I'm having trouble organizing a FEAT third-level analysis correctly. I have 2 groups, and a behavioral measurement for each participant. What I really want to see is "Behavior-correlated activity in patients" minus "Behavior-correlated activity in controls". (P*b) - (C*b).
I tried the interaction model suggested at http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/GLM#Two_Groups_with_continuous_covariate_interaction but it's not addressing the correct question. Theoretically I say this because we're looking for a subtraction, not an interaction. More convincingly/clear-to-express, it's empirically the wrong model, because it produces results wildly different from what we see comparing (P*b) and (C*b) by eye.
I've also tried a contrast of [1 -1 1] on the EVs "Patient, Control, Behavior" - i.e. like this oversimplified version:
Pat Ctl Bhvr
1 0 .4
1 0 -.2
0 1 -.3
0 1 .1
...If I do that, [1 -1 1] looks pleasingly different from [-1 1 1], but something is deeply wrong with this modeling: [1 -1 1] and [1 -1 -1] and even [1 -1 0] look nearly identical. The same areas can't be both behavior-correlated and behavior-anticorrelated, so I'm doing something wrong. [1 1 -1] and [1 1 1] also look near-identical, but at least [0 0 1] and [0 0 -1] show different areas.
Any suggestions for how to implement (P*b) - (C*b)?
Thanks,
-Benjamin Philip
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