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.
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
Copyright Date
2011
Language
en
File Format
application/pdf
Recommended Citation
Bhattacharya, Sourabh and Maitra, Ranjan, "A nonstationary nonparametric Bayesian approach to dynamically modeling effective connectivity in functional magnetic resonance imaging experiments" (2011). Statistics Publications. 69.
https://lib.dr.iastate.edu/stat_las_pubs/69
Comments
This is an article from The Annals of Applied Statistics 5 (2011): 1183, doi: 10.1214/11-AOAS470. Posted with permission.