Dynamic Causal Models for phase coupling

This paper presents an extension of the Dynamic Causal Modelling (DCM) framework to the analysis of phase-coupled data. A weakly coupled oscillator approach is used to describe dynamic phase changes in a network of oscillators. The use of Bayesian model comparison allows one to infer the mechanisms...

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Bibliographic Details
Published inJournal of neuroscience methods Vol. 183; no. 1; pp. 19 - 30
Main Authors Penny, W.D., Litvak, V., Fuentemilla, L., Duzel, E., Friston, K.
Format Journal Article
LanguageEnglish
Published Netherlands Elsevier B.V 30.09.2009
Elsevier/North-Holland Biomedical Press
Subjects
Brain - physiology
Computer Simulation
Cortical Synchronization
Dynamical causal models
Humans
Magnetoencephalography
MEG
Memory, Short-Term - physiology
Models, Biological
Nerve Net
Neurons - physiology
Nonlinear Dynamics
Synchronization
Weakly coupled oscillator
Working memory
Dynamical causal models
Weakly coupled oscillator
Working memory
Synchronization
MEG
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Summary:This paper presents an extension of the Dynamic Causal Modelling (DCM) framework to the analysis of phase-coupled data. A weakly coupled oscillator approach is used to describe dynamic phase changes in a network of oscillators. The use of Bayesian model comparison allows one to infer the mechanisms underlying synchronization processes in the brain. For example, whether activity is driven by master-slave versus mutual entrainment mechanisms. Results are presented on synthetic data from physiological models and on MEG data from a study of visual working memory.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0165-0270
1872-678X
DOI:10.1016/j.jneumeth.2009.06.029