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

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 bra

SUBMITTER: Zhang L 

PROVIDER: S-EPMC4076058 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature