Dissecting Spectral Granger Causality through Partial Information Decomposition

2026-03-08

AI Summary

This study addresses the limitations of traditional Granger causality in characterizing high-order causal interactions within complex physiological networks, particularly its inability to distinguish between redundant and synergistic information flows. To overcome this, the authors propose Partial Information Decomposition Granger Causality (PDGC), which integrates the partial information decomposition framework into spectral Granger causality analysis for the first time. Built upon a multivariate state-space model, PDGC decomposes multivariate causal effects in the frequency domain into unique, redundant, and synergistic components, enabling the extraction of high-order interaction patterns within specific frequency bands—such as the low-frequency range. Applied to physiological networks of patients with syncope susceptibility and healthy controls, PDGC successfully uncovers low-frequency cardio-cerebrovascular synergistic oscillations linked to sympathetic regulation, offering a novel mechanistic insight into autonomic dysfunction.

Technology Category

• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Machine Learning: Causal Learning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Social Networks and Social Media: Computational social science
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

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.

Problem

• Granger causality
• high-order interactions
• partial information decomposition
• spectral analysis
• physiological networks

Innovation

• Partial Information Decomposition
• Granger Causality
• Spectral Analysis
• Synergistic Causality
• Multivariate Time Series