I'd like to use the Kong 2019 parcellation on single subjects that were preprocessed with fMRIprep, yielding cifti files in 91k_fs_LR space (as described here).
However, I see that the priors for cifti data are defined in 32k_fs_LR space.
Should I resample the cifti BOLD data for 32k space before running the analysis code? If so, how could this be done? It seems like I should use the Connectome Workbench cifti-resample function, but I could not find the 91k sphere to be used for downsampling.
On the other hand, this post on NeuroStars suggests that the 91k_fs_LR output of fMRIprep, in fact reflects surface data of 32k vertices. If that's the case, then I could simply use the Kong 2019 as is. Correct?
Many thanks for you help and for making this useful code open source!
Best,
Dear CBIG lab members,
I'd like to use the Kong 2019 parcellation on single subjects that were preprocessed with fMRIprep, yielding cifti files in 91k_fs_LR space (as described here). However, I see that the priors for cifti data are defined in 32k_fs_LR space. Should I resample the cifti BOLD data for 32k space before running the analysis code? If so, how could this be done? It seems like I should use the Connectome Workbench cifti-resample function, but I could not find the 91k sphere to be used for downsampling.
On the other hand, this post on NeuroStars suggests that the 91k_fs_LR output of fMRIprep, in fact reflects surface data of 32k vertices. If that's the case, then I could simply use the Kong 2019 as is. Correct?
Many thanks for you help and for making this useful code open source! Best,
Roey