation30R9−36−24aTalairach co-ordinates (mm) refer to the voxel with the maximum FPQ (fundamental power quotient) in each regional cluster.bThe probability of false activation of each voxel in the generic brain map over all six subjects was <0.05.4. DiscussionIn two independent sets of experiments, we investigated processing of two highly overlearned faces, one's own and one's partner's, on a behavioural and neural systems level. We found a reaction time advantage for the recognition of morphs of overlearned faces compared with strangers' faces. The morphing procedure as well as the order of presentation influenced the response times significantly. Facial identity appears to be perceived categorically. There was no difference in categorical boundaries for self/novel and partner/novel. In summary, one's own face is not processed differently on a behavioural level when compared with another overlearned, emotionally salient face, the partner's. In two fMRI experiments, we demonstrated that recognition of the own face activated right limbic and left prefrontal regions, whereas recognition of the partner involved only the right insula.In a related study to ours, Beale and Keil (1995) compared the recognition of famous and novel morphed identities. They found a reaction time advantage for the famous faces. A higher processing speed for familiar faces (self or famous), whether morphed or unmorphed (Keenan et al., 1999, Klatzky and Forrest, 1984, Tong and Nakayama, 1999, Valentine and Bruce, 1986, Young et al., 1985), was replicated in our study, where the response time was faster for the overlearned faces. The faster recognition of famous or overlearned identities could be attributed to face processing models, in which the face recognition units and person identity nodes process information from very familiar (overlearned) faces more quickly than that from less familiar ones (Bruce & Young, 1986) because of ease of access to stored representation and semantic information, respectively. We extended the findings of Beale and Keil (1995) and showed that categorical perception of faces occurs also between overlearned and recently familiarized faces. Another finding is the fact that categorical boundaries depend on the order of presentation of the morphs. The boundaries are shifted towards the identity the serial presentation started with. This presumably reflects a response bias whereby subjects ‘anticipate’ the boundary.Comparing the response time of the two highly overlearned faces, self and partner, directly in a post-hoc analysis, we found a small but significantly slower processing speed for the self faces when they were morphed more (faces 4–7), but not when morphed less extensively (faces 1–3). The effect is subtle, since it was not present in the multiple regression analysis and it was found in the serial presentation (order ‘known’ first) but neither in the random presentation nor in the fMRI experiments, in which randomized presentation order was also employed. The combination of serial presentation and strong morphing produces a delayed recognition for the self compared with the partner's face. We can speculate that the delayed recognition effect might be due to a mismatch of the internal representation of the self face and reality. For example, many people think that a snapshot of themselves is not an accurate representation. The morphing procedure might exaggerate the mismatch between self-representation and photograph even further, resulting in a more complex verification process, which leads to a longer response time.Previous PET and fMRI studies have investigated cerebral areas engaged in recognition memory of faces (Grady et al., 1995, Gur et al., 1997, Haxby et al., 1996, Kapur et al., 1995, Sergent et al., 1992). In these studies subjects had to acquire new information just before or while being scanned. In newer related studies, recognition of a number of different famous or unfamiliar faces (George et al., 1999) or objects (Gauthier, Tarr, Anderson, Skudlarski, & Gore, 1999) was compared. We were not interested in mere familiarity but rather self-recognition or awareness. We matched the self face with the partner's face in order to control for emotional salience and overlearnedness (familiarity). It is assumed that subjects see their own face and their partner's face on a daily basis. The subjects were also extensively familiarized with all the stimulus material 6 weeks prior to, and through repeated presentations, during scanning. The activation consequent upon recognizing one's own face was more extensive and the pattern was striking. The right limbic regions, which were extensively activated when self was contrasted with novel, are known to be engaged in pleasant and unpleasant emotional responses (Lane et al., 1997, Phillips et al., 1997). A study of autobiographical memory has shown the engagement of right limbic regions in the recollection of personal memories (Fink et al., 1996). Preilowski (1979) measured skin conductance in split-brain patients and healthy subjects while faces of themselves, faces of persons they knew well, emotional scenes and objects were presented to one hemifield for 100 ms. The number of galvanic skin responses was higher to the own face presented to the right hemisphere than to any other stimulus-visual field combination in both groups. We interpret the activation of the right limbic system in our study as a unique, strong emotional response to seeing our own face. This emotional response seems to be present both for morphed, as in our experiment, and for unmorphed faces (Preilowski, 1979). The left prefrontal cortex, which was only activated by self faces, is thought to have an important role in executive processes such as the integration of information to form a coherent ‘whole’ from multi-modal inputs (Miller, 1992, Vandenberghe et al., 1996).In response to the partner's face, the activation of the anterior insula, about 1 cm above the activation site in the self condition, could reflect an emotional response to the both intimate and familiar partner's face. We assume that the gender of the subject's partner did not bias the results, since in both face experiments we used same sex control faces. A sex bias (i.e. males viewing female faces) should therefore have been cancelled out. Replication of the study with other intimately known individuals (e.g. siblings and women viewing male faces) would nonetheless be desirable.One explanation for the differences in activation between the two fMRI experiments is that memory and verification processes are being stimulated in the self condition only rather than processes inherent in self-recognition. This is unlikely given the closeness of the matching within each experiment and the employment of an initially unknown, but recently familiar face as the control condition in both, hence the minimal activation in the partner versus novel face experiment. Furthermore, there was little difference found between self and partner in the reaction times or categorical boundaries in the behavioural experiment. Nevertheless, we are unaccustomed to picking out our own face from a range of possible faces, unlike the more frequent task of identifying a well known person's face in a crowd. A component of the activation may therefore reflect such novelty and the additional attentional deployment. Finally, the differences in activation between the self and partner experiments may have been exaggerated by the morphing procedure, that is, morphing may have interacted with self-recognition in some way to engage attention, for example. However, even if this were the case, the presence or absence of self-relevant information is the crucial factor distinguishing the two experiments and not the morphing procedure.The combination of right limbic and left cortical activation could underlie human self-recognition. We suggest that it is this limbic-cortical connection which enables the integration of affect and cognition. Experiments with split-brain patients (Gallois et al., 1988, Preilowski, 1979, Sperry et al., 1979) have shown that although rudimentary self-recognition occurs in the disconnected right hemisphere, only transcallosal transfer of information enables the sensory experience to reach awareness. The onset of self-recognition in human infancy correlates with the myelination of fibres in the frontal lobe (Kinney, Brody, Kloman, & Gilles, 1988). Isolated failures of self-recognition have yet to emerge in the neurological literature. Such failure does not seem to occur following isolated frontal lesions or in cases of amnesia with profound loss of autobiographical memory (Tulving, 1993) where there is a preservation in the sense of self. The relatively widespread and bilateral activation we have demonstrated in response to the self stimuli suggests that many processes contribute to self-perception with some built-in redundancy, hence the resistance to disruption by common neurological lesions.5. ConclusionWe have reported evidence of a distinct neural substrate underlying facial self-recognition involving the right limbic system and left prefrontal and temporo-parietal cortex. One's own face is a stimulus that activates unique self-referential processing. We suggest that the interplay of both emotional and associative cognitive processes is necessary for the unique perception of a coherent self. Further studies are needed to clarify the specific nature of the neural correlates of visual self-recognition.AcknowledgementsT.K. was supported by the German Research Council (DFG), C.S. was supported by the Pilkington Family Trusts and the McDonnell Foundation in Cognitive Neuroscience, and E.T.B. and M.LP. were supported by the Wellcome Trust. We thank P.K. McGuire and P. Fletcher for advice on earlier versions of the manuscript, and S.C.R. Williams and C. 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Gaze cues can be evocative even when presented in static pictures of faces suggesting that responses depend on low-level visual features of gaze stimuli. The current study examined whether emotional gaze responses depend on the physical stimulus properties of an eye contact experience versus the cognitive evaluation of the social context of gaze. This was done by comparing skin conductance responses (SCR), an index of emotional arousal, during episodes of social gaze and 'self-gaze' (gazing at one's own eyes in a mirror), keeping other aspects of the viewing conditions constant. We compared SCRs during social gaze and self-gaze in forty participant pairs. Each participant engaged in ten, 20 second eye contact trials, alternating between social and self-gaze. Self-gaze episodes produced significant SCRs but social gaze SCR's were larger and occurred more reliably. SCRs decreased across trials (habituation effect) in both conditions. We speculated that social gaze between opposite sex partners might yield larger SCRs but this was not found. Overall, these results conceptually replicate previous findings of (likely top-town) cognitive regulation of autonomic gaze responses based on evaluation of the social context.Are covered faces eye-catching for us? The impact of masks on attentional processing of self and other faces during the COVID-19 pandemic2022, CortexShow abstractDuring the COVID-19 pandemic, we have been confronted with faces covered by surgical-like masks. This raises a question about how our brains process this kind of visual information. Thus, the aims of the current study were twofold: (1) to investigate the role of attention in the processing of different types of faces with masks, and (2) to test whether such partial information about faces is treated similarly to fully visible faces. Participants were tasked with the simple detection of self-, close-other's, and unknown faces with and without a mask; this task relies on attentional processes. Event-related potential (ERP) findings revealed a similar impact of surgical-like masks for all faces: the amplitudes of early (P100) and late (P300, LPP) attention-related components were higher for faces with masks than for fully visible faces. Amplitudes of N170 were similar for covered and fully visible faces, and sources of brain activity were located in the fusiform gyri in both cases. Linear Discriminant Analysis (LDA) revealed that irrespective of whether the algorithm was trained to discriminate three types of faces either with or without masks, it was able to effectively discriminate faces that were not presented in the training phase.Hearing voices in the head: Two meta-analyses on structural correlates of auditory hallucinations in schizophrenia2022, NeuroImage: ClinicalShow abstractPast voxel-based morphometry (VBM) studies demonstrate reduced grey matter volume (GMV) in schizophrenia (SZ) patients’ brains in various cortical and subcortical regions. Probably due to SZ symptoms’ heterogeneity, these results are often inconsistent and difficult to integrate. We hypothesized that focusing on auditory verbal hallucinations (AVH) – one of the most common SZ symptoms – would allow reducing heterogeneity and discovering further compelling evidence of SZ neural correlates. We carried out two voxel-based meta-analyses of past studies that investigated the structural correlates of AVH in SZ. The review of whole-brain VBM studies published until June 2022 in PubMed and PsychInfo databases yielded (a) 13 studies on correlations between GMV and AVH severity in SZ patients (n = 472; 86 foci), and (b) 11 studies involving comparisons between hallucinating SZ patients (n = 504) and healthy controls (n = 524; 74 foci). Data were analyzed using the Activation Likelihood Estimation method. AVH severity was associated with decreased GMV in patients’ left superior temporal gyrus (STG) and left posterior insula. Compared with healthy controls, hallucinating SZ patients showed reduced GMV on the left anterior insula and left inferior frontal gyrus (IFG). Our findings revealed important structural dysfunctions in a left lateralized cluster of brain regions, including the insula and temporo-frontal regions, that significantly contribute to the severity and persistence of AVH. Structural atrophy found in circuits involved in generating and perceiving speech, as well as in auditory signal processing, might reasonably be considered a biological marker of AVH in SZ.Overestimation of eye size: People see themselves with bigger eyes in a holistic approach2021, Acta PsychologicaCitation Excerpt :Thus, revealing the characteristics of memory about faces is an important research topic in applied social communication as well as in basic psychology studies because it contributes to understanding the role of faces in society and a cognitive mechanism of face memory. Among face memories, own face memory has special characteristics compared to others' face memory (Alzueta et al., 2019; Keyes, 2012; Keyes & Brady, 2010; Kircher et al., 2001). Event-related potential (ERP) studies have revealed that own face perception is different from others' face perception (Alzueta et al., 2019).Show abstractA face contains crucial information for identification; moreover, face recognition is superior to other types of recognition. Notably, one's own face is recognized better than other familiar faces. However, it is unclear whether one's own face, especially one's own internal facial features, is represented more accurately than other faces. Here, we investigated how one's own internal facial features were represented. We conducted a psychological experiment in which the participants were required to adjust eye size to the real size in photos of their own or well-known celebrities' faces. To investigate why individuals' own and celebrity facial representations were different, two types of photos were prepared, with and without external features. It was found that the accuracy of eye size for one's own face was better than that for celebrities' faces in the condition without external features, in which holistic processing was less involved than in the condition with external features. This implies that the eye size of one's own face was represented more accurately than that of other familiar faces when external features were removed. Moreover, the accuracy of the eye size of one's own face in the condition with external features was worse than that in the condition without external features; the adjusted eye size in the condition with external features was larger than that in the condition without external features. In contrast, for celebrities' faces, there was no significant difference between the conditions with and without external features. The adjusted eye sizes in all conditions were overestimated compared to real eye sizes. Previous research indicated that eye size was adjusted to a larger size when evaluating as more attractive, in which the evaluation is related to holistic processing. Based on this perspective, it could be that one's own face was represented as more attractive in the condition with external features in the current study. Taken together, the results indicated that the representation of own eye size, which is an internal facial feature, was affected by the visibility of the external features.The role of structural and functional insular cortex abnormalities in body perception disturbance in schizophrenia2021, EncephaleShow abstractL’objectif de cet article est de proposer une revue de la littérature scientifique consacrée aux liens entre certaines anomalies structurelles et fonctionnelles du cortex insulaire observées dans la schizophrénie, et l’existence d’un trouble de la perception du corps, et plus généralement d’une altération du sens de l’intéroception chez les patients schizophrènes, pour comprendre comment la physiopathologie de l’insula peut être impliquée dans la symptomatologie de cette maladie.À partir d’une recherche basée sur les articles indexés dans la base de données PubMed depuis 2000, nous étudierons la fonction du cortex insulaire et son implication dans la perception et la représentation des états somatiques, puis nous passerons en revue certaines anomalies structurelles de l’insula observées dans la schizophrénie qui pourraient être impliquées symptomatologie liée à l’existence d’un trouble de l’interoception chez les patients schizophrènes.Les données de la littérature scientifique sur les anomalies du cortex insulaire dans la schizophrénie ont montré que ces dysfonctionnements pourraient constituer un substrat biologique des troubles de la perception corporelle observés dans la schizophrénie, et être à l’origine d’une altération du sentiment de soi. Enfin, des corrélations ont été observées entre ces anomalies et la symptomatologie positive.Les anomalies de l’insula pourraient participer à la physiopathologie de la schizophrénie, et être à l’origine d’un trouble de l’intéroception et d’une altération massive du sentiment de soi. Par ailleurs, ces anomalies pourraient engendrer un trouble de la discrimination entre les stimuli internes et externes, et être impliquées notamment dans les hallucinations accoustico-verbales. Les liens entre la symptomatologie de la schizophrénie et les dysfonctionnements du cortex insulaire constituent un domaine de recherche récent et encore à explorer.The present study focuses on a review of scientific literature upon structural and functional abnormalities of the insular cortex found in schizophrenic patients in order to emphasize links between the pathophysiology of this brain region and the symptomatology of schizophrenia.From a review based upon journal articles published since 2002 and indexed into the Pubmed data base, we first studied the main findings on the function of the insular cortex and its involvement in the perception and representation of the body states, as it is one of the main neural substrates for the interoception sense. Then, we highlighted various structural abnormalities found in schizophrenic patients in order to study links existing between dysfunctions in the insular cortex and an altered perception of body states and of self-awareness in schizophrenia. Eventually, we studied the links emphasized between functional abnormalities of insula in schizophrenia and a positive symptomatology, especially auditory hallucinations.The data in the neurobiological literature about abnormalities in the insular cortex in schizophrenia has demonstrated that insula dysfunctions could constitute one of the biological substrates of disorders of body perception in schizophrenia, and it could be a source of the alteration of the sense of self that is characteristic of this psychiatric pathology. Moreover, the importance of insula in processing interoceptive stimuli and their integration with exteroceptive stimuli could engender a problem in the discrimination between endogenic and exogenic stimuli, a problem that could thus be involved in the positive symptomatology of schizophrenia, such as auditory hallucinations and delusion.Scientific knowledge in the role of the insula for the perception and representation of the body states shows that the insula has a key role for interoception. Functional abnormalities of the insular cortex in schizophrenia may lead to the conclusion that this area of the brain is one of the biological substrates for the disorders of body perception in schizophrenia, and also, mainly, one of the substrates for the disorders of self-awareness which depends, according to many authors, on the representation of the body states. Moreover, the role of the insula in integrating interoceptive and exteroceptive stimuli leads to the supposition that dysfunctions of the insula could result in a problem concerning the discrimination between endogenic and exogenic stimuli, and thus could create a positive symptomatology, mainly auditory hallucinations for schizophrenic patients. It needs to be noted that the links between the symptomatology of schizophrenia and the dysfunctions of the insular cortex are still in debate among researchers. Recent researches do not allow to conclude with accuracy of a systematic correlation between psychopathology of schizophrenia and functional abnormalities of the insula, although it seems obvious to find a link between these psychopathological and neurobiological phenomena.Self-esteem and cultural worldview buffer mortality salience effects on responses to self-face: Distinct neural mediators2020, Biological PsychologyShow abstractTerror management theory proposes cultural worldview and self-esteem as two buffers against death anxiety. The neural mediators of these buffering effects, however, have not been fully understood. The present work investigated neural mediation mechanisms between self-esteem/cultural trait (self-construal) and mortality salience (MS) effects on self-face processing. We found that MS (vs. NA) priming eliminated self-face advantage in behavioral judgments of face-orientation in low self-esteem individuals and reduced self-face advantage in behavioral judgments of facial-familiarity in individuals with high interdependent self-construals. Our functional magnetic resonance imaging (fMRI) results showed that, following MS priming, insular activities mediated the relationship between self-esteem and self-face advantage in face-orientation judgments, whereas dorsal medial prefrontal activity mediated the relationship between interdependent self-construal and self-face advantage in face-familiarity judgments. Our findings suggest that distinct neural mechanisms are engaged in mediating the relationships between self-esteem/cultural trait and MS effects on the emotional and cognitive processes of self-relevant information.View all citing articles on ScopusView AbstractCopyright © 2001 Elsevier Science B.V. All rights reserved.Recommended articlesSpontaneous mentalizing in neurotypicals scoring high versus low on symptomatology of autism spectrum disorderPsychiatry Research, Volume 258, 2017, pp. 15-20Annabel.D. Nijhof, …, Jan.R. WiersemaView PDFTolerance for distorted faces: Challenges to a configural processing account of familiar face recognitionCognition, Volume 132, Issue 3, 2014, pp. 262-268Adam Sandford, A. Mike BurtonView PDFDeficits of subliminal self-face processing in schizophreniaConsciousness and Cognition, Volume 79, 2020, Article 102896Song Zhou, …, Hongxiao JiaView PDFThe P200 predominantly reflects distance-to-norm in face space whereas the N250 reflects activation of identity-specific representations of known facesBiological Psychology, Volume 140, 2019, pp. 86-95Stella J. Wuttke, Stefan R. SchweinbergerView PDFThe Integrative Self: How Self-Reference Integrates Perception and MemoryTrends in Cognitive Sciences, Volume 19, Issue 12, 2015, pp. 719-728Jie Sui, Glyn W. HumphreysView PDFIdentity-specific neural responses to three categories of face familiarity (own, friend, stranger) using fast periodic visual stimulationNeuropsychologia, Volume 141, 2020, Article 107415Alison Campbell, …, James W. TanakaView PDFShow 3 more articlesArticle MetricsCitationsCitation Indexes: 240CapturesReaders: 295Social MediaTweets: 1View detailsAbout ScienceDirectRemote accessShopping cartAdvertiseContact and supportTerms and conditionsPrivacy policyWe use cookies to help provide and enhance our service and tailor content and ads. By continuing you agree to the use of cookies.Copyright © 2023 Elsevier B.V. or its licensors or contributors. ScienceDirect® is a registered trademark of Elsevier B.V.ScienceDirect® is a registered trademark of Elsevier B.V. ",11062324,"We report two studies of facial self-perception using individually tailored, standardized facial photographs of a group of volunteers and their partners. A computerized morphing procedure was used to merge each target face with an unknown control face. In the first set of experiments, a discrimination task revealed a delayed response time for the more extensively morphed self-face stimuli. In a second set of experiments, functional magnetic resonance imaging (fMRI) was used to measure brain activation while subjects viewed morphed versions of either their own or their partner's face, alternating in blocks with presentation of an unknown face. When subjects viewed themselves (minus activation for viewing an unknown face), increased blood oxygenation was detected in right limbic (hippocampal formation, insula, anterior cingulate), left prefrontal cortex and superior temporal cortex. In the partner (versus unknown) experiment, only the right insula was activated. We suggest that a neural network involving the right hemisphere in conjunction with left-sided associative and executive regions underlies the process of visual self-recognition. Together, this combination produces the unique experience of self-awareness.",Recognizing one's own face.
-" Functional imaging of brain responses to pain. A review and meta-analysis (2000) - ScienceDirect JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main contentSkip to article ScienceDirectJournals & BooksSearchRegisterSign inView PDFDownload full issueSearch ScienceDirectOutlineAbstractRésuméKeywordsMots-cléSome methodological considerations of pain imagingResponses to acute pain in normal volunteersBrain responses in patients with painGeneral discussion and future lines of researchConclusionReferencesShow full outlineCited By (1790)Figures (6)Tables (4)Table ITable IITable IIITable IVNeurophysiologie Clinique/Clinical NeurophysiologyVolume 30, Issue 5, October 2000, Pages 263-288ReviewFunctional imaging of brain responses to pain. A review and meta-analysis (2000)Appréciation par l’imagerie fonctionnelle des réponses cérébrales à la douleur. Revue et méta-analyse.Author links open overlay panelR. Peyron 1 2 3, B. Laurent 1 2, L. García-Larrea 3 4Show moreOutlineAdd to MendeleyShareCitehttps://doi.org/10.1016/S0987-7053(00)00227-6Get rights and contentAbstractBrain responses to pain, assessed through positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) are reviewed. Functional activation of brain regions are thought to be reflected by increases in the regional cerebral blood flow (rCBF) in PET studies, and in the blood oxygen level dependent (BOLD) signal in fMRI. rCBF increases to noxious stimuli are almost constantly observed in second somatic (SII) and insular regions, and in the anterior cingulate cortex (ACC), and with slightly less consistency in the contralateral thalamus and the primary somatic area (SI). Activation of the lateral thalamus, SI, SII and insula are thought to be related to the sensory-discriminative aspects of pain processing. SI is activated in roughly half of the studies, and the probability of obtaining SI activation appears related to the total amount of body surface stimulated (spatial summation) and probably also by temporal summation and attention to the stimulus. In a number of studies, the thalamic response was bilateral, probably reflecting generalised arousal in reaction to pain. ACC does not seem to be involved in coding stimulus intensity or location but appears to participate in both the affective and attentional concomitants of pain sensation, as well as in response selection. ACC subdivisions activated by painful stimuli partially overlap those activated in orienting and target detection tasks, but are distinct from those activated in tests involving sustained attention (Stroop, etc.). In addition to ACC, increased blood flow in the posterior parietal and prefrontal cortices is thought to reflect attentional and memory networks activated by noxious stimulation. Less noted but frequent activation concerns motor-related areas such as the striatum, cerebellum and supplementary motor area, as well as regions involved in pain control such as the periaqueductal grey. In patients, chronic spontaneous pain is associated with decreased resting rCBF in contralateral thalamus, which may be reverted by analgesic procedures. Abnormal pain evoked by innocuous stimuli (allodynia) has been associated with amplification of the thalamic, insular and SII responses, concomitant to a paradoxical CBF decrease in ACC. It is argued that imaging studies of allodynia should be encouraged in order to understand central reorganisations leading to abnormal cortical pain processing. A number of brain areas activated by acute pain, particularly the thalamus and anterior cingulate, also show increases in rCBF during analgesic procedures. Taken together, these data suggest that hemodynamic responses to pain reflect simultaneously the sensory, cognitive and affective dimensions of pain, and that the same structure may both respond to pain and participate in pain control. The precise biochemical nature of these mechanisms remains to be investigated.RésuméCette revue de la littérature concerne les réponses cérébrales à la douleur appréciées par l’imagerie fonctionnelle, soit la tomographie d’émission de positons (TEP), soit l’imagerie par résonance magnétique fonctionnelle (IRMf). La première mesure les variations de débit sanguin cérébral, la seconde les variations du signal BOLD (blood oxygen level dependent) entre deux conditions. Pour l’étude de la nociception, la douleur induite par un stimulus nocif comparée à un stimulus non nocif (en dessous du seuil) s’accompagne d’une augmentation presque constante du débit sanguin cérébral et du signal BOLD dans le cortex insulaire, l’aire SII, et le gyrus cingulaire antérieur, de façon plus inconstante dans le thalamus et l’aire SI. Les réponses insulaire/SII, thalamiques et SI sont supposées refléter l’aspect discriminatif de la douleur. La réponse du cortex SI présente dans approximativement la moitié des études, apparaît liée à la surface cutanée stimulée par unité de temps, elle semble donc dépendante des sommations temporelles et spatiales; elle est modulée par l’attention portée au stimulus. La réponse thalamique, souvent bilatérale fait probablement intervenir des phénomènes attentionnels d’« éveil » en réponse à la douleur. La réponse cingulaire antérieure (aire de Brodmann 24 et 32) ne participe vraisemblablement pas au codage de l’intensité du stimulus ni de sa localisation mais reflète certainement des processus attentionnels et émotionnels associés à la perception douloureuse. Au sein de cette structure, on distingue d’ailleurs plusieurs sub-divisions, l’une se superposant partiellement avec les activités d’orientation et de détection de cibles, l’autre, plus antérieure et rostrale, correspondant plutôt à une attention soutenue (exemple : Stroop, etc.). En plus de l’augmentation de débit cingulaire, l’attention au stimulus s’accompagne d’une activité du cortex pariétal postérieur (aire de Brodman, BA 40) et du cortex pré-frontal dorsolatéral (BA 44 à 46 ) droits qui participent au réseau cortical attentionnel et/ou mnésique. Les activations du striatum, du cervelet, de l’aire motrice supplémentaire, moins commentées, pourraient intervenir dans la réponse motrice à la douleur, l’activation péri-aqueducale pouvant être impliquée dans les contrôles inhibiteurs descendants. Chez les patients, la douleur spontanée s’accompagne d’une diminution du débit thalamique controlatéral, situation réversible sous thérapeutique analgésique. L’allodynie, douleur évoquée par un stimulus non nocif, est associée à une amplification des réponses thalamiques, insulaires et de SII, alors que la réponse cingulaire rostrale est diminuée, traduisant des anomalies de réorganisations centrales postlésionnelles. Enfin, il apparaît que des procédures antalgiques, pharmacologiques ou neurochirurgicales, augmentent le débit sanguin cérébral dans les mêmes régions que celles activées par la douleur aiguë, en particulier le gyrus cingulaire antérieur et le thalamus. Ces données suggèrent que les réponses cérébrales à la douleur reflètent à la fois les aspects sensoriel, cognitif et peut-être motivationnel de la perception douloureuse, et qu‘une même structure peut à la fois répondre à la douleur et participer à son contrôle, même si la médiation biochimique de ces activités reste à inventorier.Previous article in issueNext article in issueKeywordsallodyniaanalgesiaattentioncentral painfMRIimagingmotor cortex stimulationnociceptionPETMots-cléallodynieanalgésiqueattentiondouleur spontanéeimagerieIRMfnociceptionstimulation de l’aire motriceTEPThe functional anatomy of pain in humans has been, in recent years, mainly studied with positron emission tomography (PET). This technique measures concentrations of isotopes within a given body volume; such isotopes are carried by natural molecules which are usually injected and enter the brain via the blood stream. The physical variable that is directly measured by PET cameras is therefore the distribution of radioactivity, while the associated physiological variable depends on the molecule that carries the positron-emitting isotope. In studies where relatively rapid changes in activity are to be measured, isotopes with a short half-life are preferred, which allow repeated measurements in short amounts of time. One of the choice isotopes is 15O, with a half-life of about 2 min only, which can be included in natural molecules such as water or butanol and yields information on regional cerebral blood flow (rCBF). The so-called ‘activation’ PET studies investigate variations of rCBF specifically associated to a given task or a particular stimulus. Data interpretation is based on statistical comparisons of rCBF values obtained in two clinical or experimental situations, often labelled ‘activated’ and ‘control’ conditions.PET has been applied to the study of pain since the beginning of the 1990s, mainly by comparing responses to noxious and non-noxious stimuli, and has brought relevant information to the understanding of the ‘normal’ brain processing of pain. The insights provided by the study of normal subjects have opened a large field of investigation in patients with chronic pain, with the aim to understand (and possibly to treat) the brain dysfunctions and reorganisations leading to these conditions. The finding that the hemodynamic brain response to pain is modulated by both cognitive 18, 122 and affective components [134] has joined electrophysiological data 66, 143, and connected with the view of Melzack and others 101, 102, who described pain sensation as the result of a multi-dimensional integration of sensory-discriminative, cognitive, and affective-motivational axes. However, this evolution of pain imaging complicates the interpretation of new data. Particularly, for each of the responses classically described as ‘pain-related’, the question arises now as to whether it is associated with the encoding of the sensory (intensity, location, modality), affective (fear, unpleasantness), and/or cognitive (attention, memory) aspects of pain integration, all of which contribute to the pain experience. This view is further complicated by the likely contribution of responses not linked to pain integration per se, but related to preparation or inhibition of motor responses triggered by painful stimuli [23]. Thus, the interpretation of imaging studies on pain has moved from a ‘locationist’ conception to the more fluid view of composite networks, where the interaction of interdependent processes creates the unpleasant experience that we name ‘pain’.In this overview we attempt to critically synthesize a number of results, sometimes convergent and sometimes contradictory, obtained with PET and functionnal MRI (fMRI) during the past ten years. The review will concentrate first on the responses to ‘laboratory’ pain observed in normal subjects. It will then comment on the results obtained from patients suffering from pain-related conditions and/or subject to therapeutic procedures for pain relief.Some methodological considerations of pain imagingCBF studies using PETEven though the physiological significance of rCBF changes with regard to neural activity is not clearly established, there is considerable evidence that local CBF changes are generated by metabolic products of synaptic function, and therefore reflect variations in local synaptic activity 147, 148. The short scan duration (50–120 s) and inter-scan interval (10–15 min) permit multiple studies in rapid succession, therefore allowing comparisons between consecutive functional states, including the resting state. The interpretation of results is usually based on voxel-by-voxel subtraction of images, looking for areas where rCBF is significantly changed across conditions. In most pain experiments, comparisons have been performed between two intensities of a thermal stimulus, one below and one above the pain threshold, so that the subtraction analysis extracts activity which can be ‘specifically’ attributed to nociceptive processing.Limitations of PET studies are :•low temporal resolution due to signal averaging during approximately 1 min;•the need of group analysis pooling the data of at least five to six subjects to obtain meaningful results;•the need for a nearby cyclotron facility to prepare radioactive tracers;•the need to give intravenous injections to the subjects.These drawbacks may be partly overcome in the future by fMRI, which uses similar experimental procedures as PET with a better temporal resolution, a non-radioactive environment, no injection, and a possibility for individual analysis.Studies using fMRIAnalysis of fMRI images is based on changes in the blood oxygenation level dependent (BOLD) signal, which reflects simultaneously local CBF changes and variations in deoxyhemoglobin content 140, 158. Results obtained with fMRI have been found to be strongly correlated with those from PET-CBF in identical paradigms 32, 139, 142. fMRI has some advantages over PET, including the operation in a non-radioactive environment and thus the possibility to repeat recordings. Even though new-generation PET cameras have been recently applied to single-subject analysis [25], the possibility to take into account anatomy and other individual characteristics by fMRI is a clear advantage over PET. Furthermore, the access to single-subject analysis will be an important gain in pain studies, since pain is notoriously dependent upon individual factors. Finally, the temporal resolution of fMRI, which ranges from a 300 ms theoretical value to a more realistic figure of 1–3 s in event-related fMRI studies with echo-planar systems [15], is another advantage compared to PET; fMRI appears therefore as an intermediate solution between PET resolution (tens of seconds) and electrophysiology (tens of milliseconds).Among the fMRI’s current drawbacks we should first cite the requirement of MRI-compatible (i.e., non-ferro-magnetic) equipment, as well as the need for strict timing between stimuli and acquisition in rapidly alternating conditions, all of which add technical constraints, making some experiments more difficult to conduct than with PET. In addition, a disadvantage of fMRI is the existence of pulsation artifacts, which currently impairs analysis of brainstem and thalamic responses. More important, fMRI remains currently limited to ‘activation’ studies, and is neither able to provide information on the resting state nor on neurotransmitters or receptors; this may represent a shortcoming in future studies on pain, which should develop strategies to describe the in vivo distribution and the functional properties of neurotransmitters related to pain processing and control 86, 87, 161. So far, the ‘pain responses’ obtained using PET and fMRI methods have been very similar, but the use of this latter is still too recent to permit definite conclusions, at least until a comparison of results using both techniques in the same population of subjects is available.Responses to acute pain in normal volunteersTable I summarises the results of previously published PET studies in normal subjects. In a decreasing order of consistency, hemodynamic responses to acute pain in normal subjects have been observed in the following brain areas: insular and SII cortices (primarily contralateral to stimulation but also ipsilateral); anterior cingulate cortex (ACC, Brodmann areas [BA] 24 and 32); thalamus (primarily contralateral to stimulation but often bilateral); SI cortex contralateral to stimulation; prefrontal (BAs 10 and 45–47) and posterior parietal (BA 40) cortices; striatum; cerebellum (vermis); periaqueductal grey (PAG); and supplementary motor area (SMA, BA 6). In figure 1, reported sites of maximally significant rCBF increases to pain have been projected onto a normalised MRI matching the Talairach and Tournoux atlas [152].Table I. Normal subjects (Nociception - Capsaicin allodynia).AuthorsYearPET fMRINStimuliT/PC/DMovementSideAnt InsulaSIImid ACC 24/32ACC (rostral)ThalamusSIDLPFC 10, 44-47MPFC 9, 10, 32Parietal 40Amygdala HippocampusSMA 6LNMidbrainCerebellumTalbot et al.1991PET8Heat thermodePC+R↑ C↑ C↑ C↑ CJones at al.1991PET6Heat thermodePD–R↑ C↑ C–ss ↑ I↑ CDerbyshire et al.1994PET6Heat thermodePD–R↑ C↑ C↑ C↑ C↑ I↑ I↑ C↑Casey et al.1994PET9Heat thermodePC+L↑ C↑ B↑ C↑ B↑ C↑↑1996PET9Heat thermodePC+L↑ C↑ C↑ C↑ B↑ C–↑ C↑↑936-43°C DiscriminationPC+L↑ C↑ C↑ C↑9Cold water bathTC–L↑ B↑ B↑ B↑ C↑ I↑ C–↑Coghill et al.1994PET9Heat thermodePD+L↑ B↑ C↑ C↑ C↑ C↓ I↑ C↑ I1999PET16Heat thermodePC+R↑ B↑ B↑ B↑ B↑ C↑ B↑ C↑ B↑Hsieh et al.1994PET10Sub-cutaneous histamineTC–R↑ R↑ R↑ C↑ B↑ R↑ B↑↑1995PET4Sub-cutaneous ethanolTC–R↑ B↑ B↑ C↑ B↑ B↑ B↑ C↑ C↑Craig et al.1996PET11Heat thermodeTC–R↑ C↑ C↑ C↑ CCold thermodeTC–R↑ C↑ C↑ Css ↑ CGrill illusion of painTC–R↑ C↑ C↑ Css ↑ CVogt et al.1996PET7Heat thermodePD–L↑ B↑ C↑ C↑ B↑ C↓ B↓ BRainville et al.1997PET8Heat water bathTC–L↑ C↑ C↑ B↑ CDerbyshire et al.1997PET12laserPD–R↑ C–↑ I↑ B↑ C↑ B↓ C↑ B↓ B↑ BXu et al.1997PET6laserPD+L↑ B↑ B–↑ C–↑ B↑ C↑ C↑ I↑Svensson et al.1997aPET11laserPD+L↑ C↑ C–↑ C↑ C↑ C↑11Electrical muscular painTC–L↑ C↑ C↑ C↑ Css ↑ C↑ Css ↑ C↑1997bPET10Heat thermodeTC–R↑ B↑ C↑ C↑ C↑ C↑ C↑9Heat thermodePD–R↑ B↑ C↑ C–↑ C↑ C↑ I↑Adler et al.1997PET9Heat thermodeTC–L↑ C↑ I↑ I↑ CAndersson et al.1997PET6Capsaicin injectionTC–R↑ C↑ C↑ C↑ C↑ I↑ IAziz et al.1997PET8Esophageal distentionPD–R/L↑ R↑ B↑ B↑ BBinkofski et al.1998fMRI5Esophageal distensionT+PC/D–R/L↑ B↑ B↑ B↑ BSilverman et al.1997PET6Visceral (rectal) painTD–R/L↑ R↑ L↑ L↑ L↑ LBaciu et al.1999fMRI6Visceral (rectal) painPD–R/L↑ B↑ B↑ R↑ R↑ RDerbyshire and Jones1998PET12Heat water bathTC–R↑ I↑ C↑ B↑ I↑ I↓ I↑ C↑Iadarola et al.1998PET13Capsaicin injection (pain)TC–L↑ B↑ C↑ B↑ C↑ C↑ C↑ I↑ B↑↑Capsaicin-induced allodyniaPC+L↑ I↑ I↑ C↑ C↑ B↑ I↑↑Baron et al.1999fMRI9Capsaicin-induced allodyniaPC–R↑ C↑ C––↑ C↑ B↑ CMay et al.1998PET7Capsaicin injection (facial)TC–R↑ B↑ I↓ C↑ C↑Paulson et al.1998PET10 MHeat thermodePD+L↑ I↑ C–↑ C↑ I↑ C↑10 FHeat thermodePD+L↑ B (*, ↑ or ↓C)↑ C↑ B (*, ↑ or ↓C)↑ C (*, ↑ or ↓)↑ I↑Tölle et al.1999PET12Heat thermodeT+PC–R↑ B↑ B↑ C↑ I↑ CPeyron et al.1999PET12Heat thermode (intensity)T+PC–R/L↑ B↑ B–↑ C↓ I––↓ I↑ B–Heat thermode (attention)T+PC–R/L––↑ C↑ B–↑ I (R)↑ B–↑ B↑ BBecerra et al.1999fMRI12Heat thermodePC–L↑ B↑ C↑ B↑ C↑ B↑ C↓ B↑ I↑T:Tonic;P:Phasic pain;C:Continuous;D:Intermittent stimulus; Stimulus displaced (+) or not (–) during experiment;R/Lside of stimulation;ss:sub-significant but discussed as relevant by authors;↑ or ↓of rCBF or BOLD signal contralateral (C) or ipsilateral (I) to stimulus; B indicates bilateral or poorly lateralized activations;*indicates abnormal (excessive) responses.Download : Download full-size imageFigure 1. Summary of the principal regions showing maximally significant rCBF changes to painful stimuli in previous literature. Thirteen studies providing stereotaxic coordinates of rCBF changes according to the Talairach and Tournoux atlas [152] are represented. The provided coordinates of maximal rCBF increases in each study (volume of 8 mm3) were projected onto a normalised magnetic resonance template (MNI, Montreal Neurologic Institute). Coordinates from studies using right-sided stimuli were taken just so, while those from studies using left-sided stimuli were flipped along the x-axis; thus, the left and right sides of the figure are respectively ‘contralateral’ and ‘ipsilateral’ to stimulation. rCBF changes are clustered in three midline regions (cerebellum/midbrain, thalamus, and anterior cingulate) and two lateral sites (around SII and the insular cortex). For more exhaustive details on insular, SII, and cingulate activations, see figures 2, 3 and 5 (represented data are from references 22, 24, 27, 34, 42, 43, 81, 99, 122, 134, 150, 162, 165).Insula/SII corticesThe most reliable pain-related activity across previous studies is bilateral, and has been located in a broad region comprising the depth of the Sylvian fissure and the parietal and frontal operculi, and therefore extending from the anterior insula to the second somatic (SII) area and associative parietal cortex (table I). It is not easy to individualise these different regions from stereotaxic coordinates provided in previous PET studies, since significant rCBF increases often extend over both sagittal and vertical axes in adjacent brain regions. In a number of studies, activations are reported as a focus of increased CBF, which overlaps on both posterior insular and SII cortices 24, 27, 41, while in other cases two separate foci of maximal significance are observed within a large ‘activated’ area 22, 116, 150, 165. This is illustrated in figure 2, which also shows that the location of activation maxima in the vertical axis ranges from z = –8 mm to z = +32 mm when projected onto the Talairach atlas. The particular anatomy of this region, with numerous cortical folds increasing the risk for partial volume effect, as well as the lack of a clear-cut anatomical delineation of SII cortex in primates and its known variability across individuals [97], are major limiting factors to discriminate among different anatomic regions when using group analysis. In accordance with the well-known bilaterality of SII and insular receptive fields in both human [63] and animal [167] investigations, more than 50% of imaging studies have described a bilateral distribution of increased CBF in insular/SII cortices during painful stimuli 5, 8, 10, 22, 24, 27, 29, 81, 99, 116, 122, 151, 156, 162, 165.Download : Download full-size imageFigure 2. Regional peaks of pain-related CBF increase in the insular and SII cortices across 34 studies. Peaks of maximal increase have been plotted (when stereotaxic coordinates were available) onto axial brain slices from the Talairach and Tournoux atlas [152]. rCBF responses in normal subjects are represented by red letters, while experimental (i) and clinical (p, s) allodynia as well as ongoing neuropathic pain (h) are displayed in green for comparison purposes. Although regions of maximal response are widely scattered, most studies found two distinct peaks of maximal response (letters represented twice). One peak was generally consistent with anterior insula (in upper row) and the other with posterior insula/SII cortex (lower row). See also text for details. Note that sites activated during noxious stimulation and allodynia are very similar. Red letters: b: see reference [8]; c: [24]; e: [43]; h: [75]; h: [74]; i: [81]; j: [41]; k: [27]; l: [28]; m: [99]; o: [116]; p: [122]; q: [34]; r: [134]; s: [150]; s: [151]; t: [153]; t: [156]; v: [162]; w: [10]; x: [165]. Green letters: h: [76]; i: [81]; p: [121]; s: [118].Notwithstanding these difficulties, analysis of figure 2 shows that in a significant proportion of cases, two spatially distinct foci of maximal rCBF increases were detected, one focus being antero-inferior, near the anterior insula, and the other postero-superior, in a region corresponding to the retro-insular/SII interface. Spatial discrimination of these two foci will probably become accessible with the use of fMRI techniques (see recent studies 7, 8, 10, 39). However, even if the two foci may be spatially discriminated, activation of SII and insula seems, despite the functional differences between the two areas, very often concomitant in response to a noxious stimulus (see table I).Previous literature suggests that neither anterior insular nor SII/retro-insular responses are specific for pain, as they have also been described in response to a wide variety of innocuous somatic stimuli, including tactile 7, 81, electrical 62, 97, vibratory [27], innocuous thermal 7, 34 and olfacto-gustatory 56, 146. Although these regions may therefore be involved in general somatosensory integration, in the context of thermal stimuli their activity dramatically increases when intensity reaches painful ranges (see table I). Thus, Casey et al. 22, 24 did not find a significant insular/SII CBF increase during discrimination of two non-noxious heat stimuli, while insula was activated by both heat and cold and SII was activated by noxious heat. In a recent PET study, both anterior insula and SII rCBF gradually increased with thermal intensity [29]. In another study which separated activities related to intensity coding from those linked to attention, specific encoding of thermal pain intensity appeared related to anterior insular activation, bilaterally [122]. Thus, in the context of thermal pain processing, both the anterior insular and the retro-insular/SII cortices appear as functionally implicated in the discrimination of stimulus intensity. This is in agreement with animal studies in primates [167], suggesting that the insular cortex may gradually encode for different intensities of stimulation, as well as with recent human recordings from SII and insular regions showing gradually incremental responses to increasingly intense laser stimuli [64]. These results are also in line with the recent claim of a specific thalamo-insular pathway for cold, including noxious cold, in primates [33] and in humans [40], which cortical projections to the insular cortex could be gradually activated by cold (including noxious cold) stimulations [35].However, there is very little evidence from the literature that points out insular activity as related to attention. In their description of the attentional network to pain, Peyron et al. [122] did not find the insular cortex to participate in brain regions involved in selective attention, while it is a major region involved in thermal discrimination. The question whether insular cortex is involved in emotional processing cannot be simply answered because of the lack of studies investigating directly this aspect of insular response to pain. A first argument for such a participation is the activation of insular cortex in emotional tasks with negative affective components such as stimulation by fearful faces [125], emotional voices 108, 109, or aversive conditioning [14]. A second argument is the observed changes in the emotional dimension of pain after lesion of the insular region, while the discriminative dimension of pain is spared. In this disconnection syndrome known as asymbolia for pain [9], the subcortical lesion has been considered as disrupting sensory-limbic connections.Anterior cingulate cortex (ACC)The ACC follows closely the anterior insula in the rank of most frequently reported sites of rCBF increase to pain (table I). In most cases the ‘activated’ sites corresponded to the ‘mid-cingular’ region (area 24), but a number of studies have also showed hemodynamic activations and deactivations of more anterior (rostral) perigenual cingulate portions (see table I).ACC, localisation of stimuli and intensity codingThe activity of anterior cingulate units is not suitable to encode sensory aspects such as stimulus location, because of very wide and overlapping receptor fields 51, 144. Accordingly, animal studies and clinical data indicated that cingulotomy, although decreasing the affective response to noxious stimuli, preserves the ability to localise such stimuli 60, 144, 159.Whether the ACC participates in the encoding of stimulus intensity is more difficult to resolve: in animals, response characteristics of ACC ‘nociceptive’ units are very similar to those of medial and intralaminar thalamic nuclei from which they receive projections [144], and which have shown some intensity coding for mechanical, heat [79], and electrical [93] noxious stimuli 17, 50. In addition, anecdotal reports in humans have suggested partial loss of the ability to code stimulus intensity after cingulotomy [154]. However, processing of intensity coding in the human ACC has not been supported so far by functional imaging studies. Casey et al. [24] did not observe significant cingulate activation during active discrimination between two non-noxious heat intensities. Peyron et al. [122] described a network of structures involved in noxious intensity encoding, which did not include mid-ACC. Using regression methods, Tölle et al. [156] did not find any relationship between ACC blood flow and stimulus intensity. Furthermore, in some pain studies, ACC activation was shown to increase in the absence of any real change in stimulus intensity. For instance, Craig et al. [34] used a ‘thermal grill’ to induce pain by employing a combination of two non-noxious stimuli (alternated warm and cool bars). Anterior cingulate was not activated by either stimulus in isolation, but showed enhanced rCBF when they were applied in combination, suggesting that ACC changes were not related to actual stimulus intensity, but rather to the subjective pain sensation. In the study of Peyron et al. [122], increased ACC activation was observed without any change in actual stimulus intensity, during a ‘distraction’ experiment entailing decreased pain sensation. We may therefore conclude that there has been, up to now, no support from imaging studies favouring a role of ACC in coding stimulus intensity.ACC and the affective reaction to painIt has been generally considered that the ACC response to noxious stimuli reflects the ‘suffering’ component of pain [83]. Vogt et al. [162] suggested that the affective reaction associated with pain unpleasantness would be principally integrated in the rostral (perigenual) sections of ACC (BA 32 and 25), whereas mid-cingulate activation (the one most commonly seen in PET and fMRI studies) would be instead associated with cognitive processes, especially response selection and motor inhibition. The implication of rostral perigenual ACC in emotional and affective reactions is supported by experimental and clinical studies 6, 47, 53 and also by recent imaging studies that have manipulated the emotional stimulus content 11, 158. However, the dichotomy between a perigenual ‘emotional’ and a middle ‘cognitive’ ACC has not been supported by other imaging studies. Neither Rainville et al. [134] nor Tölle et al. [156] found any relationship between the perigenual ACC and the affective reaction to pain. In an elegant experiment, the Montreal group [134] modulated the affective component of pain using hypnotic suggestion and reported a linear relationship between subjective unpleasantness and CBF in the mid-ACC, rather than in its perigenual portion. Tölle et al. [156] found that pain unpleasantness correlated positively with CBF in the posterior sector of ACC. Also in studies assessing the reaction to the unpleasant character of stimuli from other modalities, such as the facial expression of disgust [108], frightful animals [61], unpleasant musical dissonance [12], or words with negative semantic content [68], the main increases in CBF have been observed in the middle and posterior sections of the ACC rather than in its perigenual portions (see figure 3b, blue letters). In turn, CBF in perigenual cingulate was found to change independently of affective reactions by Svensson et al. [151], who reported rostral ACC activation by tonic, but not phasic heat, in spite of a similar affective reaction to both, as judged by both autonomic (heart rate) and subjective (unpleasantness) measures. Thus, encoding of affective and mood responses seems relatively distributed within the anterior cingulate and, according to functional imaging studies, may implicate rostral but also middle and even posterior ACC portions.Download : Download full-size imageFigure 3. Upper panel (3a): Localisation of maximal cingulate rCBF changes, as reported in published imaging studies on pain (PET and fMRI) during the 1991–1999 period. Reported peaks of maximal rCBF changes were plotted (when coordinates were available) onto a sagittal brain slice (x = 4 mm) formatted according to the Talairach and Tournoux atlas [152]. Pain-related increases of rCBF are represented by red letters; rCBF decreases by blue letters. Pain studies (red): a: see reference [1]; b: [8]; c: [24]; d: [42]; e: [43]; f: [44]; h: [75]; i: [81]; j: [41]; k: [27]; l:[28]; m: [99]; o: [116]; p: [122]; q: [34]; r: [133]; s: [150]; s: [151]; t: [153]; v: [162]; w: [10]; x: [156].Lower panel (3b): Reported loci of maximal CBF increase in imaging studies which manipulated selective, sustained attention (green letters), or emotion (blue-grey letters). Emotional manipulations consisted of increasing (intense blue) or decreasing (blue/grey) unpleasantness, or inducing stress/anxiety (grey). The letters r, f, and p in both a red and green colour refer to reports where pain and attentional responses were investigated within the same study. Although there is overlapping between clusters, cingulate ‘activation’ during selective sustained attention tends to be anterior and rostral to that obtained in pain studies. ‘Attentional’ studies (green): a: [20], (Stroop); b: see reference [16] (Stroop); c: [30] (visuospatial & divided attention); d: [96] (divided attention); f: [44] (Stroop); g: [68] (Stroop); k: [90] (go/no go (posterior) & response selection [anterior]); m: [110] (cognitive anticipation); n: [111] (visuospatial attention & target detection); o: [112] (auditory & visual attention); p: [122] (sustained attention to one hand); q: [114] (Stroop); r: [135] (hypnosis); t: [155] (stimulus-response compatibility task); w: [163] (Stroop); x: [103] (target detection). ‘Emotional’ studies (intense blue) = increased unpleasantness f: see reference [61] (frightful animals); g: [68] (sad Stroop); l: [11] (anger facial expression); m: [108] (fearful facial expression); r: [134] (increased thermal unpleasantness); t: [156] (increased thermal unpleasantness); u: [14] (aversive trace conditioning); w: [163] (negative emotion with sad Stroop); (blue/grey) = decreased unpleasantness r: [134] (decreased thermal unpleasantness); (grey/blue) = stress, anxiety, and mood. b: [13] (obsession-compulsion); r1: [136] (obsession-compulsion symptoms); d: [53]; r2: [137] (phobic symptoms); r3: [138] (stress).It is noteworthy that dramatic CBF increases have been repeatedly observed in the rostral ACC of psychiatric patients with obsessive-compulsive disorders 13, 136, phobic anxiety [137], post-traumatic stress [138], or mood disorders [53]. These data suggest that stress and anxiety, rather than unpleasantness, might be the subjective variables most closely associated to CBF increase in this portion of ACC. This could partly explain the paucity of activation sites of rostral ACC in pain studies since, due to extensive training and habituation of the subjects to the experimental paradigm, the stress and anxiety component is likely to be minimised in normal subjects. Conversely, this component is likely to persist in patients with clinical pain, in whom hemodynamic abnormalities (usually CBF decrease) have been repeatedly reported in this area (see below).ACC and cognitive-attentional response to painThe implication of the middle ACC region in cognitive responses to pain has received direct support from recent studies 38, 44, 122, 135. Using a factorial design to separate the attentional and discriminative components of the pain response, Peyron et al. [122] found that ACC was not a part of the ‘intensity coding’ network, but was activated as part of an ‘attentional matrix’ also involving the posterior parietal and prefrontal cortices. This mid-cingulate activation, mainly in BA 32, proved to be dependent on sustained attention toward the stimulus, and independent of whether the stimulus was noxious or not (figure 3b, green ‘p’). This ACC activity was spatially concordant with that observed in other attentional or ‘cognitive’ studies requiring sustained attention in the absence of pain (Stroop test, word generation, etc.) and which are illustrated in figure 3b (green letters). A second mid-ACC activation was observed in Peyron et al.’s study, with a more caudal and lower position than the previous one (see figure 3b, red ‘p’). This second cingulate activity (in BA 24) was spatially distinct from the one associated to sustained attention, and was also independent of ‘intensity coding’ since it appeared while subjects were diverted from pain and produced lower thermal intensity scores. This activity was assumed to reflect phasic attentional shifts to the sudden irruption of painful stimuli, and subjects indeed reported to have had their attention phasically drawn by noxious heat, in what they described as an orienting reaction. Since this latter activation of BA 24 was similar in location to those reported in other pain studies where attention was not controlled (figure 3b, red dots), it was suggested that at least part of mid-ACC activation in pain studies may reflect phasic orienting to the painful stimuli. Similar conclusions have been reached by Tölle et al. [156] (figure 3a, red ‘x’) who, using regression analysis, observed that mid-ACC CBF correlated positively with pain threshold intensities, and concluded that it could reflect attentional shifts toward stimuli which capture attention. Also supporting this view are some recent studies 103, 112 in which hemodynamic activation was observed in a very similar mid-ACC region (BA 24) in cases of detection of suddenly-appearing auditory or visual targets (figure 3b). These attentional experiments would share with pain studies the need to monitor the possible occurrence of sudden inputs. Therefore, in general terms we may conclude that two distinct mid-ACC hemodynamic activations can be observed in pain studies, both of which reflect the cognitive dimension of pain experience. The commonest of them is located below the intra-cingulate sulcus (BA 24) and appears to reflect attentional shifts to the painful stimulus, while the other, above the sulcus (BA 32), would appear only in cases of sustained and voluntary directed attention to the stimulated area.ACC and motor response to painPreparation and/or inhibition of motor reactions are also functional responses triggered by pain and ACC is known to participate in response selection 47, 157, motor learning 71, 88, and motor planning 47, 126. Some of the less commented ‘activations’ in PET experiments, such as those within the cerebellum, basal ganglia, supplementary motor area, and motor cortex (see table I) might be indeed considered as part of this ‘motor’ response to pain, as could also be the case, to some extent, for the ACC. To date, no functional imaging study has specifically investigated this particular aspect.ACC and anticipation of painBoth the perigenual ACC and medial prefrontal cortex modify their activities during the seconds preceding the arrival of a noxious stimulus, as previously observed during anticipation of cognitive tasks [110]. Changes described up to now are manifested either as a decreased [128] or increased 78, 127 signal, but aversive conditioning is known to increase the BOLD signal in rostral ACC [14]. It is difficult at this point to judge whether such signal changes are specifically related to the internal representation of impending stimuli (anticipatory processes per se), anger [11], or rather reflect anxiety and stress, which have also been shown to modify orbito-frontal and perigenual ACC activity 13, 136, 137, 138.ACC as a multi-integrative structureConvergent evidence summarised in previous sections suggests that ACC supports multiple functions as a subject experiences pain. From previous paragraphs, it may sometimes appear that different processing axes (sustained attention, orienting, stress, unpleasantness) are indeed represented in different subsections of the ACC. However, on the one hand, studies on groups of subjects do not adequately reflect the high spatial variability of individual responses. On the other hand, when the whole significant areas (rather than the ‘maximal’ peak responses) are considered, substantial overlap exists between ACC activations. Individual variability was demonstrated by Vogt et al. [162], while close proximity and partial overlapping of ACC activations with different functional significance (i.e., unpleasantness sensation and hypnotic suggestion) was shown in studies by Rainville et al. 134, 135. Even at a neural level, animal experiments have demonstrated that subpopulations of ACC neurons may respond in a similar way to different experimental contexts. For instance, a large proportion of ACC neurons labelled as ‘nociceptive’ has been found to respond also to pain anticipation, i.e., preceding actual stimulus delivery [93]. It is very likely that multi-functionality of ACC units and regions also exists in relation with human pain: single ACC regions may be involved in several functional networks, and their processing capacities with respect to a given function are probably modulated by other concomitant processes. Accordingly, ACC activation has been demonstrated to vary with the learning of non-motor tasks [133] as well as the learning of nociceptive stimulations, in such a way that ACC can be activated in naive subjects for an unlearned pain but is no longer activated with the practice of tasks, increased performance, and learned pain [78]. Functional interactions and internal modulations within the ACC deserve, therefore, to be specifically investigated in future studies on cortical pain imaging.Primary somatosensory (SI) cortexPrevious reports on rCBF changes to pain in the primary somatosensory cortex have been notoriously inconclusive. Thus, while a number of studies have described significant pain-related rCBF increases in SI, a comparable number of reports using similar methods have failed to do so. From the 30 experiments (from 24 studies) on somatic pain summarised in table I, significant SI ‘activation’ was observed in 15 (63% of cases), and no significant change in the other nine (46%). Thus, the contribution of SI to pain processing, as revealed by PET/fMRI experiments, is much less consistent across studies than that of the second somatosensory (SII), insular and anterior cingulate regions.Different hypotheses have been put forward to explain such inconsistent results. Derbyshire et al. [43] found that moderate painful stimuli entailed contralateral SI activation, while stimuli just above the pain threshold failed to do so. However, the intensity of the painful stimulus does not appear by itself to play a major role in SI rCBF changes, since in a number of other studies on somatic pain, such intensity was enough to produce moderate to strong pain, yet no hemodynamic activation of SI was observed 85, 122, 150, 162, 165. High intensity levels may, however, play an indirect role by increasing the level of attention toward the stimulus (see later).Since SI rCBF enhancement is more frequent for moving than for immobile stimuli (table II), the hypothesis was put forward that SI changes might depend on activation of separate groups of nociceptors by moving stimuli 83, 85, 122, 162. However, clear contralateral SI activity has also been observed when using non-moving painful stimuli such as hot or cold water baths 24, 49, 134, mechanical stimulations [36], subcutaneous injections 2, 74, 75, 81, or immobile thermodes 8, 34, 151. Furthermore, in the case of moving stimuli, SI activation was obtained by comparing painful to non-painful conditions, where the stimulus movement was identical. Thus, stimulus movement per se does not appear to be the discriminating criterion between studies activating SI or not.Table II. Relationships between surface of stimulation and SI activation*Empty CellSurfaceEmpty CellEmpty CellEmpty CellSI activationSmallLargeDerbyshire et al., 1997M- C-Talbot et al., 1991Laser (79 mm2)Heat thermode (79, 2800 mm2)M+ C+Becerra et al., 1999M- C+Casey et al., 1994M+ C+Heat thermode (900 mm2)Heat thermode (254, 1524 mm2)Casey et al., 1996M+ C+Heat thermode (254, 1524 mm2)Casey et al., 1996M- C+Cold water bath (56000 mm2)Coghill et al., 1994M+ C-Heat thermode (100, 2800 mm2)Coghill et al., 1999M+ C+Heat thermode (79, 3600 mm2)YESCraig et al., 1996M- C+Heat thermode (28000 mm2)Rainville et al., 1997M- C+Hot water bath (56000 mm2)Paulson et al., 1998 (males)M+ C-Heat thermode (254, 1524 mm2)Davis et al., 1995M- C+Electrical (median nerve, 28000 mm2)Svensson et al., 1997bM- C+Heat thermode (1256, 1600 mm2)M- C-Craig et al., 1996 (ss)M- C+Cold thermode (28000 mm2)Jones at al., 1991M- C-Derbyshire and Jones, 1998M- C+Heat thermode (1250 mm2)Hot water bath (56000 mm2)Derbyshire et al., 1994M- C-Paulson et al., 1998 (females)M+ C-Heat thermode (1250 mm2)Heat thermode (254, 1524 mm2)Vogt et al., 1996M- C-Tölle et al., 1999M - C+Heat thermode (375 mm2)Heat thermode (2304 mm2)NOPeyron et al., 1999aM- C-Heat thermode (900 mm2)M- C+Xu et al., 1997M- C-Laser (50, 625 mm2)Svensson et al., 1997aM+ C-Laser (79 mm2)Svensson et al., 1997aM- C+Electrical (10 mm)*First number in brackets indicates the surface of stimulus (thermode, laser beam, bath, etc.).The second number indicates, for stimuli applied over multiple sites (M+), the total surface of stimulation (56000 mm2 is indicative of the surface for hand immersion in a water bath) (It has been fixed by authors as approximately double of those used for the thermal grill applied on the palm).M- indicates that stimuli were not displaced during PET or fMRI recordings; C+ indicates continuous stimuli while C- indicates intermittent stimuli; ss :sub-significant activation discussed by authors as relevant.A much less explored feature that may prove relevant for SI hemodynamic activation is the stimulus’ spatial summation. In experiments using skin stimuli, this variable should be proportional to the total skin area stimulated 72, 132, which in turn is determined by 1) the intrinsic stimulus size; and 2) the repetitive application of the stimulus over different body sites. A majority of published imaging studies can be classified as activating ‘large’ or ‘small’ body surfaces depending on these parameters; table II was constructed following these lines, as applied to 30 experiments from 24 studies that used noxious skin stimuli. ‘Large’ body area experiments involved a contact thermode successively applied to multiple skin sites (usually 6) 21, 23, 27, 29, 34, 35, 115, 152, as well as studies using water baths or other stimuli covering the whole hand 24, 34, 41, 134 or electrical shocks to a large nerve trunk [37]. Conversely, ‘small’ body surfaces were considered to be stimulated in studies using either laser stimuli or point electrical stimulators 43, 150, 165, as well as those that used contact thermodes applied to one single skin site 8, 42, 85, 122, 162. As shown in table II, spatial summation appears as a relevant variable influencing SI rCBF, since a vast majority of studies involving large skin surfaces also activated the contralateral SI, while most experiments using small surfaces failed to do so (χ2 = 7.08, corrected P = 0.02; Fisher exact test, P = 0.01). The average estimated surface in studies showing SI CBF increase was about 16,300 mm2, relative to 6,400 mm2 in studies that did not describe SI activation. Published data suggest therefore that, when using painful stimuli applied on the skin, spatial summation is a crucial factor that increases the likelihood of hemodynamic SI activation in PET studies. The fact that a relatively important amount of SI surface needs to be excited to obtain significant increases of rCBF is consistent with both the very limited number of units specifically responding to pain in this region [91] and the rare evocation of pain during SI stimulation [117]. The amount of spatial summation cannot be easily estimated in studies using subcutaneous injections (capsaicin, histamine, ethanol 74, 75, 81, 99) or visceral distension with a balloon 4, 5, 10, 145, so that these figures are obviously too limited to derive conclusions at this stage.Some studies also suggest a possible role of temporal summation as a determinant of SI CBF increase. For example, data from Svensson et al. [151] reveal that for an identical stimulating surface of about 2,000 mm2, a continuous stimulus yields more important SI activation than a phasic one. However, pooled data from table II suggests that, in general terms, continuous stimulation (labelled C+) was less discriminating than the stimulation area in determining CBF increase, and therefore spatial, more than temporal summation, critically influenced the probability of SI CBF increase. Accordingly, spatial summation has been shown to modify the cognitive, affective, but also the sensory-discriminative dimension of pain appraisal 72, 113, 150. From a pragmatic point of view, increasing the total stimulated surface (and possibly also the total stimulation time) may be the simplest way of increasing the likelihood of SI activation in further imaging studies.In addition to the mechanisms described earlier, converging evidence suggests that rCBF increase in SI may partially depend on attention directed to the painful stimulus. Electrophysiological studies have shown that selective attention enhances neural activity from SI, both in animals 59, 82, 168 and in humans 46, 65, 106. PET studies have also shown that attention directed to a tactile stimulus enhances both glucose consumption and the hemodynamic response in SI 69, 105. Evidence that these mechanisms can be relevant for pain experiments has been recently provided by Bushnell and coworkers [18] in a study where the rCBF increase in contralateral SI was enhanced as attention was turned to a noxious stimulus. Based on stimulus-response characteristics of SI neurons in monkeys, it has been concluded that two different populations within SI participate respectively to the sensory and attentional aspects of pain processing 26, 59. All these results probably underlie the psychophysical observation that attention directed to a painful stimulus increases its detectability [107]. In general terms, it can be hypothesised that, in the painful ranges, moving stimuli are more likely to drive spatial attention than immobile ones, and heavily supraliminal stimuli more than stimuli barely at pain threshold. It is, however, noted that neither attention nor high intensity alone appear to be sufficient to ensure a PET-detectable rCBF increase in SI in the absence of adequate spatial summation (see [124]).In addition to increased rCBF in contralateral SI, several groups have described decreased blood flow [3], particularly in portions of the SI region that do not correspond to the stimulated body area. Decreased rCBF has been observed in SI ipsilateral to a noxious stimulus [122], or in both contralateral and ipsilateral SI regions corresponding to non-stimulated areas (for instance, the leg and face representation in case of a hand stimulus [52]). Since this phenomenon was observed even in the absence of actual stimulus, it has been attributed in part to cognitive variables such as anticipation of pain [53] or focalised attention to the site being stimulated. An rCBF decrease in sensory areas that do not receive relevant input may represent an ‘economical’ brain mechanism to facilitate stimulus detection by enhancing the contrast between regions concerned or not by stimuli [53].Prefrontal and posterior parietal corticesDorso-lateral prefrontal (DLPF) and (to a lesser extent) posterior parietal responses have been repeatedly reported as ‘pain-related’ activities (see table I). Participation of these regions in both attentional and executive functions is well known, their activation being frequently described in experiments involving attention, working memory, and goal-directed processes 21, 31, 32, 57, 70, 92, 95, 100, 111, 115, 130, 166 (for a review, see [104]). In the context of pain experiments, both posterior parietal and DLPF activations are therefore likely to mediate part of the cognitive dimension of pain processing associated with localisation and encoding of the attended stimulus [122]. Although bilateral, these cortical activities often show asymmetrical distribution and predominate on the right hemisphere, regardless of the side of stimulation 41, 42, 85, 116, 122, 162, as has been previously observed in attentional experiments 31, 70, 115.Thalamus and brainstemThalamus has been often but inconstantly activated across PET or fMRI studies on pain (table I). Thalamic activation is frequently described as bilateral 23, 24, 44, 162, suggesting that it does not merely reflect a sensory response, which would be supposed to predominate contralaterally to the noxious stimuli. Furthermore, attentional processes and vigilance have also been shown to increase thalamic activity bilaterally 61, 111, 129, 130, 131 and thus, thalamic enhancement in pain studies may also reflect a general ‘arousal’ reaction to pain ([122], see figure 4). Thus, thalamic hemodynamic responses to painful stimuli can be considered as a part of both discriminative and attentional networks involved in pain processing.Download : Download full-size imageFigure 4. Different components of the brain haemodynamic response to pain according to Peyron et al. [122]. Subjects received low- or high-intensity thermal stimuli while directing or not their attention to the stimulated hand. The ‘intensity coding’ component (top left) was obtained by subtraction of ‘low intensity’ from ‘high intensity’ conditions, regardless of attention. The ‘attention component’ (bottom) was obtained by subtraction of ‘no task’ from ‘attentional’ scans regardless of stimulus intensity. The ‘attentional component’ involved a large network including prefrontal, posterior parietal, and cingulate cortices and thalami. This component may be tentatively divided into ‘arousal’ and ‘selective attention’ systems. Decreased rCBF in the primary sensory cortex ipsilateral to the painful stimuli might participate to contrast enhancement or reflect anticipation of pain. For further details, see text and references.The thalamus contains a great number of inhibitory synaptic connections mainly involving the reticularis thalami, and these may contribute to bilateral thalamic and brainstem activations seen in pain studies. For instance, brainstem activity in response to pain is commonly reported as corresponding to the periaqueductal grey matter (PAG), but inspection of data usually shows that it greatly exceeds this localisation, includes reticular formation, and often appears as a caudal extension of thalamic activation. It may reflect synaptic activation of mesencephalic nociceptive relays related to arousing activity, to the set-up of descending pain controls, or both.Other brain regionsAmong the several other areas that have shown hemodynamic changes to pain, the presence of brain regions involved in motor functions is noteworthy, particularly the lenticular and caudate nuclei, the cerebellum (vermis and hemispheres) and the SMA. Even the primary motor cortex has been found to respond with the rCBF increase in some studies 5, 24, 151, and with the rCBF decrease in others [122]. Whether the primary motor area is activated independently of, or in conjunction with SI, and whether it reflects motor activation (withdrawal reaction) or a motor inhibition (movement refrain) cannot be ascertained at this time.Brain responses in patients with painSpontaneous pain in patients with neuropathic painSpontaneous pain is difficult to investigate using functional imaging due to the need to compare in the same subjects a painful versus a pain-free condition. This binary situation is rare in clinical practice and the literature is therefore restricted to a few reports, in patients with either cancer pain alleviated by cordotomy [48], ongoing neuropathic pain alleviated by anaesthetic blocks [76], or central pain treated with motor cortex stimulation 67, 119. One common finding in these studies was a relative decrease of thalamic rCBF during ongoing pain, which receded after analgesic treatment. In addition, a relative hypoperfusion of the thalamus contralateral to ongoing pain (compared to the ipsilateral side) has been verified in patients with either peripheral neuropathic pain 76, 80 or central pain after cortical lesions sparing the thalamus 119, 123. These findings suggest that ongoing neuropathic pain (central or peripheral) is often linked to thalamic hypoperfusion, and that a variety of analgesic treatments are mediated through an increase in thalamic blood flow.Provoked pain in patients with neuropathic painThe term ‘allodynia’ refers to abnormal pain triggered by a non-noxious stimulus (i.e., light touch, contact of the skin, brushing, non-noxious cold). Allodynia reflects, therefore, a ‘misinterpretation’ of somatosensory information, which abnormally evokes a painful experience for intensities clearly below the normal pain threshold. In patients suffering from allodynia, this symptom can be reproduced during PET or fMRI sessions, and it is thus easier to explore than spontaneous pain (figure 5). The main limitation of ‘allodynia’ paradigms is that responses to allodynic stimuli cannot be compared with innocuous stimulation of the same territory, since any skin stimulation in the affected area rapidly induces unbearable pain. The allodynic stimulation is therefore compared either to a ‘resting’ (no stimulation) condition, or to an identical stimulation of a non-affected body area. A study in patients with allodynia after a lateral medullary infarct (Wallenberg’s syndrome) showed that allodynic stimulation (light rubbing of the affected area) induced both a pain sensation and brain activities which are usually associated with pain processing, notably in the thalamus, anterior insula, SII, and posterior parietal cortex, while such activities were not observed when the same stimulus was applied to the normal side [121] (figure 6). These data were interpreted as reflecting abnormal stimulus amplification in the thalamus and thalamo-parietal loops, leading to increased rCBF in the ‘lateral’ discriminative pain system (i.e., lateral thalamus and parietal cortex), and activating attentional (posterior parietal) networks. A similar pattern of amplification of the thalamo-parieto-insular response was described in normal subjects suffering from experimental allodynia after injection of capsaicin 7, 81.Download : Download full-size imageFigure 5. Upper panel (5a): Loci of pain-related cingulate CBF increase in normal subjects (red spots) compared with those of patients with clinical pain. Capital green and blue letters refer respectively to increased and decreased blood flow during clinical pain situations: pharmacologically-induced cluster headache (H: [77]; M: [98]); nociceptive pain in atypical facial pain patients (D: [42]); dental pain patients (E: [45]); pain from rheumatoid arthritis (J: [84]); ongoing neuropathic (peripheral) pain (N: [76], S: [118]); angina pectoris (R: [141]); allodynia (P: [121]); and basal rCBF in patients with chronic central pain (W: [120]) after Wallenberg’s syndrome. Although most of the CBF changes in clinical pain are located in areas which also respond to normal nociception, a decrease of CBF has often been reported in patients. Such paradoxical responses might be relevant for the understanding of abnormal pain processing.Lower panel (5b): Pain-related ACC responses in normal controls (red spots) and foci of increased CBF during analgesic procedures (blue letters), using opioids (O: [58], O: [1]); anaesthetic blocks (A: [76]); spinal cord (S: [73] [decreased rCBF]); thalamus stimulation (T: [54]); or motor cortex (M: [67], M: [123]).Download : Download full-size imageFigure 6. The insular/SII responses to either nociception (top row) or allodynia (bottom row). Responses to nociception were obtained by PET from 12 volunteers submitted to a thermal stimulation on the right hand [122]. Responses to allodynia were obtained from patients with right-sided lesions and therefore a left allodynic pain. Note that insular/SII nociceptive responses to high intensity (painful) thermal stimuli in normal subjects (top) are very similar to allodynic responses in patients. Note also that allodynic responses in insula/SII are obtained with a low-intensity (normally non-painful) stimulus consisting of non-noxious cold rubbing on the left thigh (lower row): Left: Patient 1 had allodynia secondary to an isolated SII lesion (fMRI, unpublished data). Middle: Patient 2 had a combined SI, SII, and anterior cingulate lesion (PET and fMRI images, published as a single case, see [124]). Right: Nine patients with lateral medullary infarct (LMI) were studied as a group by PET (see [121]).In contrast with this unequivocal thalamo-parietal behaviour, allodynic responses in the ACC and medial prefrontal regions seem to be more complex, and, as for nociceptive pain, should be separated into mid-ACC and rostral ACC activations. Mid-ACC have firstly shown variability in the experimental model of capsaicin-allodynia since a bilateral rCBF increase has been observed in a first study [82] but has not been confirmed in another one [7]. Secondly, increased activity of the mid-ACC has been reported in the two studies on clinical allodynia after peripheral nerve lesions [76], while such activation was not observed in patients with allodynia after a lateral medullary (Wallenberg’s) infarct [121]. Interestingly, and thirdly, in these latter patients, investigation of the basal hemodynamic status showed that this mid-ACC region specifically had a ‘paradoxical’ decrease of rCBF [120], in a localisation highly congruent with rCBF decreases reported in other non-neuropathic pain situations [see later, 45, 84, 141]. Since the mid portion of ACC receives numerous inputs from spino-thalamic tracts 51, 144 and since different amounts of deafferentation can be observed in neuropathic pain patients (for instance, patients with Wallenberg’s syndrome have a pure spino-thalamic syndrome while those with peripheral nerve lesion had a less selective involvement), deafferentation itself may participate in hemodynamic results, independently of pain. Finally, it is noteworthy that the most consistent ACC response to allodynia, regardless of the level of the lesion (i.e., peripheral or central), is a decrease of rCBF located in the rostral portion of ACC 76, 121. In the absence of more numerous studies, it cannot yet be ascertained whether these disparities are purely methodological in origin or reflect genuine differences between experimental and clinical allodynia. If these data are confirmed by further studies, the ‘lessened’ reaction of the rostral ACC and medial prefrontal cortex to allodynic stimuli might be one characteristic of allodynia resulting from neuropathic lesions.Other clinical pain situationsApart from neuropathic pain, the mid-ACC portion has also been pointed out as a major cortical target of hemodynamic abnormalities in studies on clinical pain situations without lesion on the neuraxis. Angina pectoris [141], cluster headache [98], atypical facial pain [42], dental pain [45], and pain from rheumatoid arthritis [84] all demonstrated abnormal (i.e., increased or decreased; see figure 5, table III) activity in the mid and/or rostral portion of ACC. Hemodynamic abnormalities in a restricted area of the mid-ACC in patients compared to normal subjects 42, 45, 84 lead some authors to conclude that reduced ACC response to acute pain may be one adaptive cortical mechanism characteristic of patients with chronic pain. An alternative (or complementary) view comes from recent data showing an rCBF decrease in rostral ACC and medial prefrontal cortices during anticipation of a previously learned pain [78]. The question arises as to whether anticipation of an intensely distressful and well-learned sensation, rather than the sensation itself, might also contribute to the blunted ACC response in allodynia.Table III. Patients with chronic pain.Disease / AuthorsYearModalityNStimuliT/PMovementSideAnt InsulaSIImid ACC 24/32ACC (rostral)ThalamusSIDLPFC 10, 44-47MPFC 9, 10, 32Parietal 40 / 7Amygdala HippocampusSMA 6LNMidbrainCerebellumAtypical facial painDerbyshire et al.1994PET6Heat thermodeP–R↑ C↑ B (*↑)↑↓ I (*↓)↓ C↑↑Angina pectorisRosen et al.1994PET12Parmacological inductionT–R/L↓ B↑ L↑ B↑ B↓ L↑Neuropathic (peripheral) painIadarola et al.1995PET5Patients vs normals, restT–R/L↓ CNeuropathic (peripheral) painHsieh et al.1995PET8Ongoing pain vs reliefT–R/L↑ B–↑ R↓ B↓ C–↑ B↑ BCluster headacheHsieh et al.1996PET7Pharmacological inductionT–R/L↑ B↑ R↑ B↓ B↓ L↑ R↑ BCluster HeadacheMay et al.1998aPET9Pharmacological inductionT–R/L↑ B↑ R↑ R↑ R↑ R↑Irritable bowel syndromeSilverman et al.1997PET6Ongoing pain vs restT–R/L(*–)(*–)Rheumatoid arthritis1997PET6Heat thermodeP–R(*↓)(*↓)Dental pain1999PET6Heat thermodeP–R↑ B(*↓)(*↑)(*↑)↑ I (R) (*↓ I)(*↓)↑ C↓ I (R)↑ C (*↑)Neuropathic (central) painPeyron et al.1998PET9Wallenberg's syndromesallodyniaP+R/L↑ C↑ C↓ I↑ C↑ C↑ C↓ C↑ BElectrical painT–R/L↑ B–––↑ I↓ I↑ BNeuropathic (peripheral) painPetrovic et al.1999PET5MononeuropathyP+R/Lss ↑ I↑ B↑ C↓ C↑ B↑ C↑ C↓ B↑ C↑↑Analgesic proceduresIn that context, it may be of importance to note that analgesic procedures, including administration of opioids 1, 58 and neurostimulations for pain relief 54, 67, 73, 119, all increased rCBF in the ACC (figure 5b, table IV). Particularly, opioids and stimulation of both thalamus and motor cortex increased rCBF in the rostral ACC and basal orbitofrontal cortices, at very similar sites where it has been found to be decreased in allodynic or chronic pain patients (figure 5a, b). These convergent, although preliminary, data suggest, therefore, that regulation of activity at the orbitofrontal/ACC regions may play a role in stimulation-induced pain relief. Although the precise participation of these areas in patients’ relief remains unknown, their functional role in animals and humans suggests that they might either contribute to normalise stress, anticipatory and mood processes, the alteration of which is common to different kinds of chronic painful states, or activate descending inhibitory controls of pain.Table IV. Analgesic procedures studied with PET.Disease / AuthorsYearModalityNStimuliT/PMovementSideAnt InsulaSIImid ACC 24/32ACC (rostral)ThalamusSIDLPFC 10, 44-47MPFC 9, 10, 32Parietal 40 / 7Amygdala HippocampusSMA 6LNMidbrainCerebellumAnterior cordotomyCancer painT–R/L↓ CDi Piero et al.1991PET5Cordotomy↑ CMotor cortex stimulationCentral painT+P–R/L↓ C↓ CPeyron et al.1995PET2Stimulation↑ B↑ C↑ B↑ BGarcia-Larrea et al.1999PET10Stimulation↑ I↑ B↑ C↑ B↑ CThalamic stimulationDuncan et al.1998PET5Neuropathic painT+P–R/L↑ Iss ↑ Iss ↑ IOpioids analgesiaJones et al.1991PET1Cancer painT–L↑ C↑ CMorphine analgesia↑ B↑ B↑ B↑ BFirestone et al.1997PET6Fentanyl in normals↑ B↑ B↑ B↓ BHeat painT–L↑ I↑ I↑ I↑ CAdler et al.1997PET9Fentanyl analgesia↑ I (*, ↑ or ↓)↓ B↑ I (*, ↑ or ↓)↑ C (*, ↑ or ↓)Anaesthesic blocksHsieh et al.1995PET8Regional lidocaine blocksT–R/L↓ B↓ B↑ B↑ C↓ B↓ BT:Tonic;P:Phasic pain; Stimulus displaced (+) or not (–) during experiment;R/Lside of stimulation;ss:sub-significant but discussed as relevant by authors;↑ or ↓of rCBF or BOLD signal contralateral (C) or ipsilateral (I) to stimulus; B indicates bilateral or poorly lateralized activations;*indicates abnormal (excessive) responses.General discussion and future lines of researchFunctional imaging and subcomponents of the pain experienceImaging studies in recent years have allowed the visualisation of a number of brain regions which consistently respond to pain with changes in blood flow. These cortical targets appear to subserve different aspects of the multidimensional pain experience; thus, the sensory-discriminative aspects of pain perception appear to implicate the lateral thalamus, primary and second somatosensory regions and the insular cortex, while the additional activation of posterior parietal and prefrontal cortices appears to subserve the cognitive-attentional processing of noxious information. Different subsections of the anterior cingulate cortex are likely to underlie cognitive (orienting, response selection) and affective (aversive) reactions to pain; although some discrimination among ACC subsections may be done on the basis of meta-analyses (figure 3), it is still premature to ascribe precise ACC subdivisions to well-defined cognitive operations or especially affective reactions. Regions implicated in pain inhibition (periaqueductal grey) and in motor control (basal ganglia, SMA, cerebellum) also show inconstant rCBF increase during painful stimulation, probably reflecting the setup of descending inhibitory controls, as well as of motor and pre-motor mechanisms linked to the avoidance reactions to pain [160].As in every schematic classification, the above assertions deserve some nuance: for instance, there is little doubt that both SI and SII cortices also participate in the attentional processing of somatic stimuli, and that thalamic activation (especially when bilateral) also reflects pain-induced generalised arousal. The affective dimensions of the pain experience remain poorly investigated, probably because ‘laboratory pain’ is not a good model for inducing intense affective reactions in trained subjects [153]. It appears, however, that the hemodynamic correlates of ‘pain unpleasantness’ in normal subjects and patients with chronic pain greatly differ: while in normal controls increased unpleasantness correlates with enhanced rCBF in the mid-cingular or posterior portions of the anterior cingulate cortex 134, 156, a rCBF decrease in more rostral areas of ACC (BAs 32 and 10) has been reported in patients undergoing clinical, intensely unpleasant pain [141]. Further studies aimed at modulating specifically the emotional reactions to pain, both in healthy subjects and patients, should in the near future ameliorate our insights into these variables.The subtraction and normalisation procedures in brain imaging studiesSubtraction of ‘control’ images from those obtained during test conditions reveals changes which are associated uniquely with the test condition. The subtraction procedure has evident advantages, in that it eliminates common sources of variance between conditions, such as general anxiety or stress. Without elimination of such data results would be much more difficult to interpret. However, this methodology also entails limitations in data interpretation, and its use to study the neural substrates of cognitive activities has been recently challenged [166]. Subtraction images do not provide information about the complete network involved in stimulus processing; therefore, finding regions where the processing of innocuous and painful stimuli is different does not imply that such regions are sufficient to sustain the experience of pain. In turn, the fact that a given area is not differentially activated by innocuous and painful stimuli cannot discard its possible participation to pain processing. The activity of such a region may have a different functional content depending on whether or not it is associated with that of other areas activated by noxious stimuli. Therefore, the pain experience should not depend on the activity of regions exclusively driven by noxious input, but rather on the interaction between these regions and other areas giving similar response to noxious and innocuous stimuli, which are eliminated by subtraction.Normalisation of different conditions for global activity is also a common procedure that enhances focal changes in rCBF and eliminates the need of arterial sampling. However, this method also ignores global CBF modifications that may prove relevant to our understanding of pain processing by the brain. For instance, it has been recently demonstrated that painful stimulation may actually decrease global CBF by more than 20%, relative to resting levels [28], which represents a previously unidentified response to pain. Experimental designs using several activation levels, comparison between rCBF and electrophysiological changes including those from intracranial electrodes, and utilisation of event-related fMRI techniques are among the procedures that might overcome these difficulties in the near future.Functional significance of PET and fMRI signalsAn everlasting problem associated to both PET and fMRI results is the interpretation of blood flow changes in terms of ‘activation’ of underlying cerebral structures. Substantial evidence supports that increased rCBF reflects increased synaptic activity 147, 149, which may reflect either activating or inhibitory energy-consuming processes. The rate of blood flow increase is determined by firing rates in the synaptic terminals, and this whether an excitatory or an inhibitory neurotransmitter is released. To quote Sokoloff [148]: “To distinguish between the two, one must look one synapse downstream; if an inhibitory transmitter is released... one will observe reduced glucose utilisation in the next synapse. If an excitatory neurotransmitter is released, then glucose utilisation will increase at the next synapse.” It is obviously impossible with current PET or fMRI technology to ascertain rCBF changes “in the next synapse” of a given region. Network analysis, which seeks for statistical correlation of dynamic flow changes between interconnected regions, is one promising strategy that might partially surmount this limitation 19, 89, 164 and may help significantly to interpret the functional significance of the observed changes.Neurochemical basis of pain-related imagingA further field which deserves intensive investigation is the neurochemical basis of pain-related hemodynamic changes. Variations of rCBF associated with opioid analgesia have been explored in a few studies with rather congruent results (table IV, figure 5b). The opioid agonist fentanyl increases rCBF in the rostral part of anterior cingulate and orbito-frontal cortices in normal subjects 1, 58. This region is involved in pain integration (see earlier) and contains a high density of opioid receptors [86], the density of which is known to change during pain states. For instance, in chronic rheumatoid arthritis, the binding on opioid receptors is decreased during inflammatory phases and pain relapses, and increased during remissions [87]. On the other hand, rCBF was found to increase in very similar locations during non-pharmacological analgesic procedures such as motor cortex and thalamic stimulation 54, 67, 94, 119. Thus, increased synaptic activity in the rostral ACC/orbito-frontal boundary may be an important common effect of both drug and neurostimulation analgesia, reflecting presumably increased activity in pain control areas. Although this also suggests that part of the analgesia-related rCBF changes might depend on opioid receptors, no direct proof of this is currently available. It is likely that in vivo neuroreceptor mapping studies in coming years will be of importance for the understanding of these issues and therefore for the treatment of chronic pain.ConclusionThis review has shown a good deal of convergent data and promising results that should, in the next years, improve our understanding of pain processing in the brain. 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Proc Natl Acad Sci USA 1996 ; 93 : 10533-7Google ScholarCited by (1790)The effect of adverse childhood experiences on chronic pain and major depression in adulthood: a systematic review and meta-analysis2023, British Journal of AnaesthesiaShow abstractAdverse childhood experiences have been linked to increased multimorbidity, with physical and mental health consequences throughout life. Chronic pain is often associated with mood disorders, such as major depressive disorder (MDD); both have been linked to adverse childhood experiences. It is unclear how the effect of adverse childhood experiences on neural processing impacts on vulnerability to chronic pain, MDD, or both, and whether there are shared mechanisms. We aimed to assess evidence for central neural changes associated with adverse childhood experiences in subjects with chronic pain, MDD, or both using systematic review and meta-analysis.Electronic databases were systematically searched for neuroimaging studies of adverse childhood experiences, with chronic pain, MDD, or both. Two independent reviewers screened title, abstracts, and full text, and assessed quality. After extraction of neuroimaging data, activation likelihood estimate meta-analysis was performed to identify significant brain regions associated with these comorbidities.Forty-nine of 2414 studies were eligible, of which 43 investigated adverse childhood experiences and MDD and six investigated adverse childhood experiences and chronic pain. None investigated adverse childhood experiences, chronic pain, and MDD together. Functional and structural brain abnormalities were identified in the superior frontal, lingual gyrus, hippocampus, insula, putamen, superior temporal, inferior temporal gyrus, and anterior cerebellum in patients with MDD exposed to adverse childhood experiences. In addition, brain function abnormalities were identified for patients with MDD or chronic pain and exposure to adverse childhood experiences in the cingulate gyrus, inferior parietal lobule, and precuneus in task-based functional MRI studies.We found that adverse childhood experiences exposure can result in different functional and structural brain alterations in adults with MDD or chronic pain compared with those without adverse childhood experiences.PROSPERO CRD42021233989.The complexities of the sleep-pain relationship in adolescents: A critical review2023, Sleep Medicine ReviewsShow abstractChronic pain is a common and disabling condition in adolescents. Disturbed sleep is associated with many detrimental effects in adolescents with acute and chronic pain. While sleep and pain are known to share a reciprocal relationship, the sleep-pain relationship in adolescence warrants further contextualization within normally occurring maturation of several biopsychological processes. Since sleep and pain disorders begin to emerge in early adolescence and are often comorbid, there is a need for a comprehensive picture of their interrelation especially related to temporal relationships and mechanistic drivers. While existing reviews provide a solid foundation for the interaction between disturbed sleep and pain in youth, we will extend this review by highlighting current methodological challenges for both sleep and pain assessments, exploring the recent evidence for directionality in the sleep-pain relationship, reviewing potential mechanisms and factors underlying the relationship, and providing direction for future investigations. We will also highlight the potential role of digital technologies in advancing the understanding of the sleep and pain relationship. Ultimately, we anticipate this information will facilitate further research and inform the management of pain and poor sleep, which will ultimately improve the quality of life in adolescents and reduce the risk of pain persisting into adulthood.Criminality labelling influences reactions to others’ pain2022, HeliyonShow abstractDisparities in healthcare for underrepresented and stigmatized groups are well documented. Current understanding is that these inequalities arise, at least in part, from psychosocial factors such as stereotypes and in-group/out-group categorization. Pain management, perhaps because of the subjective nature of pain, is one area of research that has spearheaded these efforts. We investigated how observers react to the pain of individuals labelled as criminals. Face models expressing pain of different levels of intensity were portrayed as having committed a crime or not (control group). A sample of n = 327 college students were asked to estimate the intensity of the pain expressed by face models as well as their willingness to help them. Trait empathy was also measured. Data was analyzed using regression, mediation and moderation analyses. We show for the first time that observers were less willing to help individuals with a criminal history. Moreover, a moderation effect was observed whereby empathic participants were more willing to help control face models compared to less empathic participants. However, criminality history did not influence participant's pain estimation. We conclude that negative stereotypes associated with criminality can reduce willingness to help individuals in pain even when pain signals are accurately perceived.Brain-activation-based individual identification reveals individually unique activation patterns elicited by pain and touch2022, NeuroImageShow abstractPain is subjective and perceived differently in different people. However, individual differences in pain-elicited brain activations are largely overlooked and often discarded as noises. Here, we used a brain-activation-based individual identification procedure to investigate the uniqueness of the activation patterns within the whole brain or brain regions elicited by nociceptive (laser) and tactile (electrical) stimuli in each of 62 healthy participants. Specifically, brain activation patterns were used as “fingerprints” to identify each individual participant within and across sensory modalities, and individual identification accuracy was calculated to measure each individual's identifiability. We found that individual participants could be successfully identified using their brain activation patterns elicited by nociceptive stimuli, tactile stimuli, or even across modalities. However, different participants had different identifiability; importantly, the within-pain, but not within-touch or cross-modality, individual identifiability obtained from three brain regions (i.e., the left superior frontal gyrus, the middle temporal gyrus and the insular gyrus) were inversely correlated with the scores of Pain Vigilance and Awareness Questionnaire (i.e., how a person is alerted to pain) across participants. These results suggest that each individual has a unique pattern of brain responses to nociceptive stimuli which contains both modality-nonspecific and pain-specific information and may be associated with pain-related behaviors shaped by his/her own personal experiences and highlight the importance of a transition from group-level to individual-level characterization of brain activity in neuroimaging studies.Orgasm and Related Disorders Depend on Neural Inhibition Combined With Neural Excitation2022, Sexual Medicine ReviewsShow abstractPrevalent models of sexual desire, arousal and orgasm postulate that they result from an excitatory process, whereas disorders of sexual desire, arousal and orgasm result from an inhibitory process based on psychosocial, pharmacological, medical, and other factors. But neuronal excitation and active neuronal inhibition normally interact at variable intensities, concurrently and continuously. We propose herein that in conjunction with neuronal excitation, neuronal inhibition enables the generation of the intense, non-aversive pleasure of orgasm. When this interaction breaks down, pathology can result, as in disorders of sexual desire, arousal, and orgasm, and in anhedonia and pain. For perspective, we review some fundamental behavioral and (neuro-) physiological functions of neuronal excitation and inhibition in normal and pathological processes.To review evidence that the variable balance between neuronal excitation and active neuronal inhibition at different intensities can account for orgasm and its disorders.We selected studies from searches on PubMed, Google Scholar, Dialnet, and SciELO for terms including orgasm, neuronal development, Wallerian degeneration, prenatal stress, parental behavior, sensorimotor, neuronal excitation, neuronal inhibition, sensory deprivation, anhedonia, orgasmic disorder, hypoactive sexual desire disorder, persistent genital arousal disorder, sexual pain.We provide evidence that the intensity of neuronal inhibition dynamically covaries concurrently with the intensity of neuronal excitation. Differences in these relative intensities can facilitate the understanding of orgasm and disorders of orgasm.Neuronal excitation and neuronal inhibition are normal, continuously active processes of the nervous system that are necessary for survival of neurons and the organism. The ability of genital sensory stimulation to induce concurrent neuronal inhibition enables the stimulation to attain the pleasurable, non-aversive, high intensity of excitation characteristic of orgasm. Excessive or deficient levels of neuronal inhibition relative to neuronal excitation may account for disorders of sexual desire, arousal and orgasm.Komisaruk BR, Rodriguez del Cerro MC. Orgasm and Related Disorders Depend on Neural Inhibition Combined With Neural Excitation. Sex Med Rev 2022;10:481–492.Is Chronic Pain a Disease?2022, Journal of PainShow abstractIt was not until the twentieth century that pain was considered a disease. Before that it was managed medically as a symptom. The motivations for declaring chronic pain a disease, whether of the body or of the brain, include increasing its legitimacy as clinical problem and research focus worthy of attention from healthcare and research organizations alike. But 1 problem with disease concepts is that having a disease favors medical solutions and tends to reduce patient participation. We argue that chronic pain, particularly chronic primary pain (recently designated a first tier pain diagnosis in International Diagnostic Codes 11), is a learned state that is not intransigent even if it has biological correlates. Chronic pain is sometimes a symptom, and may sometimes be its own disease. But here we question the value of a disease focus for much of chronic pain for which patient involvement is essential, and which may need a much broader societal approach than is suggested by the disease designation.This article examines whether designating chronic pain a disease of the body or brain is helpful or harmful to patients. Can the disease designation help advance treatment, and is it needed to achieve future therapeutic breakthrough? Or does it make patients over-reliant on medical intervention and reduce their engagement in the process of recovery?View all citing articles on ScopusView AbstractCopyright © 2000 Éditions scientifiques et médicales Elsevier SAS. All rights reserved.Recommended articlesRelations Between Brain Alterations and Clinical Pain Measures in Chronic Musculoskeletal Pain: A Systematic ReviewThe Journal of Pain, Volume 17, Issue 9, 2016, pp. 949-962Iris Coppieters, …, Barbara CagnieView PDFDiscrepancy between perceived pain and cortical processing: A voxel-based morphometry and contact heat evoked potential studyClinical Neurophysiology, Volume 127, Issue 1, 2016, pp. 762-768J.L.K. Kramer, …, P. FreundView PDFBrain mediators of the effects of noxious heat on painPAIN®, Volume 155, Issue 8, 2014, pp. 1632-1648Lauren Y. Atlas, …, Tor D. 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ScienceDirect® is a registered trademark of Elsevier B.V.ScienceDirect® is a registered trademark of Elsevier B.V. ",11126640,"Brain responses to pain, assessed through positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) are reviewed. Functional activation of brain regions are thought to be reflected by increases in the regional cerebral blood flow (rCBF) in PET studies, and in the blood oxygen level dependent (BOLD) signal in fMRI. rCBF increases to noxious stimuli are almost constantly observed in second somatic (SII) and insular regions, and in the anterior cingulate cortex (ACC), and with slightly less consistency in the contralateral thalamus and the primary somatic area (SI). Activation of the lateral thalamus, SI, SII and insula are thought to be related to the sensory-discriminative aspects of pain processing. SI is activated in roughly half of the studies, and the probability of obtaining SI activation appears related to the total amount of body surface stimulated (spatial summation) and probably also by temporal summation and attention to the stimulus. In a number of studies, the thalamic response was bilateral, probably reflecting generalised arousal in reaction to pain. ACC does not seem to be involved in coding stimulus intensity or location but appears to participate in both the affective and attentional concomitants of pain sensation, as well as in response selection. ACC subdivisions activated by painful stimuli partially overlap those activated in orienting and target detection tasks, but are distinct from those activated in tests involving sustained attention (Stroop, etc.). In addition to ACC, increased blood flow in the posterior parietal and prefrontal cortices is thought to reflect attentional and memory networks activated by noxious stimulation. Less noted but frequent activation concerns motor-related areas such as the striatum, cerebellum and supplementary motor area, as well as regions involved in pain control such as the periaqueductal grey. In patients, chronic spontaneous pain is associated with decreased resting rCBF in contralateral thalamus, which may be reverted by analgesic procedures. Abnormal pain evoked by innocuous stimuli (allodynia) has been associated with amplification of the thalamic, insular and SII responses, concomitant to a paradoxical CBF decrease in ACC. It is argued that imaging studies of allodynia should be encouraged in order to understand central reorganisations leading to abnormal cortical pain processing. A number of brain areas activated by acute pain, particularly the thalamus and anterior cingulate, also show increases in rCBF during analgesic procedures. Taken together, these data suggest that hemodynamic responses to pain reflect simultaneously the sensory, cognitive and affective dimensions of pain, and that the same structure may both respond to pain and participate in pain control. The precise biochemical nature of these mechanisms remains to be investigated.",Functional imaging of brain responses to pain. A review and meta-analysis (2000).
-" EEG et accident vasculaire cérébral ischémique du nouveau-né à terme - ScienceDirect JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main contentSkip to article ScienceDirectJournals & BooksSearchRegisterSign inView PDFDownload full issueSearch ScienceDirectOutlineRésuméAbstractMots-cléKeywordsAbbreviations1. Introduction2. Matériel et méthodes3. Résultats4. Discussion5. ConclusionRéférencesShow full outlineCited By (11)Figures (3)Tables (3)Tableau 1Tableau 2Tableau 3Neurophysiologie Clinique/Clinical NeurophysiologyVolume 33, Issue 3, June 2003, Pages 120-129Article originalEEG et accident vasculaire cérébral ischémique du nouveau-né à termeEEG and ischemic stroke in full-term newbornsAuthor links open overlay panelD Selton, M André, J.M HascoëtShow moreOutlineAdd to MendeleyShareCitehttps://doi.org/10.1016/S0987-7053(03)00030-3Get rights and contentRésuméLe but de cette étude est de décrire les anomalies électro-encéphalographiques (EEG) observées lors d’accidents vasculaires cérébraux (AVC) unilatéraux du nouveau-né à terme, sans autre pathologie anoxo-ischémique associée, et d’établir celles qui pourraient être prédictives de l’évolution à long terme. Chez 6 nouveau-nés à terme sans hypoxo-ischémie, l’AVC a été confirmé par tomodensitométrie (TDM) et/ou imagerie en résonance magnétique (IRM) cérébrale. Vingt EEGs ont été réalisés en période néonatale, 5 en période aiguë avec crises et 15 au-delà. Les enfants ont été suivis jusque 3 à 9 ans. Les nouveau-nés ont tous présenté des crises cloniques, ayant débuté entre 14 et 48 h de vie, localisées à l’hémicorps droit (5 cas) ou gauche (1 cas). Lors du suivi, 3 enfants étaient normaux à 3, 3 et 6 ans et 3 présentaient des séquelles : une épilepsie à 9 ans, 1 hémiparésie à 3 ans et une monoparésie fruste avec des troubles importants du comportement à 4 ans. Du côté atteint ont été observées des décharges EEG critiques, ainsi que des anomalies inter ou post critiques : excès de rythmes thêta ou alpha, brève discontinuité ou diminution d’amplitude de l’activité de fond. De nombreuses pointes lentes positives rolandiques (PLPR) similaires aux pointes positives rolandiques du prématuré ont été observées chez les 2 enfants atteints de séquelles motrices. L’enfant présentant des troubles du comportement avait de nombreuses pointes plus ou moins rapides positives temporales gauches. Chez 6 nouveau-nés à terme atteints d’AVC, des PLPR du côté atteint semblent associées à des séquelles motrices controlatérales et des pointes plus ou moins rapides positives temporales gauches à des troubles comportementaux à long terme. Ces observations peuvent servir de base à l’élaboration de protocoles prospectifs destinés à préciser les relations entre certains aspects EEG et l’évolution à long terme.AbstractThe aims of this study were to describe EEG anomalies in unilateral neonatal ischemic stroke without hypoxic-ischemic encephalopathy, and to determine possible links between these abnormalities and long-term outcome. In 6 full-term newborns without severe fetal distress ischemic stroke was confirmed by computed tomography and/or magnetic resonance imaging. Twenty EEGs were recorded during the neonatal period, 5 in acute stage and 15 later. The duration of the follow-up ranged from 3 to 9 years. All newborns developed unilateral clonic seizures, right-sided (5 cases) or left-sided (1 case); seizures began between 14 and 48 h of life. At follow-up, 3 children were normal at 2 and 6 years of age, while the 3 others had sequelae: epilepsy at 9 years of age in one, and unilateral mild cerebral palsy in the 2 others (3 and 4 years of age), with behavioral problems in one of them. Critical EEG discharges, rhythmic sharp waves and/or slow waves were recorded on the injured side. Abnormalities of interictal activity were excess of alpha or theta rhythms, transitory EEG discontinuity or low voltage. The 2 children with cerebral palsy had numerous unilateral post-ictal positive rolandic slow sharp waves (PRSSWs), which were similar to the positive rolandic sharp waves of premature infants; the child with behavioral problems had numerous positive left-sided temporal fast sharp waves. PRSSWs could be associated with controlateral motor sequelae, while positive left temporal fast sharp waves were associated with long term behavioral problems. These findings may be used for future prospective studies aimed at specifying the relation between EEG abnormalities and long-term outcome.Previous article in issueNext article in issueMots-cléAccident vasculaire cérébral ischémiqueÉlectro-encéphalogrammeEnfantNouveau-né à termeInfirmité motrice cérébralePronosticTroubles du comportementKeywordsBehavior disordersCerebral palsyElectroencephalographyInfantFull-term newbornIschemic strokePrognosisAbbreviationsAVCaccident vasculaire cérébral ischémiqueEEGélectro-encéphalogrammeIRMimagerie en résonance magnétiqueSFAsouffrance fœtale aiguëPLPRpointes lentes positives rolandiquesTDMtomodensitométrie1. IntroductionL’accident vasculaire cérébral (AVC) néonatal est généralement diagnostiqué à l’occasion de convulsions dont il est responsable dans 6,1 à 14 % des cas chez les nouveau-nés à terme [10], [11], [19]. Il s’agit surtout de crises convulsives localisées ou prédominant nettement à un hémicorps, accompagnées d’anomalies focalisées, critiques [3], [5], [19] ou intercritiques [8] à l’électro-encéphalogramme.Des AVC peuvent en fait survenir à tout âge, notamment pendant la vie fœtale. Le diagnostic peut en être fait avant la naissance lors d’échographies obstétricales, en période néonatale lors de la réalisation d’une neuro-imagerie pour bilan de convulsions ou d’une encéphalopathie hypoxo-ischémique, ou enfin plus tard, au cours du deuxième semestre de vie lorsqu’une hémiplégie « congénitale » se manifeste, à distance de la période néonatale qui est exempte de déficit moteur.Pour certains auteurs l’hypoxo-ischémie est habituellement associée aux AVC du nouveau-né [24], mais pour d’autres elle est inconstante [3], [26], [37]. Dans le premier cas, les symptômes néonatals et le pronostic sont le résultat des 2 pathologies associées.L’origine de tous ces AVCs, anté– et néonataux serait multifactorielle [24]. Le mécanisme impliqué, spasme, thrombose ou embol d’origine artérielle ou placentaire, est associé à des facteurs favorisants, parmi lesquels les anomalies de la coagulation auraient une place prépondérante, notamment un taux élevé du facteur VIIIc ou une mutation du facteur V de Leiden [14], [25], [36]. La présence d’anticorps antiphospholipidiques [6], [13] chez le nouveau-né et de maladies de système chez la mère a aussi été rapportée.L’EEG présente un intérêt diagnostique certain lorsque des manifestations critiques néonatales conduisent à sa réalisation. C’est alors le caractère focalisé des anomalies EEG qui a une bonne valeur d’orientation.Si l’activité de fond EEG a un excellent pouvoir prédictif de séquelles motrices dans des situations néonatales telles que l’hypoxo-ischémie [18], [20], [32], [33], sa valeur prédictive lors d’AVCs, signalée par certains [27], n’est pas observée par tous [35]. La présence simultanée dans ces séries de cas avec et sans hypoxo-ischémie est vraisemblablement, au moins en partie, responsable de ces divergences.Nous avons donc cherché à identifier des anomalies EEG propres à l’AVC. Pour cela nous avons choisi d’analyser les EEG en période aiguë et dans les suites proches d’accidents vasculaires de survenue néonatale, chez des enfants exempts d’anoxo-ischémie associée.Nous avons recherché s’il existe des relations entre les anomalies observées et des séquelles ultérieures, aussi bien motrices que cognitives ou comportementales, plus récemment signalées dans les suites tardives d’un AVC néonatal [13], [16], [37].2. Matériel et méthodes2.1. CliniqueCette étude concerne des nouveau-nés à terme (> 36 semaines d’âge gestationnel), hospitalisés dans un service de néonatologie de niveau 3 du ler janvier 1990 au 31 décembre 2000, pour convulsions dues à un accident vasculaire cérébral ischémique artériel unilatéral. Ont été exclus les nouveau-nés dont l’histoire obstétricale ou l’état à la naissance — en particulier un score d’Apgar inférieur à 3 à 1 min et inférieur à 7 à 5 min de vie [21] — évoquaient une souffrance fœtale aiguë (SFA).2.2. Imagerie cérébraleLe diagnostic d’AVC a été porté chez tous les enfants à l’aide d’une tomodensitométrie cérébrale (TDM).Une IRM cérébrale a été faite dans les 2 cas les plus récents en période néonatale (patients 1 et 2) et à l’âge de 25 mois chez l’enfant 4 après la découverte d’une monoparésie du membre inférieur droit.2.3. Électro-encéphalogrammeUn premier EEG a été enregistré dans les 48 h suivant le début des convulsions. Cinq enfants ont été transférés depuis un établissement extérieur, ce qui explique certains délais entre les premiers symptômes cliniques et le premier EEG. Les tracés suivants l’ont été en fonction des données EEG initiales et de l’évolution clinique.Les EEGs, d’une durée d’au moins 45 min, ont été réalisés à l’aide de 8 électrodes disposées sur le scalp selon le système 10–20 international adapté aux nouveau-nés et comportaient 8 dérivations bipolaires et 2 dérivations pour l’enregistrement du rythme cardiaque et de la respiration [17]. Lors de l’enregistrement, les posologies et heures d’administration des sédatifs et anti-épileptiques ont été notées.L’analyse des EEGs a comporté la recherche de décharges critiques (localisation et durée), la description de l’activité de fond de l’hémisphère cérébral concerné et de l’hémisphère controlatéral, ainsi que des grapho-éléments anormaux surajoutés.2.4. Bilan étiologiqueUne recherche d’anomalies de la coagulation acquises ou génétiques a été effectuée chez les enfants et leurs parents, comportant les dosages sériques du facteur VIII, de l’antithrombine III, de la protéine C, de la protéine S, des anticorps antiphospholipidiques circulants [6], l’étude de la résistance de la protéine C activée ainsi que la recherche de mutation du facteur II et du facteur V de type Leiden [14], [25], [26]. Cette étude a été incomplète chez les enfants nés au cours des premières années de l’étude. Une recherche d’homocystéinurie a été pratiquée chez un enfant. En l’absence de signe d’appel, la recherche d’une maladie de système [13] n’a pas été effectuée chez les mères.2.5. ÉvolutionLe développement moteur, sensoriel, cognitif et le comportement des enfants ont été évalués à la consultation de suites du service de néonatologie. L’évaluation du développement psychomoteur a été fondée sur les critères de Brunet-Lézine [15] jusqu’à l’âge de 2 ans et demi. Le développement cognitif des enfants plus âgés a été évalué par la qualité de l’intégration scolaire et par l’évaluation clinique effectuée par un neuropédiatre à partir d’éléments de la WYPPSI-R. Les troubles du comportement révélés à l’interrogatoire et à l’évaluation clinique étaient confirmés par un pédopsychiatre.3. Résultats3.1. CliniquePendant la durée de l’étude, parmi les 150 nouveau-nés à terme hospitalisés ayant présenté des convulsions, 6 nouveau-nés, 4 garçons et 2 filles, eutrophiques, ont eu un AVC unilatéral sans SFA.Les données cliniques, tomodensitométriques et l’évolution sont résumées dans le Tableau 1. L’enfant 4, dont le score d’Apgar n’était qu’à 3 à 1 min, était né après anesthésie générale maternelle.Tableau 1. ObservationsN°AG (SA)ApgarConvulsionsManifestationsTomodensitométrieÉvolutionPN (g)1–5 minÂge de début et fincliniquesAspects(âge)SexePC (cm)(âge)1425–7H14–H49Hémiconvulsion GHypod ACM DNormaleM3540(J3)(3 A)3624110–10H32–J5Hémiconvulsion DHypod ACM G, ACP GNormaleF4220(J17)(3 A)363399–10H26–H41Hémiconvulsion DHypod ACM GÉpilepsieM3000Mouvements oculaires(J5)(9 A)33,5anormaux4383–9H26–J12Clonies MSD ApnéesHypod Aca G, ACM GMonoparésie modérée (MID)F3560(J5, 11, 13)(3 A)3553810–10H24–H48Hémiconvulsion DHypod ACM GNormaleM4210(J9)(6 A)366419–9J2– J5Clonies MID puis MSDHypod ACM GHémiparésie DM4070fixité du regardsuperficielle+ Troubles du comportement37mâchonnement(J4)(4 A)AG : âge gestationel ; SA : semaines d’aménorrhée ; PN : poids de naissance ; PC : périmètre crânien ; M : masculin ; H : heure ; G : gauche ; Hypod : hypodensité ; D : droit ; J : jour ; ACM : territoire de l’artère cérébrale moyenne ; A : ans ; F : féminin ; ACP : territoire de l’artère cérébrale postérieure ; MS : membre supérieur ; Aca : territoire de l’artère cérébrale antérieure ; MI : membre inférieur.Tous les enfants ont eu des crises partielles, de type cloniques, hémicorporelles ou focales, localisées à un membre, très transitoires (patients 3 et 5), persistantes de 1 à 3 j (patients 1 et 2) ou plus durables mais très intermittentes (patient 4).Les enfants ont reçu un traitement anti-épileptique dès que le diagnostic de convulsions a été porté, 5 fois cliniquement, 1 fois sur l’EEG (patient 6). Le traitement initial a comporté du phénobarbital (20 mg/kg), sauf chez l’enfant 5 qui a reçu du diazépam (0,3 mg/kg). Le contrôle de crises n’a été obtenu qu’après l’adjonction de clonazépam (0,1 à 0,2 mg/kg) chez 3 enfants (patients 1, 2 et 4). Le traitement par phénobarbital a été poursuivi entre 8 et 45 j chez 5 patients (1–4, 6), relayé par du valproate de sodium pendant 4,5 mois chez le patient 4.Trois enfants (patients 1, 2 et 4) ont présenté une asymétrie transitoire de tonus et de motilité corporelle. Des troubles de vigilance vraisemblablement en relation avec des barbitémies élevées à 38 et 35,1 mg/l étaient notés dans 2 cas (patients 1 et 4).3.2. Imagerie cérébraleLes explorations (TDM et IRM) ont montré que 4 enfants ont eu un AVC localisé, 3 au niveau du territoire de l’artère sylvienne gauche et 1 au niveau de celui de l’artère sylvienne droite. Deux enfants ont présenté une atteinte associant le territoire sylvien gauche à celui de la cérébrale antérieure gauche (patient 4) et à celui de la cérébrale postérieure gauche (patient 2). Chez ce dernier enfant, l’atteinte a concerné le territoire sylvien gauche superficiel et profond, atteignant le lobe temporal, le lobe pariétal et insulaire et le pédoncule cérébral sous-jacent. L’angio-IRM a révélé une thrombose complète des artères cérébrale postérieure et carotide interne gauches.3.3. EEGSur les 20 EEGs enregistrés, 5 l’ont été en phase aiguë — contemporaine des crises — (Tableau 2), 5 à 48 h après le début des convulsions, soit entre 20 h et 4 j de vie, 15 en phase « post-aiguë » de 4 à 12 j après le début des convulsions, soit entre 6 et 17 j de vie (Tableau 3).Tableau 2. Aspects EEGs en période aiguë et évolution cliniqueCasÂgeSignes de focalisation et principales anomaliesAnomalies de l'activité de fondÉvolutionNoPL et rapidesAutresAsymétrieDescription1H20PLNR D (1,7/min)B thêta R D amples, pointues,DNormalealternantes (0,1/min)Thêta en excès2J3PLNR G (1/min), P rapides N TG (2/min)Thêta en excèsGNormaleJ4PLNR G (0,9/min), P rapides N TG (0,5/min)G ( 1/min) et leur localisation, pointes lentes positives rolandiques et pointes plus ou moins rapides essentiellement positives temporales sont associées chez 2 enfants à une évolution défavorable.Les premières sont assez semblables aux pointes positives rolandiques du prématuré atteint de leucomalacie périventriculaire [2], [29] et comparables aux anomalies décrites par d’Allest et al. [9] chez des nouveau-nés à terme souffrant d’hémorragies ventriculaires avec ischémie parenchymateuse. Comme dans cette dernière situation, elles sont associées à des hypodensités parenchymateuses à la TDM cérébrale et à des séquelles motrices qui sont cependant légères chez les 2 enfants (patients 4 et 6) de notre étude, alors que ces pointes lentes positives rolandiques sont fréquentes et persistantes. Ce type d’anomalies pourrait donc être un stigmate de l’ischémie parenchymateuse due à l’AVC.Les secondes, pointes plus ou moins rapides, essentiellement positives temporales gauches, sont présentes chez l’enfant 6 et associées à la fois à un syndrome hémidéficitaire et à des troubles caractériels importants à l’âge de 4 ans. Elles sont comparables à celles décrites par Chung et Clancy [7] chez des nouveau-nés dont l’âge post-conceptionnel équivaut au terme et qui sont porteurs de lésions cérébrales diffuses ou focales dans 1 cas sur 2. Elles sont également comparables à celles, abondantes et amples, décrites par Vecchierini-Blineau et al. [38] chez des enfants présentant une asphyxie périnatale, des désordres métaboliques ou une leucomalacie périventriculaire. Elles sont associées chez l’enfant 6 à des pointes anormales diphasiques temporales, moins nombreuses.Si le syndrome hémidéficitaire semble en relation avec des lésions exprimées par des pointes lentes positives rolandiques, les troubles caractériels pourraient être liés à des anomalies de la région temporale gauche. Botez [4] a décrit chez l’adulte une relation entre des troubles mentaux à type de colères et irritabilité injustifiées et des tumeurs tempororhinencéphaliques gauches. Les pointes plus ou moins rapides essentiellement positives temporales, sans être spécifiques de l’AVC puisque retrouvées dans d’autres situations pathologiques [38], doivent cependant, de par leur localisation, amener à proposer un suivi ultérieur suffisamment prolongé des enfants qui en ont présenté en période néonatale.5. ConclusionMalgré l’homogénéité clinique des patients, tous exempts d’autre cause d’anoxo-ischémie associée, l’étude de cette petite série rétrospective n’apporte pas d’argument anamnestique, clinique ni neuroradiologique nouveau par rapport aux observations de la littérature d’AVC ischémique artériel néonatal.Dans cette pathologie, l’électro-encéphalogramme a été considéré jusqu’à présent surtout comme un moyen de mettre en évidence des signes localisés, indices de lésion cérébrale elle-même focale, et de surveiller des convulsions.Pourtant, l’analyse détaillée de l’électro-encéphalogramme fournit des données concernant les anomalies de l’activité de fond et les figures pathologiques surajoutées dont l’intérêt diagnostique et pronostique doit être souligné.En particulier, des pointes lentes positives rolandiques unilatérales fréquentes et persistantes semblent associées à des séquelles motrices hémicorporelles controlatérales et des pointes essentiellement positives, plus ou moins rapides, temporales gauches, fréquentes pourraient être prédictives de troubles comportementaux tardifs. Aucun élément particulier ne semble associé à la survenue d’une épilepsie tardive.Ces éléments devraient être pris en compte dans l’élaboration de futures études prospectives en confrontant, comme dans d’autres pathologies neurologiques néonatales déjà bien connues, les données cliniques, électro-encéphalographiques, neuroradiologiques et biologiques néonatales à un suivi rigoureux à long terme. 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Asymmetry, asynchrony, excessive focal theta activity, positive rolandic sharp waves, transitory EEG discontinuity or low voltage have been described as abnormalities of inter-ictal EEG activity associated with poor prognosis [6,8,18]. However, these abnormal graph-elements were not (or were sparsely) defined in their methodology.Show abstractTo analyze the findings in the background EEG activity of infants who suffered perinatal stroke.Eleven neonates born 2009–2014 diagnosed of ischemic stroke by MRI (three of them with multistroke) underwent continuous video-EEG monitoring. Visual and spectral (power spectrum and coherence) analyses of the background EEG was performed in three moments: 1) Onset of EEG recording (prior to initiate seizure treatment), 2) Post-ictal epoch (1–2 h after the last seizure), and 3) one–two days after seizure control. All children aged 2–6 years underwent neurodevelopmental assessment.Discontinuity, asymmetry, asynchrony, transients, and relative power spectrum in δ and θ frequency bands increased significantly (p < 0.05) in the post-ictal epoch with respect to onset of EEG recording. After seizure control, discontinuity, asynchrony, and θ power spectrum no longer had significant differences with those found at onset of EEG recording. Significant differences between the ischemic and unaffected hemispheres were found in transients and in β coherence (p = 0.002; p = 0.001, respectively) exclusively in the post-ictal epoch. Seizure burden and time-to-control ranged 5–38 min and 0.5–40 h respectively. Currently, only one child is affected by spastic monoparesis. The intelligence quotients ranged 96–123.The background EEG can undergo significant changes in the post-ictal epoch due to the seizure activity triggered by the perinatal stroke. Most of these EEG changes involve all brain activity and not exclusively the ischemic hemisphere. Many of these modifications in the EEG background reverse following the seizure control. Video-EEG monitoring allows accurate/immediate diagnosis and rapid/intensive treatment of the stroke-associated seizures.Place of EEG in the management of arterial ischemic stroke newborn2017, Archives de PediatrieShow abstractL’électroencéphalogramme (EEG) est un examen clé dans la prise en charge des convulsions néonatales, principal mode de révélation des accidents vasculaires cérébraux artériels et ischémiques du nouveau-né (AVCian). Il doit être réalisé dès que possible en cas de convulsion ou de suspicion de convulsion. Certains aspects de l’EEG sont évocateurs d’AVCian comme l’asymétrie du tracé de fond avec présence de pointes focales. L’EEG doit être prolongé et associé à une vidéo car les crises paucisymptomatiques ou infracliniques sont fréquentes, y compris sous la forme d’état de mal. Il n’a pas été démontré jusqu’à présent que les aspects EEG étaient prédictifs du devenir à long terme cognitif et épileptique.The EEG has a key role in the management of neonatal seizures, which are the typical mode of revelation of neonatal ischemic stroke. The EEG should be started as soon as possible in case of suspected seizure. Some EEG features are evocative of neonatal stroke, typically asymmetry of background activity with the presence of focal spikes and focal seizures. The EEG should be prolonged and always associated with a video as pauci-symptomatic or subclinical seizures are frequent, including some status epilepticus. Video-EEG monitoring is therefore essential to judge the efficacy of anticonvulsant therapy. There is insufficient evidence so far to state if some EEG aspects are predictive of long-term cognitive and epileptic outcomes.Neonatal arterial ischemic stroke: Review of the current guidelines2017, Archives de PediatrieShow abstractL’accident vasculaire cérébral ischémique artériel néonatal (AVCian) est une pathologie rare. Afin d’actualiser les connaissances sur ce sujet, un groupe de travail multidisciplinaire s’est constitué sous l’égide de la Société française de néonatologie et le Centre national de référence de l’AVC de l’enfant afin de proposer des recommandations sur les facteurs de risque, les modalités de transfert et de prise en charge pré-hospitalière, les modalités diagnostiques et thérapeutiques, le traitement, le pronostic et la prise en charge à court et moyen terme. Ces recommandations ont été réalisées selon la méthodologie de la Haute autorité de santé et en fonctions des thématiques proposées par un comité d’experts. Les principales recommandations issues de ce travail sont : (1) l’orientation du patient vers une unité de réanimation ou de soins intensifs néonatals disposant d’une imagerie par résonance magnétique (IRM) facilement accessible et de la possibilité de réaliser une surveillance continue par électro-encéphalogramme ; (2) le phénobarbital est le médicament de première ligne pour le traitement des crises convulsives ; (3) l’IRM réalisée entre j2 et j4 après la survenue de l’AVCian est la meilleure technique pour confirmer le diagnostic et préciser son extension ; (4) un facteur biologique de risque thrombotique ne doit pas être systématiquement recherché après un AVCian, sauf en cas d’antécédent thrombotique veineux familial ; (5) un traitement thrombolytique n’est pas recommandé ; (6) une prise en charge rééducative précoce est recommandée en cas de déficience motrice évidente.Neonatal arterial ischemic stroke (NAIS) is a rare event that occurs in approximately one in 5000 term or close-to-term infants. Most affected infants will present with seizures. Although a well-recognized clinical entity, many questions remain regarding diagnosis, risk factors, treatment, and follow-up modalities. In the absence of a known pathophysiological mechanism and lack of evidence-based guidelines, only supportive care is currently provided. To address these issues, a French national committee set up by the French Neonatal Society (Société française de néonatologie) and the national referral center (Centre national de référence) for arterial ischemic stroke in children drew up guidelines based on an HAS (Haute Autorité de santé [HAS]; French national authority for health) methodology. The main findings and recommendations established by the study group are: (1) among the risk factors, male sex, primiparity, caesarean section, perinatal hypoxia, and fetal/neonatal infection (mainly bacterial meningitis) seem to be the most frequent. As for guidelines, the study group recommends the following: (1) the transfer of neonates with suspected NAIS to a neonatal intensive care unit with available equipment to establish a reliable diagnosis with MRI imaging and neurophysiological monitoring, preferably by continuous video EEG; (2) acute treatment of suspected infection or other life-threatening processes should be addressed immediately by the primary medical team. Persistent seizures should be treated with a loading dose of phenobarbital 20 mg/kg i.v.; (3) MRI of the brain is considered optimal for the diagnosis of NAIS. Diffusion-weighted imaging with apparent diffusion coefficient is considered the most sensitive measure for identifying infarct in the neonatal brain. The location and extent of the lesions are best assessed between 2 and 4 days after the onset of stroke; (4) routine testing for thrombophilia (AT, PC PS deficiency, FV Leiden or FII20210A) or for detecting other biological risk factors such as antiphospholipid antibodies, high FVIII, homocysteinemia, the Lp(a) test, the MTHFR thermolabile variant should not be considered in neonates with NAIS. Testing for FV Leiden can be performed only in case of a documented family history of venous thromboembolic disease. Testing neonates for the presence of antiphospholipid antibodies should be considered only in case of clinical events arguing in favor of antiphospholipid syndrome in the mother; (5) unlike childhood arterial ischemic stroke, NAIS has a low 5-year recurrence rate (approximately 1 %), except in those children with congenital heart disease or multiple genetic thrombophilia. Therefore, initiation of anticoagulation or antithrombotic agents, including heparin products, is not recommended in the newborn without identifiable risk factors; (6) the study group recommends that in case of delayed motor milestones or early handedness, multidisciplinary rehabilitation is recommended as early as possible. Newborns should have physical therapy evaluation and ongoing outpatient follow-up. Given the risk of later-onset cognitive, language, and behavioral disabilities, neuropsychological testing in preschool and at school age is highly recommended.Real-time automated detection of clonic seizures in newborns2014, Clinical NeurophysiologyCitation Excerpt :Furthermore, staff recognition skills can vary significantly among different institutions. Clonic seizures recognition also carries important implications for etiologic diagnosis, as they are frequently associated with localised brain injury, mostly of vascular origin, e.g. stroke (Selton et al., 2003). An even more relevant data outlined in the aforementioned article (Murray et al., 2008) is that electroclinical seizures correspond to only 18.8% of the total seizure burden.Show abstractThe aim of this study is to apply a real-time algorithm for clonic neonatal seizures detection, based on a low complexity image processing approach extracting the differential average luminance from videotaped body movements.23 video-EEGs from 12 patients containing 78 electrographically confirmed neonatal seizures of clonic type were reviewed and all movements were divided into noise, random movements, clonic seizures or other seizure types. Six video-EEGs from 5 newborns without seizures were also reviewed. Videos were then separately analyzed using either single, double or triple windows (these latter with 50% overlap) each of a 10 s duration.With a decision threshold set at 0.5, we obtained a sensitivity of 71% (corresponding specificity: 69%) with double-window processing for clonic seizures diagnosis. The discriminatory power, indicated by the Area Under the Curve (AUC), is higher with two interlaced windows (AUC = 0.796) than with single (AUC = 0.788) or triple-window (AUC = 0.728). Among subjects without neonatal seizures, our algorithm showed a specificity of 91% with double-window processing.Our algorithm reliably detects neonatal clonic seizures and differentiates them from either noise, random movements and other seizure types.It could represent a low-cost, low complexity, real-time automated screening tool for clonic neonatal seizures.Between molecules and experience: Role of early patterns of coordinated activity for the development of cortical maps and sensory abilities2010, Brain Research ReviewsCitation Excerpt :The duration of these bursts increased continuously from the 24th to 30th postconceptional week and the IBI become shorter with age (Hahn et al., 1989; Vecchierini et al., 2003; Victor et al., 2005). Abnormally long IBI toward the end of this phase correlate with a poor neurological prognosis in premature infants (Maruyama et al., 2002; Selton et al., 2003). Not only the discontinuous aspect of the EEG trace in premature infants differs from the adult activity, but also its fine scale properties are different.Show abstractSensory systems processing information from the environment rely on precisely formed and refined neuronal networks that build maps of sensory receptor epithelia at different subcortical and cortical levels. These sensory maps share similar principles of function and emerge according to developmental processes common in visual, somatosensory and auditory systems. Whereas molecular cues set the coarse organization of cortico-subcortical topography, its refinement is known to succeed under the influence of experience-dependent electrical activity during critical periods. However, coordinated patterns of activity synchronize the cortico-subcortical networks long before the meaningful impact of environmental inputs on sensory maps. Recent studies elucidated the cellular and network mechanisms underlying the generation of these early patterns of activity and highlighted their similarities across species. Moreover, the experience-independent activity appears to act as a functional template for the maturation of sensory networks and cortico-subcortical maps. A major goal for future research will be to analyze how this early activity interacts with the molecular cues and to determine whether it is permissive or rather supporting for the establishment of sensory topography.Diagnostic management of neonatal stroke2009, Seminars in Fetal and Neonatal MedicineCitation Excerpt :This finding is not shared by others.18 Children with later cerebral palsy may keep numerous unilateral post-ictal positive rolandic slow sharp waves.46 Practice pointsShow abstractIn this paper the clinical presentation of neonatal arterial ischaemic stroke (NAIS) and neonatal cerebral sinovenous thrombosis (NCSVT) is briefly summarised; then a structured hierarchical diagnostic flow is proposed to discern clinical phenotypes underlying neonatal (ischaemic as well as haemorrhagic) stroke. The diagnostic flow proposed following clinical detection or chance imaging finding is an initial step towards standardisation of the mechanisms leading to stroke. For NAIS the sequence is: infection, trauma, embolism, arteriopathy, other, primary thrombosis and unclassified; for NCSVT the sequence is: infection, trauma, venopathy, other, primary thrombosis and unclassified. Such standardisation should guide attempts at prevention and treatment. The analysis of a retrospective personal cohort of 134 newborn infants with stroke, suggest that–for stroke in general–embolism is the most common identifiable mechanism (25%), preceding trauma (10%) and infection (8%). For NAIS the presence of an embolic phenotype is 33% in this cohort. The designation unclassifiable scored 34% for the entire stroke group, 25% for neonatal arterial ischaemic stroke. Complex arterial stroke, with multiple arteries involved–is regularly seen following embolism, infection and cranial trauma.View all citing articles on ScopusView AbstractCopyright © 2003 Éditions scientifiques et médicales Elsevier SAS. All rights reserved.Recommended articlesBurns in South Korea: An analysis of nationwide data from the Health Insurance Review and Assessment ServiceBurns, Volume 42, Issue 3, 2016, pp. 675-681Hyunjin Oh, Sunjoo BooView PDFEstimate of the prevalence and burden of food poisoning by natural toxic compounds in South KoreaFood Research International, Volume 78, 2015, pp. 108-113Myoung Su Park, Gyung Jin BahkView PDFPrévalence de l’artériopathie oblitérante des membres inférieurs et facteurs associés chez les diabétiques suivis en milieu hospitalier à Parakou en 2013Annales de Cardiologie et d'Angéiologie, Volume 65, Issue 4, 2016, pp. 260-264H.L. Codjo, …, D.M. HouénassiView PDFRespiratory variation and cardiopulmonary interactionsBest Practice & Research Clinical Anaesthesiology, Volume 28, Issue 4, 2014, pp. 407-418Russell S. 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ScienceDirect® is a registered trademark of Elsevier B.V.ScienceDirect® is a registered trademark of Elsevier B.V. ",12909390,"The aims of this study were to describe EEG anomalies in unilateral neonatal ischemic stroke without hypoxic-ischemic encephalopathy, and to determine possible links between these abnormalities and long-term outcome. In 6 full-term newborns without severe fetal distress ischemic stroke was confirmed by computed tomography and/or magnetic resonance imaging. Twenty EEGs were recorded during the neonatal period, 5 in acute stage and 15 later. The duration of the follow-up ranged from 3 to 9 years. All newborns developed unilateral clonic seizures, right-sided (5 cases) or left-sided (1 case); seizures began between 14 and 48 h of life. At follow-up, 3 children were normal at 2 and 6 years of age, while the 3 others had sequelae: epilepsy at 9 years of age in one, and unilateral mild cerebral palsy in the 2 others (3 and 4 years of age), with behavioral problems in one of them. Critical EEG discharges, rhythmic sharp waves and/or slow waves were recorded on the injured side. Abnormalities of interictal activity were excess of alpha or theta rhythms, transitory EEG discontinuity or low voltage. The 2 children with cerebral palsy had numerous unilateral post-ictal positive rolandic slow sharp waves (PRSSWs), which were similar to the positive rolandic sharp waves of premature infants; the child with behavioral problems had numerous positive left-sided temporal fast sharp waves. PRSSWs could be associated with contralateral motor sequelae, while positive left temporal fast sharp waves were associated with long term behavioral problems. These findings may be used for future prospective studies aimed at specifying the relation between EEG abnormalities and long-term outcome.",[EEG and ischemic stroke in full-term newborns].
