Ten simple rules for dynamic causal modeling

TLDR

Dynamic causal modeling is a Bayesian framework that infers hidden neuronal states and estimates effective connectivity, widely applied to neuroimaging data but requiring deep theoretical knowledge to avoid pitfalls. This article offers ten simple rules to guide DCM users, serving as a tutorial for the growing community.

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.