, Diagnostic and Statistical Manual of Mental Disorders, American Psychiatric Association, 2013.

C. Betancur, Etiological heterogeneity in autism spectrum disorders: More than 100 genetic and genomic disorders and still counting, Brain Res, vol.1380, pp.42-77, 2011.
URL : https://hal.archives-ouvertes.fr/inserm-00549873

C. Ecker, The neuroanatomy of autism spectrum disorder: An overview of structural neuroimaging findings and their translatability to the clinical setting, Autism, vol.21, pp.18-28, 2016.

C. A. Walsh, E. M. Morrow, and J. Rubenstein, Autism and brain development, Cell, vol.135, pp.396-400, 2008.

C. M. Schumann, C. S. Bloss, C. C. Barnes, G. M. Wideman, R. A. Carper et al., Longitudinal magnetic resonance imaging study of cortical development through early childhood in autism, J Neurosci, vol.30, pp.4419-4427, 2010.

C. Ecker, C. Ginestet, Y. Feng, P. Johnston, M. V. Lombardo et al., Brain surface anatomy in adults with autism: The relationship between surface area, cortical thickness, and autistic symptomsbrain surface anatomy in adults with autism, JAMA Psychiatry, vol.70, pp.59-70, 2013.

K. L. Hyde, F. Samson, A. C. Evans, and L. Mottron, Neuroanatomical differences in brain areas implicated in perceptual and other core features of autism revealed by cortical thickness analysis and voxelbased morphometry, Hum Brain Mapp, vol.31, pp.556-566, 2010.

D. Van-rooij, E. Anagnostou, C. Arango, G. Auzias, M. Behrmann 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, vol.175, pp.359-369, 2018.

C. Ecker, S. Y. Bookheimer, and D. Murphy, Neuroimaging in autism spectrum disorder: Brain structure and function across the lifespan, Lancet Neurol, vol.14, pp.1121-1134, 2015.

H. C. Hazlett, M. Poe, G. Gerig, M. Styner, C. Chappell et al., Early brain overgrowth in autism associated with an increase in cortical surface area before age 2 years, Arch Gen Psychiatry, vol.68, pp.467-476, 2011.

J. Piven, S. Arndt, J. Bailey, S. Havercamp, N. C. Andreasen et al., An MRI study of brain size in autism, Am J Psychiatry, vol.152, pp.1145-1149, 1995.

J. Piven, S. Arndt, J. Bailey, and N. Andreasen, Regional brain enlargement in autism: A magnetic resonance imaging study, J Am Acad Child Adolesc Psychiatry, vol.35, pp.530-536, 1996.

A. Y. Hardan, N. J. Minshew, M. Mallikarjuhn, and M. S. Keshavan, Brain volume in autism, J Child Neurol, vol.16, pp.421-424, 2001.

M. C. Lai, M. V. Lombardo, B. Chakrabarti, and S. Baron-cohen, Subgrouping the autism "spectrum": Reflections on DSM-5, vol.11, p.1001544, 2013.

N. Hadjikhani, R. M. Joseph, J. Snyder, and H. Tager-flusberg, Anatomical differences in the mirror neuron system and social cognition network in autism, Cereb Cortex, vol.16, pp.1276-1282, 2006.

K. M. Mak-fan, M. J. Taylor, W. Roberts, and J. P. Lerch, Measures of cortical grey matter structure and development in children with autism spectrum disorder, J Autism Dev Disord, vol.42, pp.419-427, 2012.

G. L. Wallace, N. Dankner, L. Kenworthy, J. N. Giedd, and A. Martin, Agerelated temporal and parietal cortical thinning in autism spectrum disorders, Brain, vol.133, pp.3745-3754, 2010.

C. Scheel, A. Rotarska-jagiela, L. Schilbach, F. G. Lehnhardt, B. Krug et al., Imaging derived cortical thickness reduction in high-functioning autism: Key regions and temporal slope, Neuroimage, vol.58, pp.391-400, 2011.

S. Haar, S. Berman, M. Behrmann, and I. Dinstein, Anatomical abnormalities in autism?, Cereb Cortex, vol.26, pp.1440-1452, 2016.

B. S. Khundrakpam, J. D. Lewis, P. Kostopoulos, F. Carbonell, and A. C. Evans, Cortical thickness abnormalities in autism spectrum disorders through late childhood, adolescence, and adulthood: A large-scale MRI study, Cereb Cortex, vol.27, pp.1721-1731, 2017.

