Assessment of a multi-layered diffuse correlation spectroscopy method for monitoring cerebral blood flow in adults

HyperTRCSS: A hyperspectral time-resolved compressive sensing spectrometer for depth-sensitive monitoring of cytochrome-c-oxidase and blood oxygenation

Significance

Hyperspectral time-resolved (TR) near-infrared spectroscopy offers the potential to monitor cytochrome-c-oxidase (oxCCO) and blood oxygenation in the adult brain with minimal scalp/skull contamination. We introduce a hyperspectral TR spectrometer that uses compressive sensing to minimize acquisition time without compromising spectral range or resolution and demonstrate oxCCO and blood oxygenation monitoring in deep tissue.

Aim

Develop a hyperspectral TR compressive sensing spectrometer and use it to monitor oxCCO and blood oxygenation in deep tissue.

Approach

Homogeneous tissue-mimicking phantom experiments were conducted to confirm the spectrometer's sensitivity to oxCCO and blood oxygenation. Two-layer phantoms were used to evaluate the spectrometer's sensitivity to oxCCO and blood oxygenation in the bottom layer through a 10 mm thick static top layer.

Results

The spectrometer was sensitive to oxCCO and blood oxygenation changes in the bottom layer of the two-layer phantoms, as confirmed by concomitant measurements acquired directly from the bottom layer. Measures of oxCCO and blood oxygenation by the spectrometer were highly correlated with "gold standard" measures in the homogeneous and two-layer phantom experiments.

Conclusions

The results show that the hyperspectral TR compressive sensing spectrometer is sensitive to changes in oxCCO and blood oxygenation in deep tissue through a thick static top layer.

Quantification of blood flow index in diffuse correlation spectroscopy using a robust deep learning method

Significance

Diffuse correlation spectroscopy (DCS) is a powerful, noninvasive optical technique for measuring blood flow. Traditionally the blood flow index (BFi) is derived through nonlinear least-square fitting the measured intensity autocorrelation function (ACF). However, the fitting process is computationally intensive, susceptible to measurement noise, and easily influenced by optical properties (absorption coefficient μ a and reduced scattering coefficient μ s ' ) and scalp and skull thicknesses.

Aim

We aim to develop a data-driven method that enables rapid and robust analysis of multiple-scattered light's temporal ACFs. Moreover, the proposed method can be applied to a range of source-detector distances instead of being limited to a specific source-detector distance.

Approach

We present a deep learning architecture with one-dimensional convolution neural networks, called DCS neural network (DCS-NET), for BFi and coherent factor (β ) estimation. This DCS-NET was performed using simulated DCS data based on a three-layer brain model. We quantified the impact from physiologically relevant optical property variations, layer thicknesses, realistic noise levels, and multiple source-detector distances (5, 10, 15, 20, 25, and 30 mm) on BFi and β estimations among DCS-NET, semi-infinite, and three-layer fitting models.

Results

DCS-NET shows a much faster analysis speed, around 17,000-fold and 32-fold faster than the traditional three-layer and semi-infinite models, respectively. It offers higher intrinsic sensitivity to deep tissues compared with fitting methods. DCS-NET shows excellent anti-noise features and is less sensitive to variations of μ a and μ s ' at a source-detector separation of 30 mm. Also, we have demonstrated that relative BFi (rBFi) can be extracted by DCS-NET with a much lower error of 8.35%. By contrast, the semi-infinite and three-layer fitting models result in significant errors in rBFi of 43.76% and 19.66%, respectively.

Conclusions

DCS-NET can robustly quantify blood flow measurements at considerable source-detector distances, corresponding to much deeper biological tissues. It has excellent potential for hardware implementation, promising continuous real-time blood flow measurements.

Comparison of diffuse correlation spectroscopy analytical models for measuring cerebral blood flow in adults

Significance

Although multilayer analytical models have been proposed to enhance brain sensitivity of diffuse correlation spectroscopy (DCS) measurements of cerebral blood flow, the traditional homogeneous model remains dominant in clinical applications. Rigorous in vivo comparison of these analytical models is lacking.

Aim

We compare the performance of different analytical models to estimate a cerebral blood flow index (CBFi) with DCS in adults.

Approach

Resting-state data were obtained on a cohort of 20 adult patients with subarachnoid hemorrhage. Data at 1 and 2.5 cm source-detector separations were analyzed with the homogenous, two-layer, and three-layer models to estimate scalp blood flow index and CBFi. The performance of each model was quantified via fitting convergence, fit stability, brain-to-scalp flow ratio (BSR), and correlation with transcranial Doppler ultrasound (TCD) measurements of cerebral blood flow velocity in the middle cerebral artery (MCA).

Results

The homogeneous model has the highest pass rate (100%), lowest coefficient of variation (CV) at rest (median [IQR] at 1 Hz of 0.18 [0.13, 0.22]), and most significant correlation with MCA blood flow velocities (R s = 0.59 , p = 0.010 ) compared with both the two- and three-layer models. The multilayer model pass rate was significantly correlated with extracerebral layer thicknesses. Discarding datasets with non-physiological BSRs increased the correlation between DCS measured CBFi and TCD measured MCA velocities for all models.

Conclusions

We found that the homogeneous model has the highest pass rate, lowest CV at rest, and most significant correlation with MCA blood flow velocities. Results from the multilayer models should be taken with caution because they suffer from lower pass rates and higher coefficients of variation at rest and can converge to non-physiological values for CBFi. Future work is needed to validate these models in vivo, and novel approaches are merited to improve the performance of the multimodel models.

Decoding diffuse light scattering dynamics in layered tissues: path length versus fluctuation time scale

Dynamic multiple light scattering (DMLS) has found numerous applications, including soft matter physics and biomedical optics. Yet biological tissues may have complex internal geometries, presenting a challenge for noninvasive measurements. Deciphering laminar dynamics is crucial to accurately interpret tissue or organ physiology. Seminal DMLS work noted that one can probe deeper layers indirectly by analyzing light fluctuations on shorter time scales. Recent technologies have enabled probing deeper layers directly by analyzing fluctuations at longer path lengths. The following question arises: are the indirect and direct approaches synergistic or redundant? Here, by adding an optical switch to path-length-filtered interferometric diffusing wave spectroscopy, we experimentally address this question in the context of a forearm occlusion study. We find that both approaches afford better distinction of light scattering dynamics in layered tissues than either approach alone. This motivates further development of methods that integrate both decorrelation time scale and light path length to probe layered tissues.

