In vivo local determination of tissue optical properties: applications to human brain

Using multi-modal neuroimaging to characterise social brain specialisation in infants

The specialised regional functionality of the mature human cortex partly emerges through experience-dependent specialisation during early development. Our existing understanding of functional specialisation in the infant brain is based on evidence from unitary imaging modalities and has thus focused on isolated estimates of spatial or temporal selectivity of neural or haemodynamic activation, giving an incomplete picture. We speculate that functional specialisation will be underpinned by better coordinated haemodynamic and metabolic changes in a broadly orchestrated physiological response. To enable researchers to track this process through development, we develop new tools that allow the simultaneous measurement of coordinated neural activity (EEG), metabolic rate, and oxygenated blood supply (broadband near-infrared spectroscopy) in the awake infant. In 4- to 7-month-old infants, we use these new tools to show that social processing is accompanied by spatially and temporally specific increases in coupled activation in the temporal-parietal junction, a core hub region of the adult social brain. During non-social processing, coupled activation decreased in the same region, indicating specificity to social processing. Coupling was strongest with high-frequency brain activity (beta and gamma), consistent with the greater energetic requirements and more localised action of high-frequency brain activity. The development of simultaneous multimodal neural measures will enable future researchers to open new vistas in understanding functional specialisation of the brain.

High-Speed Time-Domain Diffuse Optical Tomography with a Sensitivity Equation-based Neural Network

Steady progress in time-domain diffuse optical tomography (TD-DOT) technology is allowing for the first time the design of low-cost, compact, and high-performance systems, thus promising more widespread clinical TD-DOT use, such as for recording brain tissue hemodynamics. TD-DOT is known to provide more accurate values of optical properties and physiological parameters compared to its frequency-domain or steady-state counterparts. However, achieving high temporal resolution is still difficult, as solving the inverse problem is computationally demanding, leading to relatively long reconstruction times. The runtime is further compromised by processes that involve 'nontrivial' empirical tuning of reconstruction parameters, which increases complexity and inefficiency. To address these challenges, we present a new reconstruction algorithm that combines a deep-learning approach with our previously introduced sensitivity-equation-based, non-iterative sparse optical reconstruction (SENSOR) code. The new algorithm (called SENSOR-NET) unfolds the iterations of SENSOR into a deep neural network. In this way, we achieve high-resolution sparse reconstruction using only learned parameters, thus eliminating the need to tune parameters prior to reconstruction empirically. Furthermore, once trained, the reconstruction time is not dependent on the number of sources or wavelengths used. We validate our method with numerical and experimental data and show that accurate reconstructions with 1 mm spatial resolution can be obtained in under 20 milliseconds regardless of the number of sources used in the setup. This opens the door for real-time brain monitoring and other high-speed DOT applications.

The effects of transcranial laser photobiomodulation and neuromuscular electrical stimulation in the treatment of post-stroke dysfunctions

Post-stroke sequelae includes loss functions, such as cognitive and sensory-motor which lead to emotional and social problems, reducing quality of life and well-being. The main aim of our study was to investigate the effects of transcranial laser photobiomodulation together with neuromuscular electrical stimulation (NMES) in post-stroke patients. We performed a clinical trial and an ex vivo study. For the clinical trial, hemiplegic patients were separated into two groups: Treated Group (TG): Hemiplegics treated with transcranial laser (on) associated with NMES (on) and; Placebo Group (PG): Hemiplegics treated with placebo transcranial laser (off) associated with NMES (on). The cluster prototype includes 12 diode laser beams (4 × 660 nm, 4 × 808 nm and 4 × 980 nm) with average power of 720 mW per cluster applied during one minute, leading to 43.2 J energy per cluster. Fifteen regions for all head were irradiated by cluster, leading to 648 J energy per session. The parameters of NMES of the paretic limbs to generate extension wrist and ankle dorsiflexion were symmetrical biphasic rectangular waveforms, 50 Hz frequency, 250 μs pulse duration, and adjustable intensity to maintain the maximum range of motion (amplitude between 0 and 150 mA). Our clinical trial showed improvement of cognitive function, pain relief, greater manual dexterity, enhancement of physical and social-emotional health which lead to better quality of life and well-being. There was also increased temperature in the treated regions with laser and NMES. For the ex vivo study, the distribution of infrared and red radiation after penetration through the cranium and hemihead of cadavers were showed. Therefore, transcranial laser photobiomodulation associated with NMES can be an important therapeutic resource for rehabilitation after stroke.

