Optical monitoring of cerebral hemodynamics and oxygenation with varying anesthetic depth in a rat model

Abstract

Monitoring the depth of anesthesia (DoA) is a critical component of modern surgical practice, ensuring patient safety by preventing intraoperative awareness while avoiding the hemodynamic instability associated with anesthetic overdose. Currently, electroencephalogram (EEG)-based monitors, such as the Bispectral Index (BIS), are the standard of care. However, these modalities have significant limitations, including signal latency, susceptibility to electromyographic (EMG) artifacts, and unreliability in patients with neurological pathologies or under specific anesthetic agents like ketamine. Consequently, there is an unmet clinical need for a more physiological and universal monitoring modality. Functional near-infrared spectroscopy (fNIRS) offers a promising alternative by non-invasively measuring cerebral hemodynamic changes—specifically oxyhemoglobin (∆[HbO]), deoxyhemoglobin (∆[HbR]), and total hemoglobin (∆[HbT])—which serve as surrogate markers for neural activity via neurovascular coupling. This dissertation investigates the feasibility, validity, and generalizability of fNIRS as a novel DoA monitoring technology through three progressive preclinical studies using rat models.

The first study aimed to validate the physiological basis of fNIRS signals by correlating them with Local Field Potentials (LFP), a direct measure of neural activity. To eliminate signal attenuation and scattering caused by the skull and obtain the most accurate cortical signals, an invasive model was established in Long-Evans rats (n=8, male) where fNIRS optodes and LFP electrodes were implanted directly onto the dura mater of the prefrontal cortex. The anesthetic agent used was Isoflurane, a potent vasodilator. The results demonstrated a strong correlation between hemodynamic parameters and LFP spectral changes across varying anesthetic concentrations (2.5%, 2.0%, 1.5%, and 1.0%). Depending on the existence of a rapid increase in the ∆[HbO] and ∆[HbT] during the administration of 1.0% of isoflurane, the results are divided into ‘Awaken during Anesthesia (AA)’ group (n=4) and ‘Not Awaken during Anesthesia (NAA)’ group (n=4). In the NAA group, ∆[HbO], ∆[HbT] and the Ratio of Reflectance Intensity (RRI)— a ratiometric index developed to provide absolute values for easy comparison and minimize blood volume artifacts—showed a linear decrease as the anesthetic concentration was reduced, reflecting the washout of Isoflurane's vasodilatory effect combined with metabolic changes. Notably, in the AA group, a distinct hemodynamic surge (abrupt increase in ∆[HbO], ∆[HbT] and RRI) was observed at 1.0% concentration, immediately preceding behavioral awakening. In both groups, ∆[HbR] always showed opposite changes to ∆[HbO], ∆[HbT] and RRI. This finding suggests that fNIRS can detect the metabolic activation associated with the return of consciousness earlier than behavioral signs, highlighting its potential as a predictive marker for intraoperative awareness.

The second study explored the clinical applicability of fNIRS by transitioning to a minimally invasive model. Using Sprague-Dawley rats (n=8, male), we attached fNIRS optodes to the intact skull to mimic non-invasive clinical conditions. Despite the presence of the skull, the hemodynamic patterns observed under the same Isoflurane protocol were qualitatively identical to those in the invasive model. The linear dose-dependence in the NAA group (n=5) and the awakening surge in the AA group (n=3) were successfully reproduced. To theoretically validate these findings, Monte Carlo simulations were performed. The simulation results revealed that the skull acts as an optical diffuser, broadening the light illumination area and effectively increasing the detection depth to the brain parenchyma by approximately 23% compared to the invasive model. These results provide robust evidence that fNIRS can reliably monitor cerebral hemodynamics and anesthetic depth even when measured transcranially, supporting its feasibility for human clinical application.

The third study evaluated the generalizability of the fNIRS methodology by employing Midazolam, an intravenous anesthetic with a different pharmacological mechanism and minimal direct vasodilatory effects compared to Isoflurane. Using an invasive Sprague-Dawley rat model (n=8, male) with a jugular vein catheter and the same fNIRS setup as Study 1, hemodynamic changes were monitored across five stages: Awake, Induction, Deep Sedation, Light Sedation, and Recovery. Unlike Isoflurane, which caused an initial increase in blood volume due to vasodilation, Midazolam induction resulted in a decrease in ∆[HbO] and ∆[HbT], accurately reflecting the reduction in cerebral metabolic rate of oxygen (CMRO2) due to neural suppression. During the Deep Sedation phase, ∆[HbO], ∆[HbT] and RRI dropped to their lowest levels, significantly distinct from the Awake and Light Sedation phases. As the animals transitioned to Light Sedation and Recovery, these parameters gradually returned to baseline levels. This study confirmed that fNIRS fundamentally monitors brain metabolic changes and subsequent neural activity recovery according to anesthetic depth, demonstrating the universal efficacy of fNIRS across a variety of anesthetics and their corresponding vascular characteristics.

In conclusion, this dissertation establishes fNIRS as a powerful and reliable tool for monitoring the depth of anesthesia. By systematically verifying its physiological validity, clinical applicability, and pharmacological generalizability, this research demonstrates that fNIRS-derived parameters, particularly the RRI index, can distinguish between different states of consciousness and sedation with high sensitivity. The consistent detection of metabolic suppression during deep anesthesia and the rapid identification of pre-awakening hemodynamic surges suggest that fNIRS can complement existing EEG-based monitors. This optical approach paves the way for a new standard of "physiological DoA monitoring," potentially enabling precision anesthesia tailored to the individual patient's cerebral metabolic state and enhancing surgical safety.