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Figure 1.
Gaussian Process Classification of Biological Sex
Gaussian Process Classification of Biological Sex

A, Gaussian process classification between male and female typically developing (TD) control individuals based on normative (ie, neurotypical) variability in cortical thickness. The x-axis indicates predictive class probabilities. Therefore, a class probability of 0.5 served as a binary cutoff separating male individuals from female individuals. The y-axis indicates the position of each individual on the normative axis of sex-related phenotypic diversity in brain structure (lower panel). The upper panel shows the density (ie, frequency) of male individuals and female individuals along the normative axis of class probabilities. B, Probabilistic predictions for male individuals and female individuals with autism spectrum disorder (ASD) using the normative model for biological sex.

Figure 2.
Neuroanatomical Patterns Associated With Low and High Autism Spectrum Disorder (ASD) Probability
Neuroanatomical Patterns Associated With Low and High Autism Spectrum Disorder (ASD) Probability

Predictive maps (ie, w × CT) associated with the probability of ASD in female individuals (A) and male individuals (B). The left panel indicates ASD probability maps computed across all male individuals (or female individuals) with predictive probabilities lower than 0.5 (ie, biological male individuals or female individuals falling into the category of phenotypic female individuals). The right panel indicates ASD probability maps computed across all male individuals (or female individuals) with predictive probabilities larger than 0.5 (ie, biological male individuals or female individuals falling into the category of phenotypic male individuals). At each vertex, the color scale thus indicates the product of the weight vector w and cortical thickness (CT), averaged across all individuals within the 4 probability groups. The probability of ASD was determined as the number of male individuals (or female individuals) with ASD relative to the total number of individuals within predictive probability bins.

Figure 3.
Significant Group × Sex Interactions
Significant Group × Sex Interactions

A, Clusters with significant group × sex interactions in cortical thickness (CT) as examined by a conventional general linear model–type approach. In these regions, the difference in CT between female individuals with autism spectrum disorder (ASD) and female control individuals significantly exceeded the difference between male individuals with ASD and male controls (statistical details are available in eTable 4 in Supplement 1). The group × sex interactions were driven by reduced CT in female individuals with ASD relative to female controls and increased CT in male individuals with ASD relative to male controls (eFigure 6 in Supplement 1). B, Clusters with significantly increased CT in male individuals with ASD relative to male controls (random field theory–based, cluster-corrected P < .05). C, Clusters with significantly reduced CT in female individuals with ASD relative to female controls (random field theory–based, cluster-corrected P < .05). eFigure 5 in Supplement 1 shows results of the permutation-based cluster thresholding of the same contrasts. TD indicates typically developing.

Table.  
Participant Demographics and Global Brain Measuresa
Participant Demographics and Global Brain Measuresa
Supplement 1.

eTable 1. Frequency Ratios of ASD Cases to TD Controls, and Males to Females, Acquired Within and Across the Different Acquisition Sites

eTable 2. Population Prevalence of ASD for Biological Males and Females With Male or Female Neuroanatomical Brain Phenotype

eTable 3. Differences in Neuroanatomical Patterns Associated With Sex-Related Probability of ASD Between Men and Women

eTable 4. Results of the Vertex-wise Analysis of CT Utilizing a General Linear Model (GLM)

eTable 5. Differences in Demographic Measures Between Diagnostic Groups and Biological Sexes

eMethods. Supplementary Methods

eFigure 1. Sex-Related Pattern of Neuroanatomical Variability in Cortical Thickness

eFigure 2. Significant Differences in CT Between Male and Female TD Controls

eFigure 3. Sample Probability of ASD for Females (A) and Males (B) as a Function of Normative Sex-Related Phenotypic Variability in CT

eFigure 4. Clusters With Significant Differences in the Neuroanatomical Pattern of CT Associated With Sex-Related Probability of ASD Between Males and Females (i.e., Product of w and CT)

eFigure 5. Clusters With Significant Group-by-Sex Interactions Following a Non-parametric Clustering Approach With n = 10,000 Permutations of Group (i.e., Sex) Labels (Permutation-Based Cluster-Corrected, P < .05)

eFigure 6. Group-by-Sex Interaction Graph Based on Mean CT in the Left-Hemisphere Cluster That Extended Into the Hippocampal/Entorhinal Cortex, the Fusiform and Lingual Gyrus, and the Inferior/Middle Temporal Lobe

eFigure 7. Differences in Full-Scale, Verbal and Non-verbal (i.e., Performance) IQ Between Individual Subgroups of ASD vs TD Individuals, and Males vs Females, Examined Using t Tests for Independent Samples

eFigure 8. Spatially Distributed Pattern of Negative (Blue) and Positive (Red) Correlations Between Verbal IQ Measures and CT Assessed Across Male and Female Neurotypical Controls

