A nonstationary nonparametric Bayesian approach to dynamically modeling effective connectivity in functional magnetic resonance imaging experiments

dc.contributor.author Bhattacharya, Sourabh
dc.contributor.author Maitra, Ranjan
dc.contributor.department Statistics
dc.date 2018-02-17T18:23:51.000
dc.date.accessioned 2020-07-02T06:58:00Z
dc.date.available 2020-07-02T06:58:00Z
dc.date.copyright Sat Jan 01 00:00:00 UTC 2011
dc.date.issued 2011-06-01
dc.description.abstract <p>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.</p>
dc.description.comments <p>This is an article from <em>The Annals of Applied Statistics</em> 5 (2011): 1183, doi: <a href="http://dx.doi.org/10.1214/11-AOAS470" target="_blank">10.1214/11-AOAS470</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/stat_las_pubs/69/
dc.identifier.articleid 1067
dc.identifier.contextkey 8807061
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/69
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/90669
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/stat_las_pubs/69/2011_MaitraR_NonstationaryNonparametricBayesian.pdf|||Sat Jan 15 01:30:21 UTC 2022
dc.source.uri 10.1214/11-AOAS470
dc.subject.disciplines Statistics and Probability
dc.subject.keywords Attentional control network
dc.subject.keywords Bayesian analysis
dc.subject.keywords Dirichlet process
dc.subject.keywords effective connectivity analysis
dc.subject.keywords fMRI
dc.subject.keywords Gibbs sampling
dc.subject.keywords temporal correlation
dc.title A nonstationary nonparametric Bayesian approach to dynamically modeling effective connectivity in functional magnetic resonance imaging experiments
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
File
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
2011_MaitraR_NonstationaryNonparametricBayesian.pdf
Size:
1.25 MB
Format:
Adobe Portable Document Format
Description:
Collections