  Converging evidence indicates that the heterogeneity of cognitive profiles
may 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 brain
networks. Despite an unprecedented opportunity to uncover neurobiological
subtypes through clustering or subtyping analyses on multi-state functional
connectivity, few existing approaches are applicable here to accommodate the
network topology and unique biological architecture of functional connectivity.
To address this issue, we propose an innovative Bayesian nonparametric
network-variate clustering analysis to uncover subgroups with homogeneous brain
functional network patterns integrating different cognitive states. In light of
the existing neuroscience literature, we assume there are unknown
state-specific modular structures within functional connectivity and
simultaneously impose selection to identify informative network features for
defining subtypes within unsupervised learning. To further facilitate practical
use, we develop a computationally efficient variational inference algorithm to
perform posterior inference with satisfactory estimation accuracy. Extensive
simulations show the superior clustering accuracy and plausible result of our
method. Applying the method to the landmark Adolescent Brain Cognitive
Development (ABCD) study, we successfully establish neurodevelopmental subtypes
linked with impulsivity related behavior trait, and identify brain sub-network
phenotypes under each state to signal neurobiological heterogeneity.
