Dynamic Causal Models for phase coupling

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

doi:10.1016/j.jneumeth.2009.06.029

Dynamic Causal Models for phase coupling

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

Research output: Contribution to journal › Article › peer-review

60 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 language	English
Pages (from-to)	19-30
Number of pages	12
Journal	Journal of Neuroscience Methods
Volume	183
Issue number	1
Early online date	2 Jul 2009
DOIs	https://doi.org/10.1016/j.jneumeth.2009.06.029
Publication status	Published - 30 Sep 2009

Keywords

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

Access to Document

10.1016/j.jneumeth.2009.06.029Licence: CC BY

Cite this

@article{5074fa9f4ac84ea2b00468bb1e2f78c4,

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.",

keywords = "Brain, Computer Simulation, Cortical Synchronization, Humans, Magnetoencephalography, Short-Term Memory, Biological Models, Nerve Net, Neurons, Nonlinear Dynamics",

author = "Penny, {W D} and V Litvak and L Fuentemilla and E Duzel and K Friston",

year = "2009",

month = sep,

day = "30",

doi = "10.1016/j.jneumeth.2009.06.029",

language = "English",

volume = "183",

pages = "19--30",

journal = "Journal of Neuroscience Methods",

issn = "0165-0270",

publisher = "Elsevier",

number = "1",

}

TY - JOUR

T1 - Dynamic Causal Models for phase coupling

AU - Penny, W D

AU - Litvak, V

AU - Fuentemilla, L

AU - Duzel, E

AU - Friston, K

PY - 2009/9/30

Y1 - 2009/9/30

N2 - 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.

AB - 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.

KW - Brain

KW - Computer Simulation

KW - Cortical Synchronization

KW - Humans

KW - Magnetoencephalography

KW - Short-Term Memory

KW - Biological Models

KW - Nerve Net

KW - Neurons

KW - Nonlinear Dynamics

U2 - 10.1016/j.jneumeth.2009.06.029

DO - 10.1016/j.jneumeth.2009.06.029

M3 - Article

C2 - 19576931

VL - 183

SP - 19

EP - 30

JO - Journal of Neuroscience Methods

JF - Journal of Neuroscience Methods

SN - 0165-0270

IS - 1

ER -