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</a></noscript> </div> <div id="content"> <div xmlns="http://www.w3.org/1999/xhtml" xmlns:ol="http://www.wiley.com/namespaces/ol/xsl-lib" id="banner" style="background-image: url('http://onlinelibrary.wiley.com/store/10.1002/(ISSN)1097-0193/asset/HBM_bg.gif?v=1&s=98d730bbf0ca24890bfeedebd8b4c55b27f848d1')"><a shape="rect"><img class="center" src="http://onlinelibrary.wiley.com/store/10.1002/(ISSN)1097-0193/asset/HBM_centre.gif?v=1&s=8ec180dd87bbc24c1b35fbfc48370a4e41c34713" /></a></div> <div id="mainContent"> <div xmlns="http://www.w3.org/1999/xhtml" class="citation articleInformationHeader" id="titleMeta"><div id="articleDesc"><p class="articleCategory">Research Article</p><div id="articleTitle"><span class="licensedContent" title="You have full text access to this content">You have full text access to this content</span><h1 class="articleTitle"><span class="mainTitle">Executive performance is related to regional gray matter volume in healthy older individuals</span></h1></div><div id="articleMeta"><ol id="authors"><li id="au1">Ruth Ruscheweyh<sup>1,2,†,*</sup>, </li><li id="au2">Michael Deppe<sup>1,†</sup>, </li><li id="au3">Hubertus Lohmann<sup>1</sup>, </li><li id="au4">Heike Wersching<sup>1,3,5</sup>, </li><li id="au5">Catharina Korsukewitz<sup>1</sup>, </li><li id="au6">Thomas Duning<sup>1</sup>, </li><li id="au7">Saskia Bluhm<sup>1</sup>, </li><li id="au8">Christoph Stehling<sup>3,4</sup>, </li><li id="au9">Simon S. Keller<sup>1,6</sup>, </li><li id="au10">Stefan Knecht<sup>1,3</sup></li></ol><p id="publishedOnlineDate">Article first published online: 19 JUL 2012</p><p id="doi">DOI: 10.1002/hbm.22146</p><p id="copyright" class="copyright">Copyright © 2012 Wiley Periodicals, Inc.</p></div><div id="articleParent"><p class="articleCategory"> Issue </p><div id="cover" class="journal"><div class="imgShadow"><img src="http://onlinelibrary.wiley.com/store/10.1002/hbm.v34.6/asset/cover.gif?v=1&s=5f80ffdd4b9c9a339a74879fc5840c9acc5ed2e8" alt="Cover image for Vol. 34 Issue 6" title="Cover image for Vol. 34 Issue 6" /></div></div><div id="metaData"><h2 id="productTitle">Human Brain Mapping</h2><p class="earlyView"><a href="/journal/10.1002/(ISSN)1097-0193/earlyview" shape="rect">Early View (Online Version of Record published before inclusion in an issue)</a></p></div></div></div><div id="additionalInformation"><p class="articleCategory">Additional Information</p><div style="clear: left;"><div id="howToCite"><h4>How to Cite</h4><p id="citation">Ruscheweyh, R., Deppe, M., Lohmann, H., Wersching, H., Korsukewitz, C., Duning, T., Bluhm, S., Stehling, C., Keller, S. S. and Knecht, S. (2012), Executive performance is related to regional gray matter volume in healthy older individuals. Hum. Brain Mapp.. doi: 10.1002/hbm.22146</p></div><div id="authorsDetail"><h4>Author Information</h4><ol id="authorsAffiliations"><li class="affiliation"><sup>1</sup><p>Department of Neurology, University Hospital of Münster, Münster, Germany</p></li><li class="affiliation"><sup>2</sup><p>Department of Neurology, University of München, München, Germany</p></li><li class="affiliation"><sup>3</sup><p>German Atrial Fibrillation Competence NETwork (AFNET), University of Münster, Münster, Germany</p></li><li class="affiliation"><sup>4</sup><p>Department of Clinical Radiology, University of Münster, Münster, Germany</p></li><li class="affiliation"><sup>5</sup><p>Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany</p></li><li class="affiliation"><sup>6</sup><p>Department of Clinical Neuroscience, Institute of Psychiatry, King's College London, UK</p></li></ol><ol id="footnotes" class="noteGroup custom"><li id="fn1"><span class="bullet"><sup>†</sup></span><div class="text"><p>R. Ruscheweyh and M. Deppe contributed equally to this work.</p></div></li></ol><p id="contactDetails"><span class="email-label">Email: </span><span class="email">Ruth Ruscheweyh (ruth.ruscheweyh@med.uni-muenchen.de)</span></p><p id="correspondence"><sup>*</sup>Department of Neurology, University of München, Marchioninistr. 15, 81377 München, Germany</p></div><ol id="footnotes" class="noteGroup custom"><li id="fn1"><span class="bullet"><sup>†</sup></span><div class="text"><p>R. Ruscheweyh and M. Deppe contributed equally to this work.</p></div></li></ol><div id="publicationHistoryDetails"><h4>Publication History</h4><ol><li>Article first published online: 19 JUL 2012</li><li>Manuscript Revised: 23 DEC 2012</li><li>Manuscript Accepted: 20 MAY 2012</li><li>Manuscript Received: 30 SEP 2011</li></ol></div><div id="fundingInfo" class="fundingInfo"><h4>Funded by</h4><ul><li>Deutsche Forschungsgemeinschaft. Grant Number: SFB/TR3 projects A08 and A10</li><li>Volkswagen Stiftung. Grant Number: Az.: I/80 708</li><li>BMBF-Competence Network Mednet Atrial Fibrillation</li><li>Neuromedical Foundation Münster</li><li>BMBF-Research Consortium Dopaminergic Learning Enhancement. Grant Number: 01GW0520</li><li>BMBF-Research Consortium PUSH-Learning. 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xmlns="http://www.w3.org/1999/xhtml" class="tabbedContent"><ul><li><a href="/doi/10.1002/hbm.22146/abstract" title="Link to article abstract" shape="rect">Abstract</a></li><li class="active"><span>Article</span></li><li><a href="/doi/10.1002/hbm.22146/references" title="Link to article references" shape="rect">References</a></li><li><a href="/doi/10.1002/hbm.22146/suppinfo" title="Link to supporting information" shape="rect">Supporting Information</a></li><li><a href="/doi/10.1002/hbm.22146/citedby" title="Other articles that have cited this article" shape="rect">Cited By</a></li></ul></div> <div xmlns:ol="http://www.wiley.com/namespaces/ol/xsl-lib" xmlns:doc="urn://wiley-online-library/content/document" xmlns="http://www.w3.org/1999/xhtml" class="viewFullArticleAndPdf"><div class="viewFullArticleAndPdfOptions"><a href="/doi/10.1002/hbm.22146/pdf" title="Article in pdf format" class="pdfLink" shape="rect">Get PDF (1339K)</a><ul xmlns:xs="http://www.w3.org/2001/XMLSchema" class="viewOpenUrl"><li><a title="Find it at CU" href="http://libraries.colorado.edu:4550/resserv?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Human%20Brain%20Mapping&rft.stitle=Hum.%20Brain%20Mapp.&rft.atitle=Executive%20performance%20is%20related%20to%20regional%20gray%20matter%20volume%20in%20healthy%20older%20individuals&rft.spage=n%2Fa&rft.epage=n%2Fa&rft.date=2012-01-01&rft.issn=1065-9471&rft.eissn=1097-0193&rfr_id=info%3Asid%2Fwiley.com%3AOnlineLibrary" shape="rect">Find it at CU</a></li></ul></div></div> <div xmlns="http://www.w3.org/1999/xhtml" id="fulltext" class="none"><div class="keywords"><h3 class="keywordsHeading">Keywords:</h3><div class="keywordLists"><ul class="keywordList" id="abstractKeywords1"><li>age;</li><li>human;</li><li>insular cortex;</li><li>neuropsychological testing;</li><li>voxel-based morphometry;</li><li>stereology</li></ul></div></div><div id="abstract" class="fullTextAbstract"><div class="headingCont"><h3>Abstract</h3></div><ol class="jumpList"><li><a href="#leftBorder" shape="rect">Top of page</a></li><li><a href="#abstract" shape="rect">Abstract</a></li><li><a href="#sec1-1" shape="rect">INTRODUCTION</a></li><li><a href="#sec1-2" shape="rect">MATERIALS AND METHODS</a></li><li><a href="#sec1-3" shape="rect">RESULTS</a></li><li><a href="#sec1-4" shape="rect">DISCUSSION</a></li><li><a href="#sec1-5" shape="rect">LIMITATIONS</a></li><li><a href="#sec1-6" shape="rect">CONCLUSIONS</a></li><li><a href="#wol-references" shape="rect">REFERENCES</a></li><li><a href="#supp-info" shape="rect">Supporting Information</a></li></ol><div class="para"><p>Individual differences in executive functioning and brain morphology are considerable. In this study, we investigated their interrelation in a large sample of healthy older individuals. Digit span, trail-making, and Stroop tasks were used to assess different executive subfunctions in 367 nondemented community-dwelling individuals (50–81 years). Task performance was analyzed relative to brain structure using voxel-based morphometry, corrected for age and sex. Improved task performance was associated with increased local gray matter volume in task-specific patterns that showed partial, but not complete overlap with known task-specific functional imaging patterns. While all three tasks showed associations with prefrontal gray matter volume as expected for executive functioning, the strongest overlap between the three tasks was found in insular cortex, suggesting that it has a previously underestimated role for executive functions. The association between the insular cortex and executive functioning was corroborated using stereological region-of-interest measurement of insular volume in a subgroup of 93 subjects. Quantitatively, the volume of the single most strongly related region explained 2.4 ± 1.1% of the variance in executive performance over and above the variance explained by age, which amounted to 7.4 ± 4.1%. The age-independent peak associations between executive performance and gray matter described here occurred in regions that were also strongly affected by age-related gray matter atrophy, consistent with the hypothesis that age-related regional brain volume loss and age-related cognitive changes are linked. Hum Brain Mapp, 2012. © 2012 Wiley Periodicals, Inc.</p></div></div><br /><div class="section" id="sec1-1"><div class="headingCont"><h3>INTRODUCTION</h3></div><ol class="jumpList"><li><a href="#leftBorder" shape="rect">Top of page</a></li><li><a href="#abstract" shape="rect">Abstract</a></li><li><a href="#sec1-1" shape="rect">INTRODUCTION</a></li><li><a href="#sec1-2" shape="rect">MATERIALS AND METHODS</a></li><li><a href="#sec1-3" shape="rect">RESULTS</a></li><li><a href="#sec1-4" shape="rect">DISCUSSION</a></li><li><a href="#sec1-5" shape="rect">LIMITATIONS</a></li><li><a href="#sec1-6" shape="rect">CONCLUSIONS</a></li><li><a href="#wol-references" shape="rect">REFERENCES</a></li><li><a href="#supp-info" shape="rect">Supporting Information</a></li></ol><div class="para"><p>The human central executive is a multicomponent construct that modulates planning, cognitive flexibility, abstract thinking, rule acquisition, implementation, behavioral inhibition, and the selection of relevant sensory information [Baddeley and Hitch, <a href="#bib6" rel="references:#bib6" class="referenceLink" title="Link to bibliographic citation" shape="rect">1974</a>; Baddeley, <a href="#bib5" rel="references:#bib5" class="referenceLink" title="Link to bibliographic citation" shape="rect">2002</a>; Glisky, <a href="#bib31" rel="references:#bib31" class="referenceLink" title="Link to bibliographic citation" shape="rect">2007</a>], playing a key role in almost all aspects of cognition. These functions are critically dependent on the prefrontal cortex and its functional integration with other cortical and subcortical regions [Passingham, <a href="#bib62" rel="references:#bib62" class="referenceLink" title="Link to bibliographic citation" shape="rect">1995</a>; Roberts et al., <a href="#bib69" rel="references:#bib69" class="referenceLink" title="Link to bibliographic citation" shape="rect">1998</a>]. Normal aging has been shown to be associated with volume loss of the prefrontal cortex [Cowell et al., <a href="#bib19" rel="references:#bib19" class="referenceLink" title="Link to bibliographic citation" shape="rect">1994</a>, <a href="#bib18" rel="references:#bib18" class="referenceLink" title="Link to bibliographic citation" shape="rect">2007</a>; Tisserand et al., <a href="#bib78" rel="references:#bib78" class="referenceLink" title="Link to bibliographic citation" shape="rect">2002</a>; Salat et al., <a href="#bib71" rel="references:#bib71" class="referenceLink" title="Link to bibliographic citation" shape="rect">2004</a>; Tisserand et al., <a href="#bib79" rel="references:#bib79" class="referenceLink" title="Link to bibliographic citation" shape="rect">2004</a>], and given that there is a wealth of evidence to suggest that deterioration in executive function is a key contributor to specific domains of cognitive decline in normal aging [Glisky, <a href="#bib31" rel="references:#bib31" class="referenceLink" title="Link to bibliographic citation" shape="rect">2007</a>], a direct link between brain structure and executive cognition may exist, particularly in older adults. Indeed, previous studies in healthy adults suggested that executive performance is related to prefrontal volume [Elderkin-Thompson et al., <a href="#bib25" rel="references:#bib25" class="referenceLink" title="Link to bibliographic citation" shape="rect">2008</a>; Newman et al., <a href="#bib58" rel="references:#bib58" class="referenceLink" title="Link to bibliographic citation" shape="rect">2007</a>; Tisserand et al., <a href="#bib79" rel="references:#bib79" class="referenceLink" title="Link to bibliographic citation" shape="rect">2004</a>]. However, given that executive functions and higher-order fluid cognition in general are dependent on multiple cortical and subcortical networks [Knight, <a href="#bib44" rel="references:#bib44" class="referenceLink" title="Link to bibliographic citation" shape="rect">2007</a>], it is highly likely that the structural components underlying such functions are not limited to the prefrontal cortex. It is therefore important to consider how the entire brain structurally supports executive functions. The primary goal of the present study was to investigate the relationship between gray matter volume and executive function in a large sample of older individuals.