Hi,
Im trying to make classification model for disease classification using rs-fMRI data.
Im know that many studies use fALFF, Reho, Functional Connectivity as features.
I want to know how can I extract such features.
I tried to make functional connectivity using Nilearn package and I did.
But some research papers are saying that preforming ICA might provide better result.
This part is quiet tricky for me.
As far I as know, ICA gives info about which regions have similar BOLD signal patterns. Isn’t that information already included in functional Connectivity?
To de concluded, I jus wanna know whether should I perform ICA and make functional connectivity or just skip perform ICA.
Best,
Junyong Oh.