Hi, JW-

Sorry for missing this earlier.

Hmmm, how much of a gap is there in your task data between events? I don't know that one can treat the gaps between events (and this meaning the event itself, convolved with a reasonable HRF, which greatly extends the duration of what is meant by "event") as rest. While resting state acquisitions are just somewhat unconstrained periods of brain activity (and many people complain about this), I think that is still quite different than being a tiny gap between stimulated events. Many people make the case that resting state scans should be >10 mins, say, to "settle down" into stability---this is very different than what might be at most a few seconds between stimuli, and most task presentations do not have large gaps between event blocks (even before considering the HRF, which again would further lengthen the duration of what one would term part of the "event" measurement).

It is true that there is a residual output by afni_proc.py, but I also don't think this could be considered "the resting state component" of a task stimulus. Given the imperfection of GLM modeling, there would probably be too many relics of the task left in there. (And again, I can hear people asking how this would be different than a normal resting state scan, where there is audio stimulation from the scanner, visual stimulus from lights in the room, etc.---that still strikes me as a separate consideration).


So, you might be able to look at the residual and see how it looks across the brain, but that would probably not be comparable to a resting state scan. If anything, it would be closer to what people look at for measuring the smoothness of noise, for things like clustering.

--pt