Markers of criticality in phase synchronization.
Markers of criticality in phase synchronization.
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DOI：
10.3389/fnsys.2014.00176
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发表时间：
2014
影响因子：
3
通讯作者：
Berthouze L
中科院分区：
文献类型：
--
作者：
Botcharova M;Farmer SF;Berthouze L
大脑作为一个关键的动力系统的概念非常有吸引力，因为接近临界的系统被认为是信息处理和交流的最大动态范围。到目前为止，已经有两个关键的实验观察支持这一假说：(I)神经元雪崩具有大小的幂规律分布和(Ii)神经振荡幅度的长期时间相关性(LRTC)。关于这些如何使信息处理和通信的动态范围最大化的问题仍在讨论中，由于信息编码和传输的一个重要基础是神经同步，因此将同步措施与关键措施联系起来是有意义的。我们提出了一个刻画同步临界性的框架，该框架基于对基于LRTC的相位同步的时刻到时刻的波动的分析。这一框架依赖于对相位差变化率的估计和我们开发的一套检测LRTC的方法。我们针对两个经典的临界性模型(Ising和Kuramoto)测试了这个框架，并在最近描述了这些模型的变体，旨在更接近地代表人脑动力学。从这些模拟中，我们确定了这些系统在相位同步中显示LRTC证据的参数。我们通过对人类同时脑电和肌电时间序列的分析论证了原理的证明，表明在静息状态下可以检测到大脑皮层相位同步的LRTCs并进行实验操作。相同步涨落中LRTC的存在表明，这些涨落是由非局域行为控制的，所有尺度都对系统行为有贡献。这对于预期LRTC处于相位同步的条件有重要的影响。具体地说，大脑休息状态可能表现出LRTC，反映了一种准备状态，促进了与任务相关的快速向和远离废除LRTC的同步状态的转变。
The concept of the brain as a critical dynamical system is very attractive because systems close to criticality are thought to maximize their dynamic range of information processing and communication. To date, there have been two key experimental observations in support of this hypothesis: (i) neuronal avalanches with power law distribution of size and (ii) long-range temporal correlations (LRTCs) in the amplitude of neural oscillations. The case for how these maximize dynamic range of information processing and communication is still being made and because a significant substrate for information coding and transmission is neural synchrony it is of interest to link synchronization measures with those of criticality. We propose a framework for characterizing criticality in synchronization based on an analysis of the moment-to-moment fluctuations of phase synchrony in terms of the presence of LRTCs. This framework relies on an estimation of the rate of change of phase difference and a set of methods we have developed to detect LRTCs. We test this framework against two classical models of criticality (Ising and Kuramoto) and recently described variants of these models aimed to more closely represent human brain dynamics. From these simulations we determine the parameters at which these systems show evidence of LRTCs in phase synchronization. We demonstrate proof of principle by analysing pairs of human simultaneous EEG and EMG time series, suggesting that LRTCs of corticomuscular phase synchronization can be detected in the resting state and experimentally manipulated. The existence of LRTCs in fluctuations of phase synchronization suggests that these fluctuations are governed by non-local behavior, with all scales contributing to system behavior. This has important implications regarding the conditions under which one should expect to see LRTCs in phase synchronization. Specifically, brain resting states may exhibit LRTCs reflecting a state of readiness facilitating rapid task-dependent shifts toward and away from synchronous states that abolish LRTCs.