A follow-up to my follow-up above, for same reasons (so as not to confuse/misinform the hypothetical future person reading this):
Yes, it’s probably a bad idea to perform multi-stagedenoising. As documented in other places, sequential denoising runs the risk of reintroducing noise in standard fMRI analyses. Not sure if the same principle applies here since we aren’t using regressors, but from working with my data, it seems that it’s really not necessary to do a second denoising since GIFT’s ICA generally seems to do a good job of parsing signal/noise and it removed too much signal. ICs looked a lot more robust and stable when I just did the ICA-AROMA non-aggressive denoising prior to GIFT, without the second denoising step.
To get around the overfitting issue when using the fmriprep ICA-AROMA preprocessed outputs, I just specified the number of components requested in the data reduction steps instead of asking GIFT to estimate the number of components. Because the data have already had some variance removed by ICA-AROMA, I have been using a lower model order than in other papers. 30 (45 for the PCA step 1) seemed to work well for me but YMMV. You can try higher and lower numbers to see what gives the best balance between over-lumping and over-splitting.
Note also that if you’re using fALFF when identifying good/artifact components based on the Allen et al. 2011 paper (“A baseline for the multivariate comparison of resting-state networks”), their fALFF value below which components were classified as artifacts will probably be higher than when using ICA-AROMA denoised outputs, since we already removed a bunch of high-frequency noise (motion). So their fALFF = ~6 rule of thumb is not necessarily applicable.