Bassett Danielle S, Khambhati Ankit N, Grafton Scott T
Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104.
Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104.
Annu Rev Biomed Eng. 2017 Jun 21;19:327-352. doi: 10.1146/annurev-bioeng-071516-044511. Epub 2017 Mar 27.
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 are 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 that are critical for the neuroengineer's tool kit.
神经工程在修复或替换由许多相互作用部分组成的复杂神经系统时面临着独特的挑战。这些相互作用在大的时空尺度上形成复杂的模式，并产生从单个元素难以预测的涌现行为。网络科学提供了一个特别合适的框架，通过将神经元素（细胞、区域）视为图中的节点，将神经相互作用（突触、白质束）视为该图中的边，来研究和干预此类系统。在这里，我们回顾网络神经科学这一新兴学科，它使用并发展图论工具，以更好地从微观到宏观尺度理解和操纵神经系统。我们展示了如何用网络分析对人类脑成像数据进行建模的例子，并强调了潜在的陷阱。然后，我们突出当前的计算和理论前沿，并强调它们在为诊断和监测、脑机接口以及脑刺激提供信息方面的效用。作为一个灵活且快速发展的领域，网络神经科学提供了一套强大的方法和基本见解，这些对于神经工程师的工具包至关重要。
