Information-Based Principle Induces Small-World Topology and Self-Organized Criticality in a Large Scale Brain...

Area of Science:

• Neuroscience
• Network Science
• Information Theory

Background:

• Human brain information processing is crucial for cognitive functions and likely optimized under biological constraints.
• Brain networks exhibit characteristics like self-organized criticality and small-world topology, but their link to information optimization is unclear.

Purpose of the Study:

• To investigate if information optimization principles, specifically mutual information entropy maximization, explain the emergence of brain network dynamics and topology.
• To unify the understanding of self-organized criticality and small-world topology within a single information-based framework.

Main Methods:

• Analysis of functional connectome data from the human brain.
• Investigating the relationship between increasing mutual information entropy and network properties.
• Identifying phase transitions in network topology and activation dynamics.

Main Results:

• Mutual information entropy maximization induces an optimal state in the brain network.
• Self-organized criticality in brain dynamics and small-world network topology emerge simultaneously at this optimal state.
• Phase transitions for both dynamics and topology occur at the same critical point, driven by mutual information maximization.

Conclusions:

• Self-organized criticality and small-world topology are fundamentally linked and arise from the same information-based principle.
• Mutual information maximization provides a unified perspective for understanding these key brain network attributes.
• This study offers insights into the underlying mechanisms of information processing in the human brain.