Taking the lid off ambitions for optical imaging of the human brain: a conversation with Clare Elwell

Ilias Tachtsidis, professor of biomedical engineering, senior member of the Biomedical Optics Research Laboratory, and head of the Multi-Modal Spectroscopy Group at University College London (UCL), interviewed his colleague and mentor Clare Elwell, professor of medical physics at UCL and Vice Dean of Impact for UCL Engineering, about her pioneering work in fNIRS and brain imaging for global health

Chapter Developing a Model to Simulate the Effect of Hypothermia on Cerebral Blood Flow and Metabolism

Hypoxic ischemic encephalopathy (HIE) is a significant cause of death and neurological disability in newborns. Therapeutic hypothermia at 33.5 °C is one of the most common treatments in HIE and generally improves outcome; however 45–55% of injuries still result in death or severe neurodevelopmental disability. We have developed a systems biology model of cerebral oxygen transport and metabolism to model the impact of hypothermia on the piglet brain (the neonatal preclinical animal model) tissue physiology. This computational model is an extension of the BrainSignals model of the adult brain. The model predicts that during hypothermia there is a 5.1% decrease in cerebral metabolism, 1.1% decrease in blood flow and 2.3% increase in cerebral tissue oxygenation saturation. The model can be used to simulate effects of hypothermia on the brain and to help interpret bedside recordings

Evaluation of hyperspectral imaging measurements of changes in hemoglobin oxygenation and oxidation of cytochrome-c-oxidase using a liquid blood phantom

Optical imaging is a non-invasive technique that is able to monitor hemodynamic and metabolic responses during neurosurgery. However, a robust quantification is complicated to perform. To overcome this issue, phantoms that mimic biological tissues are required for the development of imaging systems in order to reach a true standardization. In this work, we explore the possibility to use a combined liquid blood phantom with cytochrome contained yeast to evaluate the reliability of hyperspectral imaging to measure oxygenation and metabolic changes. This phantom can be used to verify the reliability of intraoperative optical setups before moving on to clinical application

Watching the human retina breath in real time and the slowing of mitochondrial respiration with age

The retina has the greatest metabolic demand in the body particularly in dark adaptation when its sensitivity is enhanced. This requires elevated level of perfusion to sustain mitochondrial activity. However, mitochondrial performance declines with age leading to reduced adaptive ability. We assessed human retina metabolism in vivo using broad band near-infrared spectroscopy (bNIRS), which records colour changes in mitochondria and blood as retinal metabolism shifts in response to changes in environmental luminance. We demonstrate a significant sustained rise in mitochondrial oxidative metabolism in the first 3 min of darkness in subjects under 50 years old. This was not seen in those over 50 years. Choroidal oxygenation declines in  50 s. Significant group differences in blood oxygenation are apparent in the first 6 min, consistent with mitochondrial demand leading hemodynamic changes. A greater coupling between mitochondrial oxidative metabolism with hemodynamics is revealed in subjects older than 50, possibly due to reduced capacity in the older retina. Rapid in vivo assessment of retinal metabolism with bNIRS provides a route to understanding fundamental physiology and early identification of retinal disease before pathology is established

Systemic physiology augmented functional near-infrared spectroscopy: a powerful approach to study the embodied human brain.

In this Outlook paper, we explain why an accurate physiological interpretation of functional near-infrared spectroscopy (fNIRS) neuroimaging signals is facilitated when systemic physiological activity (e.g., cardiorespiratory and autonomic activity) is measured simultaneously by employing systemic physiology augmented functional near-infrared spectroscopy (SPA-fNIRS). The rationale for SPA-fNIRS is twofold: (i) SPA-fNIRS enables a more complete interpretation and understanding of the fNIRS signals measured at the head since they contain components originating from neurovascular coupling and from systemic physiological sources. The systemic physiology signals measured with SPA-fNIRS can be used for regressing out physiological confounding components in fNIRS signals. Misinterpretations can thus be minimized. (ii) SPA-fNIRS enables to study the embodied brain by linking the brain with the physiological state of the entire body, allowing novel insights into their complex interplay. We envisage the SPA-fNIRS approach will become increasingly important in the future

