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Technological advancements in noninvasive imaging facilitate the constructionof whole brain interconnected networks, known as brain connectivity. Existingapproaches to analyze brain connectivity frequently disaggregate the entirenetwork into a vector of unique edges or summary measures, leading to asubstantial loss of information. Motivated by the need to explore the effectmechanism among genetic exposure, brain connectivity and time to disease onset,we propose an integrative Bayesian framework to model the effect pathwaybetween each of these components while quantifying the mediating role of brainnetworks. To accommodate the biological architectures of brain connectivityconstructed along white matter fiber tracts, we develop a structural modelingframework that includes a symmetric matrix-variate accelerated failure timemodel and a symmetric matrix response regression to characterize the effectpaths. We further impose within-graph sparsity and between-graph shrinkage toidentify informative network configurations and eliminate the interference ofnoisy components. Extensive simulations confirm the superiority of our methodcompared with existing alternatives. By applying the proposed method to thelandmark Alzheimer's Disease Neuroimaging Initiative study, we obtainneurobiologically plausible insights that may inform future interventionstrategies.

Bayesian pathway analysis over brain network mediators for survival data