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直接调制指数:神经生理学数据的相位幅度耦合的度量。

Direct modulation index: A measure of phase amplitude coupling for neurophysiology data.

机构信息

Institute for Neuromodulation and Neurotechnology, University Hospital and University of Tübingen, Tübingen, Germany.

Krembil Brain Institute, University Health Network, Toronto, Canada.

出版信息

Hum Brain Mapp. 2023 Apr 1;44(5):1862-1867. doi: 10.1002/hbm.26190. Epub 2022 Dec 29.

Abstract

Neural communication across different spatial and temporal scales is a topic of great interest in clinical and basic science. Phase-amplitude coupling (PAC) has attracted particular interest due to its functional role in a wide range of cognitive and motor functions. Here, we introduce a novel measure termed the direct modulation index (dMI). Based on the classical modulation index, dMI provides an estimate of PAC that is (1) bound to an absolute interval between 0 and +1, (2) resistant against noise, and (3) reliable even for small amounts of data. To highlight the properties of this newly-proposed measure, we evaluated dMI by comparing it to the classical modulation index, mean vector length, and phase-locking value using simulated data. We ascertained that dMI provides a more accurate estimate of PAC than the existing methods and that is resilient to varying noise levels and signal lengths. As such, dMI permits a reliable investigation of PAC, which may reveal insights crucial to our understanding of functional brain architecture in key contexts such as behaviour and cognition. A Python toolbox that implements dMI and other measures of PAC is freely available at https://github.com/neurophysiological-analysis/FiNN.

摘要

不同时空尺度的神经通讯是临床和基础科学领域非常关注的话题。相位-幅度耦合 (PAC) 因其在广泛的认知和运动功能中的功能作用而引起了特别的兴趣。在这里,我们引入了一种新的度量标准,称为直接调制指数 (dMI)。基于经典的调制指数,dMI 提供了一种 PAC 的估计值,它 (1) 被限制在 0 到 +1 之间的绝对区间内,(2) 对噪声具有抵抗力,并且 (3) 即使对于少量数据也是可靠的。为了突出这个新提出的度量标准的特性,我们使用模拟数据将 dMI 与经典的调制指数、平均向量长度和锁相值进行了比较。我们确定 dMI 提供了比现有方法更准确的 PAC 估计值,并且对不同的噪声水平和信号长度具有弹性。因此,dMI 允许对 PAC 进行可靠的研究,这可能揭示对我们理解关键情境(如行为和认知)中功能大脑结构至关重要的见解。一个实现 dMI 和其他 PAC 度量标准的 Python 工具箱可在 https://github.com/neurophysiological-analysis/FiNN 上免费获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5fc6/9980882/4492ea6c7340/HBM-44-1862-g003.jpg

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