Linear and nonlinear modeling of cerebral flow autoregulation using principal dynamic modes

Area of Science:

• Neuroscience
• Physiology
• Biomedical Engineering

Background:

• Cerebral flow autoregulation (CFA) dynamically maintains brain blood flow during perfusion pressure changes.
• Static CFA describes steady-state conditions, while dynamic CFA is critical in neurological disorders like stroke.
• Previous dynamic CFA models were often linear, neglecting CO2's influence.

Purpose of the Study:

• To develop and apply a nonlinear modeling methodology for dynamic cerebral flow autoregulation.
• To investigate the impact of carbon dioxide (CO2) tension on dynamic CFA.
• To quantify dynamic CFA using Principal Dynamic Modes (PDMs) from short data records.

Main Methods:

• Utilized a nonlinear modeling approach for dynamic CFA.
• Incorporated beat-to-beat mean arterial blood pressure and end-tidal CO2 as inputs.
• Applied Principal Dynamic Modes (PDMs) for model robustness and interpretation.

Main Results:

• Demonstrated the significance of including CO2 dynamics in CFA modeling.
• Showcased the utility of nonlinear models, particularly under varying CO2 conditions (hypercapnia/hypocapnia).
• PDMs improved the robustness and physiological interpretability of the nonlinear CFA models.

Conclusions:

• Nonlinear modeling, including CO2, is essential for accurately assessing dynamic cerebral flow autoregulation.
• This approach provides a more comprehensive understanding of CFA in health and disease.
• The methodology offers improved insights into cerebrovascular regulation mechanisms.