after running fmriprep, I get a very nice tsv with the computed confounds. Would you use all of them as nuisance regressors in a GLM? Or are some more suited for resting state data vs. task-based data?
Opinions on this topic are divided and this is why FMRIPREP provides those regressors instead of cleaning up the data for you. I personally would include 6 motion parameters, FD, and aCompCor on run level and mean FD on group level (for both task and rest).
Thanks Chris! The approach of FMRIPREP makes a lot of sense, and your comment is very useful.
Let us know if there are some additional signals you would like FMRIPREP to estimate. Currently, we are planning to add: non-steady state outliers, 24 motion parameters (Friston24), and ICA-AROMA.