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Graph theory in brain-to-brain connectivity: A simulation study and an application to an EEG hyperscanning experiment
Summary
Graph theory indices can analyze brain-to-brain networks in hyperscanning studies. Global efficiency and path length effectively capture interaction dynamics and asymmetry between subjects during social tasks.
Area of Science:
- Neuroscience
- Social Neuroscience
- Network Science
Background:
- Hyperscanning enables simultaneous neural recording from multiple interacting individuals.
- Existing connectivity methods are adapted for hyperscanning, but graph theory application to multi-subject networks remains challenging.
Purpose of the Study:
- To evaluate graph theory global indices for characterizing two-subject brain-to-brain networks.
- To assess the sensitivity of these indices to interaction levels and communication asymmetry.
Main Methods:
- Simulated surrogate brain-to-brain networks representing social interactions.
- Real electroencephalography (EEG) hyperscanning data from a Joint Action task.
- Analysis using established graph theory global indices.
Main Results:
- All tested graph theory indices modulated with interaction levels in simulations.
- Global efficiency and path length specifically detected communication asymmetry.
- Real EEG data confirmed that global efficiency reflects inter-brain connectivity density, higher in social conditions.
Conclusions:
- Graph theory global indices are valuable for analyzing hyperscanning data.
- Global efficiency and path length offer insights into the dynamics and asymmetry of social interactions.
- These findings advance the application of network science to understand social brain function.
