Waterhouse, L., London, E. & Gillberg, C. ASD validity. Rev. J. Autism Dev. Disord. 3, 302–329 (2016).
Hong, S. J. et al. Toward neurosubtypes in autism. Biol. Psychiatry 88, 111–128 (2020).
Mottron, L. & Bzdok, D. Autism spectrum heterogeneity: fact or artifact? Mol. Psychiatry 25, 3178–3185 (2020).
Duan, X., Shan, X., Uddin, L. Q. & Chen, H. The future of disentangling the heterogeneity of autism with neuroimaging studies. Biol. Psychiatry 97, 428–438 (2025).
Lin, H.-Y. & Lai, M.-C. in Neurodevelopmental Pediatrics: Genetic and Environmental Influences (eds Eisenstat, D. D. et al.) 269–282 (Springer, 2023).
Lombardo, M. V. & Mandelli, V. Rethinking our concepts and assumptions about autism. Front. Psychiatry 13, 90348 (2022).
Rødgaard, E. M., Jensen, K., Vergnes, J. N., Soulières, I. & Mottron, L. Temporal changes in effect sizes of studies comparing individuals with and without autism: a meta-analysis. JAMA Psychiatry 76, 1124–1132 (2019).
Wittkopf, S. et al. Conceptualization of the latent structure of autism: further evidence and discussion of dimensional and hybrid models. Eur. Child Adolesc. Psychiatry 32, 2247–2258 (2023).
Mottron, L. & Gagnon, D. Prototypical autism: new diagnostic criteria and asymmetrical bifurcation model. Acta Psychol. 237, 103938 (2023).
Smith, J. V. et al. Time is of the essence: age at autism diagnosis, sex assigned at birth, and psychopathology. Autism 28, 2909–2922 (2024).
Arvidsson, O., Gillberg, C., Lichtenstein, P. & Lundström, S. Secular changes in the symptom level of clinically diagnosed autism. J. Child Psychol. Psychiatry 59, 744–751 (2018).
Zhang, X. et al. Polygenic and developmental profiles of autism differ by age at diagnosis. Nature 646, 1146–1155 (2025).
Hahamy, A., Behrmann, M. & Malach, R. The idiosyncratic brain: distortion of spontaneous connectivity patterns in autism spectrum disorder. Nat. Neurosci. 18, 302–309 (2015).
Hiesinger, P. R. The Self-Assembling Brain: How Neural Networks Grow Smarter (Princeton Univ. Press, 2021).
Thelen, E., Schöner, G., Scheier, C. & Smith, L. B. The dynamics of embodiment: a field theory of infant perseverative reaching. Behav. Brain Sci. 24, 1–34 (2001).
Thelen, E. & Smith, L. B. A Dynamic Systems Approach to the Development of Cognition and Action (MIT Press, 1994).
Pillai, A. S. & Jirsa, V. K. Symmetry breaking in space-time hierarchies shapes brain dynamics and behavior. Neuron 94, 1010–1026 (2017).
Raju, A., Xue, B. & Leibler, S. A theoretical perspective on Waddington’s genetic assimilation experiments. Proc. Natl Acad. Sci. USA 120, e2309760120 (2023).
Mottron, L. et al. Asymmetric developmental bifurcations in polarized environments: a new class of human variants, which may include autism. Mol. Psychiatry 30, 6155–6164 (2025).
Shlesinger, M. F., Zaslavsky, G. M. & Klafter, J. Strange kinetics. Nature 363, 31–37 (1993).
Constantino, J. N. New guidance to seekers of autism biomarkers: an update from studies of identical twins. Mol. Autism 12, 28 (2021).
Breakspear, M. Dynamic models of large-scale brain activity. Nat. Neurosci. 20, 340–352 (2017).
Thom, R. Structural Stability and Morphogenesis (CRC Press, 2018).
Hancock, F. et al. Metastability demystified—the foundational past, the pragmatic present and the promising future. Nat. Rev. Neurosci. 26, 82–100 (2025).