Deep-learning-based separation of shallow and deep layer blood flow rates in diffuse correlation spectroscopy

Diffuse correlation spectroscopy faces challenges concerning the contamination of cutaneous and deep tissue blood flow. We propose a long short-term memory network to directly quantify the flow rates of shallow and deep-layer tissues. By exploiting the different contributions of shallow and deep-layer flow rates to auto-correlation functions, we accurately predict the shallow and deep-layer flow rates (RMSE = 0.047 and 0.034 ml/min/100 g of simulated tissue, R2 = 0.99 and 0.99, respectively) in a two-layer flow phantom experiment. This approach is useful in evaluating the blood flow responses of active muscles, where both cutaneous and deep-muscle blood flow increase with exercise.

Characterization of cerebral macro- and microvascular hemodynamics during transient hypotension

The aim of the current study was to establish the interplay between blood flow patterns within a large cerebral artery and a downstream microvascular segment under conditions of transiently reduced mean arterial pressure (MAP). We report data from nine young, healthy participants (5 women; 26 ± 4 yr) acquired during a 15-s bout of sudden-onset lower body negative pressure (LBNP; -80 mmHg). Simultaneous changes in microvascular cerebral blood flow (CBF) and middle cerebral artery blood velocity (MCAvmean) were captured using diffuse correlation spectroscopy (DCS) and transcranial Doppler ultrasound (TCD), respectively. Brachial blood pressure (finger photoplethysmography) and TCD waveforms were extracted at baseline and during the nadir blood pressure (BP) response to LBNP and analyzed using a modified Windkessel model to calculate indices of cerebrovascular resistance (Ri) and compliance (Ci). Compared with baseline, rapid-onset LBNP decreased MAP by 22 ± 16% and Ri by 14 ± 10% (both P ≤ 0.03). Ci increased (322 ± 298%; P < 0.01) but MCAvmean (-8 ± 16%; P = 0.09) and CBF (-2 ± 3%; P = 0.29) were preserved. The results provide evidence that changes in both vascular resistance and compliance preserve CBF, as indexed by no significant changes in MCAvmean or DCS microvascular flow, during transient hypotension.NEW & NOTEWORTHY To characterize the relationship between cerebrovascular patterns within the large middle cerebral artery (MCA) and a downstream microvascular segment, we used a novel combination of transcranial Doppler ultrasound of the MCA and optical monitoring of a downstream microvascular segment, respectively, under conditions of transiently reduced mean arterial pressure (i.e., lower body negative pressure, -80 mmHg). A rapid increase in vessel compliance accompanied the maintenance of MCA blood velocity and downstream microvascular flow.

Agreement in cerebrovascular reactivity assessed with diffuse correlation spectroscopy across experimental paradigms improves with short separation regression

Significance

Cerebrovascular reactivity (CVR), i.e., the ability of cerebral vasculature to dilate or constrict in response to vasoactive stimuli, is a biomarker of vascular health. Exogenous administration of inhaled carbon dioxide, i.e., hypercapnia (HC), remains the "gold-standard" intervention to assess CVR. More tolerable paradigms that enable CVR quantification when HC is difficult/contraindicated have been proposed. However, because these paradigms feature mechanistic differences in action, an assessment of agreement of these more tolerable paradigms to HC is needed.

Aim

We aim to determine the agreement of CVR assessed during HC, breath-hold (BH), and resting state (RS) paradigms.

Approach

Healthy adults were subject to HC, BH, and RS paradigms. End tidal carbon dioxide (EtCO2) and cerebral blood flow (CBF, assessed with diffuse correlation spectroscopy) were monitored continuously. CVR (%/mmHg) was quantified via linear regression of CBF versus EtCO2 or via a general linear model (GLM) that was used to minimize the influence of systemic and extracerebral signal contributions.

Results

Strong agreement ( CCC ≥ 0.69 ; R ≥ 0.76 ) among CVR paradigms was demonstrated when utilizing a GLM to regress out systemic/extracerebral signal contributions. Linear regression alone showed poor agreement across paradigms ( CCC ≤ 0.35 ; R ≤ 0.45 ).

Conclusions

More tolerable experimental paradigms coupled with regression of systemic/extracerebral signal contributions may offer a viable alternative to HC for assessing CVR.

Dynamic cerebral autoregulation measured by diffuse correlation spectroscopy

Dynamic cerebral autoregulation (dCA) can be derived from spontaneous oscillations in arterial blood pressure (ABP) and cerebral blood flow (CBF). Transcranial Doppler (TCD) measures CBF-velocity and is commonly used to assess dCA. Diffuse correlation spectroscopy (DCS) is a promising optical technique for non-invasive CBF monitoring, so here we aimed to validate DCS as a tool for quantifying dCA. In 33 healthy adults and 17 acute ischemic stroke patients, resting-state hemodynamic were monitored simultaneously with high-speed (20 Hz) DCS and TCD. dCA parameters were calcaulated by a transfer function analysis using a Fourier decomposition of ABP and CBF (or CBF-velocity). Strong correlation was found between DCS and TCD measured gain (magnitude of regulation) in healthy volunteers (r = 0.73, p < 0.001) and stroke patients (r = 0.76, p = 0.003). DCS-gain retained strong test-retest reliability in both groups (ICC 0.87 and 0.82, respectively). DCS and TCD-derived phase (latency of regulation) did not significantly correlate in healthy volunteers (r = 0.12, p = 0.50) but moderately correlated in stroke patients (r = 0.65, p = 0.006). DCS-derived phase was reproducible in both groups (ICC 0.88 and 0.90, respectively). High-frequency DCS is a promising non-invasive bedside technique that can be leveraged to quantify dCA from resting-state data, but the discrepancy between TCD and DCS-derived phase requires further investigation.

