Assessment of dynamic phase amplitude coupling using matching pursuit
Assessment of dynamic phase amplitude coupling using matching pursuit
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DOI：
10.1016/j.jneumeth.2022.109610
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发表时间：
2022-05-26
影响因子：
3
通讯作者：
Aviyente，Selin
中科院分区：
文献类型：
--
作者：
Munia，Tamanna T. K.;Aviyente，Selin
背景神经元的信息传递和通讯是通过多个振荡频率之间的相互作用实现的。相位振幅耦合（PAC）量化了认知脑功能过程中的这些相互作用。PAC被定义为低频节律的相位对高频节律的幅度的调制。现有的PAC措施仅限于量化感兴趣的时间窗口内的平均耦合。然而，由于PAC是动态的，因此有必要对时变PAC进行量化。现有的时变PAC方法是基于使用滑动窗口方法。这些方法不适应信号的动态特性，因此，任意选择的窗口长度大大阻碍PAC estimation.New methodTo解决滑动窗口PAC估计方法的局限性，在本文中，我们引入了一个动态PAC措施，依赖于匹配追踪（MP）。这种方法将信号分解为最能描述信号的时间和频率局部原子。通过计算这些时间和频率局域化原子之间的耦合来量化动态PAC。因此，所提出的方法是数据驱动的，并跟踪PAC随时间的变化。我们评估所提出的方法在合成和真实的脑电图（EEG）data.ResultsThe从合成数据的结果表明，该方法检测耦合频率和耦合的时间变化正确具有高的时间和频率分辨率。EEG数据的分析揭示了响应和响应后时间间隔期间的θ-γ和α-γ PAC。与现有方法的比较与现有的基于滑动窗口的方法相比，所提出的基于MP的动态PAC测量在短时间窗口内捕获PAC时更有效，并且对噪声更鲁棒。这是因为这种方法量化的低频相位和高频振幅分量的时间和频率本地化MP原子，因此，可以捕获的信号dynamics.ConclusionsWe的建议MP基于数据驱动的方法提供了一个更强大的，可能更敏感的方法，有效地量化和跟踪动态PAC。
BackgroundNeuronal transmission and communication are enabled by the interactions across multiple oscillatory frequencies. Phase amplitude coupling (PAC) quantifies these interactions during cognitive brain functions. PAC is defined as the modulation of the amplitude of the high frequency rhythm by the phase of the low frequency rhythm. Existing PAC measures are limited to quantifying the average coupling within a time window of interest. However, as PAC is dynamic, it is necessary to quantify time-varying PAC. Existing time-varying PAC approaches are based on using a sliding window approach. These approaches do not adapt to the signal dynamics, and thus the arbitrary selection of the window length substantially hampers PAC estimation.New methodTo address the limitations of sliding window PAC estimation approaches, in this paper, we introduce a dynamic PAC measure that relies on matching pursuit (MP). This approach decomposes the signal into time and frequency localized atoms that best describe the signal. Dynamic PAC is quantified by computing the coupling between these time and frequency localized atoms. As such, the proposed approach is data-driven and tracks the change of PAC with time. We evaluate the proposed method on both synthesized and real electroencephalogram (EEG) data.ResultsThe results from synthesized data show that the proposed method detects the coupled frequencies and the time variation of the coupling correctly with high time and frequency resolution. The analysis of EEG data revealed theta-gamma and alpha-gamma PAC during response and post-response time intervals.Comparison with existing method(s)Compared to the existing sliding window based approach, the proposed MP based dynamic PAC measure is more effective at capturing PAC within a short time window and is more robust to noise. This is because this method quantifies the low frequency phase and high frequency amplitude components from the time and frequency localized MP atoms and, as such, can capture the signal dynamics.ConclusionsWe posit that the proposed MP based data-driven approach offers a more robust and possibly more sensitive method to effectively quantify and track dynamic PAC.