I'm running a functional analysis of some data from brain-lesioned (stroke) patients and am posting to confirm the following steps that I should take in the first level analysis (in addition to those required for analyses of healthy participants). These are mostly gleaned from the forums, the wiki, and direct advice from MJ.
1) I should use an inverse mask of the lesion (registered to functional space) and input it in the post-stats tab under "pre-threshold masking" so that signal from lesioned areas will be excluded before thresholding.
2) I should include a timeseries from the lesioned area (registered to functional space) as a nuisance covariate by making a timeseries of the lesioned area as follows:
fslmeants -i filtered_func_data.nii.gz -o my_meants.txt -m lesion_reg2_examplefunc.nii.gz.
Then inputting it into the "add additional confound EVs" option of the stats tab.
3) To ensure the most accurate registration, I should FNIRT the structural image using an inverse lesion mask (so that the lesion doesn't warp the registration) as follows:
fnirt --ref=MNI152_T1_2mm.nii --in=T1_brain.nii.gz --inmask=inverse_lesion_mask.nii.gz --iout=fnirted_T1_brain.nii.gz
Then I should replace the highres2standard.mat file and the highres2standard_warp.nii.gz in the .feat/reg directory with the new ones from the masked fnirt output, and use updatefeatreg to incorporate the masked registration into the first-level analysis.
Is this appropriate? Is there anything that I've missed?