Interferometric diffusing wave spectroscopy imaging with an electronically variable time-of-flight filter

Diffuse optics (DO) is a light-based technique used to study the human brain, but it suffers from low brain specificity. Interferometric diffuse optics (iDO) promises to improve the quantitative accuracy and depth specificity of DO, and particularly, coherent light fluctuations (CLFs) arising from blood flow. iDO techniques have alternatively achieved either time-of-flight (TOF) discrimination or highly parallel detection, but not both at once. Here, we break this barrier with a single iDO instrument. Specifically, we show that rapid tuning of a temporally coherent laser during the sensor integration time increases the effective linewidth seen by a highly parallel interferometer. Using this concept to create a continuously variable and user-specified TOF filter, we demonstrate a solution to the canonical problem of DO, measuring optical properties. Then, with a deep TOF filter, we reduce scalp sensitivity of CLFs by 2.7 times at 1 cm source-collector separation. With this unique combination of desirable features, i.e., TOF-discrimination, spatial localization, and highly parallel CLF detection, we perform multiparametric imaging of light intensities and CLFs via the human forehead.

Influence of oversimplifying the head anatomy on cerebral blood flow measurements with diffuse correlation spectroscopy

Significance

Diffuse correlation spectroscopy (DCS) is an emerging optical modality for non-invasive assessment of an index of regional cerebral blood flow. By the nature of this noninvasive measurement, light must pass through extracerebral layers (i.e., skull, scalp, and cerebral spinal fluid) before detection at the tissue surface. To minimize the contribution of these extracerebral layers to the measured signal, an analytical model has been developed that treats the head as a series of three parallel and infinitely extending slabs (mimicking scalp, skull, and brain). The three-layer model has been shown to provide a significant improvement in cerebral blood flow estimation over the typically used model that treats the head as a bulk homogenous medium. However, the three-layer model is still a gross oversimplification of the head geometry that ignores head curvature, the presence of cerebrospinal fluid (CSF), and heterogeneity in layer thickness.

Aim

Determine the influence of oversimplifying the head geometry on cerebral blood flow estimated with the three-layer model.

Approach

Data were simulated with Monte Carlo in a four-layer slab medium and a three-layer sphere medium to isolate the influence of CSF and curvature, respectively. Additionally, simulations were performed on magnetic resonance imaging (MRI) head templates spanning a wide-range of ages. Simulated data were fit to both the homogenous and three-layer model for CBF. Finally, to mitigate the errors in potential CBF estimation due to the difficulty in defining layer thickness, we investigated an approach to identify an equivalent, "optimized" thickness via a pressure modulation.

Results

Both head curvature and failing to account for CSF lead to significant errors in the estimation of CBF. However, the effect of curvature and CSF on relative changes in CBF is minimal. Further, we found that CBF was underestimated in all MRI-templates, although the magnitude of these underestimations was highly influenced by small variations in the source and detector optode positioning. The optimized thickness obtained from pressure modulation did not improve estimation accuracy of CBF, although it did significantly improve the estimation accuracy of relative changes in CBF.

Conclusions

In sum, these findings suggest that the three-layer model holds promise for improving estimation of relative changes in cerebral blood flow; however, estimations of absolute cerebral blood flow with the approach should be viewed with caution given that it is difficult to account for appreciable sources of error, such as curvature and CSF.

Numerical approach to quantify depth-dependent blood flow changes in real-time using the diffusion equation with continuous-wave and time-domain diffuse correlation spectroscopy

Diffuse correlation spectroscopy (DCS) is a non-invasive optical technique that can measure brain perfusion by quantifying temporal intensity fluctuations of multiply scattered light. A primary limitation for accurate quantitation of cerebral blood flow (CBF) is the fact that experimental measurements contain information about both extracerebral scalp blood flow (SBF) as well as CBF. Separating CBF from SBF is typically achieved using multiple source-detector channels when using continuous-wave (CW) light sources, or more recently with use of time-domain (TD) techniques. Analysis methods that account for these partial volume effects are often employed to increase CBF contrast. However, a robust, real-time analysis procedure that can separate and quantify SBF and CBF with both traditional CW and TD-DCS measurements is still needed. Here, we validate a data analysis procedure based on the diffusion equation in layered media capable of quantifying both extra- and cerebral blood flow in the CW and TD. We find that the model can quantify SBF and CBF coefficients with less than 5% error compared to Monte Carlo simulations using a 3-layered brain model in both the CW and TD. The model can accurately fit data at a rate of <10 ms for CW data and <250 ms for TD data when using a least-squares optimizer.

High-density diffuse correlation tomography with enhanced depth localization and minimal surface artefacts

A spatially weighted filter applied to both the measurement and the Jacobian is proposed for high-density diffuse correlation tomography (DCT) to remove unwanted extracerebral interferences and artefacts along with better depth localization in the reconstructed blood flow images. High-density DCT is implemented by appropriate modification of recently introduced Multi-speckle Diffuse Correlation Spectroscopy (M-DCS) system. Additionally, we have used autocorrelation measurements at multiple delay-times in an iterative manner to improve the reconstruction results. The proposed scheme has been validated by simulations, phantom experiments and in-vivo human experiments.

Lawrence K, Milej D. Assessing the Sensitivity of Multi-Distance Hyperspectral NIRS to Changes in the Oxidation State of Cytochrome C Oxidase in the Brain

Near-infrared spectroscopy (NIRS) measurements of tissue oxygen saturation (StO₂) are frequently used during vascular and cardiac surgeries as a non-invasive means of assessing brain health; however, signal contamination from extracerebral tissues remains a concern. As an alternative, hyperspectral (hs)NIRS can be used to measure changes in the oxidation state of cytochrome c oxidase (ΔoxCCO), which provides greater sensitivity to the brain given its higher mitochondrial concentration versus the scalp. The purpose of this study was to evaluate the depth sensitivity of the oxCCO signal to changes occurring in the brain and extracerebral tissue components. The oxCCO assessment was conducted using multi-distance hsNIRS (source-detector separations = 1 and 3 cm), and metabolic changes were compared to changes in StO₂. Ten participants were monitored using an in-house system combining hsNIRS and diffuse correlation spectroscopy (DCS). Data were acquired during carotid compression (CC) to reduce blood flow and hypercapnia to increase flow. Reducing blood flow by CC resulted in a significant decrease in oxCCO measured at r_SD = 3 cm but not at 1 cm. In contrast, significant changes in StO₂ were found at both distances. Hypercapnia caused significant increases in StO₂ and oxCCO at r_SD = 3 cm, but not at 1 cm. Extracerebral contamination resulted in elevated StO₂ but not oxCCO after hypercapnia, which was significantly reduced by applying regression analysis. This study demonstrated that oxCCO was less sensitive to extracerebral signals than StO₂.

