Correlation Controlled Adaptive Filtering for Fmri Data Analysis

In analysis of fMRI data, it is common to average neighboring voxels in order to obtain robust estimates of the correlations between voxel timeseries and the model of the signal expected to be present in activated regions. This paper presents a novel method for analysis of fMRI data, which extends this approach by averaging only neighboring voxels whose time-series have similar correlation coefficients. A comparison between the new method and two other filtering strategies is also presented, and the novel method is shown to have superior ability to discriminate between active and inactive voxels.