Hemodynamic models provide a mathematical representation and computational framework that describe the changes in blood flow, blood volume, and oxygenation levels that occur in response to neural activity and systemic changes, while near-infrared spectroscopy (NIRS) measures deoxyhemoglobin, oxyhemoglobin, and other chromophores to analyze cerebral hemodynamics and metabolism. In this study, we apply a dynamic hemometabolic model to NIRS data acquired during cardiac arrest and cardiopulmonary resuscitation (CPR) in pigs. Our goals were to test the model’s ability to accurately describe the observed phenomena, to gain an understanding of the intricate behavior of cerebral microvasculature, and to compare the obtained parameters with known values. By employing the inverse of the hemometabolic model, we measured a range of significant physiological parameters, such as the rate of oxygen diffusion from blood to tissue, the arteriole and venule volume fractions, and the Fåhraeus factor. Statistical analysis uncovered significant differences in the baseline and post-cardiac arrest values of some of the parameters.

Evaluating brain oxygen metabolism during cardiac arrest and cardiopulmonary resuscitation (CPR) is essential for improving neurological outcomes and guiding clinical interventions in high-stress medical emergencies. This study focused on two key indicators of brain oxygen metabolism: the cerebral metabolic rate of oxygen (CMRO₂) and the oxidation state of redox cytochrome c oxidase (rCCO). Using advanced techniques such as hyperspectral near-infrared spectroscopy (hNIRS) and laser Doppler flowmetry (LDF), we conducted a comprehensive analysis of their relationship in pigs during and after cardiac arrest and CPR. Both the entire duration of these experiments and specific time intervals were investigated, providing a detailed view of how these metrics interact. The data reveal a non-linear relationship between rCCO and CMRO₂. Our findings contribute to a deeper understanding of how the brain manages oxygen during critical episodes, potentially guiding future interventions in neurological care and improving outcomes in emergency medical settings.

Understanding brain oxygen metabolism is crucial for evaluating overall health, par-ticularly in high-stress medical situations such as cardiac arrest and cardiopulmonary resuscitation (CPR). This study focuses on two key indicators of brain oxygen metabo-lism: the cerebral metabolic rate of oxygen (CMRO2) and the oxidation state of redox cytochrome c oxidase (rCCO). Using advanced techniques like hyperspectral near-infrared spectroscopy (hNIRS) and laser doppler flowmetry (LDF), we have con-ducted a comprehensive analysis of their relationship in pigs during and after cardiac arrest and CPR. We investigated both the entire duration of these experiments and specific time intervals, providing a detailed view of how these metrics interact. The data reveals non-linear relationship between rCCO and CMRO2. Our findings contribute to a deeper understanding of how the brain manages oxygen during critical episodes, po-tentially guiding future interventions in neurological care and improving outcomes in emergency medical settings.

The Coherent Hemodynamic Spectroscopy (CHS) model provides a quantitative framework for modeling cerebral hemodynamics and metabolism, particularly in response to small physiological perturbations. However, in its original approximate formulation it was limited to conditions where parameter changes were constrained to 10–20%, making it unsuitable for modeling extreme physiological disruptions such as cardiac arrest. In this study, we present a detailed discussion of the algorithm using the complete CHS model, which extends the original framework by solving partial differential equations without approximations to handle large non-periodic perturbations. This model was applied to data from a previously published cardiac arrest and cardiopulmonary resuscitation (CPR) study in pigs, where cerebral blood flow changed by 100%. While our prior work demonstrated the utility of this approach for analyzing cerebral microvascular and metabolic parameters, it did not include the algorithmic details necessary for reproducibility and broader application. Here, we address this gap by describing the algorithm’s workflow, including the use of non-linear multivariate optimization, and its ability to recover multiple physiological variables, such as the capillary and venule oxygen saturations, and parameters, such as the capillary oxygen diffusion rate, and arterial oxygen saturation. The latter can be valuable when the pulse oximetry measurements are unavailable due to unstable, weak or absent pulse. This study underscores the importance of non-linear modeling in advancing the application of CHS to extreme physiological conditions and highlights its potential for translational research and clinical innovation.

The Coherent Hemodynamic Spectroscopy (CHS) model provides a quantitative framework for modeling cerebral hemodynamics and metabolism, particularly in response to small physiological perturbations. However, in its original approximate formulation it was limited to conditions where parameter changes were constrained to 10–20%, making it unsuitable for modeling extreme physiological disruptions such as cardiac arrest. In this study, we present a detailed discussion of the algorithm using the complete CHS model, which extends the original framework by solving partial differential equations without approximations to handle large non-periodic perturbations. This model was applied to data from a previously published cardiac arrest and cardiopulmonary resuscitation (CPR) study in pigs, where cerebral blood flow changed by 100%. While our prior work demonstrated the utility of this approach for analyzing cerebral microvascular and metabolic parameters, it did not include the algorithmic details necessary for reproducibility and broader application. Here we address this gap by describing the algorithm's workflow, including the use of non-linear multivariate optimization, and its ability to recover multiple physiological variables, such as the capillary and venule oxygen saturations, and parameters, such as the capillary oxygen diffusion rate, and arterial oxygen saturation. The latter can be valuable when the pulse oximetry measurements are unavailable due to unstable, weak or absent pulse. This study underscores the importance of non-linear modeling in advancing the application of CHS to extreme physiological conditions and highlights its potential for translational research and clinical innovation.

Optical neuromonitoring has matured from descriptive oxygenation trends to model-informed quantification of cerebral physiology. This review synthesizes evidence on near-infrared spectroscopy (NIRS), diffuse correlation spectroscopy (DCS), and laser Doppler flowmetry (LDF) for monitoring cerebral blood flow (CBF), blood volume (CBV), and cerebral metabolic rate of oxygen (CMRO₂) during cardiac arrest (CA) and cardiopulmonary resuscitation (CPR). We focus on using hemo-metabolic models, especially Coherent Hemodynamic Spectroscopy (CHS) and the BrainSignals models, as a framework to explain what optical signals do (and do not) tell us about microvascular oxygen transport and mitochondrial metabolism. We compare linear vs. non-linear CHS formulations for large perturbations (e.g., CA/CPR), summarize emerging depth-sensitivity and extracerebral-signal suppression strategies, and outline how DCS pairs with NIRS to link oxygen delivery with use. Across animal and human studies, we highlight convergent patterns (rapid oxygenation collapse, partial reperfusion during CPR, lagging metabolic recovery), recurring limitations (extracerebral contamination, calibration to absolutes, motion), and standardization efforts required for translation. We conclude with a pragmatic roadmap for bedside implementation: harmonized physiological endpoints (CBF, CMRO₂, rCCO), reporting standards, and model-informed thresholds to guide resuscitation. This review aims to bridge instrumentation, physiology, and modeling to enhance neuroprotective care in CA/CPR.