title: Comparing dynamic causal models using AIC, BIC and free energy.
abstract: 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.
authors.affiliation: Wellcome Trust Centre for Neuroimaging, University College, London WC1N 3BG, UK. w.penny@fil.ion.ucl.ac.uk
authors.forename: W D
authors.initials: WD
authors.lastname: Penny
mesh_terms: D001499:Bayes Theorem; D001931:Brain Mapping / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D008959:Models, Neurological; D008962:Models, Theoretical; D015203:Reproducibility of Results