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Fractionating autism based on neuroanatomical normative modeling

Abstract : Autism is a complex neurodevelopmental condition with substantial phenotypic, biological, and etiologic heterogeneity. It remains a challenge to identify biomarkers to stratify autism into replicable cognitive or biological subtypes. Here, we aim to introduce a novel methodological framework for parsing neuroanatomical subtypes within a large cohort of individuals with autism. We used cortical thickness (CT) in a large and well-characterized sample of 316 participants with autism (88 female, age mean: 17.2 ± 5.7) and 206 with neurotypical development (79 female, age mean: 17.5 ± 6.1) aged 6–31 years across six sites from the EU-AIMS multi-center Longitudinal European Autism Project. Five biologically based putative subtypes were derived using normative modeling of CT and spectral clustering. Three of these clusters showed relatively widespread decreased CT and two showed relatively increased CT. These subtypes showed morphometric differences from one another, providing a potential explanation for inconsistent case–control findings in autism, and loaded differentially and more strongly onto symptoms and polygenic risk, indicating a dilution of clinical effects across heterogeneous cohorts. Our results provide an important step towards parsing the heterogeneous neurobiology of autism.
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Submitted on : Tuesday, August 24, 2021 - 4:50:42 PM
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Mariam Zabihi, Dorothea L. Floris, Seyed Mostafa Kia, Thomas Wolfers, Julian Tillmann, et al.. Fractionating autism based on neuroanatomical normative modeling. Translational Psychiatry, Nature Pub. Group, 2020, 10 (1), pp.384. ⟨10.1038/s41398-020-01057-0⟩. ⟨pasteur-03325386⟩



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