Neuroanatomical and Neurofunctional Markers of Social Cognition in Autism Spectrum Disorder Michelle A. Patriquin, 1,2 Thomas DeRamus, 3 Lauren E. Libero, 3 Angela Laird, 4 and Rajesh K. Kana 3 * 1 The Menninger Clinic, Houston, Texas 2 Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Birmingham, Alabama 3 Department of Psychology, University of Alabama at Birmingham, Birmingham, Alabama 4 Department of Physics, Florida International University, Birmingham, Florida r r Abstract: Social impairments in autism spectrum disorder (ASD), a hallmark feature of its diagnosis, may underlie specific neural signatures that can aid in differentiating between those with and without ASD. To assess common and consistent patterns of differences in brain responses underlying social cognition in ASD, this study applied an activation likelihood estimation (ALE) meta-analysis to results from 50 neuroimaging studies of social cognition in children and adults with ASD. In addition, the group ALE clusters of activation obtained from this was used as a social brain mask to perform surface-based cortical morphometry (SBM) in an empirical structural MRI dataset collected from 55 ASD and 60 typically developing (TD) control participants. Overall, the ALE meta-analysis revealed consistent differences in activation in the posterior superior temporal sulcus at the temporoparietal junction, middle frontal gyrus, fusiform face area (FFA), inferior frontal gyrus (IFG), amygdala, insula, and cingulate cortex between ASD and TD individuals. SBM analysis showed alterations in the thick- ness, volume, and surface area in individuals with ASD in STS, insula, and FFA. Increased cortical thickness was found in individuals with ASD, the IFG. The results of this study provide functional and anatomical bases of social cognition abnormalities in ASD by identifying common signatures from a large pool of neuroimaging studies. These findings provide new insights into the quest for a neuroimaging-based marker for ASD. Hum Brain Mapp 37:3957–3978, 2016. V C 2016 Wiley Periodicals, Inc. Key words: activation likelihood estimation; social cognition; social brain; meta-analysis; autism; neu- roimaging; brain r r INTRODUCTION Social cognition has been defined as the way in which people make sense of other people and themselves [Fiske and Taylor, 1991] and the ability to construct representa- tions of the relation between oneself and others and to use those representations flexibly to guide social behavior [Adolphs, 2001]. Limited ability in social cognition in Additional Supporting Information may be found in the online version of this article. Contract grant sponsor: UAB Department of Psychology; Contract grant sponsor: McNulty-Civitan Scientist Award; Contract grant sponsor: NIH R01; Contract grant number: NIH R01-MH074457. *Correspondence to: Rajesh K. Kana, PhD; Department of Psychology, University of Alabama at Birmingham, Civitan International Research Center CIRC, 235 G, 1719 6th Ave South, Birmingham, AL 35294-0021, USA. E-mail: [email protected]Received for publication 8 September 2015; Revised 4 May 2016; Accepted 7 June 2016. DOI: 10.1002/hbm.23288 Published online 22 June 2016 in Wiley Online Library (wileyonlinelibrary.com). r Human Brain Mapping 37:3957–3978 (2016) r V C 2016 Wiley Periodicals, Inc.
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Neuroanatomical and Neurofunctional Markers ofSocial Cognition in Autism Spectrum Disorder
Michelle A. Patriquin,1,2 Thomas DeRamus,3 Lauren E. Libero,3
Angela Laird,4 and Rajesh K. Kana3*
1The Menninger Clinic, Houston, Texas2Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine,
Birmingham, Alabama3Department of Psychology, University of Alabama at Birmingham, Birmingham, Alabama
4Department of Physics, Florida International University, Birmingham, Florida
r r
Abstract: Social impairments in autism spectrum disorder (ASD), a hallmark feature of its diagnosis,may underlie specific neural signatures that can aid in differentiating between those with and withoutASD. To assess common and consistent patterns of differences in brain responses underlying socialcognition in ASD, this study applied an activation likelihood estimation (ALE) meta-analysis to resultsfrom 50 neuroimaging studies of social cognition in children and adults with ASD. In addition, thegroup ALE clusters of activation obtained from this was used as a social brain mask to performsurface-based cortical morphometry (SBM) in an empirical structural MRI dataset collected from 55ASD and 60 typically developing (TD) control participants. Overall, the ALE meta-analysis revealedconsistent differences in activation in the posterior superior temporal sulcus at the temporoparietaljunction, middle frontal gyrus, fusiform face area (FFA), inferior frontal gyrus (IFG), amygdala, insula,and cingulate cortex between ASD and TD individuals. SBM analysis showed alterations in the thick-ness, volume, and surface area in individuals with ASD in STS, insula, and FFA. Increased corticalthickness was found in individuals with ASD, the IFG. The results of this study provide functionaland anatomical bases of social cognition abnormalities in ASD by identifying common signatures froma large pool of neuroimaging studies. These findings provide new insights into the quest for aneuroimaging-based marker for ASD. Hum Brain Mapp 37:3957–3978, 2016. VC 2016 Wiley Periodicals, Inc.
Key words: activation likelihood estimation; social cognition; social brain; meta-analysis; autism; neu-roimaging; brain
r r
INTRODUCTION
Social cognition has been defined as the way in whichpeople make sense of other people and themselves [Fiske
and Taylor, 1991] and the ability to construct representa-tions of the relation between oneself and others and to usethose representations flexibly to guide social behavior[Adolphs, 2001]. Limited ability in social cognition in
Additional Supporting Information may be found in the onlineversion of this article.
Contract grant sponsor: UAB Department of Psychology; Contractgrant sponsor: McNulty-Civitan Scientist Award; Contract grantsponsor: NIH R01; Contract grant number: NIH R01-MH074457.
*Correspondence to: Rajesh K. Kana, PhD; Department ofPsychology, University of Alabama at Birmingham, Civitan
International Research Center CIRC, 235 G, 1719 6th Ave South,Birmingham, AL 35294-0021, USA. E-mail: [email protected]
Received for publication 8 September 2015; Revised 4 May 2016;Accepted 7 June 2016.
