I’m new to surface-based analyzes and reading up on both SUMA and FS-FAST in freesurfer, trying to get the lay of the land. My question is about collapsing the surface based analyzes with voxel-based analyses in sub cortex:
In FS-FAST, the analysis is basically ran three times: once for each surface hemisphere; and once in the volume-space for sub cortex (surface masked out). The resulting cluster tests (independent for the 3 spaces) are bonferonni corrected to merge the data across a single family-wise error.
In AFNI, what would the recommend pipeline be for merging SUMA-surface analyzes with subcortical voxel-wise data? Would you run AFNI_PROC once with surface flags on? and once without (with surface masked out)?
Do you need to correct p-values for surface and sub cortex the way FS-FAST does?
Your description seems good to me, though the
volumetric blur operation makes it a little messy,
at least for running a standard afni_proc.py stream.
The cortex would need to be masked out of the data
before any blur operation. Hmm, I do not think one
could import a mask and then blur within it or use it
in -mask_apply. I guess mask_apply should really
be extended to include any imported ones.
Presuming I had a subcortical mask from Freesurfer per-subject, aligned to the anatomy (or should it be aligned to the EPI?), would adding this to my volumetric afni_proc.py do the job of masking and then zero-ing out the surface/cortex voxels for analysis?
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