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Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
Published on: December 10, 2014
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Compartmental and Data-Based Modeling of Cerebral Hemodynamics: Linear Analysis
B C Henley1, D C Shin1, R Zhang2
1Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089 USA.
IEEE Access : Practical Innovations, Open Solutions
|February 23, 2016
Summary
This study compares compartmental and data-based models for cerebral blood flow regulation. Findings show qualitative similarities between the two modeling approaches for dynamic cerebral autoregulation and CO2-vasomotor reactivity.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Physiology
Background:
- Cerebral hemodynamics are studied using compartmental and data-based models.
- Dynamic cerebral autoregulation (DCA) and CO2-vasomotor reactivity (DVR) are key aspects of cerebral blood flow regulation.
Purpose of the Study:
- To examine the relationship between compartmental equivalent-circuit and data-based input-output models of DCA and DVR.
- To compare the linear dynamics of a compartmental model with data-based estimates.
Main Methods:
- Constructed a compartmental model as an equivalent-circuit based on first principles.
- Utilized previously proposed hypothesis-based models.
- Compared the linear input-output dynamics of the compartmental model with data-based estimates of the DCA-DVR process.
Main Results:
- Identified qualitative similarities between the two-input compartmental model and experimental results.
- Demonstrated potential for integrating different modeling approaches in cerebral hemodynamics.
Conclusions:
- The study suggests that compartmental and data-based models of cerebral hemodynamics share common characteristics.
- Further research can explore the synergy between these modeling techniques for a comprehensive understanding of brain blood flow regulation.
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