DOI: 10.1002/hbm.23288Published online 22 June 2016 in Wiley Online Library(wileyonlinelibrary.com).
r Human Brain Mapping 37:3957–3978 (2016) r
VC 2016 Wiley Periodicals, Inc.
individuals with autism spectrum disorder (ASD) oftenresult in poor social interaction. Recent neurobiologicalinvestigations involving human neuroimaging techniqueshave suggested several potential neural markers for ASD,primarily involving brain areas underlying social cogni-tion. For example, atypical functional activation of the fusi-form face area (FFA) [Spencer et al., 2011], superiortemporal sulcus (STS) [Kaiser et al., 2010], amygdala[Baron-Cohen et al., 2000], and disrupted connectivity ofthe theory-of-mind (ToM) network [Deshpande et al., 2013;Kana et al., 2014] have been implicated as markers ofASD. These areas are considered part of the social brain,which comprises a network of regions that include themedial prefrontal cortex (MPFC), orbitofrontal cortex(OFC), anterior cingulate cortex (ACC), amygdala (AMY),temporoparietal junction (TPJ), inferior frontal gyrus (IFG),Extrastriate Body Area (EBA), STS, and FFA [Blakemoreet al., 2007; Brothers, 1990; Easton and Emery, 2004; Frithand Frith, 2008; Kennedy and Adolphs, 2012; Pelphreyand Carter, 2008]. The social brain areas mediate differentsocial functions, such as joint attention, reading intentions,detecting agency, perceiving emotions, and processingfaces which are all critical in navigating the social world.There is emerging evidence that the anatomy, functionalactivation, and connectivity of the social brain areas arealtered in individuals with autism [Gotts et al., 2012; Ken-nedy and Adolphs, 2012; Pelphrey et al., 2011].
Among the relatively large number of functional neuroi-maging studies of autism, many have focused primarilyon individual social processes (e.g., face processing, bio-logical motion, or theory-of-mind). Prior literature has sug-gested brain areas underlying social cognition to bepotential candidates for a neuroendophenotype of ASD[Chiu et al., 2008; Kaiser et al., 2010; Spencer et al., 2011].Chiu et al. [2008] found that cingulate response during aneuroeconomic social exchange task was related to ASDsymptom severity. Kaiser et al. [2010] proposed the STS asa potential neuroendophenotype of autism based on theirfindings of differential state- and trait-related activation in
STS during biological motion perception across childrenwith ASD, their unaffected siblings, and TD children. In asimilar study, Spencer et al. [2011] found that unaffectedsiblings and individuals with ASD demonstrated similaractivity in FFA during a facial expression task, suggestingit to be a neuroendophenotype that captures both autismand the broader autism phenotype. In a recent study fromour group, Deshpande et al. [2013] found that effectiveconnectivity of the ToM network was able to successfullyclassify the participants into ASD and TD groups withabout 95% accuracy. Further, studies using voxel-basedmorphology (VBM) and diffusion tensor imaging (DTI)have provided support for alterations in cortical anatomyin social brain areas [Cauda et al., 2011a, 2014]. Thus,emerging evidence from diverse neuroimaging studiespoint to several social brain areas as potential candidatesfor neural markers of ASD. Nevertheless, within the socialbrain, there has not been an overwhelming consensus on aspecific region or network that may serve as the “best”candidate. Identifying neural signatures is critical tounderstanding the biological differences between individu-als with and without ASD. Such markers can lead to bet-ter, more accurate, and early diagnosis of ASD, and canhelp design targeted intervention for individuals withASD. As difficulties with social cognition and socialbehavior are pervasive throughout the autism spectrum,integrating inferences from numerous studies of socialcognition in ASD gives the ideal vantage point to probevalid, common, and consistent neural signatures.
While there are several meta-analyses of social cognitionin healthy individuals [Schilbach et al., 2012], there havebeen fewer attempts to consolidate the widespread andgrowing body of neuroimaging literature on social brainin autism. Using ALE meta-analysis on 24 studies of socialcognition, DiMartino et al. [2009] found that the ASD par-ticipants demonstrated a greater likelihood of hypoactiva-tion in the ACC and anterior insula. A more recent meta-analysis conducted by Sugranyes et al. [2011] analyzed 12papers that compared ASD and control groups on standar-dized facial emotion recognition (n 5 5) or ToM (n 5 7)paradigms. For these two paradigms, the meta-analysisindicated hypoactivation of MPFC, amygdala and STS inASD group primarily during ToM tasks. Developmentalapproaches to ALE have also been effectively utilized toidentify social and nonsocial functional difference (i.e.,fronto-temporal structures in particular) in children withASD, relative to adults [Dickstein et al., 2013]. Notably,the number of papers, subjects, and foci used for themeta-analysis were significantly less in these studies byonly including two social cognition paradigms. However,despite these limitations, similar findings have emergedacross these studies both in terms of hypoactivation ofASD in ACC, anterior insula [Dimartino et al., 2009],MPFC, amygdala, and STS [Sugranyes et al., 2011]. Theconsistent presence of some of these regions highlight dys-function within regions of the social brain in individuals
with ASD [Cauda et al., 2011b, 2014; DeRamus and Kana,2014; Libero et al., 2014]. It is important to consider, how-ever, that there have been a large number of studies sug-gesting that individuals with ASD differentially process,or at the very least have different BOLD activity inresponse to human faces in tasks involving face processing[Dalton et al., 2005b; Kleinhans et al., 2008; Pierce et al.,2001]. The number of face-processing studies of autism out-weighs that of other topics of social cognition, perhapsunderscoring the importance of this construct. The goal ofthis study is to comprehensively characterize the socialbrain abnormalities in autism at functional and anatomicallevels by examining the emerging patterns across a largenumber of neuroimaging studies of social cognition inASD. As such, this study of activation likelihood estimation(ALE) meta-analysis includes 50 peer-reviewed publicationsconsisting of 675 participants with ASD, 695 TD individu-als, and used a total of 1,843 foci of brain activity.
While dysfunction of the social brain in ASD has been dem-onstrated by many fMRI studies, a few studies have alsoexamined the anatomical bases of such abnormalities. VBMdata have suggested that structural alterations are withinsocial brain areas in individuals with ASD, including the pre-frontal cortex, amygdala, insula, and cingulate [Cauda et al.,2011a, 2014; DeRamus and Kana, 2014]. Meta-analyses ofVBM studies reported smaller grey matter (GM) volumes inASD in the temporal lobe, MPFC, amygdala/hippocampus,and precuneus [Duerden et al., 2012; Nickl-Jockschat et al.,2012; Stanfield et al., 2008]. Same studies have also foundlarger GM volumes in the lateral prefrontal cortex andtemporo-occipital regions. A recent study reported smallerlocal GM volumes in ASD compared to TD participants in thebilateral amygdala, left anterior insula and MPFC [Radeloffet al., 2014]. Considering these anatomical abnormalities inautism, the present study applied the social brain regions(found from meta-analysis) to test anatomical integrity (corti-cal thickness, volume, and surface area) in an empirical data-set of 115 participants (55 ASD and 60 TD). This provides avaluable and novel dimension, of relating function to struc-ture, to the present ALE-based meta-analysis. Thus, the find-ings of this study will provide important insights into thefunction and anatomy of the social brain in autism.
