Application of Fluorescence-Guided Surgery to Subsurface Cancers Requiring Wide Local Excision: Literature Review and Novel Developments Toward Indirect Visualization.

The excision of tumors by wide local excision is challenging because the mass must be removed entirely without ever viewing it directly. Positive margin rates in sarcoma resection remain in the range of 20% to 35% and are associated with increased recurrence and decreased survival. Fluorescence-guided surgery (FGS) may improve surgical accuracy and has been utilized in other surgical specialties. ABY-029, an anti-epidermal growth factor receptor Affibody molecule covalently bound to the near-infrared fluorophore IRDye 800CW, is an excellent candidate for future FGS applications in sarcoma resection; however, conventional methods with direct surface tumor visualization are not immediately applicable. A novel technique involving imaging through a margin of normal tissue is needed. We review the past and present applications of FGS and present a novel concept of indirect FGS for visualizing tumor through a margin of normal tissue and aiding in excising the entire lesion as a single, complete mass with tumor-free margins.

Quantifying cerebral blood flow in an adult pig ischemia model by a depth-resolved dynamic contrast-enhanced optical method.

Dynamic contrast-enhanced (DCE) near-infrared (NIR) methods have been proposed for bedside monitoring of cerebral blood flow (CBF). These methods have primarily focused on qualitative approaches since scalp contamination hinders quantification. In this study, we demonstrate that accurate CBF measurements can be obtained by analyzing multi-distance time-resolved DCE data with a combined kinetic deconvolution optical reconstruction (KDOR) method. Multi-distance time-resolved DCE-NIR measurements were made in adult pigs (n=8) during normocapnia, hypocapnia and ischemia. The KDOR method was used to calculate CBF from the DCE-NIR measurements. For validation, CBF was measured independently by CT under each condition. The mean CBF difference between the techniques was -1.7 mL/100 g/min with 95% confidence intervals of -16.3 and 12.9 mL/100 g/min; group regression analysis revealed a strong agreement between the two techniques (slope=1.06±0.08, y-intercept=-4.37±4.33 mL/100 g/min, p<0.001). The results of an error analysis suggest that little a priori information is needed to recover CBF, due to the robustness of the analytical method and the ability of time-resolved NIR to directly characterize the optical properties of the extracerebral tissue (where model mismatch is deleterious). The findings of this study suggest that the DCE-NIR approach presented is a minimally invasive and portable means of determining absolute hemodynamics in neurocritical care patients.

Prediction of Occult Hemorrhage in the Lower Body Negative Pressure Model: Initial Validation of Machine Learning Approaches.

INTRODUCTION: Detection of occult hemorrhage (OH) before progression to clinically apparent changes in vital signs remains an important clinical problem in managing trauma patients. The resource-intensiveness associated with continuous clinical patient monitoring and rescue from frank shock makes accurate early detection and prediction with noninvasive measurement technology a desirable innovation. Despite significant efforts directed toward the development of innovative noninvasive diagnostics, the implementation and performance of the newest bedside technologies remain inadequate. This poor performance may reflect the limitations of univariate systems based on one sensor in one anatomic location. It is possible that when signals are measured with multiple modalities in multiple locations, the resulting multivariate anatomic and temporal patterns of measured signals may provide additional discriminative power over single technology univariate measurements. We evaluated the potential superiority of multivariate methods over univariate methods. Additionally, we utilized machine learning-based models to compare the performance of noninvasive-only to noninvasive-plus-invasive measurements in predicting the onset of OH. MATERIALS AND METHODS: We applied machine learning methods to preexisting datasets derived using the lower body negative pressure human model of simulated hemorrhage. Employing multivariate measured physiological signals, we investigated the extent to which machine learning methods can effectively predict the onset of OH. In particular, we applied 2 ensemble learning methods, namely, random forest and gradient boosting. RESULTS: Analysis of precision, recall, and area under the receiver operating characteristic curve showed a superior performance of multivariate approach to that of the univariate ones. In addition, when using both invasive and noninvasive features, random forest classifier had a recall 95% confidence interval (CI) of 0.81 to 0.86 with a precision 95% CI of 0.65 to 0.72. Interestingly, when only noninvasive features were employed, the results worsened only slightly to a recall 95% CI of 0.80 to 0.85 and a precision 95% CI of 0.61 to 0.73. CONCLUSIONS: Multivariate ensemble machine learning-based approaches for the prediction of hemodynamic instability appear to hold promise for the development of effective solutions. In the lower body negative pressure multivariate hemorrhage model, predictions based only on noninvasive measurements performed comparably to those using both invasive and noninvasive measurements.

