fMRIPrep Confounds for GLM

[jdickson14]

Hello all,

I am trying to use some of the confounds generated by fMRIPrep to denoise “task” data within the FSL FEAT GLM, and I am wondering which columns to include as regressors.
I’ve followed along with this previous post so far, but wanted to ask for some further instruction/validation of methods.

I am planning to include the 6 major motion regressors (x,y,z trans & rot) and some combination of a/c/tCompCor regressors to account for physiological noise, but my question lies in which of those to include.

Based on Steven’s comments on the previous post, it is reasonable to include enough components to account for 50% variance. In evaluating the json sidecar file for the fmriprep confound tsv, the components are listed in order of ascending variance explanation. Would it be appropriate to just include the n-th component that explains 50% of the variance by itself for each masked area (combined, wm, csf, edge)? For example below, including just a_comp_cor_45 instead of some combination of a_comp_cor_00 through 44?

Additionally, I am including high-pass filtering in my FEAT setup, which means that I should omit the cosine factors from the input confound file, correct?

The study we are running involves interleaved TMS/fMRI and our “task” is a block design of 12 rounds of single pulses over 6 minutes.

Any suggestions or recommendations are welcome and appreciated.

Thanks,
Jess

[Steven]

Hi @jdickson14,

@jdickson14

a/c/tCompCor regressors to account for physiological noise, but my question lies in which of those to include.

aCompCor is most common. Given some evidence of resolvable WM bold signal, I think just using CSF mask is okay, but most common practice is CSF + WM.

@jdickson14

Based on Steven’s comments on the previous post, it is reasonable to include enough components to account for 50% variance.

Keep in mind you also want to account for temporal degrees of freedom. That is, if you do not have a lot of volumes, you don’t want a lot of regressors either. That is why you also sometimes see using top X number (e.g. top 5, top 10) components.

@jdickson14

Would it be appropriate to just include the n-th component that explains 50% of the variance by itself for each masked area (combined, wm, csf, edge)? For example below, including just a_comp_cor_45 instead of some combination of a_comp_cor_00 through 44?

No, the listed variance explained is cumulative.

@jdickson14

Additionally, I am including high-pass filtering in my FEAT setup, which means that I should omit the cosine factors from the input confound file, correct?

No, you should not do high-pass filtering in FEAT and instead use cosine regressors.

Best,
Steven