Dear FSL experts,

We are running resting-state functional connectivity analyses, in which we employ ICA AROMA to remove motion artefacts, and have some questions related to this.

1) We are wondering whether AROMA in combination with some basic preprocessing is sufficient to deal with motion-related effects, or whether one should follow up AROMA and temporal filtering with fsl motion outliers tool to identify remaining corrupted timepoints, and ignore those during subject-level statistics  (AKA motion censoring).

2) Would there be any adverse effects anticipated if AROMA was followed up by motion censoring with fsl motion outliers (DVARS or FD metric)?

3) Does AROMA deal equally well with both micro- and macro-motion effects?

4) On average, what percentage of resting state data is commonly labeled as noise by AROMA? In one post on the forum I saw 75%, however, in our dataset this is more close to 35-50%. Does this make sense?

5) In which situations would one opt to implement the “aggressive denoising” option in AROMA? And what are the potential drawbacks?