</p></div><div class="para"><p>In conjunction with the prefrontal cortex, the insula exhibits accelerated age-related decay of volume in both cerebral hemispheres [Bergfield et al., <a href="#bib9" rel="references:#bib9" class="referenceLink" title="Link to bibliographic citation" shape="rect">2010</a>; Good et al., <a href="#bib32" rel="references:#bib32" class="referenceLink" title="Link to bibliographic citation" shape="rect">2001</a>; Hutton et al., <a href="#bib38" rel="references:#bib38" class="referenceLink" title="Link to bibliographic citation" shape="rect">2009</a>; Raz et al., <a href="#bib68" rel="references:#bib68" class="referenceLink" title="Link to bibliographic citation" shape="rect">2010</a>]. Furthermore, executive functions may load on the insular cortices, as reflected in studies of task-level control and focal attention [Nelson et al., <a href="#bib56" rel="references:#bib56" class="referenceLink" title="Link to bibliographic citation" shape="rect">2010</a>], directed attention using the Stroop task [Hart et al., <a href="#bib34" rel="references:#bib34" class="referenceLink" title="Link to bibliographic citation" shape="rect">2010</a>] and decision-making [Thielscher and Pessoa, <a href="#bib77" rel="references:#bib77" class="referenceLink" title="Link to bibliographic citation" shape="rect">2007</a>]. The insula has an important role in many visceral, autonomic, and motor processes [Dupont et al., <a href="#bib24" rel="references:#bib24" class="referenceLink" title="Link to bibliographic citation" shape="rect">2003</a>; Flynn et al., <a href="#bib28" rel="references:#bib28" class="referenceLink" title="Link to bibliographic citation" shape="rect">1999</a>; Mesulam and Mufson, <a href="#bib53" rel="references:#bib53" class="referenceLink" title="Link to bibliographic citation" shape="rect">1982a</a>, <a href="#bib54" rel="references:#bib54" class="referenceLink" title="Link to bibliographic citation" shape="rect">b</a>] and in more cognitively orientated processes, such as the functional neuroanatomy of language [Dronkers, <a href="#bib23" rel="references:#bib23" class="referenceLink" title="Link to bibliographic citation" shape="rect">1996</a>; Keller et al., <a href="#bib42" rel="references:#bib42" class="referenceLink" title="Link to bibliographic citation" shape="rect">2011</a>; Ogar et al., <a href="#bib59" rel="references:#bib59" class="referenceLink" title="Link to bibliographic citation" shape="rect">2006</a>; Price, <a href="#bib67" rel="references:#bib67" class="referenceLink" title="Link to bibliographic citation" shape="rect">2000</a>; Wise et al., <a href="#bib86" rel="references:#bib86" class="referenceLink" title="Link to bibliographic citation" shape="rect">1999</a>]. On the basis of a database of published foci of imaging studies developed by Van Essen and coworkers [Van Essen, <a href="#bib81" rel="references:#bib81" class="referenceLink" title="Link to bibliographic citation" shape="rect">2009</a>; Van Essen and Dierker, <a href="#bib82" rel="references:#bib82" class="referenceLink" title="Link to bibliographic citation" shape="rect">2007</a>; Van Essen et al., <a href="#bib83" rel="references:#bib83" class="referenceLink" title="Link to bibliographic citation" shape="rect">2001</a>], Nelson et al. [<a href="#bib56" rel="references:#bib56" class="referenceLink" title="Link to bibliographic citation" shape="rect">2010</a>] point out that the left and right anterior insular cortices are among the most commonly activated brain regions across all cognitive tasks, perhaps suggesting that this region may have a general role in attention and task-level control. We were therefore particularly interested in the relationship between insular cortex volume and executive function in older adults.</p></div><div class="para"><p>Voxel-based morphometry (VBM) is widely used on magnetic resonance images (MRI) to investigate brain structural alterations in patient samples and cognitive correlates of gray matter alterations in healthy individuals. The technique removes the need of an expert anatomist for time-consuming volumetric estimation of predefined regions-of-interest (ROI), and is particularly useful to gain insight into regional brain alterations in an exploratory manner in large samples of subjects. However, there has been some discussion concerning validity of the VBM technique [Ashburner and Friston, <a href="#bib2" rel="references:#bib2" class="referenceLink" title="Link to bibliographic citation" shape="rect">2001</a>; Bookstein, <a href="#bib10" rel="references:#bib10" class="referenceLink" title="Link to bibliographic citation" shape="rect">2001</a>; Fein et al., <a href="#bib26" rel="references:#bib26" class="referenceLink" title="Link to bibliographic citation" shape="rect">2006</a>; Keller and Roberts, <a href="#bib40" rel="references:#bib40" class="referenceLink" title="Link to bibliographic citation" shape="rect">2008</a>; Pereira et al., <a href="#bib65" rel="references:#bib65" class="referenceLink" title="Link to bibliographic citation" shape="rect">2008</a>; Salmond et al., <a href="#bib72" rel="references:#bib72" class="referenceLink" title="Link to bibliographic citation" shape="rect">2002</a>]. To address this issue, and further corroborate our results, we supplemented VBM analyses with a mathematically unbiased and robust stereological approach to obtain volume estimates of the insular cortices, corresponding to our principal ROI. Stereology in conjunction with point counting has been recently shown to be a reliable and functionally significant method to estimate the volume of the insular cortices [Keller et al., <a href="#bib42" rel="references:#bib42" class="referenceLink" title="Link to bibliographic citation" shape="rect">2011</a>]. Therefore, we used both VBM and stereological approaches to examine the relationship between gray matter volume and executive function. We assessed executive functions using digit span, trail-making, and Stroop paradigms given that these tasks of attentional and executive functions are among the first to decline with age [Hedden and Gabrieli, <a href="#bib36" rel="references:#bib36" class="referenceLink" title="Link to bibliographic citation" shape="rect">2004</a>]. In a first step, we used voxel-level analyses, VBM-based ROI analyses and stereological methods in a large cohort of nondemented older individuals to identify regions where gray matter volume was related to executive functions after correction for age and sex. In a second step, we then determined the extent of age-related gray matter volume loss in these regions and the relative contributions of sex, age, and sex-and-age-independent variations in regional gray matter volume to executive performance.</p></div></div><div class="section" id="sec1-2"><div class="headingCont"><h3>MATERIALS AND METHODS</h3></div><ol class="jumpList"><li><a href="#leftBorder" shape="rect">Top of page</a></li><li><a href="#abstract" shape="rect">Abstract</a></li><li><a href="#sec1-1" shape="rect">INTRODUCTION</a></li><li><a href="#sec1-2" shape="rect">MATERIALS AND METHODS</a></li><li><a href="#sec1-3" shape="rect">RESULTS</a></li><li><a href="#sec1-4" shape="rect">DISCUSSION</a></li><li><a href="#sec1-5" shape="rect">LIMITATIONS</a></li><li><a href="#sec1-6" shape="rect">CONCLUSIONS</a></li><li><a href="#wol-references" shape="rect">REFERENCES</a></li><li><a href="#supp-info" shape="rect">Supporting Information</a></li></ol><div class="section" id="sec2-1"><h4>Subjects</h4><div class="para"><p>The Münster SEARCH study (Systematic evaluation and alteration of risk factors for cognitive health) examines the relationship between aging, cognitive functions and cardiovascular risk factors in community-dwelling individuals. The research protocol has been approved by the local ethics committee and written informed consent was obtained from every participant. Participants from 40 to 85 years of age were randomly selected based on dates of birth from the population register of the city of Münster, Germany. They were invited to participate in the study by letter and recruited after giving informed consent. From a total of 3,500 invited citizens, 792 consented to participate. All participants received a structured clinical face-to-face interview, a physical examination by a trained study physician, blood sampling, structural MRI brain imaging and comprehensive neuropsychological assessment. Although the study was open to subjects between 40 and 85 years, the initial recruitment phase focused on participants above 50 years of age, with very few subjects below 50 included in the database at the time the present investigation was conducted. For the present investigation, we therefore selected subjects between 50 and 85 years of age whose structural MRI was free of artifacts or gross morphological abnormalities (brain tumors or mengingeomas, territorial stroke, arachnoideal cysts, massive subcortical atherosclerotic encephalopathy). To reduce confounding effects on neuropsychological performance, we excluded putatively depressed subjects (Beck's depression inventory score > 17 [Beck et al., <a href="#bib7" rel="references:#bib7" class="referenceLink" title="Link to bibliographic citation" shape="rect">1961</a>]), subjects with probable mild cognitive impairment (Mini-Mental State Examination score < 27), subjects on psychotropic medication, subjects with atrial fibrillation [Knecht et al., <a href="#bib43" rel="references:#bib43" class="referenceLink" title="Link to bibliographic citation" shape="rect">2008</a>], and subjects with severe neurological or psychiatric conditions. In addition, we excluded subjects for whom gross segmentation errors occurred during the preprocessing of MRI scans for VBM. All segmented, normalized images were visually inspected and 25 subjects showing obvious segmentation errors (mostly classification of dura/scalp as gray matter) were excluded from analysis. These criteria left 367 subjects for the present study (172 male, 195 female). Characteristics of the study population and for the subsample for whom stereological analysis of the insula was performed are shown in Table <a href="#tbl1" rel="references:#tbl1" class="tableLink" title="Link to table" shape="rect">I</a>.