  Recently, the potential of dynamic brain networks as a neuroimaging
biomarkers for mental illnesses is being increasingly recognized. However,
there are several unmet challenges in developing such biomarkers, including the
need for methods to model rapidly changing network states. In one of the first
such efforts, we develop a novel approach for computing dynamic brain
functional connectivity (FC), that is guided by brain structural connectivity
(SC) computed from diffusion tensor imaging (DTI) data. The proposed approach
involving dynamic Gaussian graphical models decomposes the time course into
non-overlapping state phases determined by change points, each having a
distinct network. We develop an optimization algorithm to implement the method
such that the estimation of both the change points and the state-phase specific
networks are fully data driven and unsupervised, and guided by SC information.
The approach is scalable to large dimensions and extensive simulations
illustrate its clear advantages over existing methods in terms of network
estimation accuracy and detecting dynamic network changes. An application of
the method to a posttraumatic stress disorder (PTSD) study reveals important
dynamic resting state connections in regions of the brain previously implicated
in PTSD. We also illustrate that the dynamic networks computed under the
proposed method are able to better predict psychological resilience among
trauma exposed individuals compared to existing dynamic and stationary
connectivity approaches, which highlights its potential as a neuroimaging
biomarker.
