Is partial coherence a viable technique for identifying generators of neural oscillations?
Is partial coherence a viable technique for identifying generators of neural oscillations?
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DOI：
10.1007/s00422-004-0475-5
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发表时间：
2004-05-01
影响因子：
1.9
通讯作者：
Ding, MZ
中科院分区：
文献类型：
--
作者：
Albo, Z;Di Prisco, GV;Ding, MZ
部分相干测量去除第三个信号的影响后两个信号之间的线性关系。Gersch在1970年提出，部分相干性可以用来识别多变量时间序列的驱动力来源。这一概念在本文中被称为Gersch因果关系，得到了广泛的接受，并被广泛应用于信号处理领域的各个领域。来自给定传感器的神经生物学数据包括感兴趣的信号和其他不相关的过程，统称为测量噪声。我们发现，基于部分相干的Gersch因果关系对信噪比非常敏感；也就是说，对于一个由三个或更多同时记录的时间序列组成的组，具有最高信噪比(即相对无噪声)的时间序列通常被识别为组的“驱动因素”，而不考虑连接的真实潜在模式。这一假设在理论上和实验时间序列上都得到了验证，这些时间序列是从theta节律期间的边缘脑结构中获得的。
Partial coherence measures the linear relationship between two signals after the influence of a third signal has been removed. Gersch proposed in 1970 that partial coherence could be used to identify sources of driving for multivariate time series. This idea, referred to in this paper as Gersch Causality, has received wide acceptance and has been applied extensively to a variety of fields in the signal processing community. Neurobiological data from a given sensor include both the signals of interest and other unrelated processes collectively referred to as measurement noise. We show that partial-coherence-based Gersch Causality is extremely sensitive to signal-to-noise ratio; that is, for a group of three or more simultaneously recorded time series, the time series with the highest signal-to-noise ratio (i.e., relatively noise free) is often identified as the "driver" of the group, irrespective of the true underlying patterns of connectivity. This hypothesis is tested both theoretically and on experimental time series acquired from limbic brain structures during the theta rhythm.