  Although there is a rapidly growing literature on dynamic connectivity
methods, the primary focus has been on separate network estimation for each
individual, which fails to leverage common patterns of information. We propose
novel graph-theoretic approaches for estimating a population of dynamic
networks that are able to borrow information across multiple heterogeneous
samples in an unsupervised manner and guided by covariate information.
Specifically, we develop a Bayesian product mixture model that imposes
independent mixture priors at each time scan and uses covariates to model the
mixture weights, which results in time-varying clusters of samples designed to
pool information. The computation is carried out using an efficient
Expectation-Maximization algorithm. Extensive simulation studies illustrate
sharp gains in recovering the true dynamic network over existing dynamic
connectivity methods. An analysis of fMRI block task data with behavioral
interventions reveal sub-groups of individuals having similar dynamic
connectivity, and identifies intervention-related dynamic network changes that
are concentrated in biologically interpretable brain regions. In contrast,
existing dynamic connectivity approaches are able to detect minimal or no
changes in connectivity over time, which seems biologically unrealistic and
highlights the challenges resulting from the inability to systematically borrow
information across samples.
