Background We are considering the statistical analysis of functional magnetic resonance imaging (fMRI) data. As demonstrated in previous work, grouping voxels into regions (of interest) and carrying out a multiple test for signal detection on the basis of these regions typically leads to a higher sensitivity when compared with voxel-wise multiple testing approaches. Methods In the case of a multi-subject study, we propose to define the regions for each subject separately based on their individual brain anatomy, represented, e.g., by regional labels. The aggregation of the subject-specific evidence for the presence of signals in the different regions is then performed by means of a combination function for p -values. We validate the proposed methodology with simulated data and apply it to real fMRI data of a hypothesis-driven approach towards identifying brain regions involved in understanding software code. Results The results of our simulated data indicate that the proposed approach improves power relative to voxel-wise inference and performs comparably to a cluster-based baseline. Testing our method on real fMRI data, we found that our approach yields overlapping results with a two-stage approach for which two independent experiments are needed, one for defining the regions and one for actual signal detection. Conclusions In this paper, we overall demonstrate that our method of utilizing anatomical information is a candidate to provide a more sensitive analysis of fMRI data.
Peitek et al. (Wed,) studied this question.