Dynamic Causal Models for phase coupling

W D Penny, V Litvak, L Fuentemilla, E Duzel, K Friston

Research output: Contribution to journalArticlepeer-review

52 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)19-30
Number of pages12
JournalJournal of Neuroscience Methods
Volume183
Issue number1
Early online date2 Jul 2009
DOIs
Publication statusPublished - 30 Sep 2009

Keywords

  • Brain
  • Computer Simulation
  • Cortical Synchronization
  • Humans
  • Magnetoencephalography
  • Short-Term Memory
  • Biological Models
  • Nerve Net
  • Neurons
  • Nonlinear Dynamics

Cite this