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Figure 1.
Inclusion of studies in the meta-analysis.

Inclusion of studies in the meta-analysis.

Figure 2.
Regions of gray matter volume difference between participants with autism spectrum disorder (ASD) and healthy control individuals. A-C, Gray matter volume reductions in ASD (3-dimensional view). A, Right amygdala (coronal and radiologic view); B, right amygdala, right precuneus (sagittal view); and C, bilateral amygdala-hippocampus complex (transversal and neurologic view). D-F, Gray matter volume increase in the left middle-inferior frontal gyrus in ASD (3-dimensional view). D, Coronal and radiologic view; E, sagittal view; and F, transversal and neurologic view.

Regions of gray matter volume difference between participants with autism spectrum disorder (ASD) and healthy control individuals. A-C, Gray matter volume reductions in ASD (3-dimensional view). A, Right amygdala (coronal and radiologic view); B, right amygdala, right precuneus (sagittal view); and C, bilateral amygdala-hippocampus complex (transversal and neurologic view). D-F, Gray matter volume increase in the left middle-inferior frontal gyrus in ASD (3-dimensional view). D, Coronal and radiologic view; E, sagittal view; and F, transversal and neurologic view.

Figure 3.
Contribution of diagnostic group and age to the main results. Data shown are Signed Differential Mapping values, ranging from −1 to 1.

Contribution of diagnostic group and age to the main results. Data shown are Signed Differential Mapping values, ranging from −1 to 1.

