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Published on: July 28, 2018
Model estimation of cerebral hemodynamics between blood flow and volume changes: a data-based modeling approach
Hua-Liang Wei1, Ying Zheng, Yi Pan
1Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UK. w.Hualiang@sheffield.ac.uk
IEEE Transactions on Bio-Medical Engineering
|January 29, 2009
Summary
This study models the dynamic relationship between cerebral blood flow (CBF) and cerebral blood volume (CBV) using experimental data. A novel regularized total least-squares (RTLS) method accurately characterizes these hemodynamic changes, even with noisy data.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Medical Imaging
Background:
- The relationship between cerebral blood flow (CBF) and cerebral blood volume (CBV) is dynamic and crucial for understanding brain function.
- Functional MRI (fMRI) relies on blood oxygen-level-dependent (BOLD) signals, necessitating accurate hemodynamic modeling.
- Existing methods struggle with noisy CBF and CBV data, limiting precise hemodynamic relationship analysis.
Purpose of the Study:
- To develop an empirical, data-driven modeling framework for identifying the relationship between CBF and CBV.
- To introduce and evaluate a novel Regularized Total Least-Squares (RTLS) method for modeling noisy hemodynamic data.
- To establish an effective model for characterizing changes in CBF and CBV.
Main Methods:
- Utilized experimental CBF and CBV data for model identification.
- Employed an autoregressive with exogenous input (ARX) model structure.
- Introduced and applied a Regularized Total Least-Squares (RTLS) method to address error-in-variables problems in noisy data.
- Combined RTLS with a filtering method for enhanced model parsimony and effectiveness.
Main Results:
- The relationship between CBF and CBV changes can be parsimoniously modeled using an ARX structure.
- Ordinary Least-Squares (LS) and classical Total Least-Squares (TLS) methods yielded inaccurate estimates from noisy data.
- The proposed RTLS method demonstrated high accuracy in modeling noisy CBF and CBV data.
- A combination of RTLS and filtering provided a parsimonious yet highly effective model.
Conclusions:
- The RTLS method is a robust approach for modeling the dynamic relationship between CBF and CBV from experimental data.
- Accurate hemodynamic modeling is essential for advancing fMRI applications and understanding brain function.
- The developed modeling framework offers a powerful tool for characterizing complex physiological relationships in neuroimaging.
