Elsevier

NeuroImage

Volume 49, Issue 4, 15 February 2010, Pages 3099-3109
NeuroImage

Review
Ten simple rules for dynamic causal modeling

https://doi.org/10.1016/j.neuroimage.2009.11.015Get rights and content
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Abstract

Dynamic causal modeling (DCM) is a generic Bayesian framework for inferring hidden neuronal states from measurements of brain activity. It provides posterior estimates of neurobiologically interpretable quantities such as the effective strength of synaptic connections among neuronal populations and their context-dependent modulation. DCM is increasingly used in the analysis of a wide range of neuroimaging and electrophysiological data. Given the relative complexity of DCM, compared to conventional analysis techniques, a good knowledge of its theoretical foundations is needed to avoid pitfalls in its application and interpretation of results. By providing good practice recommendations for DCM, in the form of ten simple rules, we hope that this article serves as a helpful tutorial for the growing community of DCM users.

Keywords

Effective connectivity
DCM
Bayesian model selection
BMS
Model evidence
Model comparison
Bayes factor
Nonlinear dynamics
fMRI
EEG
MEG
Synaptic plasticity

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