eAppendix. Supplementary Appendix

1.
Lai  MC, Lombardo  MV, Auyeung  B, Chakrabarti  B, Baron-Cohen  S.  Sex/gender differences and autism: setting the scene for future research.  J Am Acad Child Adolesc Psychiatry. 2015;54(1):11-24.PubMedGoogle ScholarCrossref
2.
Fombonne  E.  Epidemiology of autistic disorder and other pervasive developmental disorders.  J Clin Psychiatry. 2005;66(suppl 10):3-8.PubMedGoogle Scholar
3.
Szatmari  P, Liu  XQ, Goldberg  J,  et al.  Sex differences in repetitive stereotyped behaviors in autism: implications for genetic liability.  Am J Med Genet B Neuropsychiatr Genet. 2012;159B(1):5-12.PubMedGoogle ScholarCrossref
4.
Dworzynski  K, Ronald  A, Bolton  P, Happé  F.  How different are girls and boys above and below the diagnostic threshold for autism spectrum disorders?  J Am Acad Child Adolesc Psychiatry. 2012;51(8):788-797.PubMedGoogle ScholarCrossref
5.
Werling  DM, Geschwind  DH.  Sex differences in autism spectrum disorders.  Curr Opin Neurol. 2013;26(2):146-153.PubMedGoogle ScholarCrossref
6.
Lai  MC, Lombardo  MV, Suckling  J,  et al; MRC AIMS Consortium.  Biological sex affects the neurobiology of autism.  Brain. 2013;136(pt 9):2799-2815.PubMedGoogle ScholarCrossref
7.
Schaer  M, Kochalka  J, Padmanabhan  A, Supekar  K, Menon  V.  Sex differences in cortical volume and gyrification in autism.  Mol Autism. 2015;6(1):42.PubMedGoogle ScholarCrossref
8.
Lai  MC, Lerch  JP, Floris  DL,  et al.  Imaging sex/gender and autism in the brain: etiological implications.  J Neurosci Res. 2017;95(1-2):380-397.PubMedGoogle ScholarCrossref
9.
Ruigrok  AN, Salimi-Khorshidi  G, Lai  MC,  et al.  A meta-analysis of sex differences in human brain structure.  Neurosci Biobehav Rev. 2014;39:34-50.PubMedGoogle ScholarCrossref
10.
Rasmussen  CE, Williams  CKI.  Gaussian Processes for Machine Learning. Cambridge, MA: MIT Press; 2006.
11.
McCarthy  MM, Arnold  AP.  Reframing sexual differentiation of the brain.  Nat Neurosci. 2011;14(6):677-683.PubMedGoogle ScholarCrossref
12.
Sowell  ER, Peterson  BS, Kan  E,  et al.  Sex differences in cortical thickness mapped in 176 healthy individuals between 7 and 87 years of age.  Cereb Cortex. 2007;17(7):1550-1560.PubMedGoogle ScholarCrossref
13.
Im  K, Lee  JM, Lee  J,  et al.  Gender difference analysis of cortical thickness in healthy young adults with surface-based methods.  Neuroimage. 2006;31(1):31-38.PubMedGoogle ScholarCrossref
14.
Ecker  C, Ginestet  C, Feng  Y,  et al; MRC AIMS Consortium.  Brain surface anatomy in adults with autism: the relationship between surface area, cortical thickness, and autistic symptoms.  JAMA Psychiatry. 2013;70(1):59-70.PubMedGoogle ScholarCrossref
15.
Wallace  GL, Dankner  N, Kenworthy  L, Giedd  JN, Martin  A.  Age-related temporal and parietal cortical thinning in autism spectrum disorders.  Brain. 2010;133(pt 12):3745-3754.PubMedGoogle ScholarCrossref
16.
Lenroot  RK, Giedd  JN.  Sex differences in the adolescent brain.  Brain Cogn. 2010;72(1):46-55.PubMedGoogle ScholarCrossref
17.
Lenroot  RK, Gogtay  N, Greenstein  DK,  et al.  Sexual dimorphism of brain developmental trajectories during childhood and adolescence.  Neuroimage. 2007;36(4):1065-1073.PubMedGoogle ScholarCrossref
18.
Geschwind  DH, Rakic  P.  Cortical evolution: judge the brain by its cover.  Neuron. 2013;80(3):633-647.PubMedGoogle ScholarCrossref
19.
Zilles  K, Palomero-Gallagher  N, Amunts  K.  Development of cortical folding during evolution and ontogeny.  Trends Neurosci. 2013;36(5):275-284.PubMedGoogle ScholarCrossref
20.
Lord  C, Rutter  M, Le 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.PubMedGoogle ScholarCrossref
21.
Lord  C, Rutter  M, Goode  S,  et al.  Autism Diagnostic Observation Schedule: a standardized observation of communicative and social behavior.  J Autism Dev Disord. 1989;19(2):185-212.PubMedGoogle ScholarCrossref
22.
Wechsler  D.  Wechsler Abbreviated Scale of Intelligence (WASI). San Antonio, TX: Harcourt Assessment; 1999.
23.
Deoni  SC, Williams  SC, Jezzard  P, Suckling  J, Murphy  DG, Jones  DK.  Standardized structural magnetic resonance imaging in multicentre studies using quantitative T1 and T2 imaging at 1.5 T.  Neuroimage. 2008;40(2):662-671.PubMedGoogle ScholarCrossref
24.
Ecker  C, Suckling  J, Deoni  SC,  et al; MRC AIMS Consortium.  Brain anatomy and its relationship to behavior in adults with autism spectrum disorder: a multicenter magnetic resonance imaging study.  Arch Gen Psychiatry. 2012;69(2):195-209.PubMedGoogle ScholarCrossref
25.
Fischl  B, Sereno  MI, Dale  AM.  Cortical surface-based analysis, II: inflation, flattening, and a surface-based coordinate system.  Neuroimage. 1999;9(2):195-207.PubMedGoogle ScholarCrossref
26.
Dale  AM, Fischl  B, Sereno  MI.  Cortical surface-based analysis, I: segmentation and surface reconstruction.  Neuroimage. 1999;9(2):179-194.PubMedGoogle ScholarCrossref
27.
Ségonne  F, Dale  AM, Busa  E,  et al.  A hybrid approach to the skull stripping problem in MRI.  Neuroimage. 2004;22(3):1060-1075.PubMedGoogle ScholarCrossref
28.
Fischl  B, Dale  AM.  Measuring the thickness of the human cerebral cortex from magnetic resonance images.  Proc Natl Acad Sci U S A. 2000;97(20):11050-11055.PubMedGoogle ScholarCrossref
29.
Marquand  A, Howard  M, Brammer  M, Chu  C, Coen  S, Mourão-Miranda  J.  Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes.  Neuroimage. 2010;49(3):2178-2189.PubMedGoogle ScholarCrossref
30.
Marquand  AF, Brammer  M, Williams  SC, Doyle  OM.  Bayesian multi-task learning for decoding multi-subject neuroimaging data.  Neuroimage. 2014;92:298-311.PubMedGoogle ScholarCrossref
31.
Baio  J; Developmental Disabilities Monitoring Network Surveillance Year 2010 Principal Investigators; Centers for Disease Control and Prevention (CDC).  Prevalence of autism spectrum disorder among children aged 8 years: Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2010.  MMWR Surveill Summ. 2014;63(2):1-21.PubMedGoogle Scholar
32.
Worsley  KJ, Andermann  M, Koulis  T, MacDonald  D, Evans  AC.  Detecting changes in nonisotropic images.  Hum Brain Mapp. 1999;8(2-3):98-101.PubMedGoogle ScholarCrossref
33.
Hagler  DJ  Jr, Saygin  AP, Sereno  MI.  Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data.  Neuroimage. 2006;33(4):1093-1103.PubMedGoogle ScholarCrossref
34.
Escorial  S, Román  FJ, Martínez  K, Burgaleta  M, Karama  S, Colom  R.  Sex differences in neocortical structure and cognitive performance: a surface-based morphometry study.  Neuroimage. 2015;104:355-365.PubMedGoogle ScholarCrossref
35.
Joel  D, McCarthy  MM.  Incorporating sex as a biological variable in neuropsychiatric research: where are we now and where should we be?  Neuropsychopharmacology. 2017;42(2):379-385.PubMedGoogle ScholarCrossref
36.
Chekroud  AM, Ward  EJ, Rosenberg  MD, Holmes  AJ.  Patterns in the human brain mosaic discriminate males from females.  Proc Natl Acad Sci U S A. 2016;113(14):E1968. doi:10.1073/pnas.1523888113PubMedGoogle ScholarCrossref
37.
Amaral  DG, Schumann  CM, Nordahl  CW.  Neuroanatomy of autism.  Trends Neurosci. 2008;31(3):137-145.PubMedGoogle ScholarCrossref
38.
Ecker  C, Bookheimer  SY, Murphy  DG.  Neuroimaging in autism spectrum disorder: brain structure and function across the lifespan.  Lancet Neurol. 2015;14(11):1121-1134.PubMedGoogle ScholarCrossref
39.
Baron-Cohen  S, Auyeung  B, Nørgaard-Pedersen  B,  et al.  Elevated fetal steroidogenic activity in autism.  Mol Psychiatry. 2015;20(3):369-376.PubMedGoogle ScholarCrossref
40.
Miller  DI, Halpern  DF.  The new science of cognitive sex differences.  Trends Cogn Sci. 2014;18(1):37-45.PubMedGoogle ScholarCrossref
41.
Hyde  JS.  Sex and cognition: gender and cognitive functions.  Curr Opin Neurobiol. 2016;38:53-56.PubMedGoogle ScholarCrossref
42.
Bölte  S, Duketis  E, Poustka  F, Holtmann  M.  Sex differences in cognitive domains and their clinical correlates in higher-functioning autism spectrum disorders.  Autism. 2011;15(4):497-511.PubMedGoogle ScholarCrossref
43.
Mandy  W, Chilvers  R, Chowdhury  U, Salter  G, Seigal  A, Skuse  D.  Sex differences in autism spectrum disorder: evidence from a large sample of children and adolescents.  J Autism Dev Disord. 2012;42(7):1304-1313.PubMedGoogle ScholarCrossref
44.
Solomon  M, Miller  M, Taylor  SL, Hinshaw  SP, Carter  CS.  Autism symptoms and internalizing psychopathology in girls and boys with autism spectrum disorders.  J Autism Dev Disord. 2012;42(1):48-59.PubMedGoogle ScholarCrossref
Original Investigation
February 8, 2017