METHODS
Meta-Analysis
This meta-analysis adhered to the Preferred ReportingItems for Systematic Reviews and Meta-Analyses(PRISMA) Statement guidelines (http://www.prisma-statement.org/). The search method for published studiesand inclusion criteria were specified in advance. Studiesincluded in this meta-analysis investigated social cognitionin participants with ASD and in TD control participants.Paradigms related to social cognition in this study are anyneuroimaging experiments involving tasks that focus on
processing information about the faces, bodies, feelings,thoughts, motions, and emotions of other humans (e.g.,viewing stimuli made up of human faces or bodies, askingto make a judgment about another person’s thoughts; seeFig. 1 for examples). Peer-reviewed and published scien-tific papers were identified through a computerized litera-ture search using Google Scholar (http://scholar.google.com/), Sleuth (http://brainmap.org/sleuth/readme.html),PubMed (http://www.ncbi.nlm.nih.gov/pubmed), and Sci-enceDirect (http://www.sciencedirect.com/). We reviewedall functional neuroimaging papers published in Englishthrough the year 2014. The publications ranged from year1992 to 2014. The following key words were used forsearch: “autism,” “social,” “cognition,” “fMRI,” “brain,”“face,” “emotion,” “theory of mind,” “empathy,”“biological motion,” “agency,” “close other,” “self-reference,” their combinations and differing terminations.The data included in the meta-analysis was conducted onprior published studies from other research groups, andnecessary data (i.e., foci of brain activation) were publiclyavailable, IRB approval from our institution was notobtained. Instead, it is assumed that each individual studyabided by high ethical standards and obtained IRBapproval prior to conducting data collection at theirinstitutions.
To meet our inclusion criteria, studies were required to(1) have both ASD and TD participants, (2) utilize fMRI orPET imaging, (3) use whole-brain image subtraction toidentify clusters of significant task-related brain activationsacross groups and conditions, and (4) report results instandard stereotactic coordinates. Studies that did notmeet these criteria were excluded from the analysis.Seventy-five functional imaging articles on autism wereretrieved initially, 50 of which met our inclusion criteria.Notably, authors who did not report stereotactic coordi-nates in their paper were contacted by email, and coordi-nates were included when provided by the author. Wealso acknowledge that there may be coordinates that werenot included due to publication bias. See Table I for anexclusive list of studies. The number of participantstotaled 675 (53 female) ASD subjects and 695 (56 female)TD subjects. Papers included child, adolescent, and adultparticipants with ASD (overall mean age: 21.7 years) andtheir TD peers (overall mean age: 21.3 years).
In addition to the number of participants and taskdescriptions, the local maxima of task-related neural activ-ity from each study were extracted and catalogued for theanalysis. Task-related neural activity from each studyencompasses any statistically significant clusters of brainactivation derived from social cognitive tasks reported ineach of the included manuscripts. Foci resulting from themeta-analysis were organized into tables for the variouscomparisons conducted. These comparisons were: (1)ASD 1 TD (within group), (2) TD (within-group), (3) ASD(within-group), (4) ASD>TD (between-group), (5)TD>ASD (between-group), (6) ASD-TD (between-group),
and (7) TD-ASD (between-group). Foci included theASD 1 TD, TD, and ASD analyses came from within-groupcluster tables for the social cognition task conditionsreported in each included study. The foci included for theASD>TD and TD>ASD between-group comparisonscame from between-group cluster tables for the social cog-nition task conditions reported in each included study.Thus, the findings reported here emerged from within-group foci as well as between-group foci from 50 studies.In addition, since there is a relatively large number of face-processing studies in autism, subanalyses were conductedon social tasks involving face processing and those that donot. Analyses of activation peaks were performed usingactivation likelihood estimation via GingerALE softwaredeveloped by the Human Brain Mapping Project (ALE)[Eickhoff et al., 2009, 2011, 2012; Turkeltaub et al., 2012].Social cognition task contrasts from individual studies werecomprised of contrasts between social (e.g., faces, directgaze) and nonsocial (e.g., fixation, neutral) conditions andwithin social conditions (e.g., ToM, emotional faces).
All coordinates were entered into GingerALE in MontrealNeurological Institute (MNI) space. Coordinates of
activation foci from studies that were not originally inMNI format were transformed to MNI from Talairachspace using the Lancaster transform (tal2icbm tool) in Gin-gerALE [Laird et al., 2010]. ALE values were computed forevery voxel in the brain, testing the null distribution (cal-culated from 1000 repetitions using a permutation analy-sis) of the ALE statistic for each voxel. For each study,peaks were selected based on subject grouping. For eachgroup, the centroid of the significant cluster uses the fociwith the shortest Euclidian distance from the center of thedistribution in each group. ALE scores from the conver-gent MA maps were then calculated on a voxel-by-voxelbasis to test for convergent (random-effects) rather thanstudy specific foci (fixed-effects). Subject information (nsubjects per study group) was used to calculate Full-widthHalf-maximum of the Gaussian function. We conductedmeta-analyses within-group (using separate within ASDgroup and within TD group coordinates) and between-group (using TD>ASD and ASD>TD cluster coordinatesreported in each study). The Cluster-level InferenceThresholding value for the ASD, TD, ASD>TD, andTD>ASD were .05 with a False Discovery Rate (FDR)
Figure 1.
Examples of social cognition tasks used in studies included in the meta-analysis. [Color figure can
pN-based (no assumptions of correlations between data)cluster-forming threshold of p< 0.05. The total number ofpermutations for each analysis was 1000. No other infor-mation (e.g., effect size, autism diagnosis, age, MRIfield strength) was used in the calculation of the ALE sta-tistic, and none of this information can be used in thealgorithm.
Cortical Surface-Based Morphology
Structural MRI data collected from 115 participants withASD (n 5 55; 49males/6 females; mean age 5 18.2) andwithout ASD (n 5 60; 55 males/5 females; mean age 5 18.5-years) were entered into a general linear model (GLM)assessing surface-based cortical morphometry. Participantswere aged 8–40 years (M 5 18.43, SD 5 6.80) and hadIQs> 70. Anatomical scans were collected on a 3 T Sie-mens Allegra head-only scanner (Siemens Medical Inc.,Erlangen, Germany) using high-resolution T1-weightedimages using a 160 slice 3D MPRAGE volume scan with arepetition time (TR) 5 200 ms, echo time (TE) 5 3.34 ms,flip angle 5 128, field of view (FOV) 5 25.6, 256 3 256matrix size, and 1 mm slice thickness. The included 3Dvolumes were the remaining images following visualexamination by three researchers independently to confirmdata quality, and exclude images with significant distor-tion due to head motion or scanner artifact [Libero et al.,2014]. Scans were segmented using the standardFreesurferTM [Fischl, 2012] pipeline, using a combination ofCasual Markov-Field modeling and probabilistic calcula-tions based on image intensity to a hand-labeled trainingset described in detail in Fischl [2004]. Statistically signifi-cant clusters (excluding the amygdala) from the modeled-activation map of the ASD 1 TD condition from all of theincluded studies (Fig. 3) were mapped from volumetricspace to the cortical surface of the fsaverage brain templatein FreesurferTM using the bbregister function to form a socialbrain mask. The masks were then mapped to each sub-ject’s native space, and a Monte-Carlo null-z distributionwas computed for the mask on the fsaverage brain tem-plate. Each participant’s cortical surface maps for thick-ness, surface area, and volume for each hemisphere werethen normalized to the fsaverage template and smoothed toa full-width half-maximum (FWHM) of 10 mm for groupcomparisons.
