Structural and functional brain networks: from connections to cognition

Area of Science:

• Neuroscience
• Computational Neuroscience
• Network Science

Background:

• The emergence of complex brain functions from underlying structural architecture is a key neuroscience challenge.
• Network theory and computational neuroscience offer frameworks for analyzing large-scale brain networks.
• Hierarchical modular networks are proposed to support both segregated local processing and integrated global function.

Purpose of the Study:

• To investigate the relationship between static structural brain networks and dynamic functional networks.
• To explore how hierarchical modularity facilitates cognitive functions.
• To highlight the importance of degeneracy in brain network organization.

Main Methods:

• Application of network theory to large-scale brain network analysis.
• Theoretical neuroscience modeling of neuronal information processing.
• Analysis of the divergence between structural and functional brain networks during cognitive tasks.

Main Results:

• Hierarchical modular brain networks are well-suited for local neuronal operations and global function integration.
• Functional brain networks diverge from structural connections during cognition, demonstrating context-sensitivity.
• Degenerate mapping (many-to-one) between function and structure is critical for brain network understanding.

Conclusions:

• Dynamic functional networks emerge from static structural connections through complex interactions.
• A formal computational approach is needed to resolve the structure-function relationship in the brain.
• Understanding degeneracy is key to deciphering how the brain supports rich cognitive functions.