Metastability indexes global changes in the dynamic working point of the brain following brain stimulation

IF 2.6 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Rishabh Bapat, Anagh Pathak, Arpan Banerjee
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Abstract

Several studies have shown that coordination among neural ensembles is a key to understand human cognition. A well charted path is to identify coordination states associated with cognitive functions from spectral changes in the oscillations of EEG or MEG. A growing number of studies suggest that the tendency to switch between coordination states, sculpts the dynamic repertoire of the brain and can be indexed by a measure known as metastability. In this article, we characterize perturbations in the metastability of global brain network dynamics following Transcranial Magnetic Stimulation that could quantify the duration for which information processing is altered. Thus allowing researchers to understand the network effects of brain stimulation, standardize stimulation protocols and design experimental tasks. We demonstrate the effect empirically using publicly available datasets and use a digital twin (a whole brain connectome model) to understand the dynamic principles that generate such observations. We observed a significant reduction in metastability, concurrent with an increase in coherence following single-pulse TMS reflecting the existence of a window where neural coordination is altered. The reduction in complexity was validated by an additional measure based on the Lempel-Ziv complexity of microstate labeled EEG data. Interestingly, higher frequencies in the EEG signal showed faster recovery in metastability than lower frequencies. The digital twin shed light on how the phase resetting introduced by the single-pulse TMS in local cortical networks can propagate globally, giving rise to changes in metastability and coherence.

脑刺激后大脑动态工作点全球变化的可转移性指数
多项研究表明,神经组合之间的协调是理解人类认知的关键。从脑电图(EEG)或脑电图(MEG)振荡的频谱变化中识别与认知功能相关的协调状态,是一条行之有效的途径。越来越多的研究表明,在协调状态之间切换的趋势是大脑动态剧目的标志,可以用一种被称为 "易变性 "的指标来衡量。在这篇文章中,我们描述了经颅磁刺激后全球大脑网络动态可变性的扰动特征,它可以量化信息处理被改变的持续时间。这样,研究人员就能了解脑刺激的网络效应、规范刺激方案和设计实验任务。我们利用公开的数据集通过经验证明了这种效应,并使用数字孪生(全脑连接组模型)来理解产生这种观察结果的动态原理。我们观察到单脉冲经颅磁刺激后,可变性明显降低,同时一致性增加,这反映出存在一个改变神经协调的窗口。基于微状态标记脑电图数据的 Lempel-Ziv 复杂性的额外测量验证了复杂性的降低。有趣的是,脑电信号中的高频率比低频率显示出更快的转移性恢复。这对数字孪生子揭示了单脉冲 TMS 在局部皮层网络中引入的相位重置如何在全球范围内传播,从而引起可变性和一致性的变化。
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来源期刊
Frontiers in Neurorobotics
Frontiers in Neurorobotics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCER-ROBOTICS
CiteScore
5.20
自引率
6.50%
发文量
250
审稿时长
14 weeks
期刊介绍: Frontiers in Neurorobotics publishes rigorously peer-reviewed research in the science and technology of embodied autonomous neural systems. Specialty Chief Editors Alois C. Knoll and Florian Röhrbein at the Technische Universität München are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neural systems include brain-inspired algorithms (e.g. connectionist networks), computational models of biological neural networks (e.g. artificial spiking neural nets, large-scale simulations of neural microcircuits) and actual biological systems (e.g. in vivo and in vitro neural nets). The focus of the journal is the embodiment of such neural systems in artificial software and hardware devices, machines, robots or any other form of physical actuation. This also includes prosthetic devices, brain machine interfaces, wearable systems, micro-machines, furniture, home appliances, as well as systems for managing micro and macro infrastructures. Frontiers in Neurorobotics also aims to publish radically new tools and methods to study plasticity and development of autonomous self-learning systems that are capable of acquiring knowledge in an open-ended manner. Models complemented with experimental studies revealing self-organizing principles of embodied neural systems are welcome. Our journal also publishes on the micro and macro engineering and mechatronics of robotic devices driven by neural systems, as well as studies on the impact that such systems will have on our daily life.
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