Standarized Resting State processing with fmriprep

Hi all,

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

Thanks,
Mitch

Hi @mitchem890,

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.

HTH, cheers, Peer

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Other options (that should work with FMRIPREP outputs):

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Any favorites for surface resting state pipelines?

A post was split to a new topic: Functional connectivity - using fMRIPrep preprocessed data with CPAC

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?

Thank you very much in advance
Jakub

https://xcp-d.readthedocs.io/en/latest/

Can handle volumetric and cifti outputs and is designed to work with 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?

Many thanks in advance

A non-BIDSapp parallel version of it developed by the same team has been published. https://www.nature.com/articles/s41596-018-0065-y

Another alternative is to do all post processing in Nilearn.

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