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Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

Cross-frequency coupling in real and virtual brain networks.

Viktor Jirsa¹, Viktor Müller

¹Institut de Neurosciences des Systèmes, Faculté de Médecine, Aix-Marseille Université, Inserm UMR1106 Marseille, France.

Frontiers in Computational Neuroscience

|July 11, 2013

Summary

Cross-frequency coupling (CFC) measures, including bispectrum and bicoherence, effectively reveal brain network dynamics. Delta-alpha CFC is prominent in resting state electroencephalography (EEG), differing between eyes open and closed conditions.

Keywords:

bicoherence bispectrum connectome cross-frequency coupling full brain model resting state simulation the virtual brain

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Area of Science:

Neuroscience
Computational Neuroscience
Signal Processing

Background:

Brain information processing involves synchronized and desynchronized oscillatory behaviors.
Cross-frequency coupling (CFC) represents complex information flow and is vital for large-scale network organization.

Purpose of the Study:

To describe and test the applicability of various CFC measures.
To evaluate CFC detection in simulated and real electroencephalographic (EEG) data under noise conditions.

Main Methods:

Derived generic oscillator equations from brain network models.
Simulated CFC scenarios with noise to model realistic brain dynamics.
Applied CFC measures, including bispectrum (BIS) and bicoherence (BIC), to simulated and resting-state EEG data.

Main Results:

Specific CFC measures accurately detected CFC under noise.
BIS and BIC effectively identified CFC in simulated data.
Resting-state EEG revealed significant delta-alpha CFC, often asymmetric and higher with eyes closed.

Conclusions:

CFC measures, particularly BIS and BIC, offer a robust toolkit for analyzing brain network couplings.
Delta-alpha CFC in resting-state EEG provides insights into brain state-dependent information processing.