Swartz Center for Computational Neurosciences, UCSD, La Jolla, CA, USA; Electric and Computer Engineering Department, Jacobs School of Engineering, UCSD, La Jolla, CA, USA.
Swartz Center for Computational Neurosciences, UCSD, La Jolla, CA, USA; Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Neuroimage. 2019 Jan 15;185:361-378. doi: 10.1016/j.neuroimage.2018.10.034. Epub 2018 Oct 18.
Here we demonstrate the suitability of a local mutual information measure for estimating the temporal dynamics of cross-frequency coupling (CFC) in brain electrophysiological signals. In CFC, concurrent activity streams in different frequency ranges interact and transiently couple. A particular form of CFC, phase-amplitude coupling (PAC), has raised interest given the growing amount of evidence of its possible role in healthy and pathological brain information processing. Although several methods have been proposed for PAC estimation, only a few have addressed the estimation of the temporal evolution of PAC, and these typically require a large number of experimental trials to return a reliable estimate. Here we explore the use of mutual information to estimate a PAC measure (MIPAC) in both continuous and event-related multi-trial data. To validate these two applications of the proposed method, we first apply it to a set of simulated phase-amplitude modulated signals and show that MIPAC can successfully recover the temporal dynamics of the simulated coupling in either continuous or multi-trial data. Finally, to explore the use of MIPAC to analyze data from human event-related paradigms, we apply it to an actual event-related human electrocorticographic (ECoG) data set that exhibits strong PAC, demonstrating that the MIPAC estimator can be used to successfully characterize amplitude-modulation dynamics in electrophysiological data.
在这里，我们展示了局部互信息度量在估计脑电生理信号中跨频耦合 (CFC) 的时间动态方面的适用性。在 CFC 中，不同频率范围内的并发活动流相互作用并瞬时耦合。CFC 的一种特殊形式，即相位-幅度耦合 (PAC)，由于其在健康和病理大脑信息处理中的可能作用的证据越来越多，因此引起了人们的兴趣。尽管已经提出了几种用于 PAC 估计的方法，但只有少数方法解决了 PAC 的时间演化估计问题，并且这些方法通常需要大量的实验试验才能返回可靠的估计。在这里，我们探索了使用互信息来估计连续和事件相关多试验数据中的 PAC 度量 (MIPAC)。为了验证所提出方法的这两种应用，我们首先将其应用于一组模拟的相位-幅度调制信号，并表明 MIPAC 可以成功地恢复连续或多试验数据中模拟耦合的时间动态。最后，为了探索 MIPAC 在分析人类事件相关范式数据中的用途，我们将其应用于一个实际的具有强 PAC 的人类事件相关脑皮层电图 (ECoG) 数据集，表明 MIPAC 估计器可用于成功地描述电生理数据中的幅度调制动态。
