Bayesian source separation of fMRI signals

In analyzing the results of functional magnetic resonance imaging, the identification of significant activation in voxels is a crucial task. In computing the activation level, a standard method is to select an assumed to be known reference function and perform a multiple regression of the time courses on it and a linear trend. Once the linear trend is found, the correlation between the assumed to be known reference function and the detrended observed time-course in each voxel is computed and voxels colored according to their correlation. But the most important question is: How do we choose the reference function? This paper develops a Bayesian statistical approch to determining the underlying source reference function based on Bayesian source separation, and uses it on both sumulated and real fMRI data. This underlying reference function is the unobserved response due the presentation of the experimental stimulus.