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Dissecting Spectral Granger Causality Through Partial Information Decomposition
IEEE Transactions on Bio-Medical Engineering
|August 12, 2026
Summary
Partial Decomposition of Granger Causality (PDGC) reveals unique, redundant, and synergistic interactions in physiological networks. This method identifies novel biomarkers for autonomic dysfunction, distinguishing syncope patients from healthy controls.
Area of Science:
- Physiology
- Network Science
- Biomedical Engineering
Background:
- Granger causality (GC) is vital for inferring directional influences in complex networks.
- GC is sensitive to high-order interactions shaping network dynamics.
- Existing methods struggle to fully capture these complex causal relationships.
Purpose of the Study:
- Introduce Partial Decomposition of Granger Causality (PDGC) to analyze redundant and synergistic interactions.
- Develop a tool to dissect multivariate GC into unique, redundant, and synergistic components.
- Investigate physiological network dynamics and identify biomarkers for autonomic dysfunction.
Main Methods:
- Utilize partial information decomposition within a multivariate Granger causality framework.
- Employ frequency-domain analysis of multivariate state-space models.
- Assess PDGC in specific frequency bands and integrated time-domain analysis.
Main Results:
- Validated PDGC on benchmark simulations, confirming reliability and accuracy in reflecting causal mechanisms.
- Applied PDGC to physiological data (arterial pressure, respiration, cerebral blood velocity, heart period variability).
- Observed distinct differences in postural stress responses between syncope-prone patients and healthy controls.
Conclusions:
- PDGC uncovers novel physiological interaction modes, including sympathetic control of cardiovascular and cerebrovascular oscillations.
- Identified distinct patterns of autonomic dysfunction.
- High-order causality patterns offer potential new biomarkers for assessing pathophysiological states.
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