A Multidimensional Similarity Measure for Bilateral Adaptive Filtering of fMRI Data

In analysis of fMRI data, it is common to average neighboring voxels in order to obtain robust estimates of the correlations between voxel time-series and the model of the signal expected to be present in activated regions. We have previously proposed a method where only voxels with similar correlation coefficients are averaged. In this paper we extend this idea, and present a novel method for analysis of fMRI data. In the proposed method, only voxels with similar correlation coefficients and similar time-series are averaged. The proposed method is compared to our previous method and to two well-known filtering strategies, and is shown to have superior ability to discriminate between active and inactive voxels.