Association Between the Probability of Autism Spectrum Disorder and Normative Sex-Related Phenotypic Diversity in Brain Structure

Author Affiliations
  • 1Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Goethe University, Frankfurt am Main, Germany
  • 2Department of Forensic and Neurodevelopmental Sciences, Sackler Institute for Translational Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, England
  • 3Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
  • 4Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, England
  • 5Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, England
  • 6Behavioural Genetics Clinic, Adult Autism Service, Behavioural and Developmental Psychiatry Clinical Academic Group, South London and Maudsley Foundation National Health Service Trust, London, England
  • 7Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, England
  • 8Child and Youth Mental Health Collaborative at the Centre for Addiction and Mental Health, The Hospital for Sick Children, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
  • 9Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei
  • 10Department of Psychology and Center for Applied Neuroscience, University of Cyprus, Nicosia
  • 11Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, England
  • 12National Autism Unit, Bethlem Royal Hospital, South London and Maudsley Foundation National Health Service Trust, London, England
JAMA Psychiatry. 2017;74(4):329-338. doi:10.1001/jamapsychiatry.2016.3990
Key Points

Question  Does the neuroanatomical male brain phenotype carry a higher intrinsic risk for autism spectrum disorder than the female neurophenotype, which could explain the male preponderant prevalence of autism spectrum disorder?

Findings  In this case-control study of 98 adults with autism spectrum disorder and 98 matched neurotypical control individuals, the neurobiological male phenotype was not associated with a higher risk for autism spectrum disorder than the female phenotype across the binary categories dictated by biological sex.

Meaning  In addition to genetic and environmental factors, normative sex-related phenotypic diversity is not sufficient to explain the male preponderant prevalence of autism spectrum disorder.

Abstract

Importance  Autism spectrum disorder (ASD) is 2 to 5 times more common in male individuals than in female individuals. While the male preponderant prevalence of ASD might partially be explained by sex differences in clinical symptoms, etiological models suggest that the biological male phenotype carries a higher intrinsic risk for ASD than the female phenotype. To our knowledge, this hypothesis has never been tested directly, and the neurobiological mechanisms that modulate ASD risk in male individuals and female individuals remain elusive.

Objectives  To examine the probability of ASD as a function of normative sex-related phenotypic diversity in brain structure and to identify the patterns of sex-related neuroanatomical variability associated with low or high probability of ASD.

Design, Setting, and Participants  This study examined a cross-sectional sample of 98 right-handed, high-functioning adults with ASD and 98 matched neurotypical control individuals aged 18 to 42 years. A multivariate probabilistic classification approach was used to develop a predictive model of biological sex based on cortical thickness measures assessed via magnetic resonance imaging in neurotypical controls. This normative model was subsequently applied to individuals with ASD. The study dates were June 2005 to October 2009, and this analysis was conducted between June 2015 and July 2016.

Main Outcomes and Measures  Sample and population ASD probability estimates as a function of normative sex-related diversity in brain structure, as well as neuroanatomical patterns associated with ASD probability in male individuals and female individuals.

Results  Among the 98 individuals with ASD, 49 were male and 49 female, with a mean (SD) age of 26.88 (7.18) years. Among the 98 controls, 51 were male and 47 female, with a mean (SD) age of 27.39 (6.44) years. The sample probability of ASD did not increase significantly with predictive probabilities for the male neuroanatomical brain phenotype. For example, biological female individuals with a more male-typic pattern of brain anatomy were equally likely to have ASD than biological female individuals with a characteristically female brain phenotype (P = .40 vs .55, respectively; χ21 = 1.11; P > .05; difference in P values, −.15; 95% CI, −.10 to .40). This finding translates to an estimated variability in population prevalence from 0.3% to 0.6%, respectively. Moreover, the patterns of sex-related neuroanatomical variability associated with ASD probability were sex specific (eg, in inferior temporal regions, where ASD has different neurobiological underpinnings in male individuals and female individuals).

Conclusions and Relevance  These findings imply that the male neuroanatomical phenotype does not carry a higher intrinsic risk for ASD than the female neurophenotype and provide important novel insights into the neurobiological mechanisms mediating sex differences in ASD prevalence.

Introduction

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that is 2 to 5 times more common in male individuals than in female individuals.1,2 While the male preponderant prevalence of ASD might partially be explained by sex differences in clinical symptoms,3,4 etiological models suggest that the biological male phenotype itself (ie, in general) carries a higher risk for ASD than the female phenotype.5 However, despite the growing number of studies examining sex differences in the brain in ASD (eg, those by Lai et al6 and by Schaer et al7), this hypothesis has never been tested directly, to our knowledge, and the neurobiological mechanisms that underpin the male preponderant prevalence of ASD remain elusive.8

This study examined the probability of ASD as a function of normative sex-related phenotypic diversity in brain structure. To do so, we initially developed a predictive model of biological sex based on multivariate differences in brain structure in a sample of typically developing (TD) male and female control individuals. This normative model was subsequently applied to male individuals and female individuals with ASD. Unlike recent studies examining sexual dimorphism of the brain (reviewed by Ruigrok et al9), we used a probabilistic pattern classification approach,10 which allowed us to accommodate interindividual phenotypic diversity within and across the binary categories dictated by biological sex.11 As a result, we were able (1) to examine the probability of ASD along a normative phenotypic axis ranging from the characteristic female to male brain phenotype and (2) to identify the patterns of sex-related neuroanatomical variability associated with low or high probability of ASD.

