Interacting brains revisited: A cross-brain network neuroscience perspective

Area of Science:

• Neuroscience
• Computational Neuroscience
• Network Science

Background:

• Understanding the neural basis of social behavior requires analyzing interacting brains, but current theoretical frameworks are limited.
• Existing hyperscanning analyses often rely on global metrics, lacking detailed regional insights.

Purpose of the Study:

• To propose a comprehensive bipartite graph framework for interbrain networks.
• To investigate if this framework offers meaningful insights into the neural underpinnings of social interactions.
• To explore the predictive power of network representations for individual social characteristics.

Main Methods:

• Utilizing bipartite graphs to model interbrain networks from hyperscanning data.
• Employing matrix decomposition to derive interpretable network representations with global and local insights.
• Applying Bayesian modeling to analyze synchrony patterns and their contribution to global effects.

Main Results:

• Nodal density in bipartite graphs of interbrain networks exhibits nonrandom properties.
• Matrix decomposition provides interpretable network insights, surpassing global metrics.
• Bayesian modeling identifies specific brain regions seeding synchrony patterns that influence global network effects.
• Graph representations outperform traditional functional connectivity estimators in predicting individual social characteristics.

Conclusions:

• The proposed bipartite graph framework offers a powerful tool for analyzing social brain interactions.
• This approach yields both global and local network insights, advancing hyperscanning analysis.
• Network representations derived from this framework can predict individual social characteristics, suggesting potential for biomarker discovery.