</p></div><div class="table" id="tbl1"><table class="topbotR"><caption><span class="tTitle"><span class="label">Table I. </span>Characterization of participants</span></caption><thead class="vBottom"><tr class="rsep"><th class="hCenter" rowspan="1" colspan="1"> </th><th class="hCenter" rowspan="1" colspan="1">Complete group (<em>n</em> = 367)</th><th class="hCenter" rowspan="1" colspan="1">Stereology subgroup (<em>n</em> = 93)</th></tr></thead><tfoot><tr><td colspan="3" rowspan="1"><div class="noteGroup"><ol id="footnotes" class="noteGroup custom"><li id="note-p-7"><span class="bullet"><sup><i></i></sup></span><div class="text"><p>Data are given as mean ± SD (range) of the raw values. Mean values in the subgroup were not significantly different from the total group.</p></div></li></ol></div></td></tr></tfoot><tbody class="vTop"><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Age [years]</td><td class="hChar" rowspan="1" colspan="1">63.7 ± 6.7 (50–81)</td><td class="hChar" rowspan="1" colspan="1">64.7 ± 6.9 (50–79)</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Gender</td><td class="hCenter" rowspan="1" colspan="1">53.1% female</td><td class="hCenter" rowspan="1" colspan="1">51.0% female</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">BDI score</td><td class="hChar" rowspan="1" colspan="1">5.2 ± 4.0 (0–17)</td><td class="hChar" rowspan="1" colspan="1">5.5 ± 4.3 (1–17)</td></tr><tr class="norsep"><td colspan="3" class="hLeft" rowspan="1">Performance on neuropsychological tests</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Digit span forward [digits]</td><td class="hChar" rowspan="1" colspan="1">7.6 ± 1.6 (3–12)</td><td class="hChar" rowspan="1" colspan="1">7.7 ± 1.6 (4–12)</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Digit span backward [digits]</td><td class="hChar" rowspan="1" colspan="1">6.7 ± 1.7 (2–12)</td><td class="hChar" rowspan="1" colspan="1">6.7 ± 1.8 (2–11)</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">TMT part A [s]</td><td class="hChar" rowspan="1" colspan="1">33 ± 10 (14–76)</td><td class="hChar" rowspan="1" colspan="1">33 ± 10 (14–68)</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">TMT part B [s]</td><td class="hChar" rowspan="1" colspan="1">83 ± 30 (30–260)</td><td class="hChar" rowspan="1" colspan="1">82 ± 27 (30–165)</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Stroop test part II [s]</td><td class="hChar" rowspan="1" colspan="1">20 ± 4 (10–35)</td><td class="hChar" rowspan="1" colspan="1">20 ± 3 (15–29)</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Stroop test part III [s]</td><td class="hChar" rowspan="1" colspan="1">40 ± 10 (20–76)</td><td class="hChar" rowspan="1" colspan="1">39 ± 8 (23–65)</td></tr></tbody></table></div></div><div class="section" id="sec2-2"><h4>Neuropsychological Assessment</h4><div class="para"><p>Trained technicians supervised by a clinical neuropsychologist conducted the neuropsychological assessment, consisting of digit span forward and backward, trail-making tests (TMT) parts A and B and Stroop tests parts II and III. Table <a href="#tbl2" rel="references:#tbl2" class="tableLink" title="Link to table" shape="rect">II</a> provides details on the procedure and the functions assessed by the different paradigms. Difference scores were calculated to isolate underlying executive subfunction as detailed in Table <a href="#tbl2" rel="references:#tbl2" class="tableLink" title="Link to table" shape="rect">II</a>.</p></div><div class="table" id="tbl2"><table class="topbotR"><caption><span class="tTitle"><span class="label">Table II. </span>Neuropsychological tests of attention and executive functions used in this study</span></caption><thead class="vBottom"><tr class="rsep"><th class="hCenter" rowspan="1" colspan="1">Task group</th><th class="hCenter" rowspan="1" colspan="1">Test</th><th class="hCenter" rowspan="1" colspan="1">Task description</th><th class="hCenter" rowspan="1" colspan="1">Performance index</th><th class="hCenter" rowspan="1" colspan="1">Measured functions</th><th class="hCenter" rowspan="1" colspan="1">Origin</th></tr></thead><tbody class="vTop"><tr class="norsep"><td rowspan="3" class="hLeft" colspan="1">Digit spans</td><td class="hCenter" rowspan="1" colspan="1">Digit span forward</td><td class="hLeft" rowspan="1" colspan="1">Digit sequences of 3 to 8 digits are read out and have to be reproduced in the presented order</td><td class="hLeft" rowspan="1" colspan="1">Number of correctly remembered digits</td><td class="hLeft" rowspan="1" colspan="1">Short-term memory [Kaufman et al, <a href="#bib39" rel="references:#bib39" class="referenceLink" title="Link to bibliographic citation" shape="rect">1991</a>]</td><td rowspan="3" class="hLeft" colspan="1">Wechsler Memory Scale Revised, German version [Härting et al., <a href="#bib35" rel="references:#bib35" class="referenceLink" title="Link to bibliographic citation" shape="rect">2000</a>]</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Digit span backward</td><td class="hLeft" rowspan="1" colspan="1">Digit sequences of 3 to 8 digits are read out and have to be reproduced in inverse order</td><td class="hLeft" rowspan="1" colspan="1">Number of correctly remembered digits</td><td class="hLeft" rowspan="1" colspan="1">Short-term memory, data manipulation [Lezak, <a href="#bib47" rel="references:#bib47" class="referenceLink" title="Link to bibliographic citation" shape="rect">1995</a>]</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Digits difference score</td><td class="hCenter" rowspan="1" colspan="1">–</td><td class="hLeft" rowspan="1" colspan="1">Calculated: Digit span forward - digit span backward</td><td class="hLeft" rowspan="1" colspan="1">Data manipulation [Wager and Smith, <a href="#bib85" rel="references:#bib85" class="referenceLink" title="Link to bibliographic citation" shape="rect">2003</a>]</td></tr><tr class="norsep"><td rowspan="3" class="hLeft" colspan="1">Trail-making tests (TMT)</td><td class="hLeft" rowspan="1" colspan="1">TMT part A</td><td class="hLeft" rowspan="1" colspan="1">25 numbers on a sheet of paper have to be connected by pencil in ascending order</td><td class="hLeft" rowspan="1" colspan="1">Time to completion</td><td class="hLeft" rowspan="1" colspan="1">Attention, visual searching, psychomotor speed [Shum et al., <a href="#bib73" rel="references:#bib73" class="referenceLink" title="Link to bibliographic citation" shape="rect">1990</a>]</td><td rowspan="3" class="hLeft" colspan="1">Compendium of neuropsychological tests [Spreen and Strauss <a href="#bib74" rel="references:#bib74" class="referenceLink" title="Link to bibliographic citation" shape="rect">1998</a>]</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">TMT part B</td><td class="hLeft" rowspan="1" colspan="1">Same as TMT A, but numbers and letters have to be alternated in ascending order</td><td class="hLeft" rowspan="1" colspan="1">Time to completion</td><td class="hLeft" rowspan="1" colspan="1">Attention, visual searching, psychomotor speed, set shifting [Shum et al., <a href="#bib73" rel="references:#bib73" class="referenceLink" title="Link to bibliographic citation" shape="rect">1990</a>]</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">TMT difference score</td><td class="hCenter" rowspan="1" colspan="1">–</td><td class="hLeft" rowspan="1" colspan="1">Calculated: TMT part B - part A</td><td class="hLeft" rowspan="1" colspan="1">Set shifting [Spreen and Strauss, <a href="#bib74" rel="references:#bib74" class="referenceLink" title="Link to bibliographic citation" shape="rect">1998</a>]</td></tr><tr class="norsep"><td rowspan="3" class="hLeft" colspan="1">Stroop tests</td><td class="hLeft" rowspan="1" colspan="1">Stroop test part II</td><td class="hLeft" rowspan="1" colspan="1">Colored rectangles are presented. Subjects have to name the colors.</td><td class="hLeft" rowspan="1" colspan="1">Time to completion</td><td class="hLeft" rowspan="1" colspan="1">Color perception, color naming, speed of processing [MacLeod, <a href="#bib50" rel="references:#bib50" class="referenceLink" title="Link to bibliographic citation" shape="rect">1991</a>]</td><td rowspan="3" class="hLeft" colspan="1">[Stroop, <a href="#bib75" rel="references:#bib75" class="referenceLink" title="Link to bibliographic citation" shape="rect">1935</a>]; German version: Nürnberger Altersinventar (Nuremberg Gerontopsychological Inventory) [Oswald and Fleischmann, <a href="#bib60" rel="references:#bib60" class="referenceLink" title="Link to bibliographic citation" shape="rect">1997</a>]</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Stroop test part III</td><td class="hLeft" rowspan="1" colspan="1">Colored rectangles with conflicting color words printed on them are presented. Subjects have to name the colors of the rectangles.</td><td class="hLeft" rowspan="1" colspan="1">Time to completion</td><td class="hLeft" rowspan="1" colspan="1">Color perception, color naming, speed of processing, response inhibition [MacLeod, <a href="#bib50" rel="references:#bib50" class="referenceLink" title="Link to bibliographic citation" shape="rect">1991</a>]</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Stroop difference score</td><td class="hCenter" rowspan="1" colspan="1">–</td><td class="hLeft" rowspan="1" colspan="1">Calculated: Stroop test part III - part II</td><td class="hLeft" rowspan="1" colspan="1">Response inhibition [Zajano and Gorman, <a href="#bib87" rel="references:#bib87" class="referenceLink" title="Link to bibliographic citation" shape="rect">1986</a>]</td></tr></tbody></table></div></div><div class="section" id="sec2-3"><h4>MRI Data Acquisition</h4><div class="para"><p>Magnetic resonance imaging (MRI) was performed on a 3 T MRI system (Gyroscan Intera T30, Philips Medical System, Best, The Netherlands) using a structural T1-weighted 3D turbo-field-echo sequence (matrix 256 × 256 × 160 over a field of view of 25.6 × 25.6 × 16 cm<sup>3</sup>, reconstructed after zero filling to 512 × 512 × 320 cubic voxels with an edge length of 0.5 mm). The system was a whole body scanner equipped with master gradients (slew rate 150 mT/m/ms; maximal gradient strength 33 mT/m). A circularly polarized transmit-receive quadrature head coil with HF reflecting screen at the cranial end was employed for spin excitation and resonance signal acquisition. All MR images were resampled to a voxel size of 1 mm × 1 mm × 1 mm and corrected for image inhomogeneity using in-house software prior to VBM and stereological analyses.</p></div></div><div class="section" id="sec2-4"><h4>Voxel-Based Morphometry</h4><div class="section" id="sec3-1"><h5>MRI data preprocessing for VBM</h5><div class="para"><p>The principles of voxel-based morphometry were employed as described by Ashburner and Friston (<a href="#bib1" rel="references:#bib1" class="referenceLink" title="Link to bibliographic citation" shape="rect">2000</a>). Data preprocessing and analysis was performed with SPM5 (Wellcome Department of Imaging Neuroscience, University College London, UK) running under Matlab 7.1 (Mathworks, Sherborn, MA), using the VBM5 toolbox (Structural Brain Mapping Group, University of Jena, Germany). Default settings were used unless otherwise indicated. Data were resliced to a voxel size of 2 × 2 × 2 mm<sup>3</sup>. The unified segmentation algorithm of SPM5 [Ashburner and Friston, <a href="#bib3" rel="references:#bib3" class="referenceLink" title="Link to bibliographic citation" shape="rect">2005</a>] was used to segment and spatially normalize the structural MR images. Voxel values of the resulting normalized gray matter segments indicate the probability (between 0 and 1) that a specific voxel belongs to gray matter. Images were corrected for volume changes during normalization by modulating each voxel by the Jacobian determinant derived from the normalization, allowing analysis of local gray matter volumes [Good et al., <a href="#bib32" rel="references:#bib32" class="referenceLink" title="Link to bibliographic citation" shape="rect">2001</a>]. Analyses of gray matter concentration from unmodulated gray matter image were not performed in this study. Finally, images were smoothed using an isotropic Gaussian kernel of 10 mm full-width at half-maximum. In a preliminary explorative analysis we did not find any impact of the intracranial volume to factors investigated in this study. Therefore, we did not adjust for differences in head size.