Assessing Brain Activity through Spatial Bayesian Variable Selection

Statistical parametric mapping (SPM), relying on the general linear
model and classical hypothesis testing, is a benchmark tool for
assessing human brain activity using data from fMRI experiments.
Friston et al. (2002a) discuss some limitations of this frequentist
approach and point out promising Bayesian perspectives. In
particular, a Bayesian formulation allows explicit modeling and
estimation of activation probabilities. In this paper, we directly
address this issue and develop a new regression based approach
using spatial Bayesian variable selection. Our method has several
advantages. First, spatial correlation is directly modeled for
activation probabilities and indirectly for activation amplitudes.
As a consequence, there is no need for spatial adjustment in a
post-processing step. Second, anatomical prior information, such as
the distribution of grey matter or expert knowledge, can be
included as part of the model. Third, the method has superior
edge-preservation properties as well as being fast to compute. When
applied to data from a simple visual experiment, the results
demonstrate improved sensitivity for detecting activated cortical
areas and for better preserving details of activated structures.