Controlling confounding variables in nested cross-validation (nilearn)

Hi all,
We are considering collapsing across a couple task-based fMRI samples that assess the same construct with slightly different stimuli and on different magnets. Specifiically, they present different tastes (kool-aide, milkshake) with the same control taste for contrast, but also with different paradigm designs. So, I’m trying to figure out a way to control for confounds in our nested CV approach. I’ve come across a few articles re: controlling for confounds in CV. Hoping to apply this to nested CV. n > 780 scans total.

any thoughts?

We have just submitted a manuscript on this. I hope that it will come out soon.

In the mean time:

I hope this helps.

Thanks so much. Looking forward to it