![]() ![]() ![]() What is the fundamental difference that causes neuroscientific data to be so challenging? Is it a lack of a mechanistic understanding of how the brain works ( 9, 10)? Or is it an inability to physically construct the hardware required to liaise with neural systems for effective interventions ( 5)? We argue that fundamental to both of these problems is the challenge of dealing with complex relational data ( 11). However, in many other arenas, the amount of data being gathered has not posed an insurmountable obstacle. In the era of big data, neural systems are no exception to the rule of ever-increasing petabytes streaming into servers around the world ( 8). Particularly exciting frontiers of neuroengineering include neuroimaging neural interfaces neural prosthetics and robotics and more general techniques for regeneration, enhancement, and refinement of neural systems ( 7). A flexible and rapidly evolving enterprise, network neuroscience provides a set of powerful approaches and fundamental insights that are critical for the neuroengineer's tool kit.Ĭould we graft new connections into the brain to give someone back the abilities they had preinjury ( 1)? Could we decode the thoughts of someone who is caged inside their own body ( 2, 3)? Could we develop adaptive brain–computer interfaces that evolve and adapt to remain effective for a child whose brain is continuously developing ( 4, 5)? Answering these and many other seemingly overambitious questions is the fundamental aim of neuroengineering ( 6), a relatively new domain of biomedical engineering that develops and uses computational and empirical techniques to understand and modulate the properties of neural systems. We then highlight current computational and theoretical frontiers and emphasize their utility in informing diagnosis and monitoring, brain–machine interfaces, and brain stimulation. We present examples of how human brain imaging data are being modeled with network analysis and underscore potential pitfalls. 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. 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. These interactions form intricate patterns over large spatiotemporal scales and produce emergent behaviors that are difficult to predict from individual elements. Neuroengineering is faced with unique challenges in repairing or replacing complex neural systems that are composed of many interacting parts. ![]()
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