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Related Experiment Video

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Brain network dynamics in the alpha band during a complex postural control task.

R Aubonnet1, M Hassan1,2, A Mheich3

  • 1Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland.

Journal of Neural Engineering
|March 9, 2023
PubMed
Summary
This summary is machine-generated.

This study reveals how brain network dynamics change during complex postural control tasks using electroencephalography (EEG). Age significantly impacts these brain network states (BNSs) and their transitions.

Keywords:
EEGbrain network statesclusteringfunctional connectivitypostural control

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Area of Science:

  • Neuroscience
  • Systems Neuroscience
  • Cognitive Neuroscience

Background:

  • Postural control (PC) is a complex sensorimotor function crucial for daily activities.
  • Understanding brain network dynamics during PC is essential for identifying neurological disorders.
  • Virtual reality (VR) and moving platforms offer advanced paradigms for studying PC.

Purpose of the Study:

  • To investigate brain network dynamic remodeling during a complex PC task using electroencephalography (EEG).
  • To identify distinct brain network states (BNSs) and their transitions during the task.
  • To explore the influence of age on these dynamic brain network changes.

Main Methods:

  • Acquired 64-channel EEG data from 158 healthy subjects performing a PC task in a VR environment with a moving platform.
  • Applied advanced source-space EEG network analysis combined with clustering algorithms.
  • Analyzed transitions between different brain network states (BNSs) across task phases.

Main Results:

  • Brain network states (BNSs) distribution coherently described the experimental phases, showing specific transitions between visual, motor, salience, and default mode networks.
  • Age was identified as a significant factor influencing the dynamic transitions of BNSs in the healthy cohort.
  • The study validated an innovative approach for quantifying brain network dynamics in the BioVRSea paradigm.

Conclusions:

  • The developed methodology effectively quantifies brain network dynamics during complex PC tasks.
  • Age-related differences in brain network transitions during PC were observed.
  • This work provides a foundation for developing brain-based biomarkers for PC-related disorders.