Significant heterogeneity across aetiologies, neurobiology and clinical phenotypes have been observed in individuals with autism spectrum disorder (ASD). Neuroimaging-based neuroanatomical studies of ASD have often reported inconsistent findings which may, in part, be attributable to an insufficient understanding of the relationship between factors influencing clinical heterogeneity and their relationship to brain anatomy. To this end, we performed a large-scale examination of cortical morphometry in ASD, with a specific focus on the impact of three potential sources of heterogeneity: sex, age and full-scale intelligence (FIQ). To examine these potentially subtle relationships, we amassed a large multi-site dataset that was carefully quality controlled (yielding a final sample of 1327 from the initial dataset of 3145 magnetic resonance images; 491 individuals with ASD). Using a meta-analytic technique to account for inter-site differences, we identified greater cortical thickness in individuals with ASD relative to controls, in regions previously implicated in ASD, including the superior temporal gyrus and inferior frontal sulcus. Greater cortical thickness was observed in sex specific regions; further, cortical thickness differences were observed to be greater in younger individuals and in those with lower FIQ, and to be related to overall clinical severity. This work serves as an important step towards parsing factors that influence neuroanatomical heterogeneity in ASD and is a potential step towards establishing individual-specific biomarkers.
Subscribe to Journal
Get full journal access for 1 year
only $9.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
R code used to conduct the prospective meta-analyses described here is available from the corresponding authors upon request.
Courchesne E, Carper R, Akshoomoff N. Evidence of brain overgrowth in the first year of life in autism. JAMA. 2003;290:337–44.
Hazlett HC, et al. Magnetic resonance imaging and head circumference study of brain size in autism: birth through age 2 years. Arch Gen Psychiatry. 2005;62:1366–76.
Wallace GL, Dankner N, Kenworthy L, Giedd JN, Martin A. Age-related temporal and parietal cortical thinning in autism spectrum disorders. Brain. 2010;133:3745–54.
Raznahan A, et al. Mapping cortical anatomy in preschool aged children with autism using surface-based morphometry. NeuroImage Clin. 2013;2:111–9.
Khundrakpam BS, Lewis JD, Kostopoulos P, Carbonell F, Evans AC. Cortical thickness abnormalities in autism spectrum disorders through late childhood, adolescence, and adulthood: a large-scale MRI study. Cereb Cortex. 2017;27:1721–31.
van Rooij D, et al. Cortical and subcortical brain morphometry differences between patients with autism spectrum disorder and healthy individuals across the lifespan: results from the ENIGMA ASD Working Group. Am J Psychiatry. 2018;175:359–69.
Ecker C, et al. Brain surface anatomy in adults with autism: the relationship between surface area, cortical thickness, and autistic symptoms. JAMA Psychiatry. 2013;70:59–70.
Ohta H, et al. Increased surface area, but not cortical thickness, in a subset of young boys with autism spectrum disorder. Autism Res. 2016;9:232–48.
Mensen VT, et al. Development of cortical thickness and surface area in autism spectrum disorder. NeuroImage Clin. 2017;13:215–22.
Hazlett HC, et al. Early brain development in infants at high risk for autism spectrum disorder. Nature. 2017;542:348–51.
Mandy W, et al. Sex differences in autism spectrum disorder: evidence from a large sample of children and adolescents. J Autism Dev Disord. 2012;42:1304–13.
Mandic-Maravic V, et al. Sex differences in autism spectrum disorders: does sex moderate the pathway from clinical symptoms to adaptive behavior? Sci Rep. 2015;5:10418.
Klin A, et al. Social and communication abilities and disabilities in higher functioning individuals with autism spectrum disorders: the Vineland and the ADOS. J Autism Dev Disord. 2007;37:748–59.
Vivanti G, Barbaro J, Hudry K, Dissanayake C, Prior M. Intellectual development in autism spectrum disorders: new insights from longitudinal studies. Front Hum Neurosci. 2013;7:354.
Haar S, Berman S, Behrmann M, Dinstein I. Anatomical abnormalities in autism? Cereb Cortex. 2016;26:1440–52.
Valk SL, Di Martino A, Milham MP, Bernhardt BC. Multicenter mapping of structural network alterations in autism. Hum Brain Mapp. 2015;36:2364–73.