-" Differential involvement of left prefrontal cortexin inductive and deductive reasoning - ScienceDirect JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main contentSkip to article ScienceDirectJournals & BooksSearchRegisterSign inView PDFDownload full issueSearch ScienceDirectOutlineAbstractKeywords1. Introduction2. Method3. Results4. DiscussionReferencesShow full outlineCited By (201)Figures (3)Tables (1)Table 1CognitionVolume 93, Issue 3, October 2004, Pages B109-B121Brief articleDifferential involvement of left prefrontal cortexin inductive and deductive reasoningAuthor links open overlay panelVinod Goel a b, Raymond J Dolan aShow moreOutlineAdd to MendeleyShareCitehttps://doi.org/10.1016/j.cognition.2004.03.001Get rights and contentAbstractWhile inductive and deductive reasoning are considered distinct logical and psychological processes, little is known about their respective neural basis. To address this issue we scanned 16 subjects with fMRI, using an event-related design, while they engaged in inductive and deductive reasoning tasks. Both types of reasoning were characterized by activation of left lateral prefrontal and bilateral dorsal frontal, parietal, and occipital cortices. Neural responses unique to each type of reasoning determined from the Reasoning Type (deduction and induction) by Task (reasoning and baseline) interaction indicated greater involvement of left inferior frontal gyrus (BA 44) in deduction than induction, while left dorsolateral (BA 8/9) prefrontal gyrus showed greater activity during induction than deduction. This pattern suggests a dissociation within prefrontal cortex for deductive and inductive reasoning.Previous article in issueNext article in issueKeywordsLeft prefrontal cortexInductive and deductive reasoningfMRI studies1. IntroductionReasoning is the cognitive process of drawing inferences from given information. All arguments involve the claim that one or more propositions (the premises) provide some grounds for accepting another proposition (the conclusion). At first pass, arguments can be divided into deduction and induction. Deductive arguments can be evaluated for validity. Validity is a function of the relationship between premises and conclusion and involves the claim that the premises provide absolute grounds for accepting the conclusion (e.g. All men are mortal; Socrates is a man; ∴ Socrates is mortal). Arguments where the premises provide only limited grounds for accepting the conclusion are broadly called inductive arguments (e.g. Socrates is a man; Socrates is mortal; ∴ All men are mortal). Inductive arguments are never valid but can be evaluated for plausibility or reasonableness (as in the above example). Thus, while validity can be reduced to a function of the logical structure of sentences and arguments, plausibility is a function of sentence content and our knowledge of the world. It is usually a matter of knowing which properties generalize in the required manner and which do not.Philosophically, induction and deduction constitute different categories of thought. They are also treated differently in the psychological literature (Garnham & Oakhill, 1994; though see Johnson-Laird (1993) for an exception). We were interested in the functional anatomy of inductive and deductive reasoning, and in particular, the role of the prefrontal cortex in the two types of reasoning. Goel, Gold, Kapur and Houle (1997) addressed this question with a O15 PET study and reported that both induction and deduction activated a similar left frontal-temporal system and that induction differed from deduction in greater activation of medial dorsal prefrontal cortex (BA 8 and 9). However, the use of a block design necessitated by the temporal window of the PET technique made it difficult to disentangle set-related activity from activity that might be specific to a particular cognitive process of interest. Here, we address the same question with a more sensitive single-event fMRI design that provides us with a finer-grained analysis of the respective neuroanatomy of inductive and deductive reasoning, uncontaminated by issues of set or expectancy.2. Method2.1. SubjectsSixteen right-handed normal subjects (8 males and 8 females), with a mean age of 27.5 years (SD 6.4) and mean education level of 17.8 years (SD 1.8), volunteered to participate in the study. All subjects gave informed consent and the study was approved by the Joint National Hospital for Neurology and Neurosurgery/Institute of Neurology Ethics Committee.2.1.1. StimuliFifty syllogisms (25 valid, 25 invalid), encompassing 24 different forms,1 fifty inductive arguments (25 plausible and 25 implausible as judged by a pilot subject), and 40 baseline trials were organized into a 2×2 factorial design (Fig. 1a). There was also a common rest condition consisting of 20 null events. The first factor was Reasoning Type, consisting of 2 levels, deduction (50 arguments, 20 baseline and 10 rest trials) and induction (50 arguments, 20 baseline and 10 rest trials) items. The two argument forms were matched for content. If there was a deductive argument about osteoporosis then there was also a matching inductive argument about osteoporosis (e.g. Deduction: No humans can get osteoporosis; Some humans are men; ∴ Some men cannot get osteoporosis; and Induction: Osteoporosis is estrogen-related; Osteoporosis is common in women; ∴ Men can also get osteoporosis).Download : Download full-size imageFig. 1. (a) Overall design of study with sample stimuli. See text. (b) Stimuli presentation. See text.The second factor was Task in which the first level was reasoning. Subjects were instructed to determine the validity of the syllogisms and plausibility of the inductive arguments. The deductive arguments were balanced for validity while the inductive arguments were balanced for plausibility. The second level (baseline condition) trials were generated by taking these arguments and switching around the 3rd sentence such that the three sentences did not constitute arguments. (e.g. No reptiles are hairy; Some elephants are hairy; ∴ No pears are green and Some elephants are hairy; George is an elephant; ∴ All pears are green). In the “rest” trials subjects simply viewed a series of Xs in the centre of a blank screen.2.1.2. Stimuli presentationTo maintain consistent task instructions and mental set the deduction and induction trials were presented in separate blocks. Stimuli within the blocks were presented randomly in an event-related design (see Fig. 1b). The order of blocks was counterbalanced. The beginning of a trial was signaled by an “*”. The sentences appeared on the screen one at a time with the first sentence appearing at 500 ms, the second at 3500 ms, and the last sentence at 6500 ms. All sentences remained on the screen until the end of the trial. The length of trials varied from 10.25 to 14.35 s, leaving subjects 3.75–7.85 s (after the presentation of the third sentence) to respond. In the rest trials sentences were replaced by a series of Xs calculated to occupy the same field of view.2.1.3. TaskSubjects were required to reason in all but the rest trials. In the deduction session subjects were required to determine whether the given conclusion followed logically from the premises (i.e. whether the argument was valid). In the induction session subjects were required to determine whether the given conclusion was plausible given the premises. In baseline trials, where the first two sentences were related, subjects would need to begin to integrate the premises and construct a representation of the problem,2 but when the third, unrelated, sentence appeared they could immediately disengage from the task and respond ‘no’. In trials where the three sentences constituted an argument (reasoning condition), subjects would need to continue with the integration of the conclusion (i.e. the reasoning component of the task) after the presentation of the third sentence. The difference between completing the reasoning task and disengaging after the presentation of the third sentence isolates the reasoning components of interest.3 In rest trials subjects simply viewed lines of Xs corresponding to the lines of text in the active trials.In the active trials subjects provided a binary response by pressing one of two buttons on a keypad after the appearance of the last sentence (valid/invalid for deduction and plausible/implausible for induction). In rest trials subjects pressed any key after the presentation of the third line of Xs. Subjects were instructed to respond as quickly as possible and move to the next trial if the stimuli advanced before they could respond. Subjects reviewed example stimuli from each condition prior to being scanned to ensure that they understood the task.2.2. fMRI scanning techniqueA 2T Siemens VISION system (Siemens, Erlangen, Germany) was used to acquire T1 anatomical volume images (1×1×1.5 mm voxels) and 48 T2*-weighted echoplanar images (64×64 3×3 mm pixels, TE 40 ms) sensitive to blood oxygenation level dependent (BOLD) contrast. Thin echoplanar images of 1.8 mm were acquired axially every 3 mm, positioned to cover the whole brain.4 Data were recorded during a single acquisition period. A total of 494 volume images were acquired over two sessions (247 volumes per session) with a repetition time (TR) of 4.1 s/volume. The first six volumes in each session were discarded (leaving 241 volumes per session) to allow for T1 equilibration effects. Trials from all conditions were randomly presented in a single-event design. The mean trial time was 12,300 ms+/−2050 ms (TR) with a random jitter. Trials thus varied from 10.25 to 14.35 s. There were 80 event presentations during a session for a total of 160 over the two sessions. Each session lasted 16.5 min. The scanner was synchronized with the presentation of every trial in each session.2.3. fMRI data analysisData were analyzed using Statistical Parametric Mapping (SPM 99) (Friston et al., 1995). All volumes were spatially realigned to the first volume (head movement was R) involvement in reasoning.The only study in the literature at variance with our present findings is a O15 PET study (Parsons & Osherson, 2001) which reported that deduction activates right hemisphere systems while induction (probabilistic reasoning) activates mostly left hemisphere systems. However, the findings in this study are at variance with the majority of imaging studies of deductive reasoning (Acuna et al., 2002, Goel et al., 2000, Goel and Dolan, 2001, Goel and Dolan, 2003, Goel et al., 1997, Goel et al., 1998, Knauff et al., 2002) as well as the patient data cited above.The apparent contradiction between studies may be explained in terms of differences in design and/or stimuli differences. Parsons and Osherson (2001) were limited to a block design and chose to use identical arguments in the induction and deduction conditions. The use of a block design precludes the possibility of differentiating the reading of the stimuli sentences from the reasoning task and the isolating of correct from incorrect trials. The use of identical stimuli in the induction and deduction conditions has merit, but comes at a cost. One can only use invalid deductive arguments, thus introducing an asymmetry into the design. This may result in differences in neural response.6 In terms of stimuli, Parsons and Osherson (2001) used complex conditional statements while our (deductive) arguments consist of categorical syllogisms. The former incorporate logical operators while the latter incorporate quantification and negation relations. It is also possible there are neural differences in how statements involving logical operators and quantification and negation relations are processed.The main interest in the present study are the findings from the Task by Reasoning Type interaction analyses that show greater left inferior frontal gyrus (BA 44) (Broca's Area) activation for deduction than induction. Conversely, the left dorsolateral (BA 9) prefrontal cortex (along with right superior occipital gyrus (BA 19)) show greater increases for induction. This result is reinforced by the simple effects analyses, where deduction minus baseline activates left inferior frontal gyrus and induction minus baseline activates more dorsal regions of left prefrontal cortex. There are several possible reasons for the greater involvement of Broca's Area (BA 44) in deduction than induction. Broca's Area is part of the phonological loop of working memory, and deductive reasoning has greater working memory requirements than inductive reasoning (Gilhooly, Logie, Wetherick, & Wynn, 1993). Broca's Area is also involved in syntactic processing, and as mentioned in the Introduction, the validity of deductive arguments is a function of logical form, which in turn is encoded in syntactic structure. Thus enhanced activity in Broca's Area during deduction may be a function of greater engagement of syntactical processing and greater working memory requirements.Inductive reasoning on the other hand, is sensitive to background knowledge rather than logical form. The increased activity in dorsolateral prefrontal cortex may thus be due to the use of world knowledge in the generation and evaluation of hypotheses (Grafman, 2002) which is the basis of inductive inference. Consistent with this dissociation between lateral and dorsolateral prefrontal cortex, lesion studies typically implicate the dorsolateral prefrontal cortex in everyday reasoning deficits (Shallice and Burgess, 1991, Stuss and Alexander, 2000), while neuroimaging studies have consistently activated lateral prefrontal cortex in logical (deductive) reasoning tasks (Goel et al., 2000, Goel and Dolan, 2003, Goel et al., 1997, Goel et al., 1998). Given that most everyday reasoning is inductive, this puzzle may be explained by the different roles for lateral and dorsolateral prefrontal cortex in reasoning suggested by our results.In summary, our findings provide additional evidence for left hemisphere dominance in human reasoning and more accurately identify brain regions unique to inductive and deductive reasoning. Contrary to common expectations in the hemispheric asymmetry literature (Springer & Deutsch, 1998)—but consistent with existent lesion data—deduction and induction do not activate left and right prefrontal cortex, respectively. Both forms of reasoning involve left prefrontal cortex. Consistent with its greater requirements for syntactic processing and working memory, deduction is characterized by increased activation in Broca's Area while induction involves greater activation in left dorsolateral prefrontal cortex, consistent with its need to access and evaluate world knowledge.Recommended articlesReferencesAcuna et al., 2002B.D Acuna, J.C Eliassen, J.P Donoghue, J.N SanesFrontal and parietal lobe activation during transitive inference in humansCerebral Cortex, 12 (12) (2002), pp. 1312-1321View in ScopusGoogle ScholarAshburner and Friston, 1999J Ashburner, K.J FristonNonlinear spatial normalization using basis functionsHuman Brain Mapping, 7 (4) (1999), pp. 254-266View in ScopusGoogle ScholarCaramazza et al., 1976A Caramazza, J Gordon, E.B Zurif, D DeLucaRight-hemispheric damage and verbal problem solving behaviorBrain and Language, 3 (1) (1976), pp. 41-46View PDFView articleView in ScopusGoogle ScholarChristoff et al., 2001K Christoff, V Prabhakaran, J Dorfman, Z Zhao, J.K Kroger, K.J Holyoak, J.D GabrieliRostrolateral prefrontal cortex involvement in relational integration during reasoningNeuroImage, 14 (5) (2001), pp. 1136-1149View PDFView articleView in ScopusGoogle ScholarDeichmann et al., 2002R Deichmann, O Josephs, C Hutton, D.R Corfield, R TurnerCompensation of susceptibility-induced BOLD sensitivity losses in echo-planar fMRI imagingNeuroImage, 15 (1) (2002), pp. 120-135View PDFView articleView in ScopusGoogle ScholarEvans et al., 1993A.C Evans, D.L Collins, S.R Mills, E.D Brown, R.L Kelly, T.M Peters3D statistical neuroanatomical models from 305 MRI volumesProceedings of the IEEE-Nuclear Science Symposium and Medical Imaging Conference (1993), pp. 1813-1817CrossRefGoogle ScholarEvans et al., 1993J.S.B.T Evans, S.E Newstead, R.M.J ByrneHuman reasoning: The psychology of deduction, Lawrence Erlbaum, Hillsdale, NJ (1993)Google ScholarFrith and Frith, 2003U Frith, C.D FrithDevelopment and neurophysiology of mentalizingPhilosophical Transactions of the Royal Society of London. 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Deductive reasoning is truth-preserving and conclusions can only be either true or false. Probabilistic reasoning is based on degrees of belief and conclusions can be more or less likely. While deductive reasoning requires people to focus on the logical structure of the inference and ignore its content, probabilistic reasoning requires the retrieval of prior knowledge from memory. Recently, however, some researchers have denied that deductive reasoning is a faculty of the human mind. What looks like deductive inference might actually also be probabilistic inference, only with extreme probabilities. We tested this assumption in an fMRI experiment with two groups of participants: one group was instructed to reason deductively, the other received probabilistic instructions. They could freely choose between a binary and a graded response to each problem. The conditional probability and the logical validity of the inferences were systematically varied. Results show that prior knowledge was only used in the probabilistic reasoning group. These participants gave graded responses more often than those in the deductive reasoning group and their reasoning was accompanied by activations in the hippocampus. Participants in the deductive group mostly gave binary responses and their reasoning was accompanied by activations in the anterior cingulate cortex, inferior frontal cortex, and parietal regions. These findings show that (1) deductive and probabilistic reasoning rely on different neurocognitive processes, (2) people can suppress their prior knowledge to reason deductively, and (3) not all inferences can be reduced to probabilistic reasoning.The left frontal lobe is critical for the AH4 fluid intelligence test2021, IntelligenceShow abstractThe frontal lobes are thought to make a fundamental contribution to fluid intelligence. However, evidence that fluid intelligence is impaired following focal frontal lobe lesions is surprisingly sparse and based on non-verbal tests of fluid intelligence. We investigated performance on Part 1 of the Alice Heim 4 (AH4–1), a verbal test of fluid intelligence, in a sample of 35 patients with focal, unilateral, left or right, frontal brain tumours and 54 healthy controls. We analysed the following variables: overall number of correct AH4–1 answers, overall AH4–1 accuracy and accuracy on four selected categories of AH4–1 questions that assess abilities previously linked to the frontal lobes, namely: synonyms, verbal analogies, numerical series and multistage calculations. We found several significant frontal effects. Thus, in comparison to healthy controls, frontal patients had a significantly lower overall number of AH4–1 answers, had significantly lower overall AH4–1 accuracy and had significantly poorer performance on verbal analogies and multistage calculations. We also found several significant lateralised left frontal effects. Thus, in comparison to healthy controls, left, but not right, frontal patents had significantly lower overall AH4–1 accuracy and poorer performance on synonyms, numerical series and multistage calculation questions. This suggests that the left frontal lobe plays a critical role in AH4–1 performance. Moreover, left frontal patients had significantly lower overall AH4–1 accuracy and poorer performance on multistage calculations than right frontal patients. These results suggest that a left lateralised frontal network is critically involved in some aspects of fluid intelligence and, in particular, multistage calculations.Cerebral underpinning of advanced mathematical activity2021, Heterogeneous Contributions to Numerical Cognition: Learning and Education in Mathematical CognitionShow abstractThe human brain is especially unique within the animal kingdom in its understanding of abstract mathematical concepts. We are able to conceive irrational numbers, idealized geometrical shapes, abstract topological properties, and so on without ever perceiving them. How, then, do such concepts develop in the human mind? And what is the neural basis underlying the manipulation of high-level math concepts? While previous work mainly focused on arithmetic processing, the work reported in the present chapter focuses on more advanced math knowledge. This gives better account for the diversity of math domains such as analysis, algebra, topology, geometry, and so on that could call upon very different skills. One way to assess the brain mechanisms underlying advanced mathematical concepts’ ontogeny is to use functional MRI and study various mathematically skilled populations such as math professors or researchers, math students, or more generally math learners.The present chapter offers an overview of various neuroimaging studies assessing the cerebral underpinnings of advanced mathematical reflection and logical reasoning, as well as the effect of mathematical expertise on the brain. In professional mathematicians, including the exceptional cases of three blind mathematicians, these studies revealed that mathematical concepts are encoded in a very abstract manner. Notably, a set of brain areas including the bilateral intraparietal sulci and bilateral inferior-temporal regions appears to be systematically involved in mathematical activity. I then discuss the relation of this math-related network with language vs numerical-spatial processing in the brain, as well as its potential link to visual processes. I also discuss the potential overlap of the math-related network with the neural correlates of deductive reasoning, a process that is at the heart of the modern mathematical practice of proofs. Finally, this chapter briefly reviews the main brain markers that have been found to accompany mathematical expertise or giftedness.Investigation of Functional Connectivity During Working Memory Task and Hemispheric Lateralization in Left- and Right- Handers Measured by fNIRS2020, OptikShow abstractWorking memory is regarded as a cognitive system with limited capacity, which is responsible for saving, manipulating, and remembering online information. The cognitive functions of the brain, such as language, understanding, planning, reasoning, and problem-solving, need working memory. Based on the previous studies, working memory is considered as a central function of frontal brain lobe. Among several protocols on assessing working memory function, the n-back task is regarded as one of the most common ones in the functional imaging studies of working memory. Functional near-infrared spectroscopy (fNIRS) is an optical imaging method for evaluating brain function, which measures nervous activities and hemodynamic response in the cerebral cortex. The present study aimed to assess functional connectivity in frontal brain lobe during conducting dual n-back task at three levels of memory loading (n = 0, 1, 2) by using fNIRS signals. The statistical population included 29 healthy volunteers, among which 11 and 18 ones were left- and right-handed, respectively. They performed a dual n-back task, and the change in the concentrations of Hb and HbO2 was measured by using 24 fNIRS channels existing in the frontal lobe. The matrix of functional connectivity was extracted and evaluated by using partial correlation criterion for all participants and each of right- and left-handed groups at every level of memory loading separately. The statistical analyses of Hb and HbO2 signals (p B and B > C), are linked in an abstract representation according to their reciprocal relations (such as A > B > C) and how this representation can be accessed and manipulated to make decisions. We show that manipulating information after learning occurs with increased difficulty as logical relationships get closer in the mental map and that the activity of neurons in the dorsal premotor cortex (PMd) encodes the difficulty level during target selection for motor decision making at the single-neuron and population levels.Cognitive and emotional predictors of real versus sham repetitive transcranial magnetic stimulation treatment response in methamphetamine use disorder2020, Journal of Psychiatric ResearchShow abstractRepetitive transcranial magnetic stimulation (rTMS) of the left dorsolateral prefrontal cortex (DLPFC) can effectively reduce cravings in methamphetamine use disorder (MUD). However, a considerable group still fails to respond. Cognitive and emotional disturbance, as well as impulsive features, are widespread in patients with MUD and might mediate the treatment response of rTMS. The purpose of this study is to figure out whether these variables can help predicting patients' responses to rTMS treatment.Ninety-seven patients with severe MUD and thirty-one gender- and age-matched healthy subjects were included. Patients were randomized to receive 20 sessions of real or sham rTMS. Intermittent theta burst protocols (iTBS) or sham iTBS were applied every weekday over the DLPFC for 20 daily sessions. Both groups received regular treatment. Craving induced by drug-related cue was measured before and after stimulation. Cognition was evaluated by using the CogState Battery. Baseline characteristics were collected through the Addiction Severity Index, Patient Health Questionnaire-9, General Anxiety Disorder Scale-7, and Barrett Impulsivity Scale-11.Results showed that patients with MUD have worse spatial working memory, problem-solving ability, as well as depression and anxiety symptoms compared with healthy controls. Cognition and emotion differed between responders (craving decrease ≥60%) and non-responders in real rTMS group but not in the sham group. Better cognitive and emotional functions means that patients have higher possibility for better response to real rTMS treatment.This study suggests that cognitive, emotional and impulsive features could be used to predict the prospective treatment responses of rTMS in patients with MUD.View all citing articles on Scopus1The following forms of syllogisms (encoded as per Evans, Newstead, and Byrne (1993)) were utilized: AA1, AA2, AA4, AE1, AE4, AI1, AO2, EI1, EE3, EI1, EI2, EI3, EI4, E03, IA3, IA4, IE1, IE3, II1, IO1, OA3, OE1, OE2, OO1. Some valid forms also had invalid “counterparts”.2Task difficulty and time limitations do not allow subjects the option of waiting until the presentation of the third sentence before deciding to begin integration of the first two sentences.3To further eliminate the neural activity associated with reading and encoding of the sentences we explicitly model them as events of no interest in all trials.4The thin 1.8 mm slices, with 1.2 mm gap, and a relatively short echo time of 40 ms serve to minimize dropout and distortion (Deichmann et al., 2002, Gorno-Tempini et al., 2002, Hutton et al., 2002).5By definition, there are no correct/incorrect answers for the induction trials. However, we compared our subjects' responses in the induction trials to that of an age and education matched pilot subject who did the task outside the scanner. Compared to this individual subject, our scanned subjects performed the induction task at 64% accuracy, comparable to the 66% for the deduction trials. However, this is an arbitrary measure. Therefore we chose not to restrict the analysis of the fMRI data of the induction trials to “correct” responses.6Indeed a direct comparison (unmasked) of invalid with valid trials reveals activation in right dorsolateral PFC (39, 42, 27; Z=3.87), though it does not survive correction.View AbstractCopyright © 2004 Published by Elsevier B.V.Recommended articlesSelf-Reported Neuropathic Pain Characteristics of Women With Provoked Vulvar Pain: A Preliminary InvestigationThe Journal of Sexual Medicine, Volume 14, Issue 4, 2017, pp. 577-591Emma Dargie, …, Caroline F. PukallPlasma acylcarnitines during insulin stimulation in humans are reflective of age-related metabolic dysfunctionBiochemical and Biophysical Research Communications, Volume 479, Issue 4, 2016, pp. 868-874Leslie A. Consitt, …, Joseph A. 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Vitevitch, Rutherford GoldsteinView PDFShow 3 more articlesArticle MetricsCapturesReaders: 254View detailsAbout ScienceDirectRemote accessShopping cartAdvertiseContact and supportTerms and conditionsPrivacy policyWe use cookies to help provide and enhance our service and tailor content and ads. By continuing you agree to the use of cookies.Copyright © 2023 Elsevier B.V. or its licensors or contributors. ScienceDirect® is a registered trademark of Elsevier B.V.ScienceDirect® is a registered trademark of Elsevier B.V. ",15178381,"While inductive and deductive reasoning are considered distinct logical and psychological processes, little is known about their respective neural basis. To address this issue we scanned 16 subjects with fMRI, using an event-related design, while they engaged in inductive and deductive reasoning tasks. Both types of reasoning were characterized by activation of left lateral prefrontal and bilateral dorsal frontal, parietal, and occipital cortices. Neural responses unique to each type of reasoning determined from the Reasoning Type (deduction and induction) by Task (reasoning and baseline) interaction indicated greater involvement of left inferior frontal gyrus (BA 44) in deduction than induction, while left dorsolateral (BA 8/9) prefrontal gyrus showed greater activity during induction than deduction. This pattern suggests a dissociation within prefrontal cortex for deductive and inductive reasoning.",Differential involvement of left prefrontal cortex in inductive and deductive reasoning.
-" A meta-analysis of structural brain abnormalities in PTSD - ScienceDirect JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main contentSkip to article ScienceDirectJournals & BooksSearchRegisterSign inView PDFDownload full issueSearch ScienceDirectOutlineAbstractKeywords1. Introduction2. Methods3. Results4. DiscussionAcknowledgementsAppendix A. Appendix B. Appendix C. Appendix D. ReferencesShow full outlineCited By (669)Figures (4)Tables (7)Table 1Table 2Table 3Table A1Table B1Table C1Show all tablesNeuroscience & Biobehavioral ReviewsVolume 30, Issue 7, 2006, Pages 1004-1031ReviewA meta-analysis of structural brain abnormalities in PTSDAuthor links open overlay panelAnke Karl a b, Michael Schaefer c, Loretta S. Malta d, Denise Dörfel a, Nicolas Rohleder a, Annett Werner eShow moreOutlineAdd to MendeleyShareCitehttps://doi.org/10.1016/j.neubiorev.2006.03.004Get rights and contentAbstractThis series of meta-analyses examined structural abnormalities of the hippocampus and other brain regions in persons with PTSD compared to trauma-exposed and non-exposed control groups. The findings were significantly smaller hippocampal volumes in persons with PTSD compared to controls with and without trauma exposure, but group differences were moderated by MRI methodology, PTSD severity, medication, age and gender. Trauma-exposed persons without PTSD also showed significantly smaller bilateral hippocampal compared to non-exposed controls. Meta-analyses also found significantly smaller left amygdala volumes in adults with PTSD compared to both healthy and trauma-exposed controls, and significantly smaller anterior cingulate cortex compared to trauma-exposed controls. Pediatric samples with PTSD exhibited significantly smaller corpus callosum and frontal lobe volumes compared to controls, but there were no group differences in hippocampal volume. The overall findings suggested a dimensional, developmental psychopathology systems model in which: (1) hippocampal volumetric differences covary with PTSD severity; (2) hippocampal volumetric differences do not become apparent until adulthood; and (3) PTSD is associated with abnormalities in multiple frontal–limbic system structures.Previous article in issueNext article in issueKeywordsMeta-analysisPTSDHippocampusMRIPlasticityMemory1. IntroductionExposure to trauma can precipitate the development of posttraumatic stress disorder (PTSD), a complex syndrome comprising re-experiencing symptoms (e.g., nightmares, flashbacks) hyperarousal symptoms (e.g., insomnia), numbing symptoms (e.g., restricted affect, anhedonia), and avoidance symptoms (e.g., avoiding trauma-related stimuli) (DSM-IV, American Psychiatric Association, 1994) in addition to poor concentration and difficulty explicitly recalling aspects of the traumatic event (DSM-IV, American Psychiatric Association, 1994). PTSD may be accompanied by other types of mild cognitive impairment, such as relatively impoverished autobiographic memory for positive events (Harvey et al., 1998; McNally et al., 1995) as well as problems with attention, working memory (Vasterling et al., 1998, Vasterling et al., 2002), and learning novel word associations (Golier et al., 2002). Studies of electro-encephalographic activity (Karl et al., 2006) have found that PTSD is associated with enhanced processing of trauma-related stimuli and reduced processing of neutral stimuli. Converging evidence from neuroimaging research suggests that this altered information processing is associated with differential functional neuroanatomical activity in PTSD (Bremner et al., 1999b, Bremner et al., 2003b; Clark et al., 2003; Matsuo et al., 2003; Rauch et al., 1996; Shaw et al., 2002; Shin et al., 2004a, Shin et al., 2004b).Studies of structural brain abnormalities in PTSD have focused in particular on the hippocampus, a grey matter structure in the limbic system that is critically involved in explicit (declarative) memory, working memory (O’Keefe and Nadel, 1978; Squire, 1992), and memory for episodic events (Eldridge et al., 2000; Tulving, 1985; Wheeler and Buckner, 2004). The hippocampus also has an important role in the regulation of stress (Jacobson and Sapolsky, 1991), and findings from animal research suggest that chronic stress may affect the hippocampus through excess release of glucocorticoids (Sapolsky et al., 1990), corticotropin-releasing hormone (Brunson et al., 2001), and glutamate (Moghaddam, 2002; Moghaddam and Bolinao, 1994), inhibition of neurogenesis (Gould et al., 1997); impaired long-term potentiation induction (Li et al., 2005); inhibition of brain-derived neurotrophic factor (BDNF, Duric and McCarson, 2005) and altered serotonergic receptor function (Harvey et al., 2003).Because of its critical role in learning and memory as well as stress regulation, alterations in the hippocampus have been proposed as contributing to the etiology of PTSD (Bremner, 2001; Sapolsky, 2000). However, findings from PTSD neuroimaging research are equivocal (Jelicic and Merckelbach, 2004). Some cross-sectional studies find reduced hippocampal volumes (e.g., Bremner et al., 1995; Gurvits et al., 1996; Stein et al., 1997) in PTSD but others do not (e.g., Pederson et al., 2004; Schuff et al., 2001). Right-sided (Bremner et al., 1995), left-sided (Gurvits et al., 1996) as well as bilateral (Bremner et al., 2003a) volumetric reductions have been reported. One longitudinal study failed to find reduced hippocampal volume at 6 months post-trauma (Bonne et al., 2001), but the sample in this study experienced only a single incident trauma rather than chronic trauma exposure. Smaller hippocampal volumes have been associated with longer time since trauma (Villarreal et al., 2002) as well as trauma severity (Bremner et al., 1997; Gurvits et al., 1996; Winter and Irle, 2004) but there are negative findings as well (Stein et al., 1997). Winter and colleagues (Winter and Irle, 2004) found reduced hippocampal volumes in burn survivors with and without PTSD, compared to non-exposed healthy controls, which suggests that trauma exposure may produce reductions in hippocampal volumes in the absence of a PTSD diagnosis. In contrast, in Gilbertson et al.'s (2002) twin study, smaller hippocampal volumes were only found in combat veterans with more severe PTSD compared to non-exposed controls, with no significant differences when veterans with less severe PTSD were included in the analyses. Perhaps most critically, they found no significant difference in hippocampal volumes between monozygotic twin pairs with and without PTSD, and concluded that smaller hippocampal volume is a premorbid risk factor for severe and chronic PTSD, rather than a consequence of PTSD or trauma exposure.In their critical review, Jelicic and Merckelbach (2004) noted that PTSD hippocampus volumetric studies are beset by a number of limitations, including small study sample sizes and low statistical power, methodological heterogeneity (e.g., neuroimaging measurements, type of control sample), and sample heterogeneity (e.g., type and severity of trauma exposure, comorbid psychopathology, medication use). Meta-analysis is a technique that can address some of these limitations, and the results of two recent meta-analyses have provided further evidence of hippocampal volumentric reduction in PTSD. Smith (2005) meta-analyzed 13 studies of adult patients with PTSD and found that persons with PTSD had left and right hippocampal volumes that were 7.2% and 7.0% smaller, respectively, than those of non-exposed controls, and 4.3% and 4.5% smaller, respectively, than those of trauma-exposed controls. Kitayama and associates (2005) also found smaller bilateral hippocampal volume in PTSD compared to both trauma-exposed and non-exposed controls in a meta-analysis of nine studies of adult patients, the majority of whom had chronic trauma exposure (combat veterans and adult survivors of childhood abuse).The objective of the research that we present in this paper was to quantitatively integrate the literature through a comprehensive series of meta-analyses of structural abnormalities in PTSD. We expanded upon the results of the two previous meta-analyses (Kitayama et al., 2005; Smith, 2005) in the following ways. As recommended by Glass et al. (1981) we did not restrict the study sample to only those studies with the best methodology, which yielded a larger and more inclusive sample of studies. We then used empirical methods to identify sample heterogeneity and to construct homogenous groups for analyses. To examine whether volumetric reductions were specific to PTSD, we also meta-analyzed comparisons of trauma-exposed samples without PTSD versus healthy controls. To address method and sample variance, we conducted an extensive series of analyses examining the effects of moderator variables, including MRI methodology, gender, age and age of trauma exposure, PTSD severity and duration, comorbid disorders, and medication. To examine whether volumetric reductions were restricted to the hippocampus, we meta-analyzed PTSD volumetric studies of other brain regions. For ease of apprehension, we have organized the series of meta-analyses into separate sections punctuated by summaries. In the discussion we summarize the overall results and explicate their implications for the formulation of comprehensive neurobiological models of PTSD.2. Methods2.1. Studies/samplesFifty English language candidate studies (23 hippocampus studies; 27 studies of other brain areas) were located through electronic indexes (Medline, PsychInfo; keywords: PTSD and MRI, hippocampal volume, amygdala volume, ACC, corpus callosum) and through perusing relevant journals from 1990 to 2005 (e.g., Neuroimage, Nature Neuroscience, Hippocampus, Biological Psychiatry, Biological Psychology). To address the “file drawer problem” (Hunter and Schmidt, 1990),1 citations and conference abstract bands (Society for Neuroscience, Human Brain Mapping) were also examined.Candidate studies were classified according to their methodology and region examined (including brain hemisphere), and those with similar methods were included in the present meta-analysis. The meta-analysis study inclusion criteria were: (1) inclusion of a PTSD group based on DSM-III-R or DSM IV diagnostic criteria and a comparison group (either a non-PTSD trauma-exposed, or non-exposed controls); (2) sufficient methodological specification (e.g. sample size, MRI methodology); and (3) sufficient reporting of statistics. Twenty-one hippocampus studies, 11 amygdala studies and 18 studies reporting other structural brain measures were included in the meta-analyses. (See Appendix A, Appendix C, Appendix D). Two studies were not included because the study did not provide statistical information (Neylan et al., 2004b) or did not report right and left hippocampal/amygdala volume (Carrion et al., 2001).2.2. Statistical proceduresMeta-analyses were computed based on the single effect size (ES) r, the Pearson product–moment correlation, a standardized form of the size of the observed effect. ES r values were calculated by the transformation of M and SD, t, F, or χ2 values into r to obtain unitary ESs (Kraemer and Thiemann, 1987), using the Meta-analysis Program version 5.3 software by Schwarzer (1989). Only one ES per construct, per study was included in the meta-analysis (Rosenthal, 1995).The meta-analysis was based on the more conservative random-effects model (Hedges and Olkin, 1985), in which both the within-study variance (used in the fixed-effects model) and the between-study variance (τ2) are incorporated in the variance component used to calculate weights (Field, 2001). ES r-values were weighted according to study sample size (Hedges and Olkin, 1985; Hedges and Vevea, 1998) and converted into the common metric of Fisher's z transformation of r (Rosenthal, 1995). The mean of z and 95% confidence interval (CI) were calculated for each set of ERP components (k=number of studies; Rosenthal, 1995). Mean ESs and CI were then converted back to r for ease of interpretation. If the 95% CI did not include zero, the null hypothesis could be rejected at a α level of .05. Cohen's (1988) guidelines for interpreting the ES of sample–weighted average correlations were used: small=.10, medium=.30, and large=.50.Sample heterogeneity was determined according to procedures proposed by Hedges and Olkin, 1985, Hedges and Vevea, 1998 and (2001), in which heterogeneity is present if the between trials variance (τ2) is greater than zero and/or the within-trials variance test (χ2) is significant. To address the file drawer problem, Orwin's fail safe N (Orwin, 1983) was computed. For a specified critical r value, for example, .10, the fail safe N is the number of studies with an ES of zero required to reduce the mean population ES to that critical value, i.e., .10.2.2.1. Moderator variablesModerator variables, which can account for sources of sample heterogeneity, were tested three ways. In analyses of MRI methodological moderators, we grouped studies according to common methodology, and then conducted meta-analyses of volumetric differences followed by tests (ANOVA, χ2) of group differences in hypothetical moderator variables. For sample-related (individual differences) variables, we used disjoint cluster analysis (Hedges and Olkin, 1985; Mullen and Rosenthal, 1985) to identify homogenous subsets of studies. Disjoint cluster analysis yields non-overlapping clusters of study effect sizes through a rank-ordering of effect sizes and a comparison of their differences to critical values at a .01 α level. For the analysis, r-values are transformed into Fisher's z-values, and z-values are then multiplied by the square root of the sample size. The resulting value (u) is rank-ordered and the gap between each pair of consecutive u-values is compared to a predetermined critical value (i.e., .01 α level, Schwarzer, 1989). We conducted two series of disjoint cluster analyses. In the first, we cluster analyzed all studies to derive homogenous clusters, and followed this with meta-analyses of volumetric differences and tests (ANOVA, χ2) of group differences in hypothetical moderator variables, with Bonferroni-corrected α levels to adjust for multiple comparisons. In the second, we used disjoint cluster analysis to identify studies that were homogenous for a particular moderator variable and then meta-analyzed volumetric differences in the homogenous clusters.3. Results3.1. Analyses 1: hippocampal volumetric studiesStudies were grouped according to type of control group: trauma-exposed (non-PTSD) or non-trauma exposed healthy controls (HC); and by hippocampal hemisphere.PTSD vs. HC: The meta-analysis included 15 studies (Studies # 1–4, 8, 9, 11, 12, 14–17, 25, 26, 35 in Appendix A), N=562. Persons with PTSD had significantly smaller bilateral hippocampal volume; see Table 1 and Fig. 1.Table 1. Meta-analysis of studies comparing hippocampal volume (PTSD vs. controls): MRI and volumetry methodsSideModerator/analysisPTSD vs.kNrwCIwτχ2Orwin's fail safe NPTSD vs. healthy controls (no trauma)RightNo moderator/all studiesHealthy controls (HC)15562−.28a−.42−.13.05b38.15b6MRI acquisitionHC, WBV+high resolution9355−.36a−.55−.13.09b31.88b7HC, other corr.+low resolution6207−.20a−.35−.04.01b6.09b0Volumetry methodHC, alveus–fornix5123−.48a−.61−.3203.487HC, mammillary bodies–fornix5274−.17−.42.11.07b16.08b−1HC, hippocampal body3103−.32a−.52−.09.01b2.642LeftNo moderator/all studiesHC15562−.29a−.43−.14.06b39.56b7MRI acquisitionHC, WBV+high resolution9355−.34a−.54−.10.10b35.27b6HC, other corr.+low resolution6207−.25a−.38−.1104.071Volumetry methodHC, alveus–fornix5123−.39a−.54−.22.0014.115HC, mammillary bodies–fornix5274−.25−.53.09.12b25.69b1HC, hippocampal body3103−.32a−.51−.10.01b2.492PTSD vs. non-PTSD (exposed to index trauma)RightNo moderator/all studiesNon-PTSD12379−.15−.29.001.03b21.50b−3MRI acquisitionNon-PTSD, WBV+high resolution9301−.17a−.32−.01.02b13.71b−1Non-PTSD, other corr.+low resolution378−.07−.49.38.12b7.87b−2Volumetry methodNon-PTSD, alveus–fornix5186−.07−.22.0803.76−3Non-PTSD, mammillary bodies–fornix380−.31−.65.13.11b6.85b2LeftNo moderator/all studiesNon-PTSD12379−.22a−.39−.04.07b33.23b1MRI acquisitionNon-PTSD, WBV+high resolution9301−.26a−.46−.04.08b27.51b3Non-PTSD, other corr.+low resolution378−.09−.42.26.064.44−2Volumetry methodNon-PTSD, alveus–fornix5186−.14−.28.0103.44−2Non-PTSD, mammillary bodies–fornix380−.53−.88.19.43b20.70b5Non-PTSD vs. HCRightNo moderator/all studiesNon-PTSD/HC6174−.32a−.47−.15.01b6.26b4Subset4119−.42a−.56−.2501.264LeftNo moderator/all studiesNon-PTSD/HC6174−.23a−.38−.0801.820Note: k=number of studies, rw=weighted r, CIw=95% confidence interval for weighted r, τ2=between trials variance; χ2=within trials variance; non-PTSD trauma exposed refers to those exposed to the study index trauma, but without PTSD.ap60). As shown in Table 2, the meta-analysis for Cluster 3 found significantly smaller right hippocampal volumes in persons with PTSD, with a large ES.Table 2. Meta-analysis of studies comparing hippocampal volume (PTSD vs. controls): homogenuos clusters and sociodemographical and clinical moderatorsSideModerator/analysisPTSD vs.kNrwCIwτχ2Orwin's fail safe NPTSD vs. healthy controls (no trauma)RightCA/all studiesHC: Cluster 17355−.07−.17.0403.72−5HC: Cluster 26184−.42a−.54−.2901.797HC: Cluster 3223−.83a−.93−.600.096AgeHC, Age 40–557183−.46a−.62−.25.05b12.63b9HC, Age 40–55 15160−.33a−.47−.1802.023HC, Age 40–55 2223−.83a−.93−.600.106PTSD severityModerate PTSD vs. HC5160−.22a−.37−.0601.221Severe PTSD vs. HC6151−.55a−.70−.36.04b9.27b11Severe PTSD vs. HC 14128−.44a−.57−.2801.725Severe PTSD vs. HC 2223−.83a−.93−.600.106GenderMale4101−.59a−.80−.25.21b9.45b8Male 1223−.83a−.93−.600.106Male 2278−.34a−.53−.130.181Mixed adult385−.46a−.62−.270.224Time since traumaTime since trauma: >10 yrs8239−.41a−.58−.20.07b19.19b8Time since trauma: >10 yrs 1344−.73a−.87−.4802.688Time since trauma: >10 yrs 25195−.25a−.40−.1004.651LeftCA/all studiesHC: Cluster 16327−.07−.18.0403.43−4HC: Cluster 27212−.37a−.48−.2401.696HC: Cluster 3223−.88a−.95−.700.197AgeHC, Age 20–395162−.27a−.41−.1203.002HC, Age 40–557183−.49a−.68−.24.10b19.50b10HC, Age 40–55 15160−.31a−.45−.1502.943HC, Age 40–55 2223−.88a−.95−.700.197PTSD SeverityModerate PTSD vs. HC5160−.20a−.36−.0304.500Severe PTSD vs. HC6151−.55a−.75−.26.13b18.16b10Severe PTSD vs. HC 14128−.33a−.48−.16.012.853Severe PTSD vs. HC 2223−.88a−.95−.700.197GenderMale4101−.59a−.85−.09.29b19.07b8Male 1 3,15223−.88a−.95−.700.197Female5159−.28a−.42−.1203.432Mixed adults385−.40a−.57−.200.343Time since traumaTime since trauma:>10 yrs8239−.44a−.62−.22.08b21.68b10Time since trauma:>10 26216−.29a−.41−.1604.163Time since trauma:>10 1223−.88a−.95−.700.197PTSD vs. non-PTSD (exposed to index trauma)RightCA/all studiesNon-PTSD: Cluster 17264−.001−.13.1305.26−7Non-PTSD: Cluster 25115−.42a−.57−.2501.916PTSD SeveritySevere PTSD vs. Non-PTSD4109−.37a−.55−.1703.643MedicationMedication no6132−.25a−.52−.07.11b15.26b3Medication no 1480−.45a−.62−.2501.575Medication yes271.02−.22.260.72−2LeftCA/all studiesNon-PTSD: Cluster 16197.03−.11.1801.20−5Non-PTSD: Cluster 25167−.34a−.48−.2002.094MedicationMedication no6132−.40a−.67−.04.18b21.86b7Medication no 1365−.46a−.64−.2303.804Medication yes271.03−.21.270.59−2Note: k=number of studies, rw=weighted r, CIw=95% confidence interval for weighted r, τ2=between trials variance; χ2=within trials variance; non-PTSD trauma exposed refers to those exposed to the study index trauma, but without PTSD.ap 2 yrsYesYesYesSevere2Fennema-Notestine et al. (2002)P-HC,34.8;MixedF1117IPVNRNRNoNoModerateP-NPTSD, HC-NPTSD34.4;111135.4;17113Hedges et al. (2003)P-HC54.4NRM44COM>10 yrsNoYesNoSevere4Pederson et al. (2004)P-HC,24.3NRF1717CSA>10 yrsYesYesNRModerateP-NPTSD, HC-NPTSD25.8171725.317175Schuff et al. (2001)P-NPTSD51.5NRM1819COMNRYesYesYesModerate6Shin et al. (2004b)P-NPTSD47RightF/M87FFNRYesNoNRModerate7Lindauer et al. (2004)P-NPTSD36.2NRF/M1414POLNRYesNoNRModerate8Bremner et al. (1995)P-HC45.2MixedM2622COM>10 yrsYesNRYesSevere9Bremner et al. (1997)P-HC40.2MixedF1717CSA>10 yrsYesNRYesNR10Bonne et al. (2001)P-NPTSDNRNRF/M1027NR0–2 yrsNRNRNRModerate11De Bellis et al. (2001)P-HC13.1NRF/M99CM>2 yrsYesYesNoNR12Stein et al. (1997)P-HC31.1MixedF2121CSA>10 yrsNRNRNRModerate13Gilbertson et al. (2002)P-NPTSD52.4NRM1223COM>10 yrsYesNRYesSevere14Bremner et al. (2003a)P-HC,36.5RightF1011CSA>10 yrsYesNoNoSevereP-NPTSD, HC-NPTSD33.5101235121115Gurvits et al. (1996)P-HC,41.25MixedM78COM>10 yrsYesNoNoSevereP-NPTSD, HC-NPTSD467742.87816De Bellis et al. (1999)P-HC12.1MixedF/M4461CM>2 yrsYesYesYesNR17De Bellis et al. (2002)P-HC11.53MixedF/M2866CM>2 yrsYesNoNRNR18Nakano et al. (2002)P-NPTSD48.5NRF2839CAN>2 yrsYesNRNROnly Re-experiencing19Winter and Irle (2004)P-HC,41.50NRM1515BS10 yrsYesNRYesSevereP-NPTSD, HC-NPTSD35.0142334.52323IPV=intimate partner violence, COM=combat, CSA=childhood sexual abuse, CM=childhood maltreatment, BS=burn survivors, Acc=accident, FF=fire fighters, POL=police officers, NR=not reported, CAN=cancer survivors, POW=prisoners of war, PA=poison attack, P-HC=PTSD vs. HC, P-NPTSD=PTSD vs. non-PTSD, HC-NPTSD=HC vs. non-PTSD.Appendix B. See Table B1 for comparison of methodical aspects (MRI protocols, analysis software, correction algorithms, anatomical borderlines).Table B1. Empty CellStudyB0-strength sequence TE/TR (ms) resolution (mm3)Manual/autom. post-processing software volume correctionNumber of raters reliability intraclass-correl. IR=INTER/IA=INTRA)Anatomical boundariesAdditional method. Info/commentEmpty CellEmpty CellEmpty CellEmpty CellEmpty CellMost anteriorMost posteriorMedial borderLateral borderInferior borderEmpty Cell1Villarreal et al. (2002)1,5 T General Electric (GE) T1-w. fast spoiled GRASS 6.9/17.7 1×1.5×1.5Manual tracing in sagittal, coronal and axial slices MEASURE (3D) yes (WBV)2 0.98/NRCSF in uncal recess of temporal horn or (when not visible) the alveusWhen crura of fornices are seen in full profileMesial edge of temporal lobeTemporal horn of lateral ventricleIncl. subicular complex and uncal cleft with the border separating the subicular complex from the parahippo-campal gyrus(Watson et al., 1992) Subicular complex, dentate gyrus, alveus and fimbria included2Fennema-Notestine et al. (2002)1,5 T G E T1-w. spoiled GRASS 5/24 NR×NR×4Semi-automated tissue segmentation manual ROI analysis in coronal slices NR2 0.85–0.99/NRWhere temporal pole is separated from frontal lobe by lateral sulcusFornixNRNRNRSubiculum included Slice thickness of 4 mm3Hedges et al. (2003)1,5 T GE Dual spin echo technique NR 1×1×1ROI-segmentation-based routines in coronal and sagittal slices ANALYZE (3D) Yes (WBV)2>0.9/NRAnterior aspect of the hippocampus or uncal recess separating amygdala from hippocampusTwo of four criteria: presence of superior colliculi, presence of medial pulvinar, visibility of oblong position of hippocampus at crura of fornices, presence of a distinct separation of the temporal horn from atriaAnterior choroidal artery or point at which boundaries of ambient cistern/ choroidal fissure are most readily identifiedMedial wall of the temoral hornNR(Bigler et al., 1997)4Pederson et al. (2004)1.5 T Siemens T1-weighted sequence 1×1×1Manual tracing in sagittal slices 2D yes (body-height)1 (3 times) NRWhite matter lamina or implicit curve of hippocampal headNRCSF from temporal hornCSF of lateral ventricle and parahippo-campal gyrusNRcorrection of brain size with body height (−), only sagittal slices for volumetry5Schuff et al. (2001)1.5 T Siemens T1-w double spin echo MPRAGE 4/10 1×1×1.4Manual tracing in sagittal, coronal and axial slices MEASURE (3D) yes (WBV)2 0.98/NRCSF in uncal recess of temporal horn or (when not visible) the alveusWhen crura of fornices are seen in full profileMesial edge of temporal lobeTemporal horn of lateral ventricleIncl. subicular complex and uncal cleft with the border separating the subicular complex from the parahippo-campal gyrus(Watson et al., 1992) Subicular complex, dentate gyrus, alveus and fimbria included6Shin et al. (2004b)1.5 T Siemens 3D MPRAGE 3/7.25 1×1×1.3Semi-automated gray matter-white matter- segmentation of hippocampus manual tracing in coronal slices NRNRLateral ventricle tip of temporal hornSegmented as a continuous gray matter mass in the primary segmental fornixSegmented as a continuous gray matterSegmented as a continuous gray matterSegmented as a continuous gray matterExcluding parahippocampal gyrus (Makris et al., 1999)7Lindauer et al. (2004)1.5 T Siemens 3D MPRAGE 4/7.4 1×1×1Manual tracing in coronal slices (HC) MRICRO FAST (brain extraction tool) for WB Yes (WBV)2 0.96/ 0.95 (left HC) 0.98/ 0.96 (right HC)When oval shape of mammillary bodies was first visibleWhen fornix was visible as a continuous tractNRNRNRBorder of hippocampus defined by its gray matter8Bremner et al. (1995)1,5 T GE T1-w spoiled GRASS 5/25 0.6×0.6×3Manual tracing in coronal slices MIND yes (body-height)2 0.78/0.75 (HC mean)First slice anterior to the superior colliculusProceed 5 contiguous 3 mm slices to bifurcation of basillary arteryMesial edge of the temporal lobeTemporal horn of the lateral ventricleIncl. subicular complex and uncal cleftOnly hippocampal body, slice thickness 3 mm (−) correction of brain size with body height (−)9Bremner et al. (1997)1,5 T GE T1-w spoiled GRASS 5/25 0.6×0.6×3Manual tracing in coronal slices ROI analyze yes (body-height)2 0.61/NR (left HC) 0.79/NR (right HC)First slice anterior to the superior colliculusProceed 5 contiguous 3 mm slices to bifurcation of basillary arteryMesial edge of the temporal lobeTemporal horn of the lateral ventricleIncl. subicular complex and uncal cleftOnly hippocampal body, slice thickness 3 mm (−) correction of brain size with body height (−)10Bonne et al. (2001)2 T Elscint Double -echo-sequence 30; 80/3000 0.9×0.9×1.5Semi-automated (clustering/connectivity algorithm) manual tracing in coronal slices yes (WBV)2 0.89/ NR (HC mean)First appearance of mammillary bodiesLast appearance of fibers coursing crux of fornix,NRNRNR28 contiguous 1,5 mm slices (Gurvits et al., 1996)11De Bellis et al. (2001)1,5 T GE 3D T1-w spoiled GRASS 5/25 1.1×1.1×1.5Manual tracing in coronal slices landmark method NR2 0.98/ NR (left HC) 0.96/ NR (right HC)Coronal slice containing the most anterior portions of the mammillary bodiesWhen fibers of fornix still visibleNRNRNRincl. Cornu ammonis, dentate gyrus, subiculum correction of brain size (Giedd et al., 1996)12Stein et al. (1997)1,5 T Siemens T2-w Turbo Spinecho 90/4000 0.5×0.5×4Manual tracing in coronal slices ALLEGRO Yes (WBV)1 (2x) NA/ 0.67 (left HC) NA/ 0.71 (right HC)First slice posterior mammillary bodies7 slices posterior up to fornixNRNRNRStandardized brain volume on first index slice slice thickness 4 mm (−)13Gilbertson et al. (2002)1,5 T GE 3D T1-w spoiled GRASS 5/35 0.9×0.9×1.5Semi-automated (clustering/connectivity algorithm) manual tracing in coronal slices yes (WBV)2 0.96/ NR (right HC), 0.92/ NR (left HC)White matter tract linking the temporal lobe with rest of brainSlice in which fibers of fornix are still visibleNRNRNRMammillary bodies used to separate amygdala and hippocampus (Shenton et al., 1992)14Bremner et al. (2003a)1,5 T GE T1-w spoiled GRASS 5/25 0.6×0.6×3Manual tracing in coronal slices MIND yes (body-height)2 0.78/ 0.75 (HC mean)First slice anterior to the superior colliculusProceed 5 contiguous 3 mm slices to bifurcation of basillary arteryMesial edge of the temporal lobeTemporal horn of the lateral ventricleIncl. subicular complex and uncal cleftOnly hippocampal body, slice thickness 3 mm (−) correction of brain size with body height (−)15Gurvits et al. (1996)1,5 T GE 3D T1-w spoiled GRASS 5/35 0.9×0.9×1.5S emi-automated (clustering/connectivity algorithm) manual tracing in coronal slices yes (WBV)3 0.78/ NR (HC/AG complex mean)First appearance of mammillary bodiesLast appearance of fibers coursing crux of fornix,NRNRNR28 contiguous 1,5 mm slices mammillary bodies to divide hippocampal and amygdala complex16De Bellis et al. (1999)1,5 T GE 3D T1-w spoiled GRASS 5/25 0.9×1.5×1.5Semi-automated (WB) manual tracing (HC) in coronal slices IMAGE Yes (WBV)2 0.99/NR (left HC/AG complex) 0.97/ NR (right HC/AG complex)Coronal slice containing the most anterior portions of the mammillary bodiesWhen fibers of fornix still visibleNRNRNRincl. cornu ammonis, dentate gyrus, subiculum correction of brain size ?? (Giedd et al., 1996)17De Bellis et al. (2002)1,5 T GE 3D T1-w spoiled GRASS 5/25 0.9×1.5×1.5Manual tracing (HC) IMAGE Yes (WBV)2 0.96/ 0.98 (HC mean)Coronal slice containing the most anterior portions of the mammillary bodiesWhen fibers of fornix still visibleNRNRNRincl. cornu ammonis, dentate gyrus, subiculum correction of brain size ?? (Giedd et al., 1996)18Nakano et al. (2002)1.5 T GE 3D-T1w spoiled GRASS 5/25 0.8×0.8×1.5Manual tracing in coronal slices (HC) semi-automated (WB) ANALYZE yes (age, IQ)1 (2 times) NA/ 0.97Where white alveus surrounds remainings of hippocampal headWhere hippocampus tail was defined as the slice in which crus of fornix is longestNRNRNRCA, dentate gyrus, fimbria subiculum19Winter and Irle, 2004)1,5 T Philips 3D T1-weighted sequence 6/24 1×1×1.3 reformatted to 1×1×1Manual tracing in coronal, sagittal and horizontal slices (HC) semiautomated (WB) CURRY (3D) Yes (WBV)1 NA/ 0.94 (HC mean)Emergence of uncal recess and alveus (one additional row of pixels anterior)Crus of fornix (gray matter attached to TLV)Tail, body and head of hippocampus included: dentate gyrus, CA, alveus, fimbria, fasciolar gyrus adjacent to CA region (Pruessner et al., 2000)20Wignall et al. (2004)1,5 T Philips T1-w spoiled Gradientecho 4.4/15 1×1×1Manual tracing in coronal slices ANALYZE (HC) SPM 99 (WB) Yes (WBV)2 IR: 0.82/ 0.82 (mean HC)When lighter band of cells forming alveus not distinguishable as border between hippocampus and amygdalaWhen crus of fornix was seen in continuity with the body of hippocampus and where hippocampus seen as distinct globular structureNRNRNRHippocampus, subiculum and dentate gyrus21Vythilingam et al. (2005)1.5 T GE 3D SPGR 5/25 0.9×1.2×1.5Manual tracing in coronal slices ANALYZE yes (body-height)2 0.90/ NR (right HC), 0.80/ NR (left HC)CSF in uncal recess of temporal horn or (when not visible) the alveus3 mm anterior to where crura of fornix separated from hippocampusMesial edge of temporal lobeCSF of temporal horn of lateral ventriclewhite matter tracts(Watson et al., 1992) gray matter of hippocampus proper, dentate gyrus, subicular complex, alveus and fimbria includedWB=whole brain, WBV=whole brain volume, HC=hippocampus, NR=not reported, NA=not applicable, CA=cornu ammonis.Appendix C. Amygdala volume studies are shown in Table C1.Table C1. Empty CellStudyIncluded in MAAgeHandednessGenderPTSDControlTrauma typeTime since trauma (years)Axis1MedicationAlcoholPTSD severity1Fennema-Notestine et al. (2002)P-HC;34.8MixedF1117IPVNRNRNoNoModerateP-NPTSD34.411112Lindauer et al. (2004)P-NPTSD36.2NRF/M1414POLNRYesNoNRModerate3Bremner et al. (1997)P-HC40.2MixedF1717CSA>10YesNRYesNR4Bonne et al. (2001)P-NPTSDNRNRF/M1027COM0–2NRNRNRModerate5De Bellis et al. (2001)P-HC13.1NRF/M99CM>2YesYesNoNR6Gilbertson et al. (2002)P-NPTSD52.4NRM1223COM>10YesNRYesSevere7Gurvits et al. (1996)P-HC;41.25MixedM78COM>10YesNoNoSevereP-NPTSD46778De Bellis et al. (1999)P-HC12.1MixedF/M4461CM>2YesYesYesNR9De Bellis et al. (2002)P-HC11.53MixedF/M2866CM>2YesNoNRNR10Wignall et al. (2004)P-HC43.00MixedF/M1511Acc10YesNRYesSevere2Bremner et al. (1997)HC40.2MixedF1717CSA>10YesNRYesNR3De Bellis et al. (2001)HC13.1NRF/M99CM>2YesYesNoNR4De Bellis et al. (1999)HC12.1MixedF/M4461CM>2YesYesYesNR5De Bellis et al. (2002)HC11.53MixedF/M2866CM>2YesnoNRNRCorpus callosum6De Bellis et al. (1999)HC12.1MixedF/M4461CM>2YesYesYesNR7De Bellis et al. (2002HC11.53MixedF/M2866CM>2YesNoNRNR8Villarreal et al. (2004)HC43.50MixedF/M1210Mixed5YesYesNoSevere10Rauch et al. (2003)Non-PTSD51.80MixedM99COM nurses>10YesNoNoSevere11Araki et al. (2005)Non-PTSD47.10RightF/M813PA>5NoNoNoMild12Woodward et al. (2005a)Non-PTSD54.80NRM3825COM>10YesNRYesSevere13Woodward et al. (2005b)Non-PTSD36.80NRF/M1323COM>10YesNRYesSeverePrefrontal/frontal lobe14De Bellis et al. (1999)HC12.1MixedF/M4461CM>2YesYesYesNR15De Bellis et al. (2002)HC11.53MixedF/M2866CM>2YesNoNRNR16Carrion et al. (2001)NR11.00MixedF/M1212NR>2YesYesNRNRCavum septum pellucidum17Myslobodsky et al. (1995)HC33.00NRM1010COM>10NRNoNoSevere18May et al. (2004)HC52.00NRM2023NRNRNRNRSevereIPV=intimate partner violence, COM=combat, CSA=childhood sexual abuse, CM=childhood maltreatment, BS=burn survivors, Acc=accident, FF=fire fighters, POL=police officers, NR=not reported, CAN=cancer survivors, POW=prisoners of war, PA=poison attack, P-HC=PTSD vs. HC, P-NPTSD=PTSD vs. non-PTSD, HC-NPTSD=HC vs. non-PTSD.Recommended articlesReferencesAggleton and Brown, 1999J.P. Aggleton, M.W. 