A Bayesian model selection approach to fMRI activation detection

Abstract

A fundamental question in functional MRI (fMRI) data analysis is to declare pixels either activated or non-activated with respect to the experimental design. A new statistical test for detecting activated pixels in fMRI data is proposed. The test is based on comparing the dimension of the parametric models fitted to the voxels fMRI time series data with and without controlled activation-baseline pattern. The Bayesian information criterion, is used for this comparison. This test has the advantage of not requiring any user-specified threshold to be estimated. The effectiveness of the proposed fMRI activation detection method is illustrated on real experimental data.