Ashwin, P. & Breakspear, M. Anisotropic properties of riddled basins. Phys. Lett. A 280, 139–145 (2001).
Goldberger, A. L. & West, B. J. Fractals in physiology and medicine. Yale J. Biol. Med. 60, 421–435 (1987).
Roberts, J. A., Boonstra, T. W. & Breakspear, M. The heavy tail of the human brain. Curr. Opin. Neurobiol. 31, 164–172 (2015).
Clauset, A., Shalizi, C. R. & Newman, M. E. Power-law distributions in empirical data. SIAM Rev. 51, 661–703 (2009).
Mottron, L., Ostrolenk, A. & Gagnon, D. Prototypical autism, the genetic ability to learn language is triggered by structured information, not only by exposure to oral language. Genes 12, 1112 (2021).
Alex, A. M. et al. A global multicohort study to map subcortical brain development and cognition in infancy and early childhood. Nat. Neurosci. 27, 176–186 (2024).
Choi, H. et al. Diagnosis-informed connectivity subtyping discovers subgroups of autism with reproducible symptom profiles. Neuroimage 256, 119212 (2022).
Hong, S. J., Valk, S. L., Di Martino, A., Milham, M. P. & Bernhardt, B. C. Multidimensional neuroanatomical subtyping of autism spectrum disorder. Cereb. Cortex 28, 3578–3588 (2018).
Tang, S. et al. Reconciling dimensional and categorical models of autism heterogeneity: a brain connectomics and behavioral study. Biol. Psychiatry 87, 1071–1082 (2020).
Andrews, D. S., Marquand, A., Ecker, C. & McAlonan, G. Using pattern classification to identify brain imaging markers in autism spectrum disorder. Curr. Top. Behav. Neurosci. 40, 413–436 (2018).
Gagnon, D. et al. Using developmental regression to reorganize the clinical importance of autistic atypicalities. Transl. Psychiatry 12, 498 (2022).
Rødgaard, E. M. et al. Clinical correlates of diagnostic certainty in children and youths with autistic disorder. Mol. Autism 15, 15 (2024).
Buch, A. M. et al. Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder. Nat. Neurosci. 26, 650–663 (2023).
Lombardo, M. V. et al. Large-scale associations between the leukocyte transcriptome and BOLD responses to speech differ in autism early language outcome subtypes. Nat. Neurosci. 21, 1680–1688 (2018).
Mandelli, V. et al. A 3D approach to understanding heterogeneity in early developing autisms. Mol. Autism 15, 41 (2024).
Huys, Q. J. M., Browning, M., Paulus, M. P. & Frank, M. J. Advances in the computational understanding of mental illness. Neuropsychopharmacology 46, 3–19 (2021).
Wiecki, T. V., Poland, J. & Frank, M. J. Model-based cognitive neuroscience approaches to computational psychiatry: clustering and classification. Clin. Psychol. Sci. 3, 378–399 (2015).
Jacob, S. et al. Neurodevelopmental heterogeneity and computational approaches for understanding autism. Transl. Psychiatry 9, 63 (2019).
Litman, A. et al. Decomposition of phenotypic heterogeneity in autism reveals underlying genetic programs. Nat. Genet. 57, 1611–1619 (2025).
Cahalan, S., Mitroff, S. R., Subiaul, F. & Rosenblau, G. Using the cognitive rigidity–flexibility dimension to deepen our understanding of the autism spectrum. Personal. Neurosci. 8, e3 (2025).
De Groot, K. & Van Strien, J. W. Evidence for a broad autism phenotype. Adv. Neurodev. Disord. 1, 129–140 (2017).
Rosenblau, G., Frolichs, K. & Korn, C. W. A neuro-computational social learning framework to facilitate transdiagnostic classification and treatment across psychiatric disorders. Neurosci. Biobehav. Rev. 149, 105181 (2023).
Itahashi, T. et al. Transdiagnostic subtyping of males with developmental disorders using cortical characteristics. Neuroimage Clin. 27, 102288 (2020).
