Ten simple rules for dynamic causal modeling

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.

Autonomic and brain responses associated with empathy deficits in autism spectrum disorder

“…These results, therefore, are generative models of the brain that provide Bayesian posterior estimates of the effective strength of synaptic connections among neuronal populations and modulatory or contextual effect of experimental manipulations (Friston, et al, 2003; Penny, et al, 2004). DCM also allows one to define models with different network properties, and then select the best model or the best family of models using Bayesian model comparison (Stephan, et al, 2009; Stephan, et al, 2010). …”

“…Therefore, changes in self-disinhibition reflect changes in gain (or precision) following experimental manipulations. Using DCM, we also modeled the directed interactions among the LPFC, AIC, and EBA, and estimated how experimental context modulates directed connections among these cortical areas (Friston, et al, 2003; Penny, et al, 2004; Stephan, et al, 2010) to test a competing hypothesis that decreased precision at the higher level of LPFC and decreased top-down connectivity from the LPFC to AIC, rather than increased interoceptive precision, contributes to empathy deficits in ASD. Our hypothesis makes a number of specific predictions: individuals with ASD would show (1) disinhibited (peripheral) autonomic responses to arousing empathetic pain stimuli; (2) disinhibited or increased cortical response to empathetic pain in brain regions subserving interoceptive and autonomic processes, such as the AIC; and (3) greater modulation of self-connectivity within the AIC by empathetic pain.…”

Depression alters “top-down” visual attention: A dynamic causal modeling comparison between depressed and healthy subjects

“…the causal influence that one brain region exerts over another brain region (Friston et al, 2003;Stephan et al, 2010). DCM for fMRI is an input-state-output deterministic model of the neuronal activity across a network of brain regions (Friston et al, 2003).…”

“…Here we sought to overcome these limitations by assessing effective connectivity with Dynamic Causal Modeling (DCM), which determines causal relationships across potentially distributed neural networks (Friston et al, 2003;Stephan et al, 2010).…”

“…Our goal was to better characterize the causal influence of topdown modulation of visual areas by parietal cortex, using a combination of DCM fitting, model space partitioning (MSP), Bayesian model selection and averaging (BMS and BMA), applied to each population (Penny et al, 2004;Stephan et al, 2009Stephan et al, , 2010. We could thus, within a Bayesian framework, characterize the brain connectivity of the two groups of subjects, as well as their differences.…”