Dear all, i would like to get some advice on the following procedure.
I performed a GLM with two parametric modulators (PM) from an event related design.
In the design there is a single event of interest and two modulators that model the event-related activity as a function of five conditions.
The two PMs have the form:
PM(1) (Conditions from 1 to 5) = - 3,-3,2,2,2
PM(2) (Conditions from 1 to 5) = - 2,-1,0,1,2
So, one resembles a step function and the other is a linear increase. The reason why I used parametric modulation is because I want to identify regions where my experimental conditions induce a linear (and not stepwise) increase in activity. Being these two patterns quite similar, I thought parametric modulation analysis in spm would be the optimal strategy, since I can get betas for the linear PM (PM(2)) after "removing" all the variance explained by PM(1).
However, when I inspect locally the pattern of activity as a function of the five conditions in clusters that result significant for the PM(2), I observe that in some clusters the pattern is different from the expected linear increase (e.g., 1 4 -3 0 2 or similar, just to make an example).
Now, unless I am doing something wrong, I think this could be due to the orthogonalization between sequential modulators applied by SPM.
So, what could be the solution to get a statistical map with exclusively (if any) significant clusters with linear (and not stepwise) increase?
I was thinking to do a conjunction analysis with the statistical map obtained for the PM(2) and a map obtained with a separate GLM in which the five conditions are regressed separately and then contrasted with the specific contrast (-2,-1,0,1,2). This should at least results in a statistical map where significant voxels (if any) are showing activity that is not explained by the mere stepwise function (PM(1)) and that resembles exclusively a linear increase (P(2)*Linear_contrast)… Could this be a reliable solution?
Since I am quite new in fmri analysis, any advice will be very appreciated.
Thanks
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