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Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder

Nature Neuroscience volume 26, pages 650–663 (2023)Cite this article

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Abstract

The mechanisms underlying phenotypic heterogeneity in autism spectrum disorder (ASD) are not well understood. Using a large neuroimaging dataset, we identified three latent dimensions of functional brain network connectivity that predicted individual differences in ASD behaviors and were stable in cross-validation. Clustering along these three dimensions revealed four reproducible ASD subgroups with distinct functional connectivity alterations in ASD-related networks and clinical symptom profiles that were reproducible in an independent sample. By integrating neuroimaging data with normative gene expression data from two independent transcriptomic atlases, we found that within each subgroup, ASD-related functional connectivity was explained by regional differences in the expression of distinct ASD-related gene sets. These gene sets were differentially associated with distinct molecular signaling pathways involving immune and synapse function, G-protein-coupled receptor signaling, protein synthesis and other processes. Collectively, our findings delineate atypical connectivity patterns underlying different forms of ASD that implicate distinct molecular signaling mechanisms.

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Fig. 1: Three brain–behavior dimensions explain individual differences in autism spectrum disorder.
Fig. 2: Functional connectivity correlates of autism spectrum disorder symptoms.
Fig. 3: Hierarchical clustering on brain–behavior dimension scores reveals four autism spectrum disorder subgroups.
Fig. 4: Autism spectrum disorder subgroups have distinct atypical connectivity patterns in dimension-related RSFC features.
Fig. 5: Transcriptomic correlates of atypical connectivity patterns in autism spectrum disorder subgroups.
Fig. 6: Protein–protein interaction networks reveal distinct connectivity-related genes with textual associations to autism spectrum disorder-related behaviors.

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Data availability

The data that support the findings of this study are publicly available. The neuroimaging datasets are available from ABIDE I and ABIDE II (https://fcon_1000.projects.nitrc.org/indi/abide/) and the the NDAR database (https://nda.nih.gov/). Users must register with the NITRC and 1000 Functional Connectomes Project to gain access to ABIDE I and ABIDE II. Users must be affiliated with a National Institutes of Health (NIH)-recognized research institution that maintains active Federalwide Assurance, be registered on NIH’s eRA Commons and complete and submit a Data Use Certification that is reviewed by the Data Access Committee to gain access to NDAR. The gene expression datasets are available from the AHBA (https://human.brain-map.org/static/download) and BrainSpan (https://www.brainspan.org/static/download.html).

Code availability

Code packages used are indicated in the Methods. Custom code for the RCCA is included in the Supplementary Information.

References

  1. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Insel, T. et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am. J. Psychiatry 167, 748–751 (2010).

    Article  PubMed  Google Scholar 

  3. Jeste, S. S. & Geschwind, D. H. Disentangling the heterogeneity of autism spectrum disorder through genetic findings. Nat. Rev. Neurol. 10, 74–81 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Lord, C., Elsabbagh, M., Baird, G. & Veenstra-Vanderweele, J. Autism spectrum disorder. Lancet 392, 508–520 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Kana, R. K., Keller, T. A., Cherkassky, V. L., Minshew, N. J. & Just, M. A. Sentence comprehension in autism: thinking in pictures with decreased functional connectivity. Brain 129, 2484–2493 (2006).

    Article  PubMed  Google Scholar 

  6. Koyama, M. S. et al. Resting-state functional connectivity indexes reading competence in children and adults. J. Neurosci. 31, 8617–8624 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Green, S. A., Hernandez, L., Bookheimer, S. Y. & Dapretto, M. Salience network connectivity in autism is related to brain and behavioral markers of sensory overresponsivity. J. Am. Acad. Child Adolesc. Psychiatry 55, 618–626 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Kana, R. K., Keller, T. A., Minshew, N. J. & Just, M. A. Inhibitory control in high-functioning autism: decreased activation and underconnectivity in inhibition networks. Biol. Psychiatry 62, 198–206 (2007).

    Article  PubMed  Google Scholar 

  9. Shafritz, K. M., Dichter, G. S., Baranek, G. T. & Belger, A. The neural circuitry mediating shifts in behavioral response and cognitive set in autism. Biol. Psychiatry 63, 974–980 (2008).

    Article  PubMed  Google Scholar 

  10. Martino, D. 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).

    Article  PubMed  Google Scholar 

  11. Martino, A. et al. Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Sci. Data 4, 170010 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  12. 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).

    Article  PubMed  Google Scholar 

  13. Yahata, N. et al. A small number of abnormal brain connections predicts adult autism spectrum disorder. Nat. Commun. 7, 11254 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Easson, A. K., Fatima, Z. & R, M. A. Functional connectivity-based subtypes of individuals with and without autism spectrum disorder. Netw. Neurosci. 3, 344–362 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  15. O’Roak, B. J. et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485, 246–250 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  16. De Rubeis, S. et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515, 209–215 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Fu, J. M. et al. Rare coding variation provides insight into the genetic architecture and phenotypic context of autism. Nat. Genet. 54, 1320–1331 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Hashem, S. et al. Genetics of structural and functional brain changes in autism spectrum disorder. Transl. Psychiatry 10, 229 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Matoba, N. et al. Common genetic risk variants identified in the SPARK cohort support DDHD2 as a candidate risk gene for autism. Transl. Psychiatry 10, 265 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Anitha, A. et al. Brain region-specific altered expression and association of mitochondria-related genes in autism. Mol. Autism 3, 12 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Zhubi, A. et al. Increased binding of MeCP2 to the GAD1 and RELN promoters may be mediated by an enrichment of 5-hmC in autism spectrum disorder (ASD) cerebellum. Transl. Psychiatry 4, e349 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Richiardi, J. et al. BRAIN NETWORKS. Correlated gene expression supports synchronous activity in brain networks. Science 348, 1241–1244 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Romme, I. A. C., de Reus, M. A., Ophoff, R. A., Kahn, R. S. & van den Heuvel, M. P. Connectome disconnectivity and cortical gene expression in patients with schizophrenia. Biol. Psychiatry 81, 495–502 (2017).