Lawrence K, Tachtsidis I, Tong Y, Torricelli A, Urner T, Wabnitz H, Wolf M, Wolf U, Xu S, Yang C, Yodh AG, Yücel MA, Zhou W. Optical imaging and spectroscopy for the study of the human brain: status report

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.

Influence of source-detector separation on diffuse correlation spectroscopy measurements of cerebral blood flow with a multilayered analytical model

Significance: Diffuse correlation spectroscopy (DCS) is an emerging noninvasive optical technology for bedside monitoring of cerebral blood flow. However, extracerebral hemodynamics can significantly influence DCS estimations of cerebral perfusion. Advanced analytical models can be used to remove the contribution of extracerebral hemodynamics; however, these models are highly sensitive to measurement noise. There is a need for an empirical determination of the optimal source-detector separation(s) (SDS) that improves the accuracy and reduces sensitivity to noise in the estimation of cerebral blood flow with these models. Aim: To determine the influence of SDS on solution uniqueness, measurement accuracy, and sensitivity to inaccuracies in model parameters when using the three-layer model to estimate cerebral blood flow with DCS. Approach: We performed a series of in silico simulations on samples spanning a wide range of physiologically-relevant layer optical properties, thicknesses, and flow. Data were simulated at SDS ranging from 0.5 to 3.0 cm using the three-layer solution to the correlation diffusion equation (with and without noise added) and using three-layer slab Monte Carlo simulations. We quantified the influence of SDS on uniqueness, accuracy, and sensitivity to inaccuracies in model parameters using the three-layer inverse model. Results: Two SDS are required to ensure a unique solution of cerebral blood flow index (CBFi). Combinations of 0.5/1.0/1.5 and 2.5 cm provide the optimal choice for balancing the depth penetration with signal-to-noise ratio to minimize the error in CBFi across a wide range of samples with varying optical properties, thicknesses, and dynamics. Conclusions: These results suggest that the choice of SDS is critical for minimizing the estimated error of cerebral blood flow when using the three-layer model to analyze DCS data.

Cerebral Blood Flow Hemispheric Asymmetry in Comatose Adults Receiving Extracorporeal Membrane Oxygenation

Peripheral veno-arterial extracorporeal membrane oxygenation (ECMO) artificially oxygenates and circulates blood retrograde from the femoral artery, potentially exposing the brain to asymmetric perfusion. Though ECMO patients frequently experience brain injury, neurologic exams and imaging are difficult to obtain. Diffuse correlation spectroscopy (DCS) non-invasively measures relative cerebral blood flow (rBF) at the bedside using an optical probe on each side of the forehead. In this study we observed interhemispheric rBF differences in response to mean arterial pressure (MAP) changes in adult ECMO recipients. We recruited 13 subjects aged 21–78 years (7 with cardiac arrest, 4 with acute heart failure, and 2 with acute respiratory distress syndrome). They were dichotomized via Glasgow Coma Scale Motor score (GCS-M) into comatose (GCS-M ≤ 4; n = 4) and non-comatose (GCS-M > 4; n = 9) groups. Comatose patients had greater interhemispheric rBF asymmetry (ASYM_rBF) vs. non-comatose patients over a range of MAP values (29 vs. 11%, p = 0.009). ASYM_rBF in comatose patients resolved near a MAP range of 70–80 mmHg, while rBF remained symmetric through a wider MAP range in non-comatose patients. Correlations between post-oxygenator pCO₂ or pH vs. ASYM_rBF were significantly different between comatose and non-comatose groups. Our findings indicate that comatose patients are more likely to have asymmetric cerebral perfusion.

Complete head cerebral sensitivity mapping for diffuse correlation spectroscopy using subject-specific magnetic resonance imaging models

We characterize cerebral sensitivity across the entire adult human head for diffuse correlation spectroscopy, an optical technique increasingly used for bedside cerebral perfusion monitoring. Sixteen subject-specific magnetic resonance imaging-derived head models were used to identify high sensitivity regions by running Monte Carlo light propagation simulations at over eight hundred uniformly distributed locations on the head. Significant spatial variations in cerebral sensitivity, consistent across subjects, were found. We also identified correlates of such differences suitable for real-time assessment. These variations can be largely attributed to changes in extracerebral thickness and should be taken into account to optimize probe placement in experimental settings.

Scanning interferometric near-infrared spectroscopy

In diffuse optics, quantitative assessment of the human brain is confounded by the skull and scalp. To better understand these superficial tissues, we advance interferometric near-infrared spectroscopy (iNIRS) to form images of the human superficial forehead blood flow index (BFI). We present a null source-collector (S-C) polarization splitting approach that enables galvanometer scanning and eliminates unwanted backscattered light. Images show an order-of-magnitude heterogeneity in superficial dynamics, implying an order-of-magnitude heterogeneity in brain specificity, depending on forehead location. Along the time-of-flight dimension, autocorrelation decay rates support a three-layer model with increasing BFI from the skull to the scalp to the brain. By accurately characterizing superficial tissues, this approach can help improve specificity for the human brain.

Accuracy of diffuse correlation spectroscopy measurements of cerebral blood flow when using a three-layer analytical model

Diffuse correlation spectroscopy (DCS) is a non-invasive optical technology for the assessment of an index of cerebral blood flow (CBFi). Analytical methods that model the head as a three-layered medium (i.e., scalp, skull, brain) are becoming more commonly used to minimize the contribution of extracerebral layers to the measured DCS signal in adult cerebral blood flow studies. However, these models rely on a priori knowledge of layer optical properties and thicknesses. Errors in these values can lead to errors in the estimation of CBFi, although the magnitude of this influence has not been rigorously characterized. Herein, we investigate the accuracy of measuring cerebral blood flow with a three-layer model when errors in layer optical properties or thicknesses are present. Through a series of in silico experiments, we demonstrate that CBFi is highly sensitive to errors in brain optical properties and skull and scalp thicknesses. Relative changes in CBFi are less sensitive to optical properties but are influenced by errors in layer thickness. Thus, when using the three-layer model, accurate estimation of scalp and skull thickness are required for reliable results.