Misaki M, Wallace GL, Dankner N, Martin A, Bandettini PA. Characteristic cortical thickness patterns in adolescents with autism spectrum disorders: interactions with age and intellectual ability revealed by canonical correlation analysis. Neuroimage. 2012;60:1890–1901.
Richter J, et al. Reduced cortical thickness and its association with social reactivity in children with autism spectrum disorder. Psychiatry Res. 2015;234:15–24.
Wallace GL, et al. Longitudinal cortical development during adolescence and young adulthood in autism spectrum disorder: increased cortical thinning but comparable surface area changes. J Am Acad Child Adolesc Psychiatry. 2015;54:464–9.
Raznahan A, et al. Cortical anatomy in autism spectrum disorder: an in vivo MRI study on the effect of age. Cereb Cortex. 2010;20:1332–40.
Greimel E, et al. Changes in grey matter development in autism spectrum disorder. Brain Struct Funct. 2013;218:929–42.
Zielinski BA, et al. Longitudinal changes in cortical thickness in autism and typical development. Brain. 2014;137:1799–812.
Lin H-Y, Ni H-C, Lai M-C, Tseng W-YI, Gau SS-F. Regional brain volume differences between males with and without autism spectrum disorder are highly age-dependent. Mol Autism. 2015;6:29.
Sussman D, et al. The autism puzzle: diffuse but not pervasive neuroanatomical abnormalities in children with ASD. NeuroImage Clin. 2015;8:170–9.
Zhang W. et al. Revisiting subcortical brain volume correlates of autism in the ABIDE dataset: effects of age and sex. Psychol Med. 2017. https://doi.org/10.1017/S003329171700201X.
Lai MC, et al. Imaging sex/gender and autism in the brain: etiological implications. J Neurosci Res. 2017;95:380–97.
Lotspeich LJ, et al. Investigation of neuroanatomical differences between autism and Asperger syndrome. Arch Gen Psychiatry. 2004;61:291–8.
Alexander-Bloch A, et al. Subtle in-scanner motion biases automated measurement of brain anatomy from in vivo MRI. Hum Brain Mapp. 2016;2397:2385–97.
Pardoe HR, Kucharsky Hiess R, Kuzniecky R. Motion and morphometry in clinical and nonclinical populations. Neuroimage. 2016;135:177–85.
Ducharme S, et al. Trajectories of cortical thickness maturation in normal brain development--the importance of quality control procedures. Neuroimage. 2016;125:267–79.
Hardan AY, Muddasani S, Vemulapalli M, Keshavan MS, Minshew NJ. An MRI study of increased cortical thickness in autism. Am J Psychiatry. 2006;163:1290–2.
Hyde KL, Samson F, Evans AC, Mottron 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;31:556–66.
Schaer M, Kochalka J, Padmanabhan A, Supekar K, Menon V. Sex differences in cortical volume and gyrification in autism. Mol Autism. 2015;6:42.
Ecker C, et al. Association between the probability of autism spectrum disorder and normative sex-related phenotypic diversity in brain structure. JAMA Psychiatry. 2017;74:329.
Lange N, et al. Longitudinal volumetric brain changes in autism spectrum disorder ages 6-35 years. Autism Res. 2015;8:82–93.
Di Martino A, et al. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatry. 2014;19:659–67.
Di Martino A. et al. Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Scientific Data. 2017;4:170010.
Zijdenbos AP, Forghani R, Evans AC. Automatic ‘pipeline’ analysis of 3-D MRI data for clinical trials: application to multiple sclerosis. IEEE Trans Med Imaging. 2002;21:1280–91.
van Erp TGM, et al. Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Mol Psychiatry. 2016;21:547–53.
Nakagawa S, Cuthill IC. Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol Rev Camb Philos Soc. 2007;82:591–605.
Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res Synth Methods. 2010;1:97–111.
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol. 1995;57:289–300.
Akaike H. A new look at the statistical model identification. IEEE Trans Automat Contr. 1974;19:716–23.
Walhovd KB, Fjell AM, Giedd J, Dale AM, Brown TT. Through thick and thin: a need to reconcile contradictory results on trajectories in human cortical development. Cereb Cortex. 2017;27:1472–81.
Chakravarty MM, et al. Striatal shape abnormalities as novel neurodevelopmental endophenotypes in schizophrenia: a longitudinal study. Hum Brain Mapp. 2015;36:1458–69.
Schuetze M, et al. Morphological alterations in the thalamus, striatum, and pallidum in autism spectrum disorder. Neuropsychopharmacology. 2016;41:2627–37.
Gotham K, Pickles A, Lord C. Standardizing ADOS scores for a measure of severity in autism spectrum disorders. J Autism Dev Disord. 2009;39:693–705.
Baron-Cohen S, Wheelwright S, Skinner R, Martin J, Clubley E. The autism-spectrum quotient (AQ): evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. J Autism Dev Disord. 2001;31:5–17.
Courchesne E, Moses P, Pierce K, Pizzo S. Unusual brain growth patterns in early life in patients with autistic disorder: an MRI study. Neurology. 2001;57:245–54.
Schumann CM, et al. Longitudinal magnetic resonance imaging study of cortical development through early childhood in autism. J Neurosci. 2010;30:4419–27.
Hazlett HC, et al. Early brain overgrowth in autism associated with an increase in cortical surface area before age 2 years. Arch Gen Psychiatry. 2011;68:467–76.
Redcay E, Courchesne E. When is the brain enlarged in autism? A meta-analysis of all brain size reports. Biol Psychiatry. 2005;58:1–9.
Bauman ML, Kemper TL. Neuroanatomic observations of the brain in autism: a review and future directions. Int J Dev Neurosci. 2005;23:183–7.
Schumann C, Noctor SC, Amaral DG. Neuropathology of autism spectrum disorders: postmortem studies. Autism Spectrum Disorders 2012;1:62–74.
Casanova MF, et al. Minicolumnar abnormalities in autism. Acta Neuropathol. 2006;112:287–303.
Huttenlocher PR. Morphometric study of human cerebral cortex development. Neuropsychologia. 1990;28:517–27.
Ecker C. The neuroanatomy of autism spectrum disorder: an overview of structural neuroimaging findings and their translatability to the clinical setting. Autism. 2017;21:18–28.
Tang G, et al. Loss of mTOR-dependent macroautophagy causes autistic-like synaptic pruning deficits. Neuron. 2014;83:1131–43.
Avino TA, Hutsler JJ. Abnormal cell patterning at the cortical gray–white matter boundary in autism spectrum disorders. Brain Res. 2010;1360:138–46.
Andrews DS, et al. In vivo evidence of reduced integrity of the gray-white matter boundary in autism spectrum disorder. Cereb Cortex. 2017;27:877–87.
Bezgin G, Lewis JD, Evans AC. Developmental changes of cortical white–gray contrast as predictors of autism diagnosis and severity. Transl Psychiatry. 2018;8:249.
Smith E, et al. Cortical thickness change in autism during early childhood. Hum Brain Mapp. 2016;2629:2616–29.
Gilmore JH, Knickmeyer RC, Gao W. Imaging structural and functional brain development in early childhood. Nat Rev Neurosci. 2018;19:123–37.
Lyall AE, et al. Dynamic development of regional cortical thickness and surface area in early childhood. Cereb Cortex. 2015;25:2204–12.
Ecker C, et al. The effect of age, diagnosis, and their interaction on vertex-based measures of cortical thickness and surface area in autism spectrum disorder. J Neural Transm. 2014;121:1157–70.
Bethlehem RAI, Seidlitz J, Romero-Garcia R, Lombardo MV. Using normative age modelling to isolate subsets of individuals with autism expressing highly age-atypical cortical thickness features. bioRxiv. 2018. https://doi.org/10.1101/252593.
Reuter M, et al. Head motion during MRI acquisition reduces gray matter volume and thickness estimates. Neuroimage. 2015;107:107–15.
Lombardo MV, Lai M-C, Baron-Cohen S. Big data approaches to decomposing heterogeneity across the autism spectrum. Mol Psychiatry. 2019. https://doi.org/10.1038/s41380-018-0321-0.
Werling DM, Geschwind DH. Understanding sex bias in autism spectrum disorder. Proc Natl Acad Sci USA. 2013;110:4868–9.
Cauvet É, et al. Sex differences along the autism continuum: a twin study of brain structure. Cereb Cortex. 2019;29:1342–50.
