Living on the edge: network neuroscience beyond nodes

Published on September 15, 2023

In the vast landscape of neural networks, scientists have primarily focused on studying the individual elements – cells, populations, and regions. However, this has overlooked the crucial aspect of how these elements are interconnected. A fresh perspective known as the ‘edge-centric’ approach is emerging in the field of network neuroscience. Instead of solely examining the nodes or elements themselves, researchers are now delving into the connections between them, also referred to as ‘edges.’ This new method is promising in unraveling unresolved questions in network neuroscience. By emphasizing the ‘edges,’ scientists can gain deeper insights into the anatomical and functional links between neural elements. The ‘edge-centric’ approach creates a more comprehensive understanding of how different elements work together within a network. This method has already been applied in various studies, and its strengths and limitations have been assessed. Researchers are also drawing connections between this approach and other existing methods in network science and neuroimaging. The possibilities for future research to expand and refine this edge-centric analysis are numerous. To explore this fascinating field further, delve into the underlying research!

Network neuroscience has emphasized the connectional properties of neural elements – cells, populations, and regions. This has come at the expense of the anatomical and functional connections that link these elements to one another. A new perspective – namely one that emphasizes ‘edges’ – may prove fruitful in addressing outstanding questions in network neuroscience. We highlight one recently proposed ‘edge-centric’ method and review its current applications, merits, and limitations. We also seek to establish conceptual and mathematical links between this method and previously proposed approaches in the network science and neuroimaging literature. We conclude by presenting several avenues for future work to extend and refine existing edge-centric analysis.

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