Dear all,
I wonder whether it would be possible to use TBSS / randomise on FA data to answer the following 2 questions:
1. I would like to visualize voxels with a significant interaction GROUP[2 levels, 1,0]*FA in correlating with a continuous COV, adjusting for AGE and GENDER. I would set up the following GLM with the EVs:
Intercept GROUP1 GROUP0 COV_G1(cont., demean) COV_G0(cont., demean) AGE(cont., demean) GENDER (0,1, demean)
Contrasts (randomise without -D option as intercept is included):
Pos. Correlation FA-COV for Group1: 0 0 0 1 0 0 0
Pos. Correlation FA-COV for Group0: 0 0 0 0 1 0 0
Sign. Interaction FA-GROUP in the explanation of COV: F contrast 0 0 0 1 -1 0 0 / 0 0 0 -1 1 0 0
Correct?
2. Now, if I would be interested in an interaction between FA and a continuous variable BEH instead of a categorial factor GROUP:
In R statistical package, the model would be - e.g. think of one single FA value in one voxel: lm(COV ~ AGE + GENDER + BEH + FA + BEH*FA, data=dat). How should I set up the model - if possible - for randomise? I could imagine
Intercept BEH(cont., demean) COV(cont., demean) BEH*COV(demean) AGE(cont., demean) GENDER (0,1, demean) with
contrast: 0 0 1 0 0.
However, here I would be modeling the interaction between BEH and COV in explaining FA, but this is actually not really what I want. I want to model the interaction BEH*FA in explaining COV (see R model). I would really appreciate any help.
Thank you very much for your help.
Kind regards, Robert
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