    Article  CAS  PubMed  Google Scholar 

  25. Rafael, R.-G., Warrier, V., Bullmore, E. T., Simon, B.-C. & Bethlehem, R. A. I. Synaptic and transcriptionally downregulated genes are associated with cortical thickness differences in autism. Mol. Psychiatry 24, 1053–1064 (2019).

    Article  Google Scholar 

  26. Morgan, S. E. et al. Cortical patterning of abnormal morphometric similarity in psychosis is associated with brain expression of schizophrenia-related genes. Proc. Natl. Acad. Sci. USA 116, 9604–9609 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Seidlitz, J. et al. Transcriptomic and cellular decoding of regional brain vulnerability to neurogenetic disorders. Nat. Commun. 11, 3358 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. BrainSpan Atlas of the Developing Human Brain [Internet]. Funded by ARRA Awards 1RC2MH089921-01, 1RC2MH090047-01 and 1RC2MH089929-01. Available from https://brainspan.org/ (2011).

  30. Satterthwaite, T. D. et al. Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth. Neuroimage 60, 623–632 (2012).

    Article  PubMed  Google Scholar 

  31. Caballero, C., Mistry, S., Vero, J. & Torres, E. B. Characterization of noise signatures of involuntary head motion in the autism brain imaging data exchange repository. Front. Integr. Neurosci. 12, 7 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L. & Petersen, S. E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59, 2142–2154 (2012).

    Article  PubMed  Google Scholar 

  33. Yan, C.-G., Craddock, R. C., Zuo, X.-N., Zang, Y.-F. & Milham, M. P. Standardizing the intrinsic brain: towards robust measurement of inter-individual variation in 1000 functional connectomes. Neuroimage 80, 246–262 (2013).

    Article  PubMed  Google Scholar 

  34. Power, J. D. et al. Functional network organization of the human brain. Neuron 72, 665–678 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Grosenick, L. et al. Functional and optogenetic approaches to discovering stable subtype-specific circuit mechanisms in depression. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 4, 554–566 (2019).

    PubMed  PubMed Central  Google Scholar 

  36. Mihalik, A., Adams, R. A. & Huys, Q. Canonical correlation analysis for identifying biotypes of depression. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 5, 478–480 (2020).

    PubMed  Google Scholar 

  37. Nadeau, C. & Bengio, Y. Inference for the generalization error. Mach. Learn. 52, 239–281 (2003).

    Article  Google Scholar 

  38. Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. https://doi.org/10.1007/978-0-387-84858-7 (Springer Science & Business Media, 2009).

  39. Koyama, M. S., Molfese, P. J., Milham, M. P., Mencl, W. E. & Pugh, K. R. Thalamus is a common locus of reading, arithmetic, and IQ: analysis of local intrinsic functional properties. Brain Lang. 209, 104835 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Achal, S., Hoeft, F. & Bray, S. Individual differences in adult reading are associated with left temporo-parietal to dorsal striatal functional connectivity. Cereb. Cortex 26, 4069–4081 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Dryburgh, E., McKenna, S. & Rekik, I. Predicting full-scale and verbal intelligence scores from functional connectomic data in individuals with autism spectrum disorder. Brain Imaging Behav. 14, 1769–1778 (2020).

    Article  PubMed  Google Scholar 

  42. Uddin, L. Q. et al. Salience network–based classification and prediction of symptom severity in children with autism. JAMA Psychiatry 70, 869–879 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Martino, A. et al. Aberrant striatal functional connectivity in children with autism. Biol. Psychiatry 69, 847–856 (2011).

    Article  PubMed  Google Scholar 

  44. Cerliani, L. et al. Increased functional connectivity between subcortical and cortical resting-state networks in autism spectrum disorder. JAMA Psychiatry 72, 767–777 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Sinclair, D., Oranje, B., Razak, K. A., Siegel, S. J. & Schmid, S. Sensory processing in autism spectrum disorders and Fragile X syndrome—from the clinic to animal models. Neurosci. Biobehav. Rev. 76, 235–253 (2017).

    Article  CAS  PubMed  Google Scholar 

  46. Abbott, A. E. et al. Repetitive behaviors in autism are linked to imbalance of corticostriatal connectivity: a functional connectivity MRI study. Soc. Cogn. Affect. Neurosci. 13, 32–42 (2018).

    Article  PubMed  Google Scholar 

  47. Supekar, K., Ryali, S., Mistry, P. & Menon, V. Aberrant dynamics of cognitive control and motor circuits predict distinct restricted and repetitive behaviors in children with autism. Nat. Commun. 12, 3537 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Iversen, R. K. & Lewis, C. Executive function skills are linked to restricted and repetitive behaviors: three correlational meta analyses. Autism Res. 14, 1163–1185 (2021).

    Article  PubMed  Google Scholar 

  49. Craddock, R. C., James, G. A., Holtzheimer, P. E. 3rd, Hu, X. P. & Mayberg, H. S. A whole-brain fMRI atlas generated via spatially constrained spectral clustering. Hum. Brain Mapp. 33, 1914–1928 (2012).

