I am pretty far away from having statistical expertise, which is why I am posing my question to the group. Recently, I have seen a multitude of papers that are using a multi-masking approach to deal with corrections for multiple comparisons (using main effect or other effects of interest contrasts masks). While on the surface this appears to seem like an optimal approach because you are restricting the number of voxels included in the multiple comparison, it seems like an opportunity for biasing the data and obtained results--especially if you are not masking the data based from a priori hypotheses (e.g., using a previously defined functional ROI mask because you're interested in face processing).
I'm not sure that I've articulated this is the best way. It seems, like I mentioned previously, to have the potential to bias results, but would greatly appreciate feedback. The questions typically asked from the lab that I've worked in have been better suited to utilizing a cluster-based approach; however, could also be served by multi-masking.
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