Compartmental and Data-Based Modeling of Cerebral Hemodynamics: Nonlinear Analysis

Area of Science:

• Neuroscience
• Physiology
• Biomedical Engineering

Background:

• Cerebral autoregulation (CA) and CO2-vasomotor reactivity (VR) are critical for maintaining stable cerebral blood flow.
• Previous studies compared linear compartmental and data-based models of CA and VR.
• A nonlinear approach is needed to fully understand the complex CA-VR process.

Purpose of the Study:

• To extend previous CA-VR modeling by investigating the process in a nonlinear context.
• To utilize principal dynamic modes (PDM) for a compact and interpretable input-output model of CA-VR.
• To compare PDM models derived from theoretical and experimental data.

Main Methods:

• Developed a nonlinear model of cerebral autoregulation and CO2-vasomotor reactivity using principal dynamic modes (PDM).
• Simulated CA and VR curves using a large dynamic range of input data in an in silico study.
• Compared PDM models derived from both theoretical and experimental data.

Main Results:

• The PDM model accurately represented simulated static CA and VR curves within associated nonlinear functions (ANFs).
• The PDM model effectively separated the pressure-flow relationship into fast linear and slow nonlinear components, mirroring experimental observations.
• Good qualitative agreement was found between theoretical and experimental CO2-flow relationship PDMs.

Conclusions:

• Hypothesize that PDMs derived from experimental data represent passive fluid dynamics and active regulatory mechanisms.
• Combining hypothesis-based and data-based modeling offers insights into the physiological basis of PDM models from human data.
• The PDM approach provides a practical method for quantifying regulatory mechanisms in the CA-VR process.