    Article  PubMed  Google Scholar 

  50. Mennes, M. et al. Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity. Neuroimage 50, 1690–1701 (2010).

    Article  PubMed  Google Scholar 

  51. Finn, E. S. et al. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat. Neurosci. 18, 1664–1671 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Seitzman, B. A. et al. Trait-like variants in human functional brain networks. Proc. Natl. Acad. Sci. USA 116, 22851–22861 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Zikopoulos, B. & Barbas, H. Altered neural connectivity in excitatory and inhibitory cortical circuits in autism. Front. Hum. Neurosci. 7, 609 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Maximo, J. O., Cadena, E. J. & Kana, R. K. The implications of brain connectivity in the neuropsychology of autism. Neuropsychol. Rev. 24, 16–31 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Arnatkeviciute, A., Fulcher, B. D. & Fornito, A. A practical guide to linking brain-wide gene expression and neuroimaging data. Neuroimage 189, 353–367 (2019).

    Article  PubMed  Google Scholar 

  56. Vértes, P. E. et al. Gene transcription profiles associated with inter-modular hubs and connection distance in human functional magnetic resonance imaging networks. Philos. Trans. R. Soc. Lond. B Biol. Sci. 371, 20150362 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Webber, W., Moffat, A. & Zobel, J. A similarity measure for indefinite rankings. ACM Trans. Inf. Syst. Secur. 28, 1–38 (2010).

    Article  Google Scholar 

  58. Korotkevich, G. et al. Fast gene set enrichment analysis. Preprint at bioRxiv https://doi.org/10.1101/060012 (2016).

  59. Enstrom, A. M., Van de Water, J. A. & Ashwood, P. Autoimmunity in autism. Curr. Opin. Investig. Drugs 10, 463–473 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Mannion, A. & Leader, G. An investigation of comorbid psychological disorders, sleep problems, gastrointestinal symptoms and epilepsy in children and adolescents with autism spectrum disorder: a two-year follow-up. Res. Autism Spectr. Disord. 22, 20–33 (2016).

    Article  Google Scholar 

  61. Pfenning, A. R. et al. Convergent transcriptional specializations in the brains of humans and song-learning birds. Science 346, 1256846 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Mi, H., Muruganujan, A., Ebert, D., Huang, X. & Thomas, P. D. PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. Nucleic Acids Res. 47, D419–D426 (2019).

    Article  CAS  PubMed  Google Scholar 

  63. Suzuki, K. et al. Microglial activation in young adults with autism spectrum disorder. JAMA Psychiatry 70, 49–58 (2013).

    Article  PubMed  Google Scholar 

  64. Zhan, Y. et al. Deficient neuron-microglia signaling results in impaired functional brain connectivity and social behavior. Nat. Neurosci. 17, 400–406 (2014).

    Article  CAS  PubMed  Google Scholar 

  65. Darnell, J. C. et al. FMRP stalls ribosomal translocation on mRNAs linked to synaptic function and autism. Cell 146, 247–261 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Porokhovnik, L. Individual copy number of ribosomal genes as a factor of mental retardation and autism risk and severity. Cells 8, 1151 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Lombardo, M. V. Ribosomal protein genes in post-mortem cortical tissue and iPSC-derived neural progenitor cells are commonly upregulated in expression in autism. Mol. Psychiatry 26, 1432–1435 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Rebholz-Schuhmann, D., Oellrich, A. & Hoehndorf, R. Text-mining solutions for biomedical research: enabling integrative biology. Nat. Rev. Genet. 13, 829–839 (2012).

    Article  CAS  PubMed  Google Scholar 

  69. Nozari, N. & Thompson-Schill, S. L. Chapter 46 - left ventrolateral prefrontal cortex in processing of words and sentences. in Neurobiology of Language (eds. G. Hickok & S. L. Small) 569–584 https://doi.org/10.1016/B978-0-12-407794-2.00046-8 (Academic Press, 2016).

  70. Antunes, F. M. & Malmierca, M. S. Corticothalamic pathways in auditory processing: recent advances and insights from other sensory systems. Front. Neural Circuits 15, 721186 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Gonzalez-Gadea, M. L. et al. Predictive coding in autism spectrum disorder and attention deficit hyperactivity disorder. J. Neurophysiol. 114, 2625–2636 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. van Laarhoven, T., Stekelenburg, J. J., Eussen, M. L. & Vroomen, J. Atypical visual–auditory predictive coding in autism spectrum disorder: electrophysiological evidence from stimulus omissions. Autism 24, 1849–1859 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Menegaux, A. et al. Aberrant cortico-thalamic structural connectivity in premature-born adults. Cortex 141, 347–362 (2021).

    Article  PubMed  Google Scholar 

  74. Crump, C., Sundquist, J. & Sundquist, K. Preterm or early term birth and risk of autism. Pediatrics 148, e2020032300 (2021).

    Article  PubMed  Google Scholar 

  75. Happé, F. & Ronald, A. The “fractionable autism triad”: a review of evidence from behavioural, genetic, cognitive and neural research. Neuropsychol. Rev. 18, 287–304 (2008).

    Article  PubMed  Google Scholar 

  76. Georgiades, S. et al. Investigating phenotypic heterogeneity in children with autism spectrum disorder: a factor mixture modeling approach. J. Child Psychol. Psychiatry 54, 206–215 (2013).

