Applying MMP ROI masks to 7T HCP CIFTI data

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1

Does anyone know of a good solution for extracting ROIs from cifti data in python? Specifically:

I have 7T HCP functional data (fsLR 59k, surface+volume)

I have the MMP parcellation (fsLR 59k, surface) it is not released in this resolution but someone projected it for now.

Say I want to apply a mask of a few different MMP parcels to extract an ROI time series for all of the grayordinates there from my functional data (without averaging over this roi). If I get_fdata() and create an index from the MMP data to apply to the functional data it does not work because they are different lengths and do not line up.

2

There is probably a better less hacky way, but if anyone else runs into this:

  • I ended up using wb_command -cifti-create-dense-from-template to make a new MMP parcellation dlabel file that includes volume (as all zeros) and matches with my functional surface+volume data that I wanted to extract the ROIs from.
  • using cifti.get_fdata() on the new dlabel and the functional file returns arrays that line up and can make a mask easily.