Hi,
I got a few questions about the CPAC output files for the PNC dataset available on RBC: PNC_CPAC/cpac_RBCv0 at main · ReproBrainChart/PNC_CPAC · GitHub
Here is the CPAC config file specific to RBC: C-PAC/CPAC/resources/configs/pipeline_config_rbc-options.yml at 0182f98c61cb7fbb495c8300e6a6a7991c859240 · FCP-INDI/C-PAC · GitHub
I’m looking under subdirectory subject/ses-PNC1/func/:
I’d like to understand differences in field of view for derivative maps across subjects. In other words, how does availability of the measure at each point in the MNI space vary across subjects? Taking ALFF maps as an example, I narrowed down to files whose names start with “sub-xxx_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6ASym_”. Here are my questions:
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Are all the derivatives for the same subject (alff, dcb, falff, lfcdv, lfcdw, reho) generated using the same mask (desc-bold_mask.nii.gz)? i.e. the availability of voxel levels should be the same for the derivative maps of a single subject?
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If I want to create an average ALFF map for all subjects, is it safe to first create a group-level mask (e.g. include voxels that are available in >0.8 of subjects), directly take an average over “…desc-sm6_alff.json”, apply the group mask, and then z-standardize the averaged map?
I’d very much appreciate any suggestions / insights!