GLMs assessing TD versus ASD structural differencesacross the ALE mask included age, diagnosis, total-brainmeasures (estimated total intracranial volume for volume,cubed-root squared transform of total intracranial volumefor surface area, and mean thickness of the left and righthemispheres for thickness), and the interaction terms forage and diagnosis, diagnosis and total-brain measure, ageand total-brain measure, and the three-way interactionbetween age, diagnosis, and total-brain measure of inter-est. Each continuous variable (age, total-brain-measure)was centered along the group mean for the participant
sample to reduce multicolinearity and increase power[Dalal and Zickar, 2012; Enders and Tofighi, 2007; Robin-son and Schumacker, 2009]. GLMs for each metric of inter-est were performed individually on the left and righthemisphere, with the appropriate statistical correction formultiple hemispheres. Results were vertex-level correctedacross the mask using a “cluster” threshold of 0.01 basedon the null-z distribution computed across the mask forthe group template.
RESULTS
The main results of this multilayered meta-analysisstudy are (1) combined fMRI meta-analysis of all partici-pant groups (ASD 1 TD) revealed increased activity inseveral regions considered part of the social brain; (2)within-group activation maps (within-ASD, within-TD)showed overlapping activation in many social brain areasacross ASD and TD groups; (3) meta-analysis of fMRIgroup differences as well as direct subtraction of within-group activation indicated reduced activity in ASD infusiform gyrus and cingulate cortex; (4) A sub analysis ofstudies involving only face processing tasks revealedreduced activity in ASD in fusiform, insula, cingulate,and amygdala; and (5) a social brain mask created basedon fMRI results to examine cortical morphology, in anempirical structural MRI dataset, revealed significantlydecreased cortical matter in the STS, insula, FFA, and leftIFG for the ASD group.
Brain Areas Associated With Social Cognition
To characterize the functional profile of the social brain,we investigated the entire sample (ASD 1 TD) as onegroup. This combined group meta-analysis (NASD 1 TD 5 89,Nfoci 5 1,109] revealed significantly increased activation inthe right insula, bilateral FFA, IFG, STG, MTG, precuneus,and amygdala, STG, left medial prefrontal cortex, left post-central gyrus, left lingual gyrus during social cognition.Most of these regions have been considered to be part ofthe social brain. The results of this analysis provided aprofile of the regions that are active in participants duringsocial cognitive tasks. The corresponding anatomicalregions and peak ALE maxima are shown in Table II andFigure 3.
Within-Group Brain Activity
When activation likelihood during social cognition wasestimated separately for each group of participants(within-ASD, within-TD), both ASD and TD group showedseveral overlapping ALE clusters of activation. Theseinclude FFA, IFG, MPFC, and STS. There were also a fewregions that showed unique activation in each group. Forexample, insula activation was only seen in ASD group,whereas the TD group showed unique activity in TPJ,
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r Social Brain in Autism r
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cingulate cortex, inferior temporal gyrus (ITG), precentralgyrus, and postcentral gyrus. Several of these regionsshowing statistically significant clusters in ASD and TDgroups, including the STS, FFA, MPFC, IFG, and TPJ, havebeen implicated in previous studies of social cognition[Pelphrey and Carter, 2008]. Although ITG, precentralgyrus, and postcentral gyrus do not appear to be as com-monly activated during social cognition tasks, some stud-ies associate them with social cognition [Baron-Cohenet al., 1999; Chiu et al., 2008]. See Tables III and IV andFigure 2 for results.
Group Differences in Social Brain Activity:
ASD > TD vs TD >ASD
The ASD group demonstrated significantly greater acti-vation in the STG, insula, amygdala, IFG, MFG, precentralgyrus, and postcentral gyrus, compared to their TD coun-terparts (NASD>TD contrast 5 23; Nfoci 5 99; Table V andFig. 3). The ASD group showed significantly lower activ-ity, when compared to TD participants (NTD>ASD contrast 5
55; Nfoci 5 279), in amygdala, hippocampus, FFA, STG,cingulate, and IFG. See Table VI and Figure 3 for
TABLE III. ALE cluster values within ASD group only
BrainSite of maximum ALE
MaximumRegion Gyrus/sulcus BA Laterality x y z
Volumea
(mm3) ALE value
ASDTemporal Fusiform 37 Right 44 266 212 9264 0.030331947Anterior Culmen Right 40 254 220 9264 0.030075628Posterior Declive Right 36 266 218 9264 0.022844706Occipital Lingual 18 Right 16 286 28 9264 0.020278946Occipital Lingual 18 Right 6 284 26 9264 0.018359212Posterior Uvula Right 30 282 226 9264 0.017297413Posterior Declive Right 30 282 214 9264 0.015294376Temporal Fusiform 37 Left 242 252 220 8848 0.025839185Occipital Fusiform 19 Left 240 274 212 8848 0.022013115Occipital Inferior Occipital 18 Left 232 284 22 8848 0.019835446Occipital Lingual 19 Left 230 280 4 8848 0.01900635Posterior Declive Left 218 282 212 8848 0.017046362Occipital Middle Occipital 18 Left 222 290 26 8848 0.016933754Sub-lobar Insula 13 Right 34 24 22 3240 0.024693143Frontal Middle Frontal 46 Right 46 30 10 3240 0.018757155Temporal Transverse Temporal 41 Right 46 224 10 3216 0.028715182Temporal Superior Temporal 13 Right 50 222 4 3216 0.024662865Temporal Superior Temporal 41 Right 58 228 8 3216 0.012479794Temporal Middle Temporal 22 Left 262 236 4 2928 0.02310238Temporal Superior Temporal 41 Left 258 226 10 2928 0.020005718Temporal Superior Temporal 22 Left 252 228 2 2928 0.019399282Temporal Superior Temporal 41 Left 248 232 12 2928 0.016045671Frontal Inferior Frontal 9 Right 46 12 24 2160 0.020109536Frontal Inferior Frontal 9 Right 54 8 18 2160 0.018232806Frontal Inferior Frontal 47 Left 244 18 216 2024 0.020636568Temporal Superior Temporal 38 Left 244 14 228 2024 0.017843021Sub-lobar Insula 13 Left 242 18 22 2024 0.013671238Frontal Inferior Frontal 44 Left 252 18 10 1560 0.020568147Frontal Inferior Frontal 45 Left 254 22 16 1560 0.018506812Frontal Inferior Frontal 9 Left 252 16 22 1560 0.01628338Parietal Postcentral 40 Left 244 228 60 1312 0.017873524Parietal Inferior Parietal 40 Left 234 240 54 1312 0.012122758Sub-lobar Lentiform Nucleus Left 224 28 212 1248 0.019479897Limbic Parahippocampal Left 218 26 216 1248 0.018269729Temporal Superior Temporal 22 Right 56 250 8 1120 0.019661412Limbic Parahippocampal Right 20 26 216 760 0.02188986Temporal Superior Temporal 38 Right 46 10 220 664 0.0152831Temporal Middle Temporal 39 Left 244 260 24 496 0.020626092Frontal Inferior Frontal 6 Left 248 6 32 496 0.018233394Frontal Paracentral Lobule 31 Left 2 28 50 464 0.015399425
Note. Minimum cluster size based on FDR correction 5 464; permutation equilibrium 5 22.aRepetition of same cluster volumes indicates that these peaks were all within the same cluster.
