I have a list of confounds (generated by fmriprep), and I was wondering if there is any substantial difference in the following approaches:
- regressing some of them out before the GLM (using nilearn.image.clean_img), and then running the GLM in SPM on the denoised data
- running the GLM on the fmriprep functionals, and include the confound as nuisance regressors in the model
side question: after the GLM I am going to perform an MVPA analysis at the subject level, so the best approach would be (ideally) the one preserving the spatial distribution of active voxels. Would a 24HMP, 8 phisiological parameters or aCompCorr, SpikeReg denoising be too aggressive for this specific case? if yes, what would you suggest?