Table 1. 
Detailed Demographic and Clinical Characteristics of the 24 Data Sets Included in the Meta-analysis
Detailed Demographic and Clinical Characteristics of the 24 Data Sets Included in the Meta-analysis
Table 2. 
Regions Showing Statistically Significant Differences in Gray Matter Volume Between Participants With ASD and Healthy Control Individualsa
Regions Showing Statistically Significant Differences in Gray Matter Volume Between Participants With ASD and Healthy Control Individualsa
Table 3. 
Jackknife Sensitivity and Quartiles Analyses
Jackknife Sensitivity and Quartiles Analyses
1.
American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders. 4thtext revision Washington, DC American Psychiatric Association2000;
2.
Baird  GSimonoff  EPickles  AChandler  SLoucas  TMeldrum  DCharman  T Prevalence of disorders of the autism spectrum in a population cohort of children in South Thames: the Special Needs and Autism Project (SNAP). Lancet 2006;368 (9531) 210- 215
PubMed
3.
Folstein  SRutter  M Infantile autism: a genetic study of 21 twin pairs. J Child Psychol Psychiatry 1977;18 (4) 297- 321
PubMed
4.
Bailey  ALuthert  PBolton  PLe Couteur  ARutter  MHarding  B Autism and megalencephaly. Lancet 1993;341 (8854) 1225- 1226
PubMed
5.
Piven  JArndt  SBailey  JHavercamp  SAndreasen  NCPalmer  P An MRI study of brain size in autism. Am J Psychiatry 1995;152 (8) 1145- 1149
PubMed
6.
Courchesne  EPierce  KSchumann  CMRedcay  EBuckwalter  JAKennedy  DPMorgan  J Mapping early brain development in autism. Neuron 2007;56 (2) 399- 413
PubMed
7.
Verhoeven  JSDe Cock  PLagae  LSunaert  S Neuroimaging of autism. Neuroradiology 2010;52 (1) 3- 14
PubMed
8.
Minshew  NJWilliams  DL The new neurobiology of autism: cortex, connectivity, and neuronal organization. Arch Neurol 2007;64 (7) 945- 950
PubMed
9.
Brambilla  PHardan  Adi Nemi  SUPerez  JSoares  JCBarale  F Brain anatomy and development in autism: review of structural MRI studies. Brain Res Bull 2003;61 (6) 557- 569
PubMed
10.
Palmen  SJMCvan Engeland  H Review on structural neuroimaging findings in autism. J Neural Transm 2004;111 (7) 903- 929
PubMed
11.
Penn  HE Neurobiological correlates of autism: a review of recent research. Child Neuropsychol 2006;12 (1) 57- 79
PubMed
12.
Levy  SEMandell  DSSchultz  RT Autism. Lancet 2009;374 (9701) 1627- 1638
PubMed
13.
Ashburner  JFriston  KJ Why voxel-based morphometry should be used. Neuroimage 2001;14 (6) 1238- 1243
PubMed
14.
Ashburner  JFriston  KJ Voxel-based morphometry: the methods. Neuroimage 2000;11 (6, pt 1) 805- 821
PubMed
15.
Costafreda  SG Pooling FMRI data: meta-analysis, mega-analysis and multi-center studies. Front Neuroinformatics 2009;333
PubMed
16.
Salimi-Khorshidi  GSmith  SMKeltner  JRWager  TDNichols  TE Meta-analysis of neuroimaging data: a comparison of image-based and coordinate-based pooling of studies. Neuroimage 2009;45 (3) 810- 823
PubMed
17.
American Psychiatric Association, DSM-5 development. 2010;American Psychiatric Association Web sitehttp://www.dsm5.org/Pages/Default.aspx11 October2010;
18.
Witwer  ANLecavalier  L Examining the validity of autism spectrum disorder subtypes. J Autism Dev Disord 2008;38 (9) 1611- 1624
PubMed
19.
Ozonoff  SSouth  MMiller  JN DSM-IV–defined Asperger syndrome: cognitive, behavioral and early history differentiation from high-functioning autism. Autism 2000;4 (1) 29- 46
20.
Howlin  P Outcome in high-functioning adults with autism with and without early language delays: implications for the differentiation between autism and Asperger syndrome. J Autism Dev Disord 2003;33 (1) 3- 13
PubMed
21.
Abell  FKrams  MAshburner  JPassingham  RFriston  KFrackowiak  RHappé  FFrith  CFrith  U The neuroanatomy of autism: a voxel-based whole brain analysis of structural scans. Neuroreport 1999;10 (8) 1647- 1651
PubMed
22.
Stroup  DFBerlin  JAMorton  SCOlkin  IWilliamson  GDRennie  DMoher  DBecker  BJSipe  TAThacker  SBMeta-analysis Of Observational Studies in Epidemiology (MOOSE) Group, Meta-analysis of observational studies in epidemiology: a proposal for reporting. JAMA 2000;283 (15) 2008- 2012
PubMed
23.
Radua  JSigned differential mapping2009;SDM Project Web sitehttp://www.sdmproject.com/11 October2010;
24.
Viechtbauer  W Bias and efficiency of meta-analytic variance estimators in the random-effects model. J Educ Behav Stat 2005;30 (3) 261- 293
25.
Turkeltaub  PEEden  GFJones  KMZeffiro  TA Meta-analysis of the functional neuroanatomy of single-word reading: method and validation. Neuroimage 2002;16 (3, pt 1) 765- 780
PubMed
26.
Wager  TDLindquist  MKaplan  L Meta-analysis of functional neuroimaging data: current and future directions. Soc Cogn Affect Neurosci 2007;2 (2) 150- 158
PubMed
27.
Radua  JMataix-Cols  D Voxel-wise meta-analysis of grey matter changes in obsessive-compulsive disorder. Br J Psychiatry 2009;195 (5) 393- 402
PubMed
28.
Radua  Jvan den Heuvel  OASurguladze  SMataix-Cols  D Meta-analytical comparison of voxel-based morphometry studies in obsessive-compulsive disorder vs other anxiety disorders. Arch Gen Psychiatry 2010;67 (7) 701- 711
PubMed
29.
Yamasue  HIshijima  MAbe  OSasaki  TYamada  HSuga  MRogers  MMinowa  ISomeya  RKurita  HAoki  SKato  NKasai  K Neuroanatomy in monozygotic twins with Asperger disorder discordant for comorbid depression. Neurology 2005;65 (3) 491- 492
PubMed
30.
McAlonan  GMCheung  VCheung  CSuckling  JLam  GYTai  KSYip  LMurphy  DGMChua  SE Mapping the brain in autism: a voxel-based MRI study of volumetric differences and intercorrelations in autism. Brain 2005;128 (pt 2) 268- 276
PubMed
31.
Salmond  CHAshburner  JConnelly  AFriston  KJGadian  DGVargha-Khadem  F The role of the medial temporal lobe in autistic spectrum disorders. Eur J Neurosci 2005;22 (3) 764- 772
PubMed
32.
Salmond  CHVargha-Khadem  FGadian  DGde Haan  MBaldeweg  T Heterogeneity in the patterns of neural abnormality in autistic spectrum disorders: evidence from ERP and MRI. Cortex 2007;43 (6) 686- 699
PubMed
33.
Salmond  CHde Haan  MFriston  KJGadian  DGVargha-Khadem  F Investigating individual differences in brain abnormalities in autism. Philos Trans R Soc Lond B Biol Sci 2003;358 (1430) 405- 413
PubMed
34.
Steinman  KLotspeich  LPatnaik  SHoefr  FReiss  A Structural brain differences between autistic children and their typically-developing siblings: a voxel-based morphometry analysis [ANA Annual Meeting abstract poster CD34]. Ann Neurol 2008;64 (6) S155
35.
Craig  MCZaman  SHDaly  EMCutter  WJRobertson  DMWHallahan  BToal  FReed  SAmbikapathy  ABrammer  MMurphy  CMMurphy  DGM Women with autistic-spectrum disorder: magnetic resonance imaging study of brain anatomy. Br J Psychiatry 2007;191224- 228
PubMed
36.
Ecker  CRocha-Rego  VJohnston  PMourao-Miranda  JMarquand  ADaly  EMBrammer  MJMurphy  CMurphy  DGMRC AIMS Consortium, Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach. Neuroimage 2010;49 (1) 44- 56
PubMed
37.
Hyde  KLSamson  FEvans  ACMottron  L Neuroanatomical differences in brain areas implicated in perceptual and other core features of autism revealed by cortical thickness analysis and voxel-based morphometry. Hum Brain Mapp 2010;31 (4) 556- 566
PubMed
38.
Kosaka  HOmori  MMunesue  TIshitobi  MMatsumura  YTakahashi  TNarita  KMurata  TSaito  DNUchiyama  HMorita  TKikuchi  MMizukami  KOkazawa  HSadato  NWada  Y Smaller insula and inferior frontal volumes in young adults with pervasive developmental disorders. Neuroimage 2010;50 (4) 1357- 1363
PubMed
39.
McAlonan  GMDaly  EKumari  VCritchley  HDvan Amelsvoort  TSuckling  JSimmons  ASigmundsson  TGreenwood  KRussell  ASchmitz  NHappe  FHowlin  PMurphy  DGM Brain anatomy and sensorimotor gating in Asperger's syndrome. Brain 2002;125 (pt 7) 1594- 1606
PubMed
40.
Rojas  DCPeterson  EWinterrowd  EReite  MLRogers  SJTregellas  JR Regional gray matter volumetric changes in autism associated with social and repetitive behavior symptoms. BMC Psychiatry 2006;656
PubMed
41.
Schmitz  NRubia  KDaly  ESmith  AWilliams  SMurphy  DGM Neural correlates of executive function in autistic spectrum disorders. Biol Psychiatry 2006;59 (1) 7- 16
PubMed
42.
Toal  FDaly  EMPage  LDeeley  QHallahan  BBloemen  OCutter  WJBrammer  MJCurran  SRobertson  DMurphy  CMurphy  KCMurphy  DG Clinical and anatomical heterogeneity in autistic spectrum disorder: a structural MRI study. Psychol Med 2010;40 (7) 1171- 1181
43.
Wilson  LBTregellas  JRHagerman  RJRogers  SJRojas  DC A voxel-based morphometry comparison of regional gray matter between fragile X syndrome and autism. Psychiatry Res 2009;174 (2) 138- 145
PubMed
44.
Boddaert  NChabane  NGervais  HGood  CDBourgeois  MPlumet  M-HBarthélémy  CMouren  MCArtiges  ESamson  YBrunelle  FFrackowiak  RSJZilbovicius  M Superior temporal sulcus anatomical abnormalities in childhood autism: a voxel-based morphometry MRI study. Neuroimage 2004;23 (1) 364- 369
PubMed
45.
Bonilha  LCendes  FRorden  CEckert  MDalgalarrondo  PLi  LMSteiner  CE Gray and white matter imbalance: typical structural abnormality underlying classic autism? Brain Dev 2008;30 (6) 396- 401
PubMed
46.
Brieber  SNeufang  SBruning  NKamp-Becker  IRemschmidt  HHerpertz-Dahlmann  BFink  GRKonrad  K Structural brain abnormalities in adolescents with autism spectrum disorder and patients with attention deficit/hyperactivity disorder. J Child Psychol Psychiatry 2007;48 (12) 1251- 1258
PubMed
47.
Freitag  CMKonrad  CHäberlen  MKleser  Cvon Gontard  AReith  WTroje  NFKrick  C Perception of biological motion in autism spectrum disorders. Neuropsychologia 2008;46 (5) 1480- 1494
PubMed
48.
Hardan  AYYorbik  OMinshew  NJDiwadkar  VAKeshavan  MS Voxel-based morphometry study of gray matter in Asperger's disorder [SOBP Annual Meeting, abstract 597]. Biol Psychiatry 2003;53211- 212
49.
Ke  XHong  STang  TZou  BLi  HHang  YZhou  ZRuan  ZLu  ZTao  GLiu  Y Voxel-based morphometry study on brain structure in children with high-functioning autism. Neuroreport 2008;19 (9) 921- 925
PubMed
50.
Kwon  HOw  AWPedatella  KELotspeich  LJReiss  AL Voxel-based morphometry elucidates structural neuroanatomy of high-functioning autism and Asperger syndrome. Dev Med Child Neurol 2004;46 (11) 760- 764
51.
Langen  MSchnack  HGNederveen  HBos  DLahuis  BEde Jonge  MVvan Engeland  HDurston  S Changes in the developmental trajectories of striatum in autism. Biol Psychiatry 2009;66 (4) 327- 333
PubMed
52.
McAlonan  GMSuckling  JWong  NCheung  VLienenkaemper  NCheung  CChua  SE Distinct patterns of grey matter abnormality in high-functioning autism and Asperger's syndrome. J Child Psychol Psychiatry 2008;49 (12) 1287- 1295
PubMed
53.
