Combined transfer function analysis and modelling of cerebral autoregulation.

Area of Science:

• Physiology
• Biomedical Engineering
• Mathematical Modeling

Background:

• Cerebral autoregulation is clinically vital, with extensive research on modeling its physiological processes.
• Existing models often fail to integrate mathematical relationships between arterial blood pressure and cerebral blood flow velocity for clinical data interpretation.

Purpose of the Study:

• To propose a simple, physiologically-based model of cerebral autoregulation.
• To estimate model parameters from experimental data and assess their clinical relevance.
• To investigate the impact of feedback gain, time constant, and intracranial pressure on autoregulation.

Main Methods:

• Developed a model based on a flow-dependent feedback mechanism adjusting arterial compliance.
• Analyzed the model's approximation to a second-order system.
• Estimated model parameters using Impulse Response (IR) data.
• Investigated the effects of feedback gain, time constant, and elevated intracranial pressure (ICP).

Main Results:

• The proposed model closely approximates a second-order system for typical physiological parameters.
• Model parameters were estimated robustly from experimental IR data, yielding physiologically reasonable values.
• Changes in feedback gain and time constant significantly affected the predicted IR.
• Elevated baseline ICP was equivalent to reduced feedback gain, though less sensitive in the model.

Conclusions:

• A transfer function approach using this physiologically-based model can clinically estimate autoregulation status.
• The model provides enhanced insight into the mechanisms governing cerebral autoregulation.
• This approach facilitates a more integrated interpretation of clinical data related to cerebral blood flow regulation.