Dear Mrtrix team,
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 so much for your help!