Hi, Juan-
Interesting that "one more question" comes enumerated as 1, 2, 3....
OK, to it then:
1) the "-inset" can be any time series data: task, resting, or anything else.
1b) "4D" data = four dimensional data, essentially, any time series data set. Three dimensions are spatial (x, y, z), and since each whole brain volume was acquired many times (t = 0, 1*TR, 2*TR, 3*TR, ....), there is another dimension along the time axis, t.
1c) Yes, make sense to do per subject.
1d) As to what time series you use, I guess it depends-- it should probably be your "final" time series; if you are using 3dDeconvolve to regress out motion, low-order polynomials (i.e., low frequency "base line"), and any other regressors of non-interest, then you might want to use the *output* from 3dDeconvolve. Using standard afni_proc.py nomenclature (hopefully you are making your life easier by building your processing with Rick's wonderful program!), for resting data this would probably be the errts* file, and for task-based data, the fitts* file.
1e) Sure, you could calculate the mean of different quantities. You could also go the multivariate modeling route, using Gang's 3dMVM function. See, for example:
[
afni.nimh.nih.gov]
afni.nimh.nih.gov/pub/dist/papers/ASF_2015_draft_BCinpress.pdf
2) Yep. A single ROI is just a set of voxels having the same integer value (they need not be contiguous), and you can certainly construct an "ROI map" in the manner described.
3) I believe you are asking what to use for the outer/whole brain limits across your group? You could probably use either method you suggest: TT_N27 mask limits if you've warped everyone there, or the 3dmask_tool 70% overlap of individual masks. Marginally, the latter seems a bit preferable to me, deriving the boundaries from your subjects themselves and their actual data (postalignment).
--pt