Waiter  GDWilliams  JHMurray  ADGilchrist  APerrett  DIWhiten  A A voxel-based investigation of brain structure in male adolescents with autistic spectrum disorder. Neuroimage 2004;22 (2) 619- 625
PubMed
54.
Lord  CRutter  MLe Couteur  A Autism Diagnostic Interview–Revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J Autism Dev Disord 1994;24 (5) 659- 685
PubMed
55.
Hedges  LVOlkin  Ieds. Statistical Methods for Meta-Analysis.  Orlando, FL Academic Press1985;
56.
Stanfield  ACMcIntosh  AMSpencer  MDPhilip  RGaur  SLawrie  SM Towards a neuroanatomy of autism: a systematic review and meta-analysis of structural magnetic resonance imaging studies. Eur Psychiatry 2008;23 (4) 289- 299
PubMed
57.
Bauman  MKemper  TL Histoanatomic observations of the brain in early infantile autism. Neurology 1985;35 (6) 866- 874
PubMed
58.
Baron-Cohen  SRing  HABullmore  ETWheelwright  SAshwin  CWilliams  SC The amygdala theory of autism. Neurosci Biobehav Rev 2000;24 (3) 355- 364
PubMed
59.
Kluver  HBucy  PC Psychic blindness and other symptoms following bilateral temporal lobectomy in rhesus monkeys [Proceedings of the American Physiological Society, 49th annual meeting]. Am J Physiol 1937;119352- 353
60.
Brothers  LRing  BKling  A Response of neurons in the macaque amygdala to complex social stimuli. Behav Brain Res 1990;41 (3) 199- 213
PubMed
61.
Thompson  CITowfighi  JT Social behavior of juvenile rhesus monkeys after amygdalectomy in infancy. Physiol Behav 1976;17 (5) 831- 836
PubMed
62.
Emery  NJCapitanio  JPMason  WAMachado  CJMendoza  SPAmaral  DG The effects of bilateral lesions of the amygdala on dyadic social interactions in rhesus monkeys (Macaca mulatta). Behav Neurosci 2001;115 (3) 515- 544
PubMed
63.
Bachevalier  JMishkin  M Effects of selective neonatal temporal lobe lesions on visual recognition memory in rhesus monkeys. J Neurosci 1994;14 (4) 2128- 2139
PubMed
64.
Adolphs  R What does the amygdala contribute to social cognition? Ann N Y Acad Sci 2010;1191 (1) 42- 61
PubMed
65.
Bachevalier  J Brief report: medial temporal lobe and autism: a putative animal model in primates. J Autism Dev Disord 1996;26 (2) 217- 220
PubMed
66.
Klin  ASparrow  SSde Bildt  ACicchetti  DVCohen  DJVolkmar  FR A normed study of face recognition in autism and related disorders. J Autism Dev Disord 1999;29 (6) 499- 508
PubMed
67.
Amaral  DGBauman  MDSchumann  CM The amygdala and autism: implications from non-human primate studies. Genes Brain Behav 2003;2 (5) 295- 302
PubMed
68.
Boraston  ZBlakemore  SJ The application of eye-tracking technology in the study of autism. J Physiol 2007;581 (pt 3) 893- 898
PubMed
69.
Sasson  NJ The development of face processing in autism. J Autism Dev Disord 2006;36 (3) 381- 394
PubMed
70.
Saitoh  OKarns  CMCourchesne  E Development of the hippocampal formation from 2 to 42 years: MRI evidence of smaller area dentata in autism. Brain 2001;124 (pt 7) 1317- 1324
PubMed
71.
Aylward  EHMinshew  NJGoldstein  GHoneycutt  NAAugustine  AMYates  KOBarta  PEPearlson  GD MRI volumes of amygdala and hippocampus in non-mentally retarded autistic adolescents and adults. Neurology 1999;53 (9) 2145- 2150
PubMed
72.
Howard  MACowell  PEBoucher  JBroks  PMayes  AFarrant  ARoberts  N Convergent neuroanatomical and behavioural evidence of an amygdala hypothesis of autism. Neuroreport 2000;11 (13) 2931- 2935
PubMed
73.
Di Martino  ARoss  KUddin  LQSklar  ABCastellanos  FXMilham  MP Functional brain correlates of social and nonsocial processes in autism spectrum disorders: an ALE meta-analysis. Biol Psychiatry 2009;65 (1) 63- 74
PubMed
74.
Brothers  L  et al.  The social brain: a project for integrating primate behavior and neurophysiology in a new domain. Cacioppo  JTeds.Foundations in Social Neuroscience. Cambridge, MA The MIT Press2002;367- 388
75.
Baron-Cohen  SLeslie  AMFrith  U Does the autistic child have a “theory of mind”? Cognition 1985;21 (1) 37- 46
PubMed
76.
Happé  FEhlers  SFletcher  PFrith  UJohansson  MGillberg  CDolan  RFrackowiak  RFrith  C “Theory of mind” in the brain: evidence from a PET scan study of Asperger syndrome. Neuroreport 1996;8 (1) 197- 201
PubMed
77.
Happé  FFrith  U The neuropsychology of autism. Brain 1996;119 (pt 4) 1377- 1400
PubMed
78.
Baron-Cohen  SRing  HAWheelwright  SBullmore  ETBrammer  MJSimmons  AWilliams  SCR Social intelligence in the normal and autistic brain: an fMRI study. Eur J Neurosci 1999;11 (6) 1891- 1898
PubMed
79.
Castelli  FFrith  CHappé  FFrith  U Autism, Asperger syndrome and brain mechanisms for the attribution of mental states to animated shapes. Brain 2002;125 (pt 8) 1839- 1849
PubMed
80.
Domes  GKumbier  EHerpertz-Dahlmann  BHerpertz  SC Social cognition in autism: a survey of functional imaging studies [in German]. Nervenarzt 2008;79 (3) 261- 274
PubMed
81.
Siegal  MVarley  R Neural systems involved in “theory of mind.” Nat Rev Neurosci 2002;3 (6) 463- 471
PubMed
82.
Hutton  JGoode  SMurphy  MLe Couteur  ARutter  M New-onset psychiatric disorders in individuals with autism. Autism 2008;12 (4) 373- 390
PubMed
83.
MacNeil  BMLopes  VAMinnes  PM Anxiety in children and adolescents with autism spectrum disorders. Res Autism Spectr Disord 2009;3 (1) 1- 21
84.
Skokauskas  NGallagher  L Psychosis, affective disorders and anxiety in autistic spectrum disorder: prevalence and nosological considerations. Psychopathology 2010;43 (1) 8- 16
PubMed
85.
Herman  JPCullinan  WE Neurocircuitry of stress: central control of the hypothalamo-pituitary-adrenocortical axis. Trends Neurosci 1997;20 (2) 78- 84
PubMed
86.
McEwen  BS Mood disorders and allostatic load. Biol Psychiatry 2003;54 (3) 200- 207
PubMed
87.
Campbell  SMarriott  MNahmias  CMacQueen  GM Lower hippocampal volume in patients suffering from depression: a meta-analysis. Am J Psychiatry 2004;161 (4) 598- 607
PubMed
88.
Diamond  DMCampbell  APark  CRVouimba  R-M Preclinical research on stress, memory, and the brain in the development of pharmacotherapy for depression. Eur Neuropsychopharmacol 2004;14(suppl 5)S491- S495
PubMed
89.
Reagan  LPGrillo  CAPiroli  GG The As and Ds of stress: metabolic, morphological and behavioral consequences. Eur J Pharmacol 2008;585 (1) 64- 75
PubMed
90.
Castro  JEVarea  EMárquez  CCordero  MIPoirier  GSandi  C Role of the amygdala in antidepressant effects on hippocampal cell proliferation and survival and on depression-like behavior in the rat. PLoS One 2010;5 (1) e8618
PubMed10.1371/journal.pone.0008618
91.
Hardan  AYMuddasani  SVemulapalli  MKeshavan  MSMinshew  NJ An MRI study of increased cortical thickness in autism. Am J Psychiatry 2006;163 (7) 1290- 1292
PubMed
92.
Jiao  YChen  RKe  XChu  KLu  ZHerskovits  EH Predictive models of autism spectrum disorder based on brain regional cortical thickness. Neuroimage 2010;50 (2) 589- 599
PubMed
93.
Cavanna  AETrimble  MR The precuneus: a review of its functional anatomy and behavioural correlates. Brain 2006;129 (pt 3) 564- 583
PubMed
94.
Lombardo  MVBarnes  JLWheelwright  SJBaron-Cohen  S Self-referential cognition and empathy in autism. PLoS One 2007;2 (9) e883
PubMed10.1371/journal.pone.0000883
95.
Lombardo  MVBaron-Cohen  S Unraveling the paradox of the autistic self. Wiley Interdiscip Rev Cogn Sci 2010;1 (3) 393- 403
96.
Rizzolatti  GFabbri-Destro  M Mirror neurons: from discovery to autism. Exp Brain Res 2010;200 (3-4) 223- 237
PubMed
97.
Ramachandran  VSOberman  LM Broken mirrors: a theory of autism. Sci Am 2006;295 (5) 62- 69
PubMed
98.
Iacoboni  MDapretto  M The mirror neuron system and the consequences of its dysfunction. Nat Rev Neurosci 2006;7 (12) 942- 951
PubMed
99.
Oberman  LMRamachandran  VS The simulating social mind: the role of the mirror neuron system and simulation in the social and communicative deficits of autism spectrum disorders. Psychol Bull 2007;133 (2) 310- 327
PubMed
100.
Williams  JH Self-other relations in social development and autism: multiple roles for mirror neurons and other brain bases. Autism Res 2008;1 (2) 73- 90
PubMed
101.
Southgate  VHamilton  AF Unbroken mirrors: challenging a theory of autism. Trends Cogn Sci 2008;12 (6) 225- 229
PubMed
102.
Hickok  G Eight problems for the mirror neuron theory of action understanding in monkeys and humans. J Cogn Neurosci 2009;21 (7) 1229- 1243
PubMed
103.
Dinstein  IThomas  CHumphreys  KMinshew  NBehrmann  MHeeger  DJ Normal movement selectivity in autism. Neuron 2010;66 (3) 461- 469
PubMed
104.
Minshew  NJKeller  TA The nature of brain dysfunction in autism: functional brain imaging studies. Curr Opin Neurol 2010;23 (2) 124- 130
PubMed
105.
Simmons  DRRobertson  AEMcKay  LSToal  EMcAleer  PPollick  FE Vision in autism spectrum disorders. Vision Res 2009;49 (22) 2705- 2739
PubMed
106.
Bowler  DMGardiner  JMBerthollier  N Source memory in adolescents and adults with Asperger's syndrome. J Autism Dev Disord 2004;34 (5) 533- 542
PubMed
107.
Lind  SEBowler  DM Recognition memory, self-other source memory, and theory-of-mind in children with autism spectrum disorder. J Autism Dev Disord 2009;39 (9) 1231- 1239
PubMed
108.
Shallice  TBurgess  PW Deficits in strategy application following frontal lobe damage in man. Brain 1991;114 (pt 2) 727- 741
PubMed
109.
Saver  JLDamasio  AR Preserved access and processing of social knowledge in a patient with acquired sociopathy due to ventromedial frontal damage. Neuropsychologia 1991;29 (12) 1241- 1249
PubMed
110.
Dimitrov  MGrafman  JHollnagel  C The effects of frontal lobe damage on everyday problem solving. Cortex 1996;32 (2) 357- 366
PubMed
111.
Adolphs  R The social brain: neural basis of social knowledge. Annu Rev Psychol 2009;60693- 716
PubMed
112.
Forbes  CEGrafman  J The role of the human prefrontal cortex in social cognition and moral judgment. Annu Rev Neurosci 2010;33299- 324
PubMed
113.
Volkmar  FRState  MKlin  A Autism and autism spectrum disorders: diagnostic issues for the coming decade. J Child Psychol Psychiatry 2009;50 (1-2) 108- 115
PubMed
114.
Macintosh  KEDissanayake  C Annotation: the similarities and differences between autistic disorder and Asperger's disorder: a review of the empirical evidence. J Child Psychol Psychiatry 2004;45 (3) 421- 434
PubMed
115.
Greimel  ESchulte-Rüther  MFink  GRPiefke  MHerpertz-Dahlmann  BKonrad  K Development of neural correlates of empathy from childhood to early adulthood: an fMRI study in boys and adult men. J Neural Transm 2010;117 (6) 781- 791
PubMed
116.
Courchesne  ECampbell  KSolso  S Brain growth across the life span in autism: age-specific changes in anatomical pathology. Brain Res 2011;1380138- 145
117.
Helt  MKelley  EKinsbourne  MPandey  JBoorstein  HHerbert  MFein  D Can children with autism recover? if so, how? Neuropsychol Rev 2008;18 (4) 339- 366
PubMed
Meta-analysis
April 04, 2011