We based our analysis on measures of cortical thickness (CT) because these measurements have previously been shown to be highly variable between neurotypical male individuals and female individuals12,13 and tend to be significantly altered in individuals with ASD (eg, as shown by Ecker et al14 and by Wallace et al15). Moreover, measures of CT are less sensitive to global confounders associated with biological sex (eg, differences in total brain volume) than other morphometric features (eg, surface area).16-19 Last, we investigated whether the cortical underpinnings of ASD are significantly modulated by biological sex and whether ASD has common or distinct neuroanatomical underpinnings in male individuals and female individuals.

Methods
Participants

Ninety-eight right-handed adults with ASD (49 male and 49 female; mean [SD] age, 26.88 [7.18] years) and 98 matched neurotypical controls (51 male and 47 female; mean [SD] age, 27.39 [6.44] years) aged 18 to 42 years were recruited locally by advertisement and assessed at the Institute of Psychiatry, Psychology and Neuroscience (IoPPN), London, England, and the Autism Research Centre, Cambridge, England (Table). Approximately equal ratios of cases to controls and male individuals to female individuals were recruited within sites (eTable 1 in Supplement 1). Exclusion criteria included a history of major psychiatric disorder (eg, psychosis), head injury, ASD-associated genetic disorders (eg, fragile X syndrome and tuberous sclerosis), or any other medical condition affecting brain function (eg, epilepsy). We also excluded individuals taking antipsychotic medication, mood stabilizers, or benzodiazepines.

All participants with ASD were diagnosed according to the International Statistical Classification of Diseases, Tenth Revision research criteria. The clinical diagnosis was confirmed using the Autism Diagnostic Interview–Revised (ADI-R)20 or the Autism Diagnostic Observation Schedule (ADOS).21 All participants diagnosed with the ADI-R reached algorithm cutoffs in the 3 domains of (1) communication (cutoff of 8), (2) reciprocal social interaction (cutoff of 10), and (3) repetitive behaviors and stereotyped patterns of interest (cutoff of 3), although failure to reach the cutoff in the repetitive domain by maximally 2 points was permitted. Because reliable informants were unavailable, we were unable to obtain ADI-R diagnostic data from 5 female individuals with ASD and confirmed diagnostic status using ADOS cutoffs. In all other participants, the ADOS scores were used to assess the severity of current symptoms. One female individual with ASD fell short by 1 point on the ADI-R communication and repetitive behavior domains but met ADOS criteria. All participants had a full-scale IQ greater than 70 assessed using the Wechsler Abbreviated Scale of Intelligence.22 Participants gave informed written consent in accord with ethics approval by the National Research Ethics Committee, Suffolk, England. The study dates were June 2005 to October 2009, and this analysis was conducted between June 2015 and July 2016.

Magnetic Resonance Imaging Data Acquisition

Imaging took place at the IoPPN and the Addenbrooke’s Hospital, Cambridge, using a 3-T system (Signa; GE Medical Systems). A specialized acquisition protocol using quantitative, T1-weighted mapping was used to ensure standardization of structural magnetic resonance imaging scans across sites,23,24 which resulted in high-resolution structural T1-weighted inversion recovery images, with 1 × 1 × 1–mm resolution, a 256 × 256 × 176–pixel matrix, repetition time of 1800 milliseconds, inversion time of 50 milliseconds, flip angle of 20°, and field of view of 25 cm.

Cortical Reconstruction Using FreeSurfer

A software program (FreeSurfer, version 5.3.0; http://surfer.nmr.mgh.harvard.edu) was used to derive cortical surface models for each T1-weighted image. These well-validated and fully automated procedures have been extensively described elsewhere.25-27 All surface models were visually inspected for errors, and scans with visible reconstruction inaccuracies were excluded from the statistical analysis (dropout <10%). We based our analysis on measures of CT (ie, the closest distance from the gray matter (GM) or white matter boundary to the GM or cerebrospinal fluid boundary at each cerebral vertex).28 Cortical thickness maps were smoothed with a 15-mm surface-based gaussian kernel.

Gaussian Process Classification

Gaussian process classification (GPC)10,29 was used for the probabilistic prediction of biological sex based on neuroanatomical variability in CT (details are available in the eMethods in Supplement 1). In brief, we initially developed a normative model of biological sex by predicting the binary class labels yi ε {−1, +1} for female and male TD controls, respectively, using CT measures estimated at approximately 320 000 vertices on the cortical surface (Gaussian Processes for Machine Learning Matlab toolbox; http://www.gaussianprocess.org/gpml/code/matlab/doc). In addition to the overall model accuracy (ie, proportion of biological male individuals or female individuals correctly classified as phenotypic male individuals or female individuals), GPC returned a set of predictive class probabilities indicating the probability of an individual belonging to the male or female category. Class probabilities ranged from 0 to 1 for the characteristic female to male brain phenotype, respectively, so that a class probability of 0.5 represents a binary cutoff separating both classes. Classifier performance was validated by cross-validation and tested for statistical significance via 1000 permutations of class labels. The normative model was subsequently applied to male individuals and female individuals with ASD.

A predictive mapping approach30 was used to identify (1) the spatially distributed patterns of neuroanatomical variability in CT characteristics for the neurotypical female or male brain phenotype and (2) the set of brain regions associated with low or high probability of ASD. Normative or risk (ie, low or high probability) patterns were derived by quantifying the extent to which the individual’s neuroanatomy interacts with the spatial representation of the decision function (ie, the weight vector w) separating neurotypical male individuals from female individuals phenotypically (ie, the product of w and CT). The high ASD probability pattern included all male individuals (or female individuals) with a male neuroanatomical phenotype (ie, class probability >0.5). The low ASD probability pattern included all male individuals (or female individuals) with a female neuroanatomical phenotype (ie, class probability <0.5). We also identified these patterns across all individuals along the normative axis of predictive class probabilities. At each vertex, the resulting maps were summarized by the mean value across all individuals within examined groups.

Estimation of ASD Probability

To examine the probability of ASD as a function of normative phenotypic variability in brain structure, we converted the continuous axis of class probabilities into a set of discrete bins (eg, from 0 to 1 in steps of 0.125). Within each bin, the sample probability of ASD was determined as the ratio of individuals with ASD relative to the total number of individuals per bin. Moreover, these data allowed us to estimate the population prevalence of ASD given that an individual exhibits a male or female neuroanatomical phenotype in addition to being biologically male or female (details are available in the eMethods in Supplement 1). In brief, using Bayes theorem, we combined our sample prevalence estimates with previously published population prevalence rates of ASD for biological male individuals and female individuals (ie, 1:42 for male individuals and 1:189 for female individuals31). In this way, we were able to estimate the population prevalence of ASD (D = 1) given a male (or female) neuroanatomical brain phenotype (M) for biological male individuals (or female individuals) (S), that is P̂ (D = 1 | M, S). Confidence intervals were determined using an exact binomial test implemented in a software package (R Project for Statistical Computing; https://www.r-project.org).

