I would like to know how to compute group analysis with structural connectomes. I have processed many subjects individually up to the level of their matrix of connectivity. What I don’t know how is to do the statistical analysis?

I have been suggested to compute a cell with the desired regions and then perform statistical tests but I don’t know where in the documentation it explains this issue. It would be helpful to know which command or code to use.

Hi @andraderenew,

How did you create the connectome? If you are interested in using the SIFT2-weighted connectome for group analysis, then you need to use the same response function to generate the FODs for all your subjects and run `mtnormalise`

after that. If you already did that, then you can use `connectomestats`

to perform your comparison.

Best regards,

Manuel

For the connectome I used tcksift2 and also tck2connectome I have applied mtnormalise previously. Then after these steps I will do connectomestats thanks you for the suggestion.

Hi.

It is also important to use the same response function for all the subjects.

Best regards,

Manuel

for connectomestats the design file and contrast file are in which format? because I have just seen in another thread of this forum what to write in the file although I don’t know what is the format though.

I have seen that the contrast and design are text format files. The issue I still have is how to actually write the design and contrast text files.

Let’s say I have one group with 3 subjects.

design

1 0

1 0

1 0

contrast

1 0

would this be correct?

Hi,

I’m not an expert on this topic, you would like to double check my answer with somebody else, but I give it a try.

The design you are proposing basically is testing if the mean of the group is greater than 0, and it will always be the case, but this is not informative at all.

Let’s say that you have one group of three subjects with a covariate of age:

design

1 33

1 45

1 27

and you want to test if you metric of interest (e.g. FD) is correlated positively with age, then your contrast will be:

0 1

or negative

0 -1

Does this make sense? I hope this helps.

Best regards,

Manuel

thanks so much I will see if it works.

I have tried but it only did 6 permutations I wonder if it is due to small sample size N=3 in one sample t test. My next question is how to understand the output. I mean which is the matrix that I can use to evaluate structural connectome of the “group”.

This are the files I have:

connectomebeta0

connectomebeta1

connectomeenchanced

connectomefwe1mpvalue

connectomenullcontributions

connectomenulldist

connectometvalue

connectomecorrectedpvalue

connectomezstat