Our lab has been looking for a standardized way to process output resting state scans from fmriprep.
We are currently using a home-brewed matlab script for filtering, scrubbing, regressing out motion noise, CSF, WM, and interpolation. Is there a more standardize tool for processing of resting state images that utilizes the bids structures and containers?
We have glanced at NiLearn, but it seems like it is geared more towards the analysis and visualization side of things
the amazing CPAC should be exactly what you’re looking for: comprehensive preprocessing, cool analytic tools and if you want everything packed within a docker container as a BIDS app.
Two years on. Has anything change?
Ie would you still recommend denoiser, xcpengine and cpac?
I am also hoping to denoise the surface based results of the cifti images. Would you suggest any denoising toolbox that might work for both volume and surface-based fMRIprep outputs?
Thanks for your response.
However, upon inspection the software seems to be still in a developmental stage?
For example, when trying to pull the docker image “docker pull pennlinc/xcp_d” it doesn’t exist - only the unstable version of the tag. Is it a good practice? How reliable do you think it is? Has the software been implemented broadly within the community?
XCP_D not a fork, per se, it is XCP built on the nipype engine to act on BIDS data. XCP_D is more actively maintained/updated and contains some features XCP classic does not, but both apps are built by the same developers.
Do you know if XCP can smooth on the surface? It’s unclear from the documentation, but smoothing appears to occur in the voxels. One of the huge benefits of fMRI processing on the surface is the ability to smooth on the surface. I suppose a work-around might be to smooth surfaces after fmriprep with mri_surf2surf prior to XCP processing.