Hi!

I have a question about the benefit of "data exploration" in the context of task-based fMRI experiment.
I think it is interesting to see the correlation between head-motion, global-signal and stimuli and also see their spectra for possible overlaps and separations.

But what exactly would you do in case of such cases? 
I understand that the high-pass cutoff shouldn't remove the spectral part that corresponds to that of stimuli.

But in other cases, like if we have high correlation between the head motion and the stimulus (very unfortunately), and if we regress out the head motion, then we would also regress out a large portion of the signal of interest. So we can see why we don't find such signal using correlation analysis (or by examining spectral overlap of motion and stimulus), but I don't know what we could do about it.

Unfortunately I couldn't read that in the ART manual. So I would very much like to learn viable remedies.

Best,
-SG