Impact of Cutaneous Blood Flow on NIR-DCS Measures of Skeletal Muscle Blood Flow Index

Near-infrared diffuse correlation spectroscopy (NIR-DCS) is an optical technique for estimating relative changes in skeletal muscle perfusion during exercise, but may be affected by changes in cutaneous blood flow, as photons emitted by the laser must first pass through the skin. Accordingly, the purpose of this investigation was to examine how increased cutaneous blood flow affects NIR-DCS blood flow index (BFI) at rest and during exercise using a passive whole-body heating protocol that increases cutaneous, but not skeletal muscle, perfusion in the uncovered limb. BFI and cutaneous perfusion (laser Doppler flowmetry) were assessed in 15 healthy young subjects before (e.g., rest) and during 5-minutes of moderate-intensity hand-grip exercise in normothermic conditions and after cutaneous blood flow was elevated via whole-body heating. Hyperthermia significantly increased both cutaneous perfusion (~7.3-fold; p≤0.001) and NIR-DCS BFI (~4.5-fold; p≤0.001). Although relative BFI (i.e., fold-change above baseline) exhibited a typical exponential increase in muscle perfusion during normothermic exercise (2.81±0.95), there was almost no change in BFI during hyperthermic exercise (1.43±0.44). A subset of 8 subjects were subsequently treated with intradermal injection of botulinum toxin-A (Botox) to block heating-induced elevations in cutaneous blood flow, which 1) nearly abolished the hyperthermia-induced increase in BFI, and 2) restored BFI kinetics during hyperthermic exercise to values that were not different from normothermic exercise (p=0.091). Collectively, our results demonstrate that cutaneous blood flow can have a substantial, detrimental impact on NIR-DCS estimates of skeletal muscle perfusion and highlight the need for technical and/or pharmacological advancements to overcome this issue moving forward.

Optimization of time domain diffuse correlation spectroscopy parameters for measuring brain blood flow

Significance: Time domain diffuse correlation spectroscopy (TD-DCS) can offer increased sensitivity to cerebral hemodynamics and reduced contamination from extracerebral layers by differentiating photons based on their travel time in tissue. We have developed rigorous simulation and evaluation procedures to determine the optimal time gate parameters for monitoring cerebral perfusion considering instrumentation characteristics and realistic measurement noise. Aim: We simulate TD-DCS cerebral perfusion monitoring performance for different instrument response functions (IRFs) in the presence of realistic experimental noise and evaluate metrics of sensitivity to brain blood flow, signal-to-noise ratio (SNR), and ability to reject the influence of extracerebral blood flow across a variety of time gates to determine optimal operating parameters. Approach: Light propagation was modeled on an MRI-derived human head geometry using Monte Carlo simulations for 765- and 1064-nm excitation wavelengths. We use a virtual probe with a source-detector separation of 1 cm placed in the pre-frontal region. Performance metrics described above were evaluated to determine optimal time gate(s) for different IRFs. Validation of simulation noise estimates was done with experiments conducted on an intralipid-based liquid phantom. Results: We find that TD-DCS performance strongly depends on the system IRF. Among Gaussian pulse shapes, ∼ 300    ps pulse length appears to offer the best performance, at wide gates (500 ps and larger) with start times 400 and 600 ps after the peak of the TPSF at 765 and 1064 nm, respectively, for a 1-s integration time at photon detection rates seen experimentally (600 kcps at 765 nm and 4 Mcps at 1064 nm). Conclusions: Our work shows that optimal time gates satisfy competing requirements for sufficient sensitivity and sufficient SNR. The achievable performance is further impacted by system IRF with ∼ 300    ps quasi-Gaussian pulse obtained using electro-optic laser shaping providing the best results.

Improved accuracy of cerebral blood flow quantification in the presence of systemic physiology cross-talk using multi-layer Monte Carlo modeling

Significance: Contamination of diffuse correlation spectroscopy (DCS) measurements of cerebral blood flow (CBF) due to systemic physiology remains a significant challenge in the clinical translation of DCS for neuromonitoring. Tunable, multi-layer Monte Carlo-based (MC) light transport models have the potential to remove extracerebral flow cross-talk in cerebral blood flow index ( CBF i ) estimates. Aim: We explore the effectiveness of MC DCS models in recovering accurate CBF i changes in the presence of strong systemic physiology variations during a hypercapnia maneuver. Approach: Multi-layer slab and head-like realistic (curved) geometries were used to run MC simulations of photon propagation through the head. The simulation data were post-processed into models with variable extracerebral thicknesses and used to fit DCS multi-distance intensity autocorrelation measurements to estimate CBF i timecourses. The results of the MC CBF i values from a set of human subject hypercapnia sessions were compared with CBF i values estimated using a semi-infinite analytical model, as commonly used in the field. Results: Group averages indicate a gradual systemic increase in blood flow following a different temporal profile versus the expected rapid CBF response. Optimized MC models, guided by several intrinsic criteria and a pressure modulation maneuver, were able to more effectively separate CBF i changes from scalp blood flow influence than the analytical fitting, which assumed a homogeneous medium. Three-layer models performed better than two-layer ones; slab and curved models achieved largely similar results, though curved geometries were closer to physiological layer thicknesses. Conclusion: Three-layer, adjustable MC models can be useful in separating distinct changes in scalp and brain blood flow. Pressure modulation, along with reasonable estimates of physiological parameters, can help direct the choice of appropriate layer thicknesses in MC models.

Dynamic response of cerebral blood flow to insulin-induced hypoglycemia

The dynamics of cerebral blood flow (CBF) at the onset of hypoglycemia may play a key role in hypoglycemia unawareness; however, there is currently a paucity of techniques that can monitor adult CBF with high temporal resolution. Herein, we investigated the use of diffuse correlation spectroscopy (DCS) to monitor the dynamics of CBF during insulin-induced hypoglycemia in adults. Plasma glucose concentrations, cortisol levels, and changes in CBF were measured before and during hypoglycemia in 8 healthy subjects. Cerebral blood flow increased by 42% following insulin injection with a delay of 17 ± 10 min, while the onset of hypoglycemia symptoms was delayed by 24 ± 11 min. The findings suggest that the onset of CBF increments precedes the appearance of hypoglycemia symptoms in nondiabetic subjects with normal awareness to hypoglycemia, and DCS could be a valuable tool for investigating the role of CBF in hypoglycemia unawareness.

