A nonstationary nonparametric Bayesian approach to dynamically modeling effective connectivity in functional magnetic resonance imaging experiments
Sourabh Bhattacharya and Ranjan Maitra
Source: Ann. Appl. Stat. Volume 5, Number 2B
(2011), 1183-1206.
Abstract
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.
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Keywords: Attentional control network; Bayesian analysis; Dirichlet process; effective connectivity analysis; fMRI; Gibbs sampling; temporal correlation
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Permanent link to this document: http://projecteuclid.org/euclid.aoas/1310562718
Digital Object Identifier: doi:10.1214/11-AOAS470
Mathematical Reviews number (MathSciNet): MR2849771
Zentralblatt MATH identifier: 1223.62011
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