Phase-amplitude coupling (PAC) measures the relationship between the phase of low-frequency oscillations (LFO) and the amplitude of high-frequency oscillations (HFO). It plays an important functional role in neural information processing and cognition. Thus, we propose a novel method based on the Jensen-Shannon (JS) divergence and correlation matrix. The method takes the amplitude distributions of the HFO located in the corresponding phase bins of the LFO as multichannel inputs to construct a correlation matrix, where the elements are calculated based on the JS divergence between pairs of amplitude distributions. Then, the omega complexity extracted from the correlation matrix is used to estimate the PAC strength. The simulation results demonstrate that the proposed method can effectively reflect the PAC strength and slightly vary with the data length. Moreover, it outperforms five frequently used algorithms in the performance against additive white Gaussian noise and spike noise and the ability of detecting PAC in wide frequency ranges. To validate our proposed method with real data, it was applied to analyze the local field potential recorded from the dorsomedial striatum in a male Sprague-Dawley rat. It can replicate previous results obtained with other PAC metrics. In conclusion, these results suggest that our proposed method is a powerful tool for measuring the PAC between neural oscillations.

中文翻译：

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基于 Jensen-Shannon 散度和相关矩阵测量相位-幅度耦合

相位幅度耦合 (PAC) 测量低频振荡 (LFO) 的相位与高频振荡 (HFO) 的幅度之间的关系。它在神经信息处理和认知中起着重要的功能作用。因此，我们提出了一种基于 Jensen-Shannon (JS) 散度和相关矩阵的新方法。该方法将位于 LFO 相应相位箱中的 HFO 的幅度分布作为多通道输入来构建相关矩阵，其中元素是根据幅度分布对之间的 JS 散度计算的。然后，从相关矩阵中提取的欧米茄复杂度用于估计 PAC 强度。仿真结果表明，所提出的方法可以有效地反映PAC强度，并且随着数据长度的变化而略有变化。此外，它在对抗加性高斯白噪声和尖峰噪声的性能以及在宽频率范围内检测 PAC 的能力方面优于五种常用算法。为了用真实数据验证我们提出的方法，它被应用于分析从雄性 Sprague-Dawley 大鼠背内侧纹状体记录的局部场电位。它可以复制以前用其他 PAC 指标获得的结果。总之，这些结果表明我们提出的方法是测量神经振荡之间 PAC 的有力工具。它在对抗加性高斯白噪声和尖峰噪声的性能以及在宽频率范围内检测 PAC 的能力方面优于五种常用算法。为了用真实数据验证我们提出的方法，它被应用于分析从雄性 Sprague-Dawley 大鼠背内侧纹状体记录的局部场电位。它可以复制以前用其他 PAC 指标获得的结果。总之，这些结果表明我们提出的方法是测量神经振荡之间 PAC 的有力工具。它在对抗加性高斯白噪声和尖峰噪声的性能以及在宽频率范围内检测 PAC 的能力方面优于五种常用算法。为了用真实数据验证我们提出的方法，它被应用于分析从雄性 Sprague-Dawley 大鼠背内侧纹状体记录的局部场电位。它可以复制以前用其他 PAC 指标获得的结果。总之，这些结果表明我们提出的方法是测量神经振荡之间 PAC 的有力工具。