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results. Although several of these regions are commonacross these comparisons (ASD>TD and TD>ASD), thepeak of activation coordinates differ in some cases.Nevertheless, it should be noted that the main regionsof group difference, where ASD group had lower activ-ity than TD, were FFA, hippocampus, and cingulatecortex.
Comparing Face-Processing Tasks vs
Non-Face-Processing Tasks
Sub analyses of social cognition tasks involving only faceversus nonface stimuli revealed several clusters of signifi-cantly reduced activation in ASD, compared to TD, individ-uals centered on right parahippocampal gyrus, left FFA,
TABLE IV. ALE cluster values within TD group only
BrainSite of maximum ALE
Maximumregion Gyrus/sulcus BA Laterality x y z
Volumea
(mm3) ALE value
TDAnterior Culmen Right 42 248 222 7672 0.038141Occipital Fusiform 19 Right 40 272 210 7672 0.033462Temporal Subgyral 37 Right 50 254 28 7672 0.028119Posterior Declive Right 34 284 214 7672 0.019529Occipital Lingual 18 Right 22 288 212 7672 0.018287027Temporal Fusiform 37 Left 242 252 218 5008 0.031164583Occipital Inferior Temporal Left 248 270 2 5008 0.017410288Temporal Fusiform 37 Left 246 268 26 5008 0.0170422Frontal Inferior Frontal 45 Right 52 26 2 4656 0.027324826Frontal Middle Frontal 46 Right 42 34 10 4656 0.02456526Sublobar Insula 13 Right 48 12 28 4656 0.022327906Sublobar Claustrum Right 30 18 0 4656 0.02062103Sublobar Insula 13 Right 34 26 4 4656 0.015006449Frontal Inferior Frontal 9 Right 50 8 28 3912 0.032349057Frontal Precentral 44 Right 52 8 10 3912 0.01981003Sublobar Insula 13 Right 44 12 18 3912 0.01948477Temporal Subgyral 21 Right 48 212 212 3688 0.02865497Sublobar Insula 13 Right 50 214 4 3688 0.025107788Temporal Superior Temporal 41 Right 40 230 14 3688 0.022448573Temporal Superior Temporal 41 Right 60 214 4 3688 0.02047863Limbic Cingulate 24 Right 4 2 46 2976 0.028651956Frontal Medial Frontal 6 Left 0 8 56 2976 0.022982934Frontal Medial Frontal 6 Left 2 2 58 2976 0.022076836Temporal Superior Temporal 39 Right 50 252 12 2848 0.029311212Temporal Superior Temporal 41 Right 52 240 6 2848 0.019602012Occipital Middle Temporal 37 Right 46 264 12 2848 0.019062284Temporal Superior Temporal 41 Left 258 222 4 2776 0.032378495Frontal Medial Frontal 9 Left 0 52 24 2352 0.025336599Frontal Superior Frontal 9 Left 26 64 18 2352 0.014481918Posterior Declive Left 218 282 216 2088 0.026589418Posterior Declive Left 232 286 214 2088 0.021471513Occipital Fusiform 19 Left 240 282 212 2088 0.01831894Sublobar Lentiform Nucleus Right 18 28 210 1272 0.03259568Sublobar Lentiform Nucleus Left 220 210 210 1216 0.03406375Temporal Superior Temporal 38 Left 248 14 222 1016 0.027216656Frontal Inferior Frontal 45 Left 236 28 2 1016 0.022899399Parietal Postcentral 3 Left 232 230 52 848 0.017779186Parietal Postcentral 2 Left 242 222 40 848 0.01583136Posterior Pyramis Right 26 280 232 736 0.01774826Temporal Superior Temporal 22 Left 258 244 10 704 0.023801552Frontal Precentral 44 Left 252 6 4 576 0.018190052Frontal Precentral 44 Left 250 6 8 576 0.01727778
Note. Minimum cluster size based on FDR correction 5 528; permutation equilibrium 5 11.aRepetition of same cluster volumes indicates that these peaks were all within the same cluster.
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cerebellum, left insula, left anterior cingulate, thalamus,bilateral cingulate, right MFG, and left IFG, during face-processing social tasks. Nonface social tasks, on the otherhand, elicited reduced activity in ASD centered within theleft precentral gyrus, STG, IFG, MTG, angular gyrus, cere-bellum, IPL, right IFG, lingual gyrus, and MTG. The ASDparticipants showed greater activity, relative to TD, in theleft parahippocampal gyrus during face processing, andgreater activity in the following regions during non-face-processing tasks: left IFG, STG, postcentral gyrus, precentralgyrus, MPFC, MTG, ITG, right insula, IFG, and MFG tasks.These results are summarized in Table VII and Figure 4.
Surface-Based Morphometry
Using Social Brain Mask
Application of the social brain mask, derived from ALEmeta-analysis of fMRI studies, to empirical structural MRIdata collected from 115 participants revealed significantmorphological changes (cortical surface area, and thickness)in several social brain areas. Cortical surface area was foundto be decreased in ASD participants in the superior tempo-ral cortex and right insula relative to total-intracranial vol-
ume (Fig. 5). The effect within the superior temporal cortexwas strongly influenced, but not fully explained by age.Analyses of cortical thickness revealed significant increasesin thickness in individuals with ASD in the left pars opercu-laris aspect of the IFG relative to age, and to mean thicknessof the left hemisphere. Interactions examining all 3 termstogether revealed that the thickness of the left pars opercula-ris decreases in individuals with ASD as a function of ageand as a function of the mean-thickness of the right hemi-sphere combined. A similar effect was also noticed in theright fusiform gyrus, with group differences heavily influ-enced by the interactions between age and mean thicknessof the right hemisphere. Finally, qualitative examination ofprevious results from surface-based [Libero et al., 2014] andvoxel-based [DeRamus and Kana, 2014] morphometry stud-ies of ASD found several regions that overlap as well as dif-fer with the findings of the current study.
