Hi everyone,
I’m looking into ways of doing seedbased resting state functional connectivity with data preprocessed in fMRIPrep. I decided to give FSL a go, and have some questions regarding seed timeseries extraction and the set up in FEAT.

I have my functional data normalised to the MNI space in fMRIprep which will be my main input in FEAT. However, I will be extracting the timeseries from my seed in another space (SUIT template) and enter these timeseries as an EV to model as shown here. Would there be any possible issues with having the seed time series extracted from a different space?

FEAT has a highpass filtering option that cannot be disabled (under Data tab). It seems like there is an option to apply these to the EVs as well, which is useful. However, if a confound.txt file is also included for denoising (under Stats option, confound EVs), would these confounds also get highpass filtered?

Would the seed timeseries included as EVs also get denoised with the confounds included in the confounds.txt? I am sorry if this is obvious. I am new to FSL and want to make sure I am getting it right. I understand how the seed timeseries would be included as an EV in the GLM here but I dont understand how this would work really if these are not denoised in the first place.
Any help would be appreciated and thank you in advance.