Phenotype Representation and Analysis via Discriminative Atypicality (PRADA) to Capture the Structural Heterogeneity of Autism Spectrum Disorder

Most current neuroimaging analyses in studies of brain disorders assume a homogenous presentation of the disorder such that traditional statistical analysis methods based on Gaussian distributions can be applied. Yet, most brain disorders present with a heterogeneous spectrum of cognitive, behavioral, morphometric as well as functional manifestations. In this paper, we introduce a novel approach called PRADA (Phenotype Representation and Analysis via Discriminant Atypicality) that embraces the heterogeneity of both typical and atypical brain morphometry. This approach employs Multiscale Score Matching Analysis (MSMA), a global and local multiscale out-of-distribution analysis via the gradients of the log density (scores). Combining MSMA and manifold-mapping, we compute a morphospace of brain phenotypes representing deviations from a population of typical subjects. Using these brain phenotypes, disorder-related subtyping can be performed. Furthermore, subject-specific profiles of atypicality can be extracted via Spatial-MSMA and summarized per subtype. We show the application of PRADA to structural MRI data in a study of Autism Spectrum Disorder (ASD). The resulting analysis detects disorder-related subtypes and reveals that subtype-specific structural atypicality correlates with cognitive and behavioral outcomes. These results highlight the potential of PRADA to discover disorder relevant phenotypes.