Wani, A. A. Comprehensive analysis of clustering algorithms: exploring limitations and innovative solutions. PeerJ Comput. Sci. 10, e2286 (2024).
Segal, A. et al. Embracing variability in the search for biological mechanisms of psychiatric illness. Trends Cogn. Sci. 29, 85–99 (2024).
Fisher, A. J., Medaglia, J. D. & Jeronimus, B. F. Lack of group-to-individual generalizability is a threat to human subjects research. Proc. Natl Acad. Sci. USA 115, E6106–E6115 (2018).
Reiter, M. A. et al. Performance of machine learning classification models of autism using resting-state fMRI is contingent on sample heterogeneity. Neural Comput. Appl. 33, 3299–3310 (2021).
Pagani, M. et al. Biological subtyping of autism via cross-species fMRI. Preprint at bioRxiv https://doi.org/10.1101/2025.03.04.641400 (2025).
Lombardo, M. V., Lai, M. C. & Baron-Cohen, S. Big data approaches to decomposing heterogeneity across the autism spectrum. Mol. Psychiatry 24, 1435–1450 (2019).
Mottron, L. Progress in autism research requires several recognition-definition-investigation cycles. Autism Res. 14, 2230–2234 (2021).
Sridhar, A. et al. Increased heterogeneity and task-related reconfiguration of functional connectivity during a lexicosemantic task in autism. Neuroimage Clin. 44, 103694 (2024).
Rabot, J. et al. Genesis, modelling and methodological remedies to autism heterogeneity. Neurosci. Biobehav. Rev. 150, 105201 (2023).
Seghier, M. L. & Price, C. J. Interpreting and utilising intersubject variability in brain function. Trends Cogn. Sci. 22, 517–530 (2018).
Pickles, A., Anderson, D. K. & Lord, C. Heterogeneity and plasticity in the development of language: a 17-year follow-up of children referred early for possible autism. J. Child Psychol. Psychiatry 55, 1354–1362 (2014).
Rødgaard, E. M., Jensen, K., Miskowiak, K. W. & Mottron, L. Autism comorbidities show elevated female-to-male odds ratios and are associated with the age of first autism diagnosis. Acta Psychiatr. Scand. 144, 475–486 (2021).
Dinstein, I., Heeger, D. J. & Behrmann, M. Neural variability: friend or foe? Trends Cogn. Sci. 19, 322–328 (2015).
Benkarim, O. et al. Connectivity alterations in autism reflect functional idiosyncrasy. Commun. Biol. 4, 1078 (2021).
Dickie, E. W. et al. Personalized intrinsic network topography mapping and functional connectivity deficits in autism spectrum disorder. Biol. Psychiatry 84, 278–286 (2018).
Tung, Y. H. et al. Whole brain white matter tract deviation and idiosyncrasy from normative development in autism and ADHD and unaffected siblings link with dimensions of psychopathology and cognition. Am. J. Psychiatry 178, 730–743 (2021).
Levin, A. R. et al. Day-to-day test-retest reliability of EEG profiles in children with autism spectrum disorder and typical development. Front. Integr. Neurosci. 14, 21 (2020).
Gordon, E. M. et al. Precision functional mapping of individual human brains. Neuron 95, 791–807.e797 (2017).
Dworetsky, A. et al. Two common and distinct forms of variation in human functional brain networks. Nat. Neurosci. 27, 1187–1198 (2024).
Mansour, L. S., Tian, Y., Yeo, B. T. T., Cropley, V. & Zalesky, A. High-resolution connectomic fingerprints: mapping neural identity and behavior. Neuroimage 229, 117695 (2021).
Finn, E. S. et al. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat. Neurosci. 18, 1664–1671 (2015).
Hawco, C. et al. Greater individual variability in functional brain activity during working memory performance in young people with autism and executive function impairment. Neuroimage Clin. 27, 102260 (2020).
