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Activity Number: 181
Type: Contributed
Date/Time: Monday, July 30, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #305791
Title: Modeling Brain Connectivity Using Multi-Modal MRI Data
Author(s): Hakmook Kang*+ and Hernando Ombao
Companies: Vanderbilt University and University of California at Irvine
Address: Department of Biostatistics, S-2323 MCN, Nashville, TN, 37232-2158, United States
Keywords: functional connectivity ; diffusion tensor imaging ; functional MRI ; Bayesiand hierarchical model

As non-invasive methods, functional magnetic resonance imaging (fMRI) plays an important role in studying the function of the human brain and diffusion tensor imaging (DTI) contributes to characterize the microstructure of the brain. Following a basic assumption that structural connectivity may be associated with functional connectivity, we propose a spatio-temporal hierarchical Bayesian model to estimate the default mode network functional connectivity between a pair of brain regions of interest by jointly utilizing both resting-state fMRI and DTI data. In this model, structural information from DTI serves as an informative prior for the resting-state functional connectivity while taking into account both spatial and temporal dependence in fMRI data. To assess the advantage of a joint DTI+fMRI analysis, we compare our approach to the fMRI-only analysis in terms of the mean squared error (MSE) of estimated functional connectivity via simulated datasets. The results demonstrate that our model produces significantly reduced MSE of functional connectivity, compared to the fMRI-only analysis. We will apply our model to analyze DTI and fMRI data from 10 healthy subjects.

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