Meta-analysis of Gray Matter Abnormalities in Autism Spectrum DisorderShould Asperger Disorder Be Subsumed Under a Broader Umbrella of Autistic Spectrum Disorder?

Author Affiliations

Author Affiliations: Departments of Psychosis Studies and Psychology (Drs Via, Radua, and Mataix-Cols) and Medical Research Council Social, Genetic, and Developmental Psychiatry Centre (Dr Happé), Institute of Psychiatry, King's College London, London, England; Hospital Universitari de Bellvitge–Institut d’ Investigació Biomèdica de Bellvitge (IDIBELL), L’Hospitalet de Llobregat (Drs Via and Cardoner) and Benito Menni Complex Assistencial en Salut Mental, Sant Boi de Llobregat (Dr Radua), and Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM) (Drs Radua and Cardoner), Barcelona, Spain.

Arch Gen Psychiatry. 2011;68(4):409-418. doi:10.1001/archgenpsychiatry.2011.27
Abstract

Context  Studies investigating abnormalities of regional gray matter volume in autism spectrum disorder (ASD) have yielded contradictory results. It is unclear whether the current subtyping of ASD into autistic disorder and Asperger disorder is neurobiologically valid.

Objectives  To conduct a quantitative meta-analysis of voxel-based morphometry studies exploring gray matter volume abnormalities in ASD, to examine potential neurobiological differences among ASD subtypes, and to create an online database to facilitate replication and further analyses by other researchers.

Data Sources  We retrieved studies from PubMed, ScienceDirect, Scopus, and Web of Knowledge databases between June 3, 1999, the date of the first voxel-based morphometry study in ASD, and October 31, 2010. Studies were also retrieved from reference lists and review articles. We contacted authors soliciting additional data.

Study Selection  Twenty-four data sets met inclusion criteria, comprising 496 participants with ASD and 471 healthy control individuals.

Data Extraction  Peak coordinates of clusters of regional gray matter differences between participants with ASD and controls, as well as demographic, clinical, and methodologic variables, were extracted from each study or obtained from the authors.

