Bayesian Hierarchical Multi-Subject Multiscale Analysis of Functional MRI Data

• Activity Number: 637
• Type: Topic Contributed
• Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
• Sponsor: International Indian Statistical Association
• Author(s): Marco Ferreira*+ and Nilotpal Sanyal
• Companies: University of Missouri and University of Missouri
• Keywords: Bayesian inference ; Image smoothing ; Mixture prior ; Multiple subjects ; Spatiotemporal analysis ; Wavelet modeling

Abstract

We develop methodology for Bayesian hierarchical multi-subject multiscale analysis of functional Magnetic Resonance Imaging (fMRI) data. Specifically, we model the brain images temporally with a standard general linear model and transform the resulting estimated standardized regression coefficient maps through a discrete wavelet transformation to obtain a sparse representation in the wavelet space. Subsequently, we assign to the wavelet coefficients a prior that is a mixture of a point mass at zero and a Gaussian distribution, and assume that the mixture probabilities for wavelet coefficients at same location and level are common across subjects. Further, we assign for the mixture probabilities a prior that depends on few hyperparameters. We develop empirical Bayes methodology that leads to fast computations. An application to computer simulated synthetic data has shown that, when compared to single-subject analysis, our multi-subject methodology performs better in terms of mean squared error. Finally, we illustrate the utility and flexibility of our multi-subject methodology with an application to an event-related fMRI dataset generated by Postle (2005).