DISCUSSION
This study attempted to consolidate the anatomy andfunction of the social brain in ASD using a comprehensive
Figure 2.
ALE estimation of social brain activity across ASD, TD, and ASD and TD participants combined
(p< 0.05, FDR cluster-forming threshold). Activity is seen in regions, such as the MPFC, bilateral
meta-analysis of fMRI studies coupled with cortical mor-phology data from an empirical structural MRI study. Themain findings point to several, but not all, regions of thesocial brain showing anatomical and functional alterationsin ASD participants. Meta-analysis of ASD and TD groupscombined resulted in an ALE map consisting of ROIs thathighly overlap with areas of the social brain. These regions
are the right cingulate cortex, left MFG, left postcentralgyrus, and bilateral: insula, FFA, amygdala, middle tempo-ral gyrus, and precuneus. Specific social processes includ-ing ToM (TPJ, MPFC, PCC), emotional and moralprocessing (insula, vmPFC, amygdala), processing humanfaces and actions (FFA, STG, TPJ, premotor/mirror neu-rons), and social reasoning and self-reflection (MPFC,
TABLE V. ALE cluster values for ASD > TD between-group analysis
BrainSite of maximum ALE
Maximumregion Gyrus/sulcus BA Hem x y z
Volumea
(mm3) ALE value
ASD>TD
Frontal Inferior frontal 9 Left 254 20 18 1232 0.02323516Parietal Postcentral 3 Left 240 226 58 968 0.013614106Parietal Inferior parietal 40 Left 250 226 50 968 0.011815298Temporal Superior temporal 22 Left 248 232 2 856 0.015697075Limbic Amygdala Left 222 24 226 752 0.014984485Frontal Precentral 4 Left 230 214 66 592 0.01119461Frontal Inferior frontal 9 Right 38 14 24 496 0.013928136Sublobar Insula 13 Right 40 22 12 488 0.013267966Frontal Middle frontal 47 Right 38 40 214 480 0.013834674Frontal Precentral 6 Left 234 4 34 480 0.013832372Frontal Medial frontal 6 Left 22 5 60 480 0.013830137
Note. Minimum cluster size based on FDR correction 5 312; permutation equilibrium 5 11.aRepetition of same cluster volumes indicates that these peaks were all within the same cluster.
Figure 3.
ALE analysis for TD>ASD (orange) and ASD>TD (green) group differences across studies:
(p< 0.05, FDR cluster-forming threshold). [Color figure can be viewed at wileyonlinelibrary.com]
precuneus/PCC) are found to be mediated by activity inthese regions [Adolphs, 2009; Fletcher et al., 1995; Gal-lagher and Frith, 2003; Iacoboni and Dapretto, 2006; Ober-man and Ramachandran, 2007; Pelphrey and Carter, 2008;Ruby and Decety, 2003; Saxe and Kanwisher, 2003;Vogeley et al., 2001]. Notably, some moderate patternsemerged regarding the clusters identified and their con-tributing studies (Table II). Broadly, the largest clusters(e.g., first) are less differentiated and appear to be relatedto more general social cognition as these experimentalparadigms involve face processing, theory-of-mind, self vsother, and imitation. However, smaller clusters, such asthe last cluster in the table, appear more function specific.For example, the smallest cluster in the left parietal (precu-neus) is related to social processing or social judgment(e.g., gaze and face processing).
When the meta-analysis was applied to each group(ASD, TD) separately, significant clusters of activity wereseen common to both groups in left FFA, right insula,right MPFC, bilateral IFG, and STG. Within-group activa-tion patterns suggest similar recruitment of social brainareas in ASD and TD groups. It should also be noted thatthere were some social brain area activity unique to eachgroup; right FFA and left insula in ASD group, andMPFC, right cingulate, and precentral gyrus in TD group.
Group difference results indicate underactivity in ASDparticipants in several social brain areas, such as theamygdala, STG, FFA, and cingulate cortex. It should benoted that dysfunction of all these regions have been pro-posed by previous neuroimaging studies as potential neu-ral markers of autism. For instance, lower level ofamygdala activation has been found to play a significant
role in social and emotional processing in autism[Baron-Cohen et al., 2000; Dalton et al., 2005a; Kliemannet al., 2013; Zalla and Sperduti, 2013]. Reduced cingulateactivation during one’s own decision (self-response) whileplaying a social exchange game has been found to predictASD symptom severity [Chiu et al., 2008]. It has been sug-gested that developmental differences in the amygdala,and possibly other limbic areas such as the cingulate,could have a cascading effect on cortical areas that medi-ate areas related to social perception (e.g., FFA) [Baron-Cohen et al., 2000; Schultz, 2005]. Dysfunction of regions,such as the STG [Kaiser et al., 2010] and FFA [Spenceret al., 2011] has been proposed by recent neuroimagingstudies as potential neuroendophenotypes of autism. TheIFG, especially BA44 (pars opercularis aspect of IFG) wasanother area of underactivation found in ASD participants.Several functional [Dapretto et al., 2006; Oberman et al.,2005] and anatomical [Hadjikhani et al., 2009] abnormal-ities have been reported in the IFG in autism by previousstudies. Thus, the group difference findings from thisstudy revealed reduced activity in important nodes of thesocial brain in ASD participants.
It is possible that the alterations in brain response to dif-ferent social cognition tasks in ASD individuals mayunderlie anatomical differences. An important and novelaspect of this study involves relating the functional MRIresults from the meta-analysis to neuroanatomy in a rela-tively large empirical dataset. Surface-based Morphometryanalysis of structural MRI data using the social brain mask(created based on the results of our ALE meta-analysis)showed reduced cortical surface area in right insula, leftSTG, and FFA in ASD participants, relative to TD controls.
