Structure Shapes Dynamics and Directionality in Diverse Brain Networks: Mathematical Principles and Empirical...

Area of Science:

• Neuroscience
• Computational Neuroscience
• Network Science

Background:

• Understanding higher cognitive functions requires knowing how the brain integrates spatially distributed information.
• Brain network structure significantly influences brain network function, but analytical methods explaining this link are limited.
• Existing research lacks clear explanations for how network topology shapes regional brain activity and information flow directionality.

Purpose of the Study:

• To develop analytical methods for explaining the role of brain network structure in shaping regional activities and directionality patterns.
• To mathematically derive the emergence of information flow directionality from underlying brain network architecture.
• To validate these analytical methods using computational models and empirical data from human, macaque, and mouse brains.

Main Methods:

• Application of analytical methods to a coupled oscillator model within inhomogeneous networks.
• Derivation of a mathematical principle linking brain network structure to information flow directionality.
• Testing the predictive power of the derived principle on anatomical brain networks and electroencephalographic data.

Main Results:

• A mathematical principle was derived, explaining how brain network structure generates directional information flow.
• The analytical methods successfully predicted simulation data and empirical electroencephalographic (EEG) data across species.
• Global directionality patterns in resting-state brain networks were shown to be predictable based solely on network structure.

Conclusions:

• Brain network structure is a primary determinant of neural information directionality.
• The developed analytical framework provides a foundation for understanding information integration in complex brain networks.
• This approach offers a novel way to predict brain activity patterns from anatomical connectivity data.