Hi everyone! Excuse my naivety as I’m quite new to the field! I am looking into the ABIDE I/II datasets. It seems that even though it was processed using
fmriprep and the output is on their website, most people who use the data for deep learning autism prediction studies prefer the C-PAC pipeline approach.
For example in this paper the authors state:
We chose the data processed through the Connectome Computation System, without global signal regression but with band-pass filtering.
but no explanation as to why. Same with more articles describing their preference for C-PAC but no further evaluation or metrics. It would be interesting if anyone has insights on that.
With this in mind, what would be a good way to compare results between different pipelines? I am especially interested for the weighted degree centrality metrics. It’s difficult to create a ‘golden average’ with degree centrality. Is there any qualitative way to compare those?
Thanks a lot for any insight given