Dynamic causal modeling: Difference between revisions

Revision as of 07:47, 20 June 2018

Revision as of 08:01, 20 June 2018

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== Model specification ==

Dynamic Causal Models (DCMs) are nonlinear state-space modelsdynamical systems in continuous time, parameterized in terms of directed effective connectivity between brain regions. Unlike [[Bayesian network|Bayesian Networks]], DCMs can be cyclic, and unlike [[Structural equation modeling|Structural Equation modelling]] and [[Granger causality]], DCM does not depend on the theory of Martingales, i.e., it does not assume that random fluctuations' are serially uncorrelated. All models in DCM have the following basic form:

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