Large-scale analyses of the relationship between sex, age and intelligence quotient heterogeneity and cortical morphometry in autism spectrum disorder.
Adolescent
Adult
Age Factors
Autism Spectrum Disorder
/ pathology
Brain
/ anatomy & histology
Cerebral Cortex
/ pathology
Child
Child, Preschool
Databases, Factual
Female
Humans
Intelligence
/ physiology
Intelligence Tests
Magnetic Resonance Imaging
/ methods
Male
Middle Aged
Neuroimaging
Sex Characteristics
Journal
Molecular psychiatry
ISSN: 1476-5578
Titre abrégé: Mol Psychiatry
Pays: England
ID NLM: 9607835
Informations de publication
Date de publication:
03 2020
03 2020
Historique:
received:
09
03
2018
accepted:
26
03
2019
revised:
25
03
2019
pubmed:
28
4
2019
medline:
15
1
2021
entrez:
28
4
2019
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 31028290
doi: 10.1038/s41380-019-0420-6
pii: 10.1038/s41380-019-0420-6
doi:
Banques de données
ClinicalTrials.gov
['NCT00001246']
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
614-628Subventions
Organisme : Medical Research Council
ID : MC_G0802534
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/N026063/1
Pays : United Kingdom
Références
Courchesne E, Carper R, Akshoomoff N. Evidence of brain overgrowth in the first year of life in autism. JAMA. 2003;290:337–44.
pubmed: 12865374
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.
pubmed: 16330725
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.
pubmed: 20926367
pmcid: 2995883
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.
pubmed: 28334080
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.
pubmed: 29145754
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.
pubmed: 23404046
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.
pubmed: 26184828
Mensen VT, et al. Development of cortical thickness and surface area in autism spectrum disorder. NeuroImage Clin. 2017;13:215–22.
pubmed: 28003960
Hazlett HC, et al. Early brain development in infants at high risk for autism spectrum disorder. Nature. 2017;542:348–51.
pubmed: 28202961
pmcid: 5336143
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.
pubmed: 21947663
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.
pubmed: 25988942
pmcid: 4437371
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.
pubmed: 17146708
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.
pubmed: 23847518
pmcid: 3701858
Haar S, Berman S, Behrmann M, Dinstein I. Anatomical abnormalities in autism? Cereb Cortex. 2016;26:1440–52.
pubmed: 25316335
Valk SL, Di Martino A, Milham MP, Bernhardt BC. Multicenter mapping of structural network alterations in autism. Hum Brain Mapp. 2015;36:2364–73.
pubmed: 25727858
pmcid: 6129398
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.
pubmed: 22326986
pmcid: 3321384
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.
pubmed: 26329119
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.
pubmed: 26004661
pmcid: 4540060
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.
pubmed: 19819933
Greimel E, et al. Changes in grey matter development in autism spectrum disorder. Brain Struct Funct. 2013;218:929–42.
pubmed: 22777602
Zielinski BA, et al. Longitudinal changes in cortical thickness in autism and typical development. Brain. 2014;137:1799–812.
pubmed: 24755274
pmcid: 4032101
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.
pubmed: 26045942
pmcid: 4455336
Sussman D, et al. The autism puzzle: diffuse but not pervasive neuroanatomical abnormalities in children with ASD. NeuroImage Clin. 2015;8:170–9.
pubmed: 26106541
pmcid: 4473820
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 .
pubmed: 28745267
Lai MC, et al. Imaging sex/gender and autism in the brain: etiological implications. J Neurosci Res. 2017;95:380–97.
pubmed: 27870420
Lotspeich LJ, et al. Investigation of neuroanatomical differences between autism and Asperger syndrome. Arch Gen Psychiatry. 2004;61:291–8.
pubmed: 14993117
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.
pubmed: 27153982
Ducharme S, et al. Trajectories of cortical thickness maturation in normal brain development--the importance of quality control procedures. Neuroimage. 2016;125:267–79.
pubmed: 26463175
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.
pubmed: 16816240
pmcid: 1509104
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.
pubmed: 19790171
Schaer M, Kochalka J, Padmanabhan A, Supekar K, Menon V. Sex differences in cortical volume and gyrification in autism. Mol Autism. 2015;6:42.
pubmed: 26146534
pmcid: 4491212
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.
pubmed: 28196230
pmcid: 5470405
Lange N, et al. Longitudinal volumetric brain changes in autism spectrum disorder ages 6-35 years. Autism Res. 2015;8:82–93.
pubmed: 25381736
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.
pubmed: 23774715
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.
pubmed: 12585710
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.
pubmed: 26033243
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.
pubmed: 17944619
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.
pubmed: 26061376
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.
