Application of Spatial Bayesian Hierarchical Model with Variable Selection to fMRI data

Abstract

We propose a spatial Bayesian hierarchical model to analyze functional magnetic resonance imaging data with complex spatial and temporal structures Several studies have found that the spatial dependence not only appear in signal changes but also in temporal correlations among voxels However currently existing statistical approaches ignore the spatial dependence of temporal correlations for the computational efficiency We consider the spatial random effect models to simultaneously model spatial dependences in both signal changes and temporal correlations but keep computationally feasible Through simulation the proposed approach improves the accuracy of identifying the activations We study the properties of the model through its performance on simulations and a real event-related fMRI data set

Date of Award	2015 Jul 21
Original language	English
Supervisor	Kuo-Jung Lee (Supervisor)