Functional connectivity scans are hierarchical – heterogeneity differentiates people according to clinical diagnosis and stage of disease. Hierarchy furthermore dictates connectivity of an individual - functional networks manifest as a hierarchy of subnetworks, each with their own unique biological function. To model functional connectivity across populations of individuals, we develop the hierarchical latent space model (HLSM), a statistical model that accounts for hierarchy as a function of clinical and connectivity features describing functional images. The HLSM reveals differences in the connectivity patterns between healthy and schizophrenia groups when applied to data from the Center for Biomedical Research Excellence (COBRE) project.
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