Mijatovic Gorana, Sparacino Laura, Antonacci Yuri, Javorka Michal, Marinazzo Daniele, Stramaglia Sebastiano, Faes Luca
Faculty of Technical SciencesUniversity of Novi Sad 21000 Novi Sad Serbia.
Department of EngineeringUniversity of Palermo 90133 Palermo Italy.
IEEE Open J Eng Med Biol. 2024 Mar 8;5:846-858. doi: 10.1109/OJEMB.2024.3374956. eCollection 2024.
The network representation is becoming increasingly popular for the description of cardiovascular interactions based on the analysis of multiple simultaneously collected variables. However, the traditional methods to assess network links based on pairwise interaction measures cannot reveal high-order effects involving more than two nodes, and are not appropriate to infer the underlying network topology. To address these limitations, here we introduce a framework which combines the assessment of high-order interactions with statistical inference for the characterization of the functional links sustaining physiological networks. The framework develops information-theoretic measures quantifying how two nodes interact in a redundant or synergistic way with the rest of the network, and employs these measures for reconstructing the functional structure of the network. The measures are implemented for both static and dynamic networks mapped respectively by random variables and random processes using plug-in and model-based entropy estimators. The validation on theoretical and numerical simulated networks documents the ability of the framework to represent high-order interactions as networks and to detect statistical structures associated to cascade, common drive and common target effects. The application to cardiovascular networks mapped by the beat-to-beat variability of heart rate, respiration, arterial pressure, cardiac output and vascular resistance allowed noninvasive characterization of several mechanisms of cardiovascular control operating in resting state and during orthostatic stress. Our approach brings to new comprehensive assessment of physiological interactions and complements existing strategies for the classification of pathophysiological states.
基于对多个同时收集的变量进行分析，网络表示法在描述心血管相互作用方面越来越受欢迎。然而，传统的基于成对相互作用度量来评估网络链接的方法无法揭示涉及两个以上节点的高阶效应，也不适用于推断潜在的网络拓扑结构。为了解决这些局限性，我们在此引入一个框架，该框架将高阶相互作用的评估与统计推断相结合，以表征维持生理网络的功能链接。该框架开发了信息理论度量，用于量化两个节点如何与网络的其余部分以冗余或协同的方式相互作用，并利用这些度量来重建网络的功能结构。这些度量分别针对由随机变量和随机过程映射的静态和动态网络，使用插件式和基于模型的熵估计器来实现。在理论和数值模拟网络上的验证证明了该框架将高阶相互作用表示为网络以及检测与级联、共同驱动和共同目标效应相关的统计结构的能力。将其应用于由心率、呼吸、动脉压、心输出量和血管阻力的逐搏变异性映射的心血管网络，能够对静息状态和直立应激期间运行的几种心血管控制机制进行无创表征。我们的方法为生理相互作用带来了新的全面评估，并补充了现有的病理生理状态分类策略。
