PhD Defence: Applying New Dynamic Models to Near-Infrared Spectroscopy Measurements of Cerebral Changes

Student: Nima Soltani

Supervisor: Dr. Vladislav Toronov

Abstract

This thesis advances the application of biophotonics and computational modeling to monitor cerebral physiology during cardiac arrest (CA) and cardiopulmonary resuscitation (CPR). Leveraging near-infrared spectroscopy (NIRS), diffuse correlation spectroscopy (DCS), and laser Doppler flowmetry (LDF), we investigate cerebral blood flow (CBF), cerebral blood volume (CBV), and the cerebral metabolic rate of oxygen (CMRO2) in a porcine model of CA. These optical methods enable real-time, non-invasive assessment of brain metabolism and microvascular dynamics during ischemia and reperfusion.

Central to this work is the non-linear extension of the Coherent Hemodynamic Spectroscopy (CHS) model, a physics-based framework that simulates light tissue interactions and oxygen transport across arteriole, capillary, and venule compartments. Applied to experimental NIRS, LDF and DCS data, the model quantifies absolute CBF and CMRO2 values and captures time- resolved changes in cerebral physiology during CA and CPR.

A key objective is to explore the coupling between CMRO2, and the redox state of cytochrome C oxidase (rCCO), a mitochondrial biomarker derived from hyperspectral NIRS. Power-law relationships between CMRO2, rCCO, and CBF are revealed, showing distinct behavior across ischemic periods and during resuscitation. These dynamics provide insight into mitochondrial function and oxygen utilization during critical care events.

In addition, a real-time algorithm was developed to extract physiological parameters from optical signals, enabling continuous bedside monitoring of cerebral states. This tool supports clinical decision-making by offering timely, quantitative insight into cerebral metabolism and perfusion.

Collectively, this work integrates physics, model-based analysis, and experimental neuroscience to deepen our understanding of cerebral responses to extreme physiological stress. By validating non-linear CHS under large perturbations and demonstrating its utility in capturing key metabolic and vascular parameters, the thesis contributes to the development of personalized monitoring tools in neurocritical care. These findings lay the groundwork for future translational research focused on reducing neurological injury and improving outcomes for patients.