Mijatovic Gorana, Pernice Riccardo, Perinelli Alessio, Antonacci Yuri, Busacca Alessandro, Javorka Michal, Ricci Leonardo, Faes Luca
Faculty of Technical Science, University of Novi Sad, Novi Sad, Serbia.
Department of Engineering, University of Palermo, Palermo, Italy.
Front Netw Physiol. 2022 Jan 28;1:765332. doi: 10.3389/fnetp.2021.765332. eCollection 2021.
The amount of information exchanged per unit of time between two dynamic processes is an important concept for the analysis of complex systems. Theoretical formulations and data-efficient estimators have been recently introduced for this quantity, known as the mutual information rate (MIR), allowing its continuous-time computation for event-based data sets measured as realizations of coupled point processes. This work presents the implementation of MIR for point process applications in Network Physiology and cardiovascular variability, which typically feature short and noisy experimental time series. We assess the bias of MIR estimated for uncoupled point processes in the frame of surrogate data, and we compensate it by introducing a corrected MIR (cMIR) measure designed to return zero values when the two processes do not exchange information. The method is first tested extensively in synthetic point processes including a physiologically-based model of the heartbeat dynamics and the blood pressure propagation times, where we show the ability of cMIR to compensate the negative bias of MIR and return statistically significant values even for weakly coupled processes. The method is then assessed in real point-process data measured from healthy subjects during different physiological conditions, showing that cMIR between heartbeat and pressure propagation times increases significantly during postural stress, though not during mental stress. These results document that cMIR reflects physiological mechanisms of cardiovascular variability related to the joint neural autonomic modulation of heart rate and arterial compliance.
两个动态过程之间每单位时间交换的信息量是分析复杂系统的一个重要概念。最近针对这个被称为互信息率（MIR）的量引入了理论公式和数据高效估计器，从而能够对作为耦合点过程实现而测量的基于事件的数据集进行连续时间计算。这项工作展示了MIR在网络生理学和心血管变异性中的点过程应用中的实现，这些应用通常具有短且有噪声的实验时间序列。我们在替代数据框架下评估了未耦合点过程估计的MIR偏差，并通过引入一种校正的MIR（cMIR）度量来补偿该偏差，该度量设计为当两个过程不交换信息时返回零值。该方法首先在合成点过程中进行了广泛测试，包括基于生理的心跳动力学模型和血压传播时间模型，我们展示了cMIR补偿MIR负偏差的能力，并且即使对于弱耦合过程也能返回具有统计学意义的值。然后在不同生理条件下从健康受试者测量的真实点过程数据中评估该方法，结果表明在姿势应激期间心跳与压力传播时间之间的cMIR显著增加，而在精神应激期间则不然。这些结果表明，cMIR反映了与心率和动脉顺应性的联合神经自主调节相关的心血管变异性的生理机制。