Hutsler JJ, Love T, Zhang H. Histological and magnetic resonance imaging assessment of cortical layering and thickness in autism spectrum disorders. Biol Psychiatry. 2007;61:449–57.
Raznahan A, et al. How does your cortex grow? J Neurosci. 2011;31:7174–7.
Shaw P, et al. Neurodevelopmental trajectories of the human cerebral cortex. J Neurosci. 2008;28:3586–94.
Tamnes CK, et al. Development of the cerebral cortex across adolescence: a multisample study of inter-related longitudinal changes in cortical volume, surface area, and thickness. J Neurosci. 2017;37:3402–12.
Gennatas ED, et al. Age-related effects and sex differences in gray matter density, volume, mass, and cortical thickness from childhood to young adulthood. J Neurosci. 2017;37:5065–73.
Brown TT, et al. Neuroanatomical assessment of biological maturity. Curr Biol. 2012;22:1693–8.
Amlien IK, et al. Organizing principles of human cortical development—thickness and area from 4 to 30 years: insights from comparative primate neuroanatomy. Cereb Cortex. 2016;26:257–67.
Schumann CM, et al. The amygdala is enlarged in children but not adolescents with autism; the hippocampus is enlarged at all ages. J Neurosci. 2004;24:6392–401.
Narr KL, et al. Relationships between IQ and regional cortical gray matter thickness in healthy adults. Cereb Cortex. 2007;17:2163–71.
Shaw P, et al. Intellectual ability and cortical development in children and adolescents. Nature. 2006;440:676–9.
Redcay E. The superior temporal sulcus performs a common function for social and speech perception: implications for the emergence of autism. Neurosci Biobehav Rev. 2008;32:123–42.
Verhoeven JS, De Cock P, Lagae L, Sunaert S. Neuroimaging of autism. Neuroradiology. 2010;52:3–14.
Herringshaw AJ, Ammons CJ, DeRamus TP, Kana RK. Hemispheric differences in language processing in autism spectrum disorders: a meta-analysis of neuroimaging studies. Autism Res. 2016;9:1046–57.
Lombardo MV, et al. Different functional neural substrates for good and poor language outcome in autism. Neuron. 2015;86:567–77.
Ellegood J, et al. Clustering autism: using neuroanatomical differences in 26 mouse models to gain insight into the heterogeneity. Mol Psychiatry. 2015;20:118–25.
de la Torre-Ubieta L, Won H, Stein JL, Geschwind DH. Advancing the understanding of autism disease mechanisms through genetics. Nat Med. 2016;22:345–61.
Yuen RKC, et al. Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder. Nat Neurosci. 2017;20:602–11.
Marshall CR, Scherer SW. Detection and characterization of copy number variation in autism spectrum disorder. Methods Mol Biol. 2012;838:115–35.
Turner TN, et al. Genomic patterns of de novo mutation in simplex autism. Cell. 2017;171:710.
Shaw P, Gogtay N, Rapoport J. Childhood psychiatric disorders as anomalies in neurodevelopmental trajectories. Hum Brain Mapp. 2010;31:917–25.
Tisdall MD, et al. Prospective motion correction with volumetric navigators (vNavs) reduces the bias and variance in brain morphometry induced by subject motion. Neuroimage. 2016;127:11–22.
Rosen AFG, et al. Quantitative assessment of structural image quality. Neuroimage. 2018;169:407–18.
White T, et al. Automated quality assessment of structural magnetic resonance images in children: comparison with visual inspection and surface-based reconstruction. Hum Brain Mapp. 2018;39:1218–31.
MRC AIMS Consortium:
Anthony J. Bailey (Oxford), Simon Baron-Cohen (Cambridge), Patrick F. Bolton (IoPPN), Edward T. Bullmore (Cambridge), Sarah Carrington (Oxford), Marco Catani (IoPPN), Bhismadev Chakrabarti (Cambridge), Michael C. Craig (IoPPN), Eileen M. Daly (IoPPN), Sean C. L. Deoni (IoPPN), Christine Ecker (IoPPN), Francesca Happé (IoPPN), Julian Henty (Cambridge), Peter Jezzard (Oxford), Patrick Johnston (IoPPN), Derek K. Jones (IoPPN), Meng-Chuan Lai (Cambridge), Michael V. Lombardo (Cambridge), Anya Madden (IoPPN), Diane Mullins (IoPPN), Clodagh M. Murphy (IoPPN), Declan G. M. Murphy (IoPPN), Greg Pasco (Cambridge), Amber N. V. Ruigrok (Cambridge), Susan A. Sadek (Cambridge), Debbie Spain (IoPPN), Rose Stewart (Oxford), John Suckling (Cambridge), Sally J. Wheelwright (Cambridge) and Steven C. Williams (IoPPN).