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SquireRelationship between magnitude of damage to the hippocampus and impaired recognition memory in monkeysHippocampus, 11 (2001), pp. 92-98View in ScopusGoogle ScholarCited by (669)Gray matter differences in adults and children with posttraumatic stress disorder: A systematic review and meta-analysis of 113 studies and 11 meta-analyses2023, Journal of Affective DisordersShow abstractIn this systematic review and meta-analysis, we aimed to provide a comprehensive overview of gray matter alterations of adult- and underage patients with posttraumatic stress disorder (PTSD) in comparison to healthy trauma-exposed (TC) and non-exposed (HC) individuals.We subdivided our groups into patients with PTSD after trauma exposure in adulthood (aa) or childhood (ac) as well as children with PTSD (cc). We identified 113 studies, including 6.800 participants in our review, which we divided into studies focusing on whole-brain and region-of-interest (ROI) analysis. We performed a coordinate-based meta-analysis on 14 studies in the group of aa-PTSD.We and found lower gray matter volume in patients with PTSD (aa) in the medial frontal gyrus (PTSD<HC/TC) and Culmen/posterior cingulate cortex (PTSD<TC). Results from ROI-based studies mainly show alterations for patients with PTSD in the prefrontal cortex, hippocampus, anterior cingulate cortex, insula, corpus callosum, and amygdala.Due to a limited number of studies reporting whole-brain results, the meta-analyses could only be performed in one subgroup and within this subgroup for a limited number of studies.Our results are in line with psychobiological models of PTSD that associate the identified regions with brain circuits involved in context processing, threat detection and emotion regulation.Digital Cognition Scale (NUMERICOG)2023, Revue Europeenne de Psychologie AppliqueeShow abstractLe développement des technologies numériques pose la question de leur impact sur nos capacités neurocognitives. Pour répondre à cette question, un premier jalon essentiel est de pouvoir caractériser les fonctions cognitives qu’un individu cherche à suppléer à l’aide du numérique.L’objectif de cette étude était de développer un instrument psychométrique, l’échelle de cognition numérique NUMERICOG, permettant de contribuer à ce premier jalon.Notre méthodologie s’est déroulée en quatre étapes. Tout d’abord, nous avons recueilli à travers une enquête (Étape 1) les usages du numérique déclarés par des participants (n = 160), nous permettant de générer 94 items que nous avons ensuite triés (Étape 2) afin d’obtenir une version de notre échelle en 24 items (n = 392). Les qualités psychométriques de celle-ci ont ensuite été testées (Étape 3 ; n = 754) afin d’aboutir à la création de normes pour l’échelle (Étape 4 ; n = 1132).Cette échelle se décompose en quatre sous-échelles permettant l’évaluation de la propension d’un individu à utiliser le numérique pour suppléer sa cognition socio-émotionnelle, sa prise de décision, sa mémoire sémantique, et sa mémoire épisodique/ses fonctions exécutives.Cette échelle étant validée au niveau psychométrique, celle-ci peut être désormais employée pour débuter l’investigation des retombées de nos usages numériques sur les quatre fonctions cognitives ciblées par l’échelle.The development of digital technologies raises the question of their impact on our neurocognitive abilities. To answer this question requires first to identify the cognitive functions that individuals attempt to support by using digital technologies.The objective was to develop a psychometric tool, the Digital Cognition Scale NUMERICOG, allowing us to identify these cognitive functions.We followed a four-step method. First, we conducted a survey (Step 1) to collect spontaneous uses of digital technologies (n = 160), allowing us to generate 94 items that we have subsequently sorted (Step 2) to produce a 24-item scale (n = 392). The psychometric qualities of our model were then tested (Step 3: n = 754) to obtain norms (Step 4; n = 1132).The scale consisted of four sub-scales that assess the propensity of an individual to use digital technologies to support their socio-emotional cognition, decision making, semantic memory, and episodic memory/executive functions.The scale can be used to start the investigation of the consequences of our use of digital technologies on the four aforementioned cognitive functions.Transcriptional signal and cell specificity of genes related to cortical structural differences of post-traumatic stress disorder2023, Journal of Psychiatric ResearchShow abstractDue to the diversity of traumatic events, the diagnosis of Post-traumatic Stress Disorder is heterogeneous. The pathogenesis has been explored in the fields of brain imaging and genomics separately, but the results are inconsistent. Previous research evidenced that there existed structural differences between PTSD and healthy controls in multiple brain regions. This study further looked into the differences of brain structure in PTSD at the whole brain level and analyzed the difference-related genomes. The brain structure imaging data of 36 patients and 32 healthy controls were taken as morphological indexes. Partial least squares regression and transcriptome data were used to extract genomes related to structural differences. Additional data sets were used to study transcription characteristics of genome. Morphological differences were found in cingulate gyrus between patients and control group. Differentially expressed genes related to Morphometric similarity networks difference space were also observed. The obtained genes (i.e., RORA, PRKG1 and FKBP5) were proved to be related to the disorder with no significant correlation with other mental illnesses. In the subsequent cell type analysis, astrocytes, excitatory neurons and inhibitory neurons were evidenced to have the most significant correlation with these genes. This study found morphologically different brain regions related to PTSD. The related genome transcription analysis connects the structural differences and molecular mechanisms.Hippocampal and amygdala volumes vary with transdiagnostic psychopathological dimensions of distress, anxious arousal, and trauma2023, Biological PsychologyShow abstractReduced hippocampal and/or amygdala volumes have been reported in patients with a variety of different anxiety diagnoses, suggesting that structural alterations may vary transdiagnostically across the internalizing disorders. The current study measured hippocampal and amygdala volumes in anxiety and mood disorder patients assessing differences that vary dimensionally with transdiagnostic factors of distress, anxious arousal, and trauma, based on a principal components analysis of questionnaires relating to symptomology. High-resolution structural images were collected in a sample of 165 patients, and volumes extracted from the hippocampal formation (including CA1, CA2/3, CA4/DG, subiculum, and molecular layer) and the amygdala. Transdiagnostically, increasing distress was associated with reduced hippocampal CA1 volume, increasing anxious arousal was associated with reduced hippocampal CA4/DG volume, and increasing trauma severity was associated with reduced amygdala volume in women. Taken together, the data indicate that subcortical brain volumes decrease as the severity of transdiagnostic psychopathological symptomology increases.Rapid volumetric brain changes after acute psychosocial stress2023, NeuroImageShow abstractStress is an important trigger for brain plasticity: Acute stress can rapidly affect brain activity and functional connectivity, and chronic or pathological stress has been associated with structural brain changes. Measures of structural magnetic resonance imaging (MRI) can be modified by short-term motor learning or visual stimulation, suggesting that they also capture rapid brain changes. Here, we investigated volumetric brain changes (together with changes in T1 relaxation rate and cerebral blood flow) after acute stress in humans as well as their relation to psychophysiological stress measures.Sixty-seven healthy men (25.8±2.7 years) completed a standardized psychosocial laboratory stressor (Trier Social Stress Test) or a control version while blood, saliva, heart rate, and psychometrics were sampled. Structural MRI (T1 mapping / MP2RAGE sequence) at 3T was acquired 45 min before and 90 min after intervention onset. Grey matter volume (GMV) changes were analysed using voxel-based morphometry. Associations with endocrine, autonomic, and subjective stress measures were tested with linear models.We found significant group-by-time interactions in several brain clusters including anterior/mid-cingulate cortices and bilateral insula: GMV was increased in the stress group relative to the control group, in which several clusters showed a GMV decrease. We found a significant group-by-time interaction for cerebral blood flow, and a main effect of time for T1 values (longitudinal relaxation time). In addition, GMV changes were significantly associated with state anxiety and heart rate variability changes.Such rapid GMV changes assessed with VBM may be induced by local tissue adaptations to changes in energy demand following neural activity. Our findings suggest that endogenous brain changes are counteracted by acute psychosocial stress, which emphasizes the importance of considering homeodynamic processes and generally highlights the influence of stress on the brain.Heritability of hippocampal functional and microstructural organisation2022, NeuroImageShow abstractThe hippocampus is a uniquely infolded allocortical structure in the medial temporal lobe that consists of the microstructurally and functionally distinct subregions: subiculum, cornu ammonis, and dentate gyrus. The hippocampus is a remarkably plastic region that is implicated in learning and memory. At the same time it has been shown that hippocampal subregion volumes are heritable, and that genetic expression varies along a posterior to anterior axis. Here, we studied how a heritable, stable, hippocampal organisation may support its flexible function in healthy adults. Leveraging the twin set-up of the Human Connectome Project with multimodal neuroimaging, we observed that the functional connectivity between hippocampus and cortex was heritable and that microstructure of the hippocampus genetically correlated with cortical microstructure. Moreover, both functional and microstructural organisation could be consistently captured by anterior-to-posterior and medial-to-lateral axes across individuals. However, heritability of functional, relative to microstructural, organisation was found reduced, suggesting individual variation in functional organisation may be explained by experience-driven factors. Last, we demonstrate that structure and function couple along an inherited macroscale organisation, suggesting an interplay of stability and plasticity within the hippocampus. Our study provides new insights on the heritability of the hippocampal of the structure and function within the hippocampal organisation.View all citing articles on Scopus1Among the general limitations of meta-analysis is the “file drawer problem”: studies that find null or difficult-to-interpret results and are therefore unpublished. Although we attempted to address this problem, we obviously would not have access to unpublished papers and all conference presentations. On the other hand, the topic itself may be somewhat protected against this problem. Because of its relative novelty there may have been a strong motivation (in both the investigators and the editors) to publish all sorts of results.2One study (Vythilingam et al., 2005) also reported hippocampal body size with a zero result. However, this study was not included in the meta-analysis because the whole hippocampus ES of that study was already included in anatomical group 1 and we followed the recommendations by (Rosenthal, 1995) that only one ES per study is to be included in the same meta-analysis.3Symptoms score from Clinician-Administered PTSD Scale (CAPS (Blake et al., 1990).4In subsequent comparisons of between-cluster differences in moderator variables, one study (#15) that was included in Cluster 3 in the PTSD vs. HC analyses was included in Cluster 2 in the PTSD vs. non-PTSD analyses because it differed from other studies in Cluster 2 only by virtue of a larger ES.View AbstractCopyright © 2006 Elsevier Ltd. All rights reserved.Recommended articlesHippocampal volume is inversely related to PTSD durationPsychiatry Research: Neuroimaging, Volume 222, Issue 3, 2014, pp. 119-123Linda L. Chao, …, Thomas C. NeylanView PDFStructural white matter changes in adults and children with posttraumatic stress disorder: A systematic review and meta-analysisNeuroImage: Clinical, Volume 19, 2018, pp. 581-598Sebastian Siehl, …, Frauke NeesView PDFGrey matter reduction associated with posttraumatic stress disorder and traumatic stressNeuroscience & Biobehavioral Reviews, Volume 43, 2014, pp. 163-172Lei Li, …, Qiyong GongView PDFStability of hippocampal subfield volumes after trauma and relationship to development of PTSD symptomsNeuroImage, Volume 236, 2021, Article 118076C.N. Weis, …, C.L. LarsonView PDFFrontal and subcortical grey matter reductions in PTSDPsychiatry Research: Neuroimaging, Volume 266, 2017, pp. 1-9Daniel C.M. O'Doherty, …, Jim LagopoulosView PDFPost-traumatic stress influences the brain even in the absence of symptoms: A systematic, quantitative meta-analysis of neuroimaging studiesNeuroscience & Biobehavioral Reviews, Volume 56, 2015, pp. 207-221E.A. Stark, …, M.L. KringelbachView PDFShow 3 more articlesArticle MetricsCitationsCitation Indexes: 666CapturesReaders: 784Exports-Saves: 211MentionsNews Mentions: 1References: 3Social MediaShares, Likes & Comments: 20Tweets: 5View detailsAbout ScienceDirectRemote accessShopping cartAdvertiseContact and supportTerms and conditionsPrivacy policyWe use cookies to help provide and enhance our service and tailor content and ads. By continuing you agree to the use of cookies.Copyright © 2023 Elsevier B.V. or its licensors or contributors. ScienceDirect® is a registered trademark of Elsevier B.V.ScienceDirect® is a registered trademark of Elsevier B.V. ",16730374,"This series of meta-analyses examined structural abnormalities of the hippocampus and other brain regions in persons with PTSD compared to trauma-exposed and non-exposed control groups. The findings were significantly smaller hippocampal volumes in persons with PTSD compared to controls with and without trauma exposure, but group differences were moderated by MRI methodology, PTSD severity, medication, age and gender. Trauma-exposed persons without PTSD also showed significantly smaller bilateral hippocampal compared to non-exposed controls. Meta-analyses also found significantly smaller left amygdala volumes in adults with PTSD compared to both healthy and trauma-exposed controls, and significantly smaller anterior cingulate cortex compared to trauma-exposed controls. Pediatric samples with PTSD exhibited significantly smaller corpus callosum and frontal lobe volumes compared to controls, but there were no group differences in hippocampal volume. The overall findings suggested a dimensional, developmental psychopathology systems model in which: (1) hippocampal volumetric differences covary with PTSD severity; (2) hippocampal volumetric differences do not become apparent until adulthood; and (3) PTSD is associated with abnormalities in multiple frontal-limbic system structures.",A meta-analysis of structural brain abnormalities in PTSD.
-" Neural Responses to Sad Facial Expressions in Major Depression Following Cognitive Behavioral Therapy - ScienceDirect JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main contentSkip to article ScienceDirectJournals & BooksSearchRegisterSign inView PDFDownload full issueSearch ScienceDirectOutlineBackgroundMethodsResultsConclusionsKey WordsMethods and MaterialsResultsDiscussionSupplementary dataReferencesShow full outlineCited By (241)Figures (3)Tables (3)Table 1Table 2Table 3Extras (5)Download all Supplement 1Supplement 2Supplement 3Supplement 4Supplement 5Biological PsychiatryVolume 64, Issue 6, 15 September 2008, Pages 505-512Archival ReportNeural Responses to Sad Facial Expressions in Major Depression Following Cognitive Behavioral TherapyAuthor links open overlay panelCynthia H.Y. Fu a, Steven C.R. Williams a, Anthony J. Cleare a, Jan Scott a b, Martina T. Mitterschiffthaler a, Nicholas D. Walsh a, Catherine Donaldson a, John Suckling c, Chris Andrew a, Herbert Steiner a, Robin M. Murray aShow moreOutlineAdd to MendeleyShareCitehttps://doi.org/10.1016/j.biopsych.2008.04.033Get rights and contentBackgroundAffective facial processing is an important component of interpersonal relationships. The neural substrate has been examined following treatment with antidepressant medication but not with psychological therapies. The present study investigated the neural correlates of implicit processing of sad facial expressions in depression pretreatment and posttreatment with cognitive behavioral therapy (CBT).MethodsThe patient group consisted of 16 medication-free subjects (mean age 40 years) with a DSM-IV diagnosis of acute unipolar major depression, and the comparison group were 16 matched healthy volunteers. Subjects participated in a prospective study with functional magnetic resonance imaging (fMRI) at weeks 0 and 16. During the fMRI scans, subjects performed an affect recognition task with facial stimuli morphed to display varying intensities of sadness. Patients received 16 sessions of CBT. Functional magnetic resonance imaging data were analyzed for the mean activation and differential response to variable intensity (load-response) of facial affect processing.ResultsDuring an acute depressive episode, patients showed elevated amygdala-hippocampal activity relative to healthy individuals. Baseline dorsal anterior cingulate activity in patients showed a significant relationship with subsequent clinical response.ConclusionsThese data provide further support for elevated amygdala activity in depression and suggest that anterior cingulate activity may be a predictor of treatment response to both pharmacotherapy and CBT.Previous article in issueNext article in issueKey WordsAmygdalaanterior cingulatecognitive behavioral therapydepressionfMRIsad facial expressionsMajor depression is characterized by impairments in affect, behavior, and cognition. Cognitive behavioral therapy (CBT) considers the interrelationship of each factor in the maintenance of depression and attempts to intervene in the vicious cycle of low mood, social withdrawal, and negative thinking style (1). Cognitive behavioral therapy and pharmacotherapy are both effective treatments for acute depressive episodes with comparable response rates (2), and there is some evidence that CBT may have greater efficacy than antidepressant medications in preventing a relapse (3, 4). However, the neurobiological mechanisms of CBT are poorly understood.Neuroimaging studies of depression have largely focused on pharmacological treatments (5). In resting state studies, antidepressant therapy has been associated with decreased limbic activity and increases in dorsal cortical activity (6, 7, 8, 9). Using experimental tasks to more directly engage affective processing, functional magnetic resonance imaging (fMRI) studies have reported excessive amygdala activation to negatively valenced stimuli in acutely depressed patients (10, 11, 12, 13), which normalizes following antidepressant treatment (10, 12). Identifying neurobiological predictors of clinical response is an important potential of longitudinal neuroimaging studies. Of particular note is the anterior cingulate cortex, as its strength of activation during an acute episode has been associated with an improved clinical response to antidepressant therapy (6, 7, 8, 9, 11, 12).Few studies have conducted psychotherapy treatment trials in depression with serial neuroimaging assessments (14, 15). Initial studies examined interpersonal psychotherapy (IPT), a time-limited therapy with a focus on individual relationships and social networks (16). Martin et al. (15) observed greater posterior cingulate and basal ganglia activation following 6 weeks of IPT, while Brody et al. (14) reported decreases in activation in the anterior cingulate and right prefrontal cortices and increases in the insula and temporal regions after 12 weeks of therapy. Few consistencies are evident from these data, although patient clinical response rates and timeframes of the follow-up scans differed substantially. More recently, there have been two prospective trials with CBT (17, 18). In a resting state positron emission tomography (PET) study, Goldapple et al. (17) found increased activity in the dorsal anterior cingulate and hippocampus and decreased activity in the prefrontal cortices following CBT, an inverse pattern of response to that observed with antidepressant treatment. However, no regions at the pretreatment scan were reported as showing an association with clinical response. In an fMRI study, Siegle et al. (18) focused on baseline predictors of treatment response to CBT. In response to negative adjectives, attenuated activity in the subgenual anterior cingulate and increased activity in the right amygdala were associated with an improved clinical outcome following CBT. Neural effects of CBT though were not described, as subjects did not have a follow-up scan.In the present study, patients with unipolar depression underwent magnetic resonance imaging (MRI) scans during an acute episode and following CBT, and healthy volunteers had the same scans at the same time points. Subjects performed an fMRI task as reported with a separate group of patients who received treatment with antidepressant medication (12). The task sought to engage implicit processing of sad facial expressions, as impairments in affective processing are evident in depression and may contribute to interpersonal difficulties (19, 20).A key neuropsychological impairment in depression is a mood-congruent processing bias in which ambiguous or positive events are experienced as negative (1). With emotional facial expressions, patients in an acute depressive episode show a negative bias, as they tend to misperceive happy faces as being neutral and neutral faces as being sad (20). A persistence of such a bias during clinical remission is associated with vulnerability for future episodes (21). The neural circuitry is postulated to consist of a core system responsible for initial visual processing of facial features and an extended system for processing additional features, such as emotional expressions (22). Core regions include the fusiform face area in the occipitotemporal cortex, which shows a selective engagement for faces, and the superior temporal sulcus, which is responsive to mouth and eye movements, while the extended system for affective processing includes the amygdala, which is reactive to stimuli of high emotional salience (22). Affective evaluation of facial expressions appears to occur initially at an implicit level (23) prior to explicit judgments of the type or intensity of affect (24), and attentional processing of affective judgments modulates amygdala activity (25).We expected that patients in an acute depressive state would show greater amygdala activation relative to healthy control subjects (10, 12, 13). Our hypothesis of the effect of CBT on amygdala activity was more provisional. Following antidepressant therapy, attenuation of abnormally elevated amygdala activity has been observed (10, 12), but changes in its activity have not been reported following treatment with IPT or CBT (14, 15, 17, 18). As a neural marker of treatment response, anterior cingulate activation during the acute depressive episode has consistently been associated with patient outcome with pharmacotherapy (6, 7, 8, 9, 11, 12) and reported with CBT (18). If anterior cingulate activity is a marker of clinical response irrespective of the treatment modality, we also expected an association with clinical improvement with CBT.Methods and MaterialsSubjectsThe study was approved by the Institute of Psychiatry and South London and Maudsley (SLAM) National Health Services (NHS) Ethics Research Committee. Seventeen participants (14 women) meeting DSM-IV criteria for major depressive disorder by Structured Clinical Interview for DSM-IV (SCID) (26) and clinical interview with a psychiatrist were recruited through local newspaper advertisements. Inclusion criteria were an acute episode of major depressive disorder, unipolar subtype (27), and a score of at least 18 on the 17-item Hamilton Rating Scale for Depression (HRSD) (28). Exclusion criteria were a history of neurological trauma resulting in a loss of consciousness, a current neurological disorder, history of diabetes or medical disorder, other Axis I disorder including an anxiety disorder, or history of substance abuse within 2 months of study participation. All patients were free of psychotropic medication for a minimum of 4 weeks at recruitment (8 weeks for fluoxetine) and remained medication-free throughout the treatment. Sixteen depressed patients (13 women; mean age 40.0 years [SD = 9.4]) completed the study, as one subject had a geographical relocation shortly following the initial scan. Sixteen age, gender, and IQ-matched healthy comparison subjects with HRSD scores less than 8 (13 women; mean age 39.2 years [SD = 9.3]) and no history of any psychiatric disorder, neurological disorder, or head injury resulting in a loss of consciousness were recruited by advertisement from the local community (Table 1). All participants provided written, informed consent.Table 1. Demographic and Clinical Characteristics of the SampleEmpty CellHealthy Control Subjects (n = 16)Depressed Patients (n = 16)Mean Age39.2 years (9.3)40.0 years (9.4)Gender M:F3:133:13Mean Duration of Current EpisodeNA1.64 years (range .2–4 years)Mean Number of Treatment Trials for Current EpisodeNA.13 (range 0–1 trials)aNumber of Previous EpisodesNA.63 (range 0–2)bMean Age of OnsetNA33.8 years (range 18–53 years)Mean IQ Full123.8 (10.9)116.6 (16.4) Verbal121.1 (12.2)115.8 (19.3) Performance122.3 (11.7)114.4 (14.1)Mean HRSD Score Week 0.3 (.7)20.9 (1.9) Week 16.6 (1.2)6.4 (5.2)Mean BDI Score Week 02.9 (3.9)38.0 (11.7) Week 162.0 (2.3)14.5 (15.4)Mean values and standard deviations are presented in parentheses, unless otherwise stated.BDI, Beck Depression Inventory; F, female; HRSD, Hamilton Rating Scale for Depression; IQ, intelligence quotient; M, male; NA, not applicable.aThree patients had medication treatment trials for their current episode.bFour patients had treatment trials with antidepressant medication for previous depressive episodes.TreatmentPatients received 16 sessions of CBT with experienced therapists (C.D., H.S., or B.S.). All sessions were audiotaped and/or reviewed in supervision to ensure the CBT followed the standard format (1) and that the therapists met the required level of competency (3). Patient progress was monitored regularly with the Beck Depression Inventory (29) and at baseline and following the course of CBT with the HRSD (28).Experimental DesignThe first neuroimaging session served to acquire a structural MRI for neuroradiological examination and to familiarize subjects with the scanning environment. The additional sessions consisted of fMRI scans of 60 to 90 min total duration at baseline or week 0 and upon study completion after 16 weeks. Each session consisted of a number of paradigms, including the same sad faces task in our fluoxetine treatment study (12) as the final task to prevent any residual effects of a possible negative mood state (30).Implicit Sad Facial Affect Recognition TaskTen faces from a standardized series (31) were morphed to represent three intensities of sadness: low, medium, and high. Facial stimuli and baseline trials (crosshair fixation point) were presented in random order in an event-related fMRI design on a neuro-optimized 1.5 Tesla IGE LX System (Maudsley Hospital, SLAM NHS Trust, London, United Kingdom). For each facial trial, subjects were asked to indicate the gender of the face (male or female) by lateral movement of a joystick (Table 4 in Supplement 1); no hand movement was required in the baseline trials. A full description of the MRI acquisition details is presented in Supplement 2.fMRI Data AnalysisFactorial effects of interest were identified with a 2 × 2 analysis of variance (ANOVA) model (12): main effect of group (patients vs. healthy control subjects at both time points), main effect of time (week 0 vs. week 16), and a group × time interaction for each measure of mean overall activation, i.e., response elicited between baseline trials and all facial trials, and facial processing load-response, i.e., the linear trend between facial trials at low, medium, and high intensities of sadness (described in full in Supplement 1).As we expected greater amygdala activity at baseline in patients relative to control subjects, the main effect of group at week 0 was also examined at a threshold of p = .05 corrected for multiple comparisons, which revealed greater right amygdala activity in patients compared with control subjects. Interaction effects in the right amygdala were examined by extraction of the activity of this functional region of interest for each group at weeks 0 and 16.To identify brain regions associated with within-group symptomatic response at baseline, the percentage change in HRSD score for each patient after 16 weeks of treatment was regressed onto the mean overall and linear load-response activity at week 0. Percentage reduction in HRSD was used as a measure of clinical improvement because there were no significant correlations in depression severity at baseline and at follow-up (measured by HRSD).ResultsClinical ResponsesAll 16 patients completed the full treatment course of CBT (Table 1). Their mean HRSD showed a decrease of 69.5% (SD = .2) (F = 118.45, df = 1,15; p 50% decrease in HRSD) and poor responders (≤50% decrease in HRSD). The inverse pattern was found in the left inferior frontal cortex (BA 44) as patients with the greatest clinical response had the highest linear load-response in this region at baseline (colored in green). BA, Brodmann area; CBT, cognitive behavioral therapy; HRSD, Hamilton Rating Scale for Depression.A significant positive association between clinical outcome and linear load-response activity was evident in the left inferior frontal gyrus (BA 44) (−47, 17, 16; 798 voxels) (r2 = .48, p happy face. Participants completed the Mood and Anxiety Symptom Questionnaire (MASQ) and the Perceived Stress Scale (PSS). Correlation analyses between amygdala activation, the aggregate immune score, and symptom were computed across groups. A mediation analysis was also performed across groups to further explore the relationship between these three variables.In line with our hypotheses and with prior work, the MDD group showed greater amygdala activation in response to threat compared to the HC group [t35 = -2.038, p = 0.049]. Internal consistency of amygdala activation to threat was found to be moderate. Response to an ex vivo immune challenge was greater in MDD than HC based on the computed immune reactivity score (PC1; t35 = 2.674, p = 0.011). Amygdala activation was positively correlated with the immune score (r = 0.331, p = 0.045). Moreover, higher amygdala activation was associated with greater anxious arousal measured by the MASQ (r = 0.390, p = 0.017). Exploring the role of stress, we found that higher perceived stress was positively associated with both inflammatory response (r = 0.367, p = 0.026) and amygdala response to threat (r = 0.325, p = 0.050). Mediation analyses showed that perceived stress predicted anxious arousal, but neither inflammation nor amygdala activation fully accounted for the effect of perceived stress on anxious arousal.These data highlight the potential importance of threat circuitry hyperactivation in MDD, consistent with prior reports. We found that higher levels of inflammatory biomarkers were associated with higher amygdala activation, which in turn was associated with anxious arousal. Future research utilizing larger sample sizes are needed to replicate these preliminary results.Recentering neuroscience on behavior: The interface between brain and environment is a privileged level of control of neural activity2022, Neuroscience and Biobehavioral ReviewsShow abstractDespite the huge and constant progress in the molecular and cellular neuroscience fields, our capability to understand brain alterations and treat mental illness is still limited. Therefore, a paradigm shift able to overcome such limitation is warranted. Behavior and the associated mental states are the interface between the central nervous system and the living environment. Since, in any system, the interface is a key regulator of system organization, behavior is proposed here as a unique and privileged level of control and orchestration of brain structure and activity. This view has relevant scientific and clinical implications. First, the study of behavior represents a singular starting point for the investigation of neural activity in an integrated and comprehensive fashion. Second, behavioral changes, accomplished through psychotherapy or environmental interventions, are expected to have the highest impact to specifically reorganize the complexity of the human mind and thus achieve a solid and long-lasting improvement in mental health.Cognitive behavioral therapy may rehabilitate abnormally functional communication pattern among the triple-network in major depressive disorder: A follow-up study2022, Journal of Affective DisordersShow abstractCognitive behavioral therapy (CBT) is an established treatment for Major Depressive Disorder (MDD). MDD is characterized by imbalanced communication patterns among three networks: the central executive network (CEN), the default mode network (DMN) and the salience network (SN). The effect of CBT in restoring communications among these networks in MDD is unknown.Thirty-three patients with MDD and 27 healthy controls (HC) participated in the study. Patients were treated with CBT. Resting-state functional magnetic resonance imaging (rs-fMRI) data were obtained in patients at three stages (T0: before treatment; T1: after 6 weeks CBT; T2: after 28 weeks CBT) and in HC (only T0). Both independent component analysis (ICA) and granger causality analysis (GCA) were used to explore dynamic causal communication patterns among the three networks (CEN, DMN, SN) over a course of CBT treatment.In the HC group, the SN had an inhibitory causal effect on CEN; the CEN and DMN had an excitatory causal effect on the SN. The SN had an inhibitory causal effect on the CEN and the DMN; only the DMN had an excitatory causal effect on the SN in the MDD patients at the T0 stage. As the CBT treatment went on for MDD patients, the CEN restored excitatory causal effect on the SN, and the SN lost inhibitory effect on the DMN. This result mimicked the one found in the HC group. Four regions, left ventromedial prefrontal cortex (lvmPFC), posterior cingulate gyrus (PCC), right inferior parietal lobule (rIPL) and right insula, were implicated in mediating network communications.The findings should be considered preliminary given the small sample sizes, and assessed only one stage in HC subjects.CBT may enhance the regulatory function of the SN, and rehabilitate the imbalanced brain network communication mode in the MDD. PCC, lvmPFC and rIPL may all be potential targets of CBT.Neural basis of positive and negative emotion regulation in remitted depression2022, NeuroImage: ClinicalShow abstractThe recurrent nature of Major Depressive Disorder (MDD) necessitates a better understanding of mechanisms facilitating relapse. MDD has often been associated with abnormal emotion regulation, underpinned by aberrant interactions between the prefrontal cortex and subcortical areas. We assessed whether neural regulation abnormalities remain after remission and relate to emotion regulation problems in daily life.At the baseline measurement of a randomized controlled trial, an emotion regulation task was performed during fMRI scanning by 46 remitted recurrent (rrMDD) patients and 24 healthy controls. We assessed both fMRI peak activity and the temporal dynamics of the neural response during passive attendance and explicit regulation of positive and negative emotions. Furthermore, we assessed regulation strategy use in daily life using questionnaires, and attentional biases using a modified attentional dot-probe task.RrMDD patients showed lower activation and different temporal dynamics in occipital, parietal, and prefrontal brain regions during passive attendance of emotional material compared to healthy controls. During explicit downregulation of negative emotions, no group differences were found. However, during explicit upregulation of positive emotions, rrMDD patients showed a different neural response over time in the insula. Behaviourally, rrMDD patients were characterized by dysfunctional regulation strategies in daily life. Within rrMDD patients, rumination was associated with activation within a limbic- prefrontal network.After remission, immediate emotional processing seems unaffected, but regulatory abnormalities remain, especially uninstructed and in daily life. Abnormal insula activation during positive upregulation suggests decreased monitoring of positive emotions. The relation between inadequate rumination and brain activity during emotion regulation suggests that regulation of both positive and negative affect is important in understanding neurocognitive underpinnings of resilience.View all citing articles on ScopusView AbstractCopyright © 2008 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.Part of special issueNeurostimulatory and Neuroablative Treatments for DepressionDownload full issueOther articles from this issueBrain Stimulation, Revolutions, and the Shifting Time Domain of Depression15 September 2008Mark S. George, Harold A. SackeimView PDFProspective Assessment of Stereotactic Ablative Surgery for Intractable Major Depression15 September 2008Donald C. Shields, …, Darin D. DoughertyView PDFVagus Nerve Stimulation: 2-Year Outcomes for Bipolar Versus Unipolar Treatment-Resistant Depression15 September 2008Andrew A. Nierenberg, …, David MischoulonView PDFView more articlesRecommended articlesNo articles found.Article MetricsCitationsCitation Indexes: 241Policy Citations: 1CapturesExports-Saves: 110Readers: 383View detailsAbout ScienceDirectRemote accessShopping cartAdvertiseContact and supportTerms and conditionsPrivacy policyWe use cookies to help provide and enhance our service and tailor content and ads. By continuing you agree to the use of cookies.Copyright © 2023 Elsevier B.V. or its licensors or contributors. ScienceDirect® is a registered trademark of Elsevier B.V.ScienceDirect® is a registered trademark of Elsevier B.V. ",18550030,"Affective facial processing is an important component of interpersonal relationships. The neural substrate has been examined following treatment with antidepressant medication but not with psychological therapies. The present study investigated the neural correlates of implicit processing of sad facial expressions in depression pretreatment and posttreatment with cognitive behavioral therapy (CBT). | The patient group consisted of 16 medication-free subjects (mean age 40 years) with a DSM-IV diagnosis of acute unipolar major depression, and the comparison group were 16 matched healthy volunteers. Subjects participated in a prospective study with functional magnetic resonance imaging (fMRI) at weeks 0 and 16. During the fMRI scans, subjects performed an affect recognition task with facial stimuli morphed to display varying intensities of sadness. Patients received 16 sessions of CBT. Functional magnetic resonance imaging data were analyzed for the mean activation and differential response to variable intensity (load-response) of facial affect processing. | During an acute depressive episode, patients showed elevated amygdala-hippocampal activity relative to healthy individuals. Baseline dorsal anterior cingulate activity in patients showed a significant relationship with subsequent clinical response. | These data provide further support for elevated amygdala activity in depression and suggest that anterior cingulate activity may be a predictor of treatment response to both pharmacotherapy and CBT.",Neural responses to sad facial expressions in major depression following cognitive behavioral therapy.
-" Regional Gray Matter Changes Are Associated with Cognitive Deficits in Remitted Geriatric Depression: An Optimized Voxel-Based Morphometry Study - ScienceDirect JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main contentSkip to article ScienceDirectJournals & BooksSearchRegisterSign inView PDFDownload full issueSearch ScienceDirectOutlineBackgroundMethodsResultsConclusionKey WordsMethods and MaterialsResultsDiscussionReferencesShow full outlineCited By (73)Figures (1)Tables (2)Table 1Table 2Biological PsychiatryVolume 64, Issue 6, 15 September 2008, Pages 541-544Brief ReportRegional Gray Matter Changes Are Associated with Cognitive Deficits in Remitted Geriatric Depression: An Optimized Voxel-Based Morphometry StudyAuthor links open overlay panelYonggui Yuan a b, Wanlin Zhu d e, Zhijun Zhang a c, Feng Bai a, Hui Yu a, Yongmei Shi c, Yun Qian a, Wen Liu b, Tianzi Jiang e, Jiayong You b, Zhening Liu fShow moreOutlineAdd to MendeleyShareCitehttps://doi.org/10.1016/j.biopsych.2008.04.032Get rights and contentBackgroundWe aimed to investigate structural abnormalities in first-episode remitted geriatric depression (RGD) with optimized voxel-based morphometry (VBM) in closely matched patients and healthy control subjects and examine the relationship of performances on neuropsychological tests with regional gray matter volumes.MethodsNineteen subjects with first-episode RGD and 16 well-matched healthy control subjects were recruited for this study, and neuropsychological tests and magnetic resonance imaging were conducted on the subjects. The differences in regional gray matter volume were determined between these two groups by optimized VBM.ResultsThe volumes of right superior frontal cortex, left postcentral cortex, and right middle temporal gyrus were significantly smaller in patients with RGD relative to healthy control subjects. However, patients with RGD had larger left cingulate gyrus volume compared with healthy control subjects. There was a significant negative correlation between left cingulate gyrus volume and Rey Auditory Verbal Learning Test delayed recall raw score in patients with RGD.ConclusionThese results reveal that RGD is associated with gray matter changes of certain brain regions hypothesized to influence cognition and might thus be involved in the psychopathology and pathophysiology of cognitive impairment in RGD.Previous article in issueNext article in issueKey WordsCognitive deficitgray mattermagnetic resonance imaging (MRI)remitted geriatric depressionvoxel-based morphometryCognitive impairment is common in late-life depression (LLD). There have been many follow-up studies of older depressed adults with varying degrees of cognitive impairment, although the findings have been mixed, including marked or mild improvement in cognitive function and persistence of cognitive impairment or deterioration after the remission of depressive symptoms (1). Late-life depression with cognitive impairment has increased risk of conversion to dementia, and it might be a preclinical stage of dementia (2).Despite increasing evidences for roles of structural and functional brain impairments in the etiology of LLD, the relationship between selective regional brain volume differences and cognitive deficits remains unclear. Previous morphometric magnetic resonance imaging (MRI) studies using region-of interest (ROI) analyses have revealed that elderly patients with major depressive disorder showed volume reduction in the prefrontal cortex (3), anterior cingulate cortex (3), hippocampus (4), and head of the caudate nucleus (5).Voxel-based morphometry (VBM) is a user-independent, fully automated method of analysis that allows for unbiased exploration of brain structures without a priori specification of ROIs and can thus identify potentially unsuspected brain structure abnormalities (6). At present, however, VBM studies in patients with major depressive disorder are few (7, 8). Only one study has addressed the association between structural alterations and cognitive dysfunction in unipolar depression (9), and no VBM study in patients with remitted geriatric depression (RGD) has investigated the relationship between cortical volume alterations and cognitive performances.In this study, we hypothesize that gray matter abnormalities of some brain regions detected by VBM are correlated with cognitive deficits, and the abnormalities might be neuroimaging markers of cognitive deficits in the RGD patients.Methods and MaterialsSubjectsA total of 19 patients (9 men and 10 women; average age 67.1 ± 7.2 years) were recruited from the Affiliated Brain Hospital of Nanjing Medical University, China, from January 2007 to June 2007. All patients were interviewed in a semi-structured interview included in the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I), Clinician Version (10), by two trained and senior psychiatrists (Y.Y. and J.Y.). They met the following inclusion criteria: 1) met the major depressive disorder in DSM-IV criteria and remitted for more than 6 months before the enrollment; 2) in their first depressive episode, and the age of onset was over 60 years; 3) Hamilton Depression Rating scale (HDRS) scores were lower than 7, and Mini-Mental State Examination (MMSE) scores were higher than 24; 4) duration of illness was .05). However, the RGD patients performed significantly worse in the delayed recall of RAVLT, Trail Making Test A and B (sec) when compared with the control group (p Control Subjects Left cingulate gyrus241913.26.001−3, 15, 29The threshold was set at p < .001 (SPM random effects analysis; uncorrected). Cluster size is more than 80 mm3; Z scores are expressed as the maximum with each area (MaxZ). Local maxima are separated by a minimum of 12 mm. RGD subjects, n = 19, healthy control subjects, n = 16. VBM, voxel-based morphometry; RGD, remitted geriatric depression; BA, Brodmann area.In addition we found a significant negative correlation between left cingulate gyrus volume and RAVLT delayed recall raw score (r = −.526, p = .036) in patients with RGD. No significant correlations were found between the change of gray matter volume and duration of disease in RGD patients.DiscussionThe major finding of the present study is that the patients with RGD have significantly smaller volumes of right superior frontal gyrus, left postcentral gyrus, and right middle temporal gyrus and larger volume in left cingulate gyrus than age-matched healthy subjects.Our findings of smaller regional gray matter volumes were consistent with previously studies of LLD (3, 11). Decreased gray matter in these areas might suggest neuronal apoptosis or a loss of neuropil (12). Interestingly, the grey matter volume reduction of left postcentral gyrus was also found to be associated with RGD. The finding is unusual. This brain region is not typically implicated in mood disorders, although a recent study found that subjects with bipolar disorder exhibited significantly decreased cortical thickness in bilateral postcentral cortices compared with well-matched healthy subjects (13). We believe this finding warrants further investigation to clarify its significance.Another major finding shows that the RGD patients have significantly worse function of episodic memory (indicated by RAVLT delayed recall) and executive function (indicated by Trail Making Test A and B) than the healthy control subjects. This is consistent with earlier reports that remitted depression had persistence of cognitive impairment (14, 15). Our previous research demonstrated that cognitive deficits were associated with white matter integrity abnormality and resting state dysfunction of central brain regions in RGD patients (16, 17). Interestingly, we find that the larger volume in left cingulate gyrus is negatively associated with episodic memory function. The finding has raised two possible explanations. One is that increased gray matter volume might be related to preapoptotic osmotic changes or hypertrophy, marking area of early neuronal pathology (12). Another explanation is that it might be associated with neuronal overgrowth or a deficit in the normal pruning process during neurogenesis and neural maturation after one successful course of antidepressant treatment (18, 19). Therefore, we tentatively speculate that larger volume in the left cingulate gyrus might be involved in the psychopathology and pathophysiology of episodic memory in RGD, and it is perhaps the important early-stage neuroimaging marker of RGD subjects who might convert to Alzheimer's disease.This study was supported by National Natural Science Foundation of China (No. 30770779 ZZ), National Basic Research Program of China (973 Program) (No. 2007CB512308 ZZ), National Hi-Tech Research and Development Program of China (863 Program) (No. 2007AA0200Z435 ZZ), and Key Program of Medical Development of Nanjing (No: ZKX07018 YY).The authors reported no biomedical financial interests or potential conflicts of interest.Special issue articlesRecommended articlesReferences1M.A. Butters, E.M. Whyte, R.D. Nebes, A.E. Begley, M.A. Dew, B.H. 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Qian, et al.Abnormal neural activity in the patients with remitted geriatric depression: A resting-state functional magnetic resonance imaging study[published online ahead of print March 25]J Affect Disord (2008)Google Scholar18C.M. Adler, M.P. DelBello, K. Jarvis, A. Levine, J. Adams, S.M. StrakowskiVoxel-based study of structural changes in first-episode patients with bipolar disorderBiol Psychiatry, 61 (2007), pp. 776-781View PDFView articleView in ScopusGoogle Scholar19H. Lavretsky, D.J. Roybal, M. Ballmaier, A.W. Toga, A. KumarAntidepressant exposure may protect against decrement in frontal gray matter volumes in geriatric depressionJ Clin Psychiatry, 66 (2005), pp. 964-967CrossRefView in ScopusGoogle ScholarCited by (73)Depression circuit adaptation in post-stroke depression2023, Journal of Affective DisordersShow abstractLesion locations of post-stroke depression (PSD) mapped to a depression circuit which centered by the left dorsolateral prefrontal cortex (DLPFC). However, it remains unknown whether the compensatory adaptations that may occur in this depression circuit due to the lesions in PSD.Rs-fMRI data were collected from 82 non-depressed stroke patients (Stroke), 39 PSD patients and 74 healthy controls (HC). We tested the existence of depression circuit, examined PSD-related alterations of DLPFC-seeded connectivity and their associations with depression severity, and analyzed the connectivity between each repetitive transcranial magnetic stimulation (rTMS) target and DLPFC to find the best treatment target for PSD.We found that: 1) the left DLPFC showed significantly stronger connectivity to lesions of PSD than Stroke group; 2) in comparison to both Stroke and HC groups, PSD exhibited increased connectivity with DLPFC in bilateral lingual gyrus, contralesional superior frontal gyrus, precuneus, and middle frontal gyrus (MFG); 3) the connectivity between DLPFC and the contralesional lingual gyrus positively correlated with depression severity; 4) the rTMS target in center of MFG showed largest between-group difference in connectivity with DLPFC, and also reported the highest predicted clinical efficacy.Longitudinal studies are required to explore the alterations of depression circuit in PSD as the disease progress.PSD underwent specific alterations in depression circuit, which may help to establish objective imaging markers for early diagnosis and interventions of the disease.Identification of specific neural circuit underlying the key cognitive deficit of remitted late-onset depression: A multi-modal MRI and machine learning study2021, Progress in Neuro-Psychopharmacology and Biological PsychiatryShow abstractNeuropsychological impairment is a key feature of late-onset depression (LOD), with deficits observed across multiple cognitive domains. And this neuropsychological impairment can persist even after the remission of depressive symptoms. However, none of previous studies have explored the pattern of cognitive deficit in remitted LOD (rLOD), and investigated the specific neural circuit underlying the key cognitive deficit of LOD. 40 rLOD patients and 36 controls underwent comprehensive neuropsychological assessments and magnetic resonance imaging (MRI) scans. The influence of executive function or information processing speed deficit on other cognitive domains was first investigated. We then applied a multivariate machine learning technique known as relevance vector regression to evaluate the potential of multiple-modal MRI (i.e., integrating whole-brain grey-matter [GM] volume and white-matter [WM] tract features) for making accurate predictions about the key cognitive deficit for individual rLOD patient. We revealed that the information processing speed appears to represent a key cognitive deficit in rLOD. Further the machine learning model identified a wide range of GM regions and WM tracts that significantly contributed to the prediction of individual performance on information processing speed (r = 0.50, P < 0.001). The GM regions mainly located in the frontal-subcortical and limbic systems; and the WM tracts mainly located in the frontal-limbic pathway, including the anterior corona radiata, fornix, posterior cingulate bundle, and uncinate fasciculus. This present study provide strongly evidence supporting the concept of rLOD that the core aspect of the cognitive deficits (i.e., information processing speed) is associated with disruption of the frontal-subcortical-limbic pathway.Neural markers of depression in MRI2021, The Neuroscience of Depression: Features, Diagnosis, and TreatmentShow abstractDepression is manifested with significant alterations in the brain, which can be revealed clearly using magnetic resonance imaging (MRI). The chapter focuses on the pathophysiology model and neural markers found by MRI in depression. In the perspectives of structural MRI, the gray matter studies showed a possible consistent pattern of alterations of gray matter volume and density in the fronto-limbic regions of depressed patients. The white matter studies also revealed a possible pattern of alterations of micro-integrity in the tracts of fronto-limbic network. For the perspectives of functional MRI, the task MRI studies suggested the importance of fronto-limbic regions, especially the anterior cingulate cortex, frontal cortex, amygdala, and hippocampus. However, the reward task showed additional regions, such as ventral striatum and nucleus accumbens. The resting-state functional MRI studies also supported the crucial roles of fronto-limbic regions. Therefore, the MRI neural markers in depression might be associated with fronto-limbic network.Altered gray matter volumes in post-stroke depressive patients after subcortical stroke2020, NeuroImage: ClinicalShow abstractStroke survivors are known to suffer from post-stroke depression (PSD). However, the likelihood of structural changes in the brains of PSD patients has not been explored. This study aims to extract changes in the gray matter of these patients and test how these changes account for the PSD symptoms. High-resolution T1 weighted images were collected from 23 PSD patients diagnosed with subcortical stroke. Voxel-based morphometry and support vector machine analyses were used to analyze the data. The results were compared with those collected from 33 non-PSD patients. PSD group showed decreased gray matter volume (GMV) in the left middle frontal gyrus (MFG) when compared to the non-PSD patients. Together with the clinical and demographic variables, the MFG's GMV predictive model was able to distinguish PSD from the non-PSD patients (0•70 sensitivity and 0•88 specificity). The changes in the left inferior frontal gyrus (61%) and dorsolateral prefrontal cortex (39%) suggest that the somatic/affective symptoms in PSD is likely to be due to patients’ problems with understanding and appraising negative emotional stimuli. The impact brought by the reduced prefrontal to limbic system connectivity needs further exploration. These findings indicate possible systemic involvement of the frontolimbic network resulting in PSD after brain lesions which is likely to be independent from the location of the lesion. The results inform specific clinical interventions to be provided for treating depressive symptoms in post-stroke patients.The neural markers of MRI to differentiate depression and panic disorder2019, Progress in Neuro-Psychopharmacology and Biological PsychiatryShow abstractDepression and panic disorder (PD) share the common pathophysiology from the perspectives of neurotransmitters. The relatively high comorbidity between depression and PD contributes to the substantial obstacles to differentiate from depression and PD, especially for the brain pathophysiology. There are significant differences in the diagnostic criteria between depression and PD. However, the paradox of similar pathophysiology and different diagnostic criteria in these two disorders were still the issues needing to be addressed. Therefore the clarification of potential difference in the field of neuroscience and pathophysiology between depression and PD can help the clinicians and scientists to understand more comprehensively about significant differences between depression and PD. The researchers should be curious about the underlying difference of pathophysiology beneath the significant distinction of clinical symptoms. In this review article, I tried to find some evidences for the differences between depression and PD, especially for neural markers revealed by magnetic resonance imaging (MRI). The distinctions of structural and functional alterations in depression and PD are reviewed. From the structural perspectives, PD seems to have less severe gray matter alterations in frontal and temporal lobes than depression. The study of white matter microintegrity reveals more widespread alterations in fronto-limbic circuit of depression patients than PD patients, such as the uncinate fasciculus and anterior thalamic radiation. PD might have a more restrictive pattern of structural alterations when compared to depression. For the functional perspectives, the core site of depression pathophysiology is the anterior subnetwork of resting-state network, such as anterior cingulate cortex, which is not significantly altered in PD. A possibly emerging pattern of fronto-limbic distinction between depression and PD has been revealed by these explorative reports. The future trend for machine learning and pattern recognition might confirm the differentiation pattern between depression and PD based on the explorative results.Brain structural abnormalities of major depressive disorder2019, Major Depressive DisorderShow abstractUnipolar major depressive disorder (MDD) is one of the leading causes of disability worldwide. This chapter highlights the importance of morphometric neuroimaging findings for a better understanding of the pathophysiology of MDD. Structural neuroimaging studies have identified several findings in MDD, including decreased volumes in prefrontal, anterior cingulate cortices, striatum, and hippocampal regions, as well as increased white matter hyperintensities. Nevertheless, the translation of these promising neuroimaging findings into the routine psychiatric practice is still at a very early stage.View all citing articles on ScopusView AbstractCopyright © 2008 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.Part of special issueNeurostimulatory and Neuroablative Treatments for DepressionDownload full issueOther articles from this issueBrain Stimulation, Revolutions, and the Shifting Time Domain of Depression15 September 2008Mark S. George, Harold A. SackeimView PDFProspective Assessment of Stereotactic Ablative Surgery for Intractable Major Depression15 September 2008Donald C. Shields, …, Darin D. DoughertyView PDFVagus Nerve Stimulation: 2-Year Outcomes for Bipolar Versus Unipolar Treatment-Resistant Depression15 September 2008Andrew A. Nierenberg, …, David MischoulonView PDFView more articlesRecommended articlesNo articles found.Article MetricsCitationsCitation Indexes: 72CapturesExports-Saves: 37Readers: 102View detailsAbout ScienceDirectRemote accessShopping cartAdvertiseContact and supportTerms and conditionsPrivacy policyWe use cookies to help provide and enhance our service and tailor content and ads. By continuing you agree to the use of cookies.Copyright © 2023 Elsevier B.V. or its licensors or contributors. ScienceDirect® is a registered trademark of Elsevier B.V.ScienceDirect® is a registered trademark of Elsevier B.V. ",18550031,"We aimed to investigate structural abnormalities in first-episode remitted geriatric depression (RGD) with optimized voxel-based morphometry (VBM) in closely matched patients and healthy control subjects and examine the relationship of performances on neuropsychological tests with regional gray matter volumes. | Nineteen subjects with first-episode RGD and 16 well-matched healthy control subjects were recruited for this study, and neuropsychological tests and magnetic resonance imaging were conducted on the subjects. The differences in regional gray matter volume were determined between these two groups by optimized VBM. | The volumes of right superior frontal cortex, left postcentral cortex, and right middle temporal gyrus were significantly smaller in patients with RGD relative to healthy control subjects. However, patients with RGD had larger left cingulate gyrus volume compared with healthy control subjects. There was a significant negative correlation between left cingulate gyrus volume and Rey Auditory Verbal Learning Test delayed recall raw score in patients with RGD. | These results reveal that RGD is associated with gray matter changes of certain brain regions hypothesized to influence cognition and might thus be involved in the psychopathology and pathophysiology of cognitive impairment in RGD.",Regional gray matter changes are associated with cognitive deficits in remitted geriatric depression: an optimized voxel-based morphometry study.
