Question on High-pass filtering using SPM

1

Dear Experts,

In our study, we used SPM to apply the high‑pass filter for fMRI data preprocessing. The task employed a block design, where the duration of each block varied across participants depending on their reaction times. The maximum block lengths across participants were as follows:

  • Inclusion 1 + Questionnaire 1: 542 seconds
  • Inclusion 2 + Questionnaire 2: 532 seconds
  • Exclusion 1 + Questionnaire 3: 487 seconds
  • Exclusion 2 + Questionnaire 1: 481 seconds

Based on these durations, we conservatively estimated the design frequency as 1/542 Hz and re‑ran the analyses using this estimated high‑pass filter to verify the robustness of our results.

However, one reviewer commented that the high‑pass filter should generally be set much higher—approximately four times the length we used—and suggested that applying a simple linear trend might be a more practical approach, possibly in combination with nuisance regressors to model low‑frequency noise.

Could you please advise how we should proceed in response to this comment? Is the reviewer’s reasoning correct in this context?

Regards
Shirley

2

Hi @ShirleyZ,

Yes, conventionally a lot of fMRI studies use high pass filters of 128 seconds (.008 Hz) or 100 seconds (.01 Hz).

Filtering in combination with nuisance regressors is important. This is because if you use something like mean WM/CSF signal in your model to correct for noise, those terms have the same signal drift embedded in them. If you filter your data before nuisance regression, and you use a nuisance regressor that has the same drift you just corrected, you can unintentionally reintroduce the signal drift.

Software like fMRIPrep make this process easy by making discrete cosine regressors that act as a 128 second filter. These can be directly included in your first level model.

In short, the reviewer is correct here.

Best,
Steven