Classification of brain activation via spatial Bayesian variable selection in fMRI regression

Functional magnetic resonance imaging (fMRI) is the most popular technique in human brain mapping, with statistical parametric mapping (SPM) as a classical benchmark tool for detecting brain activity. Smith and Fahrmeir (J Am Stat Assoc 102(478):417–431, 2007) proposed a competing method based on a spatial Bayesian variable selection in voxelwise linear regressions, with an Ising prior for latent activation indicators. In this article, we alternatively link activation probabilities to two types of latent Gaussian Markov random fields (GMRFs) via a probit model. Statistical inference in resulting high-dimensional hierarchical models is based on Markov chain Monte Carlo approaches, providing posterior estimates of activation probabilities and enhancing formation of activation clusters. Three algorithms are proposed depending on GMRF type and update scheme. An application to an active acoustic oddball experiment and a simulation study show a substantial increase in sensitivity compared to existing fMRI activation detection methods like classical SPM and the Ising model.