Hi,
We are interested in delineating network connectivity in our task-based fMRI event related design.
Basically we have 2 contrasts of interest, A & B. We have isolated 30 independent components -ICs-
using Melodic and now we want to use fsl_glm to find components with a temporal course
that is correlated with the temporal course of A and with that of B. Is this a valid approach to infer
connectivity networks correlated with our task protocol? if so
The first issue is that our fMRI design contains 3 runs per subject. In doing fsl_glm we have to specify the
.mat and .con files from the FEAT design in order run it. Then we search for correlations between each IC temporal
course with that of each contrast, by testing the ouput Z score from the fsl_glm against zero (using a t test, correcting the p
value for the number of components tested).
Since we have 3 runs we will have to ru fsl_glm 3 times per subject, one for each .mat. and ,con from the 3
lower level analyses. The question is: could we just average the 3 Z scores we get from each fsl_glm -1 per run- and test that against zero? [in order to test for statistical significance as above] I hope this make sense.
This way we expect to derive IC-based connectivity maps that are correlated with our task effects of interest A & B.
My second question is related to the dual regression approach, which to be honest am still struggling to learn whether or not
it may gives something else than Melodic and fsl_glm. I think dual regression may be useful to derive connectivity maps for each individual, based on the ICs derived at the group level
I wonder whether there may be some utility of the dual regression approach in the context of the analyses I have
described above.
Finally and I dont mean to pester, is there any short-term plan of getting a dual regression practical in the FSL website
Thanks a lot
David
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