Normalization of connectomes

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!

Welcome Julie!

If you want the values stored within a structural connectome to be directly comparable across subjects, and to exhibit the attributes that one would naturally expect when quantifying and comparing the “total amount of connectivity” per bundle as is were, then the appropriate technique for inter-subject connection density normalisation of this measure (which I’m now referring to as “Fibre Bundle Capacity (FBC)”), is explained and justified in this preprint.

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.

Personally I’m not a fan of this. In retrospect perhaps I should have said something about this in the manuscript linked above, But the argument I make there regarding intracranial volume I would extend to region volumes as well. If may well be the case that both the structural connectivity of a particular pathway involving a GM region is reduced and the volume of that region is reduced; but note how my phrasing very deliberately avoids implying causality in either direction. But I don’t think that your a priori hypothesis would be that “the connection-strength-divided-by-region-volume is different”. A more likely a priori hypothesis would be that “the connection strength is different, over and above that explained by differences in region volume”: that’s a nuisance regressor problem.


Hi Robert,

Thank you so much for your help. I have read the paper that you are referring to. First of all, congrats with this great work!

I have some follow-up questions. In the paper, it is described that, in order to calculate the FBC that can be compared between subjects, we should multiple the sum of weights as defined by SIFT2 with the proportionality coefficient (mu). I was wondering if this multiplying with mu requires a specific flag? I did see -term_mu flag with SIFT, though I am only using the SIFT2 with tcksift2 command followed by tck2connectome. My resulting connectome values are not within the 0,1 range but mostly above 1000.

Regarding my second question on the scaling by inverse regional volume: in the paper discussion section, you discuss that the FBC intrinsically handles the confound of various whole brain sizes. I am handling a neurodegenerative cohort that, depending on the disease stage of the subject, may lose brain volume in specific regions (i.e. volume loss starts in certain regions and then progresses to the rest of the brain). I was wondering if the FBC would handle this, as there still could be a certain level of causality between ‘loss of connectivity’ and ‘loss of volume’. You would recommend to add regional volume as a covariate in my analyses?

Thank you!
best regards