Towards detection of brain injury using multimodal non-invasive neuromonitoring in adults undergoing extracorporeal membrane oxygenation

Extracorporeal membrane oxygenation (ECMO) is a form of cardiopulmonary bypass that provides life-saving support to critically ill patients whose illness is progressing despite maximal conventional support. Use in adults is expanding, however neurological injuries are common. Currently, the existing brain imaging tools are a snapshot in time and require high-risk patient transport. Here we assess the feasibility of measuring diffuse correlation spectroscopy, transcranial Doppler ultrasound, electroencephalography, and auditory brainstem responses at the bedside, and developing a cerebral autoregulation metric. We report preliminary results from two patients, demonstrating feasibility and laying the foundation for future studies monitoring neurological health during ECMO.

Lawrence K. Optical monitoring of cerebral perfusion and metabolism in adults during cardiac surgery with cardiopulmonary bypass

During cardiac surgery with cardiopulmonary bypass (CPB), adequate maintenance of cerebral blood flow (CBF) is vital in preventing postoperative neurological injury - i.e. stroke, delirium, cognitive impairment. Reductions in CBF large enough to impact cerebral energy metabolism can lead to tissue damage and subsequent brain injury. Current methods for neuromonitoring during surgery are limited. This study presents the clinical translation of a hybrid optical neuromonitor for continuous intraoperative monitoring of cerebral perfusion and metabolism in ten patients undergoing non-emergent cardiac surgery with non-pulsatile CPB. The optical system combines broadband near-infrared spectroscopy (B-NIRS) to measure changes in the oxidation state of cytochrome c oxidase (oxCCO) - a direct marker of cellular energy metabolism - and diffuse correlation spectroscopy (DCS) to provide an index of cerebral blood flow (CBFi). As the heart was arrested and the CPB-pump started, increases in CBFi (88.5 ± 125.7%) and significant decreases in oxCCO (-0.5 ± 0.2 µM) were observed; no changes were noted during transitions off CPB. Fifteen hypoperfusion events, defined as large and sustained reductions in CPB-pump flow rate, were identified across all patients and resulted in significant decreases in perfusion and metabolism when mean arterial pressure dropped to 30 mmHg or below. The maximum reduction in cerebral blood flow preceded the corresponding metabolic reduction by 18.2 ± 15.0 s. Optical neuromonitoring provides a safe and non-invasive approach for assessing intraoperative perfusion and metabolism and has potential in guiding patient management to prevent adverse clinical outcomes.

Lawrence K. Direct assessment of extracerebral signal contamination on optical measurements of cerebral blood flow, oxygenation, and metabolism

Significance: Near-infrared spectroscopy (NIRS) combined with diffuse correlation spectroscopy (DCS) provides a noninvasive approach for monitoring cerebral blood flow (CBF), oxygenation, and oxygen metabolism. However, these methods are vulnerable to signal contamination from the scalp. Our work evaluated methods of reducing the impact of this contamination using time-resolved (TR) NIRS and multidistance (MD) DCS. Aim: The magnitude of scalp contamination was evaluated by measuring the flow, oxygenation, and metabolic responses to a global hemodynamic challenge. Contamination was assessed by collecting data with and without impeding scalp blood flow. Approach: Experiments involved healthy participants. A pneumatic tourniquet was used to cause scalp ischemia, as confirmed by contrast-enhanced NIRS, and a computerized gas system to generate a hypercapnic challenge. Results: Comparing responses acquired with and without the tourniquet demonstrated that the TR-NIRS technique could reduce scalp contributions in hemodynamic signals up to 4 times (r SD = 3    cm ) and 6 times (r SD = 4    cm ). Similarly, blood flow responses from the scalp and brain could be separated by analyzing MD DCS data with a multilayer model. Using these techniques, there was no change in metabolism during hypercapnia, as expected, despite large increases in CBF and oxygenation. Conclusion: NIRS/DCS can accurately monitor CBF and metabolism with the appropriate enhancement to depth sensitivity, highlighting the potential of these techniques for neuromonitoring.

Lawrence K. Characterizing dynamic cerebral vascular reactivity using a hybrid system combining time-resolved near-infrared and diffuse correlation spectroscopy

This study presents the characterization of dynamic cerebrovascular reactivity (CVR) in healthy adults by a hybrid optical system combining time-resolved (TR) near-infrared spectroscopy (NIRS) and diffuse correlation spectroscopy (DCS). Blood flow and oxygenation (oxy- and deoxy-hemoglobin) responses to a step hypercapnic challenge were recorded to characterize dynamic and static components of CVR. Data were acquired at short and long source-detector separations (r _SD) to assess the impact of scalp hemodynamics, and moment analysis applied to the TR-NIRS to further enhance the sensitivity to the brain. Comparing blood flow and oxygenation responses acquired at short and long r _SD demonstrated that scalp contamination distorted the CVR time courses, particularly for oxyhemoglobin. This effect was significantly diminished by the greater depth sensitivity of TR NIRS and less evident in the DCS data due to the higher blood flow in the brain compared to the scalp. The reactivity speed was similar for blood flow and oxygenation in the healthy brain. Given the ease-of-use, portability, and non-invasiveness of this hybrid approach, it is well suited to investigate if the temporal relationship between CBF and oxygenation is altered by factors such as age and cerebrovascular disease.

Quantification of cerebral blood flow in adults by contrast-enhanced near-infrared spectroscopy: Validation against MRI

The purpose of this study was to assess the accuracy of absolute cerebral blood flow (CBF) measurements obtained by dynamic contrast-enhanced (DCE) near-infrared spectroscopy (NIRS) using indocyanine green as a perfusion contrast agent. For validation, CBF was measured independently using the MRI perfusion method arterial spin labeling (ASL). Data were acquired at two sites and under two flow conditions (normocapnia and hypercapnia). Depth sensitivity was enhanced using time-resolved detection, which was demonstrated in a separate set of experiments using a tourniquet to temporally impede scalp blood flow. A strong correlation between CBF measurements from ASL and DCE-NIRS was observed (slope = 0.99 ± 0.08, y-intercept = -1.7 ± 7.4 mL/100 g/min, and R2 = 0.88). Mean difference between the two techniques was 1.9 mL/100 g/min (95% confidence interval ranged from -15 to 19 mL/100g/min and the mean ASL CBF was 75.4 mL/100 g/min). Error analysis showed that structural information and baseline absorption coefficient were needed for optimal CBF reconstruction with DCE-NIRS. This study demonstrated that DCE-NIRS is sensitive to blood flow in the adult brain and can provide accurate CBF measurements with the appropriate modeling techniques.

