Measuring Brain Complexity During Neural Motor Resonance

Background: EEG mu-desynchronization is an index of motor resonance (MR) and is used to study social interaction deficiencies, but finding differences in mu-desynchronization does not reveal how nonlinear brain dynamics are affected during MR. The current study explores how nonlinear brain dynamics...

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Main Authors: Brandon M. Hager, Albert C. Yang, Jennifer N. Gutsell
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-10-01
Series:Frontiers in Neuroscience
Subjects:
EEG
Online Access:https://www.frontiersin.org/article/10.3389/fnins.2018.00758/full
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spelling doaj-ac5a3da29c494c2085d067bc6a3989e72020-11-25T01:01:54ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2018-10-011210.3389/fnins.2018.00758403374Measuring Brain Complexity During Neural Motor ResonanceBrandon M. Hager0Albert C. Yang1Jennifer N. Gutsell2Department of Psychology, Brandeis University, Waltham, MA, United StatesDivision of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United StatesDepartment of Psychology, Neuroscience Program, and Volen National Center for Complex Systems, Brandeis University, Waltham, MA, United StatesBackground: EEG mu-desynchronization is an index of motor resonance (MR) and is used to study social interaction deficiencies, but finding differences in mu-desynchronization does not reveal how nonlinear brain dynamics are affected during MR. The current study explores how nonlinear brain dynamics change during MR. We hypothesized that the complexity of the mu frequency band (8–13 Hz) changes during MR, and that this change would be frequency specific. Additionally, we sought to determine whether complexity at baseline and changes in complexity during action observation would predict MR and changes in network dynamics.Methods: EEG was recorded from healthy participants (n = 45) during rest and during an action observation task. Baseline brain activity was measured followed by participants observing videos of hands squeezing stress balls. We used multiscale entropy (MSE) to quantify the complexity of the mu rhythm during MR. We then performed post-hoc graph theory analysis to explore whether nonlinear dynamics during MR affect brain network topology.Results: We found significant mu-desynchronization during the action observation task and that mu entropy was significantly increased during the task compared to rest, while gamma, beta, theta, and delta bands showed decreased entropy. Moreover, resting-state entropy was significantly predictive of the degree of mu desynchronization. We also observed a decrease in the clustering coefficient in the mu band only and a significant decrease in global alpha efficiency during action observation. MSE during action observation was strongly correlated with alpha network efficiency.Conclusions: The current findings suggest that the desynchronization of the mu wave during MR results in a local increase of mu entropy in sensorimotor areas, potentially reflecting a release from alpha inhibition. This release from inhibition may be mediated by the baseline MSE in the mu band. The dynamical complexity and network analysis of EEG may provide a useful addition for future studies of MR by incorporating measures of nonlinearity.https://www.frontiersin.org/article/10.3389/fnins.2018.00758/fullmotor resonancecomplexitynetwork connectivityEEGmu suppression
collection DOAJ
language English
format Article
sources DOAJ
author Brandon M. Hager
Albert C. Yang
Jennifer N. Gutsell
spellingShingle Brandon M. Hager
Albert C. Yang
Jennifer N. Gutsell
Measuring Brain Complexity During Neural Motor Resonance
Frontiers in Neuroscience
motor resonance
complexity
network connectivity
EEG
mu suppression
author_facet Brandon M. Hager
Albert C. Yang
Jennifer N. Gutsell
author_sort Brandon M. Hager
title Measuring Brain Complexity During Neural Motor Resonance
title_short Measuring Brain Complexity During Neural Motor Resonance
title_full Measuring Brain Complexity During Neural Motor Resonance
title_fullStr Measuring Brain Complexity During Neural Motor Resonance
title_full_unstemmed Measuring Brain Complexity During Neural Motor Resonance
title_sort measuring brain complexity during neural motor resonance
publisher Frontiers Media S.A.
series Frontiers in Neuroscience
issn 1662-453X
publishDate 2018-10-01
description Background: EEG mu-desynchronization is an index of motor resonance (MR) and is used to study social interaction deficiencies, but finding differences in mu-desynchronization does not reveal how nonlinear brain dynamics are affected during MR. The current study explores how nonlinear brain dynamics change during MR. We hypothesized that the complexity of the mu frequency band (8–13 Hz) changes during MR, and that this change would be frequency specific. Additionally, we sought to determine whether complexity at baseline and changes in complexity during action observation would predict MR and changes in network dynamics.Methods: EEG was recorded from healthy participants (n = 45) during rest and during an action observation task. Baseline brain activity was measured followed by participants observing videos of hands squeezing stress balls. We used multiscale entropy (MSE) to quantify the complexity of the mu rhythm during MR. We then performed post-hoc graph theory analysis to explore whether nonlinear dynamics during MR affect brain network topology.Results: We found significant mu-desynchronization during the action observation task and that mu entropy was significantly increased during the task compared to rest, while gamma, beta, theta, and delta bands showed decreased entropy. Moreover, resting-state entropy was significantly predictive of the degree of mu desynchronization. We also observed a decrease in the clustering coefficient in the mu band only and a significant decrease in global alpha efficiency during action observation. MSE during action observation was strongly correlated with alpha network efficiency.Conclusions: The current findings suggest that the desynchronization of the mu wave during MR results in a local increase of mu entropy in sensorimotor areas, potentially reflecting a release from alpha inhibition. This release from inhibition may be mediated by the baseline MSE in the mu band. The dynamical complexity and network analysis of EEG may provide a useful addition for future studies of MR by incorporating measures of nonlinearity.
topic motor resonance
complexity
network connectivity
EEG
mu suppression
url https://www.frontiersin.org/article/10.3389/fnins.2018.00758/full
work_keys_str_mv AT brandonmhager measuringbraincomplexityduringneuralmotorresonance
AT albertcyang measuringbraincomplexityduringneuralmotorresonance
AT jenniferngutsell measuringbraincomplexityduringneuralmotorresonance
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