首页> 美国卫生研究院文献>Frontiers in Neuroscience >Quantification of Phase-Amplitude Coupling in Neuronal Oscillations: Comparison of Phase-Locking Value, Mean Vector Length, Modulation Index, and Generalized-Linear-Modeling-Cross-Frequency-Coupling
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Quantification of Phase-Amplitude Coupling in Neuronal Oscillations: Comparison of Phase-Locking Value, Mean Vector Length, Modulation Index, and Generalized-Linear-Modeling-Cross-Frequency-Coupling

机译:神经元振荡中的幅度耦合量化:锁相值,平均矢量长度,调制指数和广义线性建模跨频耦合的比较

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摘要

Phase-amplitude coupling is a promising construct to study cognitive processes in electroencephalography (EEG) and magnetencephalography (MEG). Due to the novelty of the concept, various measures are used in the literature to calculate phase-amplitude coupling. Here, performance of the three most widely used phase-amplitude coupling measures – phase-locking value (PLV), mean vector length (MVL), and modulation index (MI) – and of the generalized linear modeling cross-frequency coupling (GLM-CFC) method is thoroughly compared with the help of simulated data. We combine advantages of previous reviews and use a realistic data simulation, examine moderators and provide inferential statistics for the comparison of all four indices of phase-amplitude coupling. Our analyses show that all four indices successfully differentiate coupling strength and coupling width when monophasic coupling is present. While the MVL was most sensitive to modulations in coupling strengths and width, only the MI and GLM-CFC can detect biphasic coupling. Coupling values of all four indices were influenced by moderators including data length, signal-to-noise-ratio, and sampling rate when approaching Nyquist frequencies. The MI was most robust against confounding influences of these moderators. Based on our analyses, we recommend the MI for noisy and short data epochs with unknown forms of coupling. For high quality and long data epochs with monophasic coupling and a high signal-to-noise ratio, the use of the MVL is recommended. Ideally, both indices are reported simultaneously for one data set.
机译:相幅耦合是研究脑电图(EEG)和脑磁图(MEG)认知过程的一种有前途的结构。由于该概念的新颖性,文献中使用了各种方法来计算相位-幅度耦合。在这里,三种最广泛使用的相-幅耦合测量(锁相值(PLV),平均矢量长度(MVL)和调制指数(MI))和广义线性建模跨频耦合(GLM- CFC)方法在模拟数据的帮助下进行了彻底比较。我们结合了以前的评论的优势,并使用了真实的数据模拟,检查了主持人,并提供了推断统计数据,以比较所有四个相幅耦合指标。我们的分析表明,当存在单相耦合时,所有四个指标都能成功地区分耦合强度和耦合宽度。虽然MVL对耦合强度和宽度的调制最敏感,但只有MI和GLM-CFC可以检测到双相耦合。当接近奈奎斯特频率时,所有四个指标的耦合值都受到仲裁者的影响,包括数据长度,信噪比和采样率。 MI对这些主持人的混杂影响最为有效。根据我们的分析,我们建议将MI用于耦合形式未知的嘈杂和短数据时期。对于具有单相耦合和高信噪比的高质量和长数据周期,建议使用MVL。理想情况下,两个索引同时报告一个数据集。

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