Hello
I have used the tck2connectome command with the “tck_weights_in” option. I have questions about how to interpret the numbers in the matrix.

Does the numbers on each edge of the connectome matrix represent the sum of streamline weights ascribed to that edge or is it the proportionality coefficient multiplied by the sum of streamline weights ?

Can I directly compare the output values of the aforementioned tck2connectome between subjects? or do I need to normalize the matrix ?

what is the best way to normalize the connectivity matrix that came from the tck2connectome command with the “tck_weights_in” option?
I will be very thankful for having an answer to the abovementioned queries.
Regards
Pratika
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Hi Pratika,
I think the connectome matrix represents the intensity of connection between nodes in the brain of different subjects so you can compare these matrices and normalization is optional.
I’m not sure whether I have misunderstood the meaning of the tck_weight_in
option in tck2connectome
. Maybe you should look back at the docs.
Best,
Volcano
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Hey volcano
Thanks for the reply. Yes, the connectivity matrix represents the intensity of connection between nodes of the brain but that can be represented in many ways, like the number of streamlines between the nodes or the sum of streamline weights (when you have used tcksift2 to optimize tracks).
And this doc here describes the use of the “tck_weight_in” option in the tck2connectome command.
It will be very helpful to get a little bit more detailed answers.
Thanks
Regards
Pratika
Hi Pratika,
I’m not sure whether you checked the sift
algorithm. In this algorithm, streamlines are reduced to a specific number as you assigned. Since sift2
is a modified version of sift
, I think the weight that the algorithm generated can do the same thing, which indicates the number of streamlines between the nodes, and the sum of streamlines’ weights may have the same meaning.
Best,
Volcano
Hey Volcano
I’m not sure whether you checked the sift
algorithm.
Yes, I have checked the algorithms behind sift and sift2 commands. This paper clearly describes the differences between the algorithms and also the normalization method but I didn’t get a total grasp of it.
I think the weight that the algorithm generated can do the same thing, which indicates the number of streamlines between the nodes, and the sum of streamlines’ weights may have the same meaning.
And the sum of streamline weights has a dimension of L^2, as it represents the total intraaxonal crosssection area of the particular pathway of interest.
I am still searching for the answers to previously asked questions. It will be very helpful to get an insight into normalization methods.
Thanks
Regards
Pratika
Hi,
As @Pratika_siwatch said, the algorithms sift and sift2 have different meanings, you can read more here.
Regarding the normalization methods, to be able to directly compare connectomes accros subjects, you need to do a couple of things (all is explained in the linked paper):
 Use the same response function to generate the FODs for all subjects
 Multiply each connectome by it’s associated
mu
Then you can safely compare them across the full population. I hope this helps.
Best regards,
Manuel
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Hey Manuel
Thanks for the reply… it resolved my query.
Regards
Pratika