Novel Brain Complexity Measures Based on Information Theory.

Area of Science:

• Neuroscience
• Network Science
• Information Theory

Background:

• Brain networks model brain topology and organization using graph theory.
• Current topological measures for brain networks lack clarity on optimal representation.
• Information theory offers a novel lens for analyzing complex network structures.

Purpose of the Study:

• To propose a new set of information theory-based measures for characterizing brain network topology.
• To provide a theoretical framework interpreting brain networks as stochastic processes.
• To derive and evaluate novel global and local network measures.

Main Methods:

• Interpreting brain networks as stochastic processes with random walks on graph nodes.
• Deriving global measures: entropy, mutual information, and erasure mutual information.
• Deriving local measures: entropic surprise, mutual surprise, mutual predictability, and erasure surprise.

Main Results:

• Global measures reveal new topological properties of brain networks.
• An optimal edge density for small-world networks was identified.
• Local measures quantify node uncertainty and path uniqueness.
• Results were consistent across synthetic and human structural/functional brain networks.

Conclusions:

• The proposed information theory measures provide a robust framework for brain network analysis.
• Novel global and local measures offer deeper insights into brain organization and node characteristics.
• The approach demonstrates consistency and robustness across different network types and subjects.