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A dynamical systems approach for estimating phase interactions between rhythms of different frequencies from experimental data
- Source :
- PLoS Computational Biology, Vol 14, Iss 1, p e1005928 (2018), PLoS Computational Biology
- Publication Year :
- 2018
- Publisher :
- Public Library of Science (PLoS), 2018.
-
Abstract
- Synchronization of neural oscillations as a mechanism of brain function is attracting increasing attention. Neural oscillation is a rhythmic neural activity that can be easily observed by noninvasive electroencephalography (EEG). Neural oscillations show the same frequency and cross-frequency synchronization for various cognitive and perceptual functions. However, it is unclear how this neural synchronization is achieved by a dynamical system. If neural oscillations are weakly coupled oscillators, the dynamics of neural synchronization can be described theoretically using a phase oscillator model. We propose an estimation method to identify the phase oscillator model from real data of cross-frequency synchronized activities. The proposed method can estimate the coupling function governing the properties of synchronization. Furthermore, we examine the reliability of the proposed method using time-series data obtained from numerical simulation and an electronic circuit experiment, and show that our method can estimate the coupling function correctly. Finally, we estimate the coupling function between EEG oscillation and the speech sound envelope, and discuss the validity of these results.<br />Author summary In this paper, we propose an estimation method to identify a dynamical system from rhythmic time-series data. Rhythmic activities have been observed frequently and are synchronized in various fields, and synchronization is an important topic in nonlinear science. It is well known that such synchronization can be described theoretically by a phase oscillator model under the condition that the rhythmic activities can be considered weakly coupled limit-cycle oscillators. Based on this theory, we propose a method to identify the interaction between rhythmic activities as a network of phase oscillators. A practical advantage of the proposed method is that, without detailed modeling, we can extract the phase oscillator model directly from time-series data. For the above theoretical and practical reasons, this method can be applied to rhythmic data from a wide range of fields. In this study, we have focused on human brain activities in which electroencephalography (EEG) signals are often synchronized with each other and with external periodic stimuli. We demonstrate that the proposed method can successfully estimate the interaction between EEG activity and speech rhythm. Consequently, the proposed method can reveal the role of neural synchronization.
- Subjects :
- Male
Periodicity
Systems Analysis
Computer science
Physiology
Normal Distribution
Social Sciences
Electroencephalography
01 natural sciences
Systems Science
0302 clinical medicine
Synchronization (computer science)
Medicine and Health Sciences
lcsh:QH301-705.5
Clinical Neurophysiology
Brain Mapping
Grammar
Ecology
medicine.diagnostic_test
Oscillation
Simulation and Modeling
Brain
Syllables
Signal Processing, Computer-Assisted
Dynamical Systems
Healthy Volunteers
Electrophysiology
Signal Filtering
Bioassays and Physiological Analysis
Computational Theory and Mathematics
Brain Electrophysiology
Neural oscillation
Modeling and Simulation
Physical Sciences
Engineering and Technology
Female
Electrical Engineering
Research Article
Adult
Computer and Information Sciences
Dynamical systems theory
Imaging Techniques
Models, Neurological
Neurophysiology
Neuroimaging
Dynamical system
Research and Analysis Methods
Phonology
Electronic Circuits
03 medical and health sciences
Cellular and Molecular Neuroscience
Young Adult
Control theory
Oscillometry
0103 physical sciences
Genetics
medicine
Speech
Humans
010306 general physics
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Quantitative Biology::Neurons and Cognition
Electrophysiological Techniques
Biology and Life Sciences
Reproducibility of Results
Linguistics
Bayes Theorem
Function (mathematics)
Coupling (physics)
lcsh:Biology (General)
Speech Signal Processing
Signal Processing
Clinical Medicine
030217 neurology & neurosurgery
Mathematics
Neuroscience
Electrical Circuits
Subjects
Details
- Language :
- English
- ISSN :
- 15537358
- Volume :
- 14
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....3bcf7a206620d931d9bc2e1eb6f2f7bc