Thanks for your email Matt.
Do you happen to know the names of those packages?
Given that our custom parcellation is in the form of an annotation file
(rather than a gifti file), can you suggest a way to get our custom
parcellation from an atlas surface to a subject's volume using Connectome
Workbench?
Alternatively, is there a way to generate a custom parcellation using
Connectome Workbench?
Simon
On 12 November 2015 at 03:33, Glasser, Matthew <glass...@wustl.edu> wrote:
> I think this might be way more complicated than is needed. I believe
> there are structural extended packages that would allow you to run a
> FreeSurfer command without needing to replicate FreeSurfer files. As for
> how to get a parcellation from an atlas surface to a subject’s volume, I
> could tell you how to do that using Connectome Workbench tools, file
> formats, and our atlas, but I don’t know the details for FreeSurfer.
>
> Peace,
>
> Matt.
>
> From: Simon Baker <simonteba...@gmail.com>
> Date: Tuesday, November 10, 2015 at 11:34 PM
> To: "hcp-users@humanconnectome.org" <hcp-users@humanconnectome.org>
> Cc: "Harms, Michael" <mha...@wustl.edu>, Timothy Coalson <tsc...@mst.edu>,
> Matt Glasser <glass...@wustl.edu>
>
> Subject: Re: [HCP-Users] Custom parcellation for HCP data
>
> Thanks for the pointers Michael, Tim, and Matt. We've tried a range of
> approaches but we're still having trouble creating the custom parcellation.
> Below describes the updated version of our pipeline. Although this version
> improves the spatial alignment between the custom parcellation and the T1w
> volume, the quality of the parcellation is still lacking compared to e.g.
> ribbon.nii.gz.
>
> --
>
> 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 xh --sval-annot highres.annot
> --trgsubject ${SUBJECTID} --srcsurfreg sphere.reg --trgsurfreg
> /MNINonLinear/Native/${SUBJECTID}.X.sphere.native.surf.gii --tval
> xh.highres.annot
>
> 3. Obtain vertices and faces data from
> /MNINonLinear/Native/${SUBJECTID}.X.white.native.surf.gii
>
> 4. Using the vertices and faces data obtained in step 3 as inputs for the
> write_surf Matlab function, create the xh.white surface file.
>
> 5. Obtain thickness data from
> /MNINonLinear/Native/${SUBJECTID}.X.thickness.native.shape.gii
>
> 6. Using T1wImage = /T1w/T1w_acpc_dc_restore.nii.gz and T1wImageBrain =
> /T1w/T1w_acpc_dc_restore_brain.nii.gz, we replicate some of the steps found
> in HCP's FreeSurferPipeline.sh in order to generate /mri/orig.mgz.
> Specifically:
>
> #Make Spline Interpolated Downsample to 1mm
> Mean=`fslstats T1wImageBrain -M`
>
> flirt -interp spline -in T1wImage -ref T1wImage -applyisoxfm 1 -out
> T1wImage_1mm
>
> applywarp --rel --interp=spline -i T1wImage -r T1wImage_1mm
> --premat=/usr/local/fsl/etc/flirtsch/ident.mat -o T1wImage_1mm
>
> applywarp --rel --interp=nn -i T1wImageBrain -r T1wImage_1mm
> --premat=/usr/local/fsl/etc/flirtsch/ident.mat -o T1wImageBrain_1mm
>
> fslmaths T1wImage_1mm -div $Mean -mul 150 -abs T1wImage_1mm
>
> mri_convert T1wImage_1mm /mri/orig/001.mgz
>
> #Initial Recon-all Steps
> recon-all -subjid ${SUBJECTID} -sd ${SUBJECTS_DIR} -motioncor -talairach
> -nuintensitycor -normalization
>
> 7. Convert the custom parcellation annotation into a custom parcellation
> volume using mri_label2vol, with /mri/orig.mgz serving as the output
> template volume.
>
> mri_label2vol --annot xh.custom.annot --temp /mri/orig.mgz --identity
> --proj frac 0 1 .1 --subject ${SUBJECTID} --hemi xh --o xh_custom_vol.mgz
>
> 8. Resample and reslice the custom parcellation volume.
>
> mri_convert --resample_type nearest --reslice_like T1wImage
> xh_custom_vol.mgz xh_custom_vol.nii
>
> 9. Combine the resampled and resliced custom parcellation volume from each
> hemisphere to create a single custom parcellation volume (custom_vol.nii).