Data Synthesis  No differences in overall gray matter volume were found between participants with ASD and healthy controls. Participants with ASD were found to have robust decreases of gray matter volume in the bilateral amygdala-hippocampus complex and the bilateral precuneus. A small increase of gray matter volume in the middle-inferior frontal gyrus was also found. No significant differences in overall or regional gray matter volumes were found between autistic disorder and Asperger disorder. Decreases of gray matter volume in the right precuneus were statistically higher in adults than in adolescents with ASD.

Conclusions  These results confirm the crucial involvement of structures linked to social cognition in ASD. The absence of significant differences between ASD subtypes may have important nosologic implications for the DSM-5. The publically available database will be a useful resource for future research.

Autism is a developmental disorder characterized by deficits in communication and reciprocal social interaction and a restricted, repetitive, or stereotyped pattern of behaviors, interests, and activities evident by the age of 3 years.1 Autism spectrum disorder (ASD) is currently used as a broad umbrella term that includes a wide range of clinical presentations, including autistic disorder, Asperger disorder, and pervasive developmental disorder not otherwise specified.1 A recent study2 estimates the prevalence of ASD to be 1% in children aged 9 to 10 years, with approximately 60% of those having associated intellectual disability.

The causes of ASD are still to be fully elucidated, although it is known to have a strong genetic component.3 It is also thought to result from maturation-related changes in various brain systems. One of the most well-replicated findings in ASD is the increased total head size and brain volume.4,5 This is mainly due to increased volumes in the frontal lobes and anterior temporal regions, most notably in the preschool years.6

Modern neuroimaging methods such as magnetic resonance imaging have provided important insights into the neurobiological basis for ASD.7 Abnormalities in brain regions known to be important for verbal and nonverbal communication, social interaction, and executive functions, as well as related white matter tracts, have been reported.7,8 However, the results of these studies have been less consistent than expected. For example, volume increases and decreases have been reported in the amygdala, a region hypothesized to be critically involved in ASD.7,911 These inconsistencies might exist partially because of the clinical and etiologic heterogeneity of the disorder,12 as well as methodologic aspects of the studies, such as the use of manual or semiautomated region of interest (ROI) methods, which focus on a priori selected regions, thus precluding the exploration of other brain regions potentially implicated in ASD.

Fully automated, whole-brain voxel-based morphometry (VBM) methods,13,14 which overcome some of the limitations of the ROI approach, provide a potentially powerful and unbiased tool to study the neural substrates of ASD. Since the advent of VBM methods, an increasing number of studies have applied them to the study of ASD, but their results have also been relatively inconsistent. These studies are often limited by relatively small sample sizes, resulting in insufficient statistical power. Voxel-based meta-analytical methods are ideal to quantify the reproducibility of neuroimaging findings and to generate insights difficult to observe in isolated studies.15,16

In this study, we searched all known published and unpublished VBM studies of gray matter in ASD and applied novel voxel-based meta-analytical methods to establish the most consistently reported abnormalities in this disorder, without the constraints of a priori hypotheses. We also examined potential differences in gray matter volume between the main subtypes of ASD, namely, autistic disorder and Asperger disorder, which remains an important nosologic issue in the DSM-5.17 Specifically, draft criteria for the DSM-5 have proposed merging Asperger disorder into ASD because of the lack of solid empirical data distinguishing Asperger and high-functioning autism subgroups with regard to cause, course, and neurocognitive profile.1820 Finally, to facilitate replication and further analyses by research groups internationally, we have also developed a readily accessible online database containing all the data and methodologic details from every study included in this meta-analysis.

METHODS
SEARCH AND INCLUSION OF STUDIES

A literature search of articles, posters, and abstracts published from June 3, 1999, the date of the first VBM study in ASD,21 through October 31, 2010, was conducted using the PubMed, ScienceDirect, Scopus, and Web of Knowledge databases. The search keywords were autism or Asperger plus morphometry, voxel based, or voxelwise. In addition, manual searches were conducted among the reference sections of the retrieved studies and several review studies. Any work comparing individuals with autistic disorder or Asperger disorder with healthy control individuals by means of whole-brain VBM was considered for inclusion in the meta-analysis. Studies containing duplicated data sets (ie, analyzing at least half of the sample in different works) and studies with fewer than 9 participants in the ASD group were excluded.

DATA EXTRACTION

Data from included studies were extracted by 2 of us (E.V. and J.R.) and then compared to minimize interpretation and data entry errors. In all but 2 studies, the corresponding authors were contacted via e-mail to obtain data not included in the original works. Meta-analysis of Observational Studies in Epidemiology guidelines22 were followed in the study.

GLOBAL DIFFERENCES IN GRAY MATTER VOLUME

Meta-analytical differences in global gray matter volumes between participants with ASD and controls were assessed using random-effects models with the Globals procedure in Signed Differential Mapping (SDM),23 which uses restricted maximum-likelihood estimation, a fitting method that has been recommended instead of previous ones for its optimal balance between lack of bias and efficiency.24

REGIONAL DIFFERENCES IN GRAY MATTER VOLUME

Regional differences in gray matter volume between participants with ASD and controls were analyzed using SDM,23 a novel voxel-based meta-analytic approach that is based on and improves on other existing methods.25,26 The method has been described in detail elsewhere27,28 and is briefly summarized herein.

First, SDM applies a strict selection of the reported peak coordinates of gray matter differences by only including those that appear statistically significant at the whole-brain level, preferably corrected for multiple comparisons. This practice is intended to avoid biases toward liberally thresholded brain regions because it is not uncommon in neuroimaging studies that the statistical threshold for some ROIs is more liberal than for the rest of the brain. Second, a map of the differences in gray matter is separately recreated for each study. This includes limiting voxel values to a maximum to avoid biases toward studies reporting various coordinates in close proximity and reconstructing increases and decreases of gray matter in the same map. Finally, the mean map is obtained by voxel-based calculation of the mean of the study maps, weighted by the squared root of the sample size of each study so that studies with large sample sizes contribute proportionally more to the final map. The statistical significance of each voxel is assessed by standard randomization tests.27

The main analysis reported herein was complemented by several additional analyses to assess the robustness and replicability of the findings,27 namely, jackknife sensitivity analyses and descriptive analyses of quartiles. The jackknife sensitivity analysis consists of iteratively repeating the same analyses excluding 1 data set at a time to establish whether the results remain significant. The analyses of quartiles describe the proportion of studies reporting changes in a particular region (ie, 25%, 50%, or 75%), regardless of P values.27,28

EFFECTS OF ASD SUBTYPE AND AGE ON GLOBAL AND REGIONAL GRAY MATTER VOLUMES

Using recently developed methods,28 we next regressed the global and regional gray matter differences between participants with ASD and controls from each study by ASD subtype (autistic vs Asperger disorder) and age (adult vs adolescent samples). Considering that some studies included participants with autistic disorder and participants with Asperger disorder, the diagnosis factor was coded as the percentage of participants with Asperger disorder.

The analysis of covariance (ANCOVA) of global gray matter differences returned a Q statistic (similar to an F statistic) that summarized the effects of diagnosis and age, a z statistic for the difference between participants with autistic disorder and participants with Asperger disorder, and a z statistic for the difference between adolescent and adult participants with ASD. Similarly, the ANCOVA of regional gray matter differences returned a map of the regional Q statistic, a map of the gray matter volume differences between participants with autistic disorder and participants with Asperger disorder, and a separate map of the differences between adolescent and adult participants with ASD. To minimize spurious findings, this ANCOVA was limited to the brain regions with significant differences between patients and controls in the main analysis.

RESULTS
INCLUDED STUDIES AND SAMPLE CHARACTERISTICS

As shown in Figure 1, the search retrieved 26 potentially suitable studies. Of those studies, 1 was excluded because it included fewer than 9 participants with ASD,29 1 was excluded because of a duplicated data set,30 and 3 additional studies were excluded because of incompatible methods (ie, bilateral VBM31,32—a new statistical VBM approach to identify only bilateral abnormalities in the autistic brain—and single-case analysis33). One further study34 had to be excluded because it was missing information that was key for our meta-analysis (ie, peak coordinates from whole-brain analysis).

After contacting the authors, no methodologic ambiguities remained regarding the design or results of the remaining 20 studies. Therefore, 20 studies could be included in this meta-analysis, comprising 24 independent data sets (that is, comparisons between groups of participants with ASD and healthy controls). Note that some studies included more than 1 sample of participants (or data sets). Nineteen of these studies were published or accepted for publication, and 1 was an unpublished analysis within an abstract. Ten studies included adult ASD samples21,3543 and another 10 studies included adolescent ASD samples.4453

The included studies comprised 496 participants with ASD and 471 healthy controls. Among them, 452 were adults (228 controls: mean [SD] age, 27 [9.7] years; 224 ASD patients: mean [SD] age, 28 [10.1] years, 46.4% with autistic disorder, 52.2% with Asperger disorder, 1.3% unspecified) and 515 were adolescents (243 controls: mean [SD] age, 12 [4.3] years; 272 ASD patients: mean [SD] age, 13 [4.6] years, 69.5% with autistic disorder, 19.1% with Asperger disorder, 11.4% unspecified). The ASD diagnosis was established by means of the Autism Diagnostic Interview–Revised54 in 17 studies and using DSM-IV criteria in the remaining 3 studies.21,38,45 As indicated in Table 1, no relevant differences between participants with ASD and healthy controls were found in terms of sex, age, handedness, or IQ, reflecting the group matching used in the original studies. With the exception of 1 study,44 the mean IQ of the participants was greater than 70. The IQ was not reported in 3 studies.21,45,50 Only 1 study35 included a completely female sample. Most of the studies (75.0%) specified that neurologic or psychiatric conditions were excluded, and only 12 participants with ASD (2.4%) were known to have comorbid conditions in 2 studies.45,46 Medication use was not often reported; across all included studies, 24 participants with ASD (4.8%) were said to be taking medication at the time of scan. More clinical and methodologic details of each of the studies can be found at http://www.sdmproject.com/database/.

