Dynamic causal modeling: Difference between revisions

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Dynamic causal modeling (DCM) is a [[Bayes factor|Bayesian model comparison]] procedure for comparing models of how data were generated. Dynamic causal models are formulated in terms of [[Stochastic differential equation|stochastic]] or [[Ordinary differential equation|ordinary differential equations]] (i.e., nonlinear [[State space|state-space]] models in continuous time). These equations model the dynamics of [[Hidden Markov model|hidden states]] in the nodes of a [[Graphical model|probabilistic graphical model]], where conditional dependencies are parameterized in terms of directed [[Brain connectivity estimators|effective connectivity]].

DCM was initially developed for identifying models of [[Dynamical system|neural dynamics]], estimating their parameters and comparing their evidence .<ref name=":2">{{Cite journal|last=Friston|first=K.J.|last2=Harrison|first2=L.|last3=Penny|first3=W.|date=2003-08|title=Dynamic causal modelling|url=https://doi.org/10.1016/S1053-8119(03)00202-7|journal=NeuroImage|volume=19|issue=4|pages=1273–1302|doi=10.1016/s1053-8119(03)00202-7|issn=1053-8119}}</ref>. DCM allows one to test competing models of interactions among neural populations (effective connectivity) using functional neuroimaging data e.g., [[functional magnetic resonance imaging]] (fMRI), [[magnetoencephalography]] (MEG) or [[electroencephalography]] (EEG).

== Procedure ==

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DCM is a hypothesis-driven approach for investigating the interactions among pre-defined regions of interest. It is not ideally suited for exploratory analyses<ref name=":0" />. Although methods have been implemented for automatically searching over reduced models ([[Bayesian model reduction|Bayesian Model Reduction]]) and for modelling large-scale brain networks<ref name=":4" />, these methods require an explicit specification of model space. in neuroimaging, other approaches such as [[Psychophysiological Interaction|psycho-physical interactions (PPI)]] analysis may be more appropriate for discovering key nodes for DCM.

The variational Bayesian methods used for model estimation are based on the on the Laplace assumption that the posterior over parameters is Gaussian. This approximation can fail in the context of highly non-linear models, where local minima can preclude the free energy from serving as a tight bound on log model evidence. Sampling approaches provide the gold standard, however are time consuming to run. These have been used to validate the variational approximations in DCM <ref>{{Cite journal|last=Chumbley|first=Justin R.|last2=Friston|first2=Karl J.|last3=Fearn|first3=Tom|last4=Kiebel|first4=Stefan J.|date=2007-11|title=A Metropolis–Hastings algorithm for dynamic causal models|url=http://dx.doi.org/10.1016/j.neuroimage.2007.07.028|journal=NeuroImage|volume=38|issue=3|pages=478–487|doi=10.1016/j.neuroimage.2007.07.028|issn=1053-8119}}</ref>.

== Software implementations ==

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== References ==

[[:Category:Neuroimaging]]

== Expanded the Dynamic Causal Modelling page to a full article ==

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