Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity

TL;DR

This paper reviews the emerging field of network neuroscience, highlighting how graph theory tools are used to understand, diagnose, and intervene in complex neural systems across multiple scales, advancing neuroengineering applications.

Contribution

It introduces the application of network science to neuroengineering, emphasizing new computational and theoretical frontiers for understanding brain connectivity.

Findings

Network analysis models human brain imaging data.

Potential pitfalls in current network modeling.

Emerging computational and theoretical tools for brain research.

Abstract

Neuroengineering is faced with unique challenges in repairing or replacing complex neural systems that are composed of many interacting parts. These interactions form intricate patterns over large spatiotemporal scales, and produce emergent behaviors that are difficult to predict from individual elements. Network science provides a particularly appropriate framework in which to study and intervene in such systems, by treating neural elements (cells, volumes) as nodes in a graph and neural interactions (synapses, white matter tracts) as edges in that graph. Here, we review the emerging discipline of network neuroscience, which uses and develops tools from graph theory to better understand and manipulate neural systems, from micro- to macroscales. We present examples of how human brain imaging data is being modeled with network analysis and underscore potential pitfalls. We then highlight current computational and theoretical frontiers, and emphasize their utility in informing diagnosis and monitoring, brain-machine interfaces, and brain stimulation. A flexible and rapidly evolving enterprise, network neuroscience provides a set of powerful approaches and fundamental insights critical to the neuroengineer's toolkit.