Hello Rob, thanks so much for your response.
So I would scale my individual connectome-matrices so that all values are between 0 and 1 to achieve a normalization. I think it is more accurate to do this for every file individually and not across all subjects, do you agree?
And one other question: My data is a comparison of patients with CNS inflammation compared to healthy controls. My preprocessing pipeline included segmentation and parcellation of the MPRAGE images, denoising, bias field correction, average group response function and CSD of the DWI images. We did NOT do the dwi group normalization since we only have a single-tissue response function and all subjects were measured using the same scanner and the same applications. To create the connectomes we performed ACT (20mio streamlines) and then SIFT using 5mio streamlines.
Do you think this approach is acceptable?
Thanks in advance!