</p></div></div><div class="section" id="sec3-2"><h5>Whole-brain VBM analysis at the voxel-level</h5><div class="para"><p>Voxel-wise multiple linear regression analyses were performed using the general linear model as implemented in SPM5, determining the association between regional gray matter volume and performance in the specified neuropsychological test, after controlling for sex and age. Voxels with a gray matter probability < 0.2 in the mean unmodulated gray matter segment (averaged over all included subjects) were excluded from statistical analysis. Given that (i) we had a priori hypotheses about the relationships between regional atrophy, aging, and executive function, (ii) that the associations between cognition and macroscopic morphology of gray matter are likely to be subtle, and (iii) that we followed up VBM analyses with stereological analysis, we applied a liberal threshold of <em>P</em> < 0.001 (uncorrected) across the whole brain for exploratory purposes.</p></div></div><div class="section" id="sec3-3"><h5>Analysis of VBM-based ROI volumes</h5><div class="para"><p>To determine if gray matter associations with cognitive functions were highly localized to peaks (as detected by voxel-based analysis) or extended to larger functional regions, we complemented the analysis by correlations of mean gray matter within each of 10 ROIs that were defined according to previous knowledge on locations of the investigated cognitive functions. Regional masks were defined using the WFU pickatlas tool (Functional MRI Laboratory, Wake Forest University School of Medicine, Winston-Salem, CA) as follows: orbitofrontal cortex (OFC, defined as the conjunction of Brodmann areas [Babcock and Salthouse, <a href="#bib4" rel="references:#bib4" class="referenceLink" title="Link to bibliographic citation" shape="rect">1990</a>]10 and 11), ventrolateral prefrontal cortex (VLPFC, BA 44/45/47), dorsolateral prefrontal cortex (DLPFC, BA46/9), anterior cingulate cortex (ACC, BA24/32), insular cortex, posterior parietal cortex (BA 5/7/40/39), medial temporal lobe (hippocampus and parahippocampal gyrus), primary visual cortex (BA17), thalamus, and cerebellum. Individual gray matter volume within each of these regions was calculated by summing voxel values of the smoothed modulated normalized images within the corresponding region. The association between gray matter volume within ROIs and cognitive performance was assessed by partial correlations, correcting for age and sex. This type of analysis is called ROI-VBM analysis throughout the article to differentiate it from stereological ROI analysis (see below).</p></div></div></div><div class="section" id="sec2-5"><h4>Stereological Measurement of Insular Volume</h4><div class="para"><p>Because of the labor-intensive nature of stereological point counting analysis, a subset of 93 participants was randomly selected for this part of the study. This subgroup was large enough to be representative of the overall cohort in terms of age, sex, and cognitive performance (Table <a href="#tbl1" rel="references:#tbl1" class="tableLink" title="Link to table" shape="rect">I</a>). Volumes of the left and right insula were estimated using the Cavalieri method of modern design stereology in conjunction with point counting [Roberts et al., <a href="#bib70" rel="references:#bib70" class="referenceLink" title="Link to bibliographic citation" shape="rect">2000</a>] using Easymeasure software [Keller et al., <a href="#bib41" rel="references:#bib41" class="referenceLink" title="Link to bibliographic citation" shape="rect">2007</a>], based on the principles of our previous study [Keller et al., <a href="#bib42" rel="references:#bib42" class="referenceLink" title="Link to bibliographic citation" shape="rect">2011</a>] (Fig. <a href="#fig4" rel="references:#fig4" class="figureLink" title="Link to figure" shape="rect">4</a>A–C). Prior to point counting, the original sagittal MR images were converted to coronal images, and contrast and brightness was optimized for all subjects using MRIcro (<!--TODO: clickthrough URL--><a href="http://www.cabiatl.com/mricro/" title="Link to external resource: http://www.cabiatl.com/mricro/" shape="rect">http://www.cabiatl.com/mricro/</a>). For point counting, an array of test probes (i.e., points) was projected over coronal sections of the T1-weighted MR images, and points intersecting the insula were counted. The insular ROI was the entire insular cortex, including all segments of the long and short insular gyri, which were demarcated from adjacent brain regions by the circular insular sulcus. Separation between test points was five points and slice interval was every fifth section (5 × 5 approach, 2.5 mm × 2.5 mm). This differed from our previous study [Keller et al., <a href="#bib42" rel="references:#bib42" class="referenceLink" title="Link to bibliographic citation" shape="rect">2011</a>], which used a 4 × 4 approach. This was due to the vastly increased sample size in the present study (93 vs. 25), and that the reduced sampling density permitted a more rapid analysis (one brain analyzed in 30 min, as opposed to the 40 min per brain using the 4 × 4 approach). Importantly, in reducing the density of the stereological parameters to 5 × 5, it was crucial to maintain a coefficient of error (CE) lower than the recommended 5% [Roberts et al., <a href="#bib70" rel="references:#bib70" class="referenceLink" title="Link to bibliographic citation" shape="rect">2000</a>]. We found that a 5 × 5 approach on MR images of voxel size 1 mm × 1 mm × 1 mm resulted in CE's of 4.23% and 4.76% for the left and right insula, respectively. An estimate of insular volume was obtained as the sum of the estimated areas of the structure transects on consecutive systematic sections multiplied by the distance between the sections. Further information for insular volume estimation can be found in [Keller et al., <a href="#bib42" rel="references:#bib42" class="referenceLink" title="Link to bibliographic citation" shape="rect">2011</a>]. Inter-rater reliability studies were performed between two raters (SB and SSK) and indicated high levels of reproducibility of measures (intraclass correlation coefficient = 0.89 left insula, 0.86 right insula). Total insular volumes were obtained by adding left and right insular volumes. The association between insular gray matter volume and cognitive performance was assessed by partial correlations, correcting for age and sex.</p></div></div><div class="section" id="sec2-6"><h4>Choice of Statistical Model</h4><div class="para"><p>When assessing the full possible range of regional gray matter volumes, one might expect a sigmoid relationship with cognition, exhibiting a threshold (below that further decrease of volume has no effect on cognition), an approximately linear middle part and a zone of saturation (above that the region contributes maximally to cognition). In this study, we assumed that the lower tail of the sigmoid curve was cut by excluding demented subjects. Using linear regression analysis, we then sensitized the analysis to regions that in cognitively healthy subjects are critically related to the function under investigation, i.e., where subjects in the normal age-adjusted range of cognitive performance fall in the approximately linear middle region of the sigmoid curve. Not finding an association between regional gray matter volume and a certain neuropsychological test may thus mean that (1) gray matter volume in the region is not related to performance in this test or (2) regional gray matter is related to performance in the test, but healthy subjects have regional gray matter volumes in the saturation part of the sigmoid curve, such that small variations in volume do not have an effect on performance.</p></div></div><div class="section" id="sec2-7"><h4>Assessment of Regional Power Differences</h4><div class="para"><p>The power to detect associations in voxel- or region-based morphometry as used in this study depends critically on the regional signal-to-noise ratio. Regional differences in the amount of “noise” (i.e., interindividual variation not caused by the investigated parameter) in gray matter volume may thus produce regional differences in the power to detect a signal, leading to preferential detection of signal in regions of low noise. As signal is small in comparison to noise in the present setting, regional variability of gray matter volume may serve as an estimation of noise. Coefficients of variation of gray matter volume were similar among gray matter regions (Supporting Information Fig. S1). The power to detect a signal should therefore be similar for all regions.</p></div></div></div><div class="section" id="sec1-3"><div class="headingCont"><h3>RESULTS</h3></div><ol class="jumpList"><li><a href="#leftBorder" shape="rect">Top of page</a></li><li><a href="#abstract" shape="rect">Abstract</a></li><li><a href="#sec1-1" shape="rect">INTRODUCTION</a></li><li><a href="#sec1-2" shape="rect">MATERIALS AND METHODS</a></li><li><a href="#sec1-3" shape="rect">RESULTS</a></li><li><a href="#sec1-4" shape="rect">DISCUSSION</a></li><li><a href="#sec1-5" shape="rect">LIMITATIONS</a></li><li><a href="#sec1-6" shape="rect">CONCLUSIONS</a></li><li><a href="#wol-references" shape="rect">REFERENCES</a></li><li><a href="#supp-info" shape="rect">Supporting Information</a></li></ol><div class="section" id="sec2-8"><h4>Association of Cognitive Performance With Local/Regional Gray Matter Volume</h4><div class="para"><p>Associations between cognitive performance and local/regional gray matter volume were assessed after correction for age and sex. For all tasks (digit span forward and backward, TMT parts A and B and Stroop test parts II and III), improved performance was associated with increased local gray matter volume in various cortical clusters as determined using whole-brain VBM statistics. These clusters included several prefrontal and posterior parietal regions, the insular cortex, cerebellum, and, in the tests relying on visual processing (TMT, Stroop), parts of the occipital cortex (Supporting Information Fig. S2, Table S1). No clusters were found where improved performance was associated with decreased gray matter volume. Correlations between task performance and gray matter were not limited to highly localized peaks (as determined by voxel-based statistics), but were also detected when analyzing mean gray matter volume within the predefined larger regions (ROI-VBM analysis), again involving various prefrontal regions, the insula, cerebellum and some regions related to specific tasks (Fig. <a href="#fig1" rel="references:#fig1" class="figureLink" title="Link to figure" shape="rect">1</a>). Both with whole-brain VBM and with ROI-VBM statistics, it was evident that while there was an association between gray matter and performance in the basic tasks (digit span forward, TMT part A, Stroop test part II), the association was considerably greater in the tasks that put additional demand on executive functioning (digit span backward, TMT part B, Stroop test part III). To isolate regions that were selectively associated with executive functions, we used the difference scores (digit span forward-backward; TMT part B-A; Stroop test part III-II). Better executive performance (as quantified by lower difference scores) was associated with larger gray matter volume both in whole-brain VBM and ROI-VBM statistics, showing task-specific patterns overlapping in several prefrontal regions, the insula and the cerebellum (Figs. <a href="#fig2" rel="references:#fig2" class="figureLink" title="Link to figure" shape="rect">2</a> and <a href="#fig3" rel="references:#fig3" class="figureLink" title="Link to figure" shape="rect">3</a>A, Table <a href="#tbl3" rel="references:#tbl3" class="tableLink" title="Link to table" shape="rect">III</a>).