Document Detail


A Spatio-Temporal Nonparametric Bayesian Variable Selection Model of fMRI Data for Clustering Correlated Time Courses.
MedLine Citation:
PMID:  24650600     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
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 an 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 complexspatial 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 timecourses, 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 ofthe 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.
Authors:
Linlin Zhang; Michele Guindani; Francesco Versace; Marina Vannucci
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-3-17
Journal Detail:
Title:  NeuroImage     Volume:  -     ISSN:  1095-9572     ISO Abbreviation:  Neuroimage     Publication Date:  2014 Mar 
Date Detail:
Created Date:  2014-3-21     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9215515     Medline TA:  Neuroimage     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2014 Elsevier Inc. All rights reserved.
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