TABLE VI. ALE cluster values for TD >ASD between-group analysis
BrainSite of maximum ALE
Maximumregion Gyrus BA Hem x y z
Volumea
(mm3) ALE value
TD>ASD
Limbic Amygdala Left 224 24 220 2656 0.02320312Limbic Hippocampus Left 230 214 216 2656 0.013125481Frontal Precentral 44 Left 252 18 0 1640 0.023291802Posterior Declive Left 226 270 216 1464 0.017044175Occipital Fusiform 19 Left 226 264 28 1464 0.013665032Limbic Parahippocampal Right 22 24 222 1096 0.014839961Temporal Superior temporal 22 Left 252 230 2 832 0.015007527Frontal Inferior frontal 13 Right 42 28 4 816 0.018335775Occipital Fusiform 19 Right 24 288 28 784 0.013432466Posterior Declive Right 26 288 218 784 0.01089274Limbic Cingulate 31 Left 224 242 34 664 0.015882928Parietal Inferior parietal 40 Left 232 246 40 664 0.014318956Temporal Middle temporal 37 Right 54 264 6 496 0.01714673Temporal Middle temporal 37 Left 264 248 210 424 0.014566092Frontal Inferior frontal 47 Left 232 14 222 408 0.015064342
Note. Minimum cluster size based on FDR correction 5 376; permutation equilibrium 5 16.aRepetition of same cluster volumes indicates that these peaks were all within the same cluster.
r Patriquin et al. r
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TABLE VII. ALE clusters of between-group comparisons for face-processing vs non-face-processing tasks
Brain
Site ofmaximum ALE
Volumea
(mm3)MaximumALE value
region Gyrus/sulcus BA Hem x y z
Face tasks:
TD>ASDb
Limbic Inferior frontal 34 Left 222 22 216 2096 0.020085309Limbic PHG Right 22 24 222 1888 0.014839509Occipital Fusiform 19 Left 226 264 28 1056 0.013205927Cerebellum Declive Left 234 266 218 1056 0.009973204Cerebellum Anterior lobe Left 236 256 230 696 0.011946438Frontal Insula 13 Left 228 234 28 480 0.015424337Frontal ACC 25 Left 2 18 216 456 0.015806857Frontal ACC 24 Left 28 26 212 456 0.015807344Limbic Cingulate 31 Right 4 238 32 456 0.015403693Limbic Cingulate 31 Left 224 242 34 456 0.015403725Thalamic Thalamus Left 24 222 10 424 0.011637608Limbic Cingulate 24 Left 0 30 16 408 0.013570925Frontal IFG 45 Left 258 20 18 392 0.012095158Frontal MFG 32 Right 2 44 210 360 0.011654614ASD>TDc
Limbic PHG 34 Left 220 0 226 384 0.008797275Nonface tasks:TD>ASDd
Frontal Precentral 44 Left 252 16 2 1544 0.018760668Temporal STG 22 Left 252 230 2 1472 0.015001407Frontal IFG 47 Left 232 14 222 736 0.014999792Temporal MTG 37 Left 264 248 210 736 0.01456575Limbic Hippocampus Left 230 214 216 488 0.012515563Parietal Angular gyrus 39 Left 254 260 40 488 0.015507407Frontal IFG 13 Right 30 12 218 480 0.013494215Cerebellum Declive Left 226 270 217 480 0.014803587Occipital Lingual gyrus 18 Right 24 290 28 480 0.011711578Temporal STG 38 Left 244 10 224 448 0.01222351Temporal MTG 37 Right 52 264 6 440 0.014093696Frontal IFG 13 Right 42 26 4 408 0.012589583Parietal IPL 40 Left 232 246 40 392 0.013886107ASD>TDe
Frontal IFG 44 Left 254 20 16 912 0.019449683Temporal STG 22 Left 248 232 2 856 0.015597242Frontal IFG 13 Right 40 22 12 504 0.013254493Parietal Postcentral 3 Left 240 226 58 504 0.013563364Frontal IFG 9 Right 38 14 24 496 0.013926981Frontal MFG 47 Right 38 40 214 480 0.013834672Frontal Precentral 6 Left 234 3 34 480 0.013830137Frontal MPFC 6 Left 22 5 60 480 0.013830137Temporal MTG 37 Left 258 269 12 384 0.013830137Temporal ITG 37 Left 262 265 28 344 0.013830137
aRepetition of same cluster volumes indicates that these peaks were all within the same clusterbMinimum cluster size based on FDR correction 5 360; permutation equilibrium 5 20.cMinimum cluster size based on FDR correction 5 200; permutation equilibrium 5 19.dMinimum cluster size based on FDR correction 5 384; permutation equilibrium 5 41.eMinimum cluster size based on FDR correction 5 288; permutation equilibrium 5 11.MFG 5 middle frontal gyrus, MPFC 5 medial prefrontal cortex, MTG 5 middle temporal gyrus, IFG 5 inferior frontal gyrus, SFG 5 supe-rior frontal gyrus, STG 5 superior temporal gyrus, ITG 5 inferior temporal gyrus, MOG 5 middle occipital gyrus, IPL 5 inferior parietallobule, PHG 5 parahippocampal gyrus.
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It should be noted that these regions were also found toshow hypoactivity in ASD participants in the meta-analysis, suggesting an anatomical basis to some of thefunctional differences. Reduced sulcal depth [Dierkeret al., 2013], cortical volume [Kosaka et al., 2010], andfunctional activation [Dimartino et al., 2009], as well asconnectivity [Ebisch et al., 2011; Kana et al., 2007; Paakkiet al., 2010] in the insula have been reported previously inthe ASD literature. The insula is also considered as thehub of the “salience network,” integrating external stimuliwith self-perceptions and emotional states, dysfunction ofwhich could relate to many of the behavioral symptoms ofASD [Silani et al., 2008; Uddin and Menon, 2009]. Altera-tions in cortical morphological features have also beenreported in the STG and FFA in individuals with ASD[Boddaert et al., 2004; Dziobek et al., 2010; Ecker et al.,2010; Gervais et al., 2004; Hadjikhani et al., 2006; Jiaoet al., 2010; McAlonan et al., 2005]. The frequency and con-sistency of functional and morphological abnormalitiesfound in STG and FFA suggest a strong role of theseregions in the pathobiology of ASD.
One region where we found an increase in cortical thick-ness in ASD was the IFG. While this finding is consistentwith a recent meta-analysis of gray matter abnormalities inASD [Via et al., 2011], it is in contrast with some previousfindings of smaller gray matter volume in ASD [Dierker
et al., 2013; Hadjikhani et al., 2006; Kosaka et al., 2010;Yamasaki et al., 2010]. It is possible that such differences infindings may reflect methodological differences, such as notincluding age and total intracranial metrics as factors in theanalysis model, or the focus of studies on a specific develop-mental window (i.e., 18–30 years). Considering the develop-mental differences in total intracranial volume in ASD[Courchesne et al., 2010; Schumann et al., 2009], age canplay a significant factor in determining cortical differences.In this context, it should also be noted that the folding pat-terns within the IFG and insula may be altered in ASD [Nor-dahl et al., 2007], which could potentially affect the waymorphometric data are interpreted in autism.
