Seed-based connectivity on the surface - Nilearn 9.2.16

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Dear community,

I intend to use my own resting-state data to get the
“Functional connectivity of the seed region to all other cortical nodes in the same hemisphere [by using] Pearson product-moment correlation coefficient.” just as in the Nilearn 9.2.16 example.(Nilearn: Statistical Analysis for NeuroImaging in Python — Machine learning for NeuroImaging).

It seems like that the data which one can fetch and use was previously “projected onto the Freesurfer fsaverage5 template (Dale et al, 1999, Fischl et al, 1999)”.

Question: How can I achieve this such that I am able to use my rs-fmri data for a connectivity analysis e.g. PCC is a seed? Is there a Nilearn/Nibabel solution?

Without this step, the mere nifti file with the resting state data is not usable (or: I miss a needed manipulation of code).

Thank you for reading and considering, truly appreciated!

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You can preprocess the data with fmriprep, being sure to include the flags that produce cifti outputs, and adding fsaverage as an output space.

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Thank you for the quick answer; I was not completely precise:

The data is preprocessed already (with Matlab), and I would like to avoid preprocessing again if possible i.e. only the analyses are left.

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I think redoing preprocessing would be the safest route. If not, If you have FreeSurfer run for your T1, you can get a T1-to-fsaverage transform. If you multiply that transform by a BOLD-to-T1 transform, then you will have a BOLD-to-fsaverage transform. However, I think getting cifti outputs from fMRIPrep would be the best way to proceed here.

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