Poulin-Lord, M. P. et al. Increased topographical variability of task-related activation in perceptive and motor associative regions in adult autistics. Neuroimage Clin. 4, 444–453 (2014).
Padmanabhan, A., Lynch, C. J., Schaer, M. & Menon, V. The default mode network in autism. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 2, 476–486 (2017).
Bernhardt, B. C., Valk, S. L., Hong, S.-J., Soulières, I. & Mottron, L. Autism-related shifts in the brain’s information processing hierarchy. Trends Cogn. Sci. 29, 942–955 (2025).
Müller, R. A. & Fishman, I. Brain connectivity and neuroimaging of social networks in autism. Trends Cogn. Sci. 22, 1103–1116 (2018).
Nunes, A. S., Peatfield, N., Vakorin, V. & Doesburg, S. M. Idiosyncratic organization of cortical networks in autism spectrum disorder. Neuroimage 190, 182–190 (2019).
Pegado, F. et al. Adults with high functioning autism display idiosyncratic behavioral patterns, neural representations and connectivity of the ‘voice area’ while judging the appropriateness of emotional vocal reactions. Cortex 125, 90–108 (2020).
Scherf, K. S., Luna, B., Minshew, N. & Behrmann, M. Location, location, location: alterations in the functional topography of face- but not object- or place-related cortex in adolescents with autism. Front. Hum. Neurosci. 4, 26 (2010).
Müller, R. A., Kleinhans, N., Kemmotsu, N., Pierce, K. & Courchesne, E. Abnormal variability and distribution of functional maps in autism: an fMRI study of visuomotor learning. Am. J. Psychiatry 160, 1847–1862 (2003).
Bolton, T. A. W., Jochaut, D., Giraud, A. L. & Van De Ville, D. Brain dynamics in ASD during movie-watching show idiosyncratic functional integration and segregation. Hum. Brain Mapp. 39, 2391–2404 (2018).
Bolton, T. A. W., Freitas, L. G. A., Jochaut, D., Giraud, A. L. & Van De Ville, D. Neural responses in autism during movie watching: inter-individual response variability co-varies with symptomatology. Neuroimage 216, 116571 (2020).
Byrge, L., Dubois, J., Tyszka, J. M., Adolphs, R. & Kennedy, D. P. Idiosyncratic brain activation patterns are associated with poor social comprehension in autism. J. Neurosci. 35, 5837–5850 (2015).
Bleimeister, I. H. et al. Idiosyncratic pupil regulation in autistic children. Autism Res. 17, 2503–2513 (2024).
Sen, S. et al. The role of visual experience in individual differences of brain connectivity. J. Neurosci. 42, 5070–5084 (2022).
Bäckström, A. et al. Motor planning and movement execution during goal-directed sequential manual movements in 6-year-old children with autism spectrum disorder: a kinematic analysis. Res. Dev. Disabil. 115, 104014 (2021).
Kojovic, N. et al. Unraveling the developmental dynamic of visual exploration of social interactions inautism. eLife 13, e85623 (2024).
Bowler, D. M., Gaigg, S. B. & Gardiner, J. M. Subjective organisation in the free recall learning of adults with Asperger’s syndrome. J. Autism Dev. Disord. 38, 104–113 (2008).
Meilleur, A. A., Jelenic, P. & Mottron, L. Prevalence of clinically and empirically defined talents and strengths in autism. J. Autism Dev. Disord. 45, 1354–1367 (2015).
Heaton, P. Pitch memory, labelling and disembedding in autism. J. Child Psychol. Psychiatry 44, 543–551 (2003).
Pellicano, E., Maybery, M., Durkin, K. & Maley, A. Multiple cognitive capabilities/deficits in children with an autism spectrum disorder: ‘weak’ central coherence and its relationship to theory of mind and executive control. Dev. Psychopathol. 18, 77–98 (2006).
van Leeuwen, T. M., Neufeld, J., Hughes, J. & Ward, J. Synaesthesia and autism: different developmental outcomes from overlapping mechanisms? Cogn. Neuropsychol. 37, 433–449 (2020).
