Wing L. The autistic spectrum. Lancet. 1997;350(9093):1761–6.

Article  CAS  PubMed  Google Scholar 

Ali MT, ElNakieb Y, Elnakib A, Shalaby A, Mahmoud A, Ghazal M, et al. the role of structure MRI in diagnosing autism. Diagnostics (Basel). 2022;12(1).

Wang J, Fu K, Chen L, Duan X, Guo X, Chen H, et al. Increased gray matter volume and resting-state functional connectivity in somatosensory cortex and their relationship with autistic symptoms in young boys with autism spectrum disorder. Front Physiol. 2017;8:588.

Article  PubMed  PubMed Central  Google Scholar 

Xie Y, Xu Z, Xia M, Liu J, Shou X, Cui Z, et al. Alterations in connectome dynamics in autism spectrum disorder: a harmonized mega- and meta-analysis study using the autism brain imaging data exchange dataset. Biol Psychiatry. 2021.

Hoogman M, van Rooij D, Klein M, Boedhoe P, Ilioska I, Li T, et al. Consortium neuroscience of attention deficit/hyperactivity disorder and autism spectrum disorder: the ENIGMA adventure. Hum Brain Mapp. 2022;43(1):37–55.

Article  PubMed  Google Scholar 

Muhle RA, Reed HE, Stratigos KA, Veenstra-VanderWeele J. The emerging clinical neuroscience of autism spectrum disorder: a review. JAMA Psychiat. 2018;75(5):514–23.

Article  Google Scholar 

Kostic A, Buxbaum JD. The promise of precision medicine in autism. Neuron. 2021;109(14):2212–5.

Article  CAS  PubMed  Google Scholar 

Shan X, Uddin LQ, Xiao J, He C, Ling Z, Li L, et al. Mapping the heterogeneous brain structural phenotype of autism spectrum disorder using the normative model. Biol Psychiatry. 2022;91(11):967–76.

Article  PubMed  Google Scholar 

Guo X, Zhang X, Chen H, Zhai G, Cao Y, Zhang T, et al. Exploring the heterogeneity of brain structure in autism spectrum disorder based on individual structural covariance network. Cereb Cortex. 2023.

Chen H, Uddin LQ, Guo X, Wang J, Wang R, Wang X, et al. Parsing brain structural heterogeneity in males with autism spectrum disorder reveals distinct clinical subtypes. Hum Brain Mapp. 2019;40(2):628–37.

Article  PubMed  Google Scholar 

Doucet GE, Moser DA, Rodrigue A, Bassett DS, Glahn DC, Frangou S. Person-based brain morphometric similarity is heritable and correlates with biological features. Cereb Cortex. 2019;29(2):852–62.

Article  PubMed  Google Scholar 

Doucet GE, Lin D, Du Y, Fu Z, Glahn DC, Calhoun VD, et al. Personalized estimates of morphometric similarity in bipolar disorder and schizophrenia. NPJ Schizophr. 2020;6(1):39.

Article  PubMed  PubMed Central  Google Scholar 

Janssen J, Diaz-Caneja CM, Alloza C, Schippers A, de Hoyos L, Santonja J, et al. Dissimilarity in sulcal width patterns in the cortex can be used to identify patients with schizophrenia with extreme deficits in cognitive performance. Schizophr Bull. 2021;47(2):552–61.

Article  PubMed  Google Scholar 

Antoniades M, Haas SS, Modabbernia A, Bykowsky O, Frangou S, Borgwardt S, et al. Personalized estimates of brain structural variability in individuals with early psychosis. Schizophr Bull. 2021;47(4):1029–38.

Article  PubMed  PubMed Central  Google Scholar 

Doucet GE, Glahn DC, Frangou S. Person-based similarity in brain structure and functional connectivity in bipolar disorder. J Affect Disord. 2020;276:38–44.

Article  PubMed  PubMed Central  Google Scholar 

Fang K, Wen B, Niu L, Wan B, Zhang W. Higher brain structural heterogeneity in schizophrenia. Front Psychiatry. 2022;13:1017399.

Article  PubMed  PubMed Central  Google Scholar 

Baldwin H, Radua J, Antoniades M, Haas SS, Frangou S, Agartz I, et al. Neuroanatomical heterogeneity and homogeneity in individuals at clinical high risk for psychosis. Transl Psychiatry. 2022;12(1):297.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhang M, Desrosiers C, Guo Y, Khundrakpam B, Al-Sharif N, Kiar G, et al. Brain status modeling with non-negative projective dictionary learning. Neuroimage. 2020;206:116226.

Article  PubMed  Google Scholar 

Di Martino A, Fair DA, Kelly C, Satterthwaite TD, Castellanos FX, Thomason ME, et al. Unraveling the miswired connectome: a developmental perspective. Neuron. 2014;83(6):1335–53.

