title: Dynamic Causal Models for phase coupling.
abstract: 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.
authors: Wellcome Trust Centre for Neuroimaging, University College, 12 Queen Square, London WC1N 3BG, UK. w.penny@fil.ion.ucl.ac.uk
authors: W D
authors: WD
authors: Penny
authors: V
authors: V
authors: Litvak
authors: L
authors: L
authors: Fuentemilla
authors: E
authors: E
authors: Duzel
authors: K
authors: K
authors: Friston
mesh_terms: D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D003344:Cortical Synchronization; D006801:Humans; D015225:Magnetoencephalography; D008570:Memory, Short-Term / Q000502:physiology; D008954:Models, Biological; D009415:Nerve Net; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics