Dear Peter and other DCM experts,
This is all very useful information. Thanks for posting it to the list, Peter. I have a couple of follow-up questions:
1. Given that it is unlikely that a single model will be a clear 'winner' by the BMS implemented at the end of the post-hoc/Bayesian model reduction procedure, would it be valid to apply Bayesian model averaging over a large set (perhaps even all 256) of the surviving/reduced models for all subjects? I've done this with my data, and it seems to reveal a sensible and theoretically meaningful model.
2. The DCM that I ran through the model reduction procedure has 13 VOIs. I've recently got reviews back on the paper, and one of the main criticisms was this large number of VOIs and thus the complexity of the model, which in the view of one reviewer "leads to lots of problems". That reviewer also said "DCM is a better technique when applied to a fairly small number of regions (3-5 perhaps)." Is this criticism warranted, do you think? If so, should I really be trying to limit my model to 8 or fewer VOIs, as is implied by what is allowable via the GUI and the code that produces the graphs?