Good morning/afternoon/evening to everybody in your various time zones!
My question is as stated in the title I know the BOLD and the T1w images are in the same space, but the masks clearly cover different regions of the brain, at least on the extremities (see the image posted below). The top mask is the T1w from the āanatā folder, while the bottom is the BOLD mask from āfuncā - the func mask clearly extends past the anat mask.
Without thinking I generated some first level models using the T1w mask as an explicit mask, but in hindsight, it seems as though the functional BOLD mask might have been more appropriate. I want to make the right choice!
Iām at this very moment attempting to comb through some papers and see what others have done, but mask parameters do not seem to be commonly reported, and doing an outright internet search isnāt returning much either. Any thoughts are appreciated!!!
As always, I appreciate the support Iāve gotten from this community, and I value the time you take to respond (I know itās finite).
if you want to analyze that particular individual and only that one, Iād take the most liberal mask: it costs you basically nothing to be a bit more generous.
but most often you deal with different subjects, and in that case, I advise not to use subject-specific masks: this makes inter-subject comparison/analysis much harder. Using a standard MNI-space brain mask is reasonable, or some statistical consensus among the individually defined masks.
HTH
Thank you for your response! I appreciate getting multiple points of view on how to address the issue.
Weāll be conducting a searchlight analysis on individuals, then averaging those accuracy maps at the group level for statistical testing. It sounds like, since we plan to go to the group level, a standard MNI mask would be more appropriate?
When you say āstatistical consensusā do you mean something like an average anatomical mask?
I think I will likely use a mask from the standard MNI template everybody was warped to, as it seems to map to the individual subject data pretty accurately when I overlay them. It is good to know about those Nilearn routines, though, so I appreciate the advice on that!