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Comparing Dynamic Causal Models using AIC, BIC and Free Energy
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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.
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“Practically, log model evidence is usually approximated with Bayesian information criteria or variational free energy (Penny, 2012).”
From Active Inference, homeostatic regulation and adaptive behavioural control (2015) · cites this paper as Penny, 2012
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Cekic, S., Grandjean, D., & Renaud, O. (2018) · Journal of Applied Statistics · doi:10.1080/02664763.2018.1455814 · Cited by 6
Metadata
- Authors
- Penny, W. D.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- Corresponding author
- Penny, W.D.
- ORCID
- Penny, W.D.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2012-01
- Volume
- 59
- Issue
- 1
- Pages
- 319-330
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3200437
- OpenAlex
- W2150847325
- Cited by
- 298
- References
- 47
- Citations by year
- 2026: 10 · 2025: 21 · 2024: 12 · 2023: 16 · 2022: 20 · 2021: 25 · 2020: 26 · 2019: 19 · 2018: 34 · 2017: 23 · 2016: 29 · 2015: 20 · 2014: 16 · 2013: 18 · 2012: 8
- FWCI
- 6.99
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- UEA Digital Repository (University of East Anglia) (submittedVersion)
- Funders
- Wellcome Trust
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Keywords
- dynamic causal modeling, model selection criteria, Akaike information criterion, Bayesian information criterion, variational free energy, brain connectivity, general linear model, model comparison, simulation study
- MeSH terms
- Bayes Theorem, Brain Mapping, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Models, Neurological, Models, Theoretical, Reproducibility of Results, Linear Models
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