Cerebral autoregulation: from models to clinical applications

Area of Science:

• Neuroscience
• Biomedical Engineering
• Physiology

Background:

• Cerebral blood flow (CBF) regulation is vital for brain health, maintained by myogenic, metabolic, and neurogenic mechanisms.
• Cerebral autoregulation (CA) ensures stable CBF despite arterial blood pressure (ABP) fluctuations.
• Dynamic CA models are crucial for understanding transient CBF-ABP relationships and clinical applications.

Purpose of the Study:

• To review the literature on the application of cerebral autoregulation (CA) models in various clinical conditions.
• To evaluate the effectiveness of linear and non-linear dynamic models for assessing CA.
• To highlight the need for advanced models and validation protocols for dynamic CA parameters.

Main Methods:

• Review of existing literature on dynamic CA modeling.
• Focus on linear input-output models like transfer function analysis (TFA) and second-order differential equations.
• Discussion of non-linear dynamic models and multivariate approaches.

Main Results:

• Linear models, particularly TFA, have been widely used and shown sensitivity to pathophysiological changes in conditions like stroke and head injury.
• Indices such as the autoregulation index (ARI) and frequency-domain parameters from TFA are clinically relevant.
• Non-linear models show promise but require further validation for clinical use.

Conclusions:

• Mathematical modeling of dynamic CA is essential for clinical insights.
• Linear models are currently prevalent, but non-linear and multivariate models are needed for comprehensive understanding.
• Further research is required to validate advanced dynamic CA models and establish normal parameter ranges.