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亚稳定性指标反映了脑刺激后大脑动态工作点的全局变化。

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

作者信息

Bapat Rishabh, Pathak Anagh, Banerjee Arpan

机构信息

Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, Haryana, India.

出版信息

Front Neurorobot. 2024 Feb 19;18:1336438. doi: 10.3389/fnbot.2024.1336438. eCollection 2024.

DOI:10.3389/fnbot.2024.1336438
PMID:38440318
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10909933/
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.

摘要

多项研究表明,神经集群之间的协调是理解人类认知的关键。一条已被充分探索的途径是从脑电图(EEG)或脑磁图(MEG)振荡的频谱变化中识别与认知功能相关的协调状态。越来越多的研究表明,在协调状态之间切换的倾向塑造了大脑的动态功能库,并且可以通过一种称为亚稳定性的指标来衡量。在本文中,我们描述了经颅磁刺激(TMS)后全脑网络动力学亚稳定性的扰动,这种扰动可以量化信息处理改变的持续时间。从而使研究人员能够理解脑刺激的网络效应,规范刺激方案并设计实验任务。我们使用公开可用的数据集通过实验证明了这种效应,并使用数字孪生(全脑连接组模型)来理解产生此类观察结果的动态原理。我们观察到,单脉冲TMS后亚稳定性显著降低,同时相干性增加,这反映了存在一个神经协调发生改变的窗口。基于标记微状态脑电图数据的Lempel-Ziv复杂度的另一种测量方法验证了复杂性的降低。有趣的是,EEG信号中较高频率的亚稳定性恢复速度比较低频率更快。数字孪生揭示了单脉冲TMS在局部皮层网络中引入的相位重置如何在全局传播,从而导致亚稳定性和相干性的变化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99d3/10909933/cf94ff029911/fnbot-18-1336438-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99d3/10909933/58cbc7837b88/fnbot-18-1336438-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99d3/10909933/4668236b9bc6/fnbot-18-1336438-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99d3/10909933/54c956006dce/fnbot-18-1336438-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99d3/10909933/5d482774aaea/fnbot-18-1336438-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99d3/10909933/7966d80aff34/fnbot-18-1336438-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99d3/10909933/cf94ff029911/fnbot-18-1336438-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99d3/10909933/58cbc7837b88/fnbot-18-1336438-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99d3/10909933/4668236b9bc6/fnbot-18-1336438-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99d3/10909933/54c956006dce/fnbot-18-1336438-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99d3/10909933/5d482774aaea/fnbot-18-1336438-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99d3/10909933/7966d80aff34/fnbot-18-1336438-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99d3/10909933/cf94ff029911/fnbot-18-1336438-g0006.jpg

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Multiscale dynamic mean field (MDMF) model relates resting-state brain dynamics with local cortical excitatory-inhibitory neurotransmitter homeostasis.多尺度动态平均场(MDMF)模型将静息态脑动力学与局部皮质兴奋性-抑制性神经递质稳态联系起来。
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