Vertexwise Between-Group Comparison of CT

Vertexwise statistical analysis of CT measures was conducted using the R2014a Matlab toolbox (SurfStat; http://www.math.mcgill.ca/keith/surfstat). For the comparison between male and female controls, parameter estimates were obtained by regression of a general linear model at each vertex i, with biological sex and center as categorical fixed-effects factors and total GM volume as a continuous covariate. To examine whether the neuroanatomy of ASD is significantly modulated by biological sex, we also included a main effect of diagnostic group and biological sex and a group × sex interaction as follows:

yi = β0 + β1 Group + β2 Sex + β3 (Group × Sex) + β4 GMtotal + β5 Center + εi,

where y refers to the variable that is predicted by the model (ie, the CT at voxel i) and εi is the residual error. Effects were estimated from the coefficients β1-3 normalized by the corresponding standard error. Corrections for multiple comparisons were performed using a random field theory–based cluster analysis for nonisotropic images at a cluster threshold of 2-tailed P < .05.32 We also confirmed our findings using a nonparametric clustering approach (2-tailed P < .05) using 10 000 permutations of the original data.33

Results
Prediction of Biological Sex Based on Normative Variability in CT

Overall, our CT-driven probabilistic classifier predicted biological sex at an accuracy of 71.4% under cross-validation in neurotypical controls, which was significantly higher than would be expected by chance (P < .001 obtained via permutation testing). In total, 68.1% (32 of 47) of all biological female individuals were correctly allocated to the category of phenotypic female individuals and 74.5% (38 of 51) of all biological male individuals to the category of phenotypic male individuals (Figure 1A, lower panel). Across individuals, class probabilities ranged continuously from 0 to 1 for the characteristic female to male brain phenotype, respectively (Figure 1A, upper panel). Moreover, the patterns of CT variability associated with the phenotypic shift of the brain from a female to male presentation resembled the set of brain regions where female individuals significantly differed from male individuals (eFigure 1 and eFigure 2 in Supplement 1). Therefore, while a perfect phenotypic differentiation between biological sexes could not be achieved, we were able to separate male individuals from female individuals in most cases based on multivariate differences in brain structure.

Phenotypic Prediction of Biological Sex for Individuals With ASD

When applying our normative predictive model of biological sex to the independent sample of individuals with ASD, we found that, in female individuals with ASD, predictive class probabilities for the male neuroanatomical brain phenotype were not significantly increased relative to female controls (t90 = −0.89, P > .05) (Figure 1B). Overall, 39 of 49 female individuals (79.6%) with ASD were correctly allocated to the category of phenotypic female individuals, which was not significantly more frequent than expected based on the typical rate of misclassification for female controls (79.6% [39 of 49] vs 68.09% [32 of 47], respectively; χ21 = 1.11, P > .05). No differences were also observed in male individuals with ASD, who were correctly allocated to the male category in 81.6% (40 of 49) of all cases and also did not differ significantly from male controls in predictive class probabilities (t98 = 1.35, 2-tailed P > .17). Therefore, when representing individuals along a single axis of normative sex-related phenotypic diversity in brain structure, female and male individuals with ASD displayed a CT pattern that resembled closely the neurotypical female or male neurophenotype, respectively. Normative sex-related phenotypic variability in brain structure is thus unlikely to influence the probability of ASD.

ASD Probability as a Function of Normative Sex-Related Phenotypic Variability in Brain Structure

In female individuals with ASD, the sample probability of ASD did not increase with increasing predictive probabilities for the male neuroanatomical brain phenotype (eFigure 3A in Supplement 1). More specifically, female individuals with class probabilities exceeding the binary sex cutoff of 0.5 (ie, biological female individuals falling into the category of phenotypic male individuals) were not significantly more likely to have a diagnosis of ASD than biological female individuals with a class probability lower than 0.5 (P = .40 vs .55, respectively; χ21 = 1.11; P > .05; difference in P values, −0.15; 95% CI, −0.10 to 0.40). Reciprocally, male individuals with class probabilities lower than 0.5 (ie, biological male individuals falling into the category of phenotypic female individuals) were not significantly less likely to have ASD than male individuals allocated to the category of phenotypic male individuals (P = .41 vs .51, respectively; χ21 = 0.38; P > .53; difference in P values, −0.10; 95% CI, −0.36 to 0.15) (eFigure 3B in Supplement 1). If one combines these sample probabilities with previously published prevalence rates of ASD in the general population (eg, 1:42 for male individuals and 1:189 for female individuals31), our study shows that biological female individuals with female neuroanatomical features were 2 times more likely to have ASD than biological female individuals with a characteristically male neuroanatomy, which translates to an estimated variability in population prevalence between 0.3% to 0.6%, respectively (eTable 2 in Supplement 1). Therefore, normative sex-related phenotypic diversity in brain structure did not significantly affect the probability of ASD in addition to biological sex alone, with female neuroanatomical characteristics carrying a similar—if not lower—intrinsic risk for ASD than male characteristics.

The particular patterns of neuroanatomical variability associated with ASD probability and phenotypic sex differed between men and women in sign and regional composition (Figure 2). For example, CT variability in the left and right inferior temporal lobe differentiated between phenotypic sex of the brain in women but not in men (eTable 3 and eFigure 4 in Supplement 1). Therefore, while it is possible to link the probability of ASD to particular patterns of neuroanatomical variability in CT, our findings suggest that these patterns are sex specific.

Biological Sex Significantly Modulates the Cortical Anatomy of ASD

Last, using a conventional general linear model, we tested for significant CT differences between men and women with ASD and group × sex interactions. We found that the cortical neuroanatomy of ASD was significantly modulated by biological sex, particularly in the bilateral parahippocampal and entorhinal cortex (Brodmann area [BA] 28/34), the fusiform and lingual gyrus (BA 20/37/19), and the inferior or middle temporal lobe (BA 21/22) (Figure 3 and eTable 4 and eFigure 5 in Supplement 1). In these regions, the degree of cortical abnormality in female individuals significantly exceeded the degree of abnormality observed in male individuals compared with their respective normative populations (t = 3.29, cluster P = 3.5 × 10−6 for the left; t = 3.42, cluster P = 5.49 × 10−4 for the right) (Figure 3B and C and eFigure 6 in Supplement 1). However, female individuals with ASD were not more significantly impaired than male individuals in clinical symptom severity (eTable 5 in Supplement 1). The clusters with significant group × sex interactions also remained significant when controlling for variability in verbal and performance IQ (eTable 5 and eFigure 7 in Supplement 1) and thus do not seem to reflect a simple functional difference in these clinical or neurocognitive measures (further details are available in eFigure 8 and the eAppendix in Supplement 1).

