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基于多脑区交叉相位-振幅传递熵的睡眠动态分析技术。

Sleep Dynamic Analysis Technology Based on Cross-Phase-Amplitude Transfer Entropy in Multiple Brain Regions.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:2953-2956. doi: 10.1109/EMBC48229.2022.9871136.

Abstract

Information flow existed across brain regions, and varies dynamically during sleep. In evaluating brain communication and neural-oscillation connectivity across spatiotemporal scales, the phase-amplitude coupling (PAC) is well-explored. However, the directional connectivity is still a deficiency. In this work, we propose a cross-phase-amplitude transfer entropy method in quantifying the characteristics of multi-regional sleep dynamics. The simulation of multivariate nonlinear and nonstationary signals verifies both effectiveness and veracity of the proposed algorithm. The results achieved in sleep EEG of healthy adults indicate that the direction of PAC is from the occipital lobe to the frontal lobe in the Awake and N1 sleep stages. And the flow of PAC turns to the opposite direction for the other sleep stages, i.e., frontal-to-occipital lobe. Besides, the δ-θ/α PAC gradually strengthens with the deepening of the sleep. Of note, the PAC results in the REM sleep stage vary across different frequency pairs. The obtained results support the proposed method as a reliable tool in evaluating brain functions during sleep with brain signals. Clinical Relevance- This manifests the brain communication and neuron-oscillation connectivity across spatiotemporal scales. The proposed framework may be useful in identifying multi-regional sleep dynamics.

摘要

信息流存在于大脑区域之间,并在睡眠期间动态变化。在评估大脑通信和神经振荡连接的时空尺度时,相位-振幅耦合(PAC)得到了很好的研究。然而,方向连接仍然是一个不足。在这项工作中,我们提出了一种跨相位-振幅传递熵方法,用于量化多区域睡眠动态的特征。多变量非线性和非平稳信号的模拟验证了所提出算法的有效性和准确性。在健康成年人的睡眠 EEG 中获得的结果表明,在清醒和 N1 睡眠阶段,PAC 的方向是从枕叶到额叶。而在其他睡眠阶段,PAC 的流向则相反,即从额叶到枕叶。此外,随着睡眠的加深,δ-θ/αPAC 逐渐增强。值得注意的是,在 REM 睡眠阶段,PAC 结果因不同的频率对而异。所获得的结果支持了该方法作为一种利用脑信号评估睡眠期间大脑功能的可靠工具。临床相关性-这表现出跨时空尺度的大脑通信和神经元振荡连接。所提出的框架可能有助于识别多区域睡眠动态。

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