Real-Time Non-invasive Assessment of Cerebral Hemodynamics With Diffuse Optical Spectroscopies in a Neuro Intensive Care Unit: An Observational Case Study

Prevention of secondary damage is an important goal in the treatment of severe neurological conditions, such as major head trauma or stroke. However, there is currently a lack of non-invasive methods for monitoring cerebral physiology. Diffuse optical methods have been proposed as an inexpensive, non-invasive bedside monitor capable of providing neurophysiology information in neurocritical patients. However, the reliability of the technique to provide accurate longitudinal measurement during the clinical evolution of a patient remains largely unaddressed. Here, we report on the translation of a hybrid diffuse optical system combining frequency domain diffuse optical spectroscopy (FD-DOS) and diffuse correlation spectroscopy (DCS) for real-time monitoring of cerebral physiology in a neuro intensive care unit (neuro-ICU). More specifically, we present a case study of a patient admitted with a high-grade aneurysmal subarachnoid hemorrhage, who was monitored throughout hospitalization. We show that the neurophysiological parameters measured by diffuse optics at the bedside are consistent with the clinical evolution of the patient at all the different stages following its brain lesion. These data provide support for clinical translation of DOS/DCS as a useful biomarker of neurophysiology in the neuro-ICU, particularly in locations where other clinical resources are limited.

Time-of-flight resolved light field fluctuations reveal deep human tissue physiology

Red blood cells (RBCs) transport oxygen to tissues and remove carbon dioxide. Diffuse optical flowmetry (DOF) assesses deep tissue RBC dynamics by measuring coherent fluctuations of multiply scattered near-infrared light intensity. While classical DOF measurements empirically correlate with blood flow, they remain far-removed from light scattering physics and difficult to interpret in layered media. To advance DOF measurements closer to the physics, here we introduce an interferometric technique, surmounting challenges of bulk motion to apply it in awake humans. We reveal two measurement dimensions: optical phase, and time-of-flight (TOF), the latter with 22 picosecond resolution. With this multidimensional data, we directly confirm the unordered, or Brownian, nature of optically probed RBC dynamics typically assumed in classical DOF. We illustrate how incorrect absorption assumptions, anisotropic RBC scattering, and layered tissues may confound classical DOF. By comparison, our direct method enables accurate and comprehensive assessment of blood flow dynamics in humans.

A multipixel diffuse correlation spectroscopy system based on a single photon avalanche diode array

The autocorrelation of laser speckles from coherent near infrared light is used for noninvasive estimates of relative changes in blood perfusion in techniques such as laser Doppler flowmetry (LDF) and diffuse correlation spectroscopy (DCS). In this study, a 2D array of single photon avalanche diodes (SPADs) was used to combine the strengths of multiple detectors in LDF with high light sensitivity in DCS. The system was tested on milk phantoms with varying detector fiber diameter (200 and 600 μm), source-detector fiber separation (4.6-10.2 mm), fiber-SPAD distance (2.5-36.5 mm), contiguous measurement time per repetition for the autocorrelation (1-33 ms) and temperature (15.6-46.7°C). An in vivo blood occlusion test was also performed. The multipixel approach improved signal-to-noise ratio (SNR) and, in our setup, the use of a multimode detector fiber was beneficial for SNR. In conclusion, the multipixel system works, but improvements and further studies regarding, for example, the data acquisition and optimal settings are still needed.

Small separation frequency-domain near-infrared spectroscopy for the recovery of tissue optical properties at millimeter depths

Millimeter-depth sensitivity with frequency domain near-infrared spectroscopy has been challenging due to the breakdown of the diffusion equation for source-detection separations < 1cm. To overcome this challenge, we employ a Monte-Carlo lookup table-based inverse algorithm to fit small separation (3-6 mm) frequency-domain near-infrared spectroscopy (FDNIRS) data for absorption and reduced scattering coefficients. We verify this small separation FDNIRS method through a series of in vitro and in vivo studies. In vitro, we observed a root mean squared percent error (RMSE) in estimation of the reduced scattering coefficient and absorption coefficient of 2.8% and 7.6%, respectively, in liquid phantoms consisting of Intralipid and Indian ink, and a RMSE in estimation of oxygen saturation and total hemoglobin concentrations of 7.8 and 11.2%, respectively, in blood-mixed liquid phantoms. Next, we demonstrate one particularly valuable in vivo application of this technique wherein we non-invasively measure the optical properties of the mouse brain (n = 4). We find that the measured resting state cerebral oxygen saturation and hemoglobin concentration are consistent with literature reported values, and we observe expected trends during a hyper-/hypoxia challenge that qualitatively mimic changes in partial pressure of oxygen (pO₂) measured simultaneously with an invasive pO₂ sensor. Further, through simulations of the mouse head geometry, we demonstrate that the skull and scalp exert minimal influence on the estimate oxygen saturation, while leading to small but systematic underestimation of total hemoglobin concentration. In total, these results demonstrate the robustness of small separation FDNIRS to assess tissue optical properties at millimeter depth resolution.

Transcranial Optical Monitoring of Cerebral Hemodynamics in Acute Stroke Patients during Mechanical Thrombectomy

INTRODUCTION

Mechanical thrombectomy is revolutionizing treatment of acute stroke due to large vessel occlusion (LVO). Unfortunately, use of the modified Thrombolysis in Cerebral Infarction score (mTICI) to characterize recanalization of the cerebral vasculature does not address microvascular perfusion of the distal parenchyma, nor provide more than a vascular "snapshot." Thus, little is known about tissue-level hemodynamic consequences of LVO recanalization. Diffuse correlation spectroscopy (DCS) and diffuse optical spectroscopy (DOS) are promising methods for continuous, noninvasive, contrast-free transcranial monitoring of cerebral microvasculature.

METHODS

Here, we use a combined DCS/DOS system to monitor frontal lobe hemodynamic changes during endovascular treatment of 2 patients with ischemic stroke due to internal carotid artery (ICA) occlusions.

RESULTS AND DISCUSSION

The monitoring instrument identified a recanalization-induced increase in ipsilateral cerebral blood flow (CBF) with little or no concurrent change in contralateral CBF and extracerebral blood flow. The results suggest that diffuse optical monitoring is sensitive to intracerebral hemodynamics in patients with ICA occlusion and can measure microvascular responses to mechanical thrombectomy.