A birefringent spectral demultiplexer enables fast hyper-spectral imaging of protoporphyrin IX during neurosurgery.

Hyperspectral imaging and spectral analysis quantifies fluorophore concentration during fluorescence-guided surgery1-6. However, acquisition of the multiple wavelengths required to implement these methods can be time-consuming and hinder surgical workflow. To this end, a snapshot hyperspectral imaging system capable of acquiring 64 channels of spectral data simultaneously was developed for rapid hyperspectral imaging during neurosurgery. The system uses a birefringent spectral demultiplexer to split incoming light and redirect wavelengths to different sections of a large format microscope sensor. Its configuration achieves high optical throughput, accepts unpolarized input light and exceeds channel count of prior image-replicating imaging spectrometers by 4-fold. Tissue-simulating phantoms consisting of serial dilutions of the fluorescent agent characterize system linearity and sensitivity, and comparisons to performance of a liquid crystal tunable filter based hyperspectral imaging device are favorable. The new instrument showed comparable, if not improved, sensitivity at low fluorophore concentrations; yet, acquired wide-field images at more than 70-fold increase in frame rate. Image data acquired in the operating room during human brain tumor resection confirm these findings. The new device is an important advance in achieving real-time quantitative imaging of fluorophore concentration for guiding surgery.

Automated motion artifact correction for dynamic contrast-enhanced fluorescence imaging during open orthopedic surgery.

Indocyanine green (ICG)-based dynamic contrast-enhanced fluorescence imaging (DCE-FI) can objectively assess bone perfusion intraoperatively. However, it is susceptible to motion artifacts due to patient's involuntary respiration during the 4.5-minute DCE-FI data acquisition. An automated motion correction approach based on mutual information (MI) frameby-frame was developed to overcome this problem. In this approach, MIs were calculated between the reference and the adjacent frame translated and the maximal MI corresponded to the optimal translation. The images obtained from eighteen amputation cases were utilized to validate the approach and the results show that this correction can significantly reduce the motion artifacts and can improve the accuracy of bone perfusion assessment.

Model-independent dynamic constraint to improve the optical reconstruction of regional kinetic parameters.

Optical dye-dilution techniques can quantify kinetic parameters in a region of tissue, but currently rely on a two-step process-spatial reconstruction of the dye concentration, repeated at every time-point, and subsequent kinetic analysis of the time-dependent change in dye concentration. Inaccuracies, in this approach, are due mainly to the ill-posed nature of the spatial reconstruction problem, which propagates into kinetic analysis and result in errors in extracted dynamic parameters. We present a hybrid kinetic deconvolution optical reconstruction algorithm, effectively combining optical reconstruction and model-independent kinetic analysis into a single inverse problem that is better posed. Kinetic parameters of multiple tissue regions can be quantified simultaneously. As proof of principle, we provide numerical experiments in reflectance-based and fluorescence molecular tomography scenarios.

Spatial and temporal patterns in dynamic-contrast enhanced intraoperative fluorescence imaging enable classification of bone perfusion in patients undergoing leg amputation.

Dynamic contrast-enhanced fluorescence imaging (DCE-FI) classification of tissue viability in twelve adult patients undergoing below knee leg amputation is presented. During amputation and with the distal bone exposed, indocyanine green contrast-enhanced images were acquired sequentially during baseline, following transverse osteotomy and following periosteal stripping, offering a uniquely well-controlled fluorescence dataset. An unsupervised classification machine leveraging 21 different spatiotemporal features was trained and evaluated by cross-validation in 3.5 million regions-of-interest obtained from 9 patients, demonstrating accurate stratification into normal, suspicious, and compromised regions. The machine learning (ML) approach also outperformed the standard method of using fluorescence intensity only to evaluate tissue perfusion by a two-fold increase in accuracy. The generalizability of the machine was evaluated in image series acquired in an additional three patients, confirming the stability of the model and ability to sort future patient image-sets into viability categories.

Dynamic contrast-enhanced fluorescence imaging compared with MR imaging in evaluating bone perfusion during open orthopedic surgery.