-" PDF Split View Views Article contents Figures & tables Video Audio Supplementary Data Cite Cite Susanne Neufang and others, Sex Differences and the Impact of Steroid Hormones on the Developing Human Brain, Cerebral Cortex, Volume 19, Issue 2, February 2009, Pages 464–473, https://doi.org/10.1093/cercor/bhn100 Select Format Select format .ris (Mendeley, Papers, Zotero) .enw (EndNote) .bibtex (BibTex) .txt (Medlars, RefWorks) Download citation Close Permissions Icon Permissions Share Icon Share Facebook Twitter LinkedIn Email Navbar Search Filter Cerebral CortexThis issueCerebral Cortex Journals Clinical NeuroscienceNeurologyNeuroscienceBooksJournalsOxford Academic Mobile Enter search term Search Close Navbar Search Filter Cerebral CortexThis issueCerebral Cortex Journals Clinical NeuroscienceNeurologyNeuroscienceBooksJournalsOxford Academic Enter search term Search Advanced Search Search Menu Abstract brain morphometry, sex differences, sexual maturation, steroid hormones Introduction Adolescence is an important developmental period in which major physical, psychological, cognitive, and social transformations occur (Blakemore and Choudhury 2006; Galvan et al. 2007; Markham et al. 2007) and gender differences emerge and manifest themselves (Lenroot et al. 2007; Schmithorst et al. 2007). Behavioral transformations are closely related to cerebral development, encompassing dramatic and widespread changes in brain morphology. Although basic developmental processes are comparable between boys and girls, such as linear increases in white matter (WM) volume and nonlinear inverted u-shaped development of regional gray matter (GM), sexual dimorphisms have been reported for global and regional brain volumes and the time course of brain development (Giedd et al. 1997, 1999). Whereas peaks in GM volume typically occur 1 year earlier in girls than in boys, the rates of global volume changes follow a steeper slope in boys than in girls with respect to both increase of WM and decrease in GM volumes. Likewise, volumes of subcortical structures like the striatum, the hippocampal, and the amygdala change during adolescence in a sex-dependent manner, with amygdala volume increasing significantly more in males than in females and hippocampal and striatal volume increasing more in females (Caviness et al. 1996; Giedd et al. 1997; Lange et al. 1997).Although it has recently been demonstrated that sexual dimorphism in total cerebral and subcortical GM and WM volumes are already present in the neonatal brain (Gilmore et al. 2007), many sex differences in brain structures seem to occur after the age of 9 or 10 (see, e.g., Goldstein et al. 1999; Giedd et al. 2006). Recently, De Bellis et al (2001) demonstrated that brain maturation is not only affected by significant interaction effects between sex and age but also by interactions between sex and measures of physical maturation (De Bellis et al. 2001) as assessed by Tanner stages (TS, Tanner and Davies 1985). It is believed that the development of GM volume in particular more or less coincides with the onset of puberty (De Bellis et al. 2001; Sowell et al. 2002; Gogtay et al. 2004). The increase of GM volume at the onset of puberty might reflect a coincident wave of synaptic proliferation and the gradual decrease in GM density that takes place after puberty might be attributed to postpubescent synaptic pruning (Bourgeois et al. 1994).The remodeling of the adolescent brain is accomplished through a variety of mechanisms, including both progressive events such as increases in cell number, dendritic elaboration and axonal sprouting, and regressive events like apoptosis and synaptic pruning. These processes are known to be influenced by both androgens (MacLusky et al. 2006) and estrogens (EST) (Galea et al. 2006). The effects of sex steroids on brain morphology are classically described as operating through 2 distinct mechanisms (Cooke et al. 1998). One mechanism, called “organization,” is defined as a developmental mechanism in which steroids act during critical periods to mediate permanent sexually dimorphic differentiation of brain morphology that gives rise to male and female sexual behavior and physiology in adulthood. The other mechanism, called “activation,” is mediated through the acute effects of gonadal hormones on the fully developed nervous system and is responsible for maintaining sex-specific behaviors in adulthood (Breedlove and Hampson 2002). Although the organization–activation framework for steroid control of reproductive behavior originally presumed a strictly activating role for gonadal steroids during adolescence, a recent modernization of this thinking incorporates dual roles for steroid hormones, proposing that they not only activate but also organize neural circuits during adolescence (Romeo et al. 2002). The sequence of events during steroid-dependent adolescent maturation of reproductive behavior may be an initial reorganization of circuits that further sensitizes them to hormone activation (Sisk and Foster 2004).To date, however, there are no empirical studies in humans which directly link sex-specific changes in brain development to the general status of pubertal development and the effects of pubertal hormones in particular. Most of the available evidence has been derived either from animal studies (Nunez et al. 2001), studies of sex hormone changes during the menstrual cycle (Hausmann 2005), or studies on abnormal brain development in anomalous hormone or sex chromosome profiles (Giedd et al. 2006).Thus, one challenge for developmental neuroscience is to identify which aspects of adolescent brain development are related to hormone levels and which are not (Giedd et al. 2006) and to understand the behavioral consequences of steroid-dependent organization and activation of the adolescent brain (Golubchik et al. 2007; Sato et al. 2008). Importantly, such knowledge of the mechanisms behind sexual differentiation of the brain might also contribute to a better understanding of the brain's susceptibility to neuropsychiatric disorders such as Attention Deficit Hyperactivity Disorder (ADHD), tics, eating disorders, and depression or schizophrenia, all of which show sex-specific prevalence rates, times of first manifestation, and courses. Data from animal studies suggest that derived sex differences in behavior are established during development by the actions of gonadal steroid hormones (Arnold and Gorski 1984). For example, odor preferences, behavioral responses to sensory stimuli, and social affiliations all change with adolescent development (Fleming and Corter 1995; Romeo et al. 1998). In addition, it has been shown that androgen deprivation causes a 40% decrease in synaptic density in the hippocampus of both rats and monkeys, and testosterone (TEST) replacement in male animals normalizes synaptic density (Leranth et al. 2003). Behaviorally, androgen deprivation by gonadectomy in male rodents impairs performance on tasks that depend on the hippocampus, such as maze learning and fear conditioning (Edinger and Frye 2004). In contrast, synapse number and dendritic spine density in the hippocampus of female rats seems to vary across the estrous cycle with low levels of estradiol being associated with lower synapse density and high estradiol levels being correlated with a higher density of synapses (Gould et al. 1990; McCarthy and Milner 2003).However, some sexual dimorphisms are not completely explained by hormonal mechanisms (De Vries et al. 2002; Arnold 2004). For example, it has been demonstrated that the adolescent remodeling of cortical and subcortical regions also involves changes in synpatic organization at both pre- and postsynaptic levels which might contribute to regional differences in GM development. Andersen et al (2002) showed that prepuberty is characterized by a high level of expression of dopamine receptors in the striatum of the rat, in contrast to receptor pruning during the postpubertal stage. This pattern is more pronounced in males than in females, but in both sexes, it proceeds even when the gonads are removed before puberty suggesting that neither the overexpression nor the pruning of the dopaminergic receptors in the striatum are dependent on pubertal hormones.Identifying the structural correlates of behavioral maturation and determining which structural features are related to steroid hormonal changes is an important area for further research. Accordingly, the aims of the present study were 1) to investigate sex differences in brain development during childhood and adolescence using fully automated voxel-based morphometry (VBM) and to discover which of these differences are influenced by sexual maturation and which are associated with the circulating level of steroid hormones and 2) to identify brain regions which are affected by circulating steroid levels using a whole-brain approach. Based on previous findings, we expected an enlargement of the amygdala in males and larger hippocampal and striatal GM volumes in females (Giedd et al. 1997). In addition, we hypothesized that amygdala and hippocampal volumes would be associated with sexual maturation as well as TEST and EST levels, whereas striatal volumes would be unaffected by circulating steroid hormones (Andersen et al. 2002). By combining direct (circulating hormonal levels) and indirect measures (TS) of puberty, we expect to get a better insight into sexual dimorphisms in brain development during childhood and adolescence. Materials and Methods Subjects Forty-six normal subjects between 8 and 15 years of age (23 boys and 23 girls, for sample characteristics, see Table 1) were recruited as part of an ongoing neurodevelopmental study at the Department of Child and Adolescent Psychiatry. All subjects were carefully screened for childhood psychiatric disorders and neurodevelopmental diseases by a standardized semi-structured interview for the diagnosis of mental disorders in children (Unnewehr 1995). Intelligence quotient (IQ) was estimated on the basis of a short version of the Wechsler Intelligence Scale for Children III (Wechsler 1991; Tewes et al. 1999). Children with an IQ below 80 were excluded. The physical level of sexual maturation was assessed via TS, a 5-point scale describing the developmental state of primary and secondary sexual organs, in a medical examination conducted by an experienced physician. None of the subjects fulfilled the criteria for “pubertas praecox” or “pubertas tarda,” and none of them were taking any medications at the time of the study, including oral contraceptives. Boys and girls did not differ significantly with respect to age, TS or IQ (see Table 1). All subjects were screened for laterality with the laterality score (LS) of the Edinburgh inventory (Oldfield 1971). Table 1Descriptive data of the whole sample (n = 46) and the subsample for the analysis of hormonal data (n =30) Mean SD Minimum Maximum Mean SD Min Max t pcorr Sample 23 boys 23 girls Age 11.70 2.28 8.00 15.09 10.92 2.06 8.01 15.03 1.23 0.14 TS 2.39 1.31 1.00 5.00 2.00 1.28 1.00 5.00 1.03 0.19 IQ 107.12 14.50 85.40 133.00 103.57 13.69 80.00 133.00 0.86 0.3 GBV/height 20.22 2.5 16.50 25.24 17.1 1.9 14.06 19.90 4.60 girls F(4,41) = 3.5, P TS 1–3 F(4,41) = 1.6, n.s. N = 30 F(1; 28) = 4.9, P girls F(1; 28) = 5.7, P pre-early F(1,28) = 0.5, n.s. F(1, 28) = 4.3, P TS 4 and 5 F(4,41) = 0.93, n.s. N = 30 F(1,28) = 4.2, P girls F(4,41) = 3.5, P TS 1–3 F(4,41) = 1.6, n.s. N = 30 F(1; 28) = 4.9, P girls F(1; 28) = 5.7, P pre-early F(1,28) = 0.5, n.s. F(1, 28) = 4.3, P TS 4 and 5 F(4,41) = 0.93, n.s. N = 30 F(1,28) = 4.2, P girls F(4,41) = 3.5, P TS 1–3 F(4,41) = 1.6, n.s. N = 30 F(1; 28) = 4.9, P girls F(1; 28) = 5.7, P pre-early F(1,28) = 0.5, n.s. F(1, 28) = 4.3, P TS 4 and 5 F(4,41) = 0.93, n.s. N = 30 F(1,28) = 4.2, P girls F(4,41) = 3.5, P TS 1–3 F(4,41) = 1.6, n.s. N = 30 F(1; 28) = 4.9, P girls F(1; 28) = 5.7, P pre-early F(1,28) = 0.5, n.s. F(1, 28) = 4.3, P TS 4 and 5 F(4,41) = 0.93, n.s. N = 30 F(1,28) = 4.2, P 0.9 for putamen, caudate, and pallidum) and neither TEST nor EST predicted the intensity values at the local maxima (P > 0.8). Steroid Effects across the Whole Brain In additional whole-brain regression analyses including circulating steroid levels as regressors and GM volume as dependent variable, EST levels were positively associated with GM volumes in the uncal cortex and parahippocampal gyrus bilaterally (xright = 12, yright = 0, zright = −30, Zright = 4.2, xleft = −18, yleft = −6, zleft = −36, Zleft = 4.1) across boys and girls. A significant sex × EST interaction effect was found for the intensity values at the local maxima bilaterally (Fright = 4.9, P < 0.01; Fleft = 4.7, P < 0.01). The following sex-specific regression analyses revealed a particularly strong impact of circulating EST on right (x = 13, y = 0, z = −29, Z = 3.9) (R2in girls = 0.187; R2in boys = 0.089) and left (x = −18, y = −7, z = −30, Z = 3.8) (R2in girls = 0.164; R2in boys = 0.061) limbic brain structures in girls (see Table 3 and Fig. 3). Table 3Hormonal effects on brain morphometry as derived from whole-brain analyses (multiple regressions, n = 30, 15 boys 15 girls) Contrast Brain region Side k MNI coordinates Z score x y z EST (+) Uncal cortex, parahippocampal gyrus Bilateral 1114 12 0 −30 4.18 653 −18 −6 −36 4.14 Sex by EST: significantly stronger positive association in girls 1917 13 0 −29 3.85 1714 −18 −7 −30 3.78 TEST (+) Diencephalon Right 5980 6 −11 −4 4.57 Sex by TEST: significantly stronger positive association in boys 6508 −2 0 1 4.69 TEST(−) Precuneus, superior parietal gyrus Left 37727 −9 −53 47 5.05 Sex by TEST: significantly stronger negative association in boys 5119 −13 −50 43 3.93 Contrast Brain region Side k MNI coordinates Z score x y z EST (+) Uncal cortex, parahippocampal gyrus Bilateral 1114 12 0 −30 4.18 653 −18 −6 −36 4.14 Sex by EST: significantly stronger positive association in girls 1917 13 0 −29 3.85 1714 −18 −7 −30 3.78 TEST (+) Diencephalon Right 5980 6 −11 −4 4.57 Sex by TEST: significantly stronger positive association in boys 6508 −2 0 1 4.69 TEST(−) Precuneus, superior parietal gyrus Left 37727 −9 −53 47 5.05 Sex by TEST: significantly stronger negative association in boys 5119 −13 −50 43 3.93 Note: In case of significant sex by steroid interaction, sex-specific findings are included in the table. P < 0.05, corrected for multiple comparisons on cluster level with an underlying threshold of P < 0.001 uncorrected on voxel level. EST, estradiol; (+) positive correlation, (−) negative correlation. Open in new tab Table 3Hormonal effects on brain morphometry as derived from whole-brain analyses (multiple regressions, n = 30, 15 boys 15 girls) Contrast Brain region Side k MNI coordinates Z score x y z EST (+) Uncal cortex, parahippocampal gyrus Bilateral 1114 12 0 −30 4.18 653 −18 −6 −36 4.14 Sex by EST: significantly stronger positive association in girls 1917 13 0 −29 3.85 1714 −18 −7 −30 3.78 TEST (+) Diencephalon Right 5980 6 −11 −4 4.57 Sex by TEST: significantly stronger positive association in boys 6508 −2 0 1 4.69 TEST(−) Precuneus, superior parietal gyrus Left 37727 −9 −53 47 5.05 Sex by TEST: significantly stronger negative association in boys 5119 −13 −50 43 3.93 Contrast Brain region Side k MNI coordinates Z score x y z EST (+) Uncal cortex, parahippocampal gyrus Bilateral 1114 12 0 −30 4.18 653 −18 −6 −36 4.14 Sex by EST: significantly stronger positive association in girls 1917 13 0 −29 3.85 1714 −18 −7 −30 3.78 TEST (+) Diencephalon Right 5980 6 −11 −4 4.57 Sex by TEST: significantly stronger positive association in boys 6508 −2 0 1 4.69 TEST(−) Precuneus, superior parietal gyrus Left 37727 −9 −53 47 5.05 Sex by TEST: significantly stronger negative association in boys 5119 −13 −50 43 3.93 Note: In case of significant sex by steroid interaction, sex-specific findings are included in the table. P < 0.05, corrected for multiple comparisons on cluster level with an underlying threshold of P < 0.001 uncorrected on voxel level. EST, estradiol; (+) positive correlation, (−) negative correlation. Open in new tab Figure 3.Open in new tabDownload slideImpact of circulating steroid levels on GM volumes across boys and girls resulting from whole-brain regression analyses, thresholded at P < 0.001 on voxel level, corrected for multiple comparisons at P < 0.05 on cluster level, and overlaid on a mean structural image of the sex-specific group. Turquoise color represents positive TEST effects, blue color negative TEST effects, and red color positive EST effects on GM volumes.In addition, a positive association was found between circulating TEST levels and GM volumes in the right diencephalic structures, including the hypothalamus, mamillary bodies, and ventral thalamus (x = 6, y = −11, z = −4, Z = 4.6) extending to the left. GM volumes in the diencephalon increased with higher levels of circulating TEST, in particular in boys (sex × TEST interaction effect: F = 5.2, P < 0.01, R2in boys = 0.65, R2in girls = 0.09).By contrast, negative associations were found for circulating TEST levels and GM volumes in the left parietal cortex including the precuneus and superior parietal gyrus (x = −9, y = −53, z = 47, Z = 5.1) (see Table 3 and Fig. 3). Here, GM volume decreased with increasing TEST levels across boys and girls. Again, the sex by TEST interaction effect was significant (F = 4.1, P < 0.05) with boys showing a significantly larger TEST effect in this particular brain region compared with girls (x = −13, y = −50, z = 43, Z = 3.9) (R2in girls = 0.26; R2in boys = 0.60). Discussion This is the first study which links sexual maturation directly to brain morphometry in normally developing children and adolescents. In line with previous studies (Giedd et al. 1997; Nunez et al. 2001) on structural brain development, typical sex differences were found, including larger GM volumes in the amygdala and smaller striatal and hippocampal GM volumes in boys compared with girls. These sex-specific developmental differences in brain morphometry have been previously described in normally developing children in both cross-sectional (Sowell et al. 2001; Gogtay et al. 2004; Wilke et al. 2007) and longitudinal studies (Giedd et al. 1999, 2006).Sexual dimorphism of brain structures may be related to sex chromosomes, hormonal effects, environmental effects, or a combination of these factors. Linear regression analyses revealed significant associations between pubertal development and sexually dimorphic brain areas in the amygdala and left hippocampus but not in the striatum. Gonadal steroid levels in children and adolescents explained 13–15% of regional GM volume variance in these specific brain regions. Thus, although there is clear evidence that sexual dimorphisms occur due to the organizing effects of sex hormones during prenatal development (for review, see Genazzani et al. 2007), our data suggest that gonadal steroid hormones also affect sexual dimorphisms later in life. Specifically, the present study showed a relationship between levels of gonadal steroid hormones and sex-dimorphic increases and decreases of regional GM volumes in the amygdala and hippocampus. Moreover, the combined analysis of TS and circulating hormonal levels might help to determine more precisely in which specific phase of pubertal development regional GM volume differences occur. It may also give first hints to whether small changes in hormonal secretion are related to brain development or only more dramatic hormonal changes as typically observed at the end of puberty impact have these effects. Thus, we were able to demonstrate that both amygdala and hippocampal volumes varied as a function of pubertal development in both sexes. The increase in amygdala volume did not occur before the end of puberty (TS 4 or TS 5) when adolescents showed increased circulating levels of TEST. In contrast, our data suggest that the larger hippocampal volume in girls might be associated with lower TEST in girls compared with boys during pubertal development. In addition, GM volumes in the medial temporal lobe/parahippocampal gyrus was positively associated with EST levels in particular in girls. This result is also interesting with regard to the neuroprotective properties of EST, as recently shown in a variety of in vitro and in vivo models of brain injury. Animal studies suggest beneficial effects of EST replacement therapy on cell death by suppressing apoptotic cell death pathways and enhancing the expression of genes that optimize cell survival (Wise 2006).With regard to the organizational–activational framework (Sisk and Zehr 2005), our results suggest that changes in the level of circulating steroids during puberty mediate permanent sexual dimorphic differences in the amygdala–hippocampus complex in the human brain. These organizing effects become obvious in adolescents and are typically seen in the adult female and male brains (e.g., Filipek et al. 1994) but not in prepubertal children (Sowell et al. 1999). This brain maturation pattern might be further associated with a sensitization of the neural networks to hormonal activation such as an EST-dependent increase in brain activity in the amygdala–hippocampus complex during reward processing in adult women within the menstrual cycle (Dreher et al. 2007). However, longitudinal studies in humans are required to prove this hypothesis.Our findings are in accordance with primate studies, which have shown that the amygdala predominantly contains androgen receptors, whereas other limbic brain structures such as hippocampal regions contain more EST receptors (Morse et al. 1986; Clark et al. 1988; Sholl and Kim 1989). However, we also found evidence for a relationship between TEST and a decrease in GM volume in the posterior hippocampus. Interestingly, Gogtay et al. (2006) recently demonstrated that the structural development of the hippocampus is indeed remarkably heterogenous. This is in line with our findings that both EST and TEST are associated with region-specific increases and decreases of GM volume within the neural circuitry of hippocampal and parahippocampal regions. Gogtay et al. (2006) described a greater loss of hippocampal volume at the posterior pole in females and at the head of the hippocampus in males. This finding is in contrast to our results of decreased GM volume in the posterior hippocampus in boys. However, note that sex differences were not tested directly in the study by Gogtay and colleagues. Interestingly, age-related changes in the functional organization of affective and memory circuits have also been observed in posterior hippocampal regions during adolescence (Nelson et al. 2003).Our results are also in agreement with recent findings from studies on abnormal development. For example, clinical studies suggested that women with gonadal hypoplasia have decreased hippocampal volume (Murphy et al. 1993). In genetic syndromes, such as Klinefelter or Turner syndrome, androgens as well as ESTs seem to impact the volume of the superior and middle temporal gyrus (Nunez et al. 2001).In addition, our results suggest that circulating TEST levels are also associated with GM volume in the parietal cortex in particular in boys. In a recent morphometric study, the most pronounced GM loss was found in the parietal lobes in normally developing children for both sexes (Wilke et al. 2007). Thus, the negative correlations between parietal structures and TEST in boys and girls might be considered within the context of the general decrease in parietal GM volumes during normal development. The development of GM in the parietal cortex might also be associated with its increasing specialization for visuospatial and attentional functions (Casey et al. 2005). This might indicate that neuronal cell death in these brain areas is directly associated with the circulating level of pubertal hormones. This is particularly interesting with regard to sex-specific differences in cognitive abilities, such as language or visuospatial skills. Behavioral studies have shown that performance in a mental rotation task improves significantly after a single injection of TEST in females, indicating a highly sensitive modulation of cognitive processes by circulating TEST in women (Hausmann et al. 2000; Aleman et al. 2004). Previous behavioral studies with healthy subjects also found associations between steroid levels and verbal skills (Gordon and Lee 1986). Our results did not reveal significant correlations between EST and GM volume in language-associated brain areas but supported an association between TEST and visuospatial skills mediated via parietal GM volume in children and adolescents in the age range of 8–15 years.In addition, an association was observed between GM of diencephalic regions and TEST in boys. Diencephalic volumes have been reported in earlier developmental studies as regions which increase in size with age (Sowell and Jernigan 1998) in children and adolescents. In the early phase of puberty, relatively sudden increases in hormonal secretion of FSH and LH take place, which activate the gonadal production of steroid hormones. Thus, our data suggest that the increase in circulating levels of hormones might parallel a volume increase within the involved structures like the hypothalamus and the hypophysis gland and this might hint to a bidirectional relationship between circulating hormonal levels and brain structure/function in this particular brain region.Boys were found to have smaller basal ganglia GM volume, which fits well with recent observations that, for example, caudate size peaks at age 7.5 in girls and at age 10.0 in boys. In line with our hypothesis, no significant association was found between pubertal development or circulating level of steroid and striatal GM volume. This is in accordance with the clinical observation that neuropsychiatric disorders are associated with striatal dysfunction. For example, ADHD and tic disorders are more frequently observed in boys than in girls. However, the typical age of onset of both neurodevelopmental disorders is clearly before puberty. This result is also in line with findings from rodent studies which have shown that the overexpression and subsequent pruning of striatal dopamine receptors is more pronounced in prepubertal males than females. However, neither process is dependent on pubertal gonadal hormones (Sisk and Zehr 2005).In contrast, psychiatric diseases associated with primary dysfunction in limbic brain areas, such as mood and anxiety disorders, occur more often in females and typically occur during or after puberty. For example, it has recently been suggested that pubertal transition to TS 3 is associated with a sharp increase in depression rates in girls, with girls at TS 3 and higher being approximately 3 times more likely to be depressed than girls at TS 1 and 2 (Forbes et al. 2004; Patton et al. 2007). This fits well with the present findings of region-specific changes in limbic brain structures associated with EST and TEST during mid-late puberty. However, more recently it has been suggested that depression is also associated with functional abnormalities within the striatum (Epstein et al. 2006; Silverman et al. 2007). Interestingly, in contrast to findings of dysfunctional brain activity in a large limbic–cortical–striatal–thalamic network in early-onset major depression, the majority of anatomical studies did not find any persistent volume loss outside the hippocampus–amygdala complex (Hickie et al. 2005; MacMaster et al. 2008).Thus, these data support the view that the timing of the interaction between structural brain development and the circulating level of pubertal hormones might affect the sex-specific risk for certain psychopathology during adolescence.The major limitation of the present study is that it investigated a relatively small cohort, so the results must be interpreted with caution and should be replicated with larger samples. In addition, these data do not clarify whether hormonal changes alter neural circuits directly or whether puberty changes the social experience of adolescents, which may in turn influence brain development. In addition, nonhormonal genetic effects on brain development have to be taken into account. For example, Dewing et al. (2003) found sexually dimorphic patterns of gene expression in mice embryos and concluded that developmental differences between the brains of male and female mice were in part due to the differential expression of genes before gonadal secretion started. Other findings have suggested a genetic influence on the circulating level of GnRH, and hence a regulating effect of genes on puberty (Seminara et al. 2003; Navarro et al. 2004). Therefore, it would be interesting to combine genetic and hormonal analysis in future studies in order to disentangle more precisely hormonal and genetic effects on brain development during puberty. Despite these several limitations, the present study has directly linked pubertal stages and hormonal data to brain morphometry in normally developing children and adolescents. In agreement with previous studies in animals and humans, the results of the present study suggest that sexual maturation in general and circulating level of gonadal hormones in particular are specifically associated with regional GM differences in brain areas related to cognitive abilities and psychopathological vulnerabilities. Funding Interdisciplinary Center of Clinical Research Aachen (IZKF grant number 38 to K.K. and G.R.F.); the Deutsche Forschungsgemeinschaft (DFG-KFO 112-II, TP5 to K.K. and B.H.-D.).Conflict of Interest: Dr. Herpertz-Dahlmann is a consultant to Eli Lilly and has received industry research funding from AstraZeneca, Eli Lilly, Novartis, and Janssen Cilag. The other authors declare that no conflicts of interest exist. References Topic: hormones amygdala hippocampus sex characteristics brain puberty steroid hormone voxel Issue Section: Articles Download all slides ",18550597,"Little is known about the hormonal effects of puberty on the anatomy of the developing human brain. In a voxel-based morphometry study, sex-related differences in gray matter (GM) volume were examined in 46 subjects aged 8-15 years. Males had larger GM volumes in the left amygdala, whereas females had larger right striatal and bilateral hippocampal GM volumes than males. Sexually dimorphic areas were related to Tanner stages (TS) of pubertal development and to circulating level of steroid hormones in a subsample of 30 subjects. Regardless of sex, amygdala and hippocampal volumes varied as a function of TS and were associated with circulating testosterone (TEST) levels. By contrast, striatal GM volumes were unrelated to pubertal development and circulating steroid hormones. Whole-brain regression analyses revealed positive associations between circulating estrogen levels and parahippocampal GM volumes as well as between TEST levels and diencephalic brain structures. In addition, a negative association was found between circulating TEST and left parietal GM volumes. These data suggest that GM development in certain brain regions is associated with sexual maturation and that pubertal hormones might have organizational effects on the developing human brain.",Sex differences and the impact of steroid hormones on the developing human brain.
+pmcid,title,keywords,abstract,body,pmid,source
+9001100.0,BMRMI Reduces Depressive Rumination Possibly through Improving Abnormal FC of Dorsal ACC,," 
+Rumination is a common symptom of major depressive disorder (MDD) and has been characterized as a vulnerability factor for the onset or recurrence of MDD. However, the neurobiological mechanisms underlying rumination and appropriate treatment strategies remain unclear. In the current study, we used resting-state functional magnetic resonance imaging to investigate the effects of body-mind relaxation meditation induction (BMRMI) intervention in MDD with rumination. To this aim, we have recruited 25 MDD and 24 healthy controls (HCs). Changes in functional connectivity (FC) of the anterior cingulate cortex (ACC) subregion and the scores of clinical measurements were examined using correlation analysis. At baseline, MDD showed stronger FC between the right dorsal ACC (dACC) and right superior frontal gyrus than did the HC group. Compared to baseline, the HC group showed a significantly enhanced FC between the right dACC and right superior frontal gyrus, and the MDD group demonstrated a significantly weaker FC between the left dACC and right middle frontal gyrus (MFG) after the intervention. Furthermore, the FC between the right dACC and right superior frontal gyrus was positively associated with rumination scores across all participants at baseline. The above results indicate that BMRMI may regulate self-referential processing and cognitive function through modulating FC of the dACC in MDD with rumination. 
+ "," 
+## 1. Introduction 
+  
+Major depressive disorder (MDD) is a severe mental disease, which is characterized by anhedonia, repetitive rumination, and cognitive impairment [ ]. The lifetime prevalence rate of MDD is about 11–15%, and it affects approximately 5–6% of people worldwide each year [ – ]. One risk factor for MDD onset or recurrence is depressive rumination, which is conceptualized as passive and repetitive attention to one's negative aspects and a tendency to focus on their possible causes and negative consequences [ ,  ]. Moreover, there is evidence that higher levels of rumination are correlated with other clinical outcomes, such as a slower treatment response [ ,  ] and inferior initial remission [ ,  ]. Recent studies have reported that body-mind relaxation meditation induction (BMRMI) can significantly reduce depressive rumination [ ]. However, the mechanisms underlying it is still not clear. 
+
+BMRMI, a kind of mindfulness meditation, has been found to reduce anxious and depressive symptoms, without any side effects [ ]. BMRMI resembles yoga; in that, it promotes the ability to change physiological behavior during a guided relaxation process and also facilitates positive emotional experiences [ ]. More importantly, BMRMI involves listening to musical melody and relaxation instructions, which enable individuals to balance their physical and mental state and promote the recovery of cognitive function and negative emotions [ ]. Clinical reports have indicated that mindfulness meditation is beneficial for developing alternative responses to negative thoughts and reducing habitual rumination [ ]. Neuroimaging studies have shown that BMRMI can affect brain regions which were related with attention and emotional processing, such as the anterior cingulate cortex (ACC), frontal cortex, insula, and sensorimotor cortex [ ]. 
+
+In recent years, neuroimaging studies have found that increased rumination was related to altered activation and connectivity of the default mode network (DMN) in MDD [ – ]. The DMN comprises the precuneus, medial prefrontal cortex (mPFC), and posterior cingulate cortex (PCC) and is implicated in self-referential processing, emotion regulating, and cognitive improving [ – ]. Cooney et al. (2010) found that there is significantly more activation in mPFC and PCC in patients with MDD compared to healthy controls (HCs) during rumination induction [ ]. Recent studies have also indicated that depressive rumination is correlated with activity in a range of regions (e.g., the ACC, amygdala, and hippocampus), which are known to be implicated in attention control and autobiographical memory [ ,  ]. Kühn et al. (2012) demonstrated that ruminative thoughts were negatively associated with gray matter density and activity in the inferior frontal gyrus and ACC [ ]. Using independent component analysis, rumination scores were found to be associated with increased functional connectivity (FC) between the ventral mPFC and ventral ACC [ ]. Moreover, ACC metabolic activity and connectivity can predict the response to antidepressants and other therapies [ – ]. The ACC therefore seems to play a particularly major role in rumination, treatment response, and the remission of MDD. 
+
+Based on its functional heterogeneity and cytoarchitecture, Margulies et al. (2007) divided the ACC into 16 seed regions that are distributed in two parallel rows [ ]. Kelly et al. (2009) proposed that the ACC can be subdivided into five seeds in each hemisphere, each of which is associated with five respective functions [ ]. The subregions of the ACC include the caudal ACC (cACC), dorsal ACC (dACC), rostral ACC, perigenual ACC (pgACC), and subgenual ACC [ ]. The cACC is commonly thought to function in tandem with fronto-parietal regions and has been proposed to integrate sensorimotor processes [ ]. The dACC activation has been associated with autobiographical memory and cognitive control and is proposed to act “circuit hub” in top-down pathway [ ,  ]. The rACC exhibits patterns of activity that are correlated with the amygdala, hippocampus, ventromedial PFC, and posterior cingulate cortex, which have been implicated in affective processing [ ,  ]. The pgACC has been demonstrated to consist a component of emotion regulation network and implicated in modulating the increased inner attention to ruminative thinking of patients with MDD [ ,  ]. The sgACC is involved in autonomic control and self-referential processing via connection with the anterior part of DMN [ ]. In this study, we used these ACC subregions as the seed regions for resting-state functional magnetic resonance imaging (rs-fMRI) to investigate the neural mechanism underlying the effect of a short-term BMRMI intervention in MDD with rumination. We hypothesized that BMRMI treatment would strengthen FC in the ACC subregions in MDD with rumination. We also expected that FC changes would be correlated with the clinical variables. 
+
+
+## 2. Methods 
+  
+### 2.1. Participants 
+  
+Participants were 25 patients with MDD who had been diagnosed by administration of the DSM-IV by two qualified psychiatrists [ ], and 24 HCs were also recruited by advertisement from the local community. Education levels and years of education were determined using self-reported information from participants [ ]. All recruited patients with MDD met the following criteria: Hamilton Depression Rating Scale (HAMD) score ≥ 17; no stable drug treatment; no other psychiatric symptoms or acute physical disease; and no history of other interventions, especially for mindfulness meditation, qigong practice, or yoga. The exclusion criteria for HCs are as follows: a history of mindfulness meditation in the last 2 months, head injury, no history of alcohol or drug abuse, pregnancy, and a family history of psychiatric illnesses. This study was approved by the Medical Ethics Committee of Guang'an Men Hospital, China Academy of Chinese Medical Sciences (Beijing, China; ethical approval number 2017-056-KY-01), and informed consent was signed from all participants before study enrollment. 
+
+
+### 2.2. Measures 
+  
+The severity of depressive and anxious symptoms was assessed in all participants by two psychiatrists using the HAMD and Hamilton Anxiety Rating Scale (HAMA). The level of rumination was assessed in all participants using the Automatic Thoughts Questionnaire (ATQ) and Ruminative Responses Scale (RRS) clinical measurements. The ATQ is a commonly used to evaluate the frequency of automatic occurrence of negative self-thoughts [ ]. The RRS evaluates repetitive responses to depressed emotions and passive self-thoughts that focused on a person's negative feelings, relative symptoms, or their causes and consequences [ ]. These measures were not implemented after the BMRMI intervention. 
+
+
+### 2.3. Intervention 
+  
+BMRMI is composed of harmonious background music and relaxation-inducing passages. The background music is called “Saishangqu,” which is soft, slow Chinese classical music played by zither [ ]. The relaxation guide passage was conducted in Mandarin by a female speaker, comprising two parts: (1) phrases that induce whole-body relaxation, such as “relax your muscles from top to bottom,” and (2) phrases that induce mind relaxation, such as “feel your body relax and take some downtime.” The BMRMI treatment session lasted 15 minutes. All participants were scanned at both baseline and immediately after the BMRMI intervention. 
+
+
+### 2.4. rs-fMRI Data Acquisition 
+  
+All participants underwent brain scans on a 1.5 T GE Signal scanner (GE Healthcare, Piscataway, NJ, USA) in Guang'an Men Hospital. The functional images were acquired in 41 axial slices from the echo planar imaging sequence (slice thickness = 3 mm, gap = 0.5 mm, repetition time (TR) = 2500 ms, echo time (TE) = 30 ms, matrix size = 64 × 64 × 20, flip angle = 90°, and field of view = 24 × 24 cm). All participants underwent a 360-second resting-state scan, and data were acquired 144 time points. All participants were instructed to keep their eyes closed in quiet state while scanning, and foam pads were used to immobilize the head. After scanning, each subject was recorded whether they had not fallen asleep or had been distracted by something during the scan. 
+
+
+### 2.5. Image Data Preprocessing 
+  
+The image data were preprocessed using the Data Processing Assistant for Resting-State fMRI Advanced Edition (DARSF 4.0) based on MATLAB 2014a software. The first 10 volumes from both scanning sessions were removed. The remaining volumes for each subject were processed to correct head motion and calculate slice timing, normalized to the standard Montreal Neurological Institute (MNI) template, and smoothed using a 6 × 6 × 6 full-width at half-maximum Gaussian kernel. Data were bandpass-filtered at 0.01–0.08 Hz and linearly detrended. The nuisance signals from white matter, cerebrospinal fluid, and six parameters of head motion were also regressed out. Any participants who had a translation larger than 2 mm and greater than 2° in any angular dimension were discarded from subsequent analysis [ ,  ]. Two subjects in the MDD group were discarded due to excessive head motion > 2.0 mm during the MRI scanning. The mean framewise displacement was not significantly different between baseline (0.124 ± 0.085) and after BMRMI treatment (0.133 ± 0.014) in the MDD group (  T   = −0.488,   p   = 0.63). 
+
+
+### 2.6. Seed Region of Interest Functional Analysis 
+  
+The ACC was divided into five subregions for each hemisphere ( ). The ACC subregions included the cACC (MNI = ±5, −10, 37), dACC (MNI = ±5, 10, 33), rostral ACC (MNI = ±5, 27, 21), pgACC (MNI = ±5, 47, 11), and sgACC (MNI = ±5, 34, −4) [ ,  ]. These seeds were selected as spheres with a 6 mm diameter in the peak center of brain regions, symmetrically in both hemispheres [ ]. For the ACC subregions in both hemispheres, the average time series of each region of interest were extracted. The   p  -correlation coefficients were computed between each ROI mean time coursed and that of each voxel of the whole brain. Fisher's   r  -to-  z   transformation was used to convert FC results to   z  -values to improve the normality. 
+
+
+### 2.7. Statistical Analyses 
+  
+All statistical analyses of the demographic data and clinical measurements were performed using SPSS version 25.0 (SPSS, Inc., Chicago, IL, USA). Baseline differences in demographic data and clinical measurements between the MDD group and HCs group were analyzed using a two-sample   t  -test, and sex differences were analyzed using the   χ   test. After verifying the normality and homogeneity of the variance of MRI data, a repeated-measures ANOVA was applied to examine the FC of ACC subregion differences between two groups in both before and after treatment, with age, education, and sex as covariates [ ]. After using Gaussian random field theory to correct the results of statistically significant FC mapping, the voxel-level threshold was   p   < 0.001, and the joint cluster-level threshold was   p   < 0.05. Next, the correlation between the mean change values of FC in ACC subregions and scores of clinical scales was assessed in both groups and in all participants before BMRMI. 
+
+
+
+## 3. Results 
+  
+### 3.1. Demographic Data and Clinical Measurements 
+  
+The are no significant differences in age (  t   = 0.10,   p   = 0.91), education (  t   = −1.22,   p   = 0.23), or sex (  χ   = 0.07,   p   = 0.792) between two group. Significant between-group differences were found in the HAMD, HAMA, ATQ, and RRS scores (for details, see  ). 
+
+
+### 3.2. ANOVA Differences in ACC Subregion Seed-Based FC 
+  
+The region of interest analysis results revealed aberrant FC between MDD group and HC group at baseline and after intervention in the right dACC and right SFG and left dACC and right middle frontal gyrus (MFG) ( ). There was a significant difference in FC between the right dACC and right SFG in the MDD group relative to the HC group before BMRMI. After BMRMI treatment, we found a stronger FC between the right dACC and right SFG in both groups relative to baseline. We also found that FC between the dACC and MFG decreased significantly after the intervention within the MDD group; conversely, the FC increased significantly after the intervention within the HC group ( ). 
+
+
+### 3.3. Correlation Analysis 
+  
+The correlation between the subregions of ACC FC and clinical measurements in all participants at baseline are shown in  . Correlation analysis revealed that the increased FC between the right dACC and right SFG was significantly correlated with the higher scores of RRS (  r   = 0.50,   p   < 0.01), ATQ (  r   = 0.54,   p   < 0.001), and HAMD (  r   = 0.62,   p   < 0.001) in all participants before BMRMI. 
+
+
+
+## 4. Discussion 
+  
+The present study used rs-fMRI to investigate the effect of BMRMI intervention on FC changes in ACC subregions in patients with MDD. At baseline, the MDD group showed stronger FC between the right dACC and right SFG than did the HC group. After BMRMI treatment, we found an enhanced FC between the right dACC and right SFG in both groups compared to baseline, but this difference was not significant within the MDD group. However, we also found a different connectivity pattern between the left dACC and right MFG in both the MDD group and HC group after BMRMI treatment. In particular, the strength of FC between the right dACC and right SFG was positively correlated with HAMD, RRS, and ATQ scores across all participants before BMRMI. These findings support the notion that BMRMI increases FC between the dACC and SFG through modulating attentional control, and these changes may play a considerable role in MDD with rumination. 
+
+In comparison with HC group, MDD patients showed a stronger FC between the right dACC and right SFG at baseline. Moreover, the increased FC between the right dACC and right SFG was associated with higher rumination scores across all participants at baseline. Previous studies have suggested the differential activity or abnormal FC of ACC subregion activity in MDD is substantively different to that of healthy individuals [ ]. Numerous research has demonstrated that the ACC has high functional coupling with core part of DMN in MDD with rumination [ – ]. For example, the decreased FC between the sgACC and right MFG has been found to be associated with higher RRS scores [ ,  ]. Negative self-focused thought has also been reported to be positively correlated with pgACC connectivity with the dorsolateral PFC [ ]. Another study detected that FC between the dACC and precuneus was positively correlated with rumination in patients with MDD [ ]. Connectivity changes between the dACC and dorsal mPFC have also been observed in the remission of patients with MDD who are prone to rumination [ ]. Importantly, the dACC is not only a hub in a network of regions involving cognitive functioning but forms a part of the salience network, which has been demanded in the processes of attentional control and monitoring conflict [ – ]. Specifically, the correlation between the increased FC of the dACC and SFG with RRS scores found in the present study indicates that depressive rumination is associated with attentional control impairments. Combined with the above results, our findings expand our understanding of abnormal FC between ACC subregions and highlight the role of the DMN as a neural mechanism underlying rumination. 
+
+After BMRMI treatment, FC between the right dACC and right SFG increased significantly in the HC group compared to baseline; however, no such significant increase in FC was found in the MDD group. Given that our intervention was a single brief session of BMRMI and clinical measurement scales were used to evaluate outcomes, we missed the correlation between the difference FC and the change of clinical measurements after treatment, so we need to interpret the results with caution. Our findings may indicate that patients with MDD require greater effort or more adequate treatment to improve higher cognitive processes and avoid negative emotion. Indeed, previous studies have also demonstrated that mindfulness-based intervention has a beneficial effect on cognitive function. For instance, 1 month of music intervention has been found to strengthen FC between the right middle temporal gyrus [ ] and between the dorsal anterior insula and ACC [ ] in patients with schizophrenia. After an 8-week mindfulness-based intervention in healthy individuals, Hölzel et al. (2010) found an increased density of gray matter in the PCC and temporo-parietal junction [ ]. After an 11-hour body-mind training meditation in patients with schizophrenia, Tang et al. (2018) found increased white matter tract integrity connecting the vACC and dACC [ ]. Using different musical and nonmusical emotion-stimuli, Lepping et al. (2016) found that patient with MDD showed the strongest activation by negative nonmusical stimuli and less activation for positive musical in the dACC after each stimulus, while the HC group exhibited greater dACC activation in response to all music than to all nonmusical stimuli [ ]. Regardless of whether an intervention is long-term or short-term, previous studies have reported that mindfulness-based therapy can improve cognitive functioning. However, we did not find a change in dACC and SFG FC after BMRMI in the MDD group, most likely because our intervention comprised a single brief session of BMRMI. In the future, we will implement a complete treatment routine and collect the relevant clinical measurements to further investigate the effect of BMRMI intervention and expand our understanding in mechanism of MDD. 
+
+We also found decreased FC between the left dACC and right MFG in the MDD group after BMRMI compared to baseline. The MFG is a major part of dorsolateral PFC, which is a component of the executive control network and involved in extensive attentional regulation, decision-making, and semantic processing [ ]. Lepping and colleagues have reported that patients with MDD showed a smaller response to positive music in the dACC [ ]. Our results extend this work and indicate that patients with MDD show a blunt response to positive emotional stimuli and require more attention and/or executive control to control competing negative information. 
+
+### 4.1. Strengths 
+  
+First, the main objective of this study was not only to determine differences in FC between patients with MDD and HCs but also to identify the specificity of these aberrant FC patterns of ACC subregions observed in neuroimaging studies. Second, our results further confirmed the relationship between depressive rumination and attentional control impairments. Third, this study was defined as a preliminary study to investigate the possible effects of BMRMI in MDD with rumination, which could provide some basic data for assessing the long-term intervention in future studies. 
+
+
+### 4.2. Limitations 
+  
+The present study has several limitations. First of all, the sample size is relatively small; we will expand the sample size to improve the reliability of data. Second, the total scanning duration was only 144 time points; in the future, we will use a longer sequence to improve measurement reliability (Birn et al., 2013). Third, only one routine therapy session was implemented in our study. Fourth, we did not measure rumination or depression severity after BMRMI treatment, so we could not ascertain whether FC changes in ACC subregions were associated with the changes of symptoms in MDD. Future studies should implement a complete treatment routine and collect the relevant clinical measurements after treatment. It would be meaningful to conduct a longitudinal study to investigate whether the altered FC in brain networks can predict the clinical outcome of BMRMI. 
+
+
+
+## 5. Conclusions 
+  
+Our findings demonstrate that an altered FC between the dACC and SFG is involved in rumination pathophysiology in patients with MDD. Furthermore, we found that BMRMI could positively improve the functional hub of the dACC through modulating attentional control in patients with MDD with rumination. Our results may provide an objective evidence supporting the effect of BMRMI intervention in regulating self-referential processing and in cognitive functioning. 
+
+ ",35419051,pubget
+5789340.0,Behavioral and Brain Activity Indices of Cognitive Control Deficits in Binge Drinkers,"binge drinking
+response conflict
+Stroop
+ventrolateral prefrontal cortex
+thalamus
+"," 
+Heavy episodic drinking is prevalent among young adults and is a public issue of increasing importance. Its initiation and maintenance are associated with deficits in the capacity to inhibit automatic processing in favor of non-habitual responses. This study used functional magnetic resonance imaging (fMRI) to examine behavioral and brain activity indices of cognitive control during the Stroop task as a function of binge drinking. Heavy episodic drinkers (HED) reported consuming 5+/6+ drinks in two hours at least five times in the past six months and were compared to light drinkers (LED) who reported two or fewer binge episodes but were matched on demographics, intelligence and family history of alcoholism. Greater conflict-induced activity in the ventrolateral prefrontal cortex (VLPFC) and thalamus was observed in HED participants and it was positively correlated with alcohol intake and alcohol-related harmful consequences. HEDs maintained intact accuracy but at a cost of prolonged reaction times to high-conflict trials and increased ratings of task difficulty. Greater activation of the areas implicated in cognitive control is consistent with compensatory network expansion to meet higher cognitive demands. These results provide further insight into degradation of cognitive control in HEDs which may benefit development of detection and prevention strategies. 
+ "," 
+## 1. Introduction 
+  
+Cognitive control is a facet of executive functioning that underlies optimization of behavior by integrating appropriate response selection with previous experiences and current goals [ , , , , ]. These top-down influences have been explored with tasks that probe decision making under conditions that induce response interference and selective inhibition [ , , ]. Extensive neuroimaging literature has characterized the predominantly frontal cortical network comprised of the anterior cingulate, ventrolateral (VLPFC) and dorsolateral prefrontal cortices (DLPFC), insula, as well as the parietal cortex and basal ganglia [ , , , , , , , , ]. The neurofunctional system subserving cognitive control is particularly vulnerable to the effects of both acute alcohol intoxication [ , , ] and long-term excessive alcohol use [ , , , ]. Structural imaging studies indicate that alcohol use disorder (AUD) is associated with compromised white matter tracts, reductions in hippocampal volume, and decreases in cortical thickness [ , , , , , ]. These measures are predictive of clinical outcomes such as the duration of abstinence after treatment [ ]. Results of functional magnetic resonance imaging (fMRI) studies using executive tasks are less consistent with some studies showing decreased [ , , ], and others reporting increased activation in participants with AUD compared with controls [ , , , , , , ]. Activation increase in AUD participants is commonly observed in the absence of executive task performance deficits suggesting that compensatory mechanisms are engaged to maintain normative behavioral performance especially on tasks imposing higher cognitive demands. 
+
+Heavy episodic drinking (HED), also termed binge drinking, is a variation of alcohol use characterized by high volume drinking episodes interspersed with periods of low or no intake. It has been on the rise especially among young and emerging adults, raising serious concerns about the neurotoxic effects of alcohol on a large scale [ ]. Neurobiologically based accounts of addiction conceptualize HED as a stage in an addiction cycle comprised of withdrawal periods followed by active alcohol seeking/craving behaviors [ , , ]. While acute alcohol-induced pharmacological effects result in enhanced neural inhibition overall [ , ], protracted periods of heavy episodic drinking elicit neuroadaptive changes to compensate for alcohol’s effects on the brain [ , ]. This is reflected in neural hyperexcitability and associated with symptoms of withdrawal, dysphoria and increased risk of relapse and dependence [ , , , ]. Evidence is accumulating in support of the “continuum hypothesis” where HED may serve as a precursor to AUD [ , , ]. Structural imaging studies have shown that the effects of HED are particularly deleterious during critical stages of brain development and frontal lobe maturation in adolescence and emerging young adulthood [ , , , ]. Furthermore, HED is associated with impaired executive functions [ , , , ]. Neuroimaging studies of cognitive control functions, however, have reported mixed results with HED participants showing decreased fMRI activation on response inhibition [ , ]. Conversely, increased activation during response inhibition was observed in heavy drinking adolescents [ ]. Increased activation in frontal regions was reported with greater reliability during more complex tasks engaging executive functions such as spatial interference [ ] and working memory [ ]. These findings have been interpreted as reflecting compensatory engagement of cortical areas needed to maintain performance accuracy during cognitively demanding tasks. They primarily encompass frontal regions suggesting an underlying dysregulation of cognitive control which is an important factor in the development of AUD. Indeed, deficient self-regulation can contribute to the formation of habitual, compulsive alcohol consumption resulting in impaired capacity to refrain from drinking [ , , , , , , ]. 
+
+Despite the prevalence of HED and its importance for public health, there is a paucity of studies on the associated harmful consequences in the neurocognitive domain. Most studies report no deficits in intelligence or on task performance in HED indicating that more sensitive neuroimaging measures are needed to characterize deficits associated with binge drinking patterns [ , , ]. Given the importance of cognitive control for the development of AUD, the aim of the present study was to examine its neural underpinnings and possible emerging signs of excessive drinking habits in HED. Cognitive control is commonly probed with tasks that involve inhibition of automatic responses in favor of those that are task-relevant but non-habitual [ , ]. Participants performed a version of the color-Stroop response conflict task, which has been shown to elicit conflict interference with high sensitivity to alcohol intoxication effects [ , ], during functional magnetic resonance imaging (fMRI). HED and matched light episodic drinkers (LED) were compared on behavioral and brain indices of response conflict in the context of a comprehensive set of questionnaires on alcohol-related behaviors, dispositional traits, personality, and intelligence measures. 
+
+
+## 2. Materials and Method 
+  
+### 2.1. Research Participants 
+  
+Thirty-one healthy, right-handed young adults (18 female, age 24.7 ± 3.9) participated in the study. They had no history of seizures, traumatic brain injury or concussions, no neurological or neuropsychiatric disorders, vision or hearing problems, and they all complied with MRI safety criteria. Participants were medication-free, they reported using no drugs or tobacco products for at least one month prior to the study and none had previously sought or been enrolled in alcohol abuse treatment. Based on the screening information on alcohol consumption rate, frequency and pattern, participants were assigned to heavy episodic drinking group (HED,   n   = 14) if they reported engaging in ≥5 binge episodes in the previous six months. Light episodic drinking (LED) group comprised individuals who had ≤2 binge episodes in that interval. A binge episode was defined as consuming 5+/6+ drinks for women/men within a two-hour time frame based on research evidence indicating that it is likely to reach a legal level of intoxication (0.08%) [ , ]. The two groups were matched on age, gender, education and family history of alcoholism (see   for group characteristics). The HED group scored higher on a wide range of alcohol-related variables, higher motor impulsivity [ ], and disinhibition and boredom symptoms [ ] ( ). Young adults were recruited from the San Diego area with flyers and on-line postings. All subjects gave written informed consent to experimental procedures approved by the relevant Institutional Review Boards. Participants were provided monetary compensation for their participation. 
+
+
+### 2.2. Experimental Protocol 
+  
+All participants provided information on multiple dimensions of their alcohol use, such as the severity of their alcohol habit (Alcohol Use Disorder Identification Test, AUDIT) [ ]; the prevalence of particular signs of alcohol misuse (Short Michigan Alcoholism Screening Test, SMAST) [ ]; the number and characteristics of drinking occasions that had occurred over the past thirty days (The Time Line Follow Back, TLFB) [ ]; the degree to which they crave alcohol (The Penn Alcohol Craving Scale, PACS) [ ]; the reasons for engaging in drinking episodes (Drinking Motive Questionnaire Revised Short Form, DMQ-R SF) [ ]; quantifying the occurrence of consequences from drinking (Brief Young Adult Alcohol Consequences Questionnaire, B-YAACQ) [ ]; and personality traits (Eysenck Personality Questionnaire, EPQ) [ ]. In addition, we collected information regarding the presence of depressive symptomology (Patient Health Questionnaire, EPQ) [ ]; anxiety (Generalized Anxiety Disorder, GAD7) [ ]; the degree of impulsive behavior for motor, attention and non-planning dimensions (Abbreviated Impulsiveness Scale, ABIS) [ ]; attention deficit/hyperactivity disorder symptomology (Adult ADHD Self-Report Scale, ASRS) [ ]; and finally the desire for novel situations and risk-taking behavior (Brief Measure of Sensation Seeking Scale, BSSS) [ ]. Intelligence was assessed with the Wechsler Abbreviated Scale of Intelligence (WASI-II) [ ]. Family history of alcoholism was assessed with a modified version of the Family History Assessment Module (FHAM) [ ]. A positive family history for alcoholism (FH+) was defined as having at least one first-degree and one first- or second-degree relative, or at least three second-degree relatives diagnosed with AUD. Statistical analysis between the alcohol use measures, personality scores, and behavioral results were conducted via SPSS 24 [ ]. Prior to scanning, subjects were screened for illicit substances via urinary analysis and women were tested for pregnancy and all tested negative. 
+
+
+### 2.3. Task 
+  
+Cognitive control processes were probed with a modified Stroop color naming task [ , , ]. The subjects were instructed to identify the color of the font (red, green, blue or yellow) and respond as quickly and accurately as possible with index and middle fingers of both hands mapped to four buttons ( ). In the congruent (CONG) condition, the color of the font matched the meaning of the color word, whereas in the incongruent (INCONG) condition the color of the font was different from the color word, inducing interference. To maintain reading dominance and automaticity, additional color words were presented in gray font and the subject responded to the meaning of the word (READ) ( ). The Stroop task was presented as a randomized event-related design and consisted of 540 stimuli across four runs comprising 20% (108) CONG, 20% (108) INCONG and 60% (324) READ trials. Words were presented for a stim duration of 300 ms followed by a 1700 ms fixation (XXXX) period for a total stimulus onset asynchrony of 2 s. In addition, 108 null fixation trials were optimally interleaved with Optseq2 ( ) randomization algorithm to allow for proper finite impulse response (FIR) deconvolution modeling during fMRI analysis [ ]. The task was programmed with Presentation V.19.0 (Neurobehavioral Systems) to sync with the scanner through transistor–transistor (TTL) pulses. 
+
+
+### 2.4. Image Acquisition and Analysis 
+  
+Structural and functional imaging data were collected with a GE Discovery MR750 3.0T whole body scanner equipped with an 8-channel head coil. The head was secured with a pillow and foam padding to minimize movement and maximize subject comfort. The subject was provided M3 earplugs (EAR Soft FX) to dampen scanner noise and protect hearing. A mirror was attached to the head coil to allow for comfortable viewing of the front-projected display. A high-resolution SPGR (spoiled gradient recalled echo) T1-weighted structural image sequence was acquired for each subject with the following parameters: TR = 7.38 ms, TE = 2.984 ms, flip angle = 8°, field of view (FOV) = 240, matrix 256 × 256, 170 slices, 1.2 mm slice thickness with a 94 × 94 mm in-plane resolution. During task administration, 4 runs of functional whole-brain blood oxygenation level dependent (BOLD) volumes (648 total) were collected with a T2*-weighted echo planar imaging sequence of 35 interleaved bottom-up 4 mm slices in AC-PC orientation (TR = 2000 ms, TE = 30 ms, flip angle = 90°, FOV = 220 mm, matrix 64 × 64, generating a 3.437 × 3.437 mm in-plane resolution). Anatomical and functional volumes were analyzed with AFNI (Analysis of Functional Neuroimages) 17.1.12 [ , ]. AFNI’s Montreal Neurological Institute (MNI; TT_avg152T1) template was used to warp the anatomical and functional datasets to standardized space. Volume registration of the echo planar imaging (EPI) runs was completed by setting the volume with the least number of outlier voxels as the base. A three-dimensional Gaussian kernel (FWHM 8.0) was used to blur the data within each volume and each voxel was scaled to percent signal change before deconvolution was performed. Motion correction was performed by removing TRs exceeding 0.25 mm rotational and translational motion, removing trials in which 25 percent or more of the voxels are considered outliers, and regressing out motion derivatives in deconvolution through six motion parameters and a third-order polynomial for drift. Hemodynamic response function (HRF) was modeled for each trial through AFNI’s version of finite impulse response model (also termed a “tent” function) within a time window from −4 to 12 s with respect to stimulus onset. The contrast matrix was generated by 3dDeconvolve and used for residual maximum likelihood (REML) and generalized least squares (GLSQ) statistical analysis to identify voxels with significant changes [ ]. For group level statistics, the coefficients and corresponding   t  -values generated from individual REML analysis were used for mixed-effects meta-analysis (MEMA). Cluster simulations were performed via 3dClustSim at the group level to adjust for multiple comparisons and to keep   p  -values below 0.05. Region-of-interest (ROI) analysis was performed to identify the pattern of BOLD activation at anatomical locations associated with the task paradigm in order to contrast activity between heavy and light episodic drinkers. ROIs were chosen from an uncorrelated orthogonal general linear model (GLM) contrast (all conditions v. fixation periods) activation map across all subjects [ , ]. The selected anatomical locations contained active voxels clusters at a   p <   0.0001 threshold corrected for multiple testing and family-wise error (FWE) via 3dClustSim. Beta weights representing percent signal change at each point in the time series were extracted from the ROIs for each subject. Time courses were analyzed using a mixed model ANOVA with drinking Group as a between-group factor and Condition as a within-subject factor. 
+
+
+
+## 3. Results 
+  
+### 3.1. Drinking and Personality Variables 
+  
+Group characteristics are displayed in  . HED and LED groups were equated on age, education, GPA, gender, family history of alcoholism, and intelligence. HED participants had higher scores on all alcohol-related variables compared to LEDs. They reported higher numbers of drinking days, more drinks per occasion, binge episodes and blackouts in the previous six months. HEDs also reported more alcoholism-related symptoms, higher alcohol cravings and more harmful consequences from drinking. They reported using alcohol as a coping strategy, to enhance their experience in social situations, and because they enjoyed the euphoria it caused. On average, HED participants started drinking around the age of sixteen, two years earlier than LEDs. In contrast, no differences between groups were detected in anxiety, depression, ADHD-like symptoms or personality measures of psychoticism, neuroticism and extraversion. However, HED individuals reported higher motor impulsivity, boredom susceptibility, and disinhibition. In addition, they rated the Stroop task as being more difficult than LEDs. 
+
+
+### 3.2. Task Performance 
+  
+Accuracy and reaction times were analyzed with a mixed-model ANOVA with the factors of Group and Condition. As shown in  , there was a main effect of Condition on task accuracy (  F   = 27.04,   p   < 0.001) and reaction time (  F   = 269,   p   < 0.0001), confirming that the task successfully elicited the Stroop interference effect. The groups did not differ on accuracy (  F   = 0.22,   p   = 0.61) but overall accuracy was the lowest on the INCONG trials compared to both the CONG (  F   = 33.0,   p   < 0.0001) and READ (  F   = 27.38,   p   < 0.0001) trials. Similarly, reaction times (RT) were the longest on the INCONG trials (M = 923 ms), followed by READ (M = 753 ms) and CONG (M = 721 ms), with all conditions differing from each other (  p  ’s < 0.0001). A significant Condition x Group interaction for RT (  F   = 5.44,   p   < 0.01) was driven by the HED individuals responding with significantly slower response times to INCONG stimuli (  F   = 4.22,   p   < 0.05) relative to LEDs ( ). There were no significant task performance differences between men and women. Altogether, this indicates that regardless of gender HEDs were particularly sensitive to response conflict. Indeed, the interference effect, calculated as the RT difference between INCONG and CONG conditions, was positively correlated with all drinking measures (  p  ’s < 0.05). Stroop task difficulty was positively correlated with binge frequency (r  = 0.55,   p   = 0.002), average drinks per occasion (r  = 0.43,   p   = 0.02, and the AUDIT (r  = 0.46,   p   = 0.013). 
+
+
+### 3.3. Neuroimaging Results 
+  
+As shown in  , voxel-wise analysis of the peak activation showed a distributed activation pattern which is broadly consistent with previous studies. The task conditions activated inferior precentral, anterior ventrolateral prefrontal, sensorimotor, parietal, occipital cortices and the insula laterally, and the supplementary/presupplementary cortex and the thalamus medially. A robust main effect of Condition with stronger activation to INCONG compared to CONG and READ trials was observed across most association fronto-parietal activated areas but not in the precentral, sensorimotor, and visual cortices ( ,  ). Extracted ROI time series were statistically assessed via mixed-model ANOVA and presented in percent signal change values ( ,  ). The largest effects of Group were observed in the VLPFC and the thalamus bilaterally. INCONG trials elicited stronger activity in HED in both the VLPFC (  F   = 13.4,   p   < 0.001) and left thalamus (  F   = 5.3,   p   < 0.05) relative to LED (  and  ). Similar to the behavioral results there were no significant gender differences in the ROI analysis. 
+
+Nonparametric correlations of Spearman’s Rho were calculated between alcohol measures and extracted time courses for ROI peak activations across groups. Bilateral peak activation of the VLPFC to INCONG trials was positively correlated with binge episodes, blackouts, and the SMAST and AUDIT alcohol severity measures (  p  ’s < 0.02). Peak activation of the left thalamus elicited by INCONG trials was positively correlated with binge episodes (r  = 0.38,   p   = 0.035). There was a significant positive relationship between the rVLPFC and Stroop difficulty during both CONG (r  = 0.43,   p   = 0.023) and INCONG (r  = 0.466,   p   = 0.013) trials. Finally, when parsing apart Groups, HED individuals have a positive correlation between the rVLPFC with non-planning impulsivity (r  = 0.601,   p   = 0.03) during INCONG trials. 
+
+
+
+## 4. Discussion 
+  
+This study used fMRI to investigate behavioral and brain activity indices of cognitive control in heavy and light episodic drinkers during response conflict evoked by the Stroop task. As expected, the high conflict (INCONG) condition evoked greater activity in the fronto-parietal association cortices. Importantly, this activation was greater for HEDs in the VLPFC and thalamus bilaterally relative to LEDs. Conflict-induced activity in the bilateral VLPFC was positively correlated with levels of alcohol intake including binge episodes and the number of drinks consumed per occasion, as well as measures of alcoholism-related symptoms (AUDIT, SMAST) and blackouts. Reported binge episodes were further correlated with activity in the left thalamus. In the absence of accuracy deficits, HEDs had significantly slower RTs to INCONG stimuli, which presumably allowed them to maintain performance levels on par with LEDs. HEDs were particularly affected by response conflict as both the Stroop interference effect and task difficulty ratings were associated with alcohol intake measures. Taken together, these results suggest that heavy episodic drinkers benefit from engaging an expanded cognitive control network and responding more slowly to resolve stimulus conflict. These effects are especially pronounced in those with heavier drinking patterns and more binge episodes which may be indicative of the deleterious effects of excessive alcohol use. 
+
+Binge drinking participants in the present study were young, healthy individuals who showed no cognitive deficits on a standardized intelligence scale. Nonetheless, the observed group differences in brain activity are consistent with those found in AUD samples. Increased activity of the VLPFC has been reported on tasks probing working memory [ , , ], response inhibition [ ], and delay discounting [ ]. Our results are most in line with the findings by Wilcox and colleagues [ ] who used a multisensory Stroop and observed bilateral VLPFC hyperactivation and an overall increase in RTs in AUD participants compared to the control group. Despite a paucity of neuroimaging studies investigating cognitive control in binge drinkers, the results that have emerged are also similar to those found in AUD. Increased activity of the VLPFC has been reported on a task probing spatial interference [ ] and declarative memory in young adult heavy drinkers [ ]. A study of spatial working memory in adolescents reported greater activation in the right inferior frontal gyrus in male but not female adolescent binge drinkers [ ]. Moreover, on the Eriksen flanker task, increased activity was induced by response conflict in the VLPFC under acute intoxication in social drinkers [ ]. Inconsistent reports notwithstanding [ , , ], accruing neuroimaging evidence indicates frontal hyperactivation in AUD and binge drinkers compared to low-drinking control groups. More studies are needed to corroborate these findings, but it appears that greater inferior frontal activation is elicited by tasks that impose higher demands on cognitive control by relying on deliberative functions and multidimensional contingencies [ ]. 
+
