Also, although I may have not felt this way initially 😅, the reviewer feedback was really great on this one. So thanks to the reviewers as well.

Sure thing! Slides and poster here: www.dropbox.com/scl/fo/re1bc...
Preprint here: www.biorxiv.org/content/10.1...

The bigger problem is when orthogonalization changes parameter interpretations such that the "adjusting" implied by the regressors isn't actually occurring. "We adjusted our group difference for age" (but age was modeled as an interaction and centered within each group, so no adjusting occurred)

Also, when looking at interactions I've never understood why people look at the main effects if the interaction is significant. If it isn't significant I omit it from the model, but I know some people don't like to do that.

Centering is the only type of orthog I find helpful, but this is often done wrong (e.g., center within groups instead of across all subjects). The big problem with orthog is people often don't realize they're effectively removing the regressors they orthogonalized with respect to from the model.