Brainnetome volumetric parcellation for DTI processing

[witherscp]

Hi!

I have downloaded the brainnetome atlas BN_Atlas_246_1mm.nii.gz[/url] to use as the parcellation for my DTI processing. How can I convert this NIFTI parcellation into subject DTI space? Previously, I have used fat_proc_map_dti to bring FreeSurfer outputs into DTI space, but this .nii.gz file is not a FreeSurfer output and it is not in the subject’s space already, so I don’t know how fat_proc_map_dti would know the correct transformation. I have also created the FreeSurfer .annot files in fsaverage space for the Brainnetome atlas by following the instructions in [url=https://pan.cstcloud.cn/s/DQov5gaAR4s]BN_Atlas_freesurfer.zip. I can convert this to subject space using mri_surf2surf and then bring these surfaces into DTI space using fat_proc_map_dti, but then I have to inflate the parcels from the surface into the volume. If I am able to, it would make more sense for me to use the parcels in the volume that have already been created by the brainnetome group.

Thanks in advance for your help!

[witherscp]

Hi! Just checking in again – any thoughts on this problem?

Thanks.

[ptaylor]

Hi-

The fat_proc_map_dti program uses affine registration, so bringing data from a subject’s own anatomical dataset to the DWI that has been aligned to it makes sense with that level of warping.

To bring ROIs from a reference template to a subject DWI/DTI dataset would likely require nonlinear alignment for reasonable accuracy; once the transformation/warp has been estimated, one can apply the warp dataset to the ROIs, bringing them into the subject anatomical space.

Reference templates are typically T1w volumes. Do you have the subject’s T1w anatomical dataset in DWI space, like from using fat_proc_map_dti? If so, you could perform nonlinear alignment to that with the default cost function (lpa+ZZ), e.g., using @SSwarper or 3dQwarp. If you want to align a template to a subject’s b=0 DWI volume or to a T2w volume, you could use 3dQwarp or @SSwarper, but use a cost function that will be OK with different dataset tissue contrasts—say, lpc+ZZ.

–pt

[tognerikm]

Hi! I will be taking over this project from Price and had a question about auto_warp.py usage. I am first registering our clinical T1 with the HCP template, using the T1 as the base and the HCP template as the input, all in MNI space. This is registering well and there are no problems. I am then wanting to align these with the brainnetome parcellation atlas, and I am adding the atlas as a follower. This is not aligning well at all, it is off by a significant amount. Is there a better way to try to get the parcellation to align with the T1/template brain? Here is what I am trying to run:

auto_warp.py -base indt.nii.gz -input HCP40_MNI_1.25mm.nii.gz -followers BN_Atlas_246_1mm.nii.gz