A Bayesian Modeling Approach of Multiple-Subject fMRI Data

• Activity Number: 668
• Type: Invited
• Date/Time: Thursday, August 13, 2015 : 10:30 AM to 12:20 PM
• Sponsor: IMS
• Author(s): Marina Vannucci* and Michele Guindani and Linlin Zhang
• Companies: Rice University and MD Anderson Cancer Center and Rice University
• Keywords: Bayesian nonparametrics ; fMRI ; spatial priors

Abstract

We present a 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 the detection of regions of the brain that activate in response to a stimulus, while simultaneously taking into account the association, or clustering, of spatially remote voxels within and across subjects. The model incorporates information on both the spatial and temporal correlation structures of the data. It also allows for voxel-dependent and subject-specific parameters. The high dimensionality of the data and the large amount of parameters to be estimated pose computational challenges. We investigate variational Bayes algorithms as an approximate computational technique and compare efficiency and estimation results with respect to a full Monte Carlo Markov chain algorithm. We explore performances of the proposed model on simulated data, with both block- and event-related designs, and on real fMRI data.