Prediction of brain tissue temperature using near-infrared spectroscopy

Broadband near-infrared spectroscopy (NIRS) can provide an endogenous indicator of tissue temperature based on the temperature dependence of the water absorption spectrum. We describe a first evaluation of the calibration and prediction of brain tissue temperature obtained during hypothermia in newborn piglets (animal dataset) and rewarming in newborn infants (human dataset) based on measured body (rectal) temperature. The calibration using partial least squares regression proved to be a reliable method to predict brain tissue temperature with respect to core body temperature in the wavelength interval of 720 to 880 nm with a strong mean predictive power of R2=0.713±0.157 (animal dataset) and R2=0.798±0.087 (human dataset). In addition, we applied regression receiver operating characteristic curves for the first time to evaluate the temperature prediction, which provided an overall mean error bias between NIRS predicted brain temperature and body temperature of 0.436±0.283°C (animal dataset) and 0.162±0.149°C (human dataset). We discuss main methodological aspects, particularly the well-known aspect of over- versus underestimation between brain and body temperature, which is relevant for potential clinical applications

Role of Optical Neuromonitoring in Neonatal Encephalopathy—Current State and Recent Advances

Neonatal encephalopathy (NE) in term and near-term infants is a significant global health problem; the worldwide burden of disease remains high despite the introduction of therapeutic hypothermia. Assessment of injury severity and effective management in the neonatal intensive care unit (NICU) relies on multiple monitoring modalities from systemic to brain-specific. Current neuromonitoring tools provide information utilized for seizure management, injury stratification, and prognostication, whilst systemic monitoring ensures multi-organ dysfunction is recognized early and supported wherever needed. The neuromonitoring technologies currently used in NE however, have limitations in either their availability during the active treatment window or their reliability to prognosticate and stratify injury confidently in the early period following insult. There is therefore a real need for a neuromonitoring tool that provides cot side, early and continuous monitoring of brain health which can reliably stratify injury severity, monitor response to current and emerging treatments, and prognosticate outcome. The clinical use of near-infrared spectroscopy (NIRS) technology has increased in recent years. Research studies within this population have also increased, alongside the development of both instrumentation and signal processing techniques. Increasing use of commercially available cerebral oximeters in the NICU, and the introduction of advanced optical measurements using broadband NIRS (BNIRS), frequency domain NIRS (FDNIRS), and diffuse correlation spectroscopy (DCS) have widened the scope by allowing the direct monitoring of oxygen metabolism and cerebral blood flow, both key to understanding pathophysiological changes and predicting outcome in NE. This review discusses the role of optical neuromonitoring in NE and why this modality may provide the next significant piece of the puzzle toward understanding the real time state of the injured newborn brain

Modelling Noninvasively Measured Cerebral Signals during a Hypoxemia Challenge: Steps towards Individualised Modelling

Noninvasive approaches to measuring cerebral circulation and metabolism are crucial to furthering our understanding of brain function. These approaches also have considerable potential for clinical use “at the bedside”. However, a highly nontrivial task and precondition if such methods are to be used routinely is the robust physiological interpretation of the data. In this paper, we explore the ability of a previously developed model of brain circulation and metabolism to explain and predict quantitatively the responses of physiological signals. The five signals all noninvasively-measured during hypoxemia in healthy volunteers include four signals measured using near-infrared spectroscopy along with middle cerebral artery blood flow measured using transcranial Doppler flowmetry. We show that optimising the model using partial data from an individual can increase its predictive power thus aiding the interpretation of NIRS signals in individuals. At the same time such optimisation can also help refine model parametrisation and provide confidence intervals on model parameters. Discrepancies between model and data which persist despite model optimisation are used to flag up important questions concerning the underlying physiology, and the reliability and physiological meaning of the signals

A digital instrument simulator to optimize the development of a hyperspectral imaging system for neurosurgery

In recent years, hyperspectral imaging (HSI) has demonstrated its capacity to non-invasively differentiate tumors from healthy tissues and identify cancerous regions during neurosurgery. Indeed, the spectral information contained in the HS images allows to identify more chromophores, refining the information provided by the imaging system, and allowing to identify the unique signature of each tissue types more accurately. Our HyperProbe project aims at developing a novel HSI system optimized for neurosurgery. As part of this project, we are developing a digital instrument simulator (DIS), based on Monte-Carlo (MC) simulations of the light propagation in tissues, in order to optimize both the hardware and data processing pipeline of our novel instrument. This framework allows us (1) to test the effect on the accuracy of the measurement of several hardware parameters, like the numerical aperture or sensitivity of the detector; (2) to be used as numerical phantoms to test various data processing algorithms; and (3) to generate generic data to develop and train machine learning (ML) algorithms. To do so, our framework is based on a 2-step method. Firstly, MC simulations are run to produce an ideal dataset of the photon transport in tissue. Then, the raw output parameters of the simulations, such as the exit positions and directions of the photons, are processed to take into account the physical parameters of an instrument in order to produce realistic images and test various scenarios. We present here the initial development of this DIS