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Abstract #5374

Bayesian Spatio-temporal Model for Brain Resting State Connectivity

Hakmook Kang1, Hernando Ombao2, Chris Fonnesbeck3, Zhaohua Ding3, and Victoria L Morgan3

1Vanderbilt University, Nashville, TN, United States, 2University of California, Irvine, 3Vanderbilt University

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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