Ten simple rules for dynamic causal modeling

K.E. Stephan, W.D. Penny, R.J. Moran, H.E.M. den Ouden, J. Daunizeau, K.J. Friston
2010 NeuroImage  
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 contextdependent 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
more » ... l 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.
doi:10.1016/j.neuroimage.2009.11.015 pmid:19914382 pmcid:PMC2825373 fatcat:nhftacuv5zfstog4xeqpo6it5m