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连续婴儿 EEG 的微状态分析:教程与可靠性。

Microstate Analysis of Continuous Infant EEG: Tutorial and Reliability.

机构信息

Department of Psychology & Neuroscience, Duke University, Reuben-Cooke Building, 417 Chapel Drive, Durham, NC, 27708, USA.

Department of Basic Neurosciences, University of Geneva, Campus Biotech, 9 Chemin des Mines, Geneva, 1202, Switzerland.

出版信息

Brain Topogr. 2024 Jul;37(4):496-513. doi: 10.1007/s10548-024-01043-5. Epub 2024 Mar 2.

Abstract

Microstate analysis of resting-state EEG is a unique data-driven method for identifying patterns of scalp potential topographies, or microstates, that reflect stable but transient periods of synchronized neural activity evolving dynamically over time. During infancy - a critical period of rapid brain development and plasticity - microstate analysis offers a unique opportunity for characterizing the spatial and temporal dynamics of brain activity. However, whether measurements derived from this approach (e.g., temporal properties, transition probabilities, neural sources) show strong psychometric properties (i.e., reliability) during infancy is unknown and key information for advancing our understanding of how microstates are shaped by early life experiences and whether they relate to individual differences in infant abilities. A lack of methodological resources for performing microstate analysis of infant EEG has further hindered adoption of this cutting-edge approach by infant researchers. As a result, in the current study, we systematically addressed these knowledge gaps and report that most microstate-based measurements of brain organization and functioning except for transition probabilities were stable with four minutes of video-watching resting-state data and highly internally consistent with just one minute. In addition to these results, we provide a step-by-step tutorial, accompanying website, and open-access data for performing microstate analysis using a free, user-friendly software called Cartool. Taken together, the current study supports the reliability and feasibility of using EEG microstate analysis to study infant brain development and increases the accessibility of this approach for the field of developmental neuroscience.

摘要

静息态 EEG 的微状态分析是一种独特的数据驱动方法,用于识别头皮潜在地形图的模式,或微状态,反映了随时间动态演化的稳定但短暂的同步神经活动期。在婴儿期 - 大脑快速发育和可塑性的关键时期 - 微状态分析为描述大脑活动的空间和时间动态提供了独特的机会。然而,从这种方法得出的测量值(例如,时间特性、转移概率、神经源)在婴儿期是否具有强心理计量特性(即可靠性)尚不清楚,这是推进我们对微状态如何受早期生活经验塑造以及它们是否与婴儿能力的个体差异相关的理解的关键信息。缺乏用于执行婴儿 EEG 微状态分析的方法学资源进一步阻碍了婴儿研究人员采用这种前沿方法。因此,在当前的研究中,我们系统地解决了这些知识空白,并报告说,除了转移概率之外,大脑组织和功能的大多数基于微状态的测量值在观看四分钟视频的静息状态数据时是稳定的,并且仅用一分钟即可高度内部一致。除了这些结果,我们还提供了一个分步教程、配套网站和用于使用名为 Cartool 的免费、用户友好的软件进行微状态分析的开放访问数据。总之,当前的研究支持使用 EEG 微状态分析来研究婴儿大脑发育的可靠性和可行性,并增加了该方法在发展神经科学领域的可及性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad2d/11199263/1f5c6668fb3a/10548_2024_1043_Fig1_HTML.jpg

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