论文标题

大脑网络的沟通和控制模型:区别,融合和未来前景

Models of communication and control for brain networks: distinctions, convergence, and future outlook

论文作者

Srivastava, Pragya, Nozari, Erfan, Kim, Jason Z., Ju, Harang, Zhou, Dale, Becker, Cassiano, Pasqualetti, Fabio, Bassett, Danielle S.

论文摘要

人脑信号传播和路由的计算模型的最新进展强调了白质结构的关键作用。一种互补的方法利用网络控制理论的框架更好地理解白色物质如何限制一个区域或一组区域可以指导或控制其他地区活动的方式。尽管这两种方法都具有增强我们对网络结构在大脑功能中的作用的理解的潜力,但很少有工作试图理解它们之间的关系。在这里,我们试图在当前文献的概念综述中明确桥接网络控制的通信和网络控制原理的计算模型。通过根据抽象的水平,动力学复杂性,对网络属性的依赖性以及多个时空量表的相互作用来进行交流和控制模型之间的比较,我们突出了两个框架之间的收敛性和区别。基于对人脑网络中交流和控制的交织性质的理解,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。

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.

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