GLOBAL DIFFERENCES IN GRAY MATTER VOLUME

Global gray matter volume could be retrieved from 12 studies.3538,40,43,46,47,49,5153 No statistically significant differences in global gray matter volumes were found between participants with ASD (n = 312) and healthy controls (n = 334) (unbiased Hedges54,55d = 0.006, z = 0.065, P = .95).

REGIONAL DIFFERENCES IN GRAY MATTER VOLUME

Data for this analysis were obtained from all 24 data sets. As indicated in Table 2 and Figure 2, participants with ASD were characterized by a significant reduction of gray matter volume in the amygdala-hippocampus complex (particularly on the right side), extending to the right claustrum. Significant reductions in gray matter volume were also found in the bilateral medial parietal cortex (precuneus, Brodmann area [BA] 7), particularly on the right side, which extended to adjacent right-sided areas: superior parietal lobule, subgyral parietal lobe, and postcentral gyrus (BA7, BA40, and BA5). Finally, a small increase in gray matter volume was found in the left middle frontal gyrus extending to the inferior frontal gyrus (BA46 and BA10).

Replicability of these results was further assessed by a jackknife sensitivity analysis consisting of the systematic exclusion of 1 study at a time. As indicated in Table 3, the main results were highly replicable, especially for the right amygdala-hippocampus complex, which remained significant in all the combinations of data sets. The left amygdala-hippocampus complex remained significant in all but 2 combinations of studies. The bilateral medial parietal cortex remained significant in all but 1 combination of data sets. The left middle-inferior frontal gyrus was somewhat less robust, failing to reach significance in 4 combinations of studies. The analyses of quartiles showed that at least 25% of the studies had found a decrease of gray matter in the right amygdala-hippocampus complex and the bilateral precuneus but not the other 2 regions.

Because there was a partial sample overlap between 2 sets of studies (Rojas et al40 with Wilson et al43 and McAlonan et al39 with Toal et al42), we next repeated the analyses excluding the studies with the smallest samples.39,43 The results remained unchanged when we excluded both of these studies.

Finally, we repeated the analyses excluding the 3 studies that did not use a criterion standard diagnostic tool, such as the Autism Diagnostic Interview–Revised.21,38,45 The exclusion of these 3 studies did not modify the results.

EFFECTS OF ASD SUBTYPE AND AGE ON GLOBAL AND REGIONAL GRAY MATTER VOLUMES

Eleven data sets (in 10 studies)3538,40,43,46,49,51,52 reported global gray matter volumes and the proportion of participants with autistic disorder and those with Asperger disorder in their samples. These studies were included in the ANCOVA of global gray matter differences comparing diagnostic and age subgroups (n = 303 healthy controls, 211 autistic disorder patients, and 67 Asperger disorder patients). The results revealed no statistically significant differences in global gray matter volumes between autistic disorder and Asperger disorder or between adult and adolescent ASD samples (Q = 0.183, df = 2, P = .91; autistic disorder vs Asperger disorder: d = 0.009, SD = 0.29, z = 0.03, P = .98; adults vs adolescents: d = −0.010, SD = 0.22, z = −0.42, P = .67).

All but 2 studies47,53 reported the proportion of participants with autistic disorder and those with Asperger disorder within their samples and could be included in the ANCOVA of regional gray matter differences (n = 440 healthy controls, 293 patients with autistic disorder, and 169 patients with Asperger disorder). The Q map of regional gray mater differences (effects of diagnosis and age) found a significant difference in the right precuneus (peak [Talairach]: 22, −48, 54, SDM = 1, P < .001). This was driven by a significantly greater decrease of gray matter volume in adults than in adolescents (peak [Talairach]: 22, −48, 54, SDM = −0.346, P < .001) (Figure 3). Crucially, no statistically significant differences were found in regional gray matter volumes between the autistic disorder and Asperger disorder groups (smallest ≥ .001) (Figure 3). An additional analysis that included only diagnostically pure samples (ie, composed of only participants with autistic disorder or only those with Asperger disorder) resulted in a nonsignificant Q map (smallest P ≥ .001), indicating no statistically significant effects of diagnosis or age.

COMMENT

To our knowledge, ours is the first meta-analysis of voxel-based gray matter abnormalities in participants with ASD. It complements other existing meta-analyses of studies using hand-tracing methods.56 The study is timely, given that findings from previous studies have been relatively inconsistent and that only recently have enough studies been accumulated to allow such meta-analysis. We took advantage of recent developments in meta-analytical methods to compare subtypes of ASD—a subject that is relevant to the current DSM-5 deliberations—and developed a publically available database, which we hope will facilitate replication and further analyses from researchers in the field. The database is readily accessible at http://www.sdmproject.com/database/.

SUMMARY OF THE MAIN FINDINGS

Twenty-four independent data sets comprising formal comparisons between participants with ASD and healthy controls met inclusion criteria. Although no significant differences in global gray matter volumes existed, participants with ASD showed robust reductions of gray matter volume in the amygdala-hippocampus complex (particularly on the right side) and medial parietal regions (precuneus, BA7). A small increase in gray matter volume was found in the left middle and inferior frontal gyri (BA46 and BA10). Overall, these findings were robust because they consistently emerged using multiple statistical approaches and various sensitivity and subgroup analyses. No significant differences in regional gray matter volume were found between participants with a diagnosis of autistic disorder vs Asperger disorder. Decreases of gray matter volume in the right precuneus were statistically higher in adults than in adolescents with ASD. Each of these findings is discussed in turn.

Reduced Gray Matter Volume in Amygdala-Hippocampus Complex

The amygdala-hippocampus complex and adjacent cortical regions (eg, parahippocampal gyrus) have long been hypothesized to be involved in ASD.57,58 For example, lesions in the amygdala cause impairment in social cognition and alterations in salience coding, including processing of emotional information from faces,5964 features that had also been demonstrated to be altered in autism.6569

Structural neuroimaging studies in ASD have reported volumetric abnormalities in the amygdala-hippocampus complex, although the direction of the changes has been inconsistent.11,56 Our results are in agreement with previous reports7072 of reduced gray matter volume in this region. Structural abnormalities in the amygdala are consistent with the results of a recent meta-analysis73 that has shown hypoactivation of this structure in functional neuroimaging tasks related to social proceses.

It is clear that the amygdala and its adjacent structures are implicated in social cognition.74 Its impairment in ASD is largely supported by tasks involving theory-of-mind processes,7580 which require making inferences regarding the mental state of others.75,81 Although the amygdala is one of the main areas active during these tasks, other brain regions, such as the precuneus and middle-inferior prefrontal areas, also found to be abnormal in this meta-analysis, are implicated too.78 An alternative, yet not incompatible, interpretation of the findings is that the reported abnormalities in the amygdala-hippocampus may be associated with the extremely high prevalence of emotional disorders in ASD. Indeed, numerous studies8284 have shown that participants with ASD have a much higher prevalence of anxiety and mood disorders than would be expected in the general population. The amygdala-hippocampus complex is crucial to the current neurobiological models of anxiety and mood disorders.8590 Unfortunately, it is not possible from the current data to establish whether the observed abnormalities predispose individuals to developing anxiety or mood disorders, are the consequence of these comorbidities, or both.

Reduced Gray Matter Volume in Medial Parietal Cortex (Precuneus)

The precuneus is a posteromedial area in the parietal lobe that has been poorly studied in the past because of its broad connections with other brain regions and the rarity of specific lesions in this area. To our knowledge, there are no previous studies in ASD in which the parietal cortex has been selected as an ROI. However, in addition to the VBM studies included in this meta-analysis, at least 2 previous ASD studies91,92 reported abnormal cortical thickness in this region using surface-based morphometry methods.

A recent functional neuroimaging study93 provides interesting information regarding the role of the precuneus and its possible role in ASD. Specifically, the precuneus has been implicated in the processing of the self (ie, the representation and awareness of the self, integral to many aspects of social cognition), which has in turn been found to be impaired in autism.94,95

Supporting its role in social functions, the precuneus and medial prefrontal areas (including those reported in this meta-analysis) are implicated in the so-called mirror neuron system, which is involved in action-perception linkage and has been hypothesized to underlie empathy and social insight.96 Dysfunction of the mirror neuron system in ASD (the broken mirror hypothesis) has been suggested and is currently a topic of considerable debate.97103

The precuneus is also involved in visuospatial imagery, and several studies104 have found unusual connectivity in participants with ASD when performing visuospatial imagery tasks. Different visual abnormalities have been proposed to cause many of the behavioral signs and symptoms of ASD, some of them specifically affecting the development of social cognitive skills.105 A third function in which the precuneus is implicated is episodic memory, which is known to be specifically impaired in ASD.106,107

Increase in Gray Matter in the Left Middle-Inferior Frontal Cortex

It is well known that lesions in prefrontal regions and their connections with other brain areas, such as the precuneus and the amygdala, cause marked impairment in social cognition.108112 Frontal brain areas have previously been proposed to be involved in ASD,7679 including the middle and inferior frontal gyri, although the precise regions obtained in our study (BA46 and BA10) have been less well studied in ASD. However, our findings in these regions were somewhat less robust because they failed to remain significant in some of the reliability analyses; thus, these findings need to be interpreted with caution.

Absence of Gray Matter Volume Differences Between Autistic Disorder and Asperger Disorder

An ongoing debate exists as to whether autistic disorder and Asperger disorder are 2 separate disorders or represent a single nosologic entity with varying degrees of severity.113,114 We did not find statistically significant differences in gray matter volume between the autistic disorder and Asperger disorder groups. These findings would suggest that both disorders, as diagnosed in studies to date, have similar neural substrates and support the view of a single nosologic entity with differing degrees of severity. Thus, our results would be consistent with the proposal in the current draft of the DSM-5 to subsume autistic disorder and Asperger disorder (as well as pervasive developmental disorder not otherwise specified) into a single, broader ASD category.17 It is, of course, still possible that each of these disorders may prove to be etiologically heterogeneous.

