Comparing dynamic causal models using AIC, BIC and free energy

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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.

Original languageEnglish
Pages (from-to)319-330
Number of pages12
JournalNeuroImage
Volume59
Issue number1
Early online date27 Jul 2011
DOIs
Publication statusPublished - 2 Jan 2012

Keywords

  • Bayes Theorem
  • Brain Mapping
  • Computer-Assisted Image Processing
  • Linear Models
  • Magnetic Resonance Imaging
  • Neurological Models
  • Theoretical Models
  • Reproducibility of Results
  • Comparative Study

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