Ibáñez-Molina Antonio José, Soriano María Felipa, Iglesias-Parro Sergio
Department of Psychology, University of Jaén, Jaén, Spain.
Unidad de salud mental, Hospital San Agustín, Linares, Spain.
Front Neurosci. 2020 Dec 14;14:574796. doi: 10.3389/fnins.2020.574796. eCollection 2020.
Electroencephalograms (EEG) are one of the most commonly used measures to study brain functioning at a macroscopic level. The structure of the EEG time series is composed of many neural rhythms interacting at different spatiotemporal scales. This interaction is often named as cross frequency coupling, and consists of transient couplings between various parameters of different rhythms. This coupling has been hypothesized to be a basic mechanism involved in cognitive functions. There are several methods to measure cross frequency coupling between two rhythms but no single method has been selected as the gold standard. Current methods only serve to explore two rhythms at a time, are computationally demanding, and impose assumptions about the nature of the signal. Here we present a new approach based on Information Theory in which we can characterize the interaction of more than two rhythms in a given EEG time series. It estimates the mutual information of multiple rhythms (MIMR) extracted from the original signal. We tested this measure using simulated and real empirical data. We simulated signals composed of three frequencies and background noise. When the coupling between each frequency component was manipulated, we found a significant variation in the MIMR. In addition, we found that MIMR was sensitive to real EEG time series collected with open vs. closed eyes, and intra-cortical recordings from epileptic and non-epileptic signals registered at different regions of the brain. MIMR is presented as a tool to explore multiple rhythms, easy to compute and without assumptions.
脑电图（EEG）是在宏观层面研究大脑功能最常用的测量方法之一。EEG时间序列的结构由许多在不同时空尺度上相互作用的神经节律组成。这种相互作用通常被称为交叉频率耦合，它由不同节律的各种参数之间的瞬态耦合组成。这种耦合被认为是参与认知功能的一种基本机制。有几种方法可以测量两种节律之间的交叉频率耦合，但没有一种方法被选为金标准。目前的方法一次只能探索两种节律，计算量很大，并且对信号的性质有假设。在这里，我们提出了一种基于信息论的新方法，通过该方法我们可以表征给定EEG时间序列中两种以上节律的相互作用。它估计从原始信号中提取的多个节律的互信息（MIMR）。我们使用模拟和真实的经验数据测试了这种测量方法。我们模拟了由三个频率和背景噪声组成的信号。当每个频率成分之间的耦合被操纵时，我们发现MIMR有显著变化。此外，我们发现MIMR对睁眼和闭眼时收集的真实EEG时间序列以及大脑不同区域记录的癫痫和非癫痫信号中的皮层内记录敏感。MIMR作为一种探索多种节律的工具被提出，它易于计算且无需假设。