Towards assessing subcortical "deep brain" biomarkers of PTSD with functional near-infrared spectroscopy

Assessment of brain function with functional near-infrared spectroscopy (fNIRS) is limited to the outer regions of the cortex. Previously, we demonstrated the feasibility of inferring activity in subcortical "deep brain" regions using cortical functional magnetic resonance imaging (fMRI) and fNIRS activity in healthy adults. Access to subcortical regions subserving emotion and arousal using affordable and portable fNIRS is likely to be transformative for clinical diagnostic and treatment planning. Here, we validate the feasibility of inferring activity in subcortical regions that are central to the pathophysiology of posttraumatic stress disorder (PTSD; i.e. amygdala and hippocampus) using cortical fMRI and simulated fNIRS activity in a sample of adolescents diagnosed with PTSD (N = 20, mean age = 15.3 ± 1.9 years) and age-matched healthy controls (N = 20, mean age = 14.5 ± 2.0 years) as they performed a facial expression task. We tested different prediction models, including linear regression, a multilayer perceptron neural network, and a k-nearest neighbors model. Inference of subcortical fMRI activity with cortical fMRI showed high prediction performance for the amygdala (r > 0.91) and hippocampus (r > 0.95) in both groups. Using fNIRS simulated data, relatively high prediction performance for deep brain regions was maintained in healthy controls (r > 0.79), as well as in youths with PTSD (r > 0.75). The linear regression and neural network models provided the best predictions.

Unrolled-DOT: an interpretable deep network for diffuse optical tomography

SIGNIFICANCE

Imaging through scattering media is critical in many biomedical imaging applications, such as breast tumor detection and functional neuroimaging. Time-of-flight diffuse optical tomography (ToF-DOT) is one of the most promising methods for high-resolution imaging through scattering media. ToF-DOT and many traditional DOT methods require an image reconstruction algorithm. Unfortunately, this algorithm often requires long computational runtimes and may produce lower quality reconstructions in the presence of model mismatch or improper hyperparameter tuning.

AIM

We used a data-driven unrolled network as our ToF-DOT inverse solver. The unrolled network is faster than traditional inverse solvers and achieves higher reconstruction quality by accounting for model mismatch.

APPROACH

Our model "Unrolled-DOT" uses the learned iterative shrinkage thresholding algorithm. In addition, we incorporate a refinement U-Net and Visual Geometry Group (VGG) perceptual loss to further increase the reconstruction quality. We trained and tested our model on simulated and real-world data and benchmarked against physics-based and learning-based inverse solvers.

RESULTS

In experiments on real-world data, Unrolled-DOT outperformed learning-based algorithms and achieved over 10× reduction in runtime and mean-squared error, compared to traditional physics-based solvers.

CONCLUSION

We demonstrated a learning-based ToF-DOT inverse solver that achieves state-of-the-art performance in speed and reconstruction quality, which can aid in future applications for noninvasive biomedical imaging.

The neurosurgical benefit of contactless in vivo optical coherence tomography regarding residual tumor detection: A clinical study

Purpose

In brain tumor surgery, it is crucial to achieve complete tumor resection while conserving adjacent noncancerous brain tissue. Several groups have demonstrated that optical coherence tomography (OCT) has the potential of identifying tumorous brain tissue. However, there is little evidence on human in vivo application of this technology, especially regarding applicability and accuracy of residual tumor detection (RTD). In this study, we execute a systematic analysis of a microscope integrated OCT-system for this purpose.

Experimental design

Multiple 3-dimensional in vivo OCT-scans were taken at protocol-defined sites at the resection edge in 21 brain tumor patients. The system was evaluated for its intraoperative applicability. Tissue biopsies were obtained at these locations, labeled by a neuropathologist and used as ground truth for further analysis. OCT-scans were visually assessed with a qualitative classifier, optical OCT-properties were obtained and two artificial intelligence (AI)-assisted methods were used for automated scan classification. All approaches were investigated for accuracy of RTD and compared to common techniques.