Discussion

Our study demonstrates that normative sex-related phenotypic diversity in brain structure does not affect the prevalence of ASD in addition to biological sex, with female neuroanatomical characteristics carrying a similar intrinsic risk for ASD as male characteristics. More specifically, female individuals with ASD, who displayed a pattern of neuroanatomical variability in CT that resembled more closely the neurotypical female rather than male neurophenotype overall had a similar ASD risk as females with a more male-characteristic neuroanatomical pattern. Moreover, our study links the risk (ie, probability) of ASD to particular patterns of sex-related neuroanatomical variability, thus providing important novel insights into the neurobiological mechanisms underpinning the male preponderant prevalence of ASD.

An important feature of GPC is that it is possible to summarize the highly complex and multivariate pattern of normative sex-related phenotypic diversity in brain structure to a single measure (ie, class probability) indicative of the individual’s neurophenotypic sex. This ability sets our approach apart from existing studies (eg, those by Ruigrok et al9 and by Escorial et al34) examining sexual dimorphism of the brain based on univariate group differences between male individuals and female individuals, which precludes the investigation of interindividual phenotypic diversity across the binary categories dictated by biological sex. This study is of importance because it has recently been suggested that, on a phenotypic scale, the brain should be considered a “mosaic” of regions, each of relative maleness or femaleness, resulting in significant interindividual variability both within and between sexes.11,35 Therefore, the multivariate patterns derived from the classification between neurotypical male and female controls may be interpreted as being representative of such a mosaic, which drives the brain toward a female or male phenotypic end point overall (see also the study by Chekroud et al36).

Furthermore, we visualized the normative pattern of sex-related neuroanatomical variability associated with ASD probability within and across the binary categories for biological sex. These patterns not only included many of the brain regions where women typically differ from men in CT12,13 but also highlighted brain areas that have previously been linked to the core behavioral deficits characteristic of ASD (reviewed by Amaral et al37 and by Ecker et al38). Notably, some of these brain regions (eg, inferior temporal lobes) carried low or high probability of ASD in women but not in men. Herein, we found that the degree of cortical abnormality in female individuals with ASD significantly exceeded the degree of abnormality in male individuals compared with their respective normative population. Yet, female individuals with ASD were not more severely impaired than male individuals on the level of autistic symptoms. Therefore, our finding agrees with previous reports suggesting that (1) biological sex significantly modulates the neurobiological basis of ASD (eg, the studies by Lai et al6 and by Schaer et al7) and (2) female individuals may have a higher threshold (ie, minimum liability sufficient to cause ASD) for reaching affection status than male individuals (reviewed by Werling and Geschwind5) and may need to deviate more neurobiologically from their normative population to demonstrate a clinical ASD phenotype.

Limitations

While our approach holds promise for future investigations into the neurobiological mechanisms that underpin risk and resilience for conditions with a sex difference in prevalence, this study has several limitations. First, our study was designed to establish the statistical association between normative sex–related phenotypic diversity and ASD probability. Therefore, future studies are needed to examine the causal mechanisms for this association (eg, as shown by Lai et al8 and Baron-Cohen et al39). Second, because of reasons outlined above, we based our study on measures of CT in a sample of high-functioning adults with ASD. While this scope is sufficient to provide proof of concept, it will be necessary in the future to extend our approach to other neurobiological features that have previously been linked to ASD (eg, cortical surface area and regional volumes14) and to replicate our findings in other subgroups on the autism spectrum (eg, different age groups and individuals with learning disabilities). Third, in future research, it will be crucial to further explore the functional relevance of our findings and to examine how normative sex-related phenotypic diversity in brain structure relates to sex differences in general cognitive or behavioral profiles (eg, as shown by Miller and Halpern40 and by Hyde41) and to different clinical ASD phenotypes.3,42-44

Conclusions

Our findings suggest that the neurobiological male phenotype does not carry a higher intrinsic risk for ASD. However, there is considerable phenotypic diversity within and across the binary categories dictated by biological sex. In addition to genetic and environmental factors, such normative sex-related phenotypic diversity might thus be taken into account when examining sex differences in ASD. Therefore, our approach to modeling normative sex-related phenotypic diversity may be more widely used in the future to elucidate the neurobiological mechanisms that underpin risk and resilience for mental health disorders.

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Article Information

Corresponding Author: Christine Ecker, PhD, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Goethe University, Deutschordenstrasse 50, 60528 Frankfurt am Main, Germany (christine.ecker@kgu.de).

Accepted for Publication: November 30, 2016.

Published Online: February 8, 2017. doi:10.1001/jamapsychiatry.2016.3990

Retraction and Replacement: This article was retracted and replaced on February 27, 2019, to fix errors throughout the article and figures (see Supplement 2 for the retracted article with errors highlighted and Supplement 3 for the replacement article with corrections highlighted).

Author Contributions: Dr Ecker had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Drs Craig and D. G. M. Murphy contributed equally to this article.

Study concept and design: Ecker, Andrews, Marquand, C. M. Murphy, Lai, Bullmore, Williams, Baron-Cohen, Craig, D. G. M. Murphy.

Acquisition, analysis, or interpretation of data: Ecker, Andrews, Gudbrandsen, Marquand, Ginestet, Daly, C. M. Murphy, Lai, Lombardo, Ruigrok, Suckling, Williams, Craig, D. G. M. Murphy.

Drafting of the manuscript: Ecker, Andrews, Ginestet, Daly, Lai, Lombardo, Suckling, Williams, Craig, D. G. M. Murphy.

Critical revision of the manuscript for important intellectual content: Ecker, Gudbrandsen, Marquand, Ginestet, C. M. Murphy, Lai, Ruigrok, Bullmore, Williams, Baron-Cohen, Craig, D. G. M. Murphy.

Statistical analysis: Ecker, Andrews, Marquand, Ginestet, Daly, Lai, Suckling, D. G. M. Murphy.

Obtained funding: Bullmore, Williams, Baron-Cohen, D. G. M. Murphy.

Administrative, technical, or material support: Gudbrandsen, C. M. Murphy, Lombardo, Bullmore, Williams, Craig, D. G. M. Murphy.

Study supervision: Williams, Baron-Cohen, Craig, D. G. M. Murphy.

Conflict of Interest Disclosures: Dr Bullmore reported being employed half-time by GlaxoSmithKline and reported being a stockholder of GlaxoSmithKline shares. No other disclosures were reported.