    Article  PubMed  Google Scholar 

  77. Bertelsen, N. et al. Imbalanced social-communicative and restricted repetitive behavior subtypes of autism spectrum disorder exhibit different neural circuitry. Commun. Biol. 4, 574 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Fuccillo, M. V. Striatal circuits as a common node for autism pathophysiology. Front. Neurosci. 10, 27 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Chugani, D. C. et al. Efficacy of low-dose buspirone for restricted and repetitive behavior in young children with autism spectrum disorder: a randomized trial. J. Pediatr. 170, 45–53 (2016).

    Article  CAS  PubMed  Google Scholar 

  80. Dunn, J. T., Mroczek, J., Patel, H. R. & Ragozzino, M. E. Tandospirone, a partial 5-HT1A receptor agonist, administered systemically or into anterior cingulate attenuates repetitive behaviors in Shank3b mice. Int. J. Neuropsychopharmacol. 23, 533–542 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Yahya, S. M., Gebril, O., Abdel Raouf, E. R. & Elhadidy, M. E. A preliminary investigation of HTR1A gene expression levels in autism spectrum disorders. Int. J. Pharm. Pharm. Sci. 11, 1–3 (2019).

    Article  CAS  Google Scholar 

  82. Kieran, N., Ou, X.-M. & Iyo, A. H. Chronic social defeat downregulates the 5-HT1A receptor but not Freud-1 or NUDR in the rat prefrontal cortex. Neurosci. Lett. 469, 380–384 (2010).

    Article  CAS  PubMed  Google Scholar 

  83. Dölen, G., Darvishzadeh, A., Huang, K. W. & Malenka, R. C. Social reward requires coordinated activity of nucleus accumbens oxytocin and serotonin. Nature 501, 179–184 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Kohls, G., Yerys, B. E. & Schultz, R. T. Striatal development in autism: repetitive behaviors and the reward circuitry. Biol. Psychiatry 76, 358–359 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Langen, M. et al. Changes in the development of striatum are involved in repetitive behavior in autism. Biol. Psychiatry 76, 405–411 (2014).

    Article  PubMed  Google Scholar 

  86. Wilkes, B. J. & Lewis, M. H. The neural circuitry of restricted repetitive behavior: magnetic resonance imaging in neurodevelopmental disorders and animal models. Neurosci. Biobehav. Rev. 92, 152–171 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Dickie, E. W. et al. Personalized intrinsic network topography mapping and functional connectivity deficits in autism spectrum disorder. Biol. Psychiatry 84, 278–286 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Geschwind, D. H. & Levitt, P. Autism spectrum disorders: developmental disconnection syndromes. Curr. Opin. Neurobiol. 17, 103–111 (2007).

    Article  CAS  PubMed  Google Scholar 

  89. Zuo, X.-N. et al. Growing together and growing apart: regional and sex differences in the lifespan developmental trajectories of functional homotopy. J. Neurosci. 30, 15034–15043 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Gee, D. G. et al. A developmental shift from positive to negative connectivity in human amygdala–prefrontal circuitry. J. Neurosci. 33, 4584–4593 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Menon, V. Developmental pathways to functional brain networks: emerging principles. Trends Cogn. Sci. 17, 627–640 (2013).

    Article  PubMed  Google Scholar 

  92. Fulcher, B. D., Arnatkeviciute, A. & Fornito, A. Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data. Nat. Commun. 12, 2669 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Arnatkeviciute, A. et al. Genetic influences on hub connectivity of the human connectome. Nat. Commun. 12, 4237 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Smith, S. M. et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23, S208–S219 (2004).

    Article  PubMed  Google Scholar 

  95. Cox, R. W. AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29, 162–173 (1996).

    Article  CAS  PubMed  Google Scholar 

  96. Smith, S. M. Fast robust automated brain extraction. Hum. Brain Mapp. 17, 143–155 (2002).

    Article  PubMed  PubMed Central  Google Scholar 

  97. Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825–841 (2002).

    Article  PubMed  Google Scholar 

  98. Jenkinson, M. & Smith, S. A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5, 143–156 (2001).

    Article  CAS  PubMed  Google Scholar 

  99. Collins, L. D., Holmes, C. J., Peters, T. M. & Evans, A. C. Automatic 3D model-based neuroanatomical segmentation. Hum. Brain Mapp. 3, 190–208 (1995).

    Article  Google Scholar 

  100. Mazziotta, J. et al. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos. Trans. R. Soc. Lond. B Biol. Sci. 356, 1293–1322 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Andersson, J. L. R., Jenkinson, M., Smith, S. & Andersson, J. Non-linear registration, aka spatial normalisation. FMRIB Technial Report TR07JA2. https://www.fmrib.ox.ac.uk/datasets/techrep/tr07ja2/tr07ja2.pdf (2007).

  102. Jo, H. J., Saad, Z. S., Simmons, W. K., Milbury, L. A. & Cox, R. W. Mapping sources of correlation in resting-state fMRI, with artifact detection and removal. Neuroimage 52, 571–582 (2010).

    Article  PubMed  Google Scholar 

  103. Murphy, K., Bodurka, J. & Bandettini, P. A. How long to scan? The relationship between fMRI temporal signal to noise ratio and necessary scan duration. Neuroimage 34, 565–574 (2007).

    Article  PubMed  Google Scholar 

  104. Gotham, K., Pickles, A. & Lord, C. Standardizing ADOS scores for a measure of severity in autism spectrum disorders. J. Autism Dev. Disord. 39, 693–705 (2009).

