A spatio-temporal nonparametric Bayesian variable selection model of fMRI data for clustering correlated time courses

Simple item page
dc.citation.firstpage162en_US
dc.citation.journalTitleNeuroImageen_US
dc.citation.lastpage175en_US
dc.citation.volumeNumber95en_US
dc.contributor.authorZhang, Linlinen_US
dc.contributor.authorGuindani, Micheleen_US
dc.contributor.authorVersace, Francescoen_US
dc.contributor.authorVannucci, Marinaen_US
dc.date.accessioned2015-10-28T19:04:03Z
dc.date.available2015-10-28T19:04:03Z
dc.date.issued2014en_US
dc.description.abstractIn this paper we present a novel wavelet-based Bayesian nonparametric regression model for the analysis of functional magnetic resonance imaging (fMRI) data. Our goal is to provide a joint analytical framework that allows to detect regions of the brain which exhibit neuronal activity in response to a stimulus and, simultaneously, infer the association, or clustering, of spatially remote voxels that exhibit fMRI time series with similar characteristics. We start by modeling the data with a hemodynamic response function (HRF) with a voxel-dependent shape parameter. We detect regions of the brain activated in response to a given stimulus by using mixture priors with a spike at zero on the coefficients of the regression model. We account for the complex spatial correlation structure of the brain by using a Markov random field (MRF) prior on the parameters guiding the selection of the activated voxels, therefore capturing correlation among nearby voxels. In order to infer association of the voxel time courses, we assume correlated errors, in particular long memory, and exploit the whitening properties of discrete wavelet transforms. Furthermore, we achieve clustering of the voxels by imposing a Dirichlet process (DP) prior on the parameters of the long memory process. For inference, we use Markov Chain Monte Carlo (MCMC) sampling techniques that combine Metropolis–Hastings schemes employed in Bayesian variable selection with sampling algorithms for nonparametric DP models. We explore the performance of the proposed model on simulated data, with both block- and event-related design, and on real fMRI data.en_US
dc.identifier.citationZhang, Linlin, Guindani, Michele, Versace, Francesco, et al.. "A spatio-temporal nonparametric Bayesian variable selection model of fMRI data for clustering correlated time courses." <i>NeuroImage,</i> 95, (2014) Elsevier: 162-175. http://dx.doi.org/10.1016/j.neuroimage.2014.03.024.
dc.identifier.doihttp://dx.doi.org/10.1016/j.neuroimage.2014.03.024en_US
dc.identifier.urihttps://hdl.handle.net/1911/81935
dc.language.isoengen_US
dc.publisherElsevier
dc.rightsThis is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Elsevier.en_US
dc.subject.keywordBayesian nonparametricen_US
dc.subject.keywordDirichlet process prioren_US
dc.subject.keyworddiscrete wavelet transformen_US
dc.subject.keywordfMRIen_US
dc.subject.keywordlong memory errorsen_US
dc.subject.keywordMarkov random field prioren_US
dc.titleA spatio-temporal nonparametric Bayesian variable selection model of fMRI data for clustering correlated time coursesen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpost-printen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
nihms578292.pdf
Size:
4.02 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Faculty Publications
Statistics Publications