+This converging evidence is consistent with compensatory accounts of increased engagement across cognitive and emotional neurofunctional systems in alcoholism [ , , , , ]. Extensive functional imaging evidence suggests that the VLPFC is activated by tasks probing cognitive control [ ]. It has been proposed as a key area for inhibitory control of inappropriate motor responses [ , ] but it is also activated during attentional capture [ ] and domain-general tasks that are cognitively demanding but that do not rely on inhibition [ , ]. Meta analyses have provided further insights into functional specificity within the VLPFC subregions [ ]. However, it is increasingly clear that the VLPFC is an integral part of a network that is engaged by a range of tasks imposing attentional demands in the context of processing novelty, contingency monitoring and conflict resolution [ , , ]. Functional connectivity studies have revealed extensive connections of the VLPFC with other parts of the lateral frontal cortex, the anterior cingulate, parietal, and temporal cortices [ ]. Therefore, in response to increased demands imposed by multi-rule tasks, the VLPFC is likely to be recruited in a process of flexible network reconfiguration [ ]. On that view, the brain functions as a dynamic, interactive system that handles changing environmental demands via targeted, yet flexible and integrated engagement of the relevant neurofunctional networks in order to optimize responding [ ]. These networks interact across spatiotemporal scales, they are synchronized from local, specialized, to global-level networks and reflect typical [ ] and pathological changes [ ]. In the present study, greater activation of the VLPFC was elicited by response conflict selectively in HEDs and correlated with Stroop difficulty ratings. Furthermore, both of these variables were associated with various measures of alcohol intake and harmful drinking consequences. Therefore, the converging evidence suggests that in HEDs the task demands exceeded the normative network capacity, necessitating compensatory engagement of the VLPFC and adjustment of response strategy. The observed activation increase is associated with heavy alcohol intake and may reflect adaptation of the brain’s functional networks to the sequelae of heavy drinking. This interpretation is consistent with evidence indicating an expansion of primarily frontal networks in AUD. Muller-Oehring and colleagues [ ] examined functional connectivity in sober alcoholics compared to matched controls during wakeful rest and reported expanded attention/salience network which comprised the inferior frontal cortex. The compensatory interpretation is supported by the correlation between better task performance and network enlargement [ ]. Not all studies, however, show increased activity in brain regions implicated in cognitive control. Decreased activation has been reported in studies with simpler contingencies that rely on response inhibition such as the go/no go and stop signal tasks [ , , ]. These tasks may not be challenging enough to generate high conflict and may not engage cognitive control at the level that would necessitate activation of additional areas [ ]. The present results could serve as an indirect indication of impaired cognitive control in HED individuals. 
+
+Overwhelming evidence indicates that cognitive control is subserved by a distributed, but predominantly frontal, cortical network [ , , , ]. It has been established that AUD is associated with a range of impairments in the cognitive domain and neurophysiological changes in the frontal lobes [ , , , ]. This degradation of prefrontal functions results in impaired decision making and self-control which further contribute to the development of alcohol dependence [ , , , ]. The Stroop task probes top-down regulation by necessitating the suppression of prepotent responses in favor of controlled processing. The sensitivity of prefrontal regions to the effects of acute alcohol on response interference has been shown in previous imaging studies [ , ]. Results of the present study additionally indicate cognitive control deficits in young adult HEDs. This supports existing evidence that self-control impairments can contribute to excessive drinking [ , , ]. Indeed, automatic modes of processing are associated with the increased risk of heavy alcohol use [ ], its maintenance across time [ ], and relapse in abstinent alcoholics [ ]. 
+
+Our study revealed group differences in thalamic activity with HEDs showing greater sensitivity to response interference. The increased activation was positively correlated with the number of binge episodes. Similar results were reported by Campanella and colleagues [ ], with increased thalamic activity in HEDs during working memory in association with drinking levels. In alcohol-dependent participants, increased thalamic activity was observed during an auditory go/nogo [ ] and multisensory Stroop task [ ], but lower activity during working memory [ ]. Indeed, there is growing evidence that the thalamus plays a modulatory role in integrating activity across different levels of the neuraxis and that it contributes to cognitive control and flexible action selection [ , , , , ]. In the present study, HED participants not only exhibited greater thalamic activity, but they also rated the task as being significantly more difficult and responded with longer RTs than the LED subjects indicating that the task imposed increased cognitive demands. It is likely that the increased difficulty was accompanied by heightened arousal [ , ] which is in part subserved by the thalamus [ , ]. Another possibility is that the increased activation of the thalamus reflects neuroadaptive changes resulting from frequent bouts of heavy drinking [ , ]. The thalamus is sensitive to long-term excessive alcohol use and is implicated in a range of deficits across distributed neural circuits [ ]. Smaller thalamic volumes are predictive of subsequent relapse and alcohol intake in chronic alcoholics [ ]. Thalamic hyperactivity could, therefore, reflect its sensitivity to protracted heavy use and its compensatory engagement during increased response conflict difficulty [ ]. 
+
+Measures of impulsivity and disinhibition were positively correlated with alcohol intake variables in the present study, confirming well-established associations between AUD and traits of impulsivity, hyperactivity, and sensation/novelty seeking [ , , , ]. Dysregulation of impulse control underlies the inability to maintain inhibitory control over drinking, which has been considered fundamental to addiction [ , , , , ]. Findings suggest that the vulnerability to alcoholism shares a common genetic component with a cluster of impulsivity traits that may predispose individuals to AUD [ , , ]. Recent evidence converges on dopaminergic modulation of impulsive behavior [ , ], suggesting that the same genetic pathways may mediate both addiction and impulsivity [ , ]. Overall, these findings are strongly suggestive of shared genetic vulnerability to AUD and externalizing traits. The present study cannot speak to the issue of possible pre-existing characteristics of the HED sample unrelated to drinking levels that may have resulted in the observed group differences. However, the behavioral and brain activity findings are correlated with a range of alcohol intake measures. 
+
+Moreover, findings across different studies indicate that the compensatory activity increase may reflect deficits as a function of alcohol-induced neurotoxicity. In a working memory task, Campanella and colleagues [ ] reported positive correlation between alcohol consumption and activity in the dorsomedial prefrontal cortex in binge drinkers. Wetherill and colleagues [ ] tracked the development of alcohol habits in young adults and similarly reported a positive relationship between alcohol-induced blackouts and increased prefrontal on a response inhibition task. In a study of alcohol-dependent participants, lifetime alcohol consumption was predictive of activity in the posterior cingulate cortex and midbrain during a multisensory Stroop task [ ]. Taken together, the evidence suggests that the disinhibitory traits are implicated in drinking initiation and maintenance via impaired self-control, but that the group differences in neural function may at least in part reflect alcohol-induced neurotoxicity. 
+
+The development of addiction is an exceedingly complex process mediated by environmental risk factors, and interindividual variability [ ] due in part to a person’s genetic makeup [ , , , , ]. However, heavy episodic drinking is associated with deficits in neural functioning in response to conflict-inducing situations with increased vulnerability in adolescents and emerging adults [ , , ]. These neural compromises are often not detectable by behavioral measures but can be revealed by measures of neural function [ , , ] and may signify early trajectory toward compulsive intake characterizing AUD [ , , ]. Given that individuals who engage in most hazardous binge drinking are young and vulnerable to neurotoxicity, it is of paramount importance to better understand the neural indices associated with excessive drinking. Such insights may inform development of therapeutic, personally tailored approaches and prevention strategies. 
+
+ ",29300304,pubget
+3242169.0,Behavioral Risk Elicits Selective Activation of the Executive System in Adolescents: Clinical Implications,"risk
+behavioral risk
+decision making
+reward
+adolescence
+prefrontal brain regions
+reward response
+nucleus accumbens
+"," 
+We investigated adolescent brain processing of decisions under conditions of varying risk, reward, and uncertainty. Adolescents (  n   = 31) preformed a Decision–Reward Uncertainty task that separates decision uncertainty into behavioral and reward risk, while they were scanned using functional magnetic resonance imaging. Behavioral risk trials involved uncertainty about which action to perform to earn a fixed monetary reward. In contrast, during reward risk the decision that might lead to a reward was known, but the likelihood of earning a reward was probabilistically determined. Behavioral risk trials evoked greater activation than the reward risk and no risk conditions in the anterior cingulate, medial frontal gyrus, bilateral frontal poles, bilateral inferior parietal lobe, precuneus, bilateral superior-middle frontal gyrus, inferior frontal gyrus, and insula. Our results were similar to those of young adults using the same task (Huettel,  ) except that adolescents did not show significant activation in the posterior supramarginal gyrus during behavioral risk. During the behavioral risk condition regardless of reward outcome, overall mean frontal pole activity showed a positive correlation with age during the behavioral and reward risk conditions suggesting a developmental difference of this region of interest. Additionally, reward response to the Decision–Reward Uncertainty task in adolescents was similar to that seen in young adults (Huettel,  ). Our data did not show a correlation between age and mean ventral striatum activity during the three conditions. While our results came from a healthy high functioning non-maltreated sample of adolescents, this method can be used to address types of risks and reward processing in children and adolescents with predisposing vulnerabilities and add to the paucity of imaging studies of risk and reward processing during adolescence. 
+ "," 
+## Introduction 
+  
+Adolescence represents a period of decision making that involves increased risk taking. Risk taking is defined as engaging in behaviors that may be high in subjective desirability (i.e., associated with high perceived reward) but which expose the individual to potential injury or loss (Geier and Luna,  ). Examples of adolescent risk-taking include initiating use of alcohol and other addictive drugs (resulting in addiction) or engaging in unprotected sex (resulting in teenage pregnancies). The known increases in adolescent risk behaviors are observed across cultures (Spear,  ) and associated with less mature prefrontal inhibitory control circuits (Ernst et al.,  ). Adolescent risk taking is a major public health concern whose negative results can lead to impaired maternal–infant interactions due to addictions and/or teen parenting. However, some risk taking may be normative, in that it allows for exploration of adult roles and for development of relevant coping skills (Siegel and Shaughnessy,  ; Spear,  ; Dahl,  ; Kelley et al.,  ; Geier and Luna,  ). Consequently, the neurobiological study of adolescent decision and reward processing using functional magnetic resonance imaging (fMRI) is timely. 
+
+Brain imaging studies have demonstrated that adolescents exhibit less activation in executive brain regions during decision making in gambling tasks than adults, which suggests an immaturity of these regions during adolescence (Eshel et al.,  ; Ernst and Mueller,  ). In this investigation, we examined the neurodevelopmental maturity of adolescents using a novel task designed to challenge the dorsal lateral prefrontal executive control and ventral medial prefrontal reward circuits (Huettel,  ). This Decision–Reward Uncertainty task separates decisions into behavioral risk and reward risk (Huettel,  ). The Decision–Reward Uncertainty Task represents an innovative approach to understanding decision making and reward. While most decision-making tasks used in addiction research combine decision making, response, and reward evaluation in time, the Decision–Reward Uncertainty Task was designed to examine decision making and reward circuits separately in one task (see Figure  ). Decision-making circuits involve a set of brain structures: prefrontal cortex; dorsolateral prefrontal cortex; parietal cortex; insular cortex; and anterior and posterior cingulate (Paulus et al.,  ; Huettel,  ). Reward circuits involve a set of brain structures that receive dopaminergic input from the midbrain and include the ventral striatum (Vstr; which includes the nucleus accumbens), and ventromedial prefrontal cortex (Schott et al.,  ). 
+  
+ (A)   Geometric shape cues, button press response(s), and probabilities of reward for each risk condition. No risk cues (left button press on right hand for a star, or right button press on right hand for a square) signaled that the known behavioral response would be rewarded with 100% certainty. Reward risk cues (right button press for a trapezoid, or left button press for a circle) signaled that the known behavioral response would be rewarded with 50% probability. However, the behavioral risk cue (a triangle) signaled that the behavioral response was unknown; on each trial, either one of the two possible responses would be guaranteed a reward (“$ or $$ (not shown))” while the other would not.   (B)   Sequence of events used in each trial of the reward uncertainty task. A shape cue marked the start of each trial. After a fixed interval, a response prompt was presented. Participants were asked to press one of two buttons using their right hand as soon as the prompt appeared to make their choice. The outcome of each trial was determined by both (a) a correct right or left button response and (b) a probabilistically determined reward. Thus, each trial began with a shape cue for 250 ms in the center of the screen that indicated the trial type. After a 3-s delay, participants were prompted (i.e., “?”) for 1 s to indicate their choice with a left or right button press with the second or third finger on the right hand. After a jittered delay (1, 3, 5, or 7 s) where the fixation cross was presented, the trial outcome (reward: “$” for no and reward risk, reward: “$$”(not shown here) for behavioral risk, or no reward: “×”) was presented for 1 s, and an updated tally of cumulative earnings was displayed in the lower portion of the screen. A fixation cross was displayed in the center of the screen during a jittered inter-trial interval (2, 4, 6, or 8 s). Participants completed 150 trials on average, split evenly among six 6-min runs. Optimal performance could yield up to an additional $25 (e.g., $0.15 per correct response; for one dollar sign; $0.30 for two dollar signs). 
+  
+The Decision–Reward Uncertainty task is an advance because most previous research failed to differentiate decisions into risk types (i.e., reward risk versus behavioral risk) and reward response (Bolla et al.,  ; Huettel et al.,  ; Verdejo-Garcia et al.,  ) Thus, in most studies, decision making (also called response selection) was contingent in time upon reward and not separated from reward delivery (Xiangrui et al.,  ). Reward risk is defined as certainty about behavior but uncertainty about possible outcomes (i.e., reward presence). In other words, one knows what actions to take for a reward but the probability of reward is not certain. Reward risk activates reward circuits in the ventromedial prefrontal cortex, striatum, and other subcortical components of reward networks (Huettel,  ). Behavioral risk is defined as uncertainty about which decisions and actions should be taken to earn a reward or achieve a desired goal. Under these conditions, one does not know what actions to take for a reward. The Decision–Reward Uncertainty Task examines three types of risk: reward risk, behavioral risk, and no risk. In reward risk trials, the action required to earn a reward is known, but the outcome of each trial is probabilistic. In behavioral risk trials, there is limited knowledge about which action to take (i.e., button to press), and the participant chooses between two possible button presses, one of which randomly determines a reward on that trial. The only difference between these conditions is whether a subject knows the correct action (reward risk) or not (behavioral risk). In other words, in reward risk, the decision and action to take are certain and in behavioral risk, the decision and action to take are uncertain. The Decision–Reward Uncertainty Task includes a no risk or certainty condition as a control, where the action required to earn a reward is known and reward is certain. 
+
+While undergoing the Decision–Reward Uncertainty Task during the behavioral risk condition, healthy young adults activated executive-control circuits including the prefrontal, parietal, and insular regions, within which no effect of reward risk was observed (Huettel,  ), Reward delivery, in comparison to no reward, evoked increased activity in the ventromedial prefrontal cortex and the Vstr which includes the nucleus accumbens (Huettel,  ). In healthy young adults undergoing this task, reward risk activated nucleus accumbens and ventromedial prefrontal cortex suggesting that distinct brain systems are recruited for the resolution of these different forms of risk (Huettel,  ). 
+
+However, the Decision–Reward Uncertainty Task results were derived from samples of young-adult participants, and it is not clear whether they generalize to adolescence, when the prefrontal cortex is actively undergoing maturational changes. Indeed, the dorsolateral prefrontal cortex completes its pruning of gray matter only toward the end of adolescence (Gogtay et al.,  ). Consequently, conclusions about decision processes derived from adult samples may not generalize well to adolescents. Given the differences in behavior and levels of brain maturation in adolescents and adults, an important question for current research is whether these differences are evident in both behavioral and reward risk, and their brain circuitry regions of interest. To date, previous studies have not investigated how different types of risk are represented in adolescent executive and reward networks. This is a potentially important distinction, because it may have social and policy implications. We hypothesize that in adolescents, behavioral risk will activate executive-control circuits their associated functional regions of interests while reward risk will activate reward circuits and their associated functional regions of interest as suggested in the Huettel ( ) study. However, in this study, we wished to examine in adolescents the neural correlates of decision making with respect to reward and behavioral risks. Furthermore, we predict an association with executive control and reward circuits regions of interest and age. 
+
+
+## Materials and Methods 
+  
+### Participants 
+  
+Thirty-one healthy adolescents (mean age and SD: 15.5 ± 1.5 years; age range: 12.3–17.7 years; 21 females, 10 males) participated in a detailed clinical research assessment, and then engaged in the Decision–Reward Uncertainty Task while undergoing fMRI on another day. There were no gender or age differences (mean age females 15.5 ± 1.6, mean age males 15.6 ± 1.2 years:   F   = 0.01, df = 21,   p   = 0.94) in the control group. Healthy adolescent participants were recruited from the community by IRB approved advertisements. Adolescents provided written assent and legal guardians provided written informed consent before participation. Male and females did not differ in handedness, IQ, or socioeconomic status. All participants came from a range of socioeconomic environments (middle to upper socioeconomic strata). 
+
+The clinical assessment portion of the study was undertaken at the Healthy Childhood Brain Development Developmental Traumatology Research Program and included interviews of both adolescents and their legal guardians using the Schedule for Affective Disorders and Schizophrenia for School Aged Children Present and Lifetime Version (KSADS-PL), which includes a comprehensive post-traumatic stress disorder interview (Kaufman et al.,  ). This semi-structured interview was administered to caregivers and adolescents. We also used archival records as additional sources of information. The KSADS-PL was modified to include additional information about: (1) life events, including traumatic events from the Child and Adolescent Psychiatric Assessment (Angold et al.,  ); and (2) disorders not present in the KSADS-PL. Modifications also included: (3) an added structured scale to quantify symptom frequency with a minimum score of 0 = no history of a symptom and maximum score of 10 = symptoms present several times a day; and (4) algorithms to determine Axis I psychiatric disorders based on DSM-IV criteria. Disorders were assigned a severity score of mild, moderate, or severe. This modified version is available upon request. Interviewers were individually trained to obtain over 90% agreement for the presence of any lifetime major Axis I disorder with a board certified child and adolescent psychiatrist and experienced child trauma interviewer (MDDB). Discrepancies were resolved by reviewing archival information (e.g., school records, birth, and pediatric medical records) or by re-interviewing the child or caregiver. If diagnostic disagreements were not resolved with this method, consensus diagnoses were reached among a child psychiatrist (MDDB) and child psychologist (SRH). Subjects also underwent extensive neuropsychological testing to verify that they were age-typical. This included a two-subtest short-form of the Wechsler Intelligence Scale for Children-III (WISC-III; Wechsler,  ) comprised of Vocabulary and Block Design, to generate an IQ score. Mean IQ was 113.1 ± 11.0 (IQ range 90–132). Adolescents also received saliva and urine toxicology screens to confirm the absence of alcohol, tobacco, or other drug use on the day of interview and imaging data collection. Participants with an Axis I diagnosis, who were not age-typical on neuropsychological testing or had a positive alcohol or drug screen, were excluded. 
+
+Exclusion criteria for subjects were: (1) current or lifetime history of DSM-IV Axis I psychiatric disorders including alcohol and substance use disorders, (2) significant medical, neurological, or psychiatric disorder, (3) history of head injury or loss of consciousness, (4) pregnancy, (5) history of prenatal or birth confounds that could have influenced brain maturation such as significant prenatal exposure to substances, severe birth complications, or birth weight under 5 lb or severe postnatal compromise with neonatal intensive care unit (NICU) stay; (6) morbid obesity or growth failure, (7) full scale IQ lower than 90, (8) history of trauma or child maltreatment, or (9) contraindications to safe participation in MRI research. The Institutional Review Board of the Duke University Medical Center approved this study. 
+
+
+### Experimental design 
+  
+We used an experimental paradigm, the Decision–Reward Uncertainty task, that we have used previously to examine neural correlates of risky decision making in young-adult participants (Huettel,  ). Critically, the task was designed to temporally isolate three phases of decision making: (1) choice selection, (2) action execution, and (3) outcome or reward evaluation (Ernst and Paulus,  ; Rangel et al.,  ). Our analyses focus on the initial choice selection and outcome evaluation phases of decision making. 
+
+In this task, we manipulated two types of risk: Reward risk and behavioral risk. In reward risk trials, the action required to earn a reward was known to the participant, but the outcome of each trial was probabilistic: if the correct button was pressed, there was a 50% probability of a reward. In behavioral risk trials, the participant chose between two possible button presses, one of which (randomly determined) guaranteed a reward on that trial. Note that the behavioral risk and reward risk conditions were matched on probability and expected value, in that each contained a 50% chance of receiving a constant-size reward. The only difference between these conditions was in whether the participant knew the correct action (reward risk) or not (behavioral risk). We also included a no risk condition as a control. In the no risk condition, the action required to earn a reward was known and the likelihood of earning a reward was certain. 
+
+Each condition was represented by a visual cue (square, star, circle, trapezoid, or triangle) and mapped directly to a response [left (second digit) or right (third digit) button press with right hand; see Figure  A]. No risk cues (left button press on right hand for a star, or right button press on right hand for a square) signaled that the known behavioral response would be rewarded with 100% certainty. Reward risk cues (right button press for a trapezoid, or left button press for a circle) signaled that the known behavioral response would be rewarded with 50% probability. However, the behavioral risk cue (a triangle) signaled that the behavioral response was unknown; on each trial, either one of the two possible responses would be guaranteed a reward while the other would not. Each trial (Figure  B) began with a shape cue for 250 ms in the center of the screen that indicated the trial type. After a 3-s delay, participants were prompted (i.e., “?”) for 1 s to indicate their choice with a left or right button press with the second or third finger on their right hand. After a jittered delay (1, 3, 5, or 7 s) where the fixation cross was presented, the trial outcome (reward: “$” for no risk, and “$$” for behavioral risk or reward risk, or no reward: “×”) was presented for 1 s, and an updated tally of cumulative earnings was displayed in the lower portion of the screen. A fixation cross was displayed in the center of the screen during a jittered inter-trial interval (2, 4, 6, or 8 s). Participants completed 150 trials on average, split evenly among six 6-min runs. As a developmental adaptation for the younger adolescents, the duration of each run was reduced to six 6-min runs for the adolescent group from the 10 min used in the young-adult group (Huettel,  ). This adaptation greatly improved adolescent cooperation with the task. Optimal performance could yield up to an additional $25 (e.g., $0.15 per correct response; for one dollar sign; $0.30 for two dollar signs) above the regular compensation for participation. Participants were trained on the task’s cue–response contingencies in a prior behavioral testing session before scanning. To minimize practice or learning effects, all subjects practice the task until they showed that they had mastered the rules of the Decision–Reward Uncertainty task. 
+
+The experiment was programmed in MATLAB (MathWorks ) using the Psychophysics Toolbox (Brainard,  ). Stimuli were displayed on goggles at a video resolution of 800 × 600 pixels and an apparent field of view of approximately 20°. Responses were collected on a four-button box, where only the first two buttons were used. 
+
+
+### Image acquisition 
+  
+The fMRI data for our adolescent participants were acquired using a 3.0-T General Electric (Waukesha, WI, USA) scanner. Whole-brain images sensitive to blood-oxygenation-level-dependent (BOLD) contrast were acquired using a high-throughput   -weighted spiral-in pulse sequence (TR = 2 s, TE = 28 ms, flip angle = 90°, 34 slices, voxel size: 3.75 mm × 3.75 mm × 3.8 mm). Data were acquired in a series of six sessions, each comprising 180 volumes. We additionally acquired whole-brain high-resolution images using a T -weighted 3D spoiled gradient-recalled sequence to aid in normalization and registration of the functional images. 
+
+
+### fMRI data analysis 
+  
+Functional images were analyzed using fMRI Expert Analysis Tool (version 5.98, Analysis Group, FMRIB, Oxford, UK). These images were corrected for slice acquisition time (interleaved ascending), corrected for motion with MCFLIRT, normalized into the standard Montreal Neurological Institute stereotaxic space (MNI, Montreal, QC, Canada), and subjected to a high-pass filter (pass frequency > 1/100 Hz). FSL’s Brain Extraction Tool (BET) was used to exclude non-brain voxels from our analyses. Four volumes from the start of each session were discarded to allow image intensity to stabilize. First-level (i.e., within-run) regression analyses included three regressors time-locked to the onset of the decision phase, defined as first second from the onset of the stimulus, of each trial type (behavioral risk, reward risk, and no risk), one nuisance regressor for all responses, and one nuisance regressor for missed responses. Second-level analyses collapsed across runs, within each subject, using a fixed-effects model. Across-subjects comparisons used a random-effects model that included an additional regressor for between-group comparisons. All reported results, including figures and tables, show activation that survived a whole-brain cluster family wise error (FWE) correction with a voxelwise   z  -statistic threshold of 2.3 (  p   ≤ 0.01). 
+
+To examine the relationship between maturation and brain region of interests (ROI), we used mean ROI BOLD activity of brain regions which showed significant differences in our third level analyses and correlated these with age using parametric statistics (Pearson’s correlations) and jmp 9.0.2  (2010 SAS Institute Inc). Before Pearson’s correlations were applied, data was tested for fit to the normal distribution using the Goodness of Fit Test (i.e., Shapiro–Wilk   W   Test) in jmp. 
+
+
+
+## Results 
+  
+### Behavioral analysis 
+  
+Only correct responses performed within a 1-s window after the response prompt (e.g., “?”) was displayed were included in the analyses. Mean response times were analyzed by condition: no risk (Mean = 0.448, SD = 0.082 s), reward risk (Mean = 0.453, SD = 0.082 s), and behavioral risk (Mean = 0.451, SD = 0.091 s). Response times were submitted to a repeated measures analysis of variance and showed no main effect of condition,   F  (2, 29) = 0.373,   p   = 0.692. 
+
+
+### Regions activated by behavioral risk in adolescents 
+  
+To identify the brain regions that support decision making under behavioral risk, we contrasted activation associated with decisions in the behavioral risk (i.e., choice selection) condition with the mean activation associated with decisions in the no risk and reward risk conditions. In our adolescent sample this contrast elicited significant activations in brain regions typically implicated in risky decision making: anterior cingulate, medial frontal gyrus, bilateral frontal poles and inferior parietal lobe, precuneus, bilateral superior-middle frontal gyrus, inferior frontal gyrus (IFG), and insula (Figure,  ; Table   reports the peak voxels present using the   z  -statistic threshold of 2.3). Within these significant cluster of regions also included the dorsal lateral prefrontal cortex, anterior insula, and lateral parietal regions. This pattern of activation replicates the key results from the adult sample described by Huettel ( , indicating that adolescents activated the same decision-making network as adults during decisions involving behavioral risk. Mean percent signal change (SE) associated with the no risk (NR), reward risk (RR), and behavioral risk (BR) conditions are shown in Figure  . Signal was extracted from two regions of interest: (Figure  A) anterior cingulate and (Figure  B) frontal pole. 
+  
+ Whole-brain analysis of decisions involving behavioral risk (BR) greater than reward risk (RR) and no risk (NR) conditions in adolescents  . Behavioral risk elicited patterned activation in the insula, frontal poles (FP) and middle frontal gyrus, anterior cingulate (aCC), superior frontal gyrus, medial prefrontal cortex, precuneus (pCun), and Inferior Parietal Lobule. Within these significant cluster of regions also included the dorsolateral prefrontal cortex (dlPFC), anterior insula (alns), and lateral parietal regions (LPar). 
+    
+ Cluster and subcluster activations for the decision phase: behavioral risk > (no risk + reward risk)  . 
+  
+ Shown for each cluster of significant activation (  Z   > 2.3) are the coordinates (mm  within standard Montreal Neurological Institute stereotaxic space (MNI) space) of the peak voxel within that cluster  . 
+
+ L, left, R, right  . 
+    
+ Mean percent signal change (SE) associated with the no risk (NR), reward risk (RR), and behavioral risk (BR) conditions  . Signal was extracted from two regions of interest:   (A)   anterior cingulate and   (B)   frontal pole. 
+  
+
+### Regions activated by outcome evaluation 
+  
+To distinguish the brain regions that responded to rewarding outcomes from those activated during decisions involving risk, we contrasted trials in both the reward risk and behavioral risk conditions that led to a rewarding outcome with those trials that led to no reward. When using   z  -statistic threshold = 2.3, this contrast elicited large clusters of significant activations in three brain regions; (1) the IFG, middle frontal gyrus, and its sublobar areas; (2) the cingulate gyrus; and (3) the middle occipital gyrus. These include subclusters typically implicated in decision and reward processing: Vstr, which includes the nucleus accumbens and caudate, and putamen, and additionally activated the global pallidus and IFG, middle frontal gyrus, posterior cingulate, and large regions in the visual cortex. Because the peak activations in these regions were so large, we manually identified the subcluster in the Vstr and global pallidus. This is shown in Figure  A and Table  . 
+  
+ (A)   Whole-brain analysis of the outcome phase of the task for rewarded greater than unrewarded trials in the reward risk (RR) and behavioral risk (BR) conditions. Rewarded trials elicited greater activation in regions typically implicated in reward processing, such as the ventral striatum (Vstr), which includes the nucleus accumbens and caudate, and putamen, and additionally activated the global pallidus and inferior frontal gyrus (IFG). The nucleus accumbens was based on the standard ROI for the nucleus accumbens subcortical region as defined by the Harvard-Oxford atlas within FSL and is shown in green.   (B)   Mean percent signal change extracted from an anatomically defined ROI in the Vstr. Mean percent signal change (SE) is plotted for rewarded no risk (NR), reward risk (RR), and behavioral risk (BR) outcomes, as well as unrewarded reward risk and behavioral risk outcomes. 
+    
+ Peak activations for the outcome phase: peak activations for reward > no reward  . 
+  
+ Shown is each cluster and subclusters of significant peak activations (Z > 2.3). The coordinates (mm ) are within standard Montreal Neurological Institute stereotaxic space (MNI) of the peak voxel within that cluster  . 
+
+ L, left, R, right  . 
+  
+To examine the evoked activation in response to reward, we extracted the mean percent signal change in both rewarded and unrewarded conditions using an anatomically defined ROI in the Vstr which includes the nucleus accumbens (which was superimposed in green in Figure  A). The nucleus accumbens was based on the standard ROI for the nucleus accumbens subcortical region as defined by the Harvard-Oxford atlas within FSL and is shown in green. Mean percent signal change in response to rewarded and unrewarded outcomes were calculated for each level of risk (Figure  B). 
+
+
+### Correlations of regions of interest with age 
+  
+During the behavioral risk condition regardless of reward outcome, overall mean frontal pole BOLD activity showed a positive Pearson’s correlation with age (  F   = 11.4, df = 29,   p   = 0.002). See Figure  A. Reward risk similarly showed a positive Pearson’s correlation with age (  F   = 4.7, df = 29,   p   < 0.04). See Figure  B. These correlations suggest developmental differences during different types of decision making in the frontal pole with increasing age. We did not see significant correlations between age and mean anterior cingulate or mean Vstr BOLD activity with age. 
+  
+ (A)   During the behavioral risk condition, regardless of reward outcome, overall mean frontal pole BOLD activity showed a positive Pearson’s correlation with age (  F   = 11.4, df = 29,   p   = 0.002).   (B)   During the Reward risk condition, regardless of reward outcome overall mean frontal pole BOLD activity showed a positive Pearson’s correlation with age (  F   = 4.7, df = 29,   p   < 0.04). These correlations suggest developmental differences, during two different types of decision making, in the frontal pole with increasing age. 
+  
+
+
+## Discussion 
+  
+We investigated functional brain activity in high functioning healthy adolescents while they performed the experimental Decision–Reward Uncertainty task (Huettel,  ). We had two primary goals: to evaluate whether adolescents recruited the same decision-making network as young adults, and to examine whether the form of risk modulated these networks. Our goal was also to examine reward circuits and their regions of interest using the same simple task. Furthermore, we wanted to examine the association with executive control and reward circuits regions of interest with maturation measures (e.g., age). Our analyses focused on the decision making or choice selection and outcome evaluation phases of decisions that involved behavioral risk (i.e., decision making under uncertainty). Decision making during the task elicited activation in executive-control regions typically implicated in studies of adult decision making: frontal poles, anterior cingulate, superior, middle and medial prefrontal gyrus, precuneus, inferior parietal cortex, and insula (Huettel,  ). Behavioral risk trials, however, evoked greater activation than the other conditions in the anterior cingulate, dorsal lateral prefrontal cortex, frontal gyrus, frontal poles, inferior parietal lobe, precuneus, and anterior insula. Our results were similar to those of young adults using the same task (Huettel,  ) except that adolescents did not show significant activation in the posterior supramarginal gyrus, a brain area involved in vocabulary and declarative memory (Lee et al.,  ), during behavioral risk. Our task does involve working memory (Huettel,  ), a process that matures during adolescence. These findings show that choice selection during decisions involving behavioral risk elicits a network of brain regions including those that are involved in conflict monitoring (anterior cingulate; Kerns et al.,  ), visual attention (occipito-parietal cortex; Konrad et al.,  ), working memory and decision making (dorsolateral prefrontal cortex; Kwon et al.,  ; Huettel et al.,  ; Konrad et al.,  ), and interpreting the emotional significance and the intensity of stimuli (insula; for review see Ernst and Paulus,  ). 
+
+In our results, reward versus no reward elicited significant activations in brain regions typically implicated in decision and reward processing (i.e., Vstr, inferior frontal, gyrus, anterior to middle cingulate, posterior cingulate, and visual cortex). Similar research findings were seen in studies of reward processing in primates (Apicella et al.,  ; Schultz et al.,  ; Roesch and Olson,  ) and adults (O’Doherty et al.,  ; Delgado et al.,  ; Elliott et al.,  ). Reward response to the Decision–Reward Uncertainty task was similar to that seen in young adults using this task (Huettel,  ). There are few imaging studies of reward processing during adolescence. Those investigations also implicate neurocircuitry similar to those reported during reward response to the Decision–Reward Uncertainty task here, in that differences in BOLD activity were seen in visual cortex, Vstr, and anterior to middle cingulate during reward processing in adolescents (Bjork et al.,  ,  ; May et al.,  ; Ernst and Paulus,  ; Galvan et al.,  ; Eshel et al.,  ). 
+
+However, the reward response processing during adolescence is controversial, where some investigators believe the adolescent reward processing circuitry is hyporesponsive to rewards compared to those of adults (Spear,  ), while others believe adolescents’ reward circuits are hyperresponsive to rewards compared to adults (Chambers et al.,  ; Ernst et al.,  ). In a hypoactive reward processing system, brain areas that process rewards are not recruited as strongly as they are in adults. Our data did not provide support for this theory in that during the no risk condition, mean Vstr activity showed no positive correlation with age in carefully screened and comprehensively assessed healthy adolescents. 
+
+Decision making and reward processing in clinical populations is vastly understudied. Although speculative, a developmentally decreased sensitivity to executive function such as decision making under conditions of uncertainty may, in vulnerable adolescent populations, contribute to differences in reinforcement-related learning that lead to adolescent onset alcohol and substance use disorders (for review see Spear,  ). For example, adult studies have also shown reduced activation in control and reward processes in abstinent cannabis users (Martin-Santos et al.,  ). Additionally, childhood adverse life events are associated with basal ganglia hyporesponse during fMRI evaluation of reward (Dillon et al.,  ; Mehta et al.,  ) which may further contribute to the known risk for adolescent onset alcohol and substance use disorders seen in victims of maltreatment (Anda et al.,  ; Kilpatrick et al.,  ). Preclinical studies suggest stress in young animals lowers dopamine D2 receptors in reward regions (Papp et al.,  ; Morgan et al.,  ), making animals and humans more vulnerable to addiction (De Bellis,  ). One pediatric study, however, showed that while undergoing The Wheel of Fortune task, maltreated children with depression selected safe over risky options more frequently in the high-risk condition than did control children (i.e., they avoided selecting a large reward paired with a low chance of winning compared with maltreated children without depression and non-maltreated controls; Guyer et al.,  ). These limited data suggest that the effects of early familial adverse experiences or familial vulnerability on development of decision making and reward evaluation require further study as immaturity in executive decision making or reward systems may lead to substance use disorders and thus negatively influence the quality of care an addicted parent is able to provide. 
+
+Adolescence is a period during which the constituents of cognition develop to enable adaptive goal-directed behavior (for review see Ernst and Mueller,  ). However, the emotional intensity also associated with adolescence influences the response to rewards that may contribute to increased risk-taking behaviors. Another model of adolescent reward processing suggests that adolescents demonstrate a heightened sensitivity to rewards and over active reward system. This “triadic model” proposes three behavioral control systems (approach, avoidance, and supervisory control systems) that differ between adolescents and adults (for review see Ernst et al.,  ). In other words, normative maturational increases in dopamine neurotransmitter activity in the fronto-striatal “motivational” system coupled with relatively lower levels of inhibitory (e.g., serotoninergic) mechanisms in prefrontal systems contribute to increased reward sensitivity in adolescents (Chambers et al.,  ) and the known increases in normative adolescent risk behaviors (Dahl,  ; Kelley et al.,  ). Thus the hyper-responsivity reward processing theory suggests that an overactive Vstr is unchecked by immature prefrontal inhibitory mechanisms. In our study, the behavioral and reward risk conditions regardless of reward outcome, showed a positive correlation with age and overall mean frontal pole activity. During the reward risk condition, mean frontal pole activity also showed a positive correlations with age. Our data suggest that the prefrontal system is immature at younger ages regardless of type of risk (behavioral or reward risk) and provide no direct support for the hyper-responsivity reward processing theory. However, immaturity of prefrontal executive supervisory control systems alone may account for dysregulation of reward processing during adolescence. Its activity matures from childhood to adulthood in parallel with increased capacity for adults to make healthy mature decisions (Eshel et al.,  ). 
+
+The relationship between reduced frontal pole activation and younger age during both the behavioral and reward risk conditions may mean that less reinforced risky rewards signal the availability of reinforcement in adolescents. As greater reward was associated with greater risk in this task, a developmentally immature and less active executive system could push adolescents toward greater risk taking. Such an interpretation is consistent with findings from Bjork et al. ( ) which showed adolescents have diminished striatal activation when they are   anticipating   responding for gains, but not upon receipt of reward. Thus, adolescents may experience more risky uncertain intermittent reinforcers as more rewarding compared to adults. The more salient the reward, the more likely a prefrontal dopaminergic response will occur that is sufficient to facilitate the formation of a conditioned association. Hence immaturity in executive-control neuro-maturational systems may put an adolescent at increased risk for substance use disorders and other types of risk-taking behaviors such as suicide attempts (Shaffer and Hicks,  ; Costello et al.,  ). The data reported here are more consistent with the theory of Geier and Luna ( ), which states that adolescent risk taking may be best understood as an imbalance between inhibitory control, working memory, and reward systems that is biased toward short term goals (Geier and Luna,  ). However, while our data do suggest increased activity of the frontal pole with age during decision making, these data do not suggest any association with age and Vstr during reward evaluation. 
+
+Our data have several limitations. We studied only very healthy high functioning adolescents. Therefore, our results may not be generalizable to population-based samples. Due to our sample size, we were unable to examine for gender differences. Additionally we did not study adults using the same task parameters so we were unable to directly compare healthy adolescent responses to behavioral risk with those of adults. However, although, we did not do physical examinations for pubertal stage, we were able to associate a proxy measure of maturity (i.e., age) with a decision-making brain ROI. 
+
+While our results came from a healthy high functioning non-maltreated sample of adolescents, they point to the power of using a simple task (i.e., Decision–Reward Uncertainty task) for addressing types of risks and reward processing in children and adolescents with predisposing vulnerabilities. Given that the ability to evaluate risk and reward is a maturational process, it is important to examine the effects of early life stressors on these abilities. Conditions associated with maladaptive decision making and reward evaluation (e.g., substance use disorder) come to the fore during adolescence. A better understanding of the developmental progression of decision and reward networks will lead to more refined targets both for future research and for interventions. 
+
+
+## Conclusion 
+  
+We investigated functional brain activity in high functioning healthy adolescents while they performed the experimental Decision–Reward Uncertainty task (Huettel,  ). Our analyses focused on the decision making or choice selection and outcome evaluation phases of decisions that involved behavioral risk (i.e., decision making under uncertainty). Behavioral risk trials evoked greater activation than the reward risk and no risk conditions in the anterior cingulate, medial frontal gyrus, dorsal lateral prefrontal cortex, bilateral frontal poles and inferior parietal lobes, precuneus, bilateral superior-middle frontal gyrus, IFG, and anterior insula. Our results were similar to those of young adults using the same task during behavioral risk (Huettel,  ). During the behavioral and reward risk conditions regardless of reward outcome, overall mean frontal pole activity showed a positive correlation with age during the behavioral and reward risk conditions suggesting a developmental immaturity of this ROI. Additionally, reward response to the Decision–Reward Uncertainty task in adolescents was similar to that seen in young adults (Huettel,  ). While our results came from a healthy high functioning non-maltreated sample of adolescents, this method (i.e., Decision–Reward Uncertainty task) can be used to address types of risks and reward processing in children and adolescents with predisposing vulnerabilities and add to the paucity of imaging studies of risk and reward processing during adolescence. 
+
+
+## Conflict of Interest Statement 
+  
+The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. 
+
+ ",22194728,pubget
+7821103.0,Polymorphisms of the μ‐opioid receptor gene influence cerebral pain processing in fibromyalgia,," 
+## Background 
+  
+Dysregulation of the μ‐opioid receptor has been reported in fibromyalgia (FM) and was linked to pain severity. Here, we investigated the effect of the functional genetic polymorphism of the μ‐opioid receptor gene (OPRM1) (  rs1799971  ) on symptom severity, pain sensitivity and cerebral pain processing in FM subjects and healthy controls (HC). 
+
+
+## Methods 
+  
+Symptom severity and pressure pain sensitivity was assessed in FM subjects (  n   = 70) and HC (  n   = 35). Cerebral pain‐related activation was assessed by functional magnetic resonance imaging during individually calibrated painful pressure stimuli. 
+
+
+## Results 
+  
+Fibromyalgia subjects were more pain sensitive but no significant differences in pain sensitivity or pain ratings were observed between OPRM1 genotypes. A significant difference was found in cerebral pain processing, with carriers of at least one G‐allele showing increased activation in posterior cingulate cortex (PCC) extending to precentral gyrus, compared to AA homozygotes. This effect was significant in FM subjects but not in healthy participants, however, between‐group comparisons did not yield significant results. Seed‐based functional connectivity analysis was performed with the seed based on differences in PCC/precentral gyrus activation between OPRM1 genotypes during evoked pain across groups. G‐allele carriers displayed decreased functional connectivity between PCC/precentral gyrus and prefrontal cortex. 
+
+
+## Conclusions 
+  
+G‐allele carriers showed increased activation in PCC/precentral gyrus but decreased functional connectivity with the frontal control network during pressure stimulation, suggesting different pain modulatory processes between OPRM1 genotypes involving altered fronto‐parietal network involvement. Furthermore, our results suggest that the overall effects of the OPRM1 G‐allele may be driven by FM subjects. 
+
+
+## Significance 
+  
+We show that the functional polymorphism of the μ‐opioid receptor gene OPRM1 was associated with alterations in the fronto‐parietal network as well as with increased activation of posterior cingulum during evoked pain in FM. Thus, the OPRM1 polymorphism affects cerebral processing in brain regions implicated in salience, attention, and the default mode network. This finding is discussed in the light of pain and the opioid system, providing further evidence for a functional role of OPRM1 in cerebral pain processing. 
+
+ "," 
+## INTRODUCTION 
+  
+Fibromyalgia (FM) is a chronic musculoskeletal pain disorder characterized by chronic widespread pain, accompanied by tenderness and fatigue, disturbed sleep and psychological distress. FM is considered a nociplastic pain condition (Kosek et al.,  ) accompanied by altered nociception and changes in the central nervous system (Sluka & Clauw,  ). Importantly, FM has been associated with impaired pain inhibition (Kosek & Hansson,  ; Lannersten & Kosek,  ), also displayed in reduced activation of opioid‐rich regions of the pain modulatory system (Jensen et al.,  ,  ). Additionally, elevated endogenous opioids in the cerebrospinal fluid (Baraniuk et al.,  ) could be linked to reduced μ‐opioid receptor (MOR) availability in cerebral pain‐related areas (Harris et al.,  ). The aberration in the opioid system of FM patients is in accordance with the reports of microglia activation (Albrecht et al.,  ), as opioid‐induced hyperalgesia has been associated with glial activation (Roeckel et al.,  ). Finally, the interaction between endogenous opioids and MORs has been found to influence the pain experience (Zubieta et al.,  ) and play a role in chronic pain syndromes (Zorina‐Lichtenwalter et al.,  ), including FM (Schrepf et al.,  ). 
+
+Among pain‐relevant genetic polymorphisms is the MOR gene (OPRM1), which is of interest for pharmacogenetic research investigating opioids. The functional single nucleotide polymorphism A118G   (rs1799971)   of OPRM1 leads to an exchange of asparagine to aspartic acid at amino acid 40 and affects the putative N‐terminal site of the receptor. Although inconsistent findings have been reported, carriers of at least one G‐allele, compared to AA homozygotes, have been shown to exhibit higher receptor affinity for β‐endorphins but not for other endogenous opioids or opioid drugs (Mura et al.,  ). Additionally, the G‐allele has been associated with diminished MOR expression (Bond et al.,  ; Oertel et al.,  ; Zhang et al.,  ), reduced MOR availability (Oertel et al.,  ; Peciña et al.,  ), as well as decreased MOR G‐protein coupling efficacy and, thus, reduced signalling efficacy (Oertel et al.,  ). The G‐allele has also been associated with reduced analgesic efficacy of opioid drugs (Cajanus et al.,  ; Yu et al.,  ). 
+
+Several studies indicate that the OPRM1 polymorphism affects cognition, as G‐allele carriers exhibit higher reactivity to social rejection (Way et al.,  ), lower placebo‐induced opioid activation (Oertel et al.,  ) and aberrant response to reward in healthy individuals (Lee et al.,  ) and FM subjects (Finan et al.,  ). Moreover, an antagonistic interaction between OPRM1 and serotonin‐related genes on pain modulation was observed. Specifically, exercise‐induced hypoalgesia was pronounced in FM subjects and healthy controls (HC) with the OPRM1 G‐allele combined with genetically inferred weak serotonergic mechanisms (Tour et al.,  ). Taken together, previous studies indicate that the OPRM1 polymorphism exerts an effect in acute and FM pain. 
+
+To our knowledge, no studies have investigated the role of the functional genetic polymorphism of OPRM1 in evoked pain using fMRI in FM subjects. Here, we investigated the effect of OPRM1 (  rs1799971  ) on symptom severity, pain sensitivity, and cerebral processing in FM subjects and HC. 
+
+
+## METHODS 
+  
+### Sample 
+  
+The recruited study sample consisted of 80 FM subjects (mean 47.4 ± 7.9 years) and 40 HC (mean 47.9 ± 7.9 years). Data of one HC and one FM were excluded due to undetermined genotyping (see Section  ). Complete data sets of 79 FM patients and 39 HC were included in the behavioural analyses (  n   = 118). 
+
+Imaging data of 13 participants were excluded from further analysis due to excessive head motion (  n   = 6, see Section  ), structural brain anomalies (  n   = 1), and incomplete data sets due to technical issues and drop‐outs (  n   = 6), resulting in data of 105 participants included in the final fMRI analysis (70 FM subjects and 35 HC). 
+
+All patients underwent systematic screening by a specialist in rehabilitation medicine and pain relief (Dr. Kadetoff) to ensure that the ACR‐1990 as well as the ACR‐2011 classification criteria for FM (Wolfe et al.,  ,  ) were met. Inclusion criteria for patients also included female sex, working age (20–60 years) and right‐handedness. Exclusion criteria consisted of other dominant pain conditions than FM, painful osteoarthritis, rheumatic, or autoimmune diseases, other severe somatic diseases (neurological, cardiovascular, cancer, diabetes mellitus etc.), hypertension (>160/90 mmHg), previous brain or heart surgery, psychiatric disorders including ongoing treatment for depression or anxiety, substance abuse, pregnancy, magnetic implants, self‐reported claustrophobia, obesity (Body Mass Index > 35), smoking (>5 cigarettes/day), inability to speak and understand Swedish, medication with antidepressants or anticonvulsants, inability to refrain from analgesics, NSAID or hypnotics prior to study participation (48 hr before the first visit, and 72 hr before the second visit, that is, the fMRI examination). No FM subjects were on strong opioids. HC were right‐handed women, age‐balanced to FM subjects, and in addition to the listed exclusion criteria for FM patients also free from chronic pain conditions and without regular medications with NSAIDs, analgesics or sleep medication. 
+
+Participants were recruited through advertisement in the daily newspaper. All participants received remuneration for participation and provided written informed consent before being included in the study. The study complied with the principles of the Declaration of Helsinki and was approved by the Swedish Ethical Review Authority board (permit 2014/1604‐31/1). 
+
+Note that this study is part of a larger project (see study plan  ) including additional imaging methods and paradigms investigating pain processing in FM (Albrecht et al.,  ). One goal of the overall project was to investigate conditioned pain responses in FM subjects (Sandström et al.,  ), which have previously displayed deficits in conditioning and contingency learning (Jenewein et al.,  ; Meulders et al.,  ). Given the need to ensure successful pain conditioning in a sufficient number of participants, a larger number of FM subjects than HC was included in the project, resulting in different group sizes also in the current study. 
+
+
+### Procedure 
+  
+Data were collected for each participant over two consecutive days: on the first day all participants received information about the study procedure and provided saliva samples for genotyping. All participants filled out questionnaires regarding pain catastrophizing (Pain Catastrophizing Scale, PCS) (Sullivan et al.,  ), depression (Beck's Depression Inventory, BDI) (Beck et al.,  ), anxiety (State‐Trait Anxiety Inventory, STAI) (Spielberger et al.,  ) and health‐related quality of life with a focus on the bodily pain subscale (SF‐36 bodily pain) (Ware & Sherbourne,  ). FM subjects also completed the fibromyalgia impact questionnaire (FIQ) (Burckhardt et al.,  ). 
+
+The PCS measures pain catastrophizing tendencies on a 13‐item scale, with higher scores suggest higher catastrophizing about pain. The BDI is a 21‐item test that assesses depression with higher scores indicating more depressive severity. The STAI‐State subscale consists of 20 items assessing the current state of anxiety with a score ranging from 20 to 80 with higher scores indicative of higher levels of momentary anxiety. The SF‐36 consists of eight scales with bodily pain as a two‐item subscale resulting in a final score ranging from 0 (severe, limiting pain) to 100 (no pain or limitations due to pain). The FIQ is a questionnaire assessing FM‐specific symptoms and disability. It consists of 20 items with a score ranging from 0 to 100, where a higher value indicates a poorer state of health. 
+
+Pressure pain thresholds (PPTs) were determined in all participants to assess pain sensitivity. The pressure algometer (Somedic Sales AB) was handheld and had a round 1 cm  hard rubber probe that was applied perpendicular to the surface of the tested body part. The manual force was applied at a steady rate (approximately 30 kPa/s) until the participant's pain threshold was reached (Kosek et al.,  ). PPTs were collected bilaterally across four different sites: m. supraspinatus, elbow (lateral epicondyle), m. gluteus, and knee (at the medial fat pad proximal to the joint line) with one assessment per anatomical site. The average PPT across body sites is reported. 
+
+Pressure pain was applied to participants’ left calf using a cuff (13 × 85 cm) attached to a rapid cuff inflation system (E20/AG101; Hokanson). This method of deep tissue pain stimulation was chosen to provide higher ecological validity than, for example, cutaneous noxious stimulation, given that FM is characterized by widespread tissue pain. Applying pressure pain using a cuff inflator was similarly used in FM subjects before, for example, (Loggia et al.,  ). 
+
+In a comprehensive procedure, stimulus pressure intensity was individually calibrated to match ratings of 10 and 50 mm, respectively, on a visual analogue scale (VAS) ranging from 0 mm (no pain) to 100 mm (strongest imaginable pain), indicated as P10 and P50 throughout this paper. Participants were presented with a series of 5 s stimulations in increasing intensity steps of 25 mmHg in order to determine cuff PPT (first VAS rating >0) and the stimulation maximum (first VAS rating >60). In two following series five stimuli were presented in a randomized manner to determine the individual representation of P10 (starting from the PPT) and P50 (starting from the stimulation maximum). The randomized series to determine P10 used the PPT as a starting point and −2 steps and +2 steps of 25 mmHg. The randomized series to determine P50 used the stimulation maximum as a starting point and −4 steps of 25 mmHg. If the first subjective rating of 10 mm VAS was <100 mmHg, steps of 10 mmHg were used for the randomized series determining P10. Next, subjects were trained in front of a computer monitor to associate green circle with their individually calibrated P10 stimulation and a red circle with their individually calibrated P50 stimulation (familiarization phase), presented in a pseudo‐randomized order (10 × P10; 10 × P50). Following each stimulus, subjects rated their perceived pain on a 100 mm VAS. 
+
+On the second day, participants underwent a pressure pain paradigm during fMRI in which colour cues predicted the following stimulus intensity (Figure  ). As in the training session on the first day, a green circle was followed by the individually calibrated lower intensity pressure stimulus (10/100 VAS, P10) and a red circle was followed by the individually calibrated higher intensity stimulus (50/100 VAS, P50). Both predicting cues, green and red, and subsequent pressure stimulations, P10 and P50, respectively, were each presented 10 times in a pseudo‐randomized manner, resulting in 20 stimuli altogether. Participants were prompted to rate perceived pain intensity on a VAS after each stimulus application. 
+  
+Schematic representation of the experimental paradigm. In the instructed conditioning paradigm participants were presented with a green or red cue (2 s) that was followed by a delay (2–6 s) before pressure stimulation (5 s) of lower or higher intensity, respectively. Each stimulus presentation was followed by a rating period (8 s) using a 0–100 visual analogue scale (VAS). Intensity of pressure stimulations were individually calibrated to represent approximately 10/100 VAS (P10) and 50/100 VAS (P50). Figure adapted from (Sandström et al.,  ) 
+  
+
+### Genotyping 
+  
+Saliva samples were collected from all participants and used for genotyping, which was performed blind to phenotypic information. In accordance with previous studies, A118G single nucleotide polymorphism   rs1799971   OPRM1 genotypes were split into two groups, AA versus AG/GG (Mura et al.,  ; Tour et al.,  ). Genotyping was performed using TaqMan single nucleotide polymorphism genotyping assays and ABI 7,900 HT instrument (Applied Biosystems [ABI]). Polymerase chain reactions (PCRs), with a total volume of 5 ml, were performed in 384‐well plates containing 2.5 ml Universal Master Mix and 5 ng dried‐down genomic DNA per well. The PCR amplification protocol included two holds, 50°C for 2 min and denaturation at 95°C for 10 min, followed by 50 cycles at 92°C for 15 s and 60°C for 1 min. In one FM and one HC the genotype could not be determined, as the PCRs did not produce secure read‐outs. 
+
+
+### Statistical analysis of behavioural data 
+  
+Behavioural data analysis was performed with R (RStudio Team,  ) and included 118 participants (FM patients   n   = 79, HC   n   = 39) (Table  ). A chi‐squared test and fisher's exact test were used to analyse genotype frequencies and to assess deviations from Hardy–Weinberg equilibrium. 
+  
+Characteristics of OPRM1 genotypes reported for fibromyalgia (FM) subjects and healthy controls (HC) (  n   = 118) 
+    
+To assess differences in calibrated input pressure, a linear mixed effects model (using   nlme  , Pinheiro et al.,  ) with fixed effects pressure level (P10/P50), group (FM/HC) and OPRM1 genotype (AA/*G) including all interactions was used. Variability between participants was accounted for including random intercepts and by‐subject‐over‐pressure level random slopes accounted for individual variability between pressure level. Restricted maximum likelihood was used to estimate variances of random effects, different variances were allowed for each level of the factors OPRM1 genotype and group, and a first‐order autoregressive correlation structure was modelled to account for intra‐subject dependencies in repeated measures. 
+
+Subjective pain ratings acquired throughout the fMRI paradigm were analysed using another linear mixed effects model. Mixed model analysis was performed with the fixed effects OPRM1 variants, group, pressure level, PPTs and the continuous variable time with random intercepts, accounting for variability between participants, and by‐subject‐over‐time random slopes, accounting for individual variability over time. Interaction effects on pain ratings between OPRM1 genotype and time, group and pressure levels were also tested. As in the mixed model on pressure intensity, restricted maximum likelihood was used to estimate variances of random effects, different variances were allowed for each level of the factors OPRM1 genotype and group, and a first‐order autoregressive correlation structure was modelled to account for intra‐subject dependencies in repeated measures. 
+
+In order to investigate the effect of clinical and pain‐relevant variables on pain ratings, another mixed model analysis was performed using only FM subject data. Here, PCS, BDI, FIQ, and SF‐36 bodily pain scores were included to test for a potential association with experimental pain ratings acquired throughout the paradigm. Apart from the additionally included FM‐relevant variables, this model was set up as previously described (without the factor group). 
+
+A two‐way ANOVA with the factors genotype and group was used to test for differences in age. Given the variance differences in groups in clinical measures, that is, PCS, BDI, STAI‐State, SF‐36 bodily pain scores and PPTs, robust two‐way ANOVAs with trimmed means (trim level = 0.2) were performed (using   WRS2  , Mair & Wilcox,  ). STAI data of one FM subject and PPTs of one FM subject were missing at random. Welch tests were performed to test for differences in FM pain duration and FIQ scores between OPRM1 genotypes in FM subjects. A   p   < 0.05 was considered statistically significant in all analyses. 
+
+
+### MRI data acquisition 
+  
+fMRI data were collected on a 3T Scanner (General Electric 750) using an eight‐channel head coil. Functional images comprised 42 axial slices (slice thickness 3 mm, 0.5 mm gap) and were acquired using a T2*‐sensitive gradient echo‐planar imaging sequence (TR 2 s; TE 30 ms; flip angle 70°; field of view 220 × 220 mm, 72 × 72 mm matrix; 3 × 3 mm in‐plane resolution). The first five volumes were discarded to account for stabilization of the T1‐relaxation effects. Prior to the functional sequence, high‐resolution T1‐weighted anatomical images were acquired (BRAVO, voxel size 1 × 1 × 1 mm, 176 slices). 
+
+
+### Analysis of fMRI data 
+  
+Processing and analysis of functional data was performed using statistical parametric mapping (SPM12; Wellcome Trust Centre for Neuroimaging) running under MATLAB (The MathWorks, version R2015b). Data of 105 participants (FM = 70, HC = 35) were included in the fMRI analysis (Table  ). 
+
+First, anatomical and functional scans were reoriented manually to the anterior commissure. Functional images were spatially realigned to the mean volume using a six‐parameter affine transformation. Then, the anatomical T1‐weighted image was co‐registered to the functional images. Functional images were spatially normalized into Montreal Neurological Institute (MNI) stereotactic standard space and smoothed with a 6 mm full‐width at half‐maximum isotropic Gaussian kernel. Framewise displacement (FD) was used to assess head movement from one frame relative to the previous by calculating the sum of the absolute values of the derivatives of the six realignment parameters (Power et al.,  ). As a consequence, six participants (four FM subjects, two HC) were excluded from further analyses due to excessive head motion (FD > 0.5 in >15% of the images). There were no differences in FD between FM and HC (Wilcoxon rank sum test,   Z   = 1.58,   p   = 0.1145). The general linear model as implemented in SPM12‐7219 was used for subsequent data analysis. First level analysis included temporal high‐pass filtering (cut‐off 128 s) and correction for auto‐correlations using first‐order autoregressive modelling. The following conditions were modelled on the individual level: pressure stimulations for two intensities (P10/P50, 5 s), two cue‐anticipation phases (red preceding P50/green preceding P10, 2 s cue plus delay of 2–6 s before stimulus onset) and rating period (8 s). Six realignment‐derived motion parameters were added as regressors of no interest. In order to link variations of pain intensity perception to neural activity, additional first level models were specified that included individual pain ratings for each stimulus as a parametric modulator of the regressors representing P50 and P10. Single‐subject contrast images were then taken to second level random‐effects analyses with unequal variances between groups and genotypes being assumed. 
