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Dynamic model for the tissue concentration and oxygen saturation of hemoglobin in relation to blood volume, flow velocity, and oxygen consumption: Implications for functional neuroimaging and coherent hemodynamics spectroscopy (CHS)

机译:与血容量,流速和耗氧量相关的血红蛋白组织浓度和血氧饱和度的动态模型:对功能性神经影像学和相干血流动力学光谱学(CHS)的影响

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This article presents a dynamic model that quantifies the temporal evolution of the concentration and oxygen saturation of hemoglobin in tissue, as determined by time-varying hemodynamic and metabolic parameters: blood volume, flow velocity, and oxygen consumption. This multi-compartment model determines separate contributions from arterioles, capillaries, and venules that comprise the tissue microvasculature, and treats them as a complete network, without making assumptions on the details of the architecture and morphology of the microvascular bed. A key parameter in the model is the effective blood transit time through the capillaries and its associated probability of oxygen release from hemoglobin to tissue, as described by a rate constant for oxygen diffusion. The solution of the model in the time domain predicts the signals measured by hemodynamic-based neuroimaging techniques such as functional near-infrared spectroscopy (fNIRS) and functional magnetic resonance imaging (fMRI) in response to brain activation. In the frequency domain, the model yields an analytical solution based on a phasor representation that provides a framework for quantitative spectroscopy of coherent hemodynamic oscillations. I term this novel technique coherent hemodynamics spectroscopy (CHS), and this article describes how it can be used for the assessment of cerebral autoregulation and the study of hemodynamic oscillations resulting from a variety of periodic physiological challenges, brain activation protocols, or physical maneuvers.
机译:本文提出了一种动态模型,该模型可以量化随时间变化的血液动力学和代谢参数(血容量,流速和耗氧量)确定的组织中血红蛋白浓度和氧饱和度随时间的变化。这种多室模型确定了构成组织微脉管系统的小动脉,毛细血管和小静脉的独立贡献,并将它们视为完整的网络,而无需对微血管床的结构和形态进行任何假设。模型中的关键参数是通过毛细血管的有效血液传输时间及其相关的氧气从血红蛋白释放到组织的概率,如氧气扩散的速率常数所描述。该模型在时域中的解决方案可预测通过基于血液动力学的神经影像学技术(例如功能性近红外光谱(fNIRS)和功能性磁共振成像(fMRI))响应大脑的激活而测量的信号。在频域中,该模型基于相量表示提供了解析解决方案,为相干血液动力学振荡的定量光谱学提供了框架。我将这种新技术称为相干血流动力学光谱(CHS),本文将介绍如何将其用于评估大脑自动调节功能以及研究因各种周期性生理挑战,大脑激活协议或身体动作而引起的血液动力学振荡。

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