Hi Simon, 
Since you are using the HCP data, this may be more appropriate for the HCP-Users list.

If you look at the current HCP pipeline code, you'll see that after converting FS surfaces to .surf.gii (using 'mris_convert'), an affine transform is then
applied based on the c_ras contents (applied using 'wb_command -surface-apply-affine').  i.e., to date, mris_convert has not been applying the volume geometry information.  I believe that an option is going to exist in 'mris_convert' in the forthcoming FS release that will save out c_ras corrected surfaces.

cheers,
-MH

-- 
Michael Harms, Ph.D.
-----------------------------------------------------------
Conte Center for the Neuroscience of Mental Disorders
Washington University School of Medicine
Department of Psychiatry, Box 8134
660 South Euclid Ave. Tel: 314-747-6173
St. Louis, MO  63110 Email: mharms@wustl.edu

From: Simon Baker <simontebaker@gmail.com>
Reply-To: "freesurfer@nmr.mgh.harvard.edu" <freesurfer@nmr.mgh.harvard.edu>
Date: Monday, October 5, 2015 1:12 AM
To: "freesurfer@nmr.mgh.harvard.edu" <freesurfer@nmr.mgh.harvard.edu>
Subject: [Freesurfer] Custom parcellation for HCP data

Hi all,

We want to create a custom parcellation for use with the connectome project data. However, we have not been able to achieve accurate spatial alignment between the random parcellation volume and the T1w volume. Specifically, there appears to be an "offset," possibly due to a mismatch between the origin of these volumes. In the following we describe the relevant steps of our pipeline. Please suggest any changes that might help to resolve the issue.

1. Create a high-resolution annotation (parcellation) by randomly parcellating the fsaverage surface into N regions of approximately equal volume.

2. Map the annotation from the source subject (fsaverage) to the target subject using mri_surf2surf.

mri_surf2surf --srcsubject fsaverage --hemi lh --sval-annot highres.annot --trgsubject ${SUBJECTID} --srcsurfreg sphere.reg --trgsurfreg ${SUBJECTS_DIR}/${SUBJECTID}/MNINonLinear/Native/${SUBJECTID}.L.sphere.native.surf.gii --tval lh.highres.annot

[repeat for rh]

3. Obtain vertices and faces data from ${SUBJECTS_DIR}/${SUBJECTID}/MNINonLinear/Native/${SUBJECTID}.L.white.native.surf.gii

[repeat for rh]

4. Using the vertices and faces data obtained in step 3 as inputs for the write_surf Matlab function, create the lh.white surface file.

[repeat for rh]

5. Obtain thickness data from ${SUBJECTS_DIR}/${SUBJECTID}/MNINonLinear/Native/${SUBJECTID}.L.thickness.native.shape.gii

[repeat for rh]

6. Convert the annotation into a volume using mri_label2vol.

mri_label2vol --annot lh.highres.annot --temp ${SUBJECTS_DIR}/${SUBJECTID}/T1w/T1w_acpc_dc_restore.nii.gz --identity --proj frac 0 1 .1 --subject ${SUBJECTID} --hemi lh --o vol_lh.nii

[repeat for rh]

7. Configure the volume (remove unwanted ROIs).

[repeat for rh]

8. Combine the configured volumes from each hemisphere to create the random parcellation volume.

9. Overlay the random parcellation volume on the template volume.

See attached screenshot.jpeg showing the misalignment between the random parcellation volume and the template volume.

Kind regards,

Simon Baker
Brain & Mental Health Laboratory
Institute of Cognitive & Clinical Neuroscience
Monash University

 

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