SCALab UMR CNRS 9193, Université de Lille, Villeneuve d'Ascq, France.
Laboratory of Biological Networks, Center for Biomedical Technology (UPM), 28223, Pozuelo de Alarcón, Madrid, Spain; Complex Systems Group & G.I.S.C., Universidad Rey Juan Carlos, 28933, Móstoles, Madrid, Spain.
Neuroimage. 2019 Aug 1;196:195-199. doi: 10.1016/j.neuroimage.2019.04.029. Epub 2019 Apr 12.
Synchronization plays a fundamental role in healthy cognitive and motor function. However, how synchronization depends on the interplay between local dynamics, coupling and topology and how prone to synchronization a network is, given its topological organization, are still poorly understood issues. To investigate the synchronizability of both anatomical and functional brain networks various studies resorted to the Master Stability Function (MSF) formalism, an elegant tool which allows analysing the stability of synchronous states in a dynamical system consisting of many coupled oscillators. Here, we argue that brain dynamics does not fulfil the formal criteria under which synchronizability is usually quantified and, perhaps more importantly, this measure refers to a global dynamical condition that never holds in the brain (not even in the most pathological conditions), and therefore no neurophysiological conclusions should be drawn based on it. We discuss the meaning of synchronizability and its applicability to neuroscience and propose alternative ways to quantify brain networks synchronization.
同步在健康的认知和运动功能中起着至关重要的作用。然而，同步如何取决于局部动力学、耦合和拓扑结构之间的相互作用，以及给定其拓扑结构，网络在多大程度上容易受到同步，这些仍然是理解不足的问题。为了研究解剖和功能脑网络的同步能力，许多研究都采用了主稳定性函数（MSF）形式主义，这是一种优雅的工具，可以分析由多个耦合振荡器组成的动力系统中同步状态的稳定性。在这里，我们认为大脑动力学不符合通常用于量化同步能力的正式标准，也许更重要的是，这个度量指的是一种全局动力学条件，在大脑中从未存在过（即使在最病态的情况下也没有），因此，不应该基于它得出任何神经生理学结论。我们讨论了同步能力的含义及其在神经科学中的适用性，并提出了量化脑网络同步的替代方法。
