Does this mean that we have to include the cosine regressors (it’s 9 columns in the confounds.tsv files in my case or maybe always it’s 9 idk) in the 1st level GLM as nuisance variables, and then this way we will have high-pass filtered the bold data that we intend to use.
Is this correct?
This means that after running the basic preproc with fmriprep, only smoothing and scaling are needed, in order to proceed with fmri task connectivity analyses etc. Is this the case?
Is it the case that by including the cosine columns from the confound.tsv file to the 1st level GLM high-pass filtering is applied to the BOLD data?
(thus I conclude we do not need to apply separately high-pass filtering at all in our further pipeline by using another tool (e.g. afni, or spm etc). Right??)
That is correct! If the complete set of cosine regressors are included as regressors of no interest then data will be effectively high pass filtered during model estimation.
Hi @effigies. a related question to this thread is also from my side.
I added all the cosines to my first-level analysis in AFNI (I do not use the CompCor). I performed the first-level analysis with 3ddeconvolve and I included to option polort-A.
My question is if I am double filtering the data, and if not, how do adding the cosines and running polort-A interfere with each other?
One last question: is there a way to also apply a high pass filter equal to 180sec by adding the cosines from fmriprep?
