Date of Award
Doctor of Philosophy (PhD)
Electrical & Computer Engineering
Near-infrared spectroscopy (NIRS) is a non-invasive technology to evaluate skeletal muscle oxidative metabolism in healthy and disease states. This technology allows us to measure the dynamic response of oxygenated (Δ𝐻𝑏𝑀𝑏𝑂2) and deoxygenated (Δ𝐻𝐻𝑏𝑀𝑏) heme group concentrations during muscle contraction. These O2 kinetics are valuable for inferring the interplay between muscle oxygen delivery and utilization. However, the semi-quantitative nature of the NIRS signal limits its clinical application. Some of the challenges in interpreting the NIRS signal are related to the difficulties in quantifying the: 1) contribution of blood volume changes to the Δ𝐻𝑏𝑀𝑏𝑂2 and Δ𝐻𝐻𝑏𝑀𝑏; 2) contribution of Hb and Mb to both NIRS signals; 3) relationship between the NIRS signals and the heterogeneous O2 distribution in the muscle region investigated. Computational models of O2 transport and metabolism in skeletal muscle can be used to analyze these limitations and suggest strategies to overcome these limitations.
A computational model of O2 transport and utilization is used to analyze the NIRS data obtained from an animal model of muscle oxidative metabolism under different experimental conditions previously investigated. The aims of this analysis are to study: 1) the effects of blood flow on hemoglobin (Hb) and myoglobin (Mb) oxygenation kinetics in contracting muscles; 2) the effects of O2 delivery and blood volume changes on the NIRS signals; 3) the relationship between venous and NIRS tissue oxygenation; 4) the Hb and Mb contribution to the absolute and relative changes of heme group concentrations; and 5) the relationship between NIRS signal and oxygen distribution in the microvascular and extravascular volumes in contracting muscle. The simulation results show that the mathematical model has the capability to simulate and predict the O2 changes measured in the blood and tissue domains with invasive and non-invasive methods (NIRS) to evaluate metabolic function in contracting muscle under different experimental conditions (blood flow, metabolic rate). The main findings of the computational analysis are summarized as follows.
1) Faster O2 delivery is responsible for slower Δ𝐻𝑏𝑀𝑏𝑂2 and Δ𝐻𝐻𝑏𝑀𝑏 kinetics. Hb and Mb contributions to the oxygenated heme groups differ from those to the deoxygenated heme groups.
2) Both Δ𝐻𝑏𝑀𝑏𝑂2 and Δ𝐻𝐻𝑏𝑀𝑏 are affected by the blood volume changes. The analysis indicates that microvascular O2 saturation is a key factor in determining the sensitivity of Δ𝐻𝑏𝑀𝑏𝑂2 and Δ𝐻𝐻𝑏𝑀𝑏 to blood volume changes.
3) At low O2 delivery, the Mb contribution to NIRS signal is responsible for the non-linearity in the relationship between venous and NIRS oxygenation.
4) For an increase of O2 delivery, Hb contribution to the absolute oxygenated heme group concentrations decreases, whereases the same Hb contribution to the relative oxygenated heme group concentration increases.
5) Differential pathlength factor (DPF) for constant wave NIRS can be estimated by an integrative approach combining simulated and experimental data for NIRS kinetics. The estimated DPF can be used with the simulation of the microvascular and tissue oxygenation to predict the changes of oxygenated and deoxygenated heme group concentrations measured by NIRS.
The analysis indicates that the computational model can overcome some of the NIRS limitations in providing a quantitative relationship between NIRS signals and the oxygenation distribution in the main compartments of the muscle region investigated.
"Integrative Computational Analysis of Muscle Near-Infrared Spectroscopy Signals: Effects of Oxygen Delivery and Blood Volume"
(2021). Doctor of Philosophy (PhD), Dissertation, Electrical & Computer Engineering, Old Dominion University, DOI: 10.25777/pahw-dr59
Available for download on Wednesday, September 28, 2022