I have preprocessed a set of resting state functional images through fMRIprep version 20.2.6. This entailed slice time correction, volume registration, alignment between anat and EPI, normalization to MNI space, and confound time series estimation. The output confounds file includes the 6 realignment parameters, global signal, WM signal, CSF signal, their temporal derivatives, and the squares of each parameter and the parameters derivatives. Additionally, the file includes time series from a number of components output from aCompCor in CSF and WM and associated cosine regressors.
For the next step of the analysis, I would like to use AFNI’s 3dTproject to perform nuisance regression using the first 5 components from CSF and WM aCompCor, the 6 realignment parameters, the first temporal derivatives of the realignment parameters, apply a simultaneous bandpass filter (0.01 - 0.08 Hz), and detrend.
One issue is that the CompCor components are generated based on highpass filtered (0.008 Hz) data. However, the actual output file from fMRIprep that would be carried forward to nuisance regression is not temporally filtered in any way. So, to handle this discrepancy, the fMRIprep team suggests that when using CompCor components to also include cosine regressors into the regression matrix. My understanding is that including the cosine regressors will highpass filter the data consistently with how it was done to estimate aCompCor components originally.
Given that the data needs to be highpass filtered to accommodate the CompCor components, is it possible to also apply bandpass filtering (0.008 - 0.09 Hz)? Put another way, how could I bandpass filter the data while including aCompCor and associated cosine components in the nuisance regression?
Originally, I thought I could address this issue by excluding the cosine regressors from the nuisance regression matrix and instead in the 3dTproject command use the -passband argument with a range of 0.008 - 0.09 Hz.
Any suggestions would be helpful on how to accommodate this issue.
Thank you in advance!