Title: Efficient Bayesian estimation of brain activation with cortical surface and subcortical data using EM
Authors: Daniel Spencer - Indiana University (United States) [presenting]
Amanda Mejia - Indiana University (United States)
David Bolin - King Abdullah University of Science and Technology (KAUST) (Saudi Arabia)
Mary Beth Nebel - Kennedy Krieger Institute (United States)
Abstract: Analysis of brain imaging scans is critical to understanding the way the human brain functions. In particular, functional magnetic resonance imaging (fMRI) scans give detailed data on a living subject at relatively high spatial and temporal resolutions. Due to the high cost involved in the collection of these scans, robust methods of analysis are of critical importance in order to produce meaningful inference. Bayesian methods, in particular, allow for the inclusion of expected behavior from a prior study into an analysis, increasing the power of the results while circumventing problems that arise in classical analyses, including the effects of smoothing results and sensitivity to multiple comparison testing corrections. Recent work developed a surface-based spatial Bayesian general linear model (GLM) for cortical surface fMRI (cs-fMRI) data using stochastic partial differential equation (SPDE) priors which rely on the computational efficiencies of the integrated nested Laplace approximation (INLA) to perform powerful analyses. We develop an exact Bayesian analysis method for the GLM, employing an expectation-maximization (EM) algorithm to find estimates of effects of task-based regressors on cs-fMRI and subcortical fMRI data. Our proposed method is compared to the INLA implementation of the Bayesian GLM, as well as the classical GLM on simulated data. An analysis of data from the Human Connectome Project is also shown.