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.
Highlights
Autism is a neurodevelopmental condition marked by impairments in social communication and interaction, alongside restricted and repetitive behaviors and sensory atypicalities[1]
We build on our previous work that used normative modeling[40] further to find subtypes within the cohort. We achieve this objective by combining normative modeling with clustering, which is appealing for three reasons: first, since the normative range is defined with respect to a supervised mapping between biology and covariates relevant to the disorder, this allows the clustering algorithm to focus on clinically relevant variation
While normative modeling can predict various brain measures, here we focused on cortical thickness (CT) because firstly, alterations in CT have been extensively reported in different autism studies and secondly, it is a reliable measure of cortical morphology in autism[14,16,42,43,44,45,46,47]
Summary
Autism is a neurodevelopmental condition marked by impairments in social communication and interaction, alongside restricted and repetitive behaviors and sensory atypicalities[1]. The biologically-defined clustering approaches mostly follow the description of the condition on the group level and recapitulate the case–control paradigm, which often fails to fully capture the complexity of inter-individual alterations[5,27,39]. We build on our previous work that used normative modeling[40] further to find subtypes within the cohort We achieve this objective by combining normative modeling with clustering, which is appealing for three reasons: first, since the normative range is defined with respect to a supervised mapping between biology and covariates relevant to the disorder (while accounting for nuisance variation), this allows the clustering algorithm to focus on clinically relevant variation. Since the normative model can be learned on very large samples, this allows us to capitalize on big data cohorts to better capture the heterogeneity within clinical cohorts
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