We are trying to determine our options for applying temporal filters for the cifti outputs (dtseries.nii) of the ciftify/fmriprep pipelines. We have found that much of the other manipulations of our data are possible with the wb_command tool available from HCP, but temporal filtering in particular seems to be missing from the workbench tools.
I wrote an extra tool ciftify_clean_img into the ciftify package to help with that. Itās essentially wraps nilearnās clean_img functionality for optional temporal fitering, detrending, and nuisance regression. https://edickie.github.io/ciftify/#/usage/ciftify_clean_img.
I donāt have very nice exampleās written into the documentation yet. Let me know if the usage makes sense.
This seems very useful! Thank you so much for your response.
I want to make sure I understand the proper flags to use in our case. The only step we want to perform is temporal filtering using a low-pass + high-pass filter. Am I correct that providing the flags --detrend and --standardize asks the nilearn not to perform the steps I am not interested in?
If I use a --clean-config .json file, could I use the syntax "detrend=False, standardize=False, etc " to provide arguments to nilearn.image.clean_img through your wrapper? I look forward to trying out this tool!
Thanks for the feedback! Your right that those options are not well explained (and I think there is an error in the doc stringā¦ick). Re-reading the code, it adding --detrend and --standardize to the command-line call will set them to āTrueā when nilearn.clean_img is called (omitting them sets the options to False). If you specify only high pass and low pass filtering options than I think it should only run the temporal filtering.
If you run one participant with the āādebugā flag on it should hopefully be verbose enough for you to be sure that it is doing the right thing. It should also output a .json file with the settings so you can double check that it was correct.