A General Approach for Quantifying Nonlinear Connectivity in the Nervous System Based on Phase Coupling

Yuan Yang, Teodoro Solis-Escalante, Jun Yao, Andreas Daffertshofer, Alfred C. Schouten, Frans C.T. Van Der Helm

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

Interaction between distant neuronal populations is essential for communication within the nervous system and can occur as a highly nonlinear process. To better understand the functional role of neural interactions, it is important to quantify the nonlinear connectivity in the nervous system. We introduce a general approach to measure nonlinear connectivity through phase coupling: the multi-spectral phase coherence (MSPC). Using simulated data, we compare MSPC with existing phase coupling measures, namely n: m synchronization index and bi-phase locking value. MSPC provides a system description, including (i) the order of the nonlinearity, (ii) the direction of interaction, (iii) the time delay in the system, and both (iv) harmonic and (v) intermodulation coupling beyond the second order; which are only partly revealed by other methods. We apply MSPC to analyze data from a motor control experiment, where subjects performed isotonic wrist flexions while receiving movement perturbations. MSPC between the perturbation, EEG and EMG was calculated. Our results reveal directional nonlinear connectivity in the afferent and efferent pathways, as well as the time delay (43±8ms) between the perturbation and the brain response. In conclusion, MSPC is a novel approach capable to assess high-order nonlinear interaction and timing in the nervous system.

Original languageEnglish (US)
Article number1550031
JournalInternational Journal of Neural Systems
Volume26
Issue number1
DOIs
StatePublished - Feb 1 2016

Fingerprint

Neurology
Time delay
Intermodulation
Electroencephalography
Brain
Synchronization
Communication
Experiments

Keywords

  • EEG
  • EMG
  • Phase coupling
  • motor control
  • nonlinear interaction
  • time delay

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Yang, Yuan ; Solis-Escalante, Teodoro ; Yao, Jun ; Daffertshofer, Andreas ; Schouten, Alfred C. ; Van Der Helm, Frans C.T. / A General Approach for Quantifying Nonlinear Connectivity in the Nervous System Based on Phase Coupling. In: International Journal of Neural Systems. 2016 ; Vol. 26, No. 1.
@article{5d077ecba5f14a6b9347737b65256626,
title = "A General Approach for Quantifying Nonlinear Connectivity in the Nervous System Based on Phase Coupling",
abstract = "Interaction between distant neuronal populations is essential for communication within the nervous system and can occur as a highly nonlinear process. To better understand the functional role of neural interactions, it is important to quantify the nonlinear connectivity in the nervous system. We introduce a general approach to measure nonlinear connectivity through phase coupling: the multi-spectral phase coherence (MSPC). Using simulated data, we compare MSPC with existing phase coupling measures, namely n: m synchronization index and bi-phase locking value. MSPC provides a system description, including (i) the order of the nonlinearity, (ii) the direction of interaction, (iii) the time delay in the system, and both (iv) harmonic and (v) intermodulation coupling beyond the second order; which are only partly revealed by other methods. We apply MSPC to analyze data from a motor control experiment, where subjects performed isotonic wrist flexions while receiving movement perturbations. MSPC between the perturbation, EEG and EMG was calculated. Our results reveal directional nonlinear connectivity in the afferent and efferent pathways, as well as the time delay (43±8ms) between the perturbation and the brain response. In conclusion, MSPC is a novel approach capable to assess high-order nonlinear interaction and timing in the nervous system.",
keywords = "EEG, EMG, Phase coupling, motor control, nonlinear interaction, time delay",
author = "Yuan Yang and Teodoro Solis-Escalante and Jun Yao and Andreas Daffertshofer and Schouten, {Alfred C.} and {Van Der Helm}, {Frans C.T.}",
year = "2016",
month = "2",
day = "1",
doi = "10.1142/S0129065715500318",
language = "English (US)",
volume = "26",
journal = "International Journal of Neural Systems",
issn = "0129-0657",
publisher = "World Scientific Publishing Co. Pte Ltd",
number = "1",

}

A General Approach for Quantifying Nonlinear Connectivity in the Nervous System Based on Phase Coupling. / Yang, Yuan; Solis-Escalante, Teodoro; Yao, Jun; Daffertshofer, Andreas; Schouten, Alfred C.; Van Der Helm, Frans C.T.

In: International Journal of Neural Systems, Vol. 26, No. 1, 1550031, 01.02.2016.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A General Approach for Quantifying Nonlinear Connectivity in the Nervous System Based on Phase Coupling

AU - Yang, Yuan

AU - Solis-Escalante, Teodoro

AU - Yao, Jun

AU - Daffertshofer, Andreas

AU - Schouten, Alfred C.

AU - Van Der Helm, Frans C.T.

PY - 2016/2/1

Y1 - 2016/2/1

N2 - Interaction between distant neuronal populations is essential for communication within the nervous system and can occur as a highly nonlinear process. To better understand the functional role of neural interactions, it is important to quantify the nonlinear connectivity in the nervous system. We introduce a general approach to measure nonlinear connectivity through phase coupling: the multi-spectral phase coherence (MSPC). Using simulated data, we compare MSPC with existing phase coupling measures, namely n: m synchronization index and bi-phase locking value. MSPC provides a system description, including (i) the order of the nonlinearity, (ii) the direction of interaction, (iii) the time delay in the system, and both (iv) harmonic and (v) intermodulation coupling beyond the second order; which are only partly revealed by other methods. We apply MSPC to analyze data from a motor control experiment, where subjects performed isotonic wrist flexions while receiving movement perturbations. MSPC between the perturbation, EEG and EMG was calculated. Our results reveal directional nonlinear connectivity in the afferent and efferent pathways, as well as the time delay (43±8ms) between the perturbation and the brain response. In conclusion, MSPC is a novel approach capable to assess high-order nonlinear interaction and timing in the nervous system.

AB - Interaction between distant neuronal populations is essential for communication within the nervous system and can occur as a highly nonlinear process. To better understand the functional role of neural interactions, it is important to quantify the nonlinear connectivity in the nervous system. We introduce a general approach to measure nonlinear connectivity through phase coupling: the multi-spectral phase coherence (MSPC). Using simulated data, we compare MSPC with existing phase coupling measures, namely n: m synchronization index and bi-phase locking value. MSPC provides a system description, including (i) the order of the nonlinearity, (ii) the direction of interaction, (iii) the time delay in the system, and both (iv) harmonic and (v) intermodulation coupling beyond the second order; which are only partly revealed by other methods. We apply MSPC to analyze data from a motor control experiment, where subjects performed isotonic wrist flexions while receiving movement perturbations. MSPC between the perturbation, EEG and EMG was calculated. Our results reveal directional nonlinear connectivity in the afferent and efferent pathways, as well as the time delay (43±8ms) between the perturbation and the brain response. In conclusion, MSPC is a novel approach capable to assess high-order nonlinear interaction and timing in the nervous system.

KW - EEG

KW - EMG

KW - Phase coupling

KW - motor control

KW - nonlinear interaction

KW - time delay

UR - http://www.scopus.com/inward/record.url?scp=84953346624&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84953346624&partnerID=8YFLogxK

U2 - 10.1142/S0129065715500318

DO - 10.1142/S0129065715500318

M3 - Article

VL - 26

JO - International Journal of Neural Systems

JF - International Journal of Neural Systems

SN - 0129-0657

IS - 1

M1 - 1550031

ER -