HCP-MMP1.0 volumetric (NIfTI) masks in native structural space

We in our group received with great interest the publication of the HCP-MMP 1.0 parcellation by Glasser et al. (Nature) created using data from the Human Connectome Project earlier this year. Often in our connectivity pipelines we use volume files for parcellation in native space, so we decided to try and convert the Connectome Workbench files to volume masks in native structural space to try out in future studies. We were happy to find that someone had already gone through the trouble of generating FreeSurfer annotation files projected on fsaverage, so all we had to do was find a way to transform these annot files to each subject’s space and convert them to volume masks. So we wrote this Linux shell script that performs a series of conversion and transformation steps using only FreeSurfer commands. It first converts the annotation files (lh.HCPMMP1.annot and rh.HCPMMP1.annot, downloaded from https://figshare.com/articles/HCP-MMP1_0_projected_on_fsaverage/3498446) to labels using mri_annotation2label, then takes each label from fsaverage to each subject’s space with mri_label2label, converts transformed labels back to annotation with mri_label2annot, and finally converts these to volume files (nii.gz) with mris_label2annot. Seems like too many steps, but this is how we (who are not FreeSurfer experts) got satisfactory results. The default final file consists of a single volume containing the HCP-MMP1.0 regions plus the subcortical regions from the FreeSurfer segmentation, each assigned a unique voxel value. Left-hemisphere cortical HCP-MMP1.0 regions have values between 1001 and 1181, whereas right-sided regions have values between 2001 and 2181. The correspondence between each specific region and its value is given in a look-up table that is generated in each subject’s output folder. To identify the subcortical regions, one just needs to check the FreeSurferColorLUT.txt file provided with FreeSurfer, as they correspond to the original aseg parcellation. Optionally, one can choose to also generate individual volume files for each parcellation region. This option requires FSL, and will only create masks for the cortical HCP-MMP1.0 parcellation, not the subcortical aseg regions. By default, the latest version of the script also generates tables with anatomical information for each brain region (number of vertices, area, volume, mean thickness, etc.). Instructions on how to use the script can be found in the script itself, or here: https://cjneurolab.org/2016/11/22/hcp-mmp1-0-volumetric-nifti-masks-in-native-structural-space/ Hope this can be of use!