A mega-analysis framework for delineating autism neurosubtypes

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ABSTRACT This application proposes to lay the groundwork for precision medicine approaches to autism spectrum disorder (ASD) by identifying reproducible clinically relevant brain-connectome-based subtypes. The proposal addresses the clinical and biological heterogeneity of ASD by focusing on the intermediate level of analysis of systems neuroscience, following clues that ASD is associated with abnormalities in the brain functional connectome. Thus, we aim to identify neurosubtypes (NS), i.e., subgroups of individuals with homogeneous atypical features, based on measures of intrinsic functional connectivity (iFC). Primary aims are to: 1) generate a large, retrospectively harmonized data resource with comprehensive assessment of iFC and clinical phenotypes; 2) identify iFC-based neurosubtypes and establish their associations with clinically relevant phenotypes; 3) test the replicability of neurosubtypes and their associations with phenotypic measures in an independent sample . To this end, we propose to leverage existing large-scale ASD neuroimaging data collections from the Autism Brain Imaging Data Exchange, the National Database for Autism Research, and the Healthy Brain Network. Sample: Age/Sex: Boys and girls, 6-18 years old. Diagnosis: ASD and neurotypical (NT) individuals. Size: to date, the above neuroimaging resources contain a total N=3528; ASD n=2136, NT n=1392. Methods: Following systematic and extensive data organization, rigorous quality assurance, and preprocessing we will proceed with quantitative data harmonization using state-of-the-art methods. CovBat, the most advanced version of the Bayesian framework, ComBat, will be applied to harmonize MRI data. It has been developed by Co-I Shinohara to control for inter-scanner differences in MRI-based measures, as well as for errors arising from subject differences in measurement covariance. Recent advances in item response theory will be used to harmonize phenotypic data, informed by preliminary clinical work. To further enhance our clinical data harmonization efforts, the neuroimaging data will be aggregated with phenotypic-only collections from Co-Is Lord and Bishop (ASD n=1513). Connectopathy features: To scope the entire spectrum of ASD connectopathy, multiple features will be assessed simultaneously for the first time. Neurosubtypes: Building on our feasibility work with Co-I Yeo, homogeneous neural ASD subgroups will be identified through novel Bayesian latent factor modeling. It allows for subjects to belong to subtypes in varying degrees, identifying hybrid, categorical and dimensional, neurosubtypes. Other key questions include the relevance of MRI features studied, the diagnostic specificity of neurosubtypes, and cross-subtyping method validity. The neurosubtypes identified and methods for harmonization, along with all data generated for mega-analyses will be regularly shared, starting at the end of year two. Findings will address critical knowledge gaps and the novel resource will offer the scientific community opportunities to pursue independent inquiries transforming biological research and knowledge of ASD.