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dc.contributor.authorZhang, Linlin
Guindani, Michele
Versace, Francesco
Vannucci, Marina
dc.date.accessioned 2015-10-28T19:04:03Z
dc.date.available 2015-10-28T19:04:03Z
dc.date.issued 2014
dc.identifier.citation Zhang, Linlin, Guindani, Michele, Versace, Francesco, et al.. "A spatio-temporal nonparametric Bayesian variable selection model of fMRI data for clustering correlated time courses." NeuroImage, 95, (2014) Elsevier: 162-175. http://dx.doi.org/10.1016/j.neuroimage.2014.03.024.
dc.identifier.urihttps://hdl.handle.net/1911/81935
dc.description.abstract In 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.
dc.language.iso eng
dc.publisher Elsevier
dc.rights This is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Elsevier.
dc.title A spatio-temporal nonparametric Bayesian variable selection model of fMRI data for clustering correlated time courses
dc.type Journal article
dc.citation.journalTitle NeuroImage
dc.subject.keywordBayesian nonparametric
Dirichlet process prior
discrete wavelet transform
fMRI
long memory errors
Markov random field prior
dc.citation.volumeNumber 95
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1016/j.neuroimage.2014.03.024
dc.identifier.pmcid PMC4076058
dc.identifier.pmid 24650600
dc.type.publication post-print
dc.citation.firstpage 162
dc.citation.lastpage 175


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