Comparing dynamic causal models using AIC, BIC and free energy

Penny, W D ORCID: https://orcid.org/0000-0001-9064-1191 (2012) Comparing dynamic causal models using AIC, BIC and free energy. NeuroImage, 59 (1). pp. 319-330. ISSN 1053-8119

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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.

Item Type:	Article
Additional Information:	Copyright © 2011 Elsevier Inc. All rights reserved.
Uncontrolled Keywords:	bayes theorem,brain mapping,computer-assisted image processing,linear models,magnetic resonance imaging,neurological models,theoretical models,reproducibility of results,comparative study
Faculty \ School:	Faculty of Social Sciences > School of Psychology
UEA Research Groups:	Faculty of Social Sciences > Research Centres > Centre for Behavioural and Experimental Social Sciences
Depositing User:	Pure Connector
Date Deposited:	19 Aug 2017 05:06
Last Modified:	19 Apr 2023 22:33
URI:	https://ueaeprints.uea.ac.uk/id/eprint/64592
DOI:	10.1016/j.neuroimage.2011.07.039

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