A Spatial Bayesian Modeling Approach for Cortical Surface fMRI Data Analysis

• Activity Number: 284 - ASA Biometrics Section JSM Travel Awards (II)
• Type: Topic Contributed
• Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Amanda Mejia* and Yu Yue and David Bolin and Finn Lindgren and Martin Lindquist
• Companies: IU and The City University of New York and University of Gothenburg and University of Edinburgh and Johns Hopkins University
• Keywords: spatial statistics; Bayesian smoothing; integrated nested Laplace approximation; stochastic partial differential equation; brain imaging; functional magnetic resonance imaging

Abstract

Cortical surface fMRI (cs-fMRI) has recently grown in popularity versus traditional volumetric fMRI. In addition to offering dimension reduction, removal of extraneous tissue types, and improved alignment across subjects, it is also more compatible with Bayesian spatial models. However, as no spatial Bayesian model has been proposed for cs-fMRI data, most analyses continue to employ a massive univariate approach. Here, we propose a spatial Bayesian model for cs-fMRI, which employs sophisticated spatial processes to model latent activation fields. We make several advances compared with existing spatial Bayesian models for volumetric fMRI. First, we use integrated nested Laplacian approximations (INLA), a highly accurate and efficient Bayesian computation technique, rather than variational Bayes (VB). To identify activations, we utilize an excursions set method based on the joint, rather than the marginal, posterior distribution of the latent fields. Finally, we propose the first multi-subject spatial Bayesian modeling approach, addressing a major gap in the literature. The methods are computationally advantageous and are validated through simulations and two task fMRI studies.