Reproducibility of power spectrum, functional connectivity and network construction in resting-state EEG
Reproducibility of power spectrum, functional connectivity and network construction in resting-state EEG
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DOI：
10.1016/j.jneumeth.2020.108985
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发表时间：
2021-01-18
影响因子：
3
通讯作者：
Lei, Xu
中科院分区：
文献类型：
--
作者：
Duan, Wei;Chen, Xinyuan;Lei, Xu
背景：静息状态脑电图（rsEEG）的特征提供了认知任务和人格特征的个体差异的相关信息。由于rsEEG的应用越来越广泛，了解几种分析方法的可重复性是至关重要的。新方法：提出了一种基于简化正演模型（SFM）的脑网络构建新方法。此外，我们的目标是在传感器级和源级对功率谱的再现性和功能连通性进行广泛的检查。通过整合源成像、时程提取和网络重构等方法，系统地提出了多条新管道。结果/与现有方法的比较：我们的研究结果表明，闭眼法的重复性略高于睁眼法，相对功率比绝对功率更可重复，特别是在高频波段。无论是功率还是连通性，传感器级的再现性都高于源级。值得注意的是，连接性度量可以根据其可重复性分为两类。值得注意的是，在对体积传导效应不敏感的连通性测量中，功率包络相关（PEC）的重现性通常最高。对于全脑网络的构建，单偶极子建模优于对一个区域的多个偶极子进行均值或主成分分析（PCA）等降维方法。综上所述，我们的研究结果描述了rsEEG功率谱、连通性测量和网络构建的可重复性，可用于评估脑-行为关系的个体间差异，以及自动生物识别应用。
Background: Characteristics from resting-state electroencephalography (rsEEG) provides relevant information about individual differences in cognitive tasks and personality traits. Due to its increasing application, it is crucial to know the reproducibility of several analysis measures of rsEEG.New method: A new brain network construction method was proposed based on simplified forward model (SFM). In addition, we aimed to carry out an extensive examination of the reproducibility of the power spectrum and functional connectivity at both the sensor-level and the source-level. We systematically proposed multiple new pipelines by integration source imaging, time-course extraction and network reconstruction.Results/comparison with existing method(s): Our results revealed that the reproducibility of eyes-closed was slightly higher than that of eyes-open, and the relative power was more repeatable than the absolute power, especially in high-frequency bands. The reproducibility of the sensor-level was higher than that of the source-level, both for power and connectivity. Remarkably, connectivity measures could be separated into two classes according to their reproducibility. Notably, the reproducibility of power envelope correlation (PEC) was generally the highest among those connectivity measures which are insensitive to volume conduction effect. For the whole-brain network construction, single dipole modeling was better than the dimensionality reduction methods, such as mean or principal component analysis (PCA) of multiple dipoles of a region.Conclusions: In conclusion, our results described the reproducibility of rsEEG power spectrum, connectivity measures, and network constructions, which could be considered in assessing inter-individual differences in brain-behavior relationships, as well as automatic biometric applications.