>
> 10. Find c_ras offset and generate matrix
>
> MatrixX=$(mri_info --cras T1wImage | cut -d " " -f 1)
> MatrixY=$(mri_info --cras T1wImage | cut -d " " -f 2)
> MatrixZ=$(mri_info --cras T1wImage | cut -d " " -f 3)
> echo "1 0 0 ""$MatrixX" > c_ras_xfm.mat
> echo "0 1 0 ""$MatrixY" >> c_ras_xfm.mat
> echo "0 0 1 ""$MatrixZ" >> c_ras_xfm.mat
> echo "0 0 0 1" >> c_ras_xfm.mat
>
> 11. Invert the c_ras offset matrix
>
> convert_xfm -omat c_ras_xfm_inv.mat -inverse c_ras_xfm.mat
>
> 12. Apply the inverted c_ras offset matrix to the custom parcellation
> volume (custom_vol.nii)
>
> flirt -in custom_vol.nii -applyxfm -init c_ras_xfm_inv.mat -out
> parcellation.nii -paddingsize 0.0 -interp nearestneighbour -ref T1wImage
>
> 13. [Not sure why, but then we had to] Warp the output from the previous
> step in order to align the parcellation with T1wImage (i.e.,
> /T1w/T1w_acpc_dc_restore.nii.gz).
>
> applywarp --rel --interp=nn -i parcellation.nii -r T1wImage -w
> /MNINonLinear/xfms/standard2acpc_dc.nii.gz -o parcellation_native.nii
>
> 14. See attached screenshot2.jpeg showing /T1w/T1w_acpc_dc_restore,
> /T1w/ribbon (blue), and parcellation_native (yellow).
>
> --
>
> The alignment of parcellation_native is a bit off and it appears to be
> grainy/pixelated. Do you have any suggestions as to why this is the case?
>
> Kind regards,
>
> Simon Baker
> Brain & Mental Health Laboratory
> Institute of Cognitive & Clinical Neuroscience
> Monash University
>
>
> On 20 October 2015 at 09:20, Glasser, Matthew <glass...@wustl.edu> wrote:
>
>> FreeSurfer might do it if you simply specify the correct extension, but
>> if not, mris_convert will do the conversion.
>>
>> Peace,
>>
>> Matt.
>>
>> From: <hcp-users-boun...@humanconnectome.org> on behalf of Timothy
>> Coalson <tsc...@mst.edu>
>> Date: Monday, October 19, 2015 at 4:44 PM
>> To: "Harms, Michael" <mha...@wustl.edu>
>> Cc: "hcp-users@humanconnectome.org" <hcp-users@humanconnectome.org>,
>> Simon Baker <simonteba...@gmail.com>
>> Subject: Re: [HCP-Users] Custom parcellation for HCP data
>>
>> Additionally, if you generate a cortex-only parcellation, you shouldn't
>> need to put it into a volume to use it with the HCP CIFTI data. However,
>> I'm not sure how to get freesurfer to write GIFTI label files, and unless
>> you generate it on the fs_LR 32k mesh, you'll need to resample it (via
>> registered sphere surfaces). If you can get the label data into matlab,
>> though, you can write it as .func.gii files, and use wb_command to turn it
>> into label.gii, and then to .dlabel.nii for use with CIFTI data. Some care
>> may be needed to keep the right and left labels separate.
>>
>> Tim
>>
>>
>> On Mon, Oct 19, 2015 at 10:08 AM, Harms, Michael <mha...@wustl.edu>
>> wrote:
>>
>>>
>>> Hi,
>>> Did you see this recent thread on the list:
>>> http://www.mail-archive.com/hcp-users%40humanconnectome.org/msg01910.html
>>>
>>> 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: mha...@wustl.edu
>>>
>>> From: Simon Baker <simonteba...@gmail.com>
>>> Date: Sunday, October 18, 2015 8:16 PM
>>> To: "hcp-users@humanconnectome.org" <hcp-users@humanconnectome.org>
>>> Subject: [HCP-Users] 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
>>>
>>> _______________________________________________
>>> HCP-Users mailing list
>>> HCP-Users@humanconnectome.org
>>> http://lists.humanconnectome.org/mailman/listinfo/hcp-users
>>>
>>>
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>>
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