ICG-based dynamic contrast-enhanced fluorescence imaging (DCE-FI) and intraoperative DCE- magnetic resonance imaging (MRI) have been carried out nearly simultaneously in three lower extremity bone infection cases to investigate the relationship between these two imaging modalities for assessing bone blood perfusion during open orthopedic surgeries. Time-intensity curves in the corresponding regions of interest of two modalities were derived for comparison. The results demonstrated that ICG-based DCE-FI has higher sensitivity to perfusion changes while DCE-MRI provides superior and supplemental depth-related perfusion information. Research applying the depth-related perfusion information derived from MRI to improve the overall analytic modeling of intraoperative DCE-FI is ongoing.

Detection of subclinical hemorrhage using electrical impedance: a porcine study.

Objective.Analyze the performance of electrical impedance tomography (EIT) in an innovative porcine model of subclinical hemorrhage and investigate associations between EIT and hemodynamic trends.Approach. Twenty-five swine were bled at slow rates to create an extended period of subclinical hemorrhage during which the animal's heart rate (HR) and blood pressure (BP) remained stable from before hemodynamic deterioration, where stable was defined as <15% decrease in BP and <20% increase in HR-i.e.hemorrhages were hidden from standard vital signs of HR and BP. Continuous vital signs, photo-plethysmography, and continuous non-invasive EIT data were recorded and analyzed with the objective of developing an improved means of detecting subclinical hemorrhage-ideally as early as possible.Main results. Best area-under-the-curve (AUC) values from comparing bleed to no-bleed epochs were 0.96 at a 80 ml bleed (∼15.4 min) using an EIT-data-based metric and 0.79 at a 120 ml bleed (∼23.1 min) from invasively measured BP-i.e.the EIT-data-based metric achieved higher AUCs at earlier points compared to standard clinical metrics without requiring image reconstructions.Significance.In this clinically relevant porcine model of subclinical hemorrhage, EIT appears to be superior to standard clinical metrics in early detection of hemorrhage.

Depth resolution and multiexponential lifetime analyses of reflectance-based time-domain fluorescence data.

Time-domain fluorescence imaging is a powerful new technique that adds a rich amount of information to conventional fluorescence imaging. Specifically, time-domain fluorescence can be used to remove autofluorescence from signals, resolve multiple fluorophore concentrations, provide information about tissue microenvironments, and, for reflectance-based imaging systems, resolve inclusion depth. The present study provides the theory behind an improved method of analyzing reflectance-based time-domain data that is capable of accurately recovering mixed concentration ratios of multiple fluorescent agents while also recovering the depth of the inclusion. The utility of the approach was demonstrated in a number of simulations and in tissuelike phantom experiments using a short source-detector separation system. The major findings of this study were (1) both depth of an inclusion and accurate ratios of two-fluorophore concentrations can be recovered accurately up to depths of approximately 1 cm with only the optical properties of the medium as prior knowledge, (2) resolving the depth and accounting for the dispersion effects on fluorescent lifetimes is crucial to the accuracy of recovered ratios, and (3) ratios of three-fluorophore concentrations can be resolved at depth but only if the lifetimes of the three fluorophores are used as prior knowledge. By accurately resolving the concentration ratios of two to three fluorophores, it may be possible to remove autofluorescence or carry out quantitative techniques, such as reference tracer kinetic modeling or ratiometric approaches, to determine receptor binding or microenvironment parameters in point-based time-domain fluorescence applications.

Early Detection of Hypotension Using a Multivariate Machine Learning Approach.

INTRODUCTION: The ability to accurately detect hypotension in trauma patients at the earliest possible time is important in improving trauma outcomes. The earlier an accurate detection can be made, the more time is available to take corrective action. Currently, there is limited research on combining multiple physiological signals for an early detection of hemorrhagic shock. We studied the viability of early detection of hypotension based on multiple physiologic signals and machine learning methods. We explored proof of concept with a small (5 minutes) prediction window for application of machine learning tools and multiple physiologic signals to detecting hypotension. MATERIALS AND METHODS: Multivariate physiological signals from a preexisting dataset generated by an experimental hemorrhage model were employed. These experiments were conducted previously by another research group and the data made available publicly through a web portal. This dataset is among the few publicly available which incorporate measurement of multiple physiological signals from large animals during experimental hemorrhage. The data included two hemorrhage studies involving eight sheep. Supervised machine learning experiments were conducted in order to develop deep learning (viz., long short-term memory or LSTM), ensemble learning (viz., random forest), and classical learning (viz., support vector machine or SVM) models for the identification of physiological signals that can detect whether or not overall blood loss exceeds a predefined threshold 5 minutes ahead of time. To evaluate the performance of the machine learning technologies, 3-fold cross-validation was conducted and precision (also called positive predictive value) and recall (also called sensitivity) values were compared. As a first step in this development process, 5 minutes prediction windows were utilized. RESULTS: The results showed that SVM and random forest outperform LSTM neural networks, likely because LSTM tends to overfit the data on small sized datasets. Random forest has the highest recall (84%) with 56% precision while SVM has 62% recall with 82% precision. Upon analyzing the feature importance, it was observed that electrocardiogram has the highest significance while arterial blood pressure has the least importance among all other signals. CONCLUSION: In this research, we explored the viability of early detection of hypotension based on multiple signals in a preexisting animal hemorrhage dataset. The results show that a multivariate approach might be more effective than univariate approaches for this detection task.

