Normalization of csv.-matrix

Hey community! I need your help:-)

I created csv.matrices (84x84) and connectomes for a sample of 25 patients.
I heard that before starting the graph theory analyses, I should normalize the csv.matrices to allow inter-subject comparisons (see: https://osf.io/c67kn/)
Is it necessary? And if so, can anybody tell me, how to do it? Is there a MRTrix-Command?

Thank you in advance!

Leonie

Hi Leonie,

I can’t provide any evidence for this step being “necessary” over and above what is presented in the preprint. There do not exist studies where that step has vs. has not been applied. My suspicion is that its consequence will for most cohorts be relatively small given the magnitude of the inter-subject variance in such data. But it’s nevertheless the physically appropriate thing to do.

There is no dedicated command provided to perform this step as it is simply the multiplication of a two-dimensional numerical matrix by a scalar value, which can be done in any environment capable of manipulating numerical data; e.g. Python, MatLab / Octave, R.

Rob

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!