Differences Between Adult and Adolescent ASD Samples

Generally, the findings were more pronounced in adult than adolescent samples, but the only statistically significant difference was a greater gray matter volume reduction in the precuneus in adult compared with adolescent samples. Whether these differences are the result of developmental maturational processes or reflect secondary effects of living with cognitive differences is unclear because little is known about the role of this brain region in ASD. One functional neuroimaging study115 has recently reported age-related changes in the neural correlates of empathy (including decreases in activation in the right precuneus) in healthy participants.

Age-related changes in ASD may result from dynamic processes in brain structure and function across the lifespan.116 It is known that children and adolescents with ASD can show a surprising degree of age-related clinical improvement.117 It is therefore possible that a selection bias exists, whereby older participants may have a more disabling form of the disorder and thus display more prominent gray matter abnormalities. Unfortunately, few magnetic resonance imaging studies have included young children, and it was difficult to fully explore age-related differences in brain structure in this meta-analysis. Longitudinal studies tracking individuals with a range of outcomes are clearly needed to examine this question.

In any case, these results need to be interpreted cautiously because additional analyses that include only diagnostically pure samples (ie, composed of only participants with autistic disorder or only those with Asperger disorder) revealed no statistically significant age effects. This finding could be indicative of insufficient statistical power to examine age-related effects.

STRENGTHS AND LIMITATIONS

The main strengths of this study are the unbiased inclusion of published and unpublished studies, even if their results were negative (ie, when no significant differences between participants with ASD and controls were found) and the use of novel voxel-wise meta-analytic methods.27,28 The online database containing all the data and methodologic details from every study included in this meta-analysis will be a useful resource. It is important to highlight several limitations of this study, some of which are inherent to all meta-analytical approaches. First, despite our attempts to include as many unpublished VBM studies as possible, the possibility of publication bias cannot be entirely ruled out. Second, voxel-based meta-analyses are based on summarized (ie, coordinates from published studies) rather than raw data, which may result in less accurate results.16 However, obtaining the raw images from the original studies is logistically difficult. Third, although our method provides excellent control for false-positive results, it is more difficult to completely avoid false-negative results. Fourth, our comparison of results from groups with autistic disorder vs Asperger disorder may be limited by the method and accuracy of the specific diagnoses in the contributing studies; it can be extremely hard, for example, to obtain reliable developmental history (eg, age of first words and phrases) from parents of adults. Fifth, studies retrieved were generally homogeneous in terms of sex (predominantly male), IQ (mostly high functioning), and age (predominantly adults and adolescents), which may not represent the entire autistic spectrum. For example, in the general population, approximately 60% of all participants with ASD have an IQ below 70.2 Therefore, the current results may not generalize to female participants, those with an IQ below 70, or younger patients with ASD.

In conclusion, our meta-analysis provides a quantitative summary of VBM studies in ASD and helps resolve some of the relatively inconsistent results in this literature. Abnormalities in bilateral amygdala-hippocampus, precuneus, and left prefrontal areas in ASD are all indicative of a dysfunctional network of social brain regions and consistent with the defining features of this sociocommunicative disorder. In addition, our results suggest common structural abnormalities between high-functioning autistic disorder and Asperger disorder, which support the proposal of a single category of ASD in the DSM-5.

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Article Information

Correspondence: David Mataix-Cols, PhD, PO Box 69, Department of Psychosis Studies, Institute of Psychiatry, King's College London, De Crespigny Park, London SE5 8AF, England (David.Mataix-Cols@kcl.ac.uk).

Submitted for Publication: July 27, 2010; final revision received November 2, 2010; accepted November 14, 2010.

Financial Disclosure: None reported.

Previous Presentation: Part of this work was presented at the 16th Annual Meeting of the Organization for Human Brain Mapping; June 10, 2010; Barcelona, Spain.

Additional Contributions: We thank all the authors of the included studies, particularly Antonio Youssef Hardan, MD, and Christine Ecker, PhD, for sharing their unpublished data for inclusion in this meta-analysis.