</p></div><div id="fig1" class="figure"><div class="caption" id="fig1_caption"><p><span class="label">Figure 1. </span>Association of cognitive performance with regional gray matter volume. <b>A</b>: The association between mean gray matter volume within prespecified ROIs (ROI-VBM analysis) and test scores was assessed by partial correlation (corrected for age and sex). Correlations are illustrated using color coding as indicated. *Significant at <em>P</em> < 0.05 (uncorrected); <sup>§</sup>significant at <em>P</em> < 0.05 after Bonferroni correction for 10 comparisons (corresponding to the number of ROIs tested). All correlations were positive, i.e., better performance was associated with larger gray matter volume. <b>B:</b> Partial correlation plots (after correction for age and gender) of digit span backward, TMT part B and Stroop test part III with the most strongly associated ROIs. GM, gray matter. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]</p><p class="downloadToPowerpoint"><a href="/doi/10.1002/hbm.22146/figure.pptx?figureAssetHref=image_n/nfig001.jpg" shape="rect">Download figure to PowerPoint</a></p></div><a class="figZoom" title="Link to full-size figure" href="http://onlinelibrary.wiley.com/store/10.1002/hbm.22146/asset/image_n/nfig001.jpg?v=1&t=hhvh874y&s=cc5fce13bdf18c90fbe4f14eebd5e21161bcab1e" shape="rect"><img id="fig1_img" alt="thumbnail image" src="http://onlinelibrary.wiley.com/store/10.1002/hbm.22146/asset/image_t/tfig001.gif?v=1&t=hhvh874y&s=88368ae86d07cfefca841b8df7040f8da0e983a3" /></a></div><div id="fig2" class="figure"><div class="caption" id="fig2_caption"><p><span class="label">Figure 2. </span>Association of executive performance with local gray matter volume. Significant associations (after correction for age and sex) with the respective difference score (at <em>P</em> < 0.001, uncorrected and a cluster extent threshold of 100 voxels) are illustrated superimposed on the Montreal Neurological Institute (MNI) high-resolution single subject T1 image. Note that smaller difference scores indicate better executive performance. Blobs show significant negative associations between difference scores and gray matter volume, illustrating regions where better performance was associated with larger gray matter volume. No significant positive correlations were found between difference scores and gray matter volume throughout the brain. Coordinates are according to the MNI atlas. Right side in the figure corresponds to right side of the brain. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]</p><p class="downloadToPowerpoint"><a href="/doi/10.1002/hbm.22146/figure.pptx?figureAssetHref=image_n/nfig002.jpg" shape="rect">Download figure to PowerPoint</a></p></div><a class="figZoom" title="Link to full-size figure" href="http://onlinelibrary.wiley.com/store/10.1002/hbm.22146/asset/image_n/nfig002.jpg?v=1&t=hhvh8750&s=a61744722625325d0dfa2042f873e8e180067c2f" shape="rect"><img id="fig2_img" alt="thumbnail image" src="http://onlinelibrary.wiley.com/store/10.1002/hbm.22146/asset/image_t/tfig002.gif?v=1&t=hhvh8750&s=b52e9ab2c787d48d30b204e3aafe0918dc0e653b" /></a></div><div id="fig3" class="figure"><div class="caption" id="fig3_caption"><p><span class="label">Figure 3. </span>Association of regional gray matter with executive performance and age. <b>A</b>: The association between mean gray matter volume within prespecified ROIs (ROI-VBM analysis) and the respective difference score was assessed by partial correlation (corrected for age and sex). Correlations are illustrated using color coding as indicated. Note that smaller difference scores indicate better executive performance. All correlations were negative, i.e. better performance, indicated by smaller difference scores, was associated with larger gray matter volume within the respective ROIs. <b>B:</b> Gray matter decrease within prespecified ROIs per 10 years of age was assessed using linear regression (corrected for sex), converted to percentage using the gray matter volume of a 50-year-old male as extrapolated from the linear regression as reference. *Significant at <em>P</em> < 0.05 (uncorrected); <sup>§</sup>significant at <em>P</em> < 0.05 after Bonferroni correction. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]</p><p class="downloadToPowerpoint"><a href="/doi/10.1002/hbm.22146/figure.pptx?figureAssetHref=image_n/nfig003.jpg" shape="rect">Download figure to PowerPoint</a></p></div><a class="figZoom" title="Link to full-size figure" href="http://onlinelibrary.wiley.com/store/10.1002/hbm.22146/asset/image_n/nfig003.jpg?v=1&t=hhvh8753&s=4f5a26d00fa138c0d27ec98f32dc2b0fe8ec03d5" shape="rect"><img id="fig3_img" alt="thumbnail image" src="http://onlinelibrary.wiley.com/store/10.1002/hbm.22146/asset/image_t/tfig003.gif?v=1&t=hhvh8753&s=abeeafee56ca9f81b503ecfd97e10cabf887fe68" /></a></div><div class="table" id="tbl3"><table class="topbotR"><caption><span class="tTitle"><span class="label">Table III. </span>Associations between executive functioning and local gray matter volume as determined by whole-brain VBM statistics</span></caption><thead class="vBottom"><tr class="norsep"><th class="hCenter" rowspan="1" colspan="1"> </th><th class="hCenter" rowspan="1" colspan="1"> </th><th colspan="3" class="rsep hCenter" rowspan="1">Digits (difference score)</th><th colspan="3" class="rsep hCenter" rowspan="1">TMT (difference score)</th><th colspan="3" class="rsep hCenter" rowspan="1">Stroop (difference score)</th></tr><tr class="rsep"><th class="hLeft" rowspan="1" colspan="1">Anatomical region</th><th class="hCenter" rowspan="1" colspan="1">Side</th><th class="hCenter" rowspan="1" colspan="1">Brodmann areas</th><th class="hCenter" rowspan="1" colspan="1">Peak coordinate (x y z)</th><th class="hCenter" rowspan="1" colspan="1">Peak T value</th><th class="hCenter" rowspan="1" colspan="1">Brodmann areas</th><th class="hCenter" rowspan="1" colspan="1">Peak coordinate (x y z)</th><th class="hCenter" rowspan="1" colspan="1">Peak T value</th><th class="hCenter" rowspan="1" colspan="1">Brodmann areas</th><th class="hCenter" rowspan="1" colspan="1">Peak coordinate (x y z)</th><th class="hCenter" rowspan="1" colspan="1">Peak T value</th></tr></thead><tfoot><tr><td colspan="11" rowspan="1"><div class="noteGroup"><ol id="footnotes" class="noteGroup custom"><li id="note-p-20"><span class="bullet"><sup><i></i></sup></span><div class="text"><p>Clusters of ≥100 contiguous voxels above a threshold of <em>P</em> < 0.001 (uncorrected) are tabulated in terms of the brain region and the corresponding Brodmann's area. Note that smaller difference scores indicate better executive performance. All associations shown in the table were negative, i.e., better performance was related to larger local gray matter. Coordinates are according to the Montreal Neurological Institute (MNI) atlas.</p></div></li></ol></div></td></tr></tfoot><tbody class="vTop"><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Insula</td><td class="hCenter" rowspan="1" colspan="1">R</td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1">50 8 4</td><td class="hChar" rowspan="1" colspan="1">4.45</td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1">40 8 16</td><td class="hChar" rowspan="1" colspan="1">3.91</td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Insula</td><td class="hCenter" rowspan="1" colspan="1">L</td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hChar" rowspan="1" colspan="1">−46 −18 16</td><td class="hChar" rowspan="1" colspan="1">3.58</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1"><b>Frontal</b></td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Frontopolar cortex</td><td class="hCenter" rowspan="1" colspan="1">L</td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1">10</td><td class="hCenter" rowspan="1" colspan="1">−18 66 −2</td><td class="hChar" rowspan="1" colspan="1">3.83</td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Medial prefrontal cortex</td><td class="hCenter" rowspan="1" colspan="1">R</td><td class="hCenter" rowspan="1" colspan="1">8</td><td class="hCenter" rowspan="1" colspan="1">18 30 44</td><td class="hChar" rowspan="1" colspan="1">4.08</td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Medial prefrontal cortex</td><td class="hCenter" rowspan="1" colspan="1">R + L</td><td class="hCenter" rowspan="1" colspan="1">9/8</td><td class="hCenter" rowspan="1" colspan="1">2 54 38</td><td class="hChar" rowspan="1" colspan="1">3.74</td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Lateral prefrontal cortex</td><td class="hCenter" rowspan="1" colspan="1">L</td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1">45/47/46</td><td class="hCenter" rowspan="1" colspan="1">−46 40 2</td><td class="hChar" rowspan="1" colspan="1">3.78</td><td class="hCenter" rowspan="1" colspan="1">46/9</td><td class="hChar" rowspan="1" colspan="1">−52 20 32</td><td class="hChar" rowspan="1" colspan="1">3.76</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Lateral prefrontal cortex</td><td class="hCenter" rowspan="1" colspan="1">R</td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1">47/45</td><td class="hChar" rowspan="1" colspan="1">48 32 −16</td><td class="hChar" rowspan="1" colspan="1">3.75</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Premotor cortex</td><td class="hCenter" rowspan="1" colspan="1">L</td><td class="hCenter" rowspan="1" colspan="1">6</td><td class="hCenter" rowspan="1" colspan="1">−62 2 10</td><td class="hChar" rowspan="1" colspan="1">3.99</td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Premotor cortex</td><td class="hCenter" rowspan="1" colspan="1">L</td><td class="hCenter" rowspan="1" colspan="1">6</td><td class="hCenter" rowspan="1" colspan="1">−24 10 58</td><td class="hChar" rowspan="1" colspan="1">3.60</td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1"><b>Temporal and parietal</b></td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Medial temporal lobe</td><td class="hCenter" rowspan="1" colspan="1">R</td><td class="hCenter" rowspan="1" colspan="1">28, Amygdala</td><td class="hCenter" rowspan="1" colspan="1">14 −4 −14</td><td class="hChar" rowspan="1" colspan="1">3.82</td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1">28/34</td><td class="hChar" rowspan="1" colspan="1">14 −10 −20</td><td class="hChar" rowspan="1" colspan="1">3.59</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Superior temporal gyrus/ inferior parietal lobe</td><td class="hCenter" rowspan="1" colspan="1">R</td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1">22/40</td><td class="hChar" rowspan="1" colspan="1">58 −46 16</td><td class="hChar" rowspan="1" colspan="1">3.93</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1"><b>Occipital</b></td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Occipital cortex</td><td class="hCenter" rowspan="1" colspan="1">R</td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1">18</td><td class="hCenter" rowspan="1" colspan="1">22 −78 −12</td><td class="hChar" rowspan="1" colspan="1">3.66</td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Occipital cortex</td><td class="hCenter" rowspan="1" colspan="1">L</td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1">18/17</td><td class="hCenter" rowspan="1" colspan="1">−12 −90 −12</td><td class="hChar" rowspan="1" colspan="1">3.91</td><td class="hCenter" rowspan="1" colspan="1">18/19</td><td class="hChar" rowspan="1" colspan="1">−42 −78 −16</td><td class="hChar" rowspan="1" colspan="1">3.55</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Occipital cortex</td><td class="hCenter" rowspan="1" colspan="1">L</td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1">18/19</td><td class="hCenter" rowspan="1" colspan="1">−4 90 12</td><td class="hChar" rowspan="1" colspan="1">3.90</td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1"><b>Cerebellum</b></td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Cerebellum</td><td class="hCenter" rowspan="1" colspan="1">L</td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1">−2 −66 −18</td><td class="hChar" rowspan="1" colspan="1">3.57</td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hChar" rowspan="1" colspan="1">−18 −60 −62</td><td class="hChar" rowspan="1" colspan="1">3.94</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Cerebellum</td><td class="hCenter" rowspan="1" colspan="1">R</td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1">8 −68 −18</td><td class="hChar" rowspan="1" colspan="1">3.54</td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hCenter" rowspan="1" colspan="1"> </td></tr></tbody></table></div><div class="para"><p>To further corroborate the association between insular volume and executive performance, we obtained stereological measures of insular volume from a randomly chosen subgroup (<em>n</em> = 93, Fig. <a href="#fig4" rel="references:#fig4" class="figureLink" title="Link to figure" shape="rect">4</a>A–C). The subgroup did not significantly differ from the complete group in age, gender, BDI scores, and neuropsychological performance (Table <a href="#tbl1" rel="references:#tbl1" class="tableLink" title="Link to table" shape="rect">I</a>). Insular volumes as quantified by VBM (sum of voxel values within the insular ROI) and by stereology were significantly correlated (Fig. <a href="#fig4" rel="references:#fig4" class="figureLink" title="Link to figure" shape="rect">4</a>D). Correlations of both measures with the digit span difference score were similar (Fig. <a href="#fig4" rel="references:#fig4" class="figureLink" title="Link to figure" shape="rect">4</a>E,F). Correlations with the TMT and Stroop difference scores did not reach significance for either insular volume parameter within this smaller group.