Autoregulation in a simulator-based educational model of intracranial physiology

Area of Science:

• Biomedical Engineering
• Neuroscience Education
• Physiological Modeling

Background:

• Existing educational brain models lack realistic autoregulation mechanisms.
• Accurate simulation of cerebral hemodynamics is crucial for neuroanesthesia training.

Purpose of the Study:

• To implement a dynamic autoregulation mechanism in an educational brain model.
• To enhance the real-time display of key cerebrovascular parameters (CMRO2, CBF, CBV, ICP, CPP).

Main Methods:

• Developed a cerebrovascular resistance (CVR) feedback loop to maintain cerebral blood flow (CBF) relative to cerebral perfusion pressure (CPP).
• Integrated a patient simulator for physiological parameters and incorporated oxygen demand and CO2 responsivity as key CBF determinants.
• Modeled increased oxygen extraction (up to 70%) when CBF is insufficient to meet metabolic demand (CMRO2).

Main Results:

• The enhanced model demonstrated autoregulation, with CVR, CBF, CBV, and ICP changes consistent with literature.
• Model responses showed less than 9% variation from literature data.
• Strong correlations were found between model predictions and experimental data for CBF-PaCO2 relationships at different mean arterial blood pressures (R2=0.92 and R2=0.70).

Conclusions:

• The autoregulated brain model accurately simulates cerebrovascular dynamics and intracranial pressure.
• The model's enhanced features, including CO2 responsivity and variable oxygen extraction, improve its educational utility for neuroanesthesia.
• Future enhancements will expand its application to more complex clinical scenarios like herniation and drug effects.