Funding/Support: This work was supported by the Autism Imaging Multicentre Study Consortium, funded by Medical Research Council United Kingdom grant G0400061, and by the European Union–Autism Imaging Multicentre Study Consortium, supported by the Innovative Medicines Initiative Joint Undertaking under grant 115300, which includes financial contributions from the European Union Seventh Framework Programme (FP7/2007-2013), the European Federation of Pharmaceutical Industries and Associations companies in kind, and Autism Speaks. Dr Ecker acknowledges support by grants EC480/1-1 and EC480/2-1 from the German Research Foundation under the Heisenberg Programme. Dr Marquand acknowledges support by grant 024.001.006 from the Netherlands Institute for Scientific Research under the Gravitation Programme. Dr Lai acknowledges support from the William Binks Autism Neuroscience Fellowship, Cambridge, England, and the O’Brien Scholars Program within the Child and Youth Mental Health Collaborative at the Centre for Addiction and Mental Health and The Hospital for Sick Children, Toronto, Ontario, Canada. Drs Ruigrok and Baron-Cohen acknowledge support from the Autism Research Trust.

Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Group Information: The Medical Research Council Autism Imaging Multicentre Study (MRC AIMS) Consortium members were (in alphabetical order) Anthony J. Bailey, FRCPsych (University of Oxford, Oxford, England), Simon Baron-Cohen, PhD (University of Cambridge, Cambridge, England), Patrick F. Bolton, PhD, FRCPsych (Institute of Psychiatry, Psychology and Neuroscience [IoPPN], London, England), Edward T. Bullmore, PhD, FRCP, FRCPsych, FMedSci (University of Cambridge), Sarah Carrington, PhD (University of Oxford), Marco Catani, PhD (IoPPN), Bhismadev Chakrabarti, PhD (University of Cambridge), Michael C. Craig, PhD (IoPPN), Eileen M. Daly, PhD (IoPPN), Sean C. L. Deoni, PhD (IoPPN), Christine Ecker, PhD (IoPPN), Francesca Happé, PhD (IoPPN), Julian Henty, PhD (University of Cambridge), Peter Jezzard, PhD (University of Oxford), Patrick Johnston, PhD (IoPPN), Derek K. Jones, PhD (IoPPN), Meng-Chuan Lai, PhD (University of Cambridge), Michael V. Lombardo, PhD (University of Cambridge), Anya Madden, MSc (IoPPN), Diane Mullins, MD (IoPPN), Clodagh M. Murphy, PhD (IoPPN), Declan G. M. Murphy, FRCPsych (IoPPN), Greg Pasco, PhD (University of Cambridge), Amber N. V. Ruigrok, PhD (University of Cambridge), Susan A. Sadek, DClinPsy (University of Cambridge), Debbie Spain, MSc (IoPPN), Rose Stewart, MSc (University of Oxford), John Suckling, PhD (University of Cambridge), Sally J. Wheelwright, MSc (University of Cambridge), Steven C. R. Williams, PhD (IoPPN), and C. Ellie Wilson, PhD (IoPPN).

Additional Contributions: We thank the National Institute for Health Research Biomedical Research Centre for Mental Health and The Dr Mortimer and Theresa Sackler Foundation. The MRC AIMS Consortium is a United Kingdom collaboration between the Institute of Psychiatry, Psychology and Neuroscience at King’s College London, the Autism Research Centre at the University of Cambridge, and the Autism Research Group at the University of Oxford.