Small separation diffuse correlation spectroscopy for measurement of cerebral blood flow in rodents

Diffuse correlation spectroscopy (DCS) has shown promise as a means to non-invasively measure cerebral blood flow in small animal models. Here, we characterize the validity of DCS at small source-detector reflectance separations needed for small animal measurements. Through Monte Carlo simulations and liquid phantom experiments, we show that DCS error increases as separation decreases, although error remains below 12% for separations > 0.2 cm. In mice, DCS measures of cerebral blood flow have excellent intra-user repeatability and strongly correlate with MRI measures of blood flow (R = 0.74, p<0.01). These results are generalizable to other DCS applications wherein short-separation reflectance geometries are desired.

Shedding light on the neonatal brain: probing cerebral hemodynamics by diffuse optical spectroscopic methods

Investigating the cerebral physiology of healthy term newborns' brains is important for better understanding perinatal brain injuries, of which the most common etiologies are hypoxia and ischemia. Hence, cerebral blood flow and cerebral oxygenation are important biomarkers of brain health. In this study, we employed a hybrid diffuse optical system consisting of diffuse correlation spectroscopy (DCS) and frequency-domain near infrared spectroscopy (FDNIRS) to measure hemoglobin concentration, oxygen saturation, and indices of cerebral blood flow and metabolism. We measured 30 term infants to assess the optical and physiological characteristics of the healthy neonatal brain in the frontal, temporal, and parietal lobes. We observed higher metabolism in the right hemisphere compared to the left and a positive correlation between gestational age and the level of cerebral hemoglobin concentration, blood volume, and oxygen saturation. Moreover, we observed higher cerebral blood flow and lower oxygen saturation in females compared to males. The delayed maturation in males and the sexual dimorphism in cerebral hemodynamics may explain why males are more vulnerable to perinatal brain injuries than females.

Shedding light on the neonatal brain: probing cerebral hemodynamics by diffuse optical spectroscopic methods

Investigating the cerebral physiology of healthy term newborns' brains is important for better understanding perinatal brain injuries, of which the most common etiologies are hypoxia and ischemia. Hence, cerebral blood flow and cerebral oxygenation are important biomarkers of brain health. In this study, we employed a hybrid diffuse optical system consisting of diffuse correlation spectroscopy (DCS) and frequency-domain near infrared spectroscopy (FDNIRS) to measure hemoglobin concentration, oxygen saturation, and indices of cerebral blood flow and metabolism. We measured 30 term infants to assess the optical and physiological characteristics of the healthy neonatal brain in the frontal, temporal, and parietal lobes. We observed higher metabolism in the right hemisphere compared to the left and a positive correlation between gestational age and the level of cerebral hemoglobin concentration, blood volume, and oxygen saturation. Moreover, we observed higher cerebral blood flow and lower oxygen saturation in females compared to males. The delayed maturation in males and the sexual dimorphism in cerebral hemodynamics may explain why males are more vulnerable to perinatal brain injuries than females.

Mapping breast cancer blood flow index, composition, and metabolism in a human subject using combined diffuse optical spectroscopic imaging and diffuse correlation spectroscopy

Diffuse optical spectroscopic imaging (DOSI) and diffuse correlation spectroscopy (DCS) are model-based near-infrared (NIR) methods that measure tissue optical properties (broadband absorption, ? a , and reduced scattering, ? s ? ) and blood flow (blood flow index, BFI), respectively. DOSI-derived ? a values are used to determine composition by calculating the tissue concentration of oxy- and deoxyhemoglobin ( HbO 2 , HbR), water, and lipid. We developed and evaluated a combined, coregistered DOSI/DCS handheld probe for mapping and imaging these parameters. We show that uncertainties of 0.3 ?? mm ? 1 (37%) in ? s ? and 0.003 ?? mm ? 1 (33%) in ? a lead to ? 53 % and 9% errors in BFI, respectively. DOSI/DCS imaging of a solid tissue-simulating flow phantom and

Clinical applications of near-infrared diffuse correlation spectroscopy and tomography for tissue blood flow monitoring and imaging

OBJECTIVE

Blood flow is one such available observable promoting a wealth of physiological insight both individually and in combination with other metrics.

APPROACH

Near-infrared diffuse correlation spectroscopy (DCS) and, to a lesser extent, diffuse correlation tomography (DCT), have increasingly received interest over the past decade as noninvasive methods for tissue blood flow measurements and imaging. DCS/DCT offers several attractive features for tissue blood flow measurements/imaging such as noninvasiveness, portability, high temporal resolution, and relatively large penetration depth (up to several centimeters).

MAIN RESULTS

This review first introduces the basic principle and instrumentation of DCS/DCT, followed by presenting clinical application examples of DCS/DCT for the diagnosis and therapeutic monitoring of diseases in a variety of organs/tissues including brain, skeletal muscle, and tumor.

SIGNIFICANCE

Clinical study results demonstrate technical versatility of DCS/DCT in providing important information for disease diagnosis and intervention monitoring.

Brain Monitoring in Critically Neurologically Impaired Patients

Assessment of neurologic injury and the evolution of severe neurologic injury is limited in comatose or critically ill patients that lack a reliable neurologic examination. For common yet severe pathologies such as the comatose state after cardiac arrest, aneurysmal subarachnoid hemorrhage (aSAH), and severe traumatic brain injury (TBI), critical medical decisions are made on the basis of the neurologic injury. Decisions regarding active intensive care management, need for neurosurgical intervention, and withdrawal of care, depend on a reliable, high-quality assessment of the true state of neurologic injury, and have traditionally relied on limited assessments such as intracranial pressure monitoring and electroencephalogram. However, even within TBI there exists a spectrum of disease that is likely not captured by such limited monitoring and thus a more directed effort towards obtaining a more robust biophysical signature of the individual patient must be undertaken. In this review, multimodal monitoring including the most promising serum markers of neuronal injury, cerebral microdialysis, brain tissue oxygenation, and pressure reactivity index to access brain microenvironment will be discussed with their utility among specific pathologies that may help determine a more complete picture of the neurologic injury state for active intensive care management and long-term outcomes. Goal-directed therapy guided by a multi-modality approach appears to be superior to standard intracranial pressure (ICP) guided therapy and should be explored further across multiple pathologies. Future directions including the application of optogenetics to evaluate brain injury and recovery and even as an adjunct monitoring modality will also be discussed.