E. Courchesne, C. M. Karns, H. R. Davis, R. Ziccardi, R. A. Carper et al., Unusual brain growth patterns in early life in patients with autistic disorder: An MRI study, Neurology, vol.57, pp.245-254, 2011.

C. Ecker, A. Shahidiani, Y. Feng, E. Daly, C. Murphy et al., The effect of age, diagnosis, and their interaction on vertexbased measures of cortical thickness and surface area in autism spectrum disorder, J Neural Transm, vol.121, pp.1157-1170, 2014.

G. Ramaswami and D. H. Geschwind, Genetics of autism spectrum disorder, Handb Clin Neurol, vol.147, pp.321-329, 2018.

W. Zhang, W. Groen, M. Mennes, C. Greven, J. Buitelaar et al., Revisiting subcortical brain volume correlates of autism in the ABIDE dataset: Effects of age and sex, Psychol Med, vol.48, pp.654-668, 2018.

T. Wolfers, J. K. Buitelaar, C. F. Beckmann, B. Franke, and A. F. Marquand, From estimating activation locality to predicting disorder: A review of pattern recognition for neuroimaging-based psychiatric diagnostics, Neurosci Biobehav Rev, vol.57, pp.328-349, 2015.

M. R. Sabuncu and E. Konukoglu, Clinical prediction from structural brain MRI scans: A large-scale empirical study, Neuroinformatics, vol.13, pp.31-46, 2014.

C. R. Damiano, C. A. Mazefsky, S. W. White, and G. S. Dichter, Future directions for research in autism spectrum disorders, J Clin Child Adolesc Psychol, vol.43, pp.828-843, 2014.

T. R. Insel and B. N. Cuthbert, Brain disorders? Precisely. Science, vol.348, pp.499-500, 2015.

M. V. Lombardo, K. Pierce, L. T. Eyler, C. Barnes, C. Ahrensbarbeau et al., Different functional neural substrates for good and poor language outcome in autism, Neuron, vol.86, pp.567-577, 2015.

C. Fountain, A. S. Winter, and P. S. Bearman, Six developmental trajectories characterize children with autism, Pediatrics, vol.129, pp.1112-1120, 2012.

D. A. Fair, D. Bathula, M. A. Nikolas, and J. T. Nigg, Distinct neuropsychological subgroups in typically developing youth inform heterogeneity in children with ADHD, Proc Natl Acad Sci U S A, vol.109, pp.6769-6774, 2012.

C. Dias, T. G. Iyer, S. P. Carpenter, S. D. Cary, R. P. Wilson et al., Characterizing heterogeneity in children with and without ADHD based on reward system connectivity, Dev Cogn Neurosci, vol.11, pp.155-174, 2015.

H. M. Van-loo, P. De-jonge, J. Romeijn, R. C. Kessler, and R. A. Schoevers, Data-driven subtypes of major depressive disorder: A systematic review, BMC Med, vol.10, p.156, 2012.

M. D. Bell, S. Corbera, J. K. Johannesen, J. M. Fiszdon, and B. E. Wexler, Social cognitive impairments and negative symptoms in schizophrenia: Are there subtypes with distinct functional correlates?, Schizophr Bull, vol.39, pp.186-196, 2013.

A. F. Marquand, T. Wolfers, M. Mennes, J. Buitelaar, and C. F. Beckmann, Beyond lumping and splitting: A review of computational approaches for stratifying psychiatric disorders, Biol Psychiatry Cogn Neurosci Neuroimaging, vol.1, pp.433-447, 2016.

A. F. Marquand, I. Rezek, J. Buitelaar, and C. F. Beckmann, Understanding heterogeneity in clinical cohorts using normative models: Beyond case-control studies, Biol Psychiatry, vol.80, pp.552-561, 2016.

, Dissecting Heterogeneity of ASD With Normative Modeling Biological Psychiatry, Cognitive Neuroscience and Neuroimaging, 2019.

T. Wolfers, N. Doan, T. Kaufmann, D. Alnaes, T. Moberget et al., Mapping the heterogeneous phenotype of schizophrenia and bipolar disorder using normative models, JAMA Psychiatry, vol.75, pp.1146-1155, 2018.

