Work

Comparing Dynamic Causal Models using AIC, BIC and Free Energy

Year: 2011

Type: article

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.

Source: NeuroImage

DOI: 10.1016/j.neuroimage.2011.07.039

Author: W.D. Penny

Institutions: Wellcome Centre for Human Neuroimaging, University College London

Language: English

Topic: Functional Brain Connectivity Studies

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: hybrid

Estimated APC paid: $3,540

Funder: Wellcome Trust