Thanks, Kevin for the additional information. Kevin's paper is here https://www.sciencedirect.com/science/article/pii/S0960982217308138

Dealing with motion artefacts in the functional data is another class of sometimes complex registration problem. I have sometimes done a stack reg on the the functional data for motion correction and then used an average image of that to bridge to anatomical data. I'd be happy to see some more details of using AFNI's 3dvolreg that you mention.

Besides the points already mentioned mapping a mask in template space on your functional data has the advantage of reformatting much less image data. Your functional data will be very oversampled if you transform it from 6 to 101 slices.