Mixture model mapping of the brain activation in functional magnetic resonance images

We report on a novel method of identifying brain regions activated by periodic experimental design in functional magnetic resonance imaging data. This involves fitting a mixture distribution with two components to a test statistic estimated at each voxel in an image. The two parameters of this distribution, the proportion of nonactivated voxels, and the effect size can be estimated using maximum likelihood methods. Standard errors of the parameters can also be estimated. The fitted distribution can be used to derive brain activation maps and two examples are described, one involving a visual stimulation task, the other an auditory stimulation task. The method appears to have some advantages over direct use of the P-values corresponding to each voxel's value of the test statistic.