+
+Our functional imaging analysis aimed to test whether the genetic polymorphism of OPRM1 affects pain‐related processing in FM subjects and HC and examine which brain regions may be functionally connected to areas differing between genotypes. In addition, we explored whether OPRM1 influences processing during the cue‐anticipation phase (red preceding P50/green preceding P10), as an indicator for anticipatory and/or psychological processes. First, the effect of painful pressure stimulation and cue‐anticipation was tested separately for each group and pressure level (P10/P50). To test for a possible interaction between OPRM1 genotype and pressure level as well as group and pressure level, two‐sample   t   tests were performed using individual contrast images of (a) pressure intensity (P50‐P10) and (b) cue‐anticipation colour (red preceding P50‐green preceding P10). Groups and genotypes were contrasted separately for each cue‐anticipation colour and during noxious stimulation using parametric trial‐by‐trial responses. An OPRM1 genotype‐by‐group interaction during noxious stimulation as well as during cue‐anticipation was tested using full factorial models. 
+
+A region‐of‐interest (ROI) approach was used to test whether differences between OPRM1 genotypes are present in opioid‐rich brain areas, that is, regions demonstrating a correlation between MOR availability/binding potential and blood oxygen level dependent (BOLD) signal in FM subjects during evoked pain (Schrepf et al.,  ). ROIs were based on findings by Schrepf and colleagues using anatomical masks derived from the Harvard‐Oxford Atlas freely distributed with FSL ( ). The probability maps, namely left posterior cingulate cortex (PCC), right precentral gyrus (encompasses the primary motor cortex, M1), left anterior cingulate cortex (ACC), and left middle temporal gyrus (temporo‐occipital part) were conservatively thresholded at 50%. Note that the right ACC mask was thresholded at 25%, as a more conservative threshold excluded the subgenual portions, which were reported by Schrepf et al. ( ). Also note that the dorsolateral prefrontal gyrus (DLPFC) is not an anatomical region per se but the coordinates reported by Schrepf were best represented by the left middle frontal gyrus (thresholded at 25%). The mask for the cerebellum was also based on the provided coordinates located in crus II based on the SUIT cerebellum atlas ( ). Given that the ROIs were based on data from FM subjects, differences between OPRM1 genotypes were first tested in FM subjects. For completion, and after testing for an interaction effect with group, the analysis was also performed on pooled data. To test for a main effect of OPRM1 within ROIs, two‐sample   t  ‐tests were performed between OPRM1 genotypes for painful pressure stimulations (P10 + P50, that is, pain > baseline). Next, OPRM1 variants were contrasted in a two‐sample   t   test using a whole brain approach to identify regions displaying functional differences outside of opioid‐rich brain areas included in the ROI approach. To further investigate contributions to OPRM1 genotype differences, we explored differences between OPRM1 variants separately in each group. 
+
+A psychophysiological interaction analysis (PPI) analysis (Friston et al.,  ) was performed to identify differences in functional connectivity associated with the OPRM1 polymorphism pooled across groups. A PPI analysis tests for an interaction between a predetermined seed region in the brain (physiological factor) with other brain areas during an experimental condition (psychological factor). Here, individual time series were extracted from the cluster showing differences in activation between OPRM1 genotypes during evoked pain across group and pressure levels (peak at [−2 –28 48]). We then contrasted the OPRM1 genotypes to identify differences in coupling with other brain regions related to the differential processing of evoked pain between genetic variants. 
+
+Extracted betas from the cluster found to differ between OPRM1 variants in FM subjects were correlated with clinical and pain‐relevant factors for each OPRM1 genotype. Previously, PCC activity has been shown to be increased during FM pain catastrophizing (Lee et al.,  ) and has been associated with trait pain catastrophizing (Galambos et al.,  ). For this reason, betas extracted from the ROI PCC analysis were correlated with PCS scores in FM subjects in an additional analysis. 
+
+For all fMRI analysed the initial statistical threshold was set to   p   < 0.001 and a cluster threshold of   p   < 0.05 (family wise error corrected) was applied, unless otherwise reported. Results are presented stating   x  ,   y  ,   z   coordinates in MNI space. 
+
+
+
+## Results 
+  
+### Behavioural data results 
+  
+#### Demographics and questionnaires 
+  
+The participant characteristics are described in Table  . As expected, FM subjects scored significantly higher than HC on PCS (  Q   = 67.743,   p   < 0.001), BDI (  Q   = 180.33,   p   < 0.001), STAI (  Q   = 28.862,   p   < 0.001) and SF‐36 bodily pain (  Q   = 412.284,   p   < 0.001). There was neither a difference between OPRM1 variants nor an interaction between genotype and group with respect to BDI, PCS, STAI, SF‐36 bodily pain and age (all   p   > 0.3). In FM subjects, there was no difference between OPRM1 genotypes in pain duration (  t  (44.7) = −0.193,   p   = 0.848) and FIQ scores (  t  (34.5) = 0.479,   p   = 0.635). 
+
+
+#### Genotype frequencies 
+  
+The OPRM1 genotype frequency, that is, the distribution of homozygous AA and G‐allele carriers, in the sample did not deviate from the Hardy–Weinberg equilibrium, (  χ  (1) = 2.17,   p   = 0.141) and was similar in FM subjects and HC (  χ  (1) = 0.546,   p   = 0.46). 
+
+
+#### Pressure pain thresholds 
+  
+As expected, FM subjects had significantly lower PPTs than HC (  Q   = 50.86,   p   < 0.001), indicating higher pain sensitivity. No difference between OPRM1 genotypes or interaction between group and genotype was observed in PPTs. 
+
+
+#### Input pressure 
+  
+In line with previous findings, FM displayed increased pain sensitivity compared to HC by requiring lower input pressure stimuli for pain intensities equivalent to P10 and P50 (  β   = 56.717 [95% confidence interval (CI) 39.9, 73.4],   t   = 17.38,   SE   = 8.445,   p   < 0.001). In addition, a main effect for the input pressure was found (  β   = 123.731 [CI 109.6, 137.8],   t   = 6.716,   SE   = 7.119,   p   < 0.001). The observed main effects group and input pressure were qualified by statistically significant group × pressure intensity interaction (  β   = 40.331 [CI 17.3, 63.4],   t   = 3.469,   SE   = 11.625,   p   < 0.001). There were no differences between OPRM1 genotypes or interactions including the OPRM1 variant (all   p   > 0.2) (Table  ; Figure  ). 
+  
+Calibrated pressure input in mmHg for OPRM1 genotypes (*/G vs. AA) to match 10/100 VAS (P10) and 50/100 VAS (P50) pressure intensity. There was a significant difference in input pressure (i) between groups and (ii) between P50 (white boxplot) and P10 (grey boxplot) pressure in mmHg. No difference between OPRM1 genotypes was observed. Whiskers represent the maximum 1.5 interquartile range (IQR). Circles represent data outside the IQR. FM, fibromyalgia; HC, healthy controls; VAS, visual analogue scale 
+  
+
+#### Pain ratings 
+  
+There was a difference in pain ratings between stimulus intensities (P10/P50), indicating successful calibration within participants (results are presented in Table  ; Table  ). Pain ratings also differed between groups, indicating FM subjects rated pain intensity higher than HC, despite individual pressure intensity calibration (Figure  ). No difference in ratings over time or between OPRM1 genotypes was observed. No interactions between OPRM1 genotype × stimulus intensity, time or group were found in pain ratings (Figure  ). 
+  
+Linear mixed model results for predictors of pain ratings 
+      
+Pain ratings for OPRM1 genotype A (  n   = 88) and OPRM1 G‐carriers (  n   = 30) during 10/100 VAS (P10) and 50/100 VAS (P50) stimulus pressure intensity. Individual pain ratings were acquired using a VAS ranging from ‘no pain’ (0) to ‘worst pain imaginable’ (100). There was a significant difference between groups (FM/HC) and between stimulus intensities (P10/P50) but no difference between OPRM1 genotypes (AA/*G). (a) Pain ratings are displayed for stimuli of lower intensity (P10) and (b) for stimuli of higher intensity (P50). Error bars represent the standard error of the mean. FM, fibromyalgia; HC, healthy controls; VAS, visual analogue scale 
+  
+Given differing group sizes and genotype distributions, Cook's distance was calculated across groups to identify potentially influential data points (118 subjects × 20 ratings, that is, 2,360 observations) in the model. Cook's distance was overall small (mean = 2.03e‐04, median = 6.213e‐05), indicating single data points did not affect the model in a substantial way. Nonetheless, a commonly used cut‐off value of 4/number of observations was adopted to determine potentially influential data points, resulting in 42/2,360 observations (1.78%) surpassing the threshold (1.69e‐03). In order to investigate the influence of these observations, the mixed model was repeated without influential data points, yielding similar results. 
+
+In FM subjects, there was a significant difference in pain ratings between stimulus intensity (P10/P50) but no effect of OPRM1 genotype, time or interaction between them (results are presented in Table  ). Of the included pain‐related measures, only PCS showed a significant effect, with higher PCS scores being linked to higher experimental pain ratings. There was no evidence of PPTs, BDI, FIQ, or SF‐36 bodily pain scores influencing experimental pressure pain ratings in FM subjects. 
+  
+Linear mixed model results for predictors of pain ratings in fibromyalgia subjects 
+    
+As in the previous model, Cook's distance in the FM model was overall small (mean = 2.49e‐04, median = 8.01e‐05), with 17 out of 1,580 observations (1.08%) surpassing Cook's distance of 4/number of observations (2.53e‐03). A comparison of mixed models, with and without potentially influential data points, yielded comparable results. 
+
+Analysing only data of participants included in the fMRI analysis (  n   = 105) revealed similar results in all behavioural analyses (see Tables   and  ; Figure  ). 
+
+
+
+### Functional imaging results 
+  
+#### BOLD responses to painful pressure stimulation 
+  
+Analysis of fMRI data showed increased BOLD response during painful pressure stimulation in brain regions commonly associated with pain processing (Apkarian et al.,  ; Peyron et al.,  ), including insula, postcentral gyrus (primary somatosensory cortex, S1) and parietal operculum (secondary somatosensory cortex, S2) (Figure  ; Table  ). Neural activity linked to pain ratings using parametric modulation displayed increased activation in operculum, ACC, postcentral gyrus, and thalamus (Table  ). BOLD responses during pressure stimulation separately in both FM subjects and HC for each pressure level (P10/P50) are presented in Table  . 
+  
+Main effect of pressure pain stimulation. A main effect of pressure stimulus was observed in regions associated with pain processing, including insula and somatosensory cortices/parietal operculum. Maps are displayed whole brain family wise error‐corrected at a threshold of   p   < 0.05 using a one‐sample   t   test resulting in   t  ‐maps, overlaid on a group‐average structural image 
+    
+Localization of significant clusters (  p   < 0.05) during noxious stimulation (pain > baseline) across participants (Figure  ) and using experimental pain ratings as a parametric modulator 
+   Note    
+
+#### Group differences 
+  
+Parametric response did not differ between groups, providing no evidence of differing pain processing in individually calibrated pressure intensities. 
+
+
+#### ROI analyses of OPRM1 differences during painful pressure stimulation 
+  
+First, we focused on ROIs that had previously shown a correlation between MOR availability/binding potential and BOLD signal during evoked pain in FM subjects, that is, left middle frontal gyrus/DLPFC, left perigenual ACC, left middle temporal gyrus, left PCC, right subgenual ACC and cerebellum (Schrepf et al.,  ). Here, there were significant differences between OPRM1 genotypes in PCC and precentral gyrus (both thresholded at 50%) but not in other predetermined ROIs in FM subjects. Specifically, FM OPRM1 G‐carriers showed increased activation compared to AA homozygotes in PCC and precentral gyrus (Table  ), two opioid‐rich brain regions previously shown to be functionally connected to pain‐evoked BOLD signal in FM subjects. 
+  
+Localization of significant clusters (  p   < 0.05) in region‐of‐interest analysis (based on Schrepf et al.,  ) during noxious stimulation (pain > baseline) 
+   Note    
+No group‐by‐genotype effect was observed in predetermined ROIs, indicating no systematic difference between groups with respect to OPRM1 genotype. A subsequent ROI analysis on pooled group data revealed very similar results as observed in FM subjects. Specifically, OPRM1 */G displayed increased activation compared to AA homozygotes in PCC and precentral gyrus (Table  ). No effect in the opposite direction, that is, OPRM1 AA> */G, emerged in any ROI analysis. 
+
+
+#### Whole brain analyses of OPRM1 differences during painful pressure stimulation 
+  
+For completion, we performed whole brain analyses to test for differences outside of predetermined ROIs, finding no significant interaction between genotype × pressure level (P10/P50), suggesting no substantial difference in BOLD response between the two painful pressure intensities depending on the polymorphism variant. In addition, there was no interaction between OPRM1 genotype (AA/ */G) × group (FM/HC), suggesting genotypes did not differ systematically in neural response between groups. This is in line with the findings in the previous ROI analysis, where no group‐by‐genotype interaction was observed. 
+
+Given that no interactions between OPRM1 genotype × group as well as OPRM1 genotype × pressure level were found, we further investigated whether OPRM1 variants exert an effect on the cerebral processing of noxious stimulation (pain > baseline). We found that OPRM1 genotypes differed significantly in BOLD signal during the processing of painful stimuli in just one prominent cluster, which largely overlapped with findings from the ROI analysis. OPRM1 G‐carriers showed increased activation compared to AA homozygotes in a cluster encompassing PCC and precentral gyrus/postcentral gyrus (Figure  ). Note that while we preserved laterality in the ROI analysis (Table  ), the whole brain analysis revealed that the OPRM1 effect stretched across both hemispheres (Table  ). There was no effect in the opposite direction, that is, no increased activation in OPRM1 genotype AA compared to */G. 
+  
+Cortical brain activity during processing of painful pressure stimulation for OPRM1 genotype A (  n   = 77) compared to OPRM1 G‐carriers (  n   = 28). Carriers of at least one G allele displayed increased activation in a cluster encompassing the posterior cingulate cortex (PCC) and precentral gyrus (peak at [−2 –28 48], Table  ) with an enlarged image of the finding from the region‐of‐interest PCC analysis (peak at [−2 –28 46], Table  ). Results are overlaid on a group‐average structural image (visualization threshold   p   < 0.001 uncorrected). The bar plot shows group means and standard errors of parameter estimates extracted from the activation cluster of the contrast OPRM1 */G > OPRM1 AA. a.u., arbitrary units; R, right 
+    
+Localization of significant clusters (  p   < 0.05) showing differences between OPRM1 genotypes during noxious stimulation (pain > baseline) 
+   Note    
+To further identify contributions to the observed OPRM1 genotype differences across groups, we explored differences between OPRM1 variants in FM subjects and HC separately across the whole brain. In FM subjects (  n   = 71), we observed that G‐allele carriers displayed increased activation compared to the AA homozygotes in PCC/precentral gyrus (Figure  ; Table  ). As in the ROI approach, the observed differences between OPRM1 genotypes in FM subjects were found only in a very similar location as observed in the previous analysis including all participants. Comparing OPRM1 genotypes in HC only (  n   = 34), we did not observe differences during evoked pain at the applied threshold. No effect was found in either group for the opposite direction (OPRM1 AA > */G). 
+  
+Cortical brain activity in FM subjects (  n   = 71) during processing of evoked pain for OPRM1 genotype A (  n   = 50) compared to OPRM1 G‐carriers (  n   = 21). FM carriers of at least one G allele displayed increased activation in a cluster encompassing the posterior cingulate cortex (PCC) and precentral gyrus (peak at [−2 –28 48], Table  ) with an enlarged image of the finding from the region‐of‐interest PCC analysis (peak at [−2 –28 46], Table  ). Results are overlaid on a group‐average structural image (visualization threshold   p   < 0.001 uncorrected). The bar plot shows group means and standard errors of parameter estimates extracted from the activation cluster of the contrast OPRM1 */G > OPRM1 AA. a.u., arbitrary units; R, right 
+  
+
+#### PPI analysis 
+  
+On the basis of the increased activation in PCC/precentral gyrus in G‐allele carriers observed in the whole brain analysis pooled across groups (peak at [−2 –28 48]), we explored whether different functional connectivity accompanies differences in BOLD activation between genotypes. Here, we observed decreased coupling in OPRM1 genotype */G compared to AA between the described seed cluster comprised of PCC/precentral gyrus with left middle frontal gyrus/DLPFC extending to left precentral gyrus (Figure  ; Table  ). The observed target cluster encompassing the left precentral gyrus was located more lateral than the seed cluster of PCC/precentral gyrus. In addition, decreased functional connectivity in OPRM1 */G was observed from the same seed region with the left angular gyrus extending to the left supramarginal gyrus, that is, areas comprising the inferior parietal lobe (Figure  ; Table  ). There was no effect in the opposite direction, that is, no increased connectivity between PCC/precentral gyrus and other brain regions in OPRM1 */G compared to homozygous AA. 
+  
+Psychophysiological interaction analysis. Psychophysiological interaction analysis revealed increased functional coupling in homozygote OPRM1 AA compared to OPRM1 G‐carriers between posterior cingulate cortex/precentral gyrus and middle frontal gyrus/DLPFC (peak at [−34 2 60], Table  ) extending to the precentral gyrus. In addition, increased coupling was observed with the inferior parietal lobe (peak at [−58 –54 30]), that is, angular gyrus extending to the supramarginal gyrus. Results are overlaid on a group‐average structural image (visualization threshold   p   < 0.001 uncorrected). The bar plots display group means and standard errors of parameter estimates extracted from the respective activation cluster. a.u., arbitrary units; R, right 
+    
+Localization of significant clusters (  p   < 0.05) showing differences in functional connectivity during pressure pain stimulation (pain > baseline) 
+   Note    
+
+#### OPRM1 differences during the cue‐anticipation phase 
+  
+There were neither differences observed between groups in the cue‐anticipation phase nor an interaction between groups and cue‐anticipation colour (red preceding P50/green preceding P10). BOLD responses during cue‐anticipation phase for each group and cue‐anticipation colour are presented in Table  . In addition, there was no significant difference between OPRM1 genotypes during the cue‐anticipation phase or between cue‐anticipation colour depending on OPRM1 variant. These results provide no evidence that anticipatory and/or psychological effects processes are affected by OPRM1. 
+
+
+#### Correlation between BOLD response and clinical measures in FM subjects 
+  
+Finally, extracted BOLD response in FM subjects from the observed OPRM1 effect (pain > baseline) in the cluster encompassing PCC/precentral gyrus were correlated with clinical and pain‐relevant measures for each OPRM1 genotype. No correlation for either OPRM1 genotype was observed for PPTs, SF‐36 bodily pain or FIQ (all   p   > 0.3), finding no association between BOLD response to evoked pressure pain or any clinical measures in FM subjects depending on OPRM1 genotype. 
+
+In addition, there was no correlation observed between PCC activation obtained from the ROI analysis and PCS scores. Note that using data from the same project we recently found that higher catastrophizing in FM is associated with increased BOLD response in prefrontal cortices and reduced functional connectivity between inferior parietal lobe and thalamus during pressure pain stimulation in a previously conditioned low‐pain condition (Sandström et al.,  ). Importantly, the previously reported correlation between the BOLD response and catastrophizing was located in other brain regions than the OPRM1 effect observed in the current data. 
+
+
+
+
+## DISCUSSION 
+  
+In this study, we investigated the influence of the functional polymorphism of the MOR gene (OPRM1, A118G   rs1799971  ) on processing of evoked pain in FM subjects and HC using fMRI. Our data showed no systematic difference in neural response to nociceptive pressure stimulation between groups with respect to OPRM1 genotypes. Pooled across groups, we found that OPRM1 G‐carriers (AG or GG) displayed increased activation in PCC extending to the precentral gyrus, compared to AA homozygotes. This finding was observed in FM subjects alone but not in HC, indicating that FM subjects may drive the effect, even though the group‐by‐genotype interaction yielded no significant result. Across groups, decreased functional connectivity was found in OPRM1 */G compared to AA between PCC/precentral gyrus and (a) the fronto‐parietal network, that is, DLPFC/middle frontal gyrus, and (b) inferior parietal lobe, that is, angular and supramarginal gyrus. Our findings suggest that differences in pain‐evoked neural response and functional coupling may mirror differing modulatory mechanisms between OPRM1 variants. 
+
+In accordance with previous studies (Peciña et al.,  ; Tour et al.,  ), OPRM1 genotypes did not differ in pain sensitivity, suggesting that both variants similarly modulate pain but through varying routes. Schrepf and colleagues link the less reactive endogenous opioid system in FM, displayed as reduced MOR availability using positron emission tomography (PET) (Harris et al.,  ) and decreased BOLD response during evoked pain in antinociceptive brain regions (Schrepf et al.,  ). Similar to FM subjects (Baraniuk et al.,  ), OPRM1 G‐carriers could hypothetically have a higher baseline opioid tone and, thus, possess a hyporeactive opioid system when challenged by noxious stimulation, consistent with reduced placebo responses (Peciña et al.,  ). The observed differences between genotypes were located in the PCC, a key node in the default mode network (DMN) (Fransson & Marrelec,  ), suggested to be relevant for internally directed cognition and attention (Leech & Sharp,  ). While differences in PCC were regarded as particularly interesting, the precentral gyrus may contribute to the observed effect, for example, through the discussed role of M1 in experimental muscle pain (Burns et al.,  ). Given our results in two out of several ROIs, our findings suggest that functional consequences of OPRM1 may not be specific to opioid‐rich regions per se, which is corroborated by the expression of MOR throughout the brain (Mansour et al.,  ). G‐carriers displayed reduced functional connectivity between PCC/precentral gyrus and DLPFC (Figure  ), crucially involved in the fronto‐parietal network (Zanto & Gazzaley,  ) and pain modulation (Seminowicz & Moayedi,  ), with DLPFC previously showing reduced MOR binding potential in G‐carriers (Peciña et al.,  ). Our findings indicate alternative modulatory patterns with engagement of the fronto‐partietal network in OPRM1 AA and PCC/precentral gyrus in OPRM1 */G. Interestingly, ROIs (Schrepf et al.,  ) overlapped not only where our data displayed OPRM1 differences in neural processing (PCC/precentral gyrus) but also in functional connections (DLPFC). 
+
+An increased endogenous opioid tone in FM, suggested by elevated endogenous opioids in the CSF (Baraniuk et al.,  ), may lead to opioid‐induced hyperalgesia, shown to be associated with glial activation (Roeckel et al.,  ). Microglia activation was recently demonstrated in precuneus/PCC, S1/M1 and DLPFC, providing evidence for glial involvement in FM pathophysiology (Albrecht et al.,  ). Several of those regions were involved in differing functional patterns between OPRM1 genotypes in the current data. Some drugs presumably acting on glial cells have shown favourable effects in FM, for example, milnacipran (Clauw et al.,  ). Besides its primary mechanism, milnacipran has been shown to mitigate microglia activation in a mouse model (Shadfar et al.,  ), suggesting an effect through microglia modulation. A study in FM subjects showed that the degree of antinociceptive effect of milnacipran correlated with pain‐related BOLD signal in PCC (Jensen et al.,  ). Notably, the PCC peak coordinate in this study [−2 –28 46] was very similar to the location of the positive milnacipran response [−4 –30 46], stressing the clinical role of PCC in FM‐relevant pain modulation. As the OPRM1 effect was significant in FM subjects but not in HC, we speculate that FM subjects drive this finding. 
+
+However, we cannot exclude that OPRM1 */G may confer diminished endogenous opioid tone, due to reduced expression of MOR (Bond et al.,  ). In this sense, the diminished MOR binding potential in OPRM1 G‐carriers (Peciña et al.,  ) is inconclusive, as it could result from increased binding of endogenous ligands, therefore, preventing PET ligand binding and/or reduced MOR expression (Loggia,  ). Arguably, the effect of OPRM1 may be due to reduced MOR availability in FM (Harris et al.,  ) caused by lower expression of opioid receptors in response to long‐term exposure to high levels of endogenous opioids. The latter would be in accordance with reports of OPRM1 */G preventing upregulation of MOR by decreasing OPRM1 mRNA expression following chronic opioid exposure in opioid addicts (Oertel et al.,  ). The effect of OPRM1 */G would then be more pronounced in FM subjects than HC, which is suggested in our data. 
+
+Decreased resting state connectivity in FM has been found between pain‐relevant and sensorimotor areas (Flodin et al.,  ). FM subjects also showed decreased coupling between supramarginal gyrus and S1/M1, brain regions partly displaying reduced connectivity with PCC in G‐allele carriers (Figure  ). In HC, prefrontal cortex and PCC/precuneus increased coupling during painful disruptions, suggesting alternative connectivity patterns between DMN and pain‐relevant regions during evoked pain (Mantini et al.,  ). We found that OPRM1 G‐carriers showed decreased connectivity between PCC/precentral gyrus and inferior parietal lobe, considered a hub for integrating multisensory information (Seghier,  ) and DMN (Davey et al.,  ). Here, increased PCC activation in G‐carriers may indicate failed appropriate deactivation during painful stimulation, where attentional focus is presumably external. Increased connectivity in AA homozygotes may indicate deactivation of the DMN and presumably activation of the fronto‐parietal network (Leech & Sharp,  ). One could argue that reduced control over pain‐relevant pathways in FM during rest may be complemented by additional modulations in connectivity through genetic dispositions independent of disease. 
+
+Functional data did not reveal a significant group‐by‐genotype interaction, indicating similar effects of OPRM1 in FM subjects and HC. As no correlation between PCC/precentral gyrus activation and clinical measures was found, no FM‐specific effects of OPRM1 were identified. With respect to perception, higher catastrophizing scores were linked to higher experimental pain ratings in FM subjects, regardless of genotype. Additionally, no differences in pain‐evoked brain activation between FM and HC were found with perceived pain intensity adjusted between groups, which is in line with previous reports (López‐Solà et al.,  ). However, the interaction analysis may have been insufficiently powered and differential OPRM1 effects between groups cannot be ruled out, in fact, we suggest that the OPRM1 effects were driven by the FM group. Further research is needed to investigate whether cerebral OPRM1 differences are specific to FM. 
+
+Importantly, a combination of the OPRM1 G‐allele and other factors may, nonetheless, play a role in chronic pain. G‐carriers in clinical pain cohorts have been associated with less preferential characteristics (Menon et al.,  ; Tan et al.,  ; Wei et al.,  ). However, contrasting results (Ballina et al.,  ; Linnstaedt et al.,  ) emphasize the need for more research on the role of OPRM1 in acute and chronic pain. 
+
+We did not observe OPRM1 differences in pain sensitivity or pain ratings, which is in line with studies in FM and HC (Peciña et al.,  ; Solak et al.,  ; Tour et al.,  ), however, there have been mixed reports in clinical cohorts (Menon et al.,  ) and HC (Fillingim et al.,  ). Similar to our results, no differences in other FM characteristics, for example, depression, have been observed (Solak et al.,  ; Tour et al.,  ). Given the lacking OPRM1 effect on behavioural/physiological measures, we cannot conclude that the differences in neural processing are accompanied by perceptual or clinically relevant discrepancies. We, thus, suggest that differing cerebral modulatory processes may lead to similar behavioural/perceptual outcome. 
+
+We acknowledge some caveats associated with this study. First, due to the genotype frequency in OPRM1 the absolute number of G‐allele carriers was smaller than homozygous AA, resulting in uneven subgroup sizes. Thus, some analyses may have been insufficiently powered, which limits the interpretability, particularly absent interaction effects between genotype, group and pressure level. We cannot conclude that the OPRM1 effect is specific to a certain pain intensity. Importantly, we emphasize that the observed OPRM1 differences are in need of replication in a larger sample. Second, due to lacking an affective pain measure, the current data cannot corroborate a reported association between the OPRM1 G‐allele and affective regulation (Finan et al.,  ). Finally, as FM is predominant in women, we included only female participants. Our results may, therefore, not be applicable to males. 
+
+To conclude, this study provides further evidence for a functional role of the OPRM1 polymorphism in the neural processing of evoked pain. Specifically, G‐allele carriers showed increased activation in PCC/precentral gyrus and decreased functional connectivity with the fronto‐parietal network, suggesting alternative pain modulatory mechanisms between OPRM1 variants. Finally, we speculate that the OPRM1 effect may be driven by FM subjects. 
+
+
+## CONFLICT OF INTEREST 
+  
+There are no conflicts of interest to declare. 
+
+
+## AUTHORS' CONTRIBUTIONS 
+  
+E.K. and K.B.J. conceptualized and designed the study. E.K. acquired funding. E.K. and M.S. provided resources. D.K. screened participants. A.S. and J.T. collected the data. M.S. provided expertise on genotyping. I.E. analysed the data and prepared the original draft. I.E., E.K. and A.S. interpreted the data. All authors discussed the results and commented on the manuscript. 
+
+
+## Supporting information 
+  
+ ",33064887,pubget
+3960334.0,Girls’ challenging social experiences in early adolescence predict neural response to rewards and depressive symptoms,"BA, Brodmann Area
+BOLD, blood-oxygen-level-dependent
+EPI, echo planar imaging
+fMRI, functional magnetic resonance imaging
+LN, natural log transformation
+mPFC, medial prefrontal cortex
+MNI, Montreal Neurological Institute
+OFC, orbitofrontal cortex
+PGS, Pittsburgh Girls Study
+PGS-E, Pittsburgh Girls Study-Emotions Substudy
+ROI, region of interest
+TE, echo time
+TR, repetition time
+Parental warmth
+Peer victimization
+Reward
+fMRI
+Adolescence
+Depression
+","  Highlights  
+  
+Adolescent psychosocial stress prospectively predicted neural response to potential rewards. 
+  
+Low parental warmth predicted increased reward response in the mPFC, striatum, and amygdala. 
+  
+Peer victimization predicted decreased reward response in the mPFC. 
+  
+Stress-related neural response to potential rewards was correlated with depressive symptoms. 
+  
+Results support reward-focused neurodevelopmental models of depression in girls. 
+  
+  
+Developmental models of psychopathology posit that exposure to social stressors may confer risk for depression in adolescent girls by disrupting neural reward circuitry. The current study tested this hypothesis by examining the relationship between early adolescent social stressors and later neural reward processing and depressive symptoms. Participants were 120 girls from an ongoing longitudinal study of precursors to depression across adolescent development. Low parental warmth, peer victimization, and depressive symptoms were assessed when the girls were 11 and 12 years old, and participants completed a monetary reward guessing fMRI task and assessment of depressive symptoms at age 16. Results indicate that low parental warmth was associated with increased response to potential rewards in the medial prefrontal cortex (mPFC), striatum, and amygdala, whereas peer victimization was associated with decreased response to potential rewards in the mPFC. Furthermore, concurrent depressive symptoms were associated with increased reward anticipation response in mPFC and striatal regions that were also associated with early adolescent psychosocial stressors, with mPFC and striatal response mediating the association between social stressors and depressive symptoms. These findings are consistent with developmental models that emphasize the adverse impact of early psychosocial stressors on neural reward processing and risk for depression in adolescence. 
+ "," 
+## Introduction 
+  
+Depression is a leading cause of global disease burden with a 16.6% lifetime prevalence ( ,  ). Although the prevalence of depression during childhood is less than 3% ( ), rates of depression increase sharply during adolescence with the first onset occurring between the ages of 12 and 19 years in 20% of individuals who experience depression during their lifetimes ( ). Rates of depression are particularly high in adolescent girls (cumulative prevalence of 20.8%), who are twice as likely to become depressed compared to adolescent boys ( ). Because depression is a recurrent disorder, experiencing depression for the first time in childhood or adolescence, compared with onset later in life, results in greater lifetime depression-related disability ( ). Thus, studies that examine risk factors for the development of depression in adolescent girls are particularly relevant for prevention and intervention efforts. 
+
+Parent and peer relationships are both important to adolescent development, and stressors in either social domain can increase risk for psychopathology. There is a large body of research documenting the impact that parenting behaviors, such as emotional responsiveness and warmth, have on children's emotional development broadly ( ,  ), and on depressive symptoms specifically ( ). Additional data from longitudinal studies indicate that low parental warmth increases risk for depression in children and adolescents ( ,  ,  ) and decreases resilience in adolescents with a high genetic and socioeconomic risk for the disorder ( ,  ). Early adolescence in particular is characterized by decreases in parent–child relationship quality ( ,  ), which could contribute to the higher risk for psychopathology during this developmental period relative to childhood. 
+
+Although parents continue to be important sources of social support and play a role in adolescents’ mental health, peer relationships become increasingly important as adolescents individuate from parents and form social hierarchies with peers. Stressful interactions with peers, including emotional exclusion and aggression, are particularly difficult experiences for adolescents ( ,  ), and approximately 50% of sixth and seventh graders experience these forms of peer victimization ( ). Peer victimization is also associated with increased risk for depression. A meta-analysis of cross-sectional studies indicated that peer victimization during childhood or adolescence was moderately associated with depression, and had a stronger relationship with depression than other negative psychosocial outcomes such as anxiety ( ). Peer victimization also predicted later depressive symptoms in several longitudinal studies of children and adolescents ( ,  ,  ). 
+
+Although there is substantial evidence that low parental warmth and peer victimization are both associated with risk for depression, few studies have examined the potential neural mechanisms of these effects. Several developmental models of depression have focused on the interface between adolescent social development and brain development in conceptualizing vulnerability to depression ( ,  ,  ). In this view, adolescent development of neural reward circuitry is a key process in the etiology of depression and depressive anhedonia. Furthermore, stressors that occur during adolescence may disrupt the development of reward-related circuitry, such as the medial prefrontal cortex (mPFC) – a region implicated in self-relevant and social processing as well as reward function ( ,  ) – and the ventral striatum – a region implicated in motivation to obtain rewards ( ). Consistent with these neurodevelopmental models of depression, neural response during reward anticipation and following rewarding outcomes is disrupted in adolescents and adults with depression ( ,  ,  ,  ). There is also evidence that exposure to early life stress, such as childhood maltreatment, is associated with reductions in reward-directed behavior ( ), and maternal deprivation produces anhedonic behaviors (e.g., decreased sucrose preference) in rodents and non-human primates ( ), behaviors that are supported by neural reward circuitry ( ). 
+
+The aim of the present paper was to examine the relationship between social stressors experienced in early adolescence and neural response to rewards and depressive symptoms in later adolescence. Low parental warmth, peer victimization, and depressive symptoms were assessed at ages 11 and 12 and used to predict neural response during reward anticipation at age 16 in a large sub-sample of adolescent girls from the ongoing Pittsburgh Girls Study (PGS). Depressive symptoms were also assessed at age 16 and used to test associations with neural response to potential rewards in areas that were also associated with early adolescent social stressors. Based on previous studies showing increased mPFC response during reward anticipation in depressed adolescents ( ) and adults ( ), we expected that low parental warmth, peer victimization, and depressive symptoms would be associated with increased mPFC response during reward anticipation. We also expected that low parental warmth, peer victimization, and depressive symptoms would be associated with decreased ventral striatum response to potential rewards, consistent with other studies that found decreased striatal response to rewards in clinically depressed samples ( ,  ,  ). Finally, we hypothesized that neural response to potential rewards in the mPFC and ventral striatum would mediate the association between early adolescent social stress and depressive symptoms at age 16, even after controlling for early adolescent depressive symptoms. 
+
+
+## Materials and methods 
+  
+### Participants 
+  
+Participants were girls and their birth mothers recruited from the longitudinal Pittsburgh Girls Study ( ). The PGS sample was formed following an enumeration of households with girls between the ages of 5 and 8 in the city of Pittsburgh. Of the 2992 eligible families, 2451 (85%) were successfully re-contacted and agreed to participate in a prospective study. Girls in the current fMRI study were participants in the ongoing longitudinal PGS Emotions sub-study (PGS-E), a study of precursors to depression beginning with laboratory assessments of girls and their mothers at age 9 years. PGS-E participants were drawn from the youngest participants in the PGS who either screened high on measures of depressive symptoms at age 8, or who were included in a random selection from the remaining 8-year old PGS girls. Girls whose scores fell at or above the 75th percentile by their own report on the Short Moods and Feelings Questionnaire ( ) and/or by their mother's report on the Child Symptom Inventory ( ) comprised the screen-high group (  N   = 135). There were significantly more African American than European American girls in the screen-high group. Thus, the girls selected from the remainder were matched to the screen-high group on race. Of the 263 families eligible to participate in the PGS-E, 232 (88.2%) agreed to participate and completed the first laboratory assessment when the girls were nine years of age. Retention in each year was above 95%. 
+
+At age 16, 194 participants completed the annual PGS-E assessment and 147 completed the reward task and fMRI scan (22 refused to be scanned or could not be scheduled, 25 were ineligible for scanning at the time of the study due to pregnancy, braces, or other scanning exclusions). An additional 27 participants who completed the scan were excluded from analyses. Reasons for exclusion included <80% striatum coverage (  n   = 12), >2 mm or 2° average movement in any direction during the scan (  n   = 6), poor quality scan (  n   = 2), incidental findings (  n   = 1), <80% response rate on the reward task (  n   = 2), and not understanding the reward task (  n   = 4). Subsequently, data from 120 participants were available for analyses. Of this sample, 65% were Black, 27% were White, and 8% were multi-racial. Sixty percent of the study families received some form of public assistance when the girls were between the ages of 9 and 12, with 26% of the families receiving public assistance continuously during that period. The mean number of years that participants’ families received public assistance was 0.43 (  SD   = 0.42), indicating that participants’ families received public assistance slightly less than 2 out of 4 years (ages 9–12) on average. The study distribution for race and public assistance was representative of the full PGS-E sample. 
+
+
+### Questionnaires and interviews 
+  
+Low parental warmth was assessed by parent report using six items from the Parent–Child Rating Scale ( ). Items (e.g., “How often have you wished [your daughter] would just leave you alone”) were scored on a three-point scale (1 = ‘almost never’ to 3 = ‘often’). Higher scores were indicative of lower parental warmth. Cronbach's α for low parental warmth items ranged from 0.75 (age 11) to 0.76 (age 12). Scores at ages 11 and 12 were correlated with one another (Pearson's   r   = 0.59,   p   < 0.001) and were averaged for analysis (  M   = 8.71,   SD   = 2.09). 
+
+Peer victimization was assessed using nine items from the Peer Experiences Scale ( ). Items assessed victimization by physical aggression and social exclusion on a five-point scale (0 = ‘never’ to 4 = ‘a few times per week’). Cronbach's   α   for the nine victimization items ranged from 0.85 (age 11) to 0.83 (age 12). Scores at ages 11 and 12 were correlated with one another (Pearson's   r   = 0.63,   p   < 0.001) and were averaged for analysis (  M   = 3.33,   SD   = 4.30). 
+
+Current symptoms of depression (i.e., past month) were measured in each year using the Schedule for Affective Disorders and Schizophrenia for School-Age Children–Present and Lifetime Version ( ), a semi-structured psychiatric diagnostic interview, which was administered separately to the mother and the girl by research assistants who were trained and monitored by a licensed clinical psychologist (KK). Each of the nine symptoms of depression was assessed on a three-point scale (1 = ‘not present’, 2 = ‘subthreshold’, 3 = ‘threshold’) regardless of whether disturbance in mood or anhedonia were endorsed, thereby providing a continuous measure of depression symptom counts. Thirteen percent of the girls’ interviews were randomly selected and coded for assessing interrater reliability. For youth-report data, the average intraclass correlation coefficient for total number of symptoms was 0.92. For caregiver report, the intraclass correlation coefficient for total number of symptoms was 0.58. A symptom was considered present if it was endorsed by either informant. At each wave of data collection the alpha coefficient for the nine depression symptoms based on the combined informants was above 0.55. 
+
+
+### Reward task 
+  
+Participants performed a reward-guessing task with a slow event-related design during fMRI acquisition. This task was designed to index brain activation during anticipation of monetary incentives. Previous studies show that this task reliably elicits activation in neural reward circuitry ( ,  ). 
+
+Participants were instructed to guess whether the value of a visually presented card, with possible value from 1 to 9, would be greater than or less than five. Each trial began with the presentation of a blank card. Participants had 4 s to guess the value of the card via button press. The type of trial was then displayed for 6 s using an image with hands shuffling cards overlaid on an upward facing yellow arrow to indicate potential reward trials or a downward facing yellow arrow to indicate potential loss trials. This was followed by presentation of the “actual” value of the card for 500 ms, feedback on the trial outcome for 500 ms (a green upward-facing arrow for win, a red downward-facing arrow for loss, or a yellow circle for a no-change outcome), and a crosshair was displayed for 9 s. There were 24 trials, 20 s each, administered over a single 8 min run. Trials were presented in pseudorandom order and outcomes were predetermined with a balanced number of trial types (12 possible-win, 12 possible-loss; 6 win, 6 loss, and 12 neutral outcomes). This number of trials was previously shown to be adequate to elicit a robust BOLD response in our regions of interest ( ). Participants were told that they would receive their winnings after the scan; in fact, all participants received $10. 
+
+
+### MRI acquisition, processing, and analysis 
+  
+Neuroimaging was conducted on a Siemens 3.0 T Tim Trio scanner. BOLD functional images were acquired using a gradient echo planar imaging (EPI) sequence that included 39 axial slices (3.1 mm wide) beginning at the cerebral vertex and extending across the entire cerebrum and most of the cerebellum (TR/TE = 2000/28 ms, field of view = 20 cm, matrix = 64 × 64). Scanning parameters were selected to optimize BOLD signal quality while maximizing whole brain coverage. A reference EPI scan was acquired before fMRI data collection to visually inspect for artifacts (e.g., ghosting) and ensure adequate signal across the entire volume. In addition, a 160-slice high-resolution sagittally acquired T1-weighted anatomical image was collected for co-registration and normalization of functional images (TR/TE = 2300/2.98 ms, field of view = 20 cm, matrix = 256 × 240). 
+
+Preprocessing and analysis of imaging data were conducted using Statistical Parametric Mapping software (SPM8;  ). Anatomical images were auto-segmented in SPM8 prior to analysis. Functional image preprocessing included spatial realignment to the first volume in the time series to correct for head motion, spatial normalization to Montreal Neurological Institute (MNI) stereotaxic space using a 12-parameter affine model, and image smoothing using a Gaussian filter set at 6 mm full-width half-maximum to minimize noise and individual differences in gyral anatomy. Voxel-wise signal was ratio-normalized to the whole-brain global mean. Preprocessed data were inspected prior to second-level analysis to ensure that all participants had good whole brain coverage, ventral striatum coverage of at least 80%, and less than 2 mm or 2° average movement in any direction during the scan. 
+
+Second-level random effects models were used to estimate neural response to rewards while accounting for scan-to-scan and between-participant variability. For each participant, condition effects were calculated at each voxel using paired   t  -tests for reward anticipation > baseline. Reward anticipation was defined as the 12 potential-win intervals that included the 6 s potential-win arrow, 500 ms number presentation, 500 ms arrow feedback and the first second of fixation (8 s total). The reward anticipation period extended 2 s beyond the potential-win arrow to account for the delay in hemodynamic response relative to neural activity and capture as much of the reward anticipation response as possible while avoiding substantial overlap with BOLD response to reward outcome events. The last 3 s of fixation for all 24 trials served as the baseline condition. By averaging the last 3 s of fixation across all trial outcomes (6 reward, 6 loss, 12 neutral), the baseline condition served as a relatively neutral comparison for contrasts with reward anticipation ( ,  ). 
+
+Analysis of imaging data focused on four regions of interest (ROIs): striatum, mPFC, orbitofrontal cortex (OFC), and amygdala. ROIs were defined using PickAtlas 3.0.3 ( ). The striatal ROI was defined as a sphere with a 20 mm radius, centered on the Talairach coordinates of   x   = 0,   y   = 10 and   z   = −10, and encompassing the ventral striatum (including nucleus accumbens) and dorsal striatum bilaterally. The mPFC ROI was defined as a sphere with a 25 mm radius, centered on Talairach coordinates   x   = 0,   y   = 42,   z   = 18, and encompassing BA32 and medial regions of BA9 and BA10. Spheres were used for the striatal and mPFC ROIs because this approach focuses analyses on the striatum and medial regions of BAs 9 and 10 more precisely than atlas-based anatomical masks of those regions (especially for mPFC, which includes medial sections of large prefrontal regions such as BA10). The OFC was defined as BA11 and BA47, and the amygdala was defined using the human PickAtlas label. AlphaSim ( ) cluster extent thresholds were calculated   a priori   to determine the minimum cluster size necessary to maintain a corrected   p   < 0.05 for each ROI (cluster extent thresholds: striatum = 189 voxels, mPFC = 178 voxels, OFC = 62 voxels, amygdala = 62 voxels). 
+
+Regression analyses were performed in SPM8 to determine whether low parental warmth and peer victimization were associated with reward anticipation across participants. Using results of these analyses, a second set of regression analyses were conducted to determine whether current depressive symptoms were associated with neural response during reward anticipation in regions that were also associated with low parental warmth or peer victimization. To accomplish this, functional masks were created based on significant clusters yielded by regressions of BOLD response on early adolescent social stressors for each of the four anatomical ROIs. These functional masks were saved and used as functional ROIs for regressions of depressive symptoms on BOLD response. Because scores for low parental warmth, peer victimization, and depressive symptoms were positively skewed, these scores were log-transformed prior to analysis to better approximate a normal distribution. A constant was added to the peer victimization and depression scores prior to log-transformation because raw scores included values of zero. To account for the potential relationship between early depressive symptoms and neural response to rewards at age 16, all regression analyses included depressive symptom count averaged across ages 9–12 as a covariate. To address potential contributions of SES to development of reward circuitry, regression analyses also included, as a covariate, the average number of years that participants’ families received public assistance across ages 9–12. Regression weights and confidence intervals for significant clusters of activation were computed in SPSS using extracted SPM beta values for the average BOLD response across each significant cluster. 
+
+Finally, for each region that was significantly associated both with early adolescent social stressors and current depressive symptoms, mediation analyses were used to examine whether neural response during reward anticipation accounted for a significant portion of the association between early adolescent social stress and later depressive symptoms. To accomplish this, a second set of functional masks was created based on significant clusters yielded by regressions of BOLD response on current depressive symptoms for each functional ROI described above. These functional masks were saved and used as functional ROIs for regressions of low maternal warmth on BOLD response and peer victimization on BOLD response. Average BOLD response beta values across each significant cluster were extracted from these regressions, and tested as a mediator of the relationships between low maternal warmth and depressive symptoms, and peer victimization and depressive symptoms. Mediation analyses were implemented using the bootstrap method with the SPSS PROCESS macro ( ). Average depressive symptom count from ages 9 to 12 and average years of family public assistance from ages 9 to 12 were included as covariates in mediation analyses. 
+
+
+
+## Results 
+  
+### Participant characteristics and clinical outcomes 
+  
+Of the 120 girls with analyzable fMRI data, 3 met criteria for current major depressive disorder and an additional 7 met criteria for minor depressive disorder at age 16. The point prevalence of major depression in the sample (2.5%) is consistent with the point prevalence of depression in epidemiologic studies of adolescents (3.37 [95% CI: 1.35, 5.39];  ). The mean number of depressive symptoms at age 16 was 1.18 (  SD   = 1.35). The mean number of depressive symptoms across ages 9–12 was 2.06 (  SD   = 1.57). One-tailed Pearson's correlations indicated that depressive symptoms at age 16 were positively associated with low parental warmth (  r   = 0.42,   p   < 0.001) and peer victimization (  r   = 0.37,   p   < 0.001) in early adolescence. Low parental warmth and peer victimization were modestly associated with each other (  r   = 0.17,   p   = 0.03). 
+
+
+### Association between early adolescent social stressors and reward-related BOLD response 
+  
+Lower levels of parental warmth were associated with increased response in the dorsal and rostral mPFC (  R   = 0.10, beta = 0.26, 95% CI: 0.14, 1.02,   p   = 0.01), ventral striatum (  R   = 0.11, beta = 0.28, 95% CI: 0.21, 1.11,   p   = 0.004), and amygdala (  R   = 0.11, beta = 0.25, 95% CI: 0.10, 0.76,   p   = 0.012) during reward anticipation. Higher levels of peer victimization were associated with decreased response in the dorsal and rostral mPFC during reward anticipation (  R   = 0.10, beta = −0.26, 95% CI: −0.27, −0.04,   p   = 0.007). OFC response during reward anticipation was not associated with either low parental warmth or peer victimization. Detailed SPM8 regression results are presented in   and  .   
+Low parental warmth and peer victimization as predictors of BOLD response during reward anticipation. 
+          
+Association between social stressors and blood-oxygen-level-dependent (BOLD) response during reward anticipation. Low parental warmth was positively associated with BOLD response in the dorsal and rostral mPFC (A;   R   = 0.10), ventral striatum (B;   R   = 0.11), and amygdala (C;   R   = 0.11). LN = natural log transformation. Peer victimization was negatively associated with BOLD response in the rostral mPFC (D;   R   = 0.10). 
+  
+
+
+### Association between reward-related BOLD response and depressive symptoms 
+  
+Higher levels of concurrent depressive symptoms were associated with increased response in regions of the rostral mPFC (  R   = 0.07, beta = 0.19, 95% CI: −0.01, 0.33,   p   = 0.06) and ventral striatum (  R   = 0.06, beta = 0.22, 95% CI: 0.01, 0.32,   p   = 0.04) that were also positively associated with low parental warmth. Detailed SPM8 regression results are presented in   and  . In addition, bootstrap tests of mediation indicated that BOLD response in both the mPFC (ES = 0.15, 95% CI: 0.004, 0.40,   p   < 0.05) and ventral striatum (ES = 0.14, 95% CI: 0.001, 0.41,   p   < 0.05) significantly mediated the association between low parental warmth and depressive symptoms. Depressive symptoms were not significantly associated with neural response during reward anticipation in regions that were also associated with peer victimization.   
+Depressive symptoms predicting increased BOLD response during reward anticipation in regions that are also associated with low parental warmth. 
+      
+Association between depressive symptoms and reward-related BOLD response in regions that were also associated with low parental warmth. Depressive symptoms were positively associated with BOLD response in the rostral mPFC (A;   R   = 0.07) and ventral striatum (B;   R   = 0.06) during reward anticipation. LN = natural log transformation. 
+  
+
+
+
+## Discussion 
+  
+The results of the present study indicate that social stressors experienced by girls in early adolescence are associated with neural response to anticipated rewards at age 16. Low parental warmth at ages 11 and 12 had particularly robust associations with neural response to reward at age 16, with large clusters of increased activation in the mPFC and ventral striatum during reward anticipation. In contrast, the relationship between peer victimization at ages 11 and 12 and neural response to reward was more modest and in the opposite direction. Greater peer victimization was associated with   decreased   mPFC activation during reward anticipation, and it did not predict reward response in other reward-related ROIs. These results suggest that in early adolescence, low parental warmth may have a greater influence than peer victimization on later adolescent neural response to reward, and that different types of social stressors may influence reward circuitry in different ways. Of note, low parental warmth is likely to be more stable across child development than peer victimization ( ,  ). Girls who experience low parental warmth at ages 11 and 12 may have experienced similar parenting behaviors at multiple time points in development, with cumulative influence on their brain development. Peer groups, in contrast, tend to shift frequently during adolescence ( ). Therefore, the experience of social exclusion or aggression may be more normative, inconsistent, and time-limited, with less robust influence than parental warmth on adolescent brain function. 
+
+We also found that regions of the mPFC and striatum that were correlated with early adolescent parental warmth were also positively related to depressive symptoms at age 16. In fact, neural response to potential rewards in the mPFC and striatum mediated the relationship between low parental warmth and depressive symptoms. The associations of early adolescent parental warmth and subsequent depressive symptoms with mPFC reward anticipation response were in the predicted direction: lower warmth predicted greater dorsal and rostral mPFC response, and higher depressive symptoms predicted greater rostral mPFC response. This is consistent with previous studies that found increased mPFC reward anticipation response in depressed participants ( ,  ). The mPFC is instrumental to evaluating the relative value of rewards and coordinating reward-related behavior ( ) as well as self-relevant and social processing ( ,  ). Increased response in this region in individuals who have experienced low parental warmth or have higher depressive symptoms may reflect increased evaluation of personal performance on the task in light of previous experience or the imagined performance of others. Given that the rostral mPFC is also involved in self-related processing and internal monitoring ( ), increased response to reward in the mPFC could also reflect difficulty disengaging from this internal self-focus during the task. 
+
+In the present study, low parental warmth and higher levels of depressive symptoms were each independently associated with   increased   ventral striatal response during reward anticipation. This pattern was in the opposite direction to our prediction given the existing research in which   decreased   striatal response in depressed participants was observed relative to controls ( ,  ,  ). In those previous studies, however, participants were clinically depressed and samples were comprised of both males and females. The present study also measured reward anticipation response and depressive symptoms when the girls were 16, while other studies have assessed reward response in middle-aged adults ( ,  ) or across wider age ranges (8–17 in  ). Neurons in the mPFC and striatum undergo dramatic pruning and reorganization during adolescence ( ,  ,  ). Therefore, sample differences could reflect the developmental phase of our fMRI and depressive symptom assessment. 
+
+Another possible explanation for this inconsistency with the extant literature is that different depression phenotypes will yield different neural signatures. The striatum is involved in coding the incentive salience, or motivational value, of rewards ( ). Although we cannot tease apart depression sub-types in the current study, it may be that for depression characterized by anhedonia and low positive emotion one would expect blunted striatal activity in anticipation of reward. Depression characterized by dysphoria or irritability may be more reactive to reward opportunities. Similarly, predictable and consistent low parental warmth in the parent–child relationship may confer different risks for atypical neural processing of rewards than inconsistent or unpredictable parenting behavior. Differentiating patterns of brain activity among different depression phenotypes and risk contexts is a critical component to the development of brain-based algorithms for optimizing interventions. Our results suggest that there may be different patterns of neural activity within the broad domain of depression and contextual risks. 
+
+This is one of the first studies to use longitudinal data on early adolescent social stressors to predict brain reward processing and depressive symptoms later in adolescence, and it is the first study of social stress and reward processing in an all-female sample. Adverse parent and peer relationships may be especially influential for girls because girls are more likely than boys to value social cooperation, rely on social support to cope with stressors, attribute negative events to themselves, over-empathize with others, and suppress negative emotions to comply with the others’ expectations ( ). For example, girls who have parents who are cold or punishing may blame themselves for their parents’ behavior and go to great lengths to comply with their parents’ wishes at the expense of their own emotional expression and desire for social support. Furthermore, early-adolescent girls report greater declines in the quality of their relationships with their parents ( ) and have higher rates of relational victimization ( ) than boys. The high value that girls place on social cooperation and support, combined with the decrease in the quality of parent–child relationships and high rate of relational victimization during the transition from childhood to adolescence, may partially account for girls’ relatively greater risk for later depression. Other strengths of this study include the large sample size and the inclusion of participants who are at high risk for adverse psychosocial outcomes due to low socioeconomic status. 
+
+Conversely, because this study included an all-female sample, the relationship between low parental warmth, peer victimization, and neural reward processing in boys remains to be determined. Likewise, because many of the girls in the study were from low-income, urban neighborhoods, the results presented here may not generalize to girls from other socioeconomic backgrounds or environments. Although we included years of public assistance as a covariate in our analyses to control for the effect of poverty on reward response, we were not able to include a comprehensive indicator of socioeconomic status, such as income-to-needs, because complete income information was not provided by all participants. While possibly less fine-grained a measure of SES, public assistance represents an objective measure of family financial difficulty and indicates which of our generally low-SES participants were particularly burdened with poverty. Childhood SES has been reported to influence dorsal mPFC response to reward in adults ( ) as well as emotional processing ( ) and PFC function ( ). Financial stress may also weaken parents’ caregiving resources and the ability of children to cope with psychosocial stressors such as low parental warmth and peer victimization ( ). Additional research that examines the synergistic effects of socioeconomic status, parenting behavior, and peer stressors could delineate the neural mechanisms by which different stressors impact brain function during adolescence. 
+
+Additional study limitations include the cross-sectional fMRI assessment, the circumscribed assessment of psychosocial stress through two self-report measures, and the limited number of trials in the Reward Guessing Task. First, although our design is longitudinal and prospective, we did not assess brain functioning earlier in adolescence, and thus cannot infer that early adolescent social stressors caused disruptions in later neural response to reward. Causality has been established in animal studies, which show that early social stress produces anhedonic behavior in rodents and non-human primates ( ), and these behaviors depend on brain reward circuitry ( ). However, a number of other factors could explain the association between early social stressors and brain reward response in the present study. In particular, girls’ depressive symptoms and/or altered reward responsiveness may influence parenting and peer relationships (e.g.,  ). Furthermore, while some parental psychopathology is likely reflected by our measure of low parental warmth, we did not include a separate index of parent/family psychopathology in our analyses, nor did we include other measures of parenting that could moderate the association between warmth and reward and warmth and depression symptoms. In addition, self-reported data have some limitations, and observational measures of adolescents’ relationships with their parents and peers may provide a clearer picture of the relationship between social stress and neural response to reward. Finally, the limited number of trials in the Reward Guessing Task (12 potential win) may have reduced the signal-to-noise ratio in analyses of reward anticipation response. Notably, including too many trials and thereby extending the duration of the task also has disadvantages due to risk of task habituation, fatigue, and movement. We limited the number of trials in the Reward Guessing Task to balance these risks and because we’ve previously found that 12 reward anticipation trials is an adequate number to elicit a robust BOLD response in our regions of interest ( ). 
+
+Despite these limitations, this study is consistent with the idea that early social stress affects the neurodevelopment of reward circuits and thereby increases risk for depressive symptoms. The results of this study, particularly the divergent directions of association between neural response to reward and the two types of psychosocial stressors studied here, indicate that the influence of specific psychosocial stressors may be differentially weighted in the brain. Future studies of psychosocial stress and reward processing during adolescence should carefully consider the relative impact of parent, peer, and other stressors and their developmental timing on systems involved in the pathophysiology of depression. Furthermore, there is some evidence that neural response to monetary and social rewards differs between females and males: monetary and social rewards elicit similar patterns of striatum response in females, while men are more responsive to monetary rewards and less responsive to social rewards than women ( ,  ). Given the pronounced sex differences in the incidence of depression during adolescence and the influence of psychosocial stressors, it will be important for future studies to examine the influence of psychosocial stressors on reward processing in both sexes. Ultimately, these studies may lead to developmentally-appropriate and sex-specific interventions for reward-related brain function and other neural and behavioral precursors of depression in adolescence. 
+
+
+## Conflicts of interest statement 
+  
+There are no conflicts of interest to report in submission of this manuscript. 
+
+ ",24397999,pubget
diff --git a/store/neurostore/ingest/__init__.py b/store/neurostore/ingest/__init__.py
index f525c2dee..f819fe814 100644
--- a/store/neurostore/ingest/__init__.py
+++ b/store/neurostore/ingest/__init__.py
@@ -29,6 +29,8 @@
 )
 from neurostore.models.data import StudysetStudy, _check_type
 
