Add articles and merge papers with preprints.

grab_articles.ipynb

@@ -39,8 +39,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Total number of publications containing \"Laird AR\"[AUTH] AND (\"2012/01/01\"[PDAT] : \"3000/12/31\"[PDAT]): 103\n",
-      "Total number of publications containing \"Sutherland MT\"[AUTH] AND (\"2012/01/01\"[PDAT] : \"3000/12/31\"[PDAT]): 29\n"
+      "Total number of publications containing \"Laird AR\"[AUTH] AND (\"2012/01/01\"[PDAT] : \"3000/12/31\"[PDAT]): 106\n",
+      "Total number of publications containing \"Sutherland MT\"[AUTH] AND (\"2012/01/01\"[PDAT] : \"3000/12/31\"[PDAT]): 32\n"
      ]
     }
    ],
@@ -119,7 +119,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "118\n",
+      "119\n",
       "114\n"
      ]
     }
@@ -179,7 +179,20 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New file created for 31814997\n",
+      "New file created for 31814997\n",
+      "New file created for 31827430\n",
+      "New file created for 31872334\n",
+      "New file created for 31872334\n",
+      "New file created for 31995811\n"
+     ]
+    }
+   ],
    "source": [
     "for _, row in df.iterrows():\n",
     "    pmid = row['pmid']\n",
@@ -196,7 +209,7 @@
     "        image = '/assets/images/papers/{0}.png'.format('-'.join(journal.lower().split(' ')))\n",
     "        title = row['title'].replace('\"', \"'\")\n",
     "        completed = template.format(title=title, nickname=nickname,\n",
-    "                                    authors=', '.join(authors), year=row['year'],\n",
+    "                                    authors=', '.join(authors), year=int(row['year']),\n",
     "                                    journal=journal, volume=row['volume'],\n",
     "                                    image=image,\n",
     "                                    issue=row['issue'], pages=row['pages'],\n",

papers/_posts/2019-12-23-flannery-metaanalytic-clustering-dissociates.md

@@ -0,0 +1,40 @@
+---
+layout: paper
+title: "Meta-analytic clustering dissociates brain activity and behavior profiles across reward processing paradigms"
+nickname: 2019-12-23-flannery-metaanalytic-clustering-dissociates
+authors: "Flannery JS, Riedel MC, Bottenhorn KL, Poudel R, Salo T, Hill-Bowen LD, Laird AR, Sutherland MT"
+year: "2019"
+journal: "Cogn Affect Behav Neurosci"
+volume:
+issue:
+pages:
+is_published: true
+image: /assets/images/papers/cogn-affect-behav-neurosci.png
+projects:
+tags: [preprint]
+
+# Text
+fulltext:
+pdf:
+pdflink:
+pmcid:
+preprint: https://www.biorxiv.org/content/10.1101/818948v1
+supplement: https://www.biorxiv.org/content/biorxiv/early/2019/10/25/818948/DC1/embed/media-1.pdf
+
+# Links
+doi: "10.3758/s13415-019-00763-7"
+pmid: 31872334
+
+# Data and code
+github:
+neurovault:
+openneuro:
+figshare:
+figshare_names:
+osf:
+---
+{% include JB/setup %}
+
+# Abstract
+
+Reward learning is a ubiquitous cognitive mechanism guiding adaptive choices and behaviors, and when impaired, can lead to considerable mental health consequences. Reward-related functional neuroimaging studies have begun to implicate networks of brain regions essential for processing various peripheral influences (e.g., risk, subjective preference, delay, social context) involved in the multifaceted reward processing construct. To provide a more complete neurocognitive perspective on reward processing that synthesizes findings across the literature while also appreciating these peripheral influences, we used emerging meta-analytic techniques to elucidate brain regions, and in turn networks, consistently engaged in distinct aspects of reward processing. Using a data-driven, meta-analytic, k-means clustering approach, we dissociated seven meta-analytic groupings (MAGs) of neuroimaging results (i.e., brain activity maps) from 749 experimental contrasts across 176 reward processing studies involving 13,358 healthy participants. We then performed an exploratory functional decoding approach to gain insight into the putative functions associated with each MAG. We identified a seven-MAG clustering solution that represented dissociable patterns of convergent brain activity across reward processing tasks. Additionally, our functional decoding analyses revealed that each of these MAGs mapped onto discrete behavior profiles that suggested specialized roles in predicting value (MAG-1 & MAG-2) and processing a variety of emotional (MAG-3), external (MAG-4 & MAG-5), and internal (MAG-6 & MAG-7) influences across reward processing paradigms. These findings support and extend aspects of well-accepted reward learning theories and highlight large-scale brain network activity associated with distinct aspects of reward processing.

