low-frequency regressors from DCT included in the design matrix, e.g. cosine_XX confounds from fMRIPrep
fMRIPrep documentation advises not to use both of these methods simultaneously: Outputs of fMRIPrep — fmriprep version documentation

Then in Nilearn there is function nilearn.glm.first_level.FirstLevelModel, where we can specify drift model as polynomial, cosine or none.
The questions:

It is the same as including cosine_XX regressors in the design matrix?

If yes, then why high_pass parameter is used only if drift_model is ‘cosine’ (default=0.01)? Shouldn’t we skip high-pass filtering if we use cosine drift model?

Would it be redundant to use drift_model = cosine and then also include cosine_XX confounds in the design matrix?

@bthirion, couldn’t we use drift_model='polynomial' in addition to the cosine_XX regressors from fmriprep in the same FirstLevelModel to account for additional drifts? Would that be reasonable or is there a reason against doing it?

Hi,
Polynomial and cosine drift regressors have the same role: capturing slowly varying signals in the data. I don’t see the value of having both. Also beware that this could make the design redundant (the design matrix would be non-invertible), hence estimation would be ill-posed.
Apart from that, there is no particular issue in having redundant drifts.
My 2c,
Bertrand