FA, AD, RD connectomes


#1

Hi MRtrix experts,

I have a .tck file of my tractogtaphy and a streamlines connectomes using tck2connectome command. I however, now want FA, AD, RD connectomes for the FA, AD, RD metrics.
Is there a way I can achieve this?
Thank you in advance
Best,
Apoorva


#2

Hi,

You can take a look here here, it’s all explained.

Basically, to obtain the connectomes weighted by the mean FA (or another map) what you need to do is:

tcksample tracts.tck FA.nii.gz FA.csv -stat_tck mean
tck2connectome tracts.tck Labels.nii.gz FA.csv -scale_file FA.csv -stat_edge mean

In this case each connection will be the mean of the means of the FA. The mean FA along the tract is assigned as a weight to each tract and then each connection is the mean of all the tracts that connect the two nodes.

Best regards,

Manuel


#3

Hi Manuel,

Thank you so much! I followed the procedure and got the connectomes successfully.

Best,
Apoorva


#4

Hi everyone

I was wondering If we have to apply SIFT2 when computing connectomes weighted by some microstructural parameter (for example FA maps). By applying SIFT2 the values of the mean FA in each edges would very small and not interpretable anymore. By not applying SIFT2 the mean FA sampled through the streamlines might be biased by overestimated streamlines.
Thank you very much for your help.

Bests,
Hassna


#5

Hi Hassna,

By applying SIFT2 the values of the mean FA in each edges would very small and not interpretable anymore.

I think that perhaps this is a misunderstanding regarding how this calculation is performed.

By using the -scale_file option in tck2connectome, the contribution of each streamline is multiplied by the corresponding factor in this file; if you then additionally use -stat_edge mean, then it will be the mean of these values from across those streamlines corresponding to a particular edge that is stored in the connectome matrix.

By additionally providing per-streamline weights via the -tck_weights_in option, then yes, the contribution of each streamline is multiplied by both the streamline weight and the factor provided via -scale_file; however, in the calculation of the per-edge mean, this sum is divided by the sum of streamline weights within that edge.

So in this way, the combination of SIFT2 weights and -stat_edge mean act as a weighted mean: those values ascribed to streamlines with large weights contribute more to the final result than those ascribed to streamlines with small weights, but the magnitude of that weighted mean is still faithful to the magnitudes of those values within the file provided via -scale_file.

Rob