+META_ANALYSIS_WORDS = ['meta analysis', 'meta-analysis', 'systematic review']
+
 
 def ingest_neurovault(verbose=False, limit=20, overwrite=False, max_images=None):
     # Store existing studies for quick lookup
@@ -125,6 +127,7 @@ def add_collection(data):
             )
             images.append(image)
 
+        base_study.update_has_images_and_points()
         db.session.add_all(
             [base_study] + [s] + list(analyses.values()) + images + list(conditions)
         )
@@ -232,7 +235,7 @@ def ingest_neurosynth(max_rows=None):
                     columns = [
                         c
                         for c in source_base_study.__table__.columns
-                        if c != "versions"
+                        if c not in ("versions", "__ts_vector__")
                     ]
                     for ab in base_studies[1:]:
                         for col in columns:
@@ -360,9 +363,10 @@ def ingest_neurosynth(max_rows=None):
         for note in notes:
             to_commit.append(note.analysis)
         db.session.add_all([annot] + notes + to_commit + [d])
-        db.session.flush()
+        db.session.commit()
         for bs in base_studies:
             bs.update_has_images_and_points()
+            db.session.add_all(base_studies)
         db.session.commit()
 
 
@@ -442,17 +446,28 @@ def ingest_neuroquery(max_rows=None):
         studies=Study.query.filter_by(source="neuroquery").all(),
     )
     db.session.add(d)
-    db.session.flush()
+    db.session.commit()
     for bs in base_studies:
         bs.update_has_images_and_points()
+        db.session.add_all(base_studies)
     db.session.commit()
 
 
 def load_ace_files(coordinates_file, metadata_file, text_file):
-    coordinates_df = pd.read_table(coordinates_file, sep=",", dtype={"pmid": str})
-    metadata_df = pd.read_table(metadata_file, sep=",", dtype={"pmid": str})
-    text_df = pd.read_table(text_file, sep=",", dtype={"pmid": str})
+    coordinates_df = pd.read_table(coordinates_file, sep=",", dtype=str)
+    metadata_df = pd.read_table(metadata_file, sep=",", dtype=str)
+    text_df = pd.read_table(text_file, sep=",", dtype=str)
 
+    for col in ['x', 'y', 'z']:
+        if col in coordinates_df.columns:
+            coordinates_df[col] = pd.to_numeric(coordinates_df[col], errors='coerce')
+
+    text_df.fillna("", inplace=True)
+    metadata_df.fillna("", inplace=True)
+    coordinates_df.fillna("", inplace=True)
+
+    for df in [coordinates_df, metadata_df, text_df]:
+        df.pmid = df.pmid.str.split(".").str[0]
     # preprocessing
     metadata_df.set_index("pmid", inplace=True)
     text_df.set_index("pmid", inplace=True)
@@ -464,173 +479,130 @@ def load_ace_files(coordinates_file, metadata_file, text_file):
     return coordinates_df, metadata_df, text_df
 
 
-def ace_ingestion_logic(coordinates_df, metadata_df, text_df):
-    to_commit = []
-    # see if there are duplicates for the newly created base_studies
-    all_base_studies = []
-    with db.session.no_autoflush:
-        all_studies = {
-            s.pmid: s for s in Study.query.filter_by(source="neurosynth").all()
+def ace_ingestion_logic(coordinates_df, metadata_df, text_df, skip_existing=False):
+    def get_base_study(metadata_row):
+        doi = None if isinstance(metadata_row.doi, float) or metadata_row.doi == '' else metadata_row.doi
+        pmid = metadata_row.Index
+        base_studies = BaseStudy.query.filter(or_(BaseStudy.doi == doi, BaseStudy.pmid == pmid)).all()
+
+        if len(base_studies) == 1:
+            return base_studies[0]
+        elif len(base_studies) > 1:
+            return merge_base_studies(base_studies)
+        else:
+            created_bs = [bs for bs in all_base_studies if bs.doi == doi and bs.pmid == pmid]
+            if created_bs:
+                return created_bs[0]
+            return BaseStudy.query.filter_by(pmid=pmid).one_or_none()
+
+    def merge_base_studies(base_studies):
+        source_base_study = next(filter(lambda bs: bs.pmid == pmid and bs.doi == doi, base_studies), base_studies[0])
+        other_base_studies = [bs for bs in base_studies if bs.id != source_base_study.id]
+        columns = [c.name for c in source_base_study.__table__.columns if c.name not in ("versions", "__ts_vector__")]
+        for ab in other_base_studies:
+            for col in columns:
+                source_attr = getattr(source_base_study, col)
+                new_attr = getattr(ab, col)
+                setattr(source_base_study, col, source_attr or new_attr)
+            source_base_study.versions.extend(ab.versions)
+            db.session.delete(ab)
+        return source_base_study
+
+    def update_study_info(study, metadata_row, text_row, doi, pmcid, year, level):
+        study_info = {
+            "name": metadata_row.title,
+            "doi": doi,
+            "pmid": metadata_row.Index,
+            "pmcid": pmcid,
+            "description": text_row.abstract,
+            "authors": metadata_row.authors,
+            "publication": metadata_row.journal,
+            "year": year,
+            "level": level,
         }
-        for metadata_row, text_row in zip(
-            metadata_df.itertuples(), text_df.itertuples()
-        ):
-            base_study = None
-            doi = None if isinstance(metadata_row.doi, float) else metadata_row.doi
-            id_ = pmid = metadata_row.Index
-            year = (
-                None
-                if np.isnan(metadata_row.publication_year)
-                else int(metadata_row.publication_year)
-            )
-            # find an base_study based on available information
-            if doi is not None:
-                base_studies = BaseStudy.query.filter(
-                    or_(BaseStudy.doi == doi, BaseStudy.pmid == pmid)
-                ).all()
+        if isinstance(study, Study):
+            study_info["source"] = "neurosynth" if "ace" in metadata_row.source else "pubget",
+        for col, value in study_info.items():
+            source_attr = getattr(study, col)
+            setattr(study, col, source_attr or value)
 
-                if len(base_studies) == 1:
-                    base_study = base_studies[0]
-                elif len(base_studies) > 1:
-                    # find the first abstract study with both pmid and doi
-                    source_base_study = next(
-                        filter(
-                            lambda bs: bs.pmid == pmid and bs.doi == doi, base_studies
-                        ),
-                        base_studies[0],
-                    )
-                    other_base_studies = [
-                        bs for bs in base_studies if bs.id != source_base_study.id
-                    ]
-                    # do not overwrite the versions column
-                    # we want to append to this column
-                    columns = [
-                        c.name
-                        for c in source_base_study.__table__.columns
-                        if c != "versions"
-                    ]
-                    for ab in other_base_studies:
-                        for col in columns:
-                            source_attr = getattr(source_base_study, col)
-                            new_attr = getattr(ab, col)
-                            setattr(source_base_study, col, source_attr or new_attr)
-                        source_base_study.versions.extend(ab.versions)
-                        # delete the extraneous record
-                        db.session.delete(ab)
+    def process_coordinates(id_, s, metadata_row):
+        analyses = []
+        points = []
+        try:
+            study_coord_data = coordinates_df.loc[[id_]]
+        except KeyError:
+            print(f"pmid: {id_} has no coordinates")
+            return analyses, points
+        for order, (t_id, df) in enumerate(study_coord_data.groupby("table_id")):
+            a = Analysis.query.filter_by(table_id=str(t_id), study_id=s.id).one_or_none() or Analysis()
+            a.name = df["table_label"][0] or str(t_id)
+            a.table_id = str(t_id)
+            a.order = a.order or order
+            a.description = df["table_caption"][0] if not df["table_caption"].isna()[0] else None
+            if not a.study:
+                a.study = s
+            analyses.append(a)
+            point_idx = 0
+            for _, p in df.iterrows():
+                point = Point(
+                    x=p["x"], y=p["y"], z=p["z"],
+                    space=metadata_row.coordinate_space,
+                    kind=df["statistic"][0] if not df["statistic"].isna()[0] else "unknown",
+                    analysis=a,
+                    order=point_idx
+                )
+                points.append(point)
+                point_idx += 1
+        return analyses, points
 
-                    base_study = source_base_study
-                else:
-                    # see if it exists in the already created base_studies
-                    created_bs = [
-                        bs
-                        for bs in all_base_studies
-                        if bs.doi == doi and bs.pmid == pmid
-                    ]
-                    if created_bs:
-                        base_study = created_bs[0]
+    to_commit = []
+    all_base_studies = []
 
-            if doi is None:
-                base_study = BaseStudy.query.filter_by(pmid=pmid).one_or_none()
+    with db.session.no_autoflush:
+        all_studies = {s.pmid: s for s in Study.query.filter_by(source="neurosynth").all()}
+        for metadata_row, text_row in zip(metadata_df.itertuples(), text_df.itertuples()):
+            level = 'meta' if any(word in metadata_row.title.lower() for word in META_ANALYSIS_WORDS) else 'group'
+            base_study = get_base_study(metadata_row)
+            pmid = metadata_row.Index
+            pmcid = None if isinstance(metadata_row.pmcid, float) or metadata_row.pmcid == '' else metadata_row.pmcid
+            doi = None if isinstance(metadata_row.doi, float) or metadata_row.doi == '' else metadata_row.doi
+            year = None if isinstance(metadata_row.publication_year, float) or metadata_row.publication_year == '' else int(float(metadata_row.publication_year))
+
+            if skip_existing and base_study is not None and any(s.source == "neurosynth" for s in base_study.versions):
+                continue
 
             if base_study is None:
+
                 base_study = BaseStudy(
                     name=metadata_row.title,
                     doi=doi,
                     pmid=pmid,
-                    authors=metadata_row.authors,
-                    publication=metadata_row.journal,
-                    description=text_row.abstract,
+                    pmcid=pmcid,
+                    authors=metadata_row.authors or None,
+                    publication=metadata_row.journal or None,
+                    description=text_row.abstract or None,
                     year=year,
-                    level="group",
+                    level=level,
                 )
             else:
-                # try to update the abstract study if information is missing
-                study_info = {
-                    "name": metadata_row.title,
-                    "doi": doi,
-                    "pmid": pmid,
-                    "description": text_row.abstract,
-                    "authors": metadata_row.authors,
-                    "publication": metadata_row.journal,
-                    "year": year,
-                    "level": "group",
-                }
-                for col, value in study_info.items():
-                    source_attr = getattr(base_study, col)
-                    setattr(base_study, col, source_attr or value)
+                update_study_info(base_study, metadata_row, text_row, doi, pmcid, year, level)
 
-            # append base study to commit
             to_commit.append(base_study)
 
             s = all_studies.get(pmid, Study())
+            update_study_info(s, metadata_row, text_row, doi, pmcid, year, level)
 
-            # try to update the study if information is missing
-            study_info = {
-                "name": metadata_row.title,
-                "doi": doi,
-                "pmid": pmid,
-                "description": text_row.abstract,
-                "authors": metadata_row.authors,
-                "publication": metadata_row.journal,
-                "year": year,
-                "level": "group",
-                "source": "neurosynth",
-            }
-            for col, value in study_info.items():
-                source_attr = getattr(s, col)
-                setattr(s, col, source_attr or value)
-
-            analyses = []
-            points = []
-
-            try:
-                study_coord_data = coordinates_df.loc[[id_]]
-            except KeyError:
-                print(f"pmid: {id_} has no coordinates")
-                continue
-            for order, (t_id, df) in enumerate(study_coord_data.groupby("table_id")):
-                a = (
-                    Analysis.query.filter_by(table_id=str(t_id)).one_or_none()
-                    or Analysis()
-                )
-                a.name = df["table_label"][0] or str(t_id)
-                a.table_id = str(t_id)
-                a.order = a.order or order
-                a.description = (
-                    df["table_caption"][0]
-                    if not df["table_caption"].isna()[0]
-                    else None
-                )
-                if not a.study:
-                    a.study = s
-                analyses.append(a)
-                point_idx = 0
-                for _, p in df.iterrows():
-                    point = Point(
-                        x=p["x"],
-                        y=p["y"],
-                        z=p["z"],
-                        space=metadata_row.coordinate_space,
-                        kind=(
-                            df["statistic"][0]
-                            if not df["statistic"].isna()[0]
-                            else "unknown"
-                        ),
-                        analysis=a,
-                        entities=[Entity(label=a.name, level="group", analysis=a)],
-                        order=point_idx,
-                    )
-                    points.append(point)
-                    point_idx += 1
+            analyses, points = process_coordinates(pmid, s, metadata_row)
             to_commit.extend(points)
             to_commit.extend(analyses)
-            # append study as version of study
             base_study.versions.append(s)
 
     db.session.add_all(to_commit)
-    db.session.flush()
+    db.session.commit()
     for bs in all_base_studies:
         bs.update_has_images_and_points()
+        db.session.add_all(all_base_studies)
     db.session.commit()
 
 
diff --git a/store/neurostore/models/data.py b/store/neurostore/models/data.py
index f6a6cb996..a3866d21d 100644
--- a/store/neurostore/models/data.py
+++ b/store/neurostore/models/data.py
@@ -82,7 +82,8 @@ class Studyset(BaseMixin, db.Model):
         passive_deletes=True,
         cascade="all, delete-orphan",
     )
-    __ts_vector__ = db.Column(
+    _ts_vector = db.Column(
+        "__ts_vector__",
         TSVector(),
         db.Computed(
             "to_tsvector('english', coalesce(name, '') || ' ' || coalesce(description, ''))",
diff --git a/store/neurostore/openapi b/store/neurostore/openapi
index 3b391f873..3e4cba60f 160000
--- a/store/neurostore/openapi
+++ b/store/neurostore/openapi
@@ -1 +1 @@
-Subproject commit 3b391f8735d6d4f7191bcb0e1c6394c3506bc2a3
+Subproject commit 3e4cba60f52a6c6bdd1ac5b55cb70d0ae3399aab