Predicting Future Occurrence of Acute Hypotensive Episodes Using Noninvasive and Invasive Features.

INTRODUCTION: Early prediction of the acute hypotensive episode (AHE) in critically ill patients has the potential to improve outcomes. In this study, we apply different machine learning algorithms to the MIMIC III Physionet dataset, containing more than 60,000 real-world intensive care unit records, to test commonly used machine learning technologies and compare their performances. MATERIALS AND METHODS: Five classification methods including K-nearest neighbor, logistic regression, support vector machine, random forest, and a deep learning method called long short-term memory are applied to predict an AHE 30 minutes in advance. An analysis comparing model performance when including versus excluding invasive features was conducted. To further study the pattern of the underlying mean arterial pressure (MAP), we apply a regression method to predict the continuous MAP values using linear regression over the next 60 minutes. RESULTS: Support vector machine yields the best performance in terms of recall (84%). Including the invasive features in the classification improves the performance significantly with both recall and precision increasing by more than 20 percentage points. We were able to predict the MAP with a root mean square error (a frequently used measure of the differences between the predicted values and the observed values) of 10 mmHg 60 minutes in the future. After converting continuous MAP predictions into AHE binary predictions, we achieve a 91% recall and 68% precision. In addition to predicting AHE, the MAP predictions provide clinically useful information regarding the timing and severity of the AHE occurrence. CONCLUSION: We were able to predict AHE with precision and recall above 80% 30 minutes in advance with the large real-world dataset. The prediction of regression model can provide a more fine-grained, interpretable signal to practitioners. Model performance is improved by the inclusion of invasive features in predicting AHE, when compared to predicting the AHE based on only the available, restricted set of noninvasive technologies. This demonstrates the importance of exploring more noninvasive technologies for AHE prediction.

ICG-based dynamic contrast-enhanced fluorescence imaging guided open orthopaedic surgery: pilot patient study.

Forty two patients with high energy open fractures were involved into the study to investigate whether an indocyanine green (ICG)-based dynamic contrast-enhanced fluorescence imaging (DCE-FI) can be used to objectively assess bone perfusion and guide surgical debridement. For each patient, fluorescence images were recorded after 0.1 mg/kg of ICG was administered intravenously. By utilizing a bone-specific kinetic model to the video sequences, the perfusion-related metrics were calculated. The results of this study shown that the quantitative ICG-based DEC-FI can accurately assess the human bone perfusion during the orthopedic surgery.

Improving the Usability of 5-Aminolevulinic Acid Fluorescence-Guided Surgery by Adding an Optimized Secondary Light Source.

BACKGROUND: Tumors that take up and metabolize 5-aminolevulinic acid emit bright pink fluorescence when illuminated with blue light, aiding surgeons in identifying the margin of resection. The adoption of this method is hindered by the blue light illumination, which is too dim to safely operate under and therefore necessitates switching back and forth from white-light mode. The aim of this study was to examine the addition of an optimized secondary illuminant adapter to improve usability of blue-light mode without degrading tumor contrast. METHODS: Color science methods were used to evaluate the color of the secondary illuminant and its impact on color rendering index as well as the tumor-to-background color contrast in data collected from 7 patients with high-grade gliomas (World Health Organization grade III and IV). A secondary illuminant adapter was built to provide 475-600 nm light the intensity of which can be controlled by the surgeon and was evaluated in 2 additional patients. RESULTS: Secondary illuminant color had opposing effects on color rendering index and tumor-to-background color contrast; providing surgeon control of intensity allows this trade-off to be balanced in real time. Demonstration in 2 high-grade glioma cases confirms this, showing that additional visibility adds value when intensity can be controlled by the surgeon. CONCLUSIONS: Addition of a secondary illuminant may mitigate surgeon complaints that the operative field is too dark under the blue light illumination required for 5-aminolevulinic acid fluorescence guidance by providing improved color rendering index without completely sacrificing tumor-to-background color contrast.

