Dynamic Brain Functional Networks Guided By Anatomical Knowledge
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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.
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