E. Loth, T. Charman, L. Mason, J. Tillmann, E. Jones et al., The EU-AIMS Longitudinal European Autism Project (LEAP): Design and methodologies to identify and validate stratification biomarkers for autism spectrum disorders, Mol Autism, vol.8, p.24, 2017.
URL : https://hal.archives-ouvertes.fr/pasteur-01967232

E. Anagnostou and M. J. Taylor, Review of neuroimaging in autism spectrum disorders: What have we learned and where we go from here, Mol Autism, vol.2, p.4, 2011.

T. Charman, E. Loth, J. Tillmann, D. Crawley, C. Wooldridge et al., The EU-AIMS Longitudinal European Autism Project (LEAP): Clinical characterisation, Mol Autism, vol.8, p.27, 2017.
URL : https://hal.archives-ouvertes.fr/pasteur-01967230

M. Rutter, Autism Diagnostic Interview, 2003.

C. Lord, S. Risi, L. Lambrecht, E. H. Cook, B. L. Leventhal et al., The Autism Diagnostic Observation Schedule-Generic: A standard measure of social and communication deficits associated with the spectrum of autism, J Autism Dev Disord, vol.30, pp.205-223, 2000.

C. E. Rasmussen and C. Williams, Model selection and adaptation of hyperparameters, Gaussian Processes for Machine Learning, pp.105-128, 2006.

R. A. Fisher and L. Tippett, Limiting forms of the frequency distribution of the largest or smallest member of a sample, Math Proc Cambridge Philos Soc, vol.24, pp.180-190, 1928.

R. S. Desikan, F. Ségonne, B. Fischl, B. T. Quinn, B. C. Dickerson et al., An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest, Neuroimage, vol.31, pp.968-980, 2006.

A. M. Dale, B. Fischl, and M. I. Sereno, Cortical surface-based analysis: I. Segmentation and surface reconstruction, Neuroimage, vol.9, pp.179-184, 1999.

K. B. Walhovd, A. M. Fjell, J. Giedd, A. M. Dale, and T. T. Brown, Through thick and thin: A need to reconcile contradictory results on trajectories in human cortical development, Cereb Cortex, vol.27, pp.1472-1481, 2016.

B. A. Zielinski, M. Prigge, J. A. Nielsen, A. L. Froehlich, T. J. Abildskov et al., Longitudinal changes in cortical thickness in autism and typical development, Brain, vol.137, pp.1799-1812, 2014.

S. Ducharme, M. D. Albaugh, T. Nguyen, J. J. Hudziak, J. M. Mateos-pérez et al., NeuroImage Trajectories of cortical thickness maturation in normal brain development -The importance of quality control procedures, Neuroimage, vol.125, pp.267-279, 2016.

C. K. Tamnes, M. M. Herting, A. Goddings, R. Meuwese, S. Blakemore 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, vol.37, pp.3402-3412, 2017.

P. Shaw, N. J. Kabani, J. P. Lerch, K. Eckstrand, R. Lenroot et al., Neurodevelopmental trajectories of the human cerebral cortex, J Neurosci, vol.28, pp.3586-3594, 2008.

A. M. Fjell, H. Grydeland, S. K. Krogsrud, I. Amlien, D. A. Rohani et al., Development and aging of cortical thickness correspond to genetic organization patterns, Proc Natl Acad Sci U S A, vol.112, pp.15462-15467, 2015.

A. M. Fjell and K. B. Walhovd, Structural brain changes in aging: Courses, causes and cognitive consequences, vol.21, pp.187-221, 2010.

V. T. Mensen, L. M. Wierenga, S. Van-dijk, Y. Rijks, B. Oranje et al., Development of cortical thickness and surface area in autism spectrum disorder, Neuroimage Clin, vol.13, pp.215-222, 2016.

M. Thambisetty, J. Wan, A. Carass, Y. An, J. L. Prince et al., Longitudinal changes in cortical thickness associated with normal aging, Neuroimage, vol.52, pp.1215-1223, 2010.

B. S. Abrahams and D. H. Geschwind, Advances in autism genetics: On the threshold of a new neurobiology, Nat Rev Genet, vol.9, pp.341-355, 2008.

C. Ecker and D. Murphy, Neuroimaging in autism-from basic science to translational research, Nat Rev Neurol, vol.10, pp.82-91, 2014.