papers/_posts/2020-01-29-lesage-nicotine-dependence-trait.md

@@ -0,0 +1,40 @@
+---
+layout: paper
+title: "Nicotine dependence (trait) and acute nicotinic stimulation (state) modulate attention but not inhibitory control: converging fMRI evidence from the Go-Nogo and Flanker tasks"
+nickname: 2020-01-29-lesage-nicotine-dependence-trait
+authors: "Lesage E, Sutherland MT, Ross TJ, Salmeron BJ, Stein EA"
+year: "2020"
+journal: "Neuropsychopharmacology"
+volume:
+issue:
+pages:
+is_published: true
+image: /assets/images/papers/neuropsychopharmacology.png
+projects:
+tags: []
+
+# Text
+fulltext:
+pdf:
+pdflink:
+pmcid:
+preprint:
+supplement:
+
+# Links
+doi: "10.1038/s41386-020-0623-1"
+pmid: 31995811
+
+# Data and code
+github:
+neurovault:
+openneuro:
+figshare:
+figshare_names:
+osf:
+---
+{% include JB/setup %}
+
+# Abstract
+
+Cognitive deficits during nicotine withdrawal may contribute to smoking relapse. However, interacting effects of chronic nicotine dependence and acute nicotine withdrawal on cognitive control are poorly understood. Here we examine the effects of nicotine dependence (trait; smokers (n = 24) vs. non-smoking controls; n = 20) and acute nicotinic stimulation (state; administration of nicotine and varenicline, two FDA-approved smoking cessation aids, during abstinence), on two well-established tests of inhibitory control, the Go-Nogo task and the Flanker task, during fMRI scanning. We compared performance and neural responses between these four pharmacological manipulations in a double-blind, placebo-controlled crossover design. As expected, performance in both tasks was modulated by nicotine dependence, abstinence, and pharmacological manipulation. However, effects were driven entirely by conditions that required less inhibitory control. When demand for inhibitory control was high, abstinent smokers showed no deficits. By contrast, acutely abstinent smokers showed performance deficits in easier conditions and missed more trials. Go-Nogo fMRI results showed decreased inhibition-related neural activity in right anterior insula and right putamen in smokers and decreased dorsal anterior cingulate cortex activity on nicotine across groups. No effects were found on inhibition-related activity during the Flanker task or on error-related activity in either task. Given robust nicotinic effects on physiology and behavioral deficits in attention, we are confident that pharmacological manipulations were effective. Thus findings fit a recent proposal that abstinent smokers show decreased ability to divert cognitive resources at low or intermediate cognitive demand, while performance at high cognitive demand remains relatively unaffected, suggesting a primary attentional deficit during acute abstinence.

