I guess you could run afni_proc.py on the data, but that seems like overkill. I’d just run a smoothing function directly, if that’s what you want done to the data. But yeah, you can just use whatever smoothing kernel you want.
I’ll paraphrase @rastko here (from ICA AROMA agg vs non-agg - #6 by rastko) and say that it may be a good idea to include other nuisance regressors (e.g., WM and CSF), but since correlation with motion parameters is a criterion for AROMA classification, it probably won’t help to include the motion parameters in your denoising step. Essentially, the motion parameters are almost certainly highly correlated with the “bad” components as classified by AROMA.
Yup.