</p></div><div id="fig4" class="figure"><div class="caption" id="fig4_caption"><p><span class="label">Figure 4. </span>Comparison of stereological methods and voxel-based morphometry for measurement of insular cortex volume. <b>A–C:</b> Stereology in conjunction with point counting used to estimate the volume of the insular cortices in a randomly selected individual. A: Point counting within the left and right insula is highlighted in yellow. B: Magnification of point counting within the left insula. C: Magnification of the left insula without the projected points for anatomical clarity. <b>D:</b> Correlation between insular volumes as quantified by stereology and by ROI-VBM analysis (<em>P</em> < 0.001). <b>E, F:</b> Partial correlation plots (after correction for age and gender) of both insular measures with the digit span difference score (<em>P</em> < 0.05). Note that smaller difference scores indicate better executive performance. GM, gray matter; VBM, voxel-based morphometry. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]</p><p class="downloadToPowerpoint"><a href="/doi/10.1002/hbm.22146/figure.pptx?figureAssetHref=image_n/nfig004.jpg" shape="rect">Download figure to PowerPoint</a></p></div><a class="figZoom" title="Link to full-size figure" href="http://onlinelibrary.wiley.com/store/10.1002/hbm.22146/asset/image_n/nfig004.jpg?v=1&t=hhvh875j&s=3f3ea74b041fde0ad71ba8b03c03b48933fe94fa" shape="rect"><img id="fig4_img" alt="thumbnail image" src="http://onlinelibrary.wiley.com/store/10.1002/hbm.22146/asset/image_t/tfig004.gif?v=1&t=hhvh875k&s=ebd789c97df2cbcef8ffef45ab1a9a747bbc3890" /></a></div></div><div class="section" id="sec2-9"><h4>The Effect of Age in Cortical Regions Related to Cognitive Functions</h4><div class="para"><p>To assess if the regions showing an age-independent association between cognitive performance and gray matter volume (as described above) are also affected by age-related gray matter atrophy, we determined regional age-related gray matter decrease in the same regions (ROI-VBM analysis) investigated for cognitive functions (Fig. <a href="#fig3" rel="references:#fig3" class="figureLink" title="Link to figure" shape="rect">3</a>B). Age-related gray matter loss in these regions (ROI-VBM analysis) ranged from nonsignificant (<1.5% per 10 years) to highly significant (5% per 10 years) and was largest in the insula and posterior parietal cortex.</p></div></div><div class="section" id="sec2-10"><h4>Variance in Cognitive Functions Explained by Sex, Age, and Sex-and-Age-Independent Regional Gray Matter</h4><div class="para"><p>We used linear regression analysis to determine the cumulative association of sex, age, and sex-and-age-independent gray matter variability (within the regions most strongly related to the respective function according to ROI-VBM analysis) with cognitive functions. Regression coefficients for the difference scores are shown in Table <a href="#tbl4" rel="references:#tbl4" class="tableLink" title="Link to table" shape="rect">IV</a>. While sex made only a minor contribution to the variance of each of the three difference scores (<0.5%), age explained a significant portion of the variance of the TMT and Stroop difference scores (7.0% and 7.7%, respectively) but not of the digit span difference score. The percentage of variance explained by the gray matter volume of the most strongly related region according to ROI-VBM analysis (primary visual cortex for the TMT difference score and insular cortex for the two remaining difference scores) ranged between 1.9 and 2.4%. On average, the additional variance explained by ROI gray matter volume amounted to 41% of the variance already explained by age. For the original (not difference) test scores, results were quantitatively similar. The variance explained by age ranged from 2.6 % (Stroop test part II) to 12.8 % (TMT part A) and the variance explained by sex-and-age-independent regional gray matter within the most strongly associated ROI ranged from 0.8% (digit span forward) to 4.6% (digit span backward).</p></div><div class="table" id="tbl4"><table class="topbotR"><caption><span class="tTitle"><span class="label">Table IV. </span>Association of sex, age, and sex-and-age-independent regional gray matter variability with cognitive functions</span></caption><thead class="vBottom"><tr class="rsep"><th class="hLeft" rowspan="1" colspan="1">Independent variable</th><th class="hCenter" rowspan="1" colspan="1">% of variance explained</th><th class="hCenter" rowspan="1" colspan="1">Standardized beta (in complete model)</th><th class="hCenter" rowspan="1" colspan="1">Significance (<em>P</em>)</th></tr></thead><tfoot><tr><td colspan="4" rowspan="1"><div class="noteGroup"><ol id="footnotes" class="noteGroup custom"><li id="note-p-25"><span class="bullet"><sup><i></i></sup></span><div class="text"><p>Hierarchical linear regression was performed with cognitive test difference scores as dependent variable and sex, age and regional gray matter volume (as determined by ROI-VBM analysis, corrected for age and sex) successively entered as independent variables. For each cognitive test, the region exhibiting the strongest association with the respective difference score was used. Lower difference scores indicate better executive performance (see Table II).</p></div></li></ol></div></td></tr></tfoot><tbody class="vTop"><tr class="norsep"><td colspan="4" class="hLeft" rowspan="1">Regression 1: Digit span difference score</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Sex</td><td class="hCenter" rowspan="1" colspan="1">0.5%</td><td class="hChar" rowspan="1" colspan="1">0.072</td><td class="hChar" rowspan="1" colspan="1">0.17</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Age</td><td class="hCenter" rowspan="1" colspan="1">0.2%</td><td class="hChar" rowspan="1" colspan="1">0.046</td><td class="hChar" rowspan="1" colspan="1">0.38</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Insular cortex gray matter volume</td><td class="hCenter" rowspan="1" colspan="1">2.4%</td><td class="hChar" rowspan="1" colspan="1">−0.154</td><td class="hChar" rowspan="1" colspan="1">0.003</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Total:</td><td class="hCenter" rowspan="1" colspan="1">3.1%</td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hChar" rowspan="1" colspan="1">0.01</td></tr><tr class="norsep"><td colspan="4" class="hLeft" rowspan="1">Regression 2: TMT difference score</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Sex</td><td class="hCenter" rowspan="1" colspan="1">0.3%</td><td class="hChar" rowspan="1" colspan="1">0.071</td><td class="hChar" rowspan="1" colspan="1">0.16</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Age</td><td class="hCenter" rowspan="1" colspan="1">7.0%</td><td class="hChar" rowspan="1" colspan="1">0.266</td><td class="hChar" rowspan="1" colspan="1"><0.001</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Primary visual cortex gray matter volume</td><td class="hCenter" rowspan="1" colspan="1">2.2%</td><td class="hChar" rowspan="1" colspan="1">−0.149</td><td class="hChar" rowspan="1" colspan="1">0.003</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Total:</td><td class="hCenter" rowspan="1" colspan="1">9.5%</td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hChar" rowspan="1" colspan="1"><0.001</td></tr><tr class="norsep"><td colspan="4" class="hLeft" rowspan="1">Regression 3: Stroop difference score</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Sex</td><td class="hCenter" rowspan="1" colspan="1">0.4%</td><td class="hChar" rowspan="1" colspan="1">−0.04</td><td class="hChar" rowspan="1" colspan="1">0.42</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Age</td><td class="hCenter" rowspan="1" colspan="1">7.7%</td><td class="hChar" rowspan="1" colspan="1">0.279</td><td class="hChar" rowspan="1" colspan="1"><0.001</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Insular cortex gray matter volume</td><td class="hCenter" rowspan="1" colspan="1">1.9%</td><td class="hChar" rowspan="1" colspan="1">−0.136</td><td class="hChar" rowspan="1" colspan="1">0.07</td></tr><tr class="norsep"><td class="hLeft" rowspan="1" colspan="1">Total</td><td class="hCenter" rowspan="1" colspan="1">10.0%</td><td class="hCenter" rowspan="1" colspan="1"> </td><td class="hChar" rowspan="1" colspan="1"><0.001</td></tr></tbody></table></div></div></div><div class="section" id="sec1-4"><div class="headingCont"><h3>DISCUSSION</h3></div><ol class="jumpList"><li><a href="#leftBorder" shape="rect">Top of page</a></li><li><a href="#abstract" shape="rect">Abstract</a></li><li><a href="#sec1-1" shape="rect">INTRODUCTION</a></li><li><a href="#sec1-2" shape="rect">MATERIALS AND METHODS</a></li><li><a href="#sec1-3" shape="rect">RESULTS</a></li><li><a href="#sec1-4" shape="rect">DISCUSSION</a></li><li><a href="#sec1-5" shape="rect">LIMITATIONS</a></li><li><a href="#sec1-6" shape="rect">CONCLUSIONS</a></li><li><a href="#wol-references" shape="rect">REFERENCES</a></li><li><a href="#supp-info" shape="rect">Supporting Information</a></li></ol><div class="para"><p>This study shows that task-specific regional gray matter volume patterns for different cognitive tasks can be identified by VBM in analogy to the task-specific regional activation patterns identified by functional neuroimaging. Basic tasks were less closely related to local gray matter volume than the corresponding tasks with additional executive demand, consistent with the notion that executive functioning places special demand on cortical resources.