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- 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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- 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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- Dynamic Causal Modelling (de)
- Dynamic causal modeling (en)
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