Campus Units

Statistics

Document Type

Article

Publication Date

6-2011

Journal or Book Title

Annals of Applied Statistics

Volume

5

Issue

2B

First Page

1183

Last Page

1206

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.

Comments

This is an article from The Annals of Applied Statistics 5 (2011): 1183, doi: 10.1214/11-AOAS470. Posted with permission.

Rights

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Copyright Owner

Institute of Mathematical Statistics

Language

en

File Format

application/pdf

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