</p></div><div class="section" id="sec2-11"><h4>Gray Matter Volume Representations of Cognitive Functions and Their Relation to Previous Knowledge on Functional Representations</h4><div class="para"><p>Performance on the digit span backwards task was related to gray matter volume in various cortical regions. Some of these regions were consistent with cortical regions activated during working memory tasks in functional imaging, e.g., VLPFC [storage of information in phonological loop (Wager and Smith, <a href="#bib85" rel="references:#bib85" class="referenceLink" title="Link to bibliographic citation" shape="rect">2003</a>)], DLPFC and posterior parietal cortex [data manipulation (D'Esposito et al., <a href="#bib20" rel="references:#bib20" class="referenceLink" title="Link to bibliographic citation" shape="rect">1998</a>; Fletcher and Henson, <a href="#bib27" rel="references:#bib27" class="referenceLink" title="Link to bibliographic citation" shape="rect">2001</a>; Wager and Smith, <a href="#bib85" rel="references:#bib85" class="referenceLink" title="Link to bibliographic citation" shape="rect">2003</a>)] and ACC [attention and monitoring (Gazzaniga et al., <a href="#bib30" rel="references:#bib30" class="referenceLink" title="Link to bibliographic citation" shape="rect">2002</a>)]. However, the strongest correlation was detected with lateral temporal lobe gray matter, a region not typically associated with working memory but rather with visual processing [ventral visual pathway, (Ungerleider and Haxby, <a href="#bib80" rel="references:#bib80" class="referenceLink" title="Link to bibliographic citation" shape="rect">1994</a>)]. A possible interpretation might be that visualization of the digit sequence is a strategy used when performing the digit span backwards [Cabeza and Nyberg, <a href="#bib12" rel="references:#bib12" class="referenceLink" title="Link to bibliographic citation" shape="rect">2000</a>; Wager and Smith, <a href="#bib85" rel="references:#bib85" class="referenceLink" title="Link to bibliographic citation" shape="rect">2003</a>]. The digit span difference score, thought to isolate the data manipulation component of the task, was mainly related to the insula and dorsal medial prefrontal cortex. The latter is in accordance with the “ventral storage, dorsal executive” hypothesis of working memory, although dorsolateral rather than dorsomedial regions have been associated with data manipulation in functional imaging studies [Owen, <a href="#bib61" rel="references:#bib61" class="referenceLink" title="Link to bibliographic citation" shape="rect">2000</a>; Wager and Smith, <a href="#bib85" rel="references:#bib85" class="referenceLink" title="Link to bibliographic citation" shape="rect">2003</a>].</p></div><div class="para"><p>Performance in the TMT (both parts and difference score) was related to occipital and cerebellar gray matter, consistent with its visuomotor demands. The difference score, focusing on the set-shifting component of the task, was additionally associated with left VLPFC, DLPFC and frontopolar cortex, consistent with the functional representation of set-shifting [Collette and Van der Linden, <a href="#bib15" rel="references:#bib15" class="referenceLink" title="Link to bibliographic citation" shape="rect">2002</a>; Loose et al., <a href="#bib49" rel="references:#bib49" class="referenceLink" title="Link to bibliographic citation" shape="rect">2006</a>]. In agreement with our results, functional imaging during TMT has identified activations in left DLPFC and insula [Moll et al., <a href="#bib55" rel="references:#bib55" class="referenceLink" title="Link to bibliographic citation" shape="rect">2002</a>; Zakzanis et al., <a href="#bib88" rel="references:#bib88" class="referenceLink" title="Link to bibliographic citation" shape="rect">2005</a>]. Additional activations have been reported in premotor, mid-cingulate and posterior parietal and temporal regions [Moll et al., <a href="#bib55" rel="references:#bib55" class="referenceLink" title="Link to bibliographic citation" shape="rect">2002</a>; Zakzanis et al., <a href="#bib88" rel="references:#bib88" class="referenceLink" title="Link to bibliographic citation" shape="rect">2005</a>]. Similar to the present results, associations between TMT performance and gray matter volume have been reported in left ventrolateral cortex and occipital cortex [Newman et al., <a href="#bib58" rel="references:#bib58" class="referenceLink" title="Link to bibliographic citation" shape="rect">2007</a>]. Thus, it can be concluded that left VLPFC and DLPFC are associated with set-shifting both in functional and structural imaging studies.</p></div><div class="para"><p>Response inhibition in the Stroop task has been mainly associated with activation of the DLPFC and the dorsal ACC [Bush et al., <a href="#bib11" rel="references:#bib11" class="referenceLink" title="Link to bibliographic citation" shape="rect">2000</a>; Leung et al., <a href="#bib46" rel="references:#bib46" class="referenceLink" title="Link to bibliographic citation" shape="rect">2000</a>; Liu et al., <a href="#bib48" rel="references:#bib48" class="referenceLink" title="Link to bibliographic citation" shape="rect">2004</a>; Peterson et al., <a href="#bib66" rel="references:#bib66" class="referenceLink" title="Link to bibliographic citation" shape="rect">1999</a>]. We found an association of the Stroop difference score with gray matter volume in the left DLPFC, but association with the ACC was relatively weak (Stroop test part III) or absent (difference score).</p></div><div class="para"><p>Although functional and gray matter volume representations of the cognitive functions tested in the present study overlap in many respects, there are also major differences, e.g. the lack of association between Stroop task and gray matter volume in the ACC and the association of the digit span difference score with medial rather than lateral dorsal prefrontal cortex. These results suggest that some regions, although being strongly activated by a specific function, nonetheless have a structural and/or functional reserve for that function, so that gray matter volume decrease (within the range encountered in the presently investigated non-demented population) does not lead to reduced function. This reserve may depend on the same region and/or other regions (partially) taking over the function of the original region.</p></div></div><div class="section" id="sec2-12"><h4>Regions Associated With Cognitive Functions Across Different Tasks: DLPFC and Insular Cortex</h4><div class="para"><p>Executive functioning has been divided into different subfunctions (neurocognitive processes) such as directed attention, working memory, set-shifting and response inhibition [Suchy, <a href="#bib76" rel="references:#bib76" class="referenceLink" title="Link to bibliographic citation" shape="rect">2009</a>]. Specific activation patterns relevant for each subfunction have been identified in functional imaging studies that overlap in executive “core” regions such as the DLPFC and possibly the posterior parietal cortex [Collette and Van der Linden M., <a href="#bib15" rel="references:#bib15" class="referenceLink" title="Link to bibliographic citation" shape="rect">2002</a>; Collette et al., <a href="#bib16" rel="references:#bib16" class="referenceLink" title="Link to bibliographic citation" shape="rect">2005</a>; Suchy, <a href="#bib76" rel="references:#bib76" class="referenceLink" title="Link to bibliographic citation" shape="rect">2009</a>; Wager and Smith, <a href="#bib85" rel="references:#bib85" class="referenceLink" title="Link to bibliographic citation" shape="rect">2003</a>]. The executive subfunctions investigated here (data manipulation, set-shifting and response inhibition) were related to gray matter volume in several prefrontal regions, with a partial overlap in left DLPFC. However, both the voxel-level analysis (Table <a href="#tbl3" rel="references:#tbl3" class="tableLink" title="Link to table" shape="rect">III</a>) and the regional analysis (Fig. <a href="#fig3" rel="references:#fig3" class="figureLink" title="Link to figure" shape="rect">3</a>A) show that the largest overlap between the different subfunctions was present in the insular cortex. Given that there may be methodological issues associated with VBM that may affect results [Ashburner and Friston, <a href="#bib2" rel="references:#bib2" class="referenceLink" title="Link to bibliographic citation" shape="rect">2001</a>; Bookstein, <a href="#bib10" rel="references:#bib10" class="referenceLink" title="Link to bibliographic citation" shape="rect">2001</a>; Fein et al., <a href="#bib26" rel="references:#bib26" class="referenceLink" title="Link to bibliographic citation" shape="rect">2006</a>; Keller and Roberts, <a href="#bib40" rel="references:#bib40" class="referenceLink" title="Link to bibliographic citation" shape="rect">2008</a>; Pereira et al., <a href="#bib65" rel="references:#bib65" class="referenceLink" title="Link to bibliographic citation" shape="rect">2008</a>; Salmond et al., <a href="#bib72" rel="references:#bib72" class="referenceLink" title="Link to bibliographic citation" shape="rect">2002</a>], we complemented the analysis by obtaining stereological measures of insular volume in a subgroup of participants. As the results obtained by both methods were similar, we suggest that insular cortex volume has a relationship with executive functioning. Indeed, some previous functional imaging studies have reported prominent, mostly bilateral insular activity during different neuropsychological tasks, including Stroop test [Leung et al., <a href="#bib46" rel="references:#bib46" class="referenceLink" title="Link to bibliographic citation" shape="rect">2000</a>; Liu et al., <a href="#bib48" rel="references:#bib48" class="referenceLink" title="Link to bibliographic citation" shape="rect">2004</a>], TMT [Zakzanis et al., <a href="#bib88" rel="references:#bib88" class="referenceLink" title="Link to bibliographic citation" shape="rect">2005</a>], Go/NoGo task [Laurens et al., <a href="#bib45" rel="references:#bib45" class="referenceLink" title="Link to bibliographic citation" shape="rect">2005</a>; Menon et al., <a href="#bib52" rel="references:#bib52" class="referenceLink" title="Link to bibliographic citation" shape="rect">2001</a>], infrequent target detection [Huettel et al., <a href="#bib37" rel="references:#bib37" class="referenceLink" title="Link to bibliographic citation" shape="rect">2004</a>], selective attention [Corbetta et al., <a href="#bib17" rel="references:#bib17" class="referenceLink" title="Link to bibliographic citation" shape="rect">1991</a>] and working memory [Grasby et al., <a href="#bib33" rel="references:#bib33" class="referenceLink" title="Link to bibliographic citation" shape="rect">1994</a>; Paulesu et al., <a href="#bib63" rel="references:#bib63" class="referenceLink" title="Link to bibliographic citation" shape="rect">1993</a>]. In addition, insular lesions have been associated with speech and language disorders [Cereda et al., <a href="#bib13" rel="references:#bib13" class="referenceLink" title="Link to bibliographic citation" shape="rect">2002</a>; Dronkers, <a href="#bib23" rel="references:#bib23" class="referenceLink" title="Link to bibliographic citation" shape="rect">1996</a>; Nestor et al., <a href="#bib57" rel="references:#bib57" class="referenceLink" title="Link to bibliographic citation" shape="rect">2003</a>] and insular atrophy is related to impaired executive functioning in Huntington's disease and alcohol dependence [Chanraud et al., <a href="#bib14" rel="references:#bib14" class="referenceLink" title="Link to bibliographic citation" shape="rect">2007</a>; Peinemann et al., <a href="#bib64" rel="references:#bib64" class="referenceLink" title="Link to bibliographic citation" shape="rect">2005</a>]. Our results show association with insular volume not only in the difference scores but also in two of the three basic tasks (digit span forward and Stroop test part II), albeit at a lower level. Thus, results from both structural and functional imaging suggest that the insular cortex, in addition to other functions, may be a “core” cognitive region, most prominently involved in but not limited to executive processes.