Mottron, L., Dawson, M. & Soulières, I. Enhanced perception in savant syndrome: patterns, structure and creativity. Philos. Trans. R. Soc. Lond. B 364, 1385–1391 (2009).
Ostrolenk, A. et al. Enhanced interest in letters and numbers in autistic children. Mol. Autism 15, 26 (2024).
Kissine, M., Saint-Denis, A. & Mottron, L. Language acquisition can be truly atypical in autism: Beyond joint attention. Neurosci. Biobehav. Rev. 153, 105384 (2023).
Wodka, E. L., Mathy, P. & Kalb, L. Predictors of phrase and fluent speech in children with autism and severe language delay. Pediatrics 131, e1128–e1134 (2013).
Desrosiers, J. et al. How is calendar calculation in autism possible? A language model. Psychol. Rev. https://doi.org/10.1037/rev0000590 (2025).
Mottron, L. & Gagnon, D. Debate: How far can we modify the expression of autism by modifying the environment? Child Adolesc. Ment. Health 29, 101–103 (2024).
Green, J. Autism as emergent and transactional. Front. Psychiatry 13, 988755 (2022).
Hassan, B. A. & Hiesinger, P. R. Beyond molecular codes: simple rules to wire complex brains. Cell 163, 285–291 (2015).
Cicchetti, D. & Rogosch, F. A. Equifinality and multifinality in developmental psychopathology. Dev. Psychopathol. 8, 597–600 (1996).
Abrahams, B. S. et al. SFARI Gene 2.0: a community-driven knowledgebase for the autism spectrum disorders (ASDs). Mol Autism 4, 36 (2013).
Berkel, S. et al. Mutations in the SHANK2 synaptic scaffolding gene in autism spectrum disorder and mental retardation. Nat. Genet. 42, 489–491 (2010).
Leblond, C. S. et al. Genetic and functional analyses of SHANK2 mutations suggest a multiple hit model of autism spectrum disorders. PLoS Genet. 8, e1002521 (2012).
Hallmayer, J. et al. Genetic heritability and shared environmental factors among twin pairs with autism. Arch. Gen. Psychiatry 68, 1095–1102 (2011).
de la Torre-Ubieta, L., Won, H., Stein, J. L. & Geschwind, D. H. Advancing the understanding of autism disease mechanisms through genetics. Nat. Med. 22, 345–361 (2016).
Bourgeron, T. From the genetic architecture to synaptic plasticity in autism spectrum disorder. Nat. Rev. Neurosci. 16, 551–563 (2015).
Gaugler, T. et al. Most genetic risk for autism resides with common variation. Nat. Genet. 46, 881–885 (2014).
Krumm, N. et al. Excess of rare, inherited truncating mutations in autism. Nat. Genet. 47, 582–588 (2015).
Hartman, J. L. T., Garvik, B. & Hartwell, L. Principles for the buffering of genetic variation. Science 291, 1001–1004 (2001).
Cirnigliaro, M. et al. The contributions of rare inherited and polygenic risk to ASD in multiplex families. Proc. Natl Acad. Sci. USA 120, e2215632120 (2023).
Antaki, D. et al. A phenotypic spectrum of autism is attributable to the combined effects of rare variants, polygenic risk and sex. Nat. Genet. 54, 1284–1292 (2022).
Werling, D. M. The role of sex-differential biology in risk for autism spectrum disorder. Biol. Sex Differ. 7, 58 (2016).
Vogel Ciernia, A. & LaSalle, J. The landscape of DNA methylation amid a perfect storm of autism aetiologies. Nat. Rev. Neurosci. 17, 411–423 (2016).
Rodin, R. E. et al. The landscape of somatic mutation in cerebral cortex of autistic and neurotypical individuals revealed by ultra-deep whole-genome sequencing. Nat. Neurosci. 24, 176–185 (2021).
Fard, Y. A. et al. Epigenetic underpinnings of the autistic mind: histone modifications and prefrontal excitation/inhibition imbalance. Am. J. Med. Genet. B 195, e32986 (2024).
