Spatial mixture modeling of fMRI data

Recently, Everitt and Bullmore 1999 proposed a mixture model for a test statistic for activation in fMRI data. The distribution of the statistic was divided into two components; one for nonactivated voxels and one for activated voxels. In this framework one can calculate a posterior probability for a voxel being activated, which provides a more natural basis for thresholding the statistic image, than that based on P-values. In this article, we extend the method of Everitt and Bullmore to account for spatial coherency of activated regions. We achieve this by formulating a model for the activation in a small region of voxels and using this spatial structure when calculating the posterior probability of a voxel being activated. We have investigated several choices of spatial models but find that they all work equally well for brain imaging data. We applied the model to synthetic data from statistical image analysis, a synthetic fMRI data set and to visual stimulation data. Our conclusion is that the method improves the estimation of the activation pattern significantly, compared to the nonspatial model and to smoothing the data with a kernel of FWHM 3 voxels. The difference between FWHM 2 smoothing and our method were more modest.