The SSW reference templates are special, multivolume datasets. The th volume is a skull-less anatomical dset, and the other 4 volumes have more information used in various parts of the @SSwarper program. You can see these a bit more here:
The th volume is (from the help):
 = binary mask for gray matter plus some CSF (slightly dilated)
++ this volume is not used in this script
++ it is intended for use in restricting FMRI analyses
to the 'interesting' parts of the brain
++ this mask should be resampled to your EPI spatial
resolution (see program 3dfractionize), and then
combined with a mask from your experiment reflecting
your EPI brain coverage (see program 3dmask_tool).
So, this is the closest to being a GM mask, but it isn’t pure GM.
To your specific questions:
“when you run SSwarper on a subject do I need to input any specific command for it to produce a GM mask in the output?”
—> there isn’t such. In the animal-MRI processing world, the @animal_warper program (similar-ish to @SSwarper, but different, too) does allow one to put in other standard space maps and have those sent along the estimated transforms to subject space, or vice versa. But @SSwarper at present doesn’t.
“can this mask be used during for a single participants processing to restrict analysis to the GM only? Or is that only done during group analysis?”
—> This program is not the way to generate a GM mask (but see below for a pretty good way to generate such a beast).
“can a single mask generated from SSwarper be used for group analysis considering its warped to a specific template or would I still want to average each subjects together to try and create a better fit?”
—> SSwarper works to mask your input anatomical, but that is a whole brain (WB) mask, not a GM/tissue mask.
In order to generate a group level whole brain mask, what we might recommend if you are using afni_proc.py to process your subjects:
- take the individual masks from single subject processing, and make a new mask based on where 70% or more of them overlap
- the “70%” is just an empirically kinda useful value—a lot of overlap, but some flexibility
- for the single subject masks to use, with afni_proc.py outputs, these would likely be the mask_epi_anat*.HEAD dsets (the combination of EPI and anatomical coverage per subj)
- The explicit AFNI command to do this could look like the following, globbing over each mask in each “results” directory output by afni_proc.py:
3dmask_tool -input GROUP/*.results/mask_epi_anat*.HEAD \
-prefix group_mask.7 -frac 0.7
This step, and a a lot of these processing choices and rationales we recommend for processing (with specific details on afni_proc.py) are described here:
In terms of getting a GM-specific group mask: if you have all your data in standard space, there might be a GM tissue mask associated with that template; perhaps you could just take that and intersect it with you new WB mask. Though, in practice, and depending on your specific purpose, you might want to inflate that slightly, because while nonlinear alignment can be quite good, it is not perfect. Or, if you ran FreeSurfer’s recon-all on all of your subjects, you could pass the individually-estimated GM masks from that into afni_proc.py as well, having those sent along to your final EPI space, and then you could merge them in some fashion across your entire group. Again, what to do about a GM mask in standard space depends on your specific goal.
Hope there are some useful tidbits in this long and windy reply.