Delay-correlation landscape reveals characteristic time delays of brain rhythms and heart interactions
Delay-correlation landscape reveals characteristic time delays of brain rhythms and heart interactions
复制标题
延迟相关景观揭示了大脑节律和心脏相互作用的特征时间延迟
DOI：
10.1098/rsta.2015.0182
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发表时间：
2016-05-13
期刊：
影响因子：
5
通讯作者：
Ivanov, Plamen Ch.
中科院分区：
文献类型：
--
作者：
Lin, Aijing;Liu, Kang K. L.;Ivanov, Plamen Ch.
在“网络生理学”的框架内，我们提出了一个基本问题，即心脏动力学的调节是如何从网络化的脑-心相互作用中出现的。我们提出了一种广义的时间延迟方法来识别和量化生理相关的脑节律和心率之间的动态相互作用。我们对34名健康受试者夜间睡眠时同步连续脑电图和心电记录进行了实证分析。对于每一对脑节律和心脏相互作用，我们构建了一个延迟相关图（DCL），该图描述了个体脑节律如何与心率耦合，以及脑和心脏动力学的调节如何及时协调。我们发现了典型的时间延迟，以及大脑-心脏相互作用下的时间延迟概率分布的具体概况。这些特征在所有的受试者中都一致地观察到，表明了一种普遍的模式。通过跟踪不同睡眠阶段DCL的演化，我们发现时间延迟谱的集合在不同的生理状态下会发生变化，这表明DCL与生理状态和功能有很强的联系。本研究为心脏动力学的神经生理调控提供了新的见解，具有广泛的临床应用潜力。所提出的方法允许人们同时捕获动态相互作用的关键要素，包括特征时间延迟及其时间演化，并且可以应用于一系列耦合动力系统。
Within the framework of 'Network Physiology', we ask a fundamental question of how modulations in cardiac dynamics emerge from networked brain-heart interactions. We propose a generalized time-delay approach to identify and quantify dynamical interactions between physiologically relevant brain rhythms and the heart rate. We perform empirical analysis of synchronized continuous EEG and ECG recordings from 34 healthy subjects during night-time sleep. For each pair of brain rhythm and heart interaction, we construct a delay-correlation landscape (DCL) that characterizes how individual brain rhythms are coupled to the heart rate, and how modulations in brain and cardiac dynamics are coordinated in time. We uncover characteristic time delays and an ensemble of specific profiles for the probability distribution of time delays that underly brain-heart interactions. These profiles are consistently observed in all subjects, indicating a universal pattern. Tracking the evolution of DCL across different sleep stages, we find that the ensemble of time-delay profiles changes from one physiologic state to another, indicating a strong association with physiologic state and function. The reported observations provide new insights on neurophysiological regulation of cardiac dynamics, with potential for broad clinical applications. The presented approach allows one to simultaneously capture key elements of dynamic interactions, including characteristic time delays and their time evolution, and can be applied to a range of coupled dynamical systems.