A Framework for the Time- and Frequency-Domain Assessment of High-Order Interactions in Brain and Physiological Networks
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脑与生理网络高阶交互的时域和频域评估框架
- 研究问题
- 对跨多个时间尺度相互作用的多变量过程，如何识别三个及以上单元之间的冗余与协同交互，并区分其频率特征？
- 方法与思路
- 作者定义 O-information rate，借助向量自回归和状态空间模型的谱表示，将高阶交互分解为 Granger 因果及瞬时影响。
- 材料陈述的贡献
- 摘要称频域指标可揭示时域汇总未显示的高阶交互，并通过模拟、生理信号和动物脑信号展示该框架的应用能力。
- 阅读关注点
- 应核对指标与分解的数学定义、模型拟合和谱估计条件，以及频域交互如何与生理机制解释及统计不确定性衔接。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核验；摘要未说明完整推断条件，Granger 因果分解不能自动确认为干预因果关系。
原文摘要
While the standard network description of complex systems is based on quantifying links between pairs of system units, higher-order interactions (HOIs) involving three or more units play a major role in governing the collective network behavior. This work introduces an approach to quantify pairwise and HOIs for multivariate rhythmic processes interacting across multiple time scales. We define the so-called O-information rate (OIR) as a new metric to assess HOIs for multivariate time series, and propose a framework to decompose it into measures quantifying Granger-causal and instantaneous influences, as well as to expand it in the frequency domain. The framework exploits the spectral representation of vector autoregressive and state-space models to assess synergistic and redundant interactions among groups of processes, both in specific bands and in the time domain after whole-band integration. Validation on simulated networks illustrates how the spectral OIR can highlight redundant and synergistic HOIs emerging at specific frequencies but not using time-domain measures. The application to physiological networks described by heart period, arterial pressure and respiration measured in healthy subjects during paced breathing, and to brain networks described by ECoG signals acquired in an animal experiment during anesthesia, document the capability of our approach to identify informational circuits relevant to well-defined cardiovascular oscillations and brain rhythms and related to specific physiological mechanisms of autonomic control and altered consciousness. The proposed framework allows a hierarchically-organized evaluation of time- and frequency-domain interactions in networks mapped by multivariate time series, and its high flexibility and scalability make it suitable to investigate networks beyond pairwise interactions in neuroscience, physiology and other fields.