creating only freesurfer wm and ventricle mask with @suma_make_Spec_FS

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Dear afni experts,

I used afni_proc.py to run some preprocessing on my data and I also ran Freesurfer. I would like to generate a wm and ventricle mask using the freesurfer segmentation to then export it to CONN toolbox to run aCompCor. And then going back to afni for nuisance regression. I looked into @Suma_make_Spec_FS and I am wondering if it is possible to use this program only to generate the wm and ventricle masks from Freesurfer. So without having to run the steps in regards to surface analysis.
Also I am a little confused about the suma command and the needed directory structure so that suma can find the aparc+asec.nii file.

Right now I have free surfers prefer directory structure where I have a separate directory called freesurfer which includes directories for each subject. Do I need a specific directory structure for @suma_make_Spec_FS?

Thank you very much in advance for any help!
Carolin

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Hi, Carolin-

I think this tutorial should address most of this:
https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/tutorials/fs/fs_fsprep.html

Briefly, if you have a current version of AFNI (check with “afni -ver”; update if need be with “@update.afni.binaries -d”), then running @SUMA_Make_Spec_FS will automatically output ventricle and WM maps for you, as described in this part of the above tutorial:
https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/tutorials/fs/fs_fsprep.html#a-note-on-suma-make-spec-fs-outputs


fs_ap_wm.nii.gz, fs_ap_latvent.nii.gz

    Two volumetric datasets of masks that have been found useful for afni_proc.py scripting, namely when applying tissue-based regressors. The first is comprised of the main WM regions defined by FS, and the second is comprised of the lateral ventricles

    For more details, see the output of @SUMA_renumber_FS for more details on these.

If your subject’s FS output dir is located in /some/path/on/system/sub-001/, where sub-001 is also your subject ID, then I would try running @SUMA_Make_Spec_FS with:


@SUMA_Make_Spec_FS                         \
    -fs_setup                              \
    -NIFTI                                 \
    -sid    sub-001                             \
    -fspath /some/path/on/system/sub-001

That should make a new directory called SUMA there, adjacent to all the other ones, containing the volumes converted to NIFTI, the surfaces in the useful GIFTI format, and the surfaces also on standardized meshes.

Re. SUMA:

  1. Saad ZS, Reynolds RC, Argall B, Japee S, Cox RW (2004). SUMA: an interface for surface-based intra- and inter-subject analysis with AFNI, in: 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821). Presented at the 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821), pp. 1510-1513 Vol. 2. doi.org/10.1109/ISBI.2004.1398837
    https://ieeexplore.ieee.org/document/1398837
  2. Saad ZS, Reynolds RC (2012). SUMA. Neuroimage 62, 768–773. doi.org/10.1016/j.neuroimage.2011.09.016
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3260385/

Re. standard meshes:

  1. Argall BD, Saad ZS, Beauchamp MS (2006). Simplified intersubject averaging on the cortical surface using SUMA. Human Brain Mapping 27: 14-27.
    https://pubmed.ncbi.nlm.nih.gov/16035046/

You might also be interested in ANATICOR using those ROIs, which can be run in afni_proc.py as well; it is described here:

  1. Jo HJ, Saad ZS, Simmons WK, Milbury LA, Cox RW. Mapping sources of correlation in resting state FMRI, with artifact detection and removal. Neuroimage. 2010;52(2):571-582. doi:10.1016/j.neuroimage.2010.04.246
    https://pubmed.ncbi.nlm.nih.gov/20420926/

–pt