Hi Jeremy,
The global/wm/csf regression is somewhat controversial, I agree. However, in our experience, global signal regression seems to be clearly beneficial for control of motion artifact in traditional seed or network analyses, as motion causes large drops in BOLD signal across the brain parenchyma in a way that is effectively captured by the global timeourse. The benefits are less clear for ICA. Previously Christian and others have not recommended it prior to tc-gica (i.e., component generation). Furthermore, though I have not investigated it myself, I suspect that some of the advantages of global signal inclusion would be obviated by the fact that multiple component timecourses are included together as part of stage 2 GLM in dual regression, as the shared variance among these timecourses is likely to be similar to the global signal itself. But if you are curious, this is quite testable.
cheers,
t
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