Hi everyone,
I’m a colleague of Maarten Mennes (the original ICA-AROMA repo owner), and unfortunately, he is indeed not making any effort to support his tool. So here is what I say to people that come to me for doing fmriprep + ICA-AROMA. Since ICA-AROMA is all about time courses that are not very dependent on the frmiprep version (or at least in an arbitrary way), I tell them, until fmripost is ready, to just run fmriprep twice, once with an older fmriprep version to just get ICA-AROMA time-courses, and the second time with the latest fmriprep version. Then they can just add the ICA-AROMA nuisance regressors with the nuisance regressors from the latest fmriprep. It’s a bit of a silly solution, but I think technically such a procedure is quite ok and it works well for people that don’t know how to program the AROMA pipeline themselves.
Here’s another thing that I tell them that may interest you, which is what I call ICA-AROMA2. The thing is, the original development was totally focussed on resting-state fmri, and often took away task-related variance, even when using the “non-aggressive” option. Moreover, for people doing functional analysis, it is more optimal to estimate the task-related variance and IC-AROMA variance in one model, so using the filtered ICA-AROMA output was always a bad idea for fMRI. What I call ICA-AROMA2 is a kind of an ad hoc way of making ICA-AROMA less aggressive by re-classifying ICA nuisance components as non-nuisance when they are co-linear with the task regressors. The empirical threshold we used is to relabel (take out) the ICA AROMA nuisance regressors if the correlation is larger than 0.3. That works very well, but I must say that I think this is a safe thing to do for e.g. SPM users, who only take the betas to the second level, but for mixed effects analyses / FSL users it may see inflate the effects of interest due to the orthogonal variance that is filtered out.
A better way of doing ICA-AROMA2 would be to include the correlation with the design matrix (task regressors) directly in the LDA classification, but this needs more work / a new publication
Hope these are useful thoughts for anyone,
Best,
Marcel