Fractionating autism based on neuroanatomical normative modeling.
Floris, Dorothea L
Arenas, Alberto Llera
Beckmann, Christian F
EU-AIMS LEAP Group
Springer Science and Business Media LLC
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Zabihi, M., Floris, D. L., Kia, S. M., Wolfers, T., Tillmann, J., Arenas, A. L., Moessnang, C., et al. (2020). Fractionating autism based on neuroanatomical normative modeling.. Transl Psychiatry, 10 (1), 384. https://doi.org/10.1038/s41398-020-01057-0
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.
EU-AIMS LEAP Group, Humans, Magnetic Resonance Imaging, Case-Control Studies, Autistic Disorder, Neurobiology, Adolescent, Adult, Child, Female, Young Adult, Autism Spectrum Disorder
European Commission (278948)
European Commission and European Federation of Pharmaceutical Industries and Associations (EFPIA) FP7 Innovative Medicines Initiative (IMI) (115300)
External DOI: https://doi.org/10.1038/s41398-020-01057-0
This record's URL: https://www.repository.cam.ac.uk/handle/1810/314829
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/