2018-04-13

Hakmook Kang, PhD, Department of Biostatistics, Vanderbilt University

"A Bayesian double fusion spatio-temporal model for resting-state brain connectivity using joint functional and structural data"

In this talk, we will propose a new Bayesian spatio-temporal model to enhance estimation of functional connectivity via double fusion of resting state fMRI (rs-fMRI) and diffusion tensor imaging (DTI) data. As non-invasive methods, fMRI plays an important role in studying the function of the human brain and DTI contributes to reveal the microstructure of the brain. Given the assumption that structural connectivity and functional connectivity are intercorrelated, we propose a spatio-temporal hierarchical Bayesian model for functional brain connectivity by jointly utilizing both rs-fMRI and DTI data. In this model, structural information from DTI fused with ROI-level conventional functional connectivity serves as an informative prior for estimating structurally-adjusted functional connectivity. Moreover, indirect effect of structural connectivity is fused with direct effect of structural connectivity on structurally-adjusted functional connectivity, allowing us to take into account direct & indirect effect of structural information simultaneously. To assess the advantage of a joint rs-fMRI and DTI analysis, we compare our approach to the fMRI-only analysis in terms of the mean squared error (MSE) of estimators via simulated datasets. The results demonstrate that the rs-fMRI and DTI model produces about 54.6% smaller MSE of functional connectivity parameters compared to the conventional approach. We apply our model to analyze rs-fMRI & DTI data from 7 healthy subjects.

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