pubmed: 28365755
Chakravarty MM, et al. Striatal shape abnormalities as novel neurodevelopmental endophenotypes in schizophrenia: a longitudinal study. Hum Brain Mapp. 2015;36:1458–69.
pubmed: 25504933
Schuetze M, et al. Morphological alterations in the thalamus, striatum, and pallidum in autism spectrum disorder. Neuropsychopharmacology. 2016;41:2627–37.
pubmed: 27125303
pmcid: 5026732
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.
pubmed: 19082876
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.
pubmed: 11439754
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.
pubmed: 11468308
Schumann CM, et al. Longitudinal magnetic resonance imaging study of cortical development through early childhood in autism. J Neurosci. 2010;30:4419–27.
pubmed: 20335478
pmcid: 2859218
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.
pubmed: 21536976
pmcid: 3315057
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.
pubmed: 15935993
Bauman ML, Kemper TL. Neuroanatomic observations of the brain in autism: a review and future directions. Int J Dev Neurosci. 2005;23:183–7.
pubmed: 15749244
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.
pubmed: 16819561
Huttenlocher PR. Morphometric study of human cerebral cortex development. Neuropsychologia. 1990;28:517–27.
pubmed: 2203993
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.
pubmed: 26975670
Tang G, et al. Loss of mTOR-dependent macroautophagy causes autistic-like synaptic pruning deficits. Neuron. 2014;83:1131–43.
pubmed: 25155956
pmcid: 4159743
Avino TA, Hutsler JJ. Abnormal cell patterning at the cortical gray–white matter boundary in autism spectrum disorders. Brain Res. 2010;1360:138–46.
pubmed: 20816758
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.
pubmed: 28057721
pmcid: 6093436
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.
pubmed: 30446637
pmcid: 6240045
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.
pubmed: 29449712
pmcid: 5987539
Lyall AE, et al. Dynamic development of regional cortical thickness and surface area in early childhood. Cereb Cortex. 2015;25:2204–12.
pubmed: 24591525
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.
pubmed: 24752753
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.
pubmed: 25498430
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 .
pubmed: 30617272
pmcid: 6754748
Werling DM, Geschwind DH. Understanding sex bias in autism spectrum disorder. Proc Natl Acad Sci USA. 2013;110:4868–9.
pubmed: 23476067
Cauvet É, et al. Sex differences along the autism continuum: a twin study of brain structure. Cereb Cortex. 2019;29:1342–50.
pubmed: 30566633
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.
pubmed: 16580643
Raznahan A, et al. How does your cortex grow? J Neurosci. 2011;31:7174–7.
pubmed: 21562281
pmcid: 3157294
Shaw P, et al. Neurodevelopmental trajectories of the human cerebral cortex. J Neurosci. 2008;28:3586–94.
pubmed: 18385317
pmcid: 6671079
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.
pubmed: 28242797
pmcid: 5373125
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.
pubmed: 28432144
pmcid: 5444192
Brown TT, et al. Neuroanatomical assessment of biological maturity. Curr Biol. 2012;22:1693–8.
pubmed: 22902750
pmcid: 3461087
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.
pubmed: 25246511
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.
pubmed: 15254095
Narr KL, et al. Relationships between IQ and regional cortical gray matter thickness in healthy adults. Cereb Cortex. 2007;17:2163–71.
pubmed: 17118969
Shaw P, et al. Intellectual ability and cortical development in children and adolescents. Nature. 2006;440:676–9.
pubmed: 16572172
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.
pubmed: 17706781
Verhoeven JS, De Cock P, Lagae L, Sunaert S. Neuroimaging of autism. Neuroradiology. 2010;52:3–14.
pubmed: 20033797
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.
pubmed: 26751141
Lombardo MV, et al. Different functional neural substrates for good and poor language outcome in autism. Neuron. 2015;86:567–77.
pubmed: 25864635
pmcid: 4610713
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.
pubmed: 25199916
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.
pubmed: 27050589
pmcid: 5072455
Yuen RKC, et al. Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder. Nat Neurosci. 2017;20:602–11.
pmcid: 5501701
Marshall CR, Scherer SW. Detection and characterization of copy number variation in autism spectrum disorder. Methods Mol Biol. 2012;838:115–35.
pubmed: 22228009
Turner TN, et al. Genomic patterns of de novo mutation in simplex autism. Cell. 2017;171:710.
pubmed: 28965761
pmcid: 5679715
Shaw P, Gogtay N, Rapoport J. Childhood psychiatric disorders as anomalies in neurodevelopmental trajectories. Hum Brain Mapp. 2010;31:917–25.
pubmed: 20496382
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.
pubmed: 26654788
Rosen AFG, et al. Quantitative assessment of structural image quality. Neuroimage. 2018;169:407–18.
pubmed: 29278774
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.
pubmed: 29206318