papers/_posts/2020-01-31-bartley-brain-activity-links.md

@@ -0,0 +1,39 @@
+---
+layout: paper
+title: "Brain activity links performance in science reasoning with conceptual approach"
+nickname: 2020-01-31-bartley-brain-activity-links
+authors: "Bartley JE, Riedel MC, Salo T, Boeving ER, Bottenhorn KL, Bravo EI, Odean R, Nazareth A, Laird RW, Sutherland MT, Pruden SM, Brewe E, Laird AR"
+year: "2020"
+journal: "NPJ Sci Learn"
+volume: 4
+issue:
+pages: 20
+is_published: true
+image: /assets/images/papers/npj-sci-learn.png
+projects: [physics-learning]
+tags: [preprint]
+
+# Text
+fulltext:
+pdf:
+pdflink:
+pmcid: PMC6889284
+preprint: https://www.biorxiv.org/content/10.1101/526574v1
+supplement:
+
+# Links
+doi: "10.1038/s41539-019-0059-8"
+pmid: 31814997
+
+# Data and code
+github: ["https://github.com/NBCLab/PhysicsLearning"]
+neurovault: [4758]
+openneuro:
+osf:
+figshare:
+---
+{% include JB/setup %}
+
+# Abstract
+
+Understanding how students learn is crucial for helping them succeed. We examined brain function in 107 undergraduate students during a task known to be challenging for many students-physics problem solving-to characterize the underlying neural mechanisms and determine how these support comprehension and proficiency. Further, we applied module analysis to response distributions, defining groups of students who answered by using similar physics conceptions, and probed for brain differences linked with different conceptual approaches. We found that integrated executive, attentional, visual motion, and default mode brain systems cooperate to achieve sequential and sustained physics-related cognition. While accuracy alone did not predict brain function, dissociable brain patterns were observed when students solved problems by using different physics conceptions, and increased success was linked to conceptual coherence. Our analyses demonstrate that episodic associations and control processes operate in tandem to support physics reasoning, offering potential insight to support student learning.

papers/_posts/2020-01-31-eslami-asddiagnet-a-hybrid.md

@@ -0,0 +1,40 @@
+---
+layout: paper
+title: "ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data"
+nickname: 2020-01-31-eslami-asddiagnet-a-hybrid
+authors: "Eslami T, Mirjalili V, Fong A, Laird AR, Saeed F"
+year: "2020"
+journal: "Front Neuroinform"
+volume: 13
+issue:
+pages: 70
+is_published: true
+image: /assets/images/papers/front-neuroinform.png
+projects:
+tags: []
+
+# Text
+fulltext:
+pdf:
+pdflink:
+pmcid: PMC6890833
+preprint:
+supplement:
+
+# Links
+doi: "10.3389/fninf.2019.00070"
+pmid: 31827430
+
+# Data and code
+github:
+neurovault:
+openneuro:
+figshare:
+figshare_names:
+osf:
+---
+{% include JB/setup %}
+
+# Abstract
+
+Heterogeneous mental disorders such as Autism Spectrum Disorder (ASD) are notoriously difficult to diagnose, especially in children. The current psychiatric diagnostic process is based purely on the behavioral observation of symptomology (DSM-5/ICD-10) and may be prone to misdiagnosis. In order to move the field toward more quantitative diagnosis, we need advanced and scalable machine learning infrastructure that will allow us to identify reliable biomarkers of mental health disorders. In this paper, we propose a framework called ASD-DiagNet for classifying subjects with ASD from healthy subjects by using only fMRI data. We designed and implemented a joint learning procedure using an autoencoder and a single layer perceptron (SLP) which results in improved quality of extracted features and optimized parameters for the model. Further, we designed and implemented a data augmentation strategy, based on linear interpolation on available feature vectors, that allows us to produce synthetic datasets needed for training of machine learning models. The proposed approach is evaluated on a public dataset provided by Autism Brain Imaging Data Exchange including 1, 035 subjects coming from 17 different brain imaging centers. Our machine learning model outperforms other state of the art methods from 10 imaging centers with increase in classification accuracy up to 28% with maximum accuracy of 82%. The machine learning technique presented in this paper, in addition to yielding better quality, gives enormous advantages in terms of execution time (40 min vs. 7 h on other methods). The implemented code is available as GPL license on GitHub portal of our lab (https://github.com/pcdslab/ASD-DiagNet).