This research was undertaken thanks in part to funding from the Canada First Research Excellence Fund, awarded to McGill University for the Healthy Brains for Healthy Lives initiative, in the form of a graduate student fellowship to SAB. The Autism Imaging Multicentre Study Consortium was funded by the Medical Research Council United Kingdom grant G0400061. The Cambridge Family Study of Autism was funded by a Clinical Scientist Fellowship from the UK Medical Research Council (MRC) (G0701919). AR was supported by funding from the Intramural Research Program of the NIMH (Clinical trial reg. no. NCT00001246, clinicaltrials.gov; NIH Annual Report Number, ZIA MH002794, Protocol ID 89-M-0006). The Toronto sample was gathered from studies supported by grants MOP-119541, MOP-106582 and MOP-14237 from the Canadian Institutes of Health Research (to MT), and from the POND Network, funded by the Ontario Brain Institute (grant IDS-I l-02 to EA and JL), an independent non-profit corporation, funded partially by the Ontario government. The opinions, results and conclusions are those of the authors and no endorsement by the Ontario Brain Institute is intended or should be inferred. JL received funding from the Canadian Institute for Health Research. MMC received funding from the Canadian Institute for Health Research, the Natural Sciences and Engineering Research Council, the Fonds de recherche du Québec – Santé and McGill University’s Healthy Brains for Healthy Lives initative. SBC was supported by the Autism Research Trust. DGM was supported in this work by funding from the MRC UK, the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, and King’s College London (Medical Research Council grant no. G0400061 to DGMM). DGM, SBC, AR, JS, CE, and RH are also supported by EU-AIMS and AIMS-2 TRIALS. EU-AIMS receives support from the Innovative Medicines Initiative (IMI) Joint Undertaking (JU) under grant agreement no. 115300, the resources of which are composed of financial contributions from the European Union's Seventh Framework Programme (grant FP7/2007- 2013). AIMS-2 TRIALS received support from EFPIA and AUTISM SPEAKS, Autistica, and SFARI, and funding from the IMI 2 JU under grant agreement no. 777394, with support from the European Union's Horizon 2020 research and innovation programme. MVL was supported by an ERC Starting Grant (ERC-2017-STG; 755816). M-CL was supported by 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, and the Slifka-Ritvo Award for Innovation in Autism Research by the Alan B. Slifka Foundation and the International Society for Autism Research. The authors would like to thank the investigators and participants in the ABIDE dataset. Funding sources for each individual sites are provided on the official ABIDE website (http://fcon_1000.projects.nitrc.org/indi/abide/).
Conflict of interest
DGM reported receiving honoraria from Roche for being on a scientific advisory board. No other conflicts of interest were reported.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The Medical Research Council Autism Imaging Multicentre Study Consortium (MRC AIMS Consortium) is a UK collaboration between the Institute of Psychiatry, Psychology and Neuroscience (IoPPN) at King’s College, London, the Autism Research Centre, University of Cambridge, and the Autism Research Group, University of Oxford. Members of MRC AIMS Consortium are listed at the end of the article.
About this article
Cite this article
Bedford, S.A., Park, M.T.M., Devenyi, G.A. et al. Large-scale analyses of the relationship between sex, age and intelligence quotient heterogeneity and cortical morphometry in autism spectrum disorder. Mol Psychiatry 25, 614–628 (2020). https://doi.org/10.1038/s41380-019-0420-6
Communications Biology (2021)
Neuroscience Bulletin (2021)
Translational Psychiatry (2021)
Molecular Autism (2020)
Gray matter covariations and core symptoms of autism: the EU-AIMS Longitudinal European Autism Project
Molecular Autism (2020)