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Converging evidence indicates that the heterogeneity of cognitive profilesmay arise through detectable alternations in brain functions. Particularly,brain functional connectivity, measured under resting and cognitive states,characterizes the unique neuronal interconnections across large-scale brainnetworks. Despite an unprecedented opportunity to uncover neurobiologicalsubtypes through clustering or subtyping analyses on multi-state functionalconnectivity, few existing approaches are applicable here to accommodate thenetwork topology and unique biological architecture of functional connectivity.To address this issue, we propose an innovative Bayesian nonparametricnetwork-variate clustering analysis to uncover subgroups with homogeneous brainfunctional network patterns integrating different cognitive states. In light ofthe existing neuroscience literature, we assume there are unknownstate-specific modular structures within functional connectivity andsimultaneously impose selection to identify informative network features fordefining subtypes within unsupervised learning. To further facilitate practicaluse, we develop a computationally efficient variational inference algorithm toperform posterior inference with satisfactory estimation accuracy. Extensivesimulations show the superior clustering accuracy and plausible result of ourmethod. Applying the method to the landmark Adolescent Brain CognitiveDevelopment (ABCD) study, we successfully establish neurodevelopmental subtypeslinked with impulsivity related behavior trait, and identify brain sub-networkphenotypes under each state to signal neurobiological heterogeneity.

Bayesian subtyping for multi-state brain functional connectome with application on adolescent brain cognition