    Article  PubMed  Google Scholar 

  105. Hus, V. & Lord, C. The autism diagnostic observation schedule, module 4: revised algorithm and standardized severity scores. J. Autism Dev. Disord. 44, 1996–2012 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  106. Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B Stat. Methodol. 67, 301–320 (2005).

    Article  Google Scholar 

  107. Grosenick, L., Klingenberg, B., Katovich, K., Knutson, B. & Taylor, J. E. Interpretable whole-brain prediction analysis with GraphNet. Neuroimage 72, 304–321 (2013).

    Article  PubMed  Google Scholar 

  108. Friedman, J. H. Regularized discriminant analysis. J. Am. Stat. Assoc. 84, 165–175 (1989).

    Article  Google Scholar 

  109. Tibshirani, R., Hastie, T., Narasimhan, B. & Chu, G. Class prediction by nearest shrunken centroids, with applications to DNA microarrays. Stat. Sci. 18, 104–117 (2003).

    Article  Google Scholar 

  110. Robert, P. & Escoufier, Y. A unifying tool for linear multivariate statistical methods: the RV-coefficient. J. R. Stat. Soc. Ser. C. Appl. Stat. 25, 257–265 (1976).

    Google Scholar 

  111. de Torrenté, L. & Hastie, T. Does cross-validation work when p ≫ n? https://hastie.su.domains/Papers/does_cross-validation_work.pdf (2012).

  112. Allen Institute for Brain Science. Allen Human Brain Atlas. Available from: http://human.brain-map.org

  113. Velmeshev, D. et al. Single-cell genomics identifies cell type-specific molecular changes in autism. Science 364, 685–689 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Voineagu, I. et al. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 474, 380–384 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  115. Parikshak, N. N. et al. Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism. Nature 540, 423–427 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Sanders, S. J. et al. A framework for the investigation of rare genetic disorders in neuropsychiatry. Nat. Med. 25, 1477–1487 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. SPARK Consortium. SPARK: a US Cohort of 50,000 families to accelerate autism research. Neuron 97, 488–493 (2018).

    Article  Google Scholar 

  118. Steinberg, J. & Webber, C. The roles of FMRP-regulated genes in autism spectrum disorder: single- and multiple-hit genetic etiologies. Am. J. Hum. Genet. 93, 825–839 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Parikshak, N. N. et al. Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell 155, 1008–1021 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Nair, R. P. et al. Genome-wide scan reveals association of psoriasis with IL-23 and NF-κB pathways. Nat. Genet. 41, 199–204 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Abrahams, B. S. et al. SFARI Gene 2.0: a community-driven knowledgebase for the autism spectrum disorders (ASDs). Mol. Autism 4, 36 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  122. Davis, A. P. et al. The comparative toxicogenomics database: update 2019. Nucleic Acids Res. 47, D948–D954 (2019).

    Article  CAS  PubMed  Google Scholar 

  123. Pua, C. J. et al. Development of a comprehensive sequencing assay for inherited cardiac condition genes. J. Cardiovasc. Transl. Res. 9, 3–11 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  124. Pletscher-Frankild, S., Pallejà, A., Tsafou, K., Binder, J. X. & Jensen, L. J. DISEASES: text mining and data integration of disease-gene associations. Methods 74, 83–89 (2015).

    Article  CAS  PubMed  Google Scholar 

  125. Shimoyama, M. et al. The Rat Genome Database 2015: genomic, phenotypic and environmental variations and disease. Nucleic Acids Res. 43, D743–D750 (2015).

    Article  CAS  PubMed  Google Scholar 

  126. The Gene Ontology Consortium. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res. 47, D330–D338 (2019).

    Article  Google Scholar 

  127. Ashburner, M. et al. Gene Ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Xia, J., Benner, M. J. & Hancock, R. E. W. NetworkAnalyst—integrative approaches for protein-protein interaction network analysis and visual exploration. Nucleic Acids Res. 42, W167–W174 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Xia, J., Gill, E. E. & Hancock, R. E. W. NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat. Protoc. 10, 823–844 (2015).

    Article  CAS  PubMed  Google Scholar 

  130. Zhou, G. et al. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res. 47, W234–W241 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Szklarczyk, D. et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43, D447–D452 (2015).

    Article  CAS  PubMed  Google Scholar 

  132. Banerjee-Basu, S. & Packer, A. SFARI Gene: an evolving database for the autism research community. Dis. Models Mech. 3, 133–135 (2010).

    Article  Google Scholar 

  133. Müller, H.-M., Kenny, E. E. & Sternberg, P. W. Textpresso: an ontology-based information retrieval and extraction system for biological literature. PLoS Biol. 2, e309 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  134. Jensen, L. J., Saric, J. & Bork, P. Literature mining for the biologist: from information retrieval to biological discovery. Nat. Rev. Genet. 7, 119–129 (2006).

    Article  CAS  PubMed  Google Scholar 

  135. Singhal, A., Simmons, M. & Lu, Z. Text mining genotype—phenotype relationships from biomedical literature for database curation and precision medicine. PLoS Comput. Biol. 12, e1005017 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  136. Wei, C.-H., Kao, H.-Y. & Lu, Z. PubTator: a web-based text mining tool for assisting biocuration. Nucleic Acids Res. 41, W518–W522 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  137. Wei, C.-H., Allot, A., Leaman, R. & Lu, Z. PubTator central: automated concept annotation for biomedical full text articles. Nucleic Acids Res. 47, W587–W593 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Feinerer, I., Hornik, K. & Meyer, D. Text mining infrastructure in R. J. Stat. Softw. Artic. 25, 1–54 (2008).

