Cerebral autoregulation (CA) is a mechanism to maintain cerebral blood flow (CBF) in response to changes in cerebral perfusion pressure (CPP), through active vasoconstriction and vasodilation of arterioles in the brain. Dynamic CA is believed to act as a high-pass filter such that only low frequency changes in pressure are counteracted by an active vasculature response. With high frequency oscillations in pressure, such as those that occur at the heart rate (HR), the effects of dynamic CA are absent and changes in CPP are passively transmitted to CBF based on the cerebrovascular resistance (CVR) and compliance (CVC). These changes in CVR/CVC occur with steady-state changes in CA which can be described by Lassen’s curve. However, it is unclear what drives phase differences between pressure and flow at the respiration rate of around 0.2 Hz (12 breaths per minute). Quantifying phase differences at the physiologic respiration rate could be useful to gain a better understanding of the effects of CA and as a potential clinical monitoring tool. In this work, we looked at phase differences between arterial blood pressure (ABP) and intracranial pressure (ICP) measured with invasive pressure sensors, which serve as surrogates for CPP and CBF, to investigate how Arg(ABP)-Arg(ICP) change at the respiration rate as a function of the CPP. We quantify how Arg(ABP)-Arg(ICP) changes with respect to CPP after low-frequency oscillations, respiratory induced oscillations, and with oscillations driven by the heart rate. In each frequency regime, the trends in phase differences between Arg(ABP)-Arg(ICP) are unique with respect to CPP. At the respiration rate, the trends in Arg(ABP)-Arg(ICP) did not completely follow those predicted by a dynamic CA response or by CVC/CVR, thus we believe that there is a combination of effects influencing the phase difference between Arg(ABP)-Arg(ICP) at the respiration frequency. We also explore whether this response could be monitored completely non-invasively using near infrared spectroscopy (NIRS). We use Arg(ΔHbT)-Arg(ΔHbO) as surrogates for CPP and CBF and see a similar response of phase differences with respect to CPP at the respiration rate.
SignificanceIntracranial pressure (ICP) measurements are important for patient treatment but are invasive and prone to complications. Noninvasive ICP monitoring methods exist, but they suffer from poor accuracy, lack of generalizability, or high cost.AimWe previously showed that cerebral blood flow (CBF) cardiac waveforms measured with diffuse correlation spectroscopy can be used for noninvasive ICP monitoring. Here we extend the approach to cardiac waveforms measured with near-infrared spectroscopy (NIRS).ApproachChanges in hemoglobin concentrations were measured in eight nonhuman primates, in addition to invasive ICP, arterial blood pressure, and CBF changes. Features of average cardiac waveforms in hemoglobin and CBF signals were used to train a random forest (RF) regressor.ResultsThe RF regressor achieves a cross-validated ICP estimation of 0.937r2, 2.703-mmHg2 mean squared error (MSE), and 95% confidence interval (CI) of [ − 3.064 3.160 ] mmHg on oxyhemoglobin concentration changes; 0.946r2, 2.301-mmHg2 MSE, and 95% CI of [ − 2.841 2.866 ] mmHg on total hemoglobin concentration changes; and 0.963r2, 1.688 mmHg2 MSE, and 95% CI of [ − 2.450 2.397 ] mmHg on CBF changes.ConclusionsThis study provides a proof of concept for the use of NIRS in noninvasive ICP estimation.
Vascular impedance is a frequency dependent quantity relating a vascular compartment's flow dynamics to pressure changes. Although vascular impedance has been investigated in larger arteries using Doppler ultrasound, probing the smaller microvasculature using similar techniques is difficult due to their small cross-sectional area. However, recent developments using diffuse optics have enabled the possibility of measuring blood flow and volume in arterioles and other microvasculature. This research presents a method to estimate the arteriole impedance non-invasively using diffuse correlation spectroscopy (DCS) as well as near-infrared spectroscopy (NIRS).
Intracranial pressure (ICP) is an important metric in the management of severe head injury. We show alternatives to today’s standard of highly invasive measurement devices using near-infrared spectroscopy and diffuse correlation spectroscopy to create a real-time ICP monitor. The algorithms were developed and tested in an animal model. First results of a clinical validation will be presented.
Cerebrovascular Autoregulation failure is known to allow drastic changes in cerebral blood flow in cases of extreme Cerebral Perfusion Pressure (CPP) or Intracranial Pressure (ICP). Brain pathologies (such as traumatic brain injury, hydrocephalus, stroke, etc.) which alter CPP and ICP are also known to show impaired neurovascular coupling. We analyzed these characteristic changes in neurovascular coupling in a model to develop a non-invasive diagnostic marker of autoregulation failure using EEG, Near-Infrared Spectroscopy and Diffuse Correlation Spectroscopy.
