Technical NoteComparing Dynamic Causal Models using AIC, BIC and Free Energy

In neuroimaging it is now becoming standard practise to fit multiple models to data and compare them using a model selection criterion. This is especially prevalent in the analysis of brain connectivity. This paper describes a simulation study which compares the relative merits of three model selection criteria (i) Akaike's Information Criterion (AIC), (ii) the Bayesian Information Criterion (BIC) and (iii) the variational Free Energy. Differences in performance are examined in the context of General Linear Models (GLMs) and Dynamic Causal Models (DCMs). We find that the Free Energy has the best model selection ability and recommend it be used for comparison of DCMs.

► The Free Energy is a better model comparison criterion than AIC or BIC ► The complexity of a model is not usefully characterised by the number of parameters. ► Empirical Bayesian estimation of prior variances would improve model comparison.