Time-Resolved Directional Brain-Heart Interplay Measurement Through Synthetic Data Generation Models

Area of Science:

• Neuroscience
• Physiology
• Computational Biology

Background:

• Limited computational methods exist for estimating directed brain-heart information flow.
• Existing synthetic data generation models validate brain and cardiovascular dynamics separately.

Purpose of the Study:

• To introduce a novel computational framework for time-resolved quantification of causal brain-heart interplay.
• To leverage existing generative models for analyzing brain-heart interactions.

Main Methods:

• Coupling synthetic data models of electroencephalographic signals and heart rate variability using parameterized functions based on central autonomic network (CAN) knowledge.
• Validating the framework with data from 30 healthy volunteers undergoing a cold-pressor test.
• Comparing results with the maximal information coefficient method.

Main Results:

• Findings align with previous CAN research.
• New insights into fronto-parietal activity and temporal cortex lateralization during sympathetic stimulation.
• Identified specific time delays in brain-heart interactions.
• Afferent autonomic outflow influences δ and γ brain oscillations; efferent control involves θ, α, and β bands.

Conclusions:

• The proposed framework enables novel biomarker discovery for complex physiological networks.
• It facilitates the use of existing data generation models for brain and peripheral dynamics analysis.
• Provides a new tool for understanding brain-heart communication.