Qualitative examination of previous results from voxel-based ALE meta-data [DeRamus & Kana, 2014; see Fig. 6)and whole-brain surface-based morphometry study ofASD [Libero et al., 2014] found overlap with the socialbrain ALE analysis results in the LIFG region reported inLibero et al. [2014], but also different results in the leftmiddle temporal, and right fusiform and insula. Therewas little apparent overlap with the VBM meta-meta data(Supporting Information, Fig. 1). This divergence could bedue to a number of factors, most of which are likelyrelated to methodology and developmental level of theparticipants. Methodologically, Libero et al. [2014] used awhole-brain Monte Carlo correction for reporting the
Figure 4.
Between-group differences in social task requiring face processing (A), and social tasks that do
not require face processing (B). TD>ASD (orange), ASD>TD (green). All areas p< 0.05, FDR
cluster-forming threshold. [Color figure can be viewed at wileyonlinelibrary.com]
results. In contrast, the ALE social brain mask analysis isMonte Carlo corrected at the level of the mask (red regionsin Fig. 2 and yellow regions in Fig. 6), and metrics of ageand total intracranial volume (TICV) are centered at themean. Voxel-based morphometry (VBM) can be used tomeasure grey or white matter concentration (proportion ofmatter type within a region) or volume (weighting voxelintensity by the Jacobian determinant) [Mechelli et al.,2005], both of which calculate measures differently com-pared to surface-based approaches (see Greve [2011] for abrief review of both techniques). With regard to develop-ment, there is a large amount of literature describing theeffects of age on cortical metrics [Giedd et al., 1999] andhow these developmental trajectories may be altered inASD during development [Schumann et al., 2010; Wallaceet al., 2010]. However, the cross-sectional nature and rela-tively large age-range in ALE studies like the current onelimits the ability to pinpoint and interpret what stages ofdevelopment are associated with significant morphologicalchanges in the cortex.
One of the most widely studied areas of social cognitionin autism is face processing, with evidence supportingabnormalities emerging from behavioral, neuroimaging,and eye-tracking studies [e.g., Corbett et al., 2014; Sassonand Touchstone, 2014; Yucel et al., 2014]. ALE maps forface processing tasks suggest that ASD participants, rela-
tive to TD, showed reduced activity in FFA, cingulate cor-tex, insula, and parahippocampus. In contrast, the ASDparticipants showed increased activity only in the left par-ahippocampal gyrus. These results underscore alteredrecruitment of core areas during face processing in indi-viduals with ASD, as evidenced from numerous fMRIstudies. Activation of FFA along with other social brainareas (cingulate, insula) in TD participants may suggestricher and more meaningful face processing in them.Understanding the effects of face versus non-face process-ing is important, particularly in the context of how facesare perceived in ASD: configural or featural. A number ofstudies of face processing in ASD suggest differences inactivation and gaze/fixation preferences between TD andASD individuals. Preference in eye fixation significantlyaffects the former, and some studies controlling for fixa-tion [Hadjikhani et al., 2004] or manipulating familiarity[Pierce and Redcay, 2008] suggest that a difference in per-ceptual strategy in ASD. Studies of social cognition, espe-cially neuroimaging studies, should control for perceptualpreferences in order to improve the reliability of findings.
Notably, meta-analyses have the inherent publicationbias and suffer the “file drawer” effect such that only stud-ies with significant findings and thus published, not thosewith null findings, are included in the meta-analysis.Although the present ALE is also victim of the file drawereffect, it is more difficult to estimate the effect of publica-tion bias since ALE is a function-location meta-analysis,but not effect size meta-analysis. Prior effect size meta-analyses of neuroimaging studies have estimated publica-tion bias [Jennings and Van Horn, 2012]; however, furtherinvestigation is needed to determine the scientific methodfor estimating the bias in ALE. Additionally, although theareas identified herein appear to be related to social cogni-tion, it should be noted that the function of these areasmay not be exclusive to social cognition. For example, theFFA also responds to many visual stimuli not only socialstimuli such as faces [Zachariou et al., 2015]. Further, it ispossible that areas such as the motor, visual, or auditorycortex could play no role in social cognition but still beactive in studies of social cognition due to requirements oftasks or presentation of stimuli that are inadequately con-trolled (e.g., button presses, on/off visual or auditory stim-uli). Last, there were a few similar areas found in both theASD>TD and TD >ASD analyses (e.g., left STG), whichappears counterintuitive. It is important to highlight thatthe coordinates found with ALE analyses may be slightlydifferent because studies entered for the analyses differ.For example, a study reports ASD>TD results and didnot have any TD>ASD results; in such case, only theASD>TD coordinates will be entered in the ALE. In thiscase, however, it appears that the greater activity(ASD>TD) in ASD participants in the STG is related tomore general, even positive, emotional processing of faces[Dalton et al., 2005a; Williams et al., 2006]; whereas, lessactivity in ASD participants in the STG is related to
Figure 5.
Group differences in surface area (top) and cortical thickness
(bottom) between a sample of T1 images of ASD and TD partic-
ipants within social brain ROIs computed from the ALE mask.
Red denotes decrease ASD and blue denotes increases in ASD.
[Color figure can be viewed at wileyonlinelibrary.com]
processing of threatening (the “other” vs self, fear, trust-worthiness) face processing [Hadjikhani et al., 2009; Pink-ham et al., 2008].
In summary, the results of this ALE meta-analysis andcortical morphometry study validate the findings of manyprevious studies on activation, connectivity, and morphol-ogy in the social brain in individuals with ASD. Amongthe different social brain areas, insula, FFA, STG, and IFGseem to differentiate autism from control participants atfunctional and anatomical levels, suggesting alterations inthese regions as potential neural markers of ASD. It isimportant to note that this fMRI meta-analysis and empiri-cal structural MRI data provide a somewhat convergingpicture of multilevel abnormality in social cognition in
autism. With continuing efforts toward data-sharing andclassification analyses within the field of ASD research,meta-data approaches could be very useful in developingtargets for multilevel neuroimaging models to assist inrefining biomarkers for ASD, and develop relationshipsamong function, structure, and connectivity.
ACKNOWLEDGMENTS
The authors would like to thank Dr Mick Fox for his helpin answering all our questions related to ALE. The authorswould also like to thank Hrishikesh Deshpande for hishelp with the computational aspects of this study.
Figure 6.
The social brain mask produced across theory of mind task type across both TD and ASD par-
ticipants is displayed as a yellow overlay. Results of the surface based analysis on the mask found
regions of decreased surface area (dark blue), volume (green) and increased thickness (pink).
This is displayed in conjunction with ALE computed VBM meta-data displaying decreased (dark
blue) and increased (red) volume in ASD. [Color figure can be viewed at wileyonlinelibrary.com]
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