References
1.
American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders. 4thtext revision Washington, DC American Psychiatric Association2000;
2.
Baird  GSimonoff  EPickles  AChandler  SLoucas  TMeldrum  DCharman  T Prevalence of disorders of the autism spectrum in a population cohort of children in South Thames: the Special Needs and Autism Project (SNAP). Lancet 2006;368 (9531) 210- 215
PubMed
3.
Folstein  SRutter  M Infantile autism: a genetic study of 21 twin pairs. J Child Psychol Psychiatry 1977;18 (4) 297- 321
PubMed
4.
Bailey  ALuthert  PBolton  PLe Couteur  ARutter  MHarding  B Autism and megalencephaly. Lancet 1993;341 (8854) 1225- 1226
PubMed
5.
Piven  JArndt  SBailey  JHavercamp  SAndreasen  NCPalmer  P An MRI study of brain size in autism. Am J Psychiatry 1995;152 (8) 1145- 1149
PubMed
6.
Courchesne  EPierce  KSchumann  CMRedcay  EBuckwalter  JAKennedy  DPMorgan  J Mapping early brain development in autism. Neuron 2007;56 (2) 399- 413
PubMed
7.
Verhoeven  JSDe Cock  PLagae  LSunaert  S Neuroimaging of autism. Neuroradiology 2010;52 (1) 3- 14
PubMed
8.
Minshew  NJWilliams  DL The new neurobiology of autism: cortex, connectivity, and neuronal organization. Arch Neurol 2007;64 (7) 945- 950
PubMed
9.
Brambilla  PHardan  Adi Nemi  SUPerez  JSoares  JCBarale  F Brain anatomy and development in autism: review of structural MRI studies. Brain Res Bull 2003;61 (6) 557- 569
PubMed
10.
Palmen  SJMCvan Engeland  H Review on structural neuroimaging findings in autism. J Neural Transm 2004;111 (7) 903- 929
PubMed
11.
Penn  HE Neurobiological correlates of autism: a review of recent research. Child Neuropsychol 2006;12 (1) 57- 79
PubMed
12.
Levy  SEMandell  DSSchultz  RT Autism. Lancet 2009;374 (9701) 1627- 1638
PubMed
13.
Ashburner  JFriston  KJ Why voxel-based morphometry should be used. Neuroimage 2001;14 (6) 1238- 1243
PubMed
14.
Ashburner  JFriston  KJ Voxel-based morphometry: the methods. Neuroimage 2000;11 (6, pt 1) 805- 821
PubMed
15.
Costafreda  SG Pooling FMRI data: meta-analysis, mega-analysis and multi-center studies. Front Neuroinformatics 2009;333
PubMed
16.
Salimi-Khorshidi  GSmith  SMKeltner  JRWager  TDNichols  TE Meta-analysis of neuroimaging data: a comparison of image-based and coordinate-based pooling of studies. Neuroimage 2009;45 (3) 810- 823
PubMed
17.
American Psychiatric Association, DSM-5 development. 2010;American Psychiatric Association Web sitehttp://www.dsm5.org/Pages/Default.aspx11 October2010;
18.
Witwer  ANLecavalier  L Examining the validity of autism spectrum disorder subtypes. J Autism Dev Disord 2008;38 (9) 1611- 1624
PubMed
19.
Ozonoff  SSouth  MMiller  JN DSM-IV–defined Asperger syndrome: cognitive, behavioral and early history differentiation from high-functioning autism. Autism 2000;4 (1) 29- 46
20.
Howlin  P Outcome in high-functioning adults with autism with and without early language delays: implications for the differentiation between autism and Asperger syndrome. J Autism Dev Disord 2003;33 (1) 3- 13
PubMed
21.
Abell  FKrams  MAshburner  JPassingham  RFriston  KFrackowiak  RHappé  FFrith  CFrith  U The neuroanatomy of autism: a voxel-based whole brain analysis of structural scans. Neuroreport 1999;10 (8) 1647- 1651
PubMed
22.
Stroup  DFBerlin  JAMorton  SCOlkin  IWilliamson  GDRennie  DMoher  DBecker  BJSipe  TAThacker  SBMeta-analysis Of Observational Studies in Epidemiology (MOOSE) Group, Meta-analysis of observational studies in epidemiology: a proposal for reporting. JAMA 2000;283 (15) 2008- 2012
PubMed
23.
Radua  JSigned differential mapping2009;SDM Project Web sitehttp://www.sdmproject.com/11 October2010;
24.
Viechtbauer  W Bias and efficiency of meta-analytic variance estimators in the random-effects model. J Educ Behav Stat 2005;30 (3) 261- 293
25.
Turkeltaub  PEEden  GFJones  KMZeffiro  TA Meta-analysis of the functional neuroanatomy of single-word reading: method and validation. Neuroimage 2002;16 (3, pt 1) 765- 780
PubMed
26.
Wager  TDLindquist  MKaplan  L Meta-analysis of functional neuroimaging data: current and future directions. Soc Cogn Affect Neurosci 2007;2 (2) 150- 158
PubMed
27.
Radua  JMataix-Cols  D Voxel-wise meta-analysis of grey matter changes in obsessive-compulsive disorder. Br J Psychiatry 2009;195 (5) 393- 402
PubMed
28.
Radua  Jvan den Heuvel  OASurguladze  SMataix-Cols  D Meta-analytical comparison of voxel-based morphometry studies in obsessive-compulsive disorder vs other anxiety disorders. Arch Gen Psychiatry 2010;67 (7) 701- 711
PubMed
29.
Yamasue  HIshijima  MAbe  OSasaki  TYamada  HSuga  MRogers  MMinowa  ISomeya  RKurita  HAoki  SKato  NKasai  K Neuroanatomy in monozygotic twins with Asperger disorder discordant for comorbid depression. Neurology 2005;65 (3) 491- 492
PubMed
30.
McAlonan  GMCheung  VCheung  CSuckling  JLam  GYTai  KSYip  LMurphy  DGMChua  SE Mapping the brain in autism: a voxel-based MRI study of volumetric differences and intercorrelations in autism. Brain 2005;128 (pt 2) 268- 276
PubMed
31.
Salmond  CHAshburner  JConnelly  AFriston  KJGadian  DGVargha-Khadem  F The role of the medial temporal lobe in autistic spectrum disorders. Eur J Neurosci 2005;22 (3) 764- 772
PubMed
32.
Salmond  CHVargha-Khadem  FGadian  DGde Haan  MBaldeweg  T Heterogeneity in the patterns of neural abnormality in autistic spectrum disorders: evidence from ERP and MRI. Cortex 2007;43 (6) 686- 699
PubMed
33.
Salmond  CHde Haan  MFriston  KJGadian  DGVargha-Khadem  F Investigating individual differences in brain abnormalities in autism. Philos Trans R Soc Lond B Biol Sci 2003;358 (1430) 405- 413
PubMed
34.
Steinman  KLotspeich  LPatnaik  SHoefr  FReiss  A Structural brain differences between autistic children and their typically-developing siblings: a voxel-based morphometry analysis [ANA Annual Meeting abstract poster CD34]. Ann Neurol 2008;64 (6) S155
35.
Craig  MCZaman  SHDaly  EMCutter  WJRobertson  DMWHallahan  BToal  FReed  SAmbikapathy  ABrammer  MMurphy  CMMurphy  DGM Women with autistic-spectrum disorder: magnetic resonance imaging study of brain anatomy. Br J Psychiatry 2007;191224- 228
PubMed
36.
Ecker  CRocha-Rego  VJohnston  PMourao-Miranda  JMarquand  ADaly  EMBrammer  MJMurphy  CMurphy  DGMRC AIMS Consortium, Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach. Neuroimage 2010;49 (1) 44- 56
PubMed
37.
Hyde  KLSamson  FEvans  ACMottron  L Neuroanatomical differences in brain areas implicated in perceptual and other core features of autism revealed by cortical thickness analysis and voxel-based morphometry. Hum Brain Mapp 2010;31 (4) 556- 566
PubMed
38.
Kosaka  HOmori  MMunesue  TIshitobi  MMatsumura  YTakahashi  TNarita  KMurata  TSaito  DNUchiyama  HMorita  TKikuchi  MMizukami  KOkazawa  HSadato  NWada  Y Smaller insula and inferior frontal volumes in young adults with pervasive developmental disorders. Neuroimage 2010;50 (4) 1357- 1363
PubMed
39.
McAlonan  GMDaly  EKumari  VCritchley  HDvan Amelsvoort  TSuckling  JSimmons  ASigmundsson  TGreenwood  KRussell  ASchmitz  NHappe  FHowlin  PMurphy  DGM Brain anatomy and sensorimotor gating in Asperger's syndrome. Brain 2002;125 (pt 7) 1594- 1606
PubMed
40.
Rojas  DCPeterson  EWinterrowd  EReite  MLRogers  SJTregellas  JR Regional gray matter volumetric changes in autism associated with social and repetitive behavior symptoms. BMC Psychiatry 2006;656
PubMed
41.
Schmitz  NRubia  KDaly  ESmith  AWilliams  SMurphy  DGM Neural correlates of executive function in autistic spectrum disorders. Biol Psychiatry 2006;59 (1) 7- 16
PubMed
42.
Toal  FDaly  EMPage  LDeeley  QHallahan  BBloemen  OCutter  WJBrammer  MJCurran  SRobertson  DMurphy  CMurphy  KCMurphy  DG Clinical and anatomical heterogeneity in autistic spectrum disorder: a structural MRI study. Psychol Med 2010;40 (7) 1171- 1181
43.
Wilson  LBTregellas  JRHagerman  RJRogers  SJRojas  DC A voxel-based morphometry comparison of regional gray matter between fragile X syndrome and autism. Psychiatry Res 2009;174 (2) 138- 145
PubMed
44.
Boddaert  NChabane  NGervais  HGood  CDBourgeois  MPlumet  M-HBarthélémy  CMouren  MCArtiges  ESamson  YBrunelle  FFrackowiak  RSJZilbovicius  M Superior temporal sulcus anatomical abnormalities in childhood autism: a voxel-based morphometry MRI study. Neuroimage 2004;23 (1) 364- 369
PubMed
45.
Bonilha  LCendes  FRorden  CEckert  MDalgalarrondo  PLi  LMSteiner  CE Gray and white matter imbalance: typical structural abnormality underlying classic autism? Brain Dev 2008;30 (6) 396- 401
PubMed
46.
Brieber  SNeufang  SBruning  NKamp-Becker  IRemschmidt  HHerpertz-Dahlmann  BFink  GRKonrad  K Structural brain abnormalities in adolescents with autism spectrum disorder and patients with attention deficit/hyperactivity disorder. J Child Psychol Psychiatry 2007;48 (12) 1251- 1258
PubMed
47.
Freitag  CMKonrad  CHäberlen  MKleser  Cvon Gontard  AReith  WTroje  NFKrick  C Perception of biological motion in autism spectrum disorders. Neuropsychologia 2008;46 (5) 1480- 1494
PubMed
48.
Hardan  AYYorbik  OMinshew  NJDiwadkar  VAKeshavan  MS Voxel-based morphometry study of gray matter in Asperger's disorder [SOBP Annual Meeting, abstract 597]. Biol Psychiatry 2003;53211- 212
49.
Ke  XHong  STang  TZou  BLi  HHang  YZhou  ZRuan  ZLu  ZTao  GLiu  Y Voxel-based morphometry study on brain structure in children with high-functioning autism. Neuroreport 2008;19 (9) 921- 925
PubMed
50.
Kwon  HOw  AWPedatella  KELotspeich  LJReiss  AL Voxel-based morphometry elucidates structural neuroanatomy of high-functioning autism and Asperger syndrome. Dev Med Child Neurol 2004;46 (11) 760- 764
51.
Langen  MSchnack  HGNederveen  HBos  DLahuis  BEde Jonge  MVvan Engeland  HDurston  S Changes in the developmental trajectories of striatum in autism. Biol Psychiatry 2009;66 (4) 327- 333
PubMed
52.
McAlonan  GMSuckling  JWong  NCheung  VLienenkaemper  NCheung  CChua  SE Distinct patterns of grey matter abnormality in high-functioning autism and Asperger's syndrome. J Child Psychol Psychiatry 2008;49 (12) 1287- 1295
PubMed
53.
Waiter  GDWilliams  JHMurray  ADGilchrist  APerrett  DIWhiten  A A voxel-based investigation of brain structure in male adolescents with autistic spectrum disorder. Neuroimage 2004;22 (2) 619- 625
PubMed
54.
Lord  CRutter  MLe Couteur  A Autism Diagnostic Interview–Revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J Autism Dev Disord 1994;24 (5) 659- 685
PubMed
55.
Hedges  LVOlkin  Ieds. Statistical Methods for Meta-Analysis.  Orlando, FL Academic Press1985;
56.
Stanfield  ACMcIntosh  AMSpencer  MDPhilip  RGaur  SLawrie  SM Towards a neuroanatomy of autism: a systematic review and meta-analysis of structural magnetic resonance imaging studies. Eur Psychiatry 2008;23 (4) 289- 299
PubMed
57.
Bauman  MKemper  TL Histoanatomic observations of the brain in early infantile autism. Neurology 1985;35 (6) 866- 874
PubMed
58.
Baron-Cohen  SRing  HABullmore  ETWheelwright  SAshwin  CWilliams  SC The amygdala theory of autism. Neurosci Biobehav Rev 2000;24 (3) 355- 364
PubMed
59.
Kluver  HBucy  PC Psychic blindness and other symptoms following bilateral temporal lobectomy in rhesus monkeys [Proceedings of the American Physiological Society, 49th annual meeting]. Am J Physiol 1937;119352- 353
60.
Brothers  LRing  BKling  A Response of neurons in the macaque amygdala to complex social stimuli. Behav Brain Res 1990;41 (3) 199- 213
PubMed
61.
Thompson  CITowfighi  JT Social behavior of juvenile rhesus monkeys after amygdalectomy in infancy. Physiol Behav 1976;17 (5) 831- 836
PubMed
62.
Emery  NJCapitanio  JPMason  WAMachado  CJMendoza  SPAmaral  DG The effects of bilateral lesions of the amygdala on dyadic social interactions in rhesus monkeys (Macaca mulatta). Behav Neurosci 2001;115 (3) 515- 544
PubMed
63.
Bachevalier  JMishkin  M Effects of selective neonatal temporal lobe lesions on visual recognition memory in rhesus monkeys. J Neurosci 1994;14 (4) 2128- 2139
PubMed
64.
Adolphs  R What does the amygdala contribute to social cognition? Ann N Y Acad Sci 2010;1191 (1) 42- 61
PubMed
65.
Bachevalier  J Brief report: medial temporal lobe and autism: a putative animal model in primates. J Autism Dev Disord 1996;26 (2) 217- 220
PubMed
66.
Klin  ASparrow  SSde Bildt  ACicchetti  DVCohen  DJVolkmar  FR A normed study of face recognition in autism and related disorders. J Autism Dev Disord 1999;29 (6) 499- 508
PubMed
67.
Amaral  DGBauman  MDSchumann  CM The amygdala and autism: implications from non-human primate studies. Genes Brain Behav 2003;2 (5) 295- 302
PubMed
68.
Boraston  ZBlakemore  SJ The application of eye-tracking technology in the study of autism. J Physiol 2007;581 (pt 3) 893- 898
PubMed
69.
Sasson  NJ The development of face processing in autism. J Autism Dev Disord 2006;36 (3) 381- 394
PubMed
70.
Saitoh  OKarns  CMCourchesne  E Development of the hippocampal formation from 2 to 42 years: MRI evidence of smaller area dentata in autism. Brain 2001;124 (pt 7) 1317- 1324
PubMed
71.
Aylward  EHMinshew  NJGoldstein  GHoneycutt  NAAugustine  AMYates  KOBarta  PEPearlson  GD MRI volumes of amygdala and hippocampus in non-mentally retarded autistic adolescents and adults. Neurology 1999;53 (9) 2145- 2150
PubMed
72.
Howard  MACowell  PEBoucher  JBroks  PMayes  AFarrant  ARoberts  N Convergent neuroanatomical and behavioural evidence of an amygdala hypothesis of autism. Neuroreport 2000;11 (13) 2931- 2935
PubMed
73.
Di Martino  ARoss  KUddin  LQSklar  ABCastellanos  FXMilham  MP Functional brain correlates of social and nonsocial processes in autism spectrum disorders: an ALE meta-analysis. Biol Psychiatry 2009;65 (1) 63- 74
PubMed
74.
Brothers  L  et al.  The social brain: a project for integrating primate behavior and neurophysiology in a new domain. Cacioppo  JTeds.Foundations in Social Neuroscience. Cambridge, MA The MIT Press2002;367- 388
75.
Baron-Cohen  SLeslie  AMFrith  U Does the autistic child have a “theory of mind”? Cognition 1985;21 (1) 37- 46
PubMed
76.
Happé  FEhlers  SFletcher  PFrith  UJohansson  MGillberg  CDolan  RFrackowiak  RFrith  C “Theory of mind” in the brain: evidence from a PET scan study of Asperger syndrome. Neuroreport 1996;8 (1) 197- 201
PubMed
77.
Happé  FFrith  U The neuropsychology of autism. Brain 1996;119 (pt 4) 1377- 1400
PubMed
78.
Baron-Cohen  SRing  HAWheelwright  SBullmore  ETBrammer  MJSimmons  AWilliams  SCR Social intelligence in the normal and autistic brain: an fMRI study. Eur J Neurosci 1999;11 (6) 1891- 1898
PubMed
79.
Castelli  FFrith  CHappé  FFrith  U Autism, Asperger syndrome and brain mechanisms for the attribution of mental states to animated shapes. Brain 2002;125 (pt 8) 1839- 1849
PubMed
80.
Domes  GKumbier  EHerpertz-Dahlmann  BHerpertz  SC Social cognition in autism: a survey of functional imaging studies [in German]. Nervenarzt 2008;79 (3) 261- 274
PubMed
81.
Siegal  MVarley  R Neural systems involved in “theory of mind.” Nat Rev Neurosci 2002;3 (6) 463- 471
PubMed
82.
Hutton  JGoode  SMurphy  MLe Couteur  ARutter  M New-onset psychiatric disorders in individuals with autism. Autism 2008;12 (4) 373- 390
PubMed
83.
MacNeil  BMLopes  VAMinnes  PM Anxiety in children and adolescents with autism spectrum disorders. Res Autism Spectr Disord 2009;3 (1) 1- 21
84.
Skokauskas  NGallagher  L Psychosis, affective disorders and anxiety in autistic spectrum disorder: prevalence and nosological considerations. Psychopathology 2010;43 (1) 8- 16
PubMed
85.
Herman  JPCullinan  WE Neurocircuitry of stress: central control of the hypothalamo-pituitary-adrenocortical axis. Trends Neurosci 1997;20 (2) 78- 84
PubMed
86.
McEwen  BS Mood disorders and allostatic load. Biol Psychiatry 2003;54 (3) 200- 207
PubMed
87.
Campbell  SMarriott  MNahmias  CMacQueen  GM Lower hippocampal volume in patients suffering from depression: a meta-analysis. Am J Psychiatry 2004;161 (4) 598- 607
PubMed
88.
Diamond  DMCampbell  APark  CRVouimba  R-M Preclinical research on stress, memory, and the brain in the development of pharmacotherapy for depression. Eur Neuropsychopharmacol 2004;14(suppl 5)S491- S495
PubMed
89.
Reagan  LPGrillo  CAPiroli  GG The As and Ds of stress: metabolic, morphological and behavioral consequences. Eur J Pharmacol 2008;585 (1) 64- 75
PubMed
90.
Castro  JEVarea  EMárquez  CCordero  MIPoirier  GSandi  C Role of the amygdala in antidepressant effects on hippocampal cell proliferation and survival and on depression-like behavior in the rat. PLoS One 2010;5 (1) e8618
PubMed10.1371/journal.pone.0008618
91.
Hardan  AYMuddasani  SVemulapalli  MKeshavan  MSMinshew  NJ An MRI study of increased cortical thickness in autism. Am J Psychiatry 2006;163 (7) 1290- 1292
PubMed
92.
Jiao  YChen  RKe  XChu  KLu  ZHerskovits  EH Predictive models of autism spectrum disorder based on brain regional cortical thickness. Neuroimage 2010;50 (2) 589- 599
PubMed
93.
Cavanna  AETrimble  MR The precuneus: a review of its functional anatomy and behavioural correlates. Brain 2006;129 (pt 3) 564- 583
PubMed
94.
Lombardo  MVBarnes  JLWheelwright  SJBaron-Cohen  S Self-referential cognition and empathy in autism. PLoS One 2007;2 (9) e883
PubMed10.1371/journal.pone.0000883
95.
Lombardo  MVBaron-Cohen  S Unraveling the paradox of the autistic self. Wiley Interdiscip Rev Cogn Sci 2010;1 (3) 393- 403
96.
Rizzolatti  GFabbri-Destro  M Mirror neurons: from discovery to autism. Exp Brain Res 2010;200 (3-4) 223- 237
PubMed
97.
Ramachandran  VSOberman  LM Broken mirrors: a theory of autism. Sci Am 2006;295 (5) 62- 69
PubMed
98.
Iacoboni  MDapretto  M The mirror neuron system and the consequences of its dysfunction. Nat Rev Neurosci 2006;7 (12) 942- 951
PubMed
99.
Oberman  LMRamachandran  VS The simulating social mind: the role of the mirror neuron system and simulation in the social and communicative deficits of autism spectrum disorders. Psychol Bull 2007;133 (2) 310- 327
PubMed
100.
Williams  JH Self-other relations in social development and autism: multiple roles for mirror neurons and other brain bases. Autism Res 2008;1 (2) 73- 90
PubMed
101.
Southgate  VHamilton  AF Unbroken mirrors: challenging a theory of autism. Trends Cogn Sci 2008;12 (6) 225- 229
PubMed
102.
Hickok  G Eight problems for the mirror neuron theory of action understanding in monkeys and humans. J Cogn Neurosci 2009;21 (7) 1229- 1243
PubMed
103.
Dinstein  IThomas  CHumphreys  KMinshew  NBehrmann  MHeeger  DJ Normal movement selectivity in autism. Neuron 2010;66 (3) 461- 469
PubMed
104.
Minshew  NJKeller  TA The nature of brain dysfunction in autism: functional brain imaging studies. Curr Opin Neurol 2010;23 (2) 124- 130
PubMed
105.
Simmons  DRRobertson  AEMcKay  LSToal  EMcAleer  PPollick  FE Vision in autism spectrum disorders. Vision Res 2009;49 (22) 2705- 2739
PubMed
106.
Bowler  DMGardiner  JMBerthollier  N Source memory in adolescents and adults with Asperger's syndrome. J Autism Dev Disord 2004;34 (5) 533- 542
PubMed
107.
Lind  SEBowler  DM Recognition memory, self-other source memory, and theory-of-mind in children with autism spectrum disorder. J Autism Dev Disord 2009;39 (9) 1231- 1239
PubMed
108.
Shallice  TBurgess  PW Deficits in strategy application following frontal lobe damage in man. Brain 1991;114 (pt 2) 727- 741
PubMed
109.
Saver  JLDamasio  AR Preserved access and processing of social knowledge in a patient with acquired sociopathy due to ventromedial frontal damage. Neuropsychologia 1991;29 (12) 1241- 1249
PubMed
110.
Dimitrov  MGrafman  JHollnagel  C The effects of frontal lobe damage on everyday problem solving. Cortex 1996;32 (2) 357- 366
PubMed
111.
Adolphs  R The social brain: neural basis of social knowledge. Annu Rev Psychol 2009;60693- 716
PubMed
112.
Forbes  CEGrafman  J The role of the human prefrontal cortex in social cognition and moral judgment. Annu Rev Neurosci 2010;33299- 324
PubMed
113.
Volkmar  FRState  MKlin  A Autism and autism spectrum disorders: diagnostic issues for the coming decade. J Child Psychol Psychiatry 2009;50 (1-2) 108- 115
PubMed
114.
Macintosh  KEDissanayake  C Annotation: the similarities and differences between autistic disorder and Asperger's disorder: a review of the empirical evidence. J Child Psychol Psychiatry 2004;45 (3) 421- 434
PubMed
115.
Greimel  ESchulte-Rüther  MFink  GRPiefke  MHerpertz-Dahlmann  BKonrad  K Development of neural correlates of empathy from childhood to early adulthood: an fMRI study in boys and adult men. J Neural Transm 2010;117 (6) 781- 791
PubMed
116.
Courchesne  ECampbell  KSolso  S Brain growth across the life span in autism: age-specific changes in anatomical pathology. Brain Res 2011;1380138- 145
117.
Helt  MKelley  EKinsbourne  MPandey  JBoorstein  HHerbert  MFein  D Can children with autism recover? if so, how? Neuropsychol Rev 2008;18 (4) 339- 366
PubMed
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