First-level GLM using FSL: how to incorporate high-pass filtering and regressors?



Hi Chris,

I was considering using the cosine components to include in an FSL first-level analysis instead of using FSL’s built-in high-pass filtering tool. However, when setting up the GLM, you’re given a choice to high-pass all the regressors or not, with the recommendation that you should if thedata is high-pass filtered. I am unsure how all of this information fits together.

My feeling is that I should not use fmriprep’s cosine components and just use FSL’s high-pass filtering (with the same frequency setting as fmriprep), and then click yes to high-pass model regressors. Would this interfere with fmriprep’s confound EVs in any way if they were made assuming the data was already high-pass filtered?




I’m trying to reformulate this question to make sure I understand. In your post-fMRIPrep GLM in FSL, you want to include both aCompCor nuisance regressors from fMRIPrep and high-pass filtering—right?

This is the concern: First, fMRIPrep is internally using the Discrete Cosine Transform formulation (link) to high-pass filter the data when extracting the aCompCor regressors. FMRIPrep’s internal high-pass filtering corresponds to the Cosine0* variables output by fMRIPrep. Does this mean that we must use the Cosine0* variables in our GLM to jive with using aCompCor (as suggested here: link)? If so, I should supply fMRIPrep’s Cosine0* to FSL and tell FSL not to high-pass filter the data at all. On the other hand, what if I want to use FSL’s high-pass filtering functionality? This may not be exactly the same as fMRIPrep’s DCT formulation. If I tell FSL to also high-pass the model, I’ll be redundantly high-pass filtering the aCompCor regressors with potentially two slightly different formulations of a high-pass filter.

To me this doesn’t seem like it will be hugely problematic, but it’s definitely a bit awkward. Redundantly filtering the aCompCor variables probably won’t make a huge difference (right?). Note that this awkwardness is not limited to FSL—for example, I’ve used AFNI’s 3dTproject to regress out confounding variables including aCompCor, but instead of including Cosine0* variables, I used AFNI’s approach (first- and second-order Legendre polynomials and sine/cosine bases for high-pass filter) which differs from DCT.

Sorry that’s not an answer at all! Trying to wrap my head around this issue.