The fs_LR 32k spheres use a resolution (vertex spacing) that is suitable
for 2mm fMRI data, but it sounds like you are using structural-resolution
voxels.  As Matt says, I would put the fs_LR surface into your volume
space, and do only a single mapping, because nearest neighbor or enclosing
voxel mapping is extremely lossy - additionally, I would use the 164k
spheres instead.

Other forms of resampling, meant for continuous data, are not as lossy
because they can approximate the underlying function, but "voxel identity"
is not a continuous function.  I don't know exactly what you are doing, but
I would suggest mapping the data that *is* continuous onto fs_LR registered
surfaces, and then re-posing your "element identity" as vertex indices,
rather than T1w voxels.  If this doesn't let you do what you want, then
maybe you can do per-subject independent volume analysis, and then map the
results of that onto the individual's surface before combining across
subjects?

If you want to explain your bigger-picture goal, we might have other useful
suggestions.

Tim


On Wed, Jan 3, 2018 at 11:58 AM, Glasser, Matthew <[email protected]>
wrote:

> I think I would probably resample the subject’s own FS_LR registered
> surfaces into the FreeSurfer space (an exact transformation) and then do a
> single mapping from volume to surface.  You would need to figure out the
> affine matrix that describes this transform.
>
> Peace,
>
> Matt.
>
> From: <[email protected]> on behalf of Seán Froudist
> Walsh <[email protected]>
> Date: Wednesday, January 3, 2018 at 10:29 AM
> To: "[email protected]" <[email protected]>
> Subject: [HCP-Users] volume to average surface with Nearest Neighbour
> interpolation
>
> Dear HCP experts,
>
> I am interested in mapping individual voxels in a subject's FreeSurfer
> conformed space (orig.nii) onto the HCP template (fsaverage_LR) while
> maintaining the original voxel values.
>
> All of the voxels lie within the LH cortical ribbon in the (conformed)
> volume space. There are 186 voxels with non-zero values that act as unique
> identifiers, with all other voxels having a value of zero.
>
> I have prepared the native FreeSurfer to HCP transformations, then
> performed volume-to-surface mapping of the sample data, and finally applied
> the FreeSurfer-to-HCP transform to the sample data. I have tried to
> identify the options that perform something like Nearest Neighbour
> assignment, as I need to maintain the original values as identifiers. The
> problem I am facing is that volume-to-surface mapping as done below reduces
> the number of non-zero voxels/vertices from 186 to 94, and the
> Freesufer-to-HCP resampling reduces the number of non-zero vertices further
> from 94 to 13 non-zero points.
>
> I would greatly appreciate your guidance as to the best way to achieve my
> desired goal of obtaining all 186 vertices with their original values onto
> the HCP template. Should I map each voxel to the closest voxel on the
> FreeSurfer WM surface, or something similar?
>
> The commands I used are shown below.
>
> Many thanks,
>
> Sean
>
> wb_shortcuts -freesurfer-resample-prep lh.white.surf.gii lh.pial.surf.gii
> lh.sphere.FSave.reg.surf.gii HCP_S1200_GroupAvg_v1/standard_mesh_atlases/
> resample_fsaverage/fs_LR-deformed_to-fsaverage.L.sphere.32k_fs_LR.surf.gii
> lh.midthickness.surf.gii ${current_subject}.l.midthickness.32k_fs_LR.surf.gii
> lh.sphere.HCP.reg.surf.gii
>
> and then created a volume-to-surface mapping, while maintaining the
> original voxel/vertex values using
>
> wb_command -volume-to-surface-mapping ' 
> {current_subject}_samples_LH_cortex.nii.gz
> lh.midthickness.surf.gii   samples_native.shape.gii -enclosing
>
>
>
> and then applied the transform using
>
> wb_command -metric-resample samples_native.shape.gii
> lh.sphere.HCP.reg.surf.gii HCP_S1200_GroupAvg_v1/standard_mesh_atlases/
> resample_fsaverage/fs_LR-deformed_to-fsaverage.L.sphere.32k_fs_LR.surf.gii
> BARYCENTRIC -largest {current_subject}_samples_HCP.shape.gii
>
>
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>
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