Confounds from fmriprep: which one would you use for GLM?

Great thread! Thanks for all the contributions


Hi all, I notice that most of the answers on this post regarding the best denoising strategy (including papers and tools linked), seem to focus on resting-state data. Are there any general recommenations regarding denoising strategies or optimal fmriprep confound regressors for task-based studies (including PPI)?

It seems like the benefits and risks of different approaches would differ for an experimental design. Many of the papers evaluating different denoising strategies use outcomes (such as motion-functional connectivity relationships) which don’t really apply to task data. Would a tool like fMRIDenoise still help identify the best approach for task based data?

Also, I am specifically worried about over-fitting and/or potential reduction in degrees of freedom with too many/ non-optimal nuisance regressors in an event-related design. Any thoughts/leads would be greatly appreciated!

Hi Carlos - did you find an anwer to this question?

You will find this paper (Mascali et al., 2021) helpful for task based studies:

In addition to benchmarking denoising strategies, they also describe and provide code for optimizing acompcor regression that includes orthogonalizing the components to reduce over-fitting. In general, mean-scaling your confounds is also a good practice.

This thread is most helpful for confound inclusion! I am still wondering though… would it be redundant to include the six primary motion parameters as motion regressors in the subject-level model after ICA-AROMA has been run?

Based on the paper linked in the response above, it appears this is not a common practice… can someone further elaborate on this?

I would say it’s probably redundant; both AROMA and HMP control for motion (as opposed to aCompCor which is more for physiological signal, for example). I’m not sure if it has been explicitly shown or published, but I imagine you would find a high correlation between AROMA components and motion parameters, which would lead to multicollinearity problems in your design matrix.