Tasnim, A. et al. The developmental timing of spinal touch processing alterations predicts behavioral changes in genetic mouse models of autism spectrum disorders. Nat. Neurosci. 27, 484–496 (2024).
Pinto, D. et al. Functional impact of global rare copy number variation in autism spectrum disorders. Nature 466, 368–372 (2010).
Ayroles, J. F. et al. Behavioral idiosyncrasy reveals genetic control of phenotypic variability. Proc. Natl Acad. Sci. USA 112, 6706–6711 (2015).
Anderson, M. L. Neural reuse: a fundamental organizational principle of the brain. Behav. Brain. Sci 33, 245–266 (2010).
Mottron, L., Belleville, S., Rouleau, G. A. & Collignon, O. Linking neocortical, cognitive, and genetic variability in autism with alterations of brain plasticity: the trigger-threshold-target model. Neurosci. Biobehav. Rev. 47, 735–752 (2014).
Mueller, S. et al. Individual variability in functional connectivity architecture of the human brain. Neuron 77, 586–595 (2013).
Turkeltaub, P. E. et al. The neural basis of hyperlexic reading: an fMRI case study. Neuron 41, 11–25 (2004).
Wertheimer, O. & Hart, Y. Autism spectrum disorder variation as a computational trade-off via dynamic range of neuronal population responses. Nat. Neurosci. 27, 2476–2486 (2024).
Noel, J. P. & Angelaki, D. E. A theory of autism bridging across levels of description. Trends Cogn. Sci. 27, 631–641 (2023).
Stanley, J. et al. Large language models deconstruct the clinical intuition behind diagnosing autism. Cell 188, 2235–2248 (2025).
Lydon-Staley, D. M., Cornblath, E. J., Blevins, A. S. & Bassett, D. S. Modeling brain, symptom, and behavior in the winds of change. Neuropsychopharmacology 46, 20–32 (2021).
Ferrell, J. E. Jr. Self-perpetuating states in signal transduction: positive feedback, double-negative feedback and bistability. Curr. Opin. Cell Biol. 14, 140–148 (2002).
Kraus, B. et al. Insights from personalized models of brain and behavior for identifying biomarkers in psychiatry. Neurosci. Biobehav. Rev. 152, 105259 (2023).
Strock, A., Mistry, P. K. & Menon, V. Personalized deep neural networks reveal mechanisms of math learning disabilities in children. Sci. Adv. 11, eadq9990 (2025).
Ventola, P. et al. Heterogeneity of neural mechanisms of response to pivotal response treatment. Brain Imaging Behav. 9, 74–88 (2015).
Astle, D. E., Bassett, D. S. & Viding, E. Understanding divergence: placing developmental neuroscience in its dynamic context. Neurosci. Biobehav. Rev. 157, 105539 (2024).
Roberts, G. et al. Longitudinal changes in structural connectivity in young people at high genetic risk for bipolar disorder. Am. J. Psychiatry 179, 350–361 (2022).
Green, J. Debate: Neurodiversity, autism and healthcare. Child Adolesc. Ment. Health 28, 438–442 (2023).
Pellicano, E. et al. A capabilities approach to understanding and supporting autistic adulthood. Nat. Rev. Psychol. 1, 624–639 (2022).
Pellicano, E. & den Houting, J. Annual research review: shifting from ‘normal science’ to neurodiversity in autism science. J. Child Psychol. Psychiatry 63, 381–396 (2022).
Mottron, L. Changing perceptions: the power of autism. Nature 479, 33–35 (2011).
Heraty, S. et al. Bridge-building between communities: Imagining the future of biomedical autism research. Cell 186, 3747–3752 (2023).
Collin, C. B. et al. Computational models for clinical applications in personalized medicine-guidelines and recommendations for data integration and model validation. J. Pers. Med. 12, 166 (2022).
Friston, K. J. et al. Dynamic causal modelling revisited. NeuroImage 199, 730–744 (2019).