</p></div></div><div class="section" id="sec2-13"><h4>Functional Implications of Association Between Regional Gray Matter Volume and Cognitive Function</h4><div class="para"><p>In the present study, cognitive performance was positively associated with gray matter volume in several cortical regions. It has been emphasized that gray matter volume differences are not necessarily due to differences in neuronal numbers or density of synaptic contacts, but may also reflect different amount of glial cells or even extracellular fluid [Draganski et al., <a href="#bib22" rel="references:#bib22" class="referenceLink" title="Link to bibliographic citation" shape="rect">2006</a>; May and Gaser, <a href="#bib51" rel="references:#bib51" class="referenceLink" title="Link to bibliographic citation" shape="rect">2006</a>]. Nonetheless, the most straightforward interpretation of our results is that the cognitive task under study is critically dependent on gray matter volume in the associated brain regions, meaning that pre-existing or acquired variations in gray matter volume have a direct impact on performance in the respective task. However, gray matter volume may also change according to demand, a process that has been termed use-dependent plasticity [Draganski and May, <a href="#bib21" rel="references:#bib21" class="referenceLink" title="Link to bibliographic citation" shape="rect">2008</a>]. Thus, decreased use of specific cognitive functions during certain stages of life (e.g., retirement) might lead to both reduced cognitive performance and gray matter volume reduction in regions associated with these functions. Higher levels of education may have the contrary effect. Longitudinal studies will be needed to address direction of causality. Furthermore, it will be interesting to investigate the relationship between executive performance and white matter architecture using diffusion tensor imaging techniques. In particular, it may be hypothesized that the gray matter correlations with cognition observed in the present study relate to the integrity and connectivity of white matter between these regions [Bendlin et al., <a href="#bib8" rel="references:#bib8" class="referenceLink" title="Link to bibliographic citation" shape="rect">2010</a>; Voineskos et al., <a href="#bib84" rel="references:#bib84" class="referenceLink" title="Link to bibliographic citation" shape="rect">2010</a>].</p></div><div class="para"><p>Both executive performance and gray matter volume decrease with age [Good et al., <a href="#bib32" rel="references:#bib32" class="referenceLink" title="Link to bibliographic citation" shape="rect">2001</a>; Hedden and Gabrieli, <a href="#bib36" rel="references:#bib36" class="referenceLink" title="Link to bibliographic citation" shape="rect">2004</a>]. In a cross-sectional study, it is not possible to determine if the two relationships form a causal sequence (i.e., aging leads to gray matter atrophy, gray matter atrophy leads to cognitive decline) or are independent but parallel manifestations of age. Again, longitudinal studies will be needed to address this question. However, the age-independent peak associations between executive performance and gray matter volume described here occurred in regions that were also strongly affected by age-related gray matter atrophy. Supposing that age-dependent gray matter atrophy has the same effects as age-independent gray matter atrophy, the results are consistent with the causal sequence proposed above.</p></div></div></div><div class="section" id="sec1-5"><div class="headingCont"><h3>LIMITATIONS</h3></div><ol class="jumpList"><li><a href="#leftBorder" shape="rect">Top of page</a></li><li><a href="#abstract" shape="rect">Abstract</a></li><li><a href="#sec1-1" shape="rect">INTRODUCTION</a></li><li><a href="#sec1-2" shape="rect">MATERIALS AND METHODS</a></li><li><a href="#sec1-3" shape="rect">RESULTS</a></li><li><a href="#sec1-4" shape="rect">DISCUSSION</a></li><li><a href="#sec1-5" shape="rect">LIMITATIONS</a></li><li><a href="#sec1-6" shape="rect">CONCLUSIONS</a></li><li><a href="#wol-references" shape="rect">REFERENCES</a></li><li><a href="#supp-info" shape="rect">Supporting Information</a></li></ol><div class="para"><p>This study used a cross-sectional approach, precluding investigation of the impact of gray matter volume changes on cognitive function over time within the same individual. In addition, the study was limited to nondemented, cognitively healthy subjects. Because variations in both local gray matter volume and cognitive performance within this population were small compared with that of clinical populations with evident neuropsychological impairments, only subtle associations between gray matter volume and cognitive performance could be expected and were found. It remains to be determined if and how the associations reported here can be extrapolated into the pathological range, i.e., for populations with clearly impaired cognitive performance. Finally, this study did not use a specific control task. This is due to the fact that nonexecutive control tasks are not feasible because the performance of all cognitive tasks relies to some extent on executive functioning [Fuster, <a href="#bib29" rel="references:#bib29" class="referenceLink" title="Link to bibliographic citation" shape="rect">2001</a>]. However, this study assessed pairs of tasks with different executive demand, and indeed association of the basic tasks (digit span forward, TMT A, Stroop test part II) with the prefrontal cortex and the insula were absent or much smaller than for the corresponding tasks with high executive demand (digit span backward, TMT B, Stroop test part III), providing some degree of control for the association of these regions with executive demand.</p></div></div><div class="section" id="sec1-6"><div class="headingCont"><h3>CONCLUSIONS</h3></div><ol class="jumpList"><li><a href="#leftBorder" shape="rect">Top of page</a></li><li><a href="#abstract" shape="rect">Abstract</a></li><li><a href="#sec1-1" shape="rect">INTRODUCTION</a></li><li><a href="#sec1-2" shape="rect">MATERIALS AND METHODS</a></li><li><a href="#sec1-3" shape="rect">RESULTS</a></li><li><a href="#sec1-4" shape="rect">DISCUSSION</a></li><li><a href="#sec1-5" shape="rect">LIMITATIONS</a></li><li><a href="#sec1-6" shape="rect">CONCLUSIONS</a></li><li><a href="#wol-references" shape="rect">REFERENCES</a></li><li><a href="#supp-info" shape="rect">Supporting Information</a></li></ol><div class="para"><p>The present study showed that task-specific regional gray matter volume patterns exist and partly overlap with activation patterns seen in functional imaging. Cortical regions that did not overlap may be those that have a structural and/or functional reserve for a specific function in spite of the strong activation during performance of the task. The largest overlap between different cognitive functions was found in the insular cortex, a finding observed using more than one quantitative MRI approach. Together with the results of previous functional imaging studies, this suggests that the insular cortex, in addition to its other functions, may be a “core” cognitive region. Performance in cognitive tests is determined by multiple factors, with, as determined in the present study, on average 2-3 % of the variance explained by the gray matter volume of the single most strongly predictive region. While this is small in absolute numbers, it adds on average about one third to the variance explained by age. The interplay of multiple exogenic and endogenous factors like genetic predisposition, education, experience, use-dependent trophic effects, age, gender, nutrition, metabolism, and disease may account for the interindividual differences in regional gray matter volume.</p></div></div><div id="wol-references" class="bibliography"><div class="headingCont"><h3>REFERENCES</h3></div><ol class="jumpList"><li><a href="#leftBorder" shape="rect">Top of page</a></li><li><a href="#abstract" shape="rect">Abstract</a></li><li><a href="#sec1-1" shape="rect">INTRODUCTION</a></li><li><a href="#sec1-2" shape="rect">MATERIALS AND METHODS</a></li><li><a href="#sec1-3" shape="rect">RESULTS</a></li><li><a href="#sec1-4" shape="rect">DISCUSSION</a></li><li><a href="#sec1-5" shape="rect">LIMITATIONS</a></li><li><a href="#sec1-6" shape="rect">CONCLUSIONS</a></li><li><a href="#wol-references" shape="rect">REFERENCES</a></li><li><a href="#supp-info" shape="rect">Supporting Information</a></li></ol><ul 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href="/doi/10.1111/j.1528-1167.2007.01485.x/abstract" shape="rect">Abstract</a></li><li><a href="/doi/10.1111/j.1528-1167.2007.01485.x/full" shape="rect">Full Article (HTML)</a></li><li><a href="/doi/10.1111/j.1528-1167.2007.01485.x/pdf" shape="rect">PDF(1182K)</a></li><li><a href="/doi/10.1111/j.1528-1167.2007.01485.x/references" shape="rect">References</a></li><li><a href="/resolve/reference/ISI?id=000255480500002" shape="rect">Web of Science® Times Cited: 73</a></li></ul></div><ul xmlns:ol="http://www.wiley.com/namespaces/ol/xsl-lib" xmlns:doc="urn://wiley-online-library/content/document" xmlns:xs="http://www.w3.org/2001/XMLSchema" class="openUrl"><li><a title="Find it at CU" 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of page</a></li><li><a href="#abstract" shape="rect">Abstract</a></li><li><a href="#sec1-1" shape="rect">INTRODUCTION</a></li><li><a href="#sec1-2" shape="rect">MATERIALS AND METHODS</a></li><li><a href="#sec1-3" shape="rect">RESULTS</a></li><li><a href="#sec1-4" shape="rect">DISCUSSION</a></li><li><a href="#sec1-5" shape="rect">LIMITATIONS</a></li><li><a href="#sec1-6" shape="rect">CONCLUSIONS</a></li><li><a href="#wol-references" shape="rect">REFERENCES</a></li><li><a href="#supp-info" shape="rect">Supporting Information</a></li></ol><div class="para"><p> Additional Supporting Information may be found in the online version of this article. </p></div><table class="supportingInformation"><thead><tr><th id="articleFilename" rowspan="1" colspan="1">Filename</th><th id="articleFormat" rowspan="1" colspan="1">Format</th><th id="articleSize" rowspan="1" colspan="1">Size</th><th id="articleDescription" rowspan="1" colspan="1">Description</th></tr></thead><tbody><tr><td headers="articleFilename" rowspan="1" colspan="1"><a href="http://onlinelibrary.wiley.com/store/10.1002/hbm.22146/asset/supinfo/HBM_22146_sm_SuppFig1.TIF?v=1&s=7d465c5a4c9801036850d97722ac6e68f7de0a1c" shape="rect">HBM_22146_sm_SuppFig1.TIF</a></td><td headers="articleFormat" rowspan="1" colspan="1"></td><td headers="articleSize" rowspan="1" colspan="1">2066K</td><td headers="articleDescription" rowspan="1" colspan="1">Supporting Information Figure 1.</td></tr><tr><td headers="articleFilename" rowspan="1" colspan="1"><a href="http://onlinelibrary.wiley.com/store/10.1002/hbm.22146/asset/supinfo/HBM_22146_sm_SuppFig2.tif?v=1&s=e7f56a5df0fdc4bfdd6877f984c608d94bb55daa" shape="rect">HBM_22146_sm_SuppFig2.tif</a></td><td headers="articleFormat" rowspan="1" colspan="1"></td><td headers="articleSize" rowspan="1" colspan="1">302K</td><td headers="articleDescription" rowspan="1" colspan="1">Supporting Information Figure 2.</td></tr><tr><td headers="articleFilename" rowspan="1" colspan="1"><a href="http://onlinelibrary.wiley.com/store/10.1002/hbm.22146/asset/supinfo/HBM_22146_sm_SuppTabS1.doc?v=1&s=8b3e60d64d29c192c7be740730290bfc69748ed8" shape="rect">HBM_22146_sm_SuppTabS1.doc</a></td><td headers="articleFormat" rowspan="1" colspan="1"></td><td headers="articleSize" rowspan="1" colspan="1">107K</td><td headers="articleDescription" rowspan="1" colspan="1">Supporting Information Table S1. Associations between cognitive functions and local grey matter volume as determined by whole-brain VBM statistics. Clusters above a threshold of p<0.001 (uncorrected) are tabulated in terms of the brain region and the corresponding Brodmann' s area. * p<0.05, FWE-corrected. Extent thresholds: digit span forward: 25 voxels; TMT part A, Stroop test part II: 50 voxels, remaining tests: 100 voxels. All associations were positive, i.e. better performance was related to larger local grey matter. Coordinates are according to the Montreal Neurological Institute (MNI) atlas. Common superscripts (1) to (7) indicate regions that were part of the same cluster, respectively.</td></tr></tbody></table><p class="suppInfoDisclaimer">Please note: Wiley-Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.</p></div><style xml:space="preserve">
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