Hopefully there is a simple answer to this design problem.
We have two correlated parametric regressors, A and B. I would like to compare their contribution to the BOLD signal with each other (A - B), and between sessions (e.g. A session 1 - A session 2 …then B session 1 - B session 2). Obviously, I can not put A and B in the same GLM, so I am wondering if there is a trick to be done with contrasts, using simple linear algebra…
The difference between the values of A and B is C.
So A - B = C.
Can one then include only A and C in a model (which are not correlated), and then calculate signal associated with B with a contrast that looks like: +1 A -1 C ?
Likewise, include B and C in the model and calculate signal associated with A, by +1B and +1C ?
Calculating A with a contrast vs. a single regressor seems to give very different results at the subject and group level. We would very much like to understand why… and if there is another way of doing this, we would be very happy to hear them.
Any ideas?
Cheers,
Dan
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