Evaluation of Cerebral Blood Flow and Cerebral Autoregulation Using Synthetic Data and In Silico Modeling

Area of Science:

• Neuroscience
• Physiology
• Medical Simulation

Background:

• The classic view posits stable cerebral blood flow (CBF) across a wide mean arterial pressure (MAP) range.
• Recent studies suggest a narrower autoregulatory range due to MAP manipulation methods influencing CBF.

Purpose of the Study:

• To evaluate static cerebral autoregulatory function using simulations and synthetic data.
• To provide a theoretical framework for understanding static autoregulation in controlled settings.

Main Methods:

• Generated MAP and CBF data using a numerical model of cardio-cerebrovascular systems.
• Employed sensitivity analyses to assess parameter influence on CBF.
• Utilized partial regression analysis to determine independent hemodynamic effects on CBF.

Main Results:

• Sensitivity analysis identified systemic arteriolar resistance and arterial CO2 pressure as key CBF influencers.
• Partial regression confirmed significant effects of systemic arteriolar resistance and arterial CO2 pressure on CBF.
• Mean arterial pressure showed a significant but weak correlation with CBF.

Conclusions:

• Simulations and synthetic data are effective tools for evaluating static cerebral autoregulation.
• This approach offers a robust theoretical framework for studying autoregulation under controlled conditions.