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June 2011 A nonstationary nonparametric Bayesian approach to dynamically modeling effective connectivity in functional magnetic resonance imaging experiments
Sourabh Bhattacharya, Ranjan Maitra
Ann. Appl. Stat. 5(2B): 1183-1206 (June 2011). DOI: 10.1214/11-AOAS470

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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Sourabh Bhattacharya. Ranjan Maitra. "A nonstationary nonparametric Bayesian approach to dynamically modeling effective connectivity in functional magnetic resonance imaging experiments." Ann. Appl. Stat. 5 (2B) 1183 - 1206, June 2011. https://doi.org/10.1214/11-AOAS470

Information

Published: June 2011
First available in Project Euclid: 13 July 2011

zbMATH: 1223.62011
MathSciNet: MR2849771
Digital Object Identifier: 10.1214/11-AOAS470

Rights: Copyright © 2011 Institute of Mathematical Statistics

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Vol.5 • No. 2B • June 2011
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