A NONSTATIONARY NONPARAMETRIC BAYESIAN APPROACH TO DYNAMICALLY MODELING EFFECTIVE CONNECTIVITY IN FUNCTIONAL MAGNETIC RESONANCE IMAGING EXPERIMENTS

• 成果类型: Article
• 署名作者: Bhattacharya, Sourabh; Maitra, Ranjan
• 署名单位: Indian Statistical Institute; Indian Statistical Institute Kolkata; Iowa State University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/11-AOAS470
• 发表日期: 2011
• 页码: 1183-1206
• 关键词: hemodynamic-response function; statistical-analysis; fmri; brain; activation; inference

摘要

Effective connectivity analysis provides an understanding of the functional organization of the brain by studying how activated regions influence one other. We propose a nonparametric Bayesian approach to model effective connectivity assuming a dynamic nonstationary neuronal system. Our approach uses the Dirichlet process to specify an appropriate (most plausible according to our prior beliefs) dynamic model as the expectation of a set of plausible models upon which we assign a probability distribution. This addresses model uncertainty associated with dynamic effective connectivity. We derive a Gibbs sampling approach to sample from the joint (and marginal) posterior distributions of the unknowns. Results on simulation experiments demonstrate our model to be flexible and a better candidate in many situations. We also used our approach to analyzing functional Magnetic Resonance Imaging (fMRI) data on a Stroop task: our analysis provided new insight into the mechanism by which an individual brain distinguishes and learns about shapes of objects.