About: Dynamic causal modeling

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Dynamic causal modeling (DCM) is a framework for specifying models, fitting them to data and comparing their evidence using Bayesian model comparison. It uses nonlinear state-space models in continuous time, specified using stochastic or ordinary differential equations. DCM was initially developed for testing hypotheses about neural dynamics. In this setting, differential equations describe the interaction of neural populations, which directly or indirectly give rise to functional neuroimaging data e.g., functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG) or electroencephalography (EEG). Parameters in these models quantify the directed influences or effective connectivity among neuronal populations, which are estimated from the data using Bayesian statistical methods

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rdfs:label	Dynamic Causal Modelling (de) Dynamic causal modeling (en)
rdfs:comment	Dynamic Causal Modelling (DCM) bezeichnet eine Methode zur Interpretation von Daten der funktionellen Magnetresonanztomographie. Das Verfahren wurde im Jahre 2003 entwickelt und ist Teil der Analysesoftware . Eine Vielzahl wissenschaftlicher Artikel zum Thema DCM wurden am in London veröffentlicht. (de) Dynamic causal modeling (DCM) is a framework for specifying models, fitting them to data and comparing their evidence using Bayesian model comparison. It uses nonlinear state-space models in continuous time, specified using stochastic or ordinary differential equations. DCM was initially developed for testing hypotheses about neural dynamics. In this setting, differential equations describe the interaction of neural populations, which directly or indirectly give rise to functional neuroimaging data e.g., functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG) or electroencephalography (EEG). Parameters in these models quantify the directed influences or effective connectivity among neuronal populations, which are estimated from the data using Bayesian statistical methods (en)
foaf:depiction
dcterms:subject	Neuroimaging
Wikipage page ID	57381799 (xsd:integer)
Wikipage revision ID	1079482196 (xsd:integer)
Link from a Wikipage to another Wikipage	Bayes factor Bayesian inference Bayesian model reduction Predictive coding Electroencephalography Dynamical system Wilson–Cowan model Convolution General linear model Generalized filtering Functional magnetic resonance imaging Morris–Lecar model State space Statistical parametric mapping Psychophysiological Interaction Neuroimaging Magnetoencephalography Prior probability Taylor series Laplace's method Hodgkin–Huxley model Ordinary differential equation Stochastic differential equation Sigmoid function Variational Bayesian methods Factorial experiment Evoked potential Resting state fMRI Bayesian model comparison
Link from a Wikipage to an external page	http://www.fil.ion.ucl.ac.uk/spm/software/spm12/). https://mbb-team.github.io/VBA-toolbox/). https://www.tnu.ethz.ch/en/software/tapas.html) http://www.scholarpedia.org/article/Conductance-based_models http://www.scholarpedia.org/article/Dynamic_causal_modeling
sameAs	Dynamic causal modeling Dynamic causal modeling Dynamic causal modeling
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thumbnail	wiki-commons:Special:FilePath/DCM_for_fMRI.svg?width=300
has abstract	Dynamic Causal Modelling (DCM) bezeichnet eine Methode zur Interpretation von Daten der funktionellen Magnetresonanztomographie. Das Verfahren wurde im Jahre 2003 entwickelt und ist Teil der Analysesoftware . Eine Vielzahl wissenschaftlicher Artikel zum Thema DCM wurden am in London veröffentlicht. (de) Dynamic causal modeling (DCM) is a framework for specifying models, fitting them to data and comparing their evidence using Bayesian model comparison. It uses nonlinear state-space models in continuous time, specified using stochastic or ordinary differential equations. DCM was initially developed for testing hypotheses about neural dynamics. In this setting, differential equations describe the interaction of neural populations, which directly or indirectly give rise to functional neuroimaging data e.g., functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG) or electroencephalography (EEG). Parameters in these models quantify the directed influences or effective connectivity among neuronal populations, which are estimated from the data using Bayesian statistical methods. (en)
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page length (characters) of wiki page	31947 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Dynamic_causal_modeling
is Link from a Wikipage to another Wikipage of	Brain connectivity estimators Psychophysiological Interaction Dynamic causal modelling Affect labeling
is Wikipage redirect of	Dynamic causal modelling
is foaf:primaryTopic of	wikipedia-en:Dynamic_causal_modeling

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