Current approaches separately analyze concurrently acquired diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) data. The primary limitation of these approaches is not to use all available information in estimation of resting state functional connectivity (FC). To overcome this limitation, we developed a Bayesian hierarchical spatio-temporal model that incorporated structural connectivity (SC) into estimating FC, where SC based on DTI was used to construct a prior for FC based on resting state fMRI (rs-fMRI) data. Simulations and data analysis concluded that our model achieved smaller false positive rates and was robust to data decimation compared to the conventional approach.
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