Censor motion vs. Regress motion derivatives


I have a question regarding censoring vs. regressing motion derivatives in the uber_subject.py. Does it make sense to censor motion AND regress motion derivatives, or should I only complete one? My concern would be that even after TRs with lots of movement has been censored, other TRs with movement remain, which can impact the quality of my data?

Thank you so much for your help.


The debate is almost as old as (fMRI) time, with some people suggesting that you never regress out motion. But the AFNI way is to do both censor and regress out motion. Note that censoring means that those TRs don’t contribute to the final stats, so the motion related to those TRs is also not considered.

I’ve played with motion censoring and regressors a decent amount, and while it’s an imperfect solution, it works better than not including them in the model. There are suggestions of using more parameters to model motion, and in some AFNI processing there are derivatives and per-run things modeled.

You might start with this paper[/url], and then farm the references. There’s also some handy references mentioned in the [url=http://preprocessed-connectomes-project.org/abide/dparsf.html]ABIDE project info.