Journal
NEUROIMAGE
Volume 59, Issue 1, Pages 319-330Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2011.07.039
Keywords
Bayesian; Model comparison; Brain connectivity; Dynamic Causal Modelling; fMRI
Funding
- Wellcome Trust
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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. (C) 2011 Elsevier Inc. All rights reserved.
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