脑网络的通信和控制模型：区别，收敛和未来展望
Models of communication and control for brain networks: distinctions, convergence, and future outlook
摘要：
最近在人脑中信号传播和路由的计算模型方面的进展强调了白质结构的关键作用。一种补充方法利用网络控制理论的框架来更好地理解白质如何约束一个区域或一组区域可以指导或控制其他区域活动的方式。尽管这两种方法都有可能增强我们对网络结构在大脑功能中的作用的理解，但很少有工作试图了解它们之间的关系。在这里，我们寻求明确的桥梁计算模型的通信和网络控制的原则，在目前的文献的概念审查。通过绘制通信和控制模型之间的抽象层次，动态复杂性，对网络属性的依赖性，以及多个时空尺度的相互作用的比较，我们突出了这两个框架之间的收敛性和区别。基于对人脑网络中通信和控制相互交织本质的理解，这项工作为该领域提供了综合视角，并概述了未来工作的令人兴奋的方向。
英文摘要：
Recent advances in computational models of signal propagation and routing in the human brain have underscored the critical role of white matter structure. A complementary approach has utilized the framework of network control theory to better understand how white matter constrains the manner in which a region or set of regions can direct or control the activity of other regions. Despite the potential for both of these approaches to enhance our understanding of the role of network structure in brain function, little work has sought to understand the relations between them. Here, we seek to explicitly bridge computational models of communication and principles of network control in a conceptual review of the current literature. By drawing comparisons between communication and control models in terms of the level of abstraction, the dynamical complexity, the dependence on network attributes, and the interplay of multiple spatiotemporal scales, we highlight the convergence of and distinctions between the two frameworks. Based on the understanding of the intertwined nature of communication and control in human brain networks, this work provides an integrative perspective for the field and outlines exciting directions for future work.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
