Dear SPM- and DCM-Experts,
I performed a rfx - Bayesian model selection analysis with DCMs modelling the influence of a drug on the connectivity of different pain-processing regions (direct input: pain, modulatory input: drug). This resulted in three models among 64 models that best explain the influence of the drug.
In a further approach, I estimated the best model of the first study for another data set investigating the same drug in the same study design but in another experimental pain condition and compared for these data the best model (model 1) with a model without drug influence (model 0, no modulatory input). Here, the model lacking drug influence turned out to be superior.
However, when I test not only the best, but the 3 best models of the first analysis against a model without drug influence (model 0), the exceedance probability of model 0 is relatively worse than model 1. Why does the presence or absence of other models reverses the ratio of these two models?
Thank you for your help!!
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