Bristol Centre for Complexity Sciences, University of Bristol, UK.
Prog Biophys Mol Biol. 2011 Mar;105(1-2):49-57. doi: 10.1016/j.pbiomolbio.2010.09.007. Epub 2010 Sep 30.
Neuroscience time series data from a range of techniques and species reveal complex, non-linear interactions between different frequencies of neuronal network oscillations within and across brain regions. Here, we briefly review the evidence that these nested, cross-frequency interactions act in concert with linearly covariant (within-frequency) activity to dynamically coordinate functionally related neuronal ensembles during behaviour. Such studies depend upon reliable quantification of cross-frequency coordination, and we compare the properties of three techniques used to measure phase-amplitude coupling (PAC)--Envelope-to-Signal Correlation (ESC), the Modulation Index (MI) and Cross-Frequency Coherence (CFC)--by standardizing their filtering algorithms and systematically assessing their robustness to noise and signal amplitude using artificial signals. Importantly, we also introduce a freely-downloadable method for estimating statistical significance of PAC, a step overlooked in the majority of published studies. We find that varying data length and noise levels leads to the three measures differentially detecting false positives or correctly identifying frequency bands of interaction; these conditions should therefore be taken into careful consideration when selecting PAC analyses. Finally, we demonstrate the utility of the three measures in quantifying PAC in local field potential data simultaneously recorded from rat hippocampus and prefrontal cortex, revealing a novel finding of prefrontal cortical theta phase modulating hippocampal gamma power. Future adaptations that allow detection of time-variant PAC should prove essential in deciphering the roles of cross-frequency coupling in mediating or reflecting nervous system function.
神经科学时间序列数据来自一系列技术和物种，揭示了不同脑区和脑区之间神经元网络振荡的不同频率之间复杂的非线性相互作用。在这里，我们简要回顾了这些嵌套的跨频相互作用与线性协变（同频）活动协同作用的证据，这些活动在行为过程中动态协调功能相关的神经元集合。这些研究依赖于对跨频协调的可靠量化，我们通过标准化滤波算法并使用人工信号系统地评估它们对噪声和信号幅度的稳健性，比较了用于测量相位-幅度耦合 (PAC) 的三种技术的特性 - 包络到信号相关 (ESC)、调制指数 (MI) 和 跨频相干性 (CFC)。重要的是，我们还引入了一种用于估计 PAC 统计显著性的免费下载方法，这是大多数已发表研究中忽略的步骤。我们发现，数据长度和噪声水平的变化导致三种测量方法以不同的方式检测假阳性或正确识别相互作用的频带；因此，在选择 PAC 分析时，应仔细考虑这些条件。最后，我们展示了这三种测量方法在同时记录自大鼠海马体和前额叶皮层的局部场电位数据中量化 PAC 的效用，揭示了前额叶皮层 theta 相位调制海马体 gamma 功率的新发现。未来的适应可以检测时间变化的 PAC，这对于解析跨频耦合在介导或反映神经系统功能中的作用应该是必不可少的。