References
1.
Lai  MC, Lombardo  MV, Auyeung  B, Chakrabarti  B, Baron-Cohen  S.  Sex/gender differences and autism: setting the scene for future research.  J Am Acad Child Adolesc Psychiatry. 2015;54(1):11-24.PubMedGoogle ScholarCrossref
2.
Fombonne  E.  Epidemiology of autistic disorder and other pervasive developmental disorders.  J Clin Psychiatry. 2005;66(suppl 10):3-8.PubMedGoogle Scholar
3.
Szatmari  P, Liu  XQ, Goldberg  J,  et al.  Sex differences in repetitive stereotyped behaviors in autism: implications for genetic liability.  Am J Med Genet B Neuropsychiatr Genet. 2012;159B(1):5-12.PubMedGoogle ScholarCrossref
4.
Dworzynski  K, Ronald  A, Bolton  P, Happé  F.  How different are girls and boys above and below the diagnostic threshold for autism spectrum disorders?  J Am Acad Child Adolesc Psychiatry. 2012;51(8):788-797.PubMedGoogle ScholarCrossref
5.
Werling  DM, Geschwind  DH.  Sex differences in autism spectrum disorders.  Curr Opin Neurol. 2013;26(2):146-153.PubMedGoogle ScholarCrossref
6.
Lai  MC, Lombardo  MV, Suckling  J,  et al; MRC AIMS Consortium.  Biological sex affects the neurobiology of autism.  Brain. 2013;136(pt 9):2799-2815.PubMedGoogle ScholarCrossref
7.
Schaer  M, Kochalka  J, Padmanabhan  A, Supekar  K, Menon  V.  Sex differences in cortical volume and gyrification in autism.  Mol Autism. 2015;6(1):42.PubMedGoogle ScholarCrossref
8.
Lai  MC, Lerch  JP, Floris  DL,  et al.  Imaging sex/gender and autism in the brain: etiological implications.  J Neurosci Res. 2017;95(1-2):380-397.PubMedGoogle ScholarCrossref
9.
Ruigrok  AN, Salimi-Khorshidi  G, Lai  MC,  et al.  A meta-analysis of sex differences in human brain structure.  Neurosci Biobehav Rev. 2014;39:34-50.PubMedGoogle ScholarCrossref
10.
Rasmussen  CE, Williams  CKI.  Gaussian Processes for Machine Learning. Cambridge, MA: MIT Press; 2006.
11.
McCarthy  MM, Arnold  AP.  Reframing sexual differentiation of the brain.  Nat Neurosci. 2011;14(6):677-683.PubMedGoogle ScholarCrossref
12.
Sowell  ER, Peterson  BS, Kan  E,  et al.  Sex differences in cortical thickness mapped in 176 healthy individuals between 7 and 87 years of age.  Cereb Cortex. 2007;17(7):1550-1560.PubMedGoogle ScholarCrossref
13.
Im  K, Lee  JM, Lee  J,  et al.  Gender difference analysis of cortical thickness in healthy young adults with surface-based methods.  Neuroimage. 2006;31(1):31-38.PubMedGoogle ScholarCrossref
14.
Ecker  C, Ginestet  C, Feng  Y,  et al; MRC AIMS Consortium.  Brain surface anatomy in adults with autism: the relationship between surface area, cortical thickness, and autistic symptoms.  JAMA Psychiatry. 2013;70(1):59-70.PubMedGoogle ScholarCrossref
15.
Wallace  GL, Dankner  N, Kenworthy  L, Giedd  JN, Martin  A.  Age-related temporal and parietal cortical thinning in autism spectrum disorders.  Brain. 2010;133(pt 12):3745-3754.PubMedGoogle ScholarCrossref
16.
Lenroot  RK, Giedd  JN.  Sex differences in the adolescent brain.  Brain Cogn. 2010;72(1):46-55.PubMedGoogle ScholarCrossref
17.
Lenroot  RK, Gogtay  N, Greenstein  DK,  et al.  Sexual dimorphism of brain developmental trajectories during childhood and adolescence.  Neuroimage. 2007;36(4):1065-1073.PubMedGoogle ScholarCrossref
18.
Geschwind  DH, Rakic  P.  Cortical evolution: judge the brain by its cover.  Neuron. 2013;80(3):633-647.PubMedGoogle ScholarCrossref
19.
Zilles  K, Palomero-Gallagher  N, Amunts  K.  Development of cortical folding during evolution and ontogeny.  Trends Neurosci. 2013;36(5):275-284.PubMedGoogle ScholarCrossref
20.
Lord  C, Rutter  M, Le 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.PubMedGoogle ScholarCrossref
21.
Lord  C, Rutter  M, Goode  S,  et al.  Autism Diagnostic Observation Schedule: a standardized observation of communicative and social behavior.  J Autism Dev Disord. 1989;19(2):185-212.PubMedGoogle ScholarCrossref
22.
Wechsler  D.  Wechsler Abbreviated Scale of Intelligence (WASI). San Antonio, TX: Harcourt Assessment; 1999.
23.
Deoni  SC, Williams  SC, Jezzard  P, Suckling  J, Murphy  DG, Jones  DK.  Standardized structural magnetic resonance imaging in multicentre studies using quantitative T1 and T2 imaging at 1.5 T.  Neuroimage. 2008;40(2):662-671.PubMedGoogle ScholarCrossref
24.
Ecker  C, Suckling  J, Deoni  SC,  et al; MRC AIMS Consortium.  Brain anatomy and its relationship to behavior in adults with autism spectrum disorder: a multicenter magnetic resonance imaging study.  Arch Gen Psychiatry. 2012;69(2):195-209.PubMedGoogle ScholarCrossref
25.
Fischl  B, Sereno  MI, Dale  AM.  Cortical surface-based analysis, II: inflation, flattening, and a surface-based coordinate system.  Neuroimage. 1999;9(2):195-207.PubMedGoogle ScholarCrossref
26.
Dale  AM, Fischl  B, Sereno  MI.  Cortical surface-based analysis, I: segmentation and surface reconstruction.  Neuroimage. 1999;9(2):179-194.PubMedGoogle ScholarCrossref
27.
Ségonne  F, Dale  AM, Busa  E,  et al.  A hybrid approach to the skull stripping problem in MRI.  Neuroimage. 2004;22(3):1060-1075.PubMedGoogle ScholarCrossref
28.
Fischl  B, Dale  AM.  Measuring the thickness of the human cerebral cortex from magnetic resonance images.  Proc Natl Acad Sci U S A. 2000;97(20):11050-11055.PubMedGoogle ScholarCrossref
29.
Marquand  A, Howard  M, Brammer  M, Chu  C, Coen  S, Mourão-Miranda  J.  Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes.  Neuroimage. 2010;49(3):2178-2189.PubMedGoogle ScholarCrossref
30.
Marquand  AF, Brammer  M, Williams  SC, Doyle  OM.  Bayesian multi-task learning for decoding multi-subject neuroimaging data.  Neuroimage. 2014;92:298-311.PubMedGoogle ScholarCrossref
31.
Baio  J; Developmental Disabilities Monitoring Network Surveillance Year 2010 Principal Investigators; Centers for Disease Control and Prevention (CDC).  Prevalence of autism spectrum disorder among children aged 8 years: Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2010.  MMWR Surveill Summ. 2014;63(2):1-21.PubMedGoogle Scholar
32.
Worsley  KJ, Andermann  M, Koulis  T, MacDonald  D, Evans  AC.  Detecting changes in nonisotropic images.  Hum Brain Mapp. 1999;8(2-3):98-101.PubMedGoogle ScholarCrossref
33.
Hagler  DJ  Jr, Saygin  AP, Sereno  MI.  Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data.  Neuroimage. 2006;33(4):1093-1103.PubMedGoogle ScholarCrossref
34.
Escorial  S, Román  FJ, Martínez  K, Burgaleta  M, Karama  S, Colom  R.  Sex differences in neocortical structure and cognitive performance: a surface-based morphometry study.  Neuroimage. 2015;104:355-365.PubMedGoogle ScholarCrossref
35.
Joel  D, McCarthy  MM.  Incorporating sex as a biological variable in neuropsychiatric research: where are we now and where should we be?  Neuropsychopharmacology. 2017;42(2):379-385.PubMedGoogle ScholarCrossref
36.
Chekroud  AM, Ward  EJ, Rosenberg  MD, Holmes  AJ.  Patterns in the human brain mosaic discriminate males from females.  Proc Natl Acad Sci U S A. 2016;113(14):E1968. doi:10.1073/pnas.1523888113PubMedGoogle ScholarCrossref
37.
Amaral  DG, Schumann  CM, Nordahl  CW.  Neuroanatomy of autism.  Trends Neurosci. 2008;31(3):137-145.PubMedGoogle ScholarCrossref
38.
Ecker  C, Bookheimer  SY, Murphy  DG.  Neuroimaging in autism spectrum disorder: brain structure and function across the lifespan.  Lancet Neurol. 2015;14(11):1121-1134.PubMedGoogle ScholarCrossref
39.
Baron-Cohen  S, Auyeung  B, Nørgaard-Pedersen  B,  et al.  Elevated fetal steroidogenic activity in autism.  Mol Psychiatry. 2015;20(3):369-376.PubMedGoogle ScholarCrossref
40.
Miller  DI, Halpern  DF.  The new science of cognitive sex differences.  Trends Cogn Sci. 2014;18(1):37-45.PubMedGoogle ScholarCrossref
41.
Hyde  JS.  Sex and cognition: gender and cognitive functions.  Curr Opin Neurobiol. 2016;38:53-56.PubMedGoogle ScholarCrossref
42.
Bölte  S, Duketis  E, Poustka  F, Holtmann  M.  Sex differences in cognitive domains and their clinical correlates in higher-functioning autism spectrum disorders.  Autism. 2011;15(4):497-511.PubMedGoogle ScholarCrossref
43.
Mandy  W, Chilvers  R, Chowdhury  U, Salter  G, Seigal  A, Skuse  D.  Sex differences in autism spectrum disorder: evidence from a large sample of children and adolescents.  J Autism Dev Disord. 2012;42(7):1304-1313.PubMedGoogle ScholarCrossref
44.
Solomon  M, Miller  M, Taylor  SL, Hinshaw  SP, Carter  CS.  Autism symptoms and internalizing psychopathology in girls and boys with autism spectrum disorders.  J Autism Dev Disord. 2012;42(1):48-59.PubMedGoogle ScholarCrossref
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