Dynamic causal modeling

Definition: Dynamic causal modeling (DCM) is a Bayesian model comparison framework used to estimate the effective connectivity between brain regions. DCM models are based on stochastic estimation of the parameters of an underpinning assumed generative model. One common DCM model is the bilinear model based on ordinary differential equations.

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References:

https://doi.org/10.1016/s1053-8119(03)00202-7

https://doi.org/10.1016/j.neuroimage.2015.02.035

https://doi.org/10.1016/j.neuroimage.2018.04.022

https://doi.org/10.1016/j.schres.2020.11.012

Related terms: Granger causality