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