Lehnertz Klaus, Bröhl Timo, Rings Thorsten
Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany.
Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany.
Front Physiol. 2020 Dec 21;11:598694. doi: 10.3389/fphys.2020.598694. eCollection 2020.
The field of Network Physiology aims to advance our understanding of how physiological systems and sub-systems interact to generate a variety of behaviors and distinct physiological states, to optimize the organism's functioning, and to maintain health. Within this framework, which considers the human organism as an integrated network, vertices are associated with organs while edges represent time-varying interactions between vertices. Likewise, vertices may represent networks on smaller spatial scales leading to a complex mixture of interacting homogeneous and inhomogeneous networks of networks. Lacking adequate analytic tools and a theoretical framework to probe interactions within and among diverse physiological systems, current approaches focus on inferring properties of time-varying interactions-namely strength, direction, and functional form-from time-locked recordings of physiological observables. To this end, a variety of bivariate or, in general, multivariate time-series-analysis techniques, which are derived from diverse mathematical and physical concepts, are employed and the resulting time-dependent networks can then be further characterized with methods from network theory. Despite the many promising new developments, there are still problems that evade from a satisfactory solution. Here we address several important challenges that could aid in finding new perspectives and inspire the development of theoretic and analytical concepts to deal with these challenges and in studying the complex interactions between physiological systems.
网络生理学领域旨在深化我们对生理系统及子系统如何相互作用以产生各种行为和不同生理状态、优化机体功能并维持健康的理解。在此将人体视为一个整合网络的框架内,节点与器官相关联,而边代表节点之间随时间变化的相互作用。同样,节点可能代表较小空间尺度上的网络,从而形成相互作用的同质性和非均匀性网络的复杂混合。由于缺乏足够的分析工具和理论框架来探究不同生理系统内部及之间的相互作用,当前的方法侧重于从生理可观测指标的锁时记录中推断随时间变化的相互作用的特性,即强度、方向和功能形式。为此,人们采用了多种源自不同数学和物理概念的双变量或一般多变量时间序列分析技术,然后可以用网络理论方法进一步表征由此产生的随时间变化的网络。尽管有许多前景广阔的新进展,但仍存在一些问题难以得到令人满意的解决。在此,我们探讨几个重要挑战,这些挑战有助于找到新的视角,并激发理论和分析概念的发展,以应对这些挑战,并研究生理系统之间的复杂相互作用。