Edges in brain networks: Contributions to models of structure and function

Area of Science:

• Neuroscience
• Network Science
• Computational Biology

Background:

• Brain organization is often modeled using network science, with a focus on nodes and their specialized functions.
• Node functions are intrinsically linked to the complex web of interrelationships, represented by network edges.
• Current research predominantly emphasizes node-centric perspectives, potentially overlooking critical information conveyed by edges.

Purpose of the Study:

• To underscore the significant contributions of brain network edges to understanding distributed brain organization.
• To explore how different definitions and types of edges impact network analysis and interpretation.
• To highlight the potential of edges to reveal higher-order, dynamic, and community-based network topology.

Main Methods:

• Network analysis of brain data, focusing on the properties and relationships of network edges.
• Comparative analysis of network interpretations based on varying edge definitions (e.g., connectivity, similarity).
• Investigation of edge collectives, higher-order arrangements, time series data, and edge communities.

Main Results:

• Different edge types and definitions fundamentally alter brain network analysis and interpretation.
• Edges can form higher-order structures and communities, offering insights complementary to node-centric approaches.
• Focusing on edges reveals previously underappreciated aspects of structural and functional brain network organization.

Conclusions:

• Brain network edges are critical for a comprehensive understanding of brain organization, complementing traditional node-focused research.
• Edge-centric analysis provides novel perspectives on brain topology, dynamics, and function.
• Future research should integrate edge-based analyses to fully capture the complexity of brain networks.