Conceptually AROMA and ME-ICA are similar in that they are transforming the data into ICA components and identifying components to remove. The big difference is that AROMA decides on which components to remove based on how much each component correlates to head motion parameters or had edge artifacts while ME-ICA decides on components to remove based on whether the echo time dependence across echoes is unlikely to be BOLD-weighted. tedana is a re-implementation of the ME-ICA method.
A key point is that these methods are not inherently exclusive. You can theoretically take the same ICA components, run them through AROMA and tedana and remove the components and are rejected by either method. I can point you towards some efforts in this area and talk more about my own experiences working on merged methods, but there’s no push-button code that does both yet.
Conceptually, multi-echo methods can be superior because they can do everything AROMA does and also use additional empirical information from comparing echoes to more quantitatively remove non-T2* noise.
I use AFNI & not fmriprep so I can’t give advise there. I’m generally against using GSR as a standard pre-processing step, but there are specific use-cases where it may be appropriate.
Hope this helps
Dan