Assessing Soft Tissue Perfusion Using Laser-Assisted Angiography in Tibial Plateau and Pilon Fractures: A Pilot Study.

OBJECTIVES: To determine whether skin perfusion surrounding tibial plateau and pilon fractures is associated with the Tscherne classification for severity of soft tissue injury. The secondary aim was to determine if soft tissue perfusion improves from the time of injury to the time of definitive fracture fixation in fractures treated using a staged protocol. DESIGN: Prospective cohort study. SETTING: Academic trauma center. PATIENTS: Eight pilon fracture patients and 19 tibial plateau fracture patients who underwent open reduction internal fixation. MAIN OUTCOME MEASURES: Skin perfusion (fluorescence units) as measured by LA-ICGA. RESULTS: Six patients were classified as Tscherne grade 0, 9 as grade 1, 10 as grade 2, and 2 as grade 3. Perfusion decreased by 14 fluorescence units (95% confidence interval, -21 to -6; P < 0.01) with each increase in Tscherne grade. Sixteen patients underwent staged fixation with an external fixator (mean time to definitive fixation 14.1 days). The mean perfusion increased significantly at the time of definitive fixation by a mean of 13.9 fluorescence units (95% confidence interval 4.8-22.9; P = 0.01). CONCLUSIONS: LA-ICGA perfusion measures are associated with severity of soft tissue injury surrounding orthopaedic trauma fractures and appear to improve over time when fractures are stabilized in an external fixator. Further research is warranted to investigate whether objective perfusion measures are predictive of postoperative wound healing complications and whether this tool can be used to effectively guide timing of safe surgical fixation. LEVEL OF EVIDENCE: Prognostic Level II. See Instructions for Authors for a complete description of levels of evidence.

Evaluation of bone perfusion during open orthopedic surgery using quantitative dynamic contrast-enhanced fluorescence imaging.

In this study, an indocyanine green (ICG)-based dynamic contrast- enhanced fluorescence imaging (DCE-FI) technique was evaluated as a method to provide objective real-time data on bone perfusion using a porcine osteotomy model. DCE-FI with sequentially increasing injury to osseous blood supply was performed in 12 porcine tibias. There were measurable, reproducible and predictable changes to DCE-FI data across each condition have been observed on simple kinetic curve-derived variables as well variables derived from a novel bone-specific kinetic model. The best accuracy, sensitivity and specificity of 89%, 88% and 90%, have been achieved to effectively differentiate injured from normal/healthy bone.

Intraoperative fluorescence perfusion assessment should be corrected by a measured subject-specific arterial input function.

SIGNIFICANCE: The effects of varying the indocyanine green injection dose, injection rate, physiologic dispersion of dye, and intravenous tubing volume propagate into the shape and magnitude of the arterial input function (AIF) during intraoperative fluorescence perfusion assessment, thereby altering the observed kinetics of the fluorescence images in vivo. AIM: Numerical simulations are used to demonstrate the effect of AIF on metrics derived from tissue concentration curves such as peak fluorescence, time-to-peak (TTP), and egress slope. APPROACH: Forward models of tissue concentration were produced by convolving simulated AIFs with the adiabatic approximation to the tissue homogeneity model using input parameters representing six different tissue examples (normal brain, glioma, normal skin, ischemic skin, normal bone, and osteonecrosis). RESULTS: The results show that AIF perturbations result in variations in estimates of total intensity of up to 80% and TTP error of up to 200%, with the errors more dominant in brain, less in skin, and less in bone. Interestingly, error in ingress slope was as high as 60% across all tissue types. These are key observable parameters used in fluorescence imaging either implicitly by viewing the image or explicitly through intensity fitting algorithms. Correcting by deconvolving the image with a measured subject-specific AIF provides an intuitive means of visualizing the data while also removing the source of variance and allowing intra- and intersubject comparisons. CONCLUSIONS: These results suggest that intraoperative fluorescence perfusion assessment should be corrected by patient-specific AIFs measured by pulse dye densitometry.

Endosteal and periosteal blood flow quantified with dynamic contrast-enhanced fluorescence to guide open orthopaedic surgery.

