Edges in Brain Networks: Contributions to Models of Structure and Function

TL;DR

This paper emphasizes the crucial role of edges in brain network models, highlighting how different types of edges and their higher-order arrangements offer new insights into brain structure and function beyond traditional node-centric approaches.

Contribution

It introduces a focus on edges and their higher-order and dynamic properties, revealing underappreciated aspects of brain network organization.

Findings

Edges represent diverse relationships like connectivity and similarity.

Edge communities reveal new topological insights.

Dynamic and higher-order edges enhance understanding of brain organization.

Abstract

Network models describe the brain as sets of nodes and edges that represent its distributed organization. So far, most discoveries in network neuroscience have prioritized insights that highlight distinct groupings and specialized functional contributions of network nodes. Importantly, these functional contributions are determined and expressed by the web of their interrelationships, formed by network edges. Here, we underscore the important contributions made by brain network edges for understanding distributed brain organization. Different types of edges represent different types of relationships, including connectivity and similarity among nodes. Adopting a specific definition of edges can fundamentally alter how we analyze and interpret a brain network. Furthermore, edges can associate into collectives and higher-order arrangements, describe time series, and form edge communities that provide insights into brain network topology complementary to the traditional node-centric perspective. Focusing on the edges, and the higher-order or dynamic information they can provide, discloses previously underappreciated aspects of structural and functional network organization.