I have a pipeline where data is preprocessed by fmriprep and is extracted for analysis using nilearn’s Nifti Masker objects. Often times, the data are used for connectivity analysis; typical confound regression is performed using confounds produced from fmriprep, along with standardization, temporal filtering, and detrending.
I’ve looked at recent papers (e.g., Arbabshirani et al (2014), Bright et al (2017), Afyouni et al (2019), Honari et al (2019); Olszowy et al (2019) for recent analysis in task-based fMRI) discussing the impact of high autocorrelations in fMRI data, and relatedly, pre-whitening data and the confound regressors.
My question mainly concerns the confound regressors. Can I continue to use nilearn for confound regression, or should I be switching over to something like AFNI’s
3dREMLfit or FSL’s
fsl_glm? Looking into
nilearn.signal.clean, I can’t seem to find anything (but I could be missing something).
I’m also just curious as to what others do when it comes to pre-whitening (either the fmri data itself or the confounds regressors), and what additional perspectives are there to consider?