Tracking EEG network dynamics through transitions between eyes-closed, eyes-open, and task states

Area of Science:

• Neuroscience
• Computational Neuroscience
• Brain Network Dynamics

Background:

• Understanding brain state transitions is crucial for cognitive neuroscience.
• Dynamic functional connectivity analysis offers insights into brain network reorganization.
• Chronnectomics provides a framework for studying time-varying brain networks.

Purpose of the Study:

• To apply chronnectomics for reconstructing dynamic network transitions between eyes-closed rest, eyes-open rest, and task states.
• To investigate dynamic changes in functional connectivity during transitions between different brain states.
• To analyze coupling strength and variability in alpha and beta frequencies during state changes.

Main Methods:

• Dense electroencephalography (EEG) recordings were utilized.
• Source-level time-courses of EEG signals were reconstructed.
• Functional connectivity was quantified using the phase lag index (PLI).
• Dynamic analyses focused on coupling strength and variability in alpha and beta bands.

Main Results:

• Significant, dynamically specific transitions were observed in the alpha band during eye-opening/closing and eyes-closed-to-task shifts.
• These transitions involved global, default mode, and central executive networks.
• Eye-opening decreased connectivity strength and variability faster than synchronization increased, indicating different reorganization times.
• Resting state network characteristics differ depending on whether eyes are open or closed.

Conclusions:

• Chronnectomics is effective for mapping dynamic brain network transitions.
• Alpha band activity plays a key role in state-dependent network reorganization.
• The temporal dynamics of network transitions vary, with distinct reorganization periods.
• The definition of resting state networks is influenced by ocular condition (eyes-open vs. eyes-closed).