    Google Scholar 

  139. Benoit, K. et al. quanteda: an R package for the quantitative analysis of textual data. J. Open Source Softw. 3, 774 (2018).

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by grants from the NIMH (MH118388, MH114976, MH123154, MH118451, MH109685 and MH109685-04S1), the National Institute on Drug Abuse (DA047851), the Hope for Depression Research Foundation, the Pritzker Neuropsychiatric Disorders Research Consortium, the Klingenstein–Simons Foundation Fund, the One Mind Institute, the Rita Allen Foundation, the Dana Foundation, the Foundation for OCD Research, the Hartwell Foundation and the Brain and Behavior Research Foundation (NARSAD).

Author information

Authors and Affiliations

  1. Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA

    Amanda M. Buch, So Hyun Kim, Logan Grosenick & Conor Liston

  2. Department of Psychiatry, University of Cambridge, Cambridge, UK

    Petra E. Vértes

  3. Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA

    Jakob Seidlitz

  4. Department of Child and Adolescent Psychiatry and Behavioral Science, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Jakob Seidlitz

  5. Center for Autism and the Developing Brain, Weill Cornell Medicine, White Plains, NY, USA

    So Hyun Kim

  6. School of Psychology, Korea University, Seoul, South Korea

    So Hyun Kim

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  1. Amanda M. Buch
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Contributions

A.M.B. and C.L. developed the concept for the study. A.M.B., L.G. and C.L. designed the analyses, which were implemented by A.M.B. S.H.K. provided consultation on interpreting the results, and P.E.V. and J.S. advised on the implementation of the transcriptomic and bioinformatic analyses. All authors contributed to writing the manuscript.

Corresponding authors

Correspondence to Logan Grosenick or Conor Liston.

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Competing interests

C.L. is listed as an inventor for Cornell University patent applications on neuroimaging biomarkers for depression that are pending or in preparation. C.L. has served as a scientific advisor or consultant to Compass Pathways, Delix Therapeutics, Magnus Medical and Brainify.AI. The authors declare no other competing interests.

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Nature Neuroscience thanks Jingyu Liu, Lucina Uddin and Aristotle Voineskos for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Connectivity score loadings on RSFC and atypical RSFC in 247 × 247 heatmaps.

Heatmaps of 247 × 247 regions of interest (ROIs) corresponding to panels in Fig. 2 sorted and labeled by functional network. (a) Correlation between verbal IQ-related dimension (dimension 1) and RSFC (FDR < 0.05; see Fig. 2a). (b) Correlation between social affect-related dimension (dimension 2) and RSFC (FDR < 0.05; see Fig. 2b). (c) Correlation between RRB-related dimension (dimension 3) and RSFC (FDR < 0.05; see Fig. 2c). (d) Atypical connectivity in ASD subjects versus controls (Welch’s t-test; FDR < 0.05; see Fig. 2d). Abbreviations described previously in Figs. 1–2.

Extended Data Fig. 2 Autism spectrum disorder subgroups replicate when using different clustering methods.

(a, b) K-means clustering with cosine distance, (c, d) spectral clustering with cosine distance, and (e, f) hierarchical clustering with Euclidean distance and Ward linkage across 1,000 training set replicates (N = 284). In (g, h) we show the original analysis using hierarchical clustering with cosine distance and average linkage (see Methods for more details). Boxplots show distribution of clinical symptom z-scores (superimposed bar graphs depict the median) for social affect, repetitive, restrictive behaviors and interests (RRB), verbal IQ, and total severity (color indicates subgroup). Plots include 284 subjects x 1,000 training sets to indicate distribution of clinical behaviors across all 1,000 training set cluster assignments. Box bounds: [25th,75th percentile]; center: median; whiskers: 99.3% data in + /–2.7 σ; outliers: circles). Heatmaps show patterns of mean atypical connectivity across replicates in each subgroup across brain regions (rows) and functional networks (columns), and were thresholded for significant atypical connectivity (two-sided Welch’s t-test, mean FDR < 0.05), evaluated relative to N = 907 neurotypical controls. See additional comparisons in Supplementary Fig. 9.

Extended Data Fig. 3 Functional connectivity differences reveal subgroup-specific atypical connectivity.

Subgroups were defined as the modal subgroup assignment over the 1,000 training set replicates, which is used in the main text for Figs. 3–6. (a-d) Heatmaps show patterns of atypical connectivity in each subgroup across brain regions (rows) and functional networks (columns). Thresholded for significant atypical connectivity (two-sided Welch’s t-test, FDR < 0.05), evaluated in N = 69 ASD subjects in subgroup 1, N = 87 ASD subjects in subgroup 2, N = 67 ASD subjects in subgroup 3, N = 76 ASD subjects in subgroup 4, relative to N = 907 neurotypical controls.

Extended Data Fig. 4 Cross-validation of the clinical symptom and atypical connectivity differences between subgroups.

