Dear Mike,
> Also I think I should report decision-related regions identified in (A). But confirmation is needed
If you don't care about average activations during DEC you don't have to look at it. However, it might make a difference for interpretation whether the activation level is modulated by the PM in a certain region, with its average activation level being sig. negative or positive. Besides, even if it's not the main topic of your study the average activation pattern can serve as a confirmation that the design worked, that you find regions that have been reported before.
> So which one is correct?
If you want to find out something about the unique variance associated with ATT and DIF you will have to go with something like Analysis 2 (but e.g. step-wise model building might be an alternative depending on exact purpose).
> I won't be able to identify brain regions that are correlated to both ATT and DIF?
The shared variance is always attributed to the first PM.
> a study published in NeuroImage (Mùˆller-Pinzler et al., 2015)
I had a look at the study to get an impression of what you might want to implement, and I stumbled across the following:
"The parametric modulators of hemodynamic responses during feedback contained first, the exact percent values of PERFORMANCE, second, the deviation from 50% to control for any within-subject variance in neural activation due to PERFORMANCE, and third, the trial-by-trial pupil dilation value"
Not sure, but the second PM sounds like a rescaled version of the first PM to me. If serial orthogonalization is disabled the two PM regressors would be collinear then (they would only differ in their amplitude, if at all), and they would correlate with the condition regressor (as all three predict a BOLD response after trial onset). If serial orthogonalization is enabled the second PM regressor would be a constant term, as it has no unique variance. If mean-centered performance values were entered, with the serial orthogonalization turned off, then the two PMs would not correlate (highly) with the condition regressor, but the two PMs should still be collinear then. So basically I'm not sure what their approach with the two performance PMs is about. In any case, it can be quite useful to add a figure with the predictors as soon as designs become more complex.
Best
Helmut
|