A0692
Title: A comparison of whole brain connectivity between depressed and non-depressed using a Bayesian spatio-temporal model
Authors: Hakmook Kang - Vanderbilt University (United States) [presenting]
Ilwoo Lyu - Ulsan National Institute of Science and Technology (Korea, South)
Kim Albert - Vanderbilt University Medical Center (United States)
Brian Boyd - Vanderbilt University Medical Center (United States)
Bennett Landman - Vanderbilt University (United States)
Warren Taylor - Vanderbilt University Medical Center (United States)
Abstract: A Bayesian double-fusion technique is introduced for enhancing the estimation of resting-state functional connectivity (FC) based on functional magnetic resonance imaging (fMRI) data between brain regions by using structural connectivity (SC) based on diffusion tensor imaging (DTI) data. Our previous work has been expanded to accommodate estimating the whole-brain functional connectivity matrix instead of focusing on a small number of regions of interest. Concurrently acquired two imaging data will be simultaneously used for FC estimation, which allows us to precisely investigate the relationship between FC and SC, or alterations in white matter microstructural integrity. The method is applied to multi-subject data ($n=45$) with depression ($n=20$) and without depression ($n=25$) to examine how SC differences are related to differences in function (i.e., FC) and in turn related to cognitive task performance in depression.
