格兰杰因果关系（Granger causality, GC）是一种广泛使用的统计方法，用于推断复杂网络所测时间序列之间的定向影响；但其对高阶（非成对）相互作用敏感，而此类相互作用从根本上塑造了网络的集体动力学。本研究提出格兰杰因果关系的部分分解（Partial Decomposition of Granger Causality, PDGC），一种用于揭示生理网络子系统间信息流模式中冗余性与协同性因果相互作用的工具。该工具基于部分信息分解（partial information decomposition）框架，将从一组驱动随机过程到目标过程的多元GC分解为三类成分：仅由各驱动变量单独携带的独特效应、由多个驱动变量以相同方式携带的冗余效应，以及由若干驱动变量共同携带但任一驱动变量单独均不携带的协同效应。计算基于频域扩展的多元状态空间模型，从而可在特定生理相关频段及全频段积分后的时域中评估PDGC。谱PDGC在易发生神经介导性晕厥患者的生理网络中进行了验证，所用数据包括动脉压、心周期、呼吸及脑血流速度的变异性序列，并与健康对照组比较。该应用揭示了前所未有的生理相互作用模式，这些模式与低频心血管及脑血管振荡的交感神经调控相关，表征了自主神经功能障碍的独特模式。从谱GC中提取高阶因果模式，有助于在众多以数据驱动的网络科学应用中解析振荡过程间多元相互作用背后的因果影响机制。

Granger causality (GC), a popular statistical method for the inference of directional influences between time series measured from a complex network, is sensitive to high-order (non-pairwise) interactions which fundamentally shape the collective network dynamics. This work introduces Partial Decomposition of Granger Causality (PDGC), a tool eliciting redundant and synergistic causal interactions in the pattern of information flow between the subsystems of physiological networks. The tool exploits the framework of partial information decomposition to dissect the multivariate GC from a set of driver random processes to a target process into unique effects carried exclusively by each driver, redundant effects carried identically by more drivers, and synergistic effects carried jointly by some drivers but not by any of them individually. Computation is based on multivariate state-space models expanded in the frequency domain to assess PDGC both in specific bands of physiological interest and in the time domain after whole-band integration. The spectral PDGC was tested in physiological networks probed by measuring the variability series of arterial pressure, heart period, respiration and cerebral blood velocity in patients prone to neurally-mediated syncope compared to healthy controls. This application revealed unprecedented modes of physiological interaction, related to the sympathetic control of low-frequency cardiovascular and cerebrovascular oscillations, characterizing distinctive patterns of autonomic dysfunction. The extraction of high-order causality patterns from the spectral GC favors dissecting the mechanisms of causal influence underlying multivariate interactions among oscillatory processes in many data-driven applications of network science.