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Phase-Amplitude Coupling between Neuronal Wideband Low-Frequency Oscillations and Broadband Gamma Activity

机译:神经元宽带低频振荡与宽带伽马活动之间的相位幅度耦合

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Phase-amplitude coupling (PAC) between the phase of low-frequency oscillations and the power of high-frequency activity plays a functional role in neuronal computation and information transfer. Traditional Hilbert transform-based PAC methods assume that neuronal activity is narrowband, sinusoidal, and sustained. However, natural neuronal signals often violate these three assumptions, creating a potential confound for the interpretation of PAC results. In this study, we present a new method, called Tau-Modulation, that does not require these assumptions to be met. We use this method to identify task-relevant neuronal networks in human electrocorticographic signals. Our results show that Tau-Modulation can identify these networks and characterize the strength and frequency of wideband low-frequency coupling with broadband gamma activity. Thus, Tau-Modulation might provide for a robust approach to analyzing neuronal signals and pave the way for new insights on brain functions.
机译:低频振荡相位与高频活动的功率之间的相位幅度耦合(PAC)在神经元计算和信息传输中起着功能作用。 基于传统的希尔伯特变革的PAC方法假设神经元活动是窄带,正弦和持续的。 然而,天然神经元信号经常违反这三个假设,为解释PAC结果产生了潜在的困惑。 在这项研究中,我们提出了一种称为TAU调制的新方法,这不需要满足这些假设。 我们使用该方法来识别人类电加理信号中的任务相关的神经元网络。 我们的结果表明,TAU调制可以识别这些网络,并表征宽带低频耦合的强度和频率与宽带伽马活动。 因此,TAU调制可能提供稳健的方法来分析神经元信号,并为大脑功能的新见解铺平道路。

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