Hi @ryanhucla, sorry for the slow reply. Can you clarify two points?
When you write that you “added aCompCor50” to get your second plot, do you mean that there were two separate sets of confounds that you regressed out of the data (in two steps), or did you regress out all confounds in one single step? Given that you’re talking about the reintroduction of noise, I’m assuming you did the latter (one step). That latter step would be the correct choice (unless you separately orthogonalized the regressors).
When using all aCompCor components that explain 50% of the variance, which mask were you using? (I’m not sure it matters, but if someone that knows more about AROMA chimes in the mask might be useful information)
But I agree with your inference that including the components is reintroducing noise. I’m really not familiar with AROMA; it seems like deciding which confounds to use on the smoothAROMAnonaggr data is even trickier than data which haven’t gone through non-aggressive AROMA (maybe you’ve seen them, but this thread and this one with their associated links seem particularly relevant. There may be others). ICA and PCA are closely related, in of that they’re both finding components that explain variance in the data but have different definitions of what it means for the components to be unrelated to each other. The aCompCor components are calculated prior to denoising with unaggressive AROMA, so regressing out many aCompCor components from the smoothAROMAnonaggr would reintroduce some of the variability AROMA had taken out (Lindquist et al. 2019).
To check this inference, you could make similar plots for outputs besides smoothAROMAnonaggr. E.g., I’d expect less that regressing out aCompCor50 from desc-preproc_bold wouldn’t result in such terrible voxels, while regressing out the first 5 components might have slightly more of an effect. Though, again, using the broken stick threshold I’ve often found that no aCompCor components explain significant variance, which would mean a lack of much effect from regressing out just a few components is at least somewhat likely.