Normalization of the structural connectome

Dear experts, I am reaching out to you with a question regarding the normalization of DWI-based connectomes. My pipeline was the following: group-based response function, ACT with 20M streamlines, dynamic seeding with cropping at the border and SIFT2 correction to generate my connectomes. I would now like to compare the connectomes between diagnostic groups to detect which connections in the brain are significantly different and also do some graph theory analyses.
I have been contemplating as to whether a normalization of each subject’s connectome to its max amount of streamlines (for example with bct.normalize) per subject is necessary/recommended. My diagnostic group involves a neurodegenerative disease and therefore has generally a lower amount of streamlines. As such, when I normalize every subject’s connectome by its max amount of streamlines, my group differences in connectivity and graph theory metrics disappear. I was wondering if it would be accepted to not normalize by the max amount of streamlines, since I also used a group-based response function (i.e., the same response function for all my subjects irrespective of their diagnostic group). I assume this is also a normalization in a certain way? Second, I also wanted to ask if you would recommend scaling the connectomes by inverse ROI volume, as this may take care of atrophy or individual differences in ROI volumes. Thank you!