To cross-validate the clinical symptom and atypical connectivity differences between subgroups in Figs. 3–4 and Extended Data Fig. 3, we first subsampled 95% of the data in 1,000 replicates. Second, we calculated canonical variates (connectivity score and clinical score for each brain–behavior dimension) in each replicate. Third, in each replicate, we hierarchically clustered on connectivity scores using cosine similarity distance and average linkage and identified four subgroups. Fourth, we used the Hungarian method to match clusters between replicates (numerical assignment of subgroups can change without changing subject composition in cluster). Fifth, we calculated the distribution of clinical symptom z-scores for each subgroup across replicates. Sixth, in each replicate, we calculated atypical connectivity per subgroup versus N = 907 neurotypical controls (two-sided Welch’s t-test). Seventh, we calculated the mean and standard deviation (σ) of atypical connectivity (t) on RSFC over 1,000 subsampled replicates. (a-d) Note similarity to Fig. 3b-e: Subgroups differ with respect to clinical symptoms, similar to subgroup differences identified when subgroups were calculated as modal cluster assignment across 1,000 training sets (mode analysis) shown in Fig. 3b-e. Plots include 284 subjects x 1,000 training sets to indicate distribution of clinical behaviors across all 1,000 training set cluster assignments. Box bounds: [25th,75th percentile]; center: median; whiskers: 99.3% data in + /–2.7 σ; outliers: circles). (e-h) Heatmaps show patterns of mean atypical connectivity across replicates in each subgroup across brain regions (rows) and functional networks (columns), and were thresholded for significant atypical connectivity (two-sided Welch’s t-test, mean FDR < 0.05). (i-l) Heatmaps show patterns of the standard deviation of atypical connectivity across replicates in each subgroup across brain regions (rows) and functional networks (columns).

Extended Data Fig. 5 RCCA and clustering analysis using narrower age range (ages 8–18) yields ASD subgroups with clinical symptoms and atypical connectivity consistent with main analysis.

We repeated all the main analyses (shown in box, i-p) using a smaller age range, including only ASD and neurotypical individuals of ages 8–18 (shown in a-d and i-l). This reduced our ASD sample from N = 299 ages 5–35 to N = 243 ages 8–18 and reduced our neurotypical sample from N = 907 to N = 573. In this secondary analysis, we found similar clinical symptom profiles associated with each subgroup (a-d vs. i-l). Boxplots of the distribution of clinical symptom z-scores (superimposed bar graphs depict the median) for (a,e) social affect, (b,f) repetitive, restrictive behaviors and interests (RRB), (c,g) verbal IQ, and (d,h) total severity (color indicates subgroup). Note that higher social affect, RRB, and total severity scores and lower verbal IQ indicate greater impairment. Box bounds: [25th,75th percentile]; center: median; whiskers: 99.3% data in + /–2.7 σ; outliers: circles). Next, we found similar atypical connectivity associated with each subtype (e-h vs. m-p). (e-h) Atypical connections that were significant (P < 0.05) in the narrower age range, thresholded for significant atypical connectivity (two-sided Welch’s t-test, FDR < 0.05). (m-p) Atypical connections that were significant (P < 0.05) in the full age range, thresholded for connections that were significant in the main analysis (two-sided Welch’s t-test, FDR < 0.05). Heatmaps show patterns of atypical connectivity in each subgroup across brain regions (rows) and functional networks (columns). Thresholded for significant atypical connectivity (two-sided Welch’s t-test, FDR < 0.05), evaluated relative to N = 907 neurotypical controls. For additional results, see Supplementary Figs. 13, 14 and 17–20.

Extended Data Fig. 6 RCCA and clustering analysis using the Craddock 200 atlas yields ASD subgroups with clinical symptoms and atypical connectivity consistent when analyzed using the Power atlas.

We reparcellated the brains using the Craddock 200 atlas69, recalculated functional connectivity for each subject, and repeated the full analysis following the original pipeline (feature selection, RCCA, clustering, and PLS). Key findings from the primary analysis using the Power parcellation replicate in this secondary analysis using the Craddock atlas. Here we plot the clinical symptom scores (boxplots as in Extended Data Fig. 5) for each subgroup when (a-d) we used the Craddock 200 parcellation for functional connectivity versus (i-l) the Power parcellation for functional connectivity (main text analysis). Next, we measured atypical connectivity using the Craddock parcellation and mapped it onto the Power atlas for visual comparison between the two parcellations. We plot the atypical connectivity for each subgroup for (e-h) the analysis in the Craddock 200 parcellation thresholded the significant connections from the Power parcellation, and (m-p) the analysis in the Power atlas. Heatmaps show patterns of atypical connectivity in each subgroup across brain regions (rows) and functional networks (columns). Thresholded for significant atypical connectivity (two-sided Welch’s t-test, FDR < 0.05), each evaluated separately relative to N = 907 neurotypical controls. For additional results, see Supplementary Figs. 15, 16.

Extended Data Fig. 7 Out-of-sample replication of ASD subgroup clinical symptoms and atypical connectivity in NDA dataset (NNDA = 85 ASD subjects).

We repeated the main analyses to define ASD subgroups using the NDA dataset (RCCA and clustering). This analysis replicated key results from ABIDE, such that the four NDA subgroups (NNDA_1 = 20, NNDA_2 = 21; NNDA_3 = 27; NNDA_4 = 17) exhibited clinical symptom / behavior profiles and atypical connectivity patterns that were highly similar to those observed in the ABIDE subgroups (NABIDE_1 = 69, NABIDE_2 = 87; NABIDE_3 = 67; NABIDE_4 = 76). In this summary figure, we plot the clinical symptom scores (NDA: a-d, ABIDE: i-l; boxplots as in Extended Data Fig. 5) and atypical connectivity patterns for each subgroup (NDA: e-h, ABIDE: m-p). As expected, statistical power to detect significant atypical connectivity was reduced due to the smaller sample size of NDA. Here, the heatmaps show atypical functional connectivity in NDA and ABIDE subgroups, with the NDA subgroups thresholded by significance from ABIDE for comparison (that is, we set elements in the NDA heatmaps with FDR < 0.05 from a connectivity (two-sided Welch’s t-test in ABIDE heatmaps to 0). However, we confirmed that compared to an empirical null (100 shuffles, see Methods for details), atypical connectivity patterns in the NDA ASD subgroups were more correlated with ABIDE ASD subgroups than expected by chance (P1 = 0.0099, P2 = 0.0297, P3 = 0.0099, P4 = 0.0198). Note that the P values correspond to the probability of obtaining the observed sum of ranks statistic (sum of observed ranks across a range of FDR thresholds, FDR in {1, 0.05, 0.01, 0.005, 0.001, 0.0005, 0.0001, 0.00005}) under the empirical null. For additional results, see Supplementary Fig. 21.