Due to the lack of objectively measurable or quantifiable methods to assess the bone perfusion, the success of removing devitalized bone is based almost entirely on surgeon's experience and varies widely across surgeons and centers. In this study, an indocyanine green (ICG)-based dynamic contrast-enhanced fluorescence imaging (DCE-FI) has been developed to objectively assess bone perfusion and guide surgical debridement. A porcine trauma model (n = 6 pigs × 2 legs) with up to 5 conditions of severity in loss of flow in each, was imaged by a commercial fluorescence imaging system. By applying the bone-specific hybrid plug-compartment (HyPC) kinetic model to four-minute video sequences, the perfusion-related metrics, such as peak intensity, total bone blood flow (TBBF) and endosteal bone blood flow to TBBF fraction (EFF) were calculated. The results shown that the combination of TBBF and EFF can effectively differentiate injured from normal bone with the accuracy, sensitivity and specificity of 89%, 88% and 90%, respectively. Our subsequent first in human bone blood flow imaging study confirmed DCE-FI can be successfully translated into human orthopaedic trauma patients.

Perspective on optical imaging for functional assessment in musculoskeletal extremity trauma surgery.

SIGNIFICANCE: Extremity injury represents the leading cause of trauma hospitalizations among adults under the age of 65 years, and long-term impairments are often substantial. Restoring function depends, in large part, on bone and soft tissue healing. Thus, decisions around treatment strategy are based on assessment of the healing potential of injured bone and/or soft tissue. However, at the present, this assessment is based on subjective clinical clues and/or cadaveric studies without any objective measure. Optical imaging is an ideal method to solve several of these issues. AIM: The aim is to highlight the current challenges in assessing bone and tissue perfusion/viability and the potentially high impact applications for optical imaging in orthopaedic surgery. APPROACH: The prospective will review the current challenges faced by the orthopaedic surgeon and briefly discuss optical imaging tools that have been published. With this in mind, it will suggest key research areas that could be evolved to help make surgical assessments more objective and quantitative. RESULTS: Orthopaedic surgical procedures should benefit from incorporation of methods to measure functional blood perfusion or tissue metabolism. The types of measurements though can vary in the depth of tissue sampled, with some being quite superficial and others sensing several millimeters into the tissue. Most of these intrasurgical imaging tools represent an ideal way to improve surgical treatment of orthopaedic injuries due to their inherent point-of-care use and their compatibility with real-time management. CONCLUSION: While there are several optical measurements to directly measure bone function, the choice of tools can determine also the signal strength and depth of sampling. For orthopaedic surgery, real-time data regarding bone and tissue perfusion should lead to more effective patient-specific management of common orthopaedic conditions, requiring deeper penetrance commonly seen with indocyanine green imaging. This will lower morbidity and result in decreased variability associated with how these conditions are managed.

Cerebral metabolic rate of oxygen and amplitude-integrated electroencephalography during early reperfusion after hypoxia-ischemia in piglets.

The therapeutic window following perinatal hypoxia-ischemia is brief, and early clinical signs of injury can be subtle. Electroencephalography (EEG) represents the most promising early diagnostic of hypoxia-ischemia; however, some studies have questioned the sensitivity and specificity of EEG. The present study investigated the use of both near-infrared spectroscopy (NIRS) measurements of the cerebral metabolic rate of oxygen (CMRO(2)) and amplitude-integrated EEG (aEEG) to detect the severity of hypoxia-ischemia after 1 h of reperfusion in newborn piglets (10 insult, 3 control). The CMRO(2) was measured before and after 1 h of reperfusion from hypoxia-ischemia, the duration of which was varied from piglet to piglet with a range of 3-24 min, under fentanyl/nitrous oxide anesthesia to mimic awake-like levels of cerebral metabolism. EEG data were collected throughout the study. On average, the CMRO(2) and mean aEEG background signals were significantly depressed following the insult (P < 0.05). Mean CMRO(2) and mean aEEG background were 2.61 +/- 0.11 ml O(2).min(-1).100 g(-1) and 20.4 +/- 2.7 microV before the insult and 1.58 +/- 0.09 ml O(2).min(-1).100 g(-1) and 11.8 +/- 2.9 microV after 1 h of reperfusion, respectively. Both CMRO(2) and aEEG displayed statistically significant correlations with duration of ischemia (P < 0.05; r = 0.71 and r = 0.89, respectively); however, only CMRO(2) was sensitive to milder injuries (<5 min). This study highlights the potential for combining NIRS measures of CMRO(2) with EEG in the neonatal intensive care unit to improve early detection of perinatal hypoxia-ischemia.