Title: Predictive Information Decomposition as a Tool to Quantify Emergent Dynamical Behaviors In Physiological Networks Show Chinese title

Title: 预测信息分解作为量化生理网络中涌现动力学行为的工具

Abstract: Objective: This work introduces a framework for multivariate time series analysis aimed at detecting and quantifying collective emerging behaviors in the dynamics of physiological networks. Methods: Given a network system mapped by a vector random process, we compute the predictive information (PI) between the present and past network states and dissect it into amounts quantifying the unique, redundant and synergistic information shared by the present of the network and the past of each unit. Emergence is then quantified as the prevalence of the synergistic over the redundant contribution. The framework is implemented in practice using vector autoregressive (VAR) models. Results: Validation in simulated VAR processes documents that emerging behaviors arise in networks where multiple causal interactions coexist with internal dynamics. The application to cardiovascular and respiratory networks mapping the beat-to-beat variability of heart rate, arterial pressure and respiration measured at rest and during postural stress reveals the presence of statistically significant net synergy, as well as its modulation with sympathetic nervous system activation. Conclusion: Causal emergence can be efficiently assessed decomposing the PI of network systems via VAR models applied to multivariate time series. This approach evidences the synergy/redundancy balance as a hallmark of integrated short-term autonomic control in cardiovascular and respiratory networks. Significance: Measures of causal emergence provide a practical tool to quantify the mechanisms of causal influence that determine the dynamic state of cardiovascular and neural network systems across distinct physiopathological conditions. Show Chinese abstract

Abstract: 目的：本研究介绍了一个用于多元时间序列分析的框架，旨在检测和量化生理网络动力学中的集体涌现行为。 方法：对于由向量随机过程映射的网络系统，我们计算网络当前状态与过去状态之间的预测信息（PI），并将其分解为衡量当前网络与各单元过去状态之间独特、冗余和协同共享的信息量。然后，将协同作用量化为协同贡献超过冗余贡献的程度。该框架在实践中使用向量自回归（VAR）模型实现。 结果：在模拟的VAR过程中验证表明，当多个因果相互作用与内部动态共存时，网络中会出现涌现行为。应用于心率、动脉血压和呼吸变异性的心血管和呼吸网络的研究揭示了统计上显著的净协同效应，并且发现这种协同效应会随着交感神经系统激活而被调节。 结论：通过应用到多元时间序列上的VAR模型，可以有效评估网络系统的因果涌现。这种方法展示了协同/冗余平衡作为心血管和呼吸网络短期自主控制整合性的标志。 意义：因果涌现的测量提供了一种实用工具，用以量化决定心血管和神经网络系统在不同生理病理条件下动态状态的因果影响机制。