Extended Data Fig. 8 Replication of transcriptomic correlates of subgroup atypical connectivity using BrainSpan gene expression.

We mapped data from the BrainSpan gene expression atlas to the Power atlas, and repeated the PLS and gene set enrichment analyses described in the main text. We found similar results to the original analysis in which we had used the AHBA gene expression dataset, including highly similar transcriptomic correlates of subgroup atypical connectivity. For the PLS analysis, we first calculated gene expression at each brain region (ROI) and atypical connectivity (RSFC) summed over ROIs for each subgroup. Second, we performed PLS regression for each subgroup. Third, we ranked genes by PLS gene weights in each model. The results were highly similar to those observed in the original analysis using the AHBA gene expression atlas. Heatmaps of gene set enrichment for each subgroup’s ranked gene weights for (a vs. b) ASD-related gene sets, (c vs. d) nonpsychiatric disease-related gene sets, (e vs. f) psychiatric disorder-related gene sets, (g vs. h) synaptic signaling gene sets, (i vs. j) immune signaling gene sets, and (k vs. l) protein translation gene sets. All subgroups were enriched for ASD-related gene sets, but not for unrelated diseases. Color indicates strength of negative log transformed FDR for normalized enrichment score multiplied by sign of gene weight (+1 or −1). The P values were calculated and FDR-corrected as in Fig. 5.

Extended Data Fig. 9 Transcriptomic correlates of atypical connectivity patterns associated with ASD-related behaviors.

To further assess relationships between gene expression with atypical connectivity and behavior in larger useable samples (that is, now including subjects with usable fMRI data who were excluded from primary analyses due to incomplete behavioral assessments) we started with the N = 782 subjects with usable scan data, and split the NVIQ = 590 subjects with VIQ into VIQ bins (ASD subjects with [NVIQ>120 = 127] VIQ > = 120, [N85≤VIQ≤120 = 383] VIQ 85–120, or [NVIQ<85 = 80] VIQ < = 85). We also split the NADOS-2 = 353 subjects with ADOS-2 assessment into bins by calculating social affect divided by RRB. The social affect > RRB bin (social affect / RRB > 1) had NSA>RRB = 113 ASD subjects and the RRB > social affect bin (social affect / RRB > 1) had NSA<RRB = 171 ASD subjects; the NSA=RRB = 69 ASD subjects with SA/RRB = 1 were not included in either ADOS-2 bin. The overlap of subjects between the NVIQ = 590 subjects with VIQ and NADOS-2 = 353 subjects with ADOS-2 was the NVIQ;ADOS-2 = 299 ASD subjects in the main analysis. We used the same PLS and gene set enrichment procedure as in Fig. 5 (see b,d,f,h,j,l in box) to assess the relationship of these binned subjects’ atypical connectivity with gene expression. Heatmaps of gene set enrichment for each subgroup’s ranked gene weights for (a-b) ASD-related gene sets, (c-d) nonpsychiatric disease-related gene sets, (e-f) psychiatric disorder-related gene sets, (g-h) synaptic signaling gene sets, (i-j) immune signaling gene sets, and (k-l) protein translation gene sets. Color indicates strength of negative log transformed FDR for normalized enrichment score multiplied by sign of gene weight (+1 or −1). The results were consistent with our predictions: gene set enrichments for the low-VIQ bin resembled those for subgroup 2 (featured low Verbal IQ) and enrichments for the high-VIQ bin resembled those for subgroup 1 (featured above-average VIQ). See further description of results in Supplementary Discussion. The P values were calculated and FDR-corrected as in Fig. 5.

Extended Data Fig. 10 Zero-order protein-protein interaction (PPI) networks for genes associated with multiple subgroups.

Zero-order protein-protein interaction (PPI) networks for (a) genes associated with all four subgroups and (b) genes associated with at least 3 subgroups (STRING database; see Methods). Blue genes are known to be transcriptionally regulated in ASD while red genes are genes not known to be transcriptionally regulated but that have been associated with ASD in the SFARI database. The significance of each PPI module is the two-sample Wilcoxon rank sum test (unpaired, two-sided) of within-module degrees versus cross-module degrees (no adjustments for multiple comparisons of modules). For each gene in the module, the within-module degree is the number of connected genes within the module and the cross-module degree is the number of connected genes outside of the module.

Supplementary information

Supplementary Information

Supplementary Discussion, Tables 1–3 and 6 and Figs. 1–25

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Supplementary Table

Supplementary Tables 4 and 5

Supplementary Code

Custom code for the RCCA with an example.

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Buch, A.M., Vértes, P.E., Seidlitz, J. et al. Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder. Nat Neurosci 26, 650–663 (2023). https://doi.org/10.1038/s41593-023-01259-x

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