Jin, J., Zeidman, P., Friston, K. J. & Kotov, R. Inferring trajectories of psychotic disorders using dynamic causal modeling. Comput. Psychiatr. 7, 60–75 (2023).
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 19, 659–667 (2014).
King, J. B. et al. Generalizability and reproducibility of functional connectivity in autism. Mol. Autism 10, 27 (2019).
Rodriguez, R. X., Noble, S., Camp, C. C. & Scheinost, D. Connectome caricatures remove large-amplitude coactivation patterns in resting-state fMRI to emphasize individual differences. Nat. Neurosci. 28, 2601–2611 (2025).
Rutherford, S. et al. The normative modeling framework for computational psychiatry. Nat. Protoc. 17, 1711–1734 (2022).
Chien, Y. L. et al. Neurodevelopmental model of schizophrenia revisited: similarity in individual deviation and idiosyncrasy from the normative model of whole-brain white matter tracts and shared brain-cognition covariation with ADHD and ASD. Mol. Psychiatry 27, 3262–3271 (2022).
Elfadaly, F. G., Garthwaite, P. H. & Crawford, J. R. On point estimation of the abnormality of a Mahalanobis index. Comput. Stat. Data Anal. 99, 115–130 (2016).
Bottema-Beutel, K., Kapp, S. K., Lester, J. N., Sasson, N. J. & Hand, B. N. Avoiding ableist language: suggestions for autism researchers. Autism Adulthood 3, 18–29 (2021).
Haatveit, B. et al. Intra- and inter-individual cognitive variability in schizophrenia and bipolar spectrum disorder: an investigation across multiple cognitive domains. Schizophrenia 9, 89 (2023).
Brugger, S. P. & Howes, O. D. Heterogeneity and homogeneity of regional brain structure in schizophrenia: a meta-analysis. JAMA Psychiatry 74, 1104–1111 (2017).
Tepper, Á et al. Intra and inter-individual variability in functional connectomes of patients with first episode of psychosis. Neuroimage Clin. 38, 103391 (2023).
Aoki, Y. et al. Association of white matter structure with autism spectrum disorder and attention-deficit/hyperactivity disorder. JAMA Psychiatry 74, 1120–1128 (2017).
de Lange, S. C. et al. Shared vulnerability for connectome alterations across psychiatric and neurological brain disorders. Nat. Hum. Behav. 3, 988–998 (2019).
Ostrolenk, A., Courchesne, V. & Mottron, L. A longitudinal study on language acquisition in monozygotic twins concordant for autism and hyperlexia. Brain Cogn. 173, 106099 (2023).
Gagnon, D., Ostrolenk, A. & Mottron, L. Early manifestations of unexpected bilingualism in minimally verbal autism. J. Child Psychol. Psychiatry https://doi.org/10.1111/jcpp.70032 (2025).
Gollo, L. L. et al. Fragility and volatility of structural hubs in the human connectome. Nat. Neurosci. 21, 1107–1116 (2018).
McClellan, J. M. et al. An evolutionary perspective on complex neuropsychiatric disease. Neuron 112, 7–24 (2024).
Schindler, D. E. et al. Population diversity and the portfolio effect in an exploited species. Nature 465, 609–612 (2010).
Kardos, M. et al. The crucial role of genome-wide genetic variation in conservation. Proc. Natl Acad. Sci. USA 118, e2104642118 (2021).
Friston, K. Life as we know it. J. R. Soc. Interface 10, 20130475 (2013).
Frankel, N. W. et al. Adaptability of non-genetic diversity in bacterial chemotaxis. eLife 3, e03526 (2014).
Hiesinger, P. R. & Hassan, B. A. The evolution of variability and robustness in neural development. Trends Neurosci. 41, 577–586 (2018).
Hunt, A. D. & Jaeggi, A. V. The DCIDE framework: systematic investigation of evolutionary hypotheses, exemplified with autism. Biol. Rev. Camb. Philos. Soc. 100, 1484–1511 (2025).