Measuring intracranial pressure (ICP) is typically a highly invasive procedure, in which a ventricular catheter or pressure sensor is placed into the brain. To improve the availability of ICP measurements in non-intensive care patients and research and to reduce the invasiveness and underlying risks of ICP sensing, we developed a non-invasive method to measure ICP with Diffuse Correlation Spectroscopy (DCS) and machine learning. ICP baseline changes were induced in non-human primates (Macaca mulatta) through adjusting the height of a saline reservoir connected to the lateral ventricle via a catheter. ICP was precisely measured with an invasive parenchymal pressure sensor. Cerebral blood flow (CBF) was measured with DCS. The DCS system was operated by a software correlator able to resolve cardiac pulse waves at a sampling rate of 100Hz. To increase signal-to-noise ratio, multiple cardiac pulse waves in CBF were averaged based on systolic peak maximum in invasively measured arterial blood pressure. We hypothesized that the cerebral blood flow pulse waves will change their shape with increasing ICP. The shape of the curve was expressed in numerical features and passed into a regression forest training algorithm. Preliminary results show successful prediction of underlying ICP baselines by the decision forest in one animal. The prediction of non-invasive ICP was achieved with a sampling rate of 1 Hz, an equivalent of about 120 averaged pulses. A larger data set for increased generalizability is the next step to push this approach further.
Diffuse correlation spectroscopy (DCS) is an optical method for non-invasive measurements of blood flow in deep tissue microvasculature, such as the brain, without the need for tracers or ionizing radiation. The technique relies on determining temporal autocorrelations of light intensity fluctuations which arise due to time changing speckle patterns of moving scatterers when illuminated by a long coherence length laser. Measurements of blood flow using DCS have extensively been validated and have found some clinical translation already. High temporal resolution by fast sampling of the autocorrelation curves has recently been achieved by software based correlators. Here we demonstrate a new software correlator approach which uses components that are an order of magnitude cheaper than current approaches. We will present on the instrument design, as well as measurements of pulsatile blood flow on healthy volunteers. We will show blood flow measurements with a signal bandwidth of 50Hz and present on signal to noise ratios (SNR) of extracted pulse waveforms as a function of sampling rate. We will show how using an EKG based timing of the signal for averaging increases the fidelity of extracting the blood flow waveform even in low SNR environments. We will further present results of the pulsatile waveforms and the latency of the dicrotic notch as affected by posture changes in healthy volunteers.
Guiding treatment in traumatic brain injury based on managing and optimizing cerebral perfusion pressure, which is the difference between mean arterial blood pressure and intracranial pressure (ICP), has been demonstrated to improve patient outcome. However, this requires ICP to be measured, which currently is only possible by placing pressure probes inside the brain. The feasibility of optical systems to measure ICP non-invasively has shown preliminary promising evidence of feasibility. To pursue the goal of non-invasive ICP acquisition further, an understanding of the influence of different pressure changes on the brain and their hemodynamic response is necessary. To investigate the frequency content of hemodynamic reactions to pressure changes in both ICP as well as arterial blood pressure (ABP), we induced changes of both pressures in non-human primates. We then demonstrate that ABP and ICP changes both influence cerebral blood flow and hemoglobin concentrations, measured with diffuse correlation spectroscopy (DCS) and near-infrared spectroscopy (NIRS), respectively. We found that the magnitude of induced oscillations is dependent on the frequency of the oscillation. Our data suggests, changes in ABP and ICP influence the hemodynamics differently, which we can use as a basis for non-invasive ICP measurements.
Cerebral microvascular changes are influenced by intracranial pressure (ICP) as well as mean arterial blood pressure (MAP). The mechanism maintaining blood flow despite changes in either pressure is called cerebral autoregulation. This mechanism is known to be impaired in many diseases, including traumatic brain injury and stroke. Maintaining adequate cerebral blood flow and autoregulation is known to improve long term patient outcomes. However, the influence on the microvasculature and autoregulation of blood pressure vs. fluid increase, hence intracranial pressure, is not well understood. Furthermore, while blood pressure changes can readily be measured, intracranial pressure sensors are invasive and there is a need to overcome this invasiveness. We have recently shown that changes in cerebral perfusion pressure, which is the difference between blood pressure and intracranial pressure, can be correlated to total hemoglobin concentration, as measured non-invasively with near-infrared spectroscopy (NIRS) in non-human primates. These results showed that non-invasive intracranial pressure monitoring should be possible by means of vascular changes as measured with NIRS. In order to quantify autoregulation and differentiate between blood pressure and fluid increase driven vascular changes, we collected data on non-human primates. The primates’ brains were cannulated to induce rapid changes in ICP. Exsanguination was performed to reduce blood pressure. Data was collected with a combined frequency domain NIRS (OxiplexTS, ISS Inc.) and diffuse correlation spectroscopy (DCS) system for measuring hemoglobin concentration changes as well as blood flow changes, respectively. We will present on the experimental implementation as well as data analysis for quantifying cerebral autoregulation.
