I know that fmriprep used to also produce the aggressively denoised image as an output but doesn’t do so anymore.
I do not believe we did. Perhaps @jdkent can confirm?
Would the recommendation be to use the
smoothAROMAnonaggr_bold.nii.gz. image and include motion, fd, and global signal variables and/or the AROMA mixing vectors as first-level covariates in CONN, for not so arbitrary example, or to use thepreproc_bold.nii.gz?
I believe AROMA advocates would say that AROMA should capture motion-related components, and so motion and FD would be at best redundant and at worst reintroduce motion-correlated noise. If you wish to do GSR, then that should be calculated post-denoising, I would think. There’s an open issue to recalculate some of the regressors post-AROMA. I would be very hesitant to use most of the regressors calculated on the raw BOLD data when analyzing the the AROMA-denoised data.
In any event, the AROMA regressors are only to be used if you want to do aggressive denoising on the desc-preproc_bold.nii.gz files. I do not think there is any good way to use them with the denoised data. (I may be misinterpreting part of your question, but just in case I’m not I want to make that clear.)
To all of this I add the caveat: I am not a specialist in rest fMRI or functional connectivity, and don’t know what is currently considered the best practice. And I have no experience with CONN, so cannot make a recommendation there. I invite further comment from others. @mmennes will probably be the best authority on AROMA.