Hey Paul,

To further follow up on the second part of your message: I again checked the output of the whole sample (50 subjects) and the initial alignment (init_qc_00_overlap_usrc_obase.jpg) after running @Align_centers was actually comparably good/bad across all of them - probably as suggested by you due to considerably large neck/cheek areas. Whilst this not-so-great initial alignment was overcome in most cases, the fact that some subjects (like the data I uploaded) have clippings suggests that there is indeed a problem in their data and I am not quite sure what the best steps would be to deal with this. I don't know whether it helps, but we also processed the anatomical data in FreeSurfer (recon-all with defaults) and ran @SUMA_Make_Spec_FS -sid ${subject} -NIFTI afterwards, so we have those output files (i.e., T1.nii & ${subject}_SurfVol.nii) and I was wondering whether either of those could be a better input for @SSwarper? If so, should any steps be omitted (e.g., using -unifize_off or -aniso_off)? I have uploaded the T1.nii & ${subject}_SurfVol.nii as well for you to have a look at. Before "switching" to @SSwarper, I used T1.nii as anatomical input for afni_proc.py.

Again, thanks so much for your help, this is really appreciated!

Best wishes,
Stef