Article  PubMed  PubMed Central  Google Scholar 

Cole JH, Marioni RE, Harris SE, Deary IJ. Brain age and other bodily “ages”: implications for neuropsychiatry. Mol Psychiatry. 2019;24(2):266–81.

Article  PubMed  Google Scholar 

Bethlehem RAI, Seidlitz J, White SR, Vogel JW, Anderson KM, Adamson C, et al. Brain charts for the human lifespan. Nature. 2022;604(7906):525–33.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Xi YB, Wu XS, Cui LB, Bai LJ, Gan SQ, Jia XY, et al. Neuroimaging-based brain-age prediction of first-episode schizophrenia and the alteration of brain age after early medication. Br J Psychiatry. 2021:1–8.

Wang Q, Hu K, Wang M, Zhao Y, Liu Y, Fan L, et al. Predicting brain age during typical and atypical development based on structural and functional neuroimaging. Hum Brain Mapp. 2021;42(18):5943–55.

Article  PubMed  PubMed Central  Google Scholar 

Franke K, Gaser C. Ten years of BrainAGE as a neuroimaging biomarker of brain aging: What insights have we gained? Front Neurol. 2019;10:789.

Article  PubMed  PubMed Central  Google Scholar 

Geschwind DH, State MW. Gene hunting in autism spectrum disorder: on the path to precision medicine. Lancet Neurol. 2015;14(11):1109–20.

Article  PubMed  PubMed Central  Google Scholar 

Panizzon MS, Fennema-Notestine C, Eyler LT, Jernigan TL, Prom-Wormley E, Neale M, et al. Distinct genetic influences on cortical surface area and cortical thickness. Cereb Cortex. 2009;19(11):2728–35.

Article  PubMed  PubMed Central  Google Scholar 

Hawrylycz M, Miller JA, Menon V, Feng D, Dolbeare T, Guillozet-Bongaarts AL, et al. Canonical genetic signatures of the adult human brain. Nat Neurosci. 2015;18(12):1832–44.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, Shen EH, Ng L, Miller JA, et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature. 2012;489(7416):391–9.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Romero-Garcia R, Warrier V, Bullmore ET, Baron-Cohen S, Bethlehem RAI. Synaptic and transcriptionally downregulated genes are associated with cortical thickness differences in autism. Mol Psychiatry. 2019;24(7):1053–64.

Article  CAS  PubMed  Google Scholar 

Xie Y, Zhang X, Liu F, Qin W, Fu J, Xue K, et al. Brain mRNA expression associated with cortical volume alterations in autism spectrum disorder. Cell Rep. 2020;32(11):108137.

Article  CAS  PubMed  Google Scholar 

Di Martino A, Yan CG, Li Q, Denio E, Castellanos FX, Alaerts K, et al. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatry. 2014;19(6):659–67.

Article  PubMed  Google Scholar 

Di Martino A, O’Connor D, Chen B, Alaerts K, Anderson JS, Assaf M, et al. Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Sci Data. 2017;4:170010.

Article  PubMed  PubMed Central  Google Scholar 

Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage. 2007;38(1):95–113.

Article  PubMed  Google Scholar 

Glasser MF, Coalson TS, Robinson EC, Hacker CD, Harwell J, Yacoub E, et al. A multi-modal parcellation of human cerebral cortex. Nature. 2016;536(7615):171–8.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Fortin JP, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA, et al. Harmonization of cortical thickness measurements across scanners and sites. Neuroimage. 2018;167:104–20.

Article  PubMed  Google Scholar 

Cortes C. Support-Vector Networks. Machine Learning. 1995;20.

Tipping ME. Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res. 2001;1(3):211–44.

Google Scholar 

Williams C, Rasmussen CE. Gaussian processes for regression. Advances in neural information processing systems. 1996;8.

Franke K, Ziegler G, Kloppel S, Gaser C, Alzheimer's Disease Neuroimaging I. Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: exploring the influence of various parameters. Neuroimage. 2010;50(3):883–92.

Baecker L, Dafflon J, da Costa PF, Garcia-Dias R, Vieira S, Scarpazza C, et al. Brain age prediction: a comparison between machine learning models using region- and voxel-based morphometric data. Hum Brain Mapp. 2021;42(8):2332–46.

Article  PubMed  PubMed Central  Google Scholar 

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12:2825–30.

Google Scholar 

Arnatkeviciute A, Fulcher BD, Fornito A. A practical guide to linking brain-wide gene expression and neuroimaging data. Neuroimage. 2019;189:353–67.

Article  PubMed  Google Scholar 

Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10(1):1523.

Article  PubMed  PubMed Central  Google Scholar 

Frigaux A, Evrard R, Lighezzolo-Alnot J. ADI-R and ADOS and the differential diagnosis of autism spectrum disorders: Interests, limits and openings. Encephale. 2019;45(5):441–8.

Article  CAS