Xcp_d vs xcpengine?

I am trying to understand what’s the difference between the xcp_d and xcpengine workflow pipelines. The idea is to post-process the fMRI bold from fmriprep, correct them using co-founder and use for voxel-wise connectivity analysis.

I am unable to run xcp_d though as I am getting an error on the singularity pipeline. Could I use xcpengine for the same?

I’ve reported the error on the GitHub in this issue


The difference is that XCP_D is more actively maintained compared to XCPEngine, and XCP_D works more like regular BIDS apps in the way it indexes directories and meta data. XCP_D is more hard-coded for certain preprocessing pipelines (like fMRIPREP, DCAN, nibabies etc), and does not require a cohort tsv file like xcpengine does. The functionality should by-and-large be the same, but I think XCP_D has more preconfigured denoising schemes and has a respiratory/head motion temporal filter option that xcpengine does not. Short answer: I would use xcp_d.

In regards to your issue, I will follow up on the github page.