I would like to apply temporal-band-pass-equivalent filtering to my fmriprep output. I know there are concerns with “reintroducing” noise if one sequentially regresses out confounds (e.g., motion parameters) and proceeds in a subsequent step with a temporal filter such as a Butterworth (e.g., Hallquist et al., 2013, NeuroImage; Lindquist et al., Sept 13, 2018, BioRxiv). I currently high-pass filter my fmriprep-data using the CosineXX functions (included in the *confounds.tsv) along with other noise regressors (e.g., motion parameters, WM, CSF, etc.), but I am curious if this sort of basis function filtering is possible in the case of low-pass?
How might I obtain “low-pass CosineXX” equivalents for the stop-band above a certain frequency? Are there nipype functions for this? Am I understanding these basis functions in the correct way?