D. H. Geschwind and P. Levitt, Autism spectrum disorders: Developmental disconnection syndromes, Curr Opin Neurobiol, vol.17, pp.103-111, 2007.

C. R. Marshall, A. Noor, J. B. Vincent, A. C. Lionel, L. Feuk et al., Structural variation of chromosomes in autism spectrum disorder, Am J Hum Genet, vol.82, pp.477-488, 2008.

L. A. Croen, J. K. Grether, and S. Selvin, Descriptive epidemiology of autism in a California population: Who is at risk?, J Autism Dev Disord, vol.32, pp.217-224, 2002.

M. M. Seltzer, P. Shattuck, L. Abbeduto, and J. S. Greenberg, Trajectory of development in adolescents and adults with autism, Ment Retard Dev Disabil Res Rev, vol.10, pp.234-247, 2004.

A. Ronald, F. Happé, P. Bolton, L. M. Butcher, T. S. Price et al., Genetic heterogeneity between the three components of the autism spectrum: A twin study, J Am Acad Child Adolesc Psychiatry, vol.45, pp.691-699, 2006.

A. Y. Hardan, S. Muddasani, M. Vemulapalli, M. S. Keshavan, and N. J. Minshew, An MRI study of increased cortical thickness in autism, Am J Psychiatry, vol.163, pp.1290-1292, 2006.

D. Martino, A. Yan, C. G. Li, Q. Denio, E. Castellanos et al., The autism brain imaging data exchange: Towards a largescale evaluation of the intrinsic brain architecture in autism, Mol Psychiatry, vol.19, pp.659-667, 2014.

R. Bethlehem, J. Seidlitz, R. Romero-garcia, and M. V. Lombardo, Using normative age modelling to isolate subsets of individuals with autism expressing highly age-atypical cortical thickness features, 2018.

D. G. Amaral, C. M. Schumann, and C. W. Nordahl, Neuroanatomy of autism, vol.31, pp.137-145, 2008.

Y. Jiao, R. Chen, X. Ke, K. Chu, Z. Lu et al., Predictive models of autism spectrum disorder based on brain regional cortical thickness, Neuroimage, vol.50, pp.589-599, 2011.

C. Ecker, D. Andrews, F. Dell'acqua, E. Daly, C. Murphy et al., Relationship between cortical gyrification, white matter connectivity, and autism spectrum disorder, Cereb Cortex, vol.26, pp.3297-3309, 2016.

C. Ecker, L. Ronan, Y. Feng, E. Daly, C. Murphy et al., Intrinsic gray-matter connectivity of the brain in adults with autism spectrum disorder, Proc Natl Acad Sci U S A, vol.110, pp.13222-13227, 2013.

E. Moradi, B. Khundrakpam, J. D. Lewis, A. C. Evans, and J. Tohka, Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data, Neuroimage, vol.144, pp.128-141, 2017.

K. Doyle-thomas, E. G. Duerden, M. J. Taylor, J. P. Lerch, L. V. Soorya et al., Effects of age and symptomatology on cortical thickness in autism spectrum disorders, Res Autism Spectr Disord, vol.7, pp.141-150, 2013.

J. N. Giedd, J. Blumenthal, N. O. Jeffries, F. X. Castellanos, H. Liu et al., Brain development during childhood and adolescence: A longitudinal MRI study, Nat Neurosci, vol.2, pp.861-863, 1999.

A. Y. Hardan, R. A. Libove, M. S. Keshavan, N. M. Melhem, and N. J. Minshew, A preliminary longitudinal magnetic resonance imaging study of brain volume and cortical thickness in autism, Biol Psychiatry, vol.66, pp.320-326, 2009.

G. M. Mcalonan, V. Cheung, C. Cheung, J. Suckling, G. Y. Lam et al., Mapping the brain in autism. A voxel-based MRI study of volumetric differences and intercorrelations in autism, Brain, vol.128, pp.268-276, 2005.

G. M. Mcalonan, J. Suckling, N. Wong, V. Cheung, N. Lienenkaemper et al., Distinct patterns of grey matter abnormality in high-functioning autism and Asperger's syndrome, J Child Psychol Psychiatry, vol.49, pp.1287-1295, 2008.

, Dissecting Heterogeneity of ASD With Normative Modeling