I am using python to calculate degree score centrality for each ROI of BOLD resting state nifti using generated fMRIPrep confounds and with Schaefer 400Parcellations for extracting ROI references. Can anyone guide me with some references or examples? as I am not sure from where to start. Thanks
What is the interpretation of degree score centrality for a functional connectivity matrix? Unless you were to binarize the connectivity matrix, wouldn’t each node be connected to every other node in the matrix, and thus each node would have the same degree centrality?
For extracting the connectome, this tutorial may be helpful: 3.1. Extracting times series to build a functional connectome - Nilearn
Just keep in mind that you do not want to use ALL the confounds in the fmriprep confound file. How to use the fmriprep nilearn interface to load selected confounds is shown further down in that tutorial/
Degree score centrality is a measure of how important the node is based on the number of its connections with other nodes. I am not binarizing the matrix instead I am normalizing it using fisher z normalization followed by thresholding.
Every node in a functional connectome will have a non-zero connection with another node (unless it is binarized), so without thresholding/binarizing, each node should have the same degree centrality.