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Ten simple rules for dynamic causal modeling
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
doi:10.1016/j.neuroimage.2009.11.015
pmid:19914382
pmcid:PMC2825373
fatcat:nhftacuv5zfstog4xeqpo6it5m