A Bayesian Model of Activation and Functional Connectivity for Event-Related fMRI

• Activity Number: 108
• Type: Invited
• Date/Time: Monday, August 5, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Wesley K Thompson*+ and Dongli Zhou
• Companies: University of California, San Diego and Forest Labs
• Keywords: fMRI ; Functional Data Analysis ; Brain Imaging ; Bayesian ; Functional Principal Components ; Smoothing

Abstract

Neuroscientists are increasingly focused on the determination of functional relationships among anatomically-distinct brain regions, termed functional connectivity (FC). FC has been evaluated using data obtained from functional magnetic resonance imaging (fMRI) studies. We propose a novel multivariate methodology for event-related fMRI that simultaneously smooths the BOLD responses and determines FC among several regions. Smoothing is accomplished via empirical bases obtained from functional principal components analysis. The coefficients of the basis are allowed to be correlated across regions, and the nature and strength of FC is derived from this correlation matrix. The model is implemented within a Bayesian framework by using a Markov Chain Monte Carlo (MCMC) sampling algorithm. We demonstrate our methodology on a sample of clinically depressed subjects and healthy controls in examining relationships among three brain regions implicated in depression and emotion during emotional information processing.