Thanks for further clarification on GSR concerns, Alfonso.

When using aCompCor, have there been documented recommendations for *how* to choose the explicit number of principal components to be regressed from WM and CSF regions?  The Chai et al. (2012) methods paper gives results with regression of 1, 3, 5, and 10 principal components per each WM and CSF regions and eventually settles on 5 for extensive analyses; however, the choice of 5 seems pretty arbitrary. There are bound to be differences in the PC structure of research subjects' data, and I worry that choosing an arbitrary cutoff would result in either insufficient removal of "noise" or distortion of "neural signal." The latter case I think would be increasingly likely when choosing aCompCor with large numbers (e.g., 5 per CSF and WM) of components removed (is anything >1 safe? if not, why not just use the average?). 

A method to determine on a single-subject basis the optimal number of artifact components to regress using aCompCor would be greatly appreciated. Are you aware if anyone out there in the CONN-world has implemented this?