KEYWORDS: In vivo imaging, Optical coherence tomography, Image registration, Eye, 3D metrology, 3D microstructuring, 3D image processing, Tissues, Optic nerve, Blood vessels
Elevated intraocular pressure (IOP) deforms the lamina cribrosa (LC), a structure within the optic nerve head (ONH) in the back of the eye. Evidence suggests that these deformations trigger events that eventually cause irreversible blindness, and have therefore been studied in-vivo using optical coherence tomography (OCT), and ex-vivo using OCT and a diversity of techniques. To the best of our knowledge, there have been no in-situ ex-vivo studies of LC mechanics. Our goal was two-fold: to introduce a technique for measuring 3D LC deformations from OCT, and to determine whether deformations of the LC induced by elevated IOP differ between in-vivo and in-situ ex-vivo conditions. A healthy adult rhesus macaque monkey was anesthetized and IOP was controlled by inserting a 27- gauge needle into the anterior chamber of the eye. Spectral domain OCT was used to obtain volumetric scans of the ONH at normal and elevated IOPs. To improve the visibility of the LC microstructure the scans were first processed using a novel denoising technique. Zero-normalized cross-correlation was used to find paired corresponding locations between images. For each location pair, the components of the 3D strain tensor were determined using non-rigid image registration. A mild IOP elevation from 10 to 15mmHg caused LC effective strains as large as 3%, and about 50% larger in-vivo than in-situ ex-vivo. The deformations were highly heterogeneous, with substantial 3D components, suggesting that accurate measurement of LC microstructure deformation requires high-resolution volumes. This technique will help improve understanding of LC biomechanics and how IOP contributes to glaucoma.
Although it is well documented that abnormal levels of either intraocular (IOP) or intracranial pressure (ICP) can lead to potentially blinding conditions, such as glaucoma and papilledema, little is known about how the pressures actually affect the eye. Even less is known about potential interplay between their effects, namely how the level of one pressure might alter the effects of the other. Our goal was to measure in-vivo the pressure-induced stretch and compression of the lamina cribrosa due to acute changes of IOP and ICP. The lamina cribrosa is a structure within the optic nerve head, in the back of the eye. It is important because it is in the lamina cribrosa that the pressure-induced deformations are believed to initiate damage to neural tissues leading to blindness. An eye of a rhesus macaque monkey was imaged in-vivo with optical coherence tomography while IOP and ICP were controlled through cannulas in the anterior chamber and lateral ventricle, respectively. The image volumes were analyzed with a newly developed digital image correlation technique. The effects of both pressures were highly localized, nonlinear and non-monotonic, with strong interactions. Pressure variations from the baseline normal levels caused substantial stretch and compression of the neural tissues in the posterior pole, sometimes exceeding 20%. Chronic exposure to such high levels of biomechanical insult would likely lead to neural tissue damage and loss of vision. Our results demonstrate the power of digital image correlation technique based on non-invasive imaging technologies to help understand how pressures induce biomechanical insults and lead to vision problems.
The mechanism that maintains a stable blood flow in the brain despite changes in cerebral perfusion pressure (CPP), and therefore guaranties a constant supply of oxygen and nutrients to the neurons, is known as cerebral auto-regulation (CA). In a certain range of CPP, blood flow is mediated by a vasomotor adjustment in vascular resistance through dilation of blood vessels. CA is known to be impaired in diseases like traumatic brain injury, Parkinson’s disease, stroke, hydrocephalus and others. If CA is impaired, blood flow and pressure changes are coupled and thee oxygen supply might be unstable. Lassen’s blood flow auto-regulation curve describes this mechanism, where a plateau of stable blood flow in a specific range of CPP corresponds to intact auto-regulation. Knowing the limits of this plateau and maintaining CPP within these limits can improve patient outcome. Since CPP is influenced by both intracranial pressure and arterial blood pressure, long term changes in either can lead to auto-regulation impairment. Non-invasive methods for monitoring blood flow auto-regulation are therefore needed. We propose too use Near infrared spectroscopy (NIRS) too fill this need. NIRS is an optical technique, which measures microvascular changes in cerebral hemoglobin concentration. We performed experiments on non-human primates during exsanguination to demonstrate that thee limits of blood flow auto-regulation can be accessed with NIRS.
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