Results

Visual OCT-scan classification correlated well with histopathological findings. Classification with measured OCT image-properties achieved a balanced accuracy of 85%. A neuronal network approach for scan feature recognition achieved 82% and an auto-encoder approach 85% balanced accuracy. Overall applicability showed need for improvement.

Conclusion

Contactless in vivo OCT scanning has shown to achieve high values of accuracy for RTD, supporting what has well been described for ex vivo OCT brain tumor scanning, complementing current intraoperative techniques and even exceeding them in accuracy, while not yet in applicability.

Cot-side imaging of functional connectivity in the developing brain during sleep using wearable high-density diffuse optical tomography

Studies of cortical function in newborn infants in clinical settings are extremely challenging to undertake with traditional neuroimaging approaches. Partly in response to this challenge, functional near-infrared spectroscopy (fNIRS) has become an increasingly common clinical research tool but has significant limitations including a low spatial resolution and poor depth specificity. Moreover, the bulky optical fibres required in traditional fNIRS approaches present significant mechanical challenges, particularly for the study of vulnerable newborn infants. A new generation of wearable, modular, high-density diffuse optical tomography (HD-DOT) technologies has recently emerged that overcomes many of the limitations of traditional, fibre-based and low-density fNIRS measurements. Driven by the development of this new technology, we have undertaken the first cot-side study of newborn infants using wearable HD-DOT in a clinical setting. We use this technology to study functional brain connectivity (FC) in newborn infants during sleep and assess the effect of neonatal sleep states, active sleep (AS) and quiet sleep (QS), on resting state FC. Our results demonstrate that it is now possible to obtain high-quality functional images of the neonatal brain in the clinical setting with few constraints. Our results also suggest that sleep states differentially affect FC in the neonatal brain, consistent with prior reports.

Reliability and similarity of resting state functional connectivity networks imaged using wearable, high-density diffuse optical tomography in the home setting

BACKGROUND

When characterizing the brain's resting state functional connectivity (RSFC) networks, demonstrating networks' similarity across sessions and reliability across different scan durations is essential for validating results and possibly minimizing the scanning time needed to obtain stable measures of RSFC. Recent advances in optical functional neuroimaging technologies have resulted in fully wearable devices that may serve as a complimentary tool to functional magnetic resonance imaging (fMRI) and allow for investigations of RSFC networks repeatedly and easily in non-traditional scanning environments.

METHODS

Resting-state cortical hemodynamic activity was repeatedly measured in a single individual in the home environment during COVID-19 lockdown conditions using the first ever application of a 24-module (72 sources, 96 detectors) wearable high-density diffuse optical tomography (HD-DOT) system. Twelve-minute recordings of resting-state data were acquired over the pre-frontal and occipital regions in fourteen experimental sessions over three weeks. As an initial validation of the data, spatial independent component analysis was used to identify RSFC networks. Reliability and similarity scores were computed using metrics adapted from the fMRI literature.

RESULTS

We observed RSFC networks over visual regions (visual peripheral, visual central networks) and higher-order association regions (control, salience and default mode network), consistent with previous fMRI literature. High similarity was observed across testing sessions and across chromophores (oxygenated and deoxygenated haemoglobin, HbO and HbR) for all functional networks, and for each network considered separately. Stable reliability values (described here as a <10% change between time windows) were obtained for HbO and HbR with differences in required scanning time observed on a network-by-network basis.

DISCUSSION

Using RSFC data from a highly sampled individual, the present work demonstrates that wearable HD-DOT can be used to obtain RSFC measurements with high similarity across imaging sessions and reliability across recording durations in the home environment. Wearable HD-DOT may serve as a complimentary tool to fMRI for studying RSFC networks outside of the traditional scanning environment and in vulnerable populations for whom fMRI is not feasible.

Two-layer analytical model for estimation of layer thickness and flow using Diffuse Correlation Spectroscopy

Diffuse correlation spectroscopy (DCS) has been widely explored for its ability to measure cerebral blood flow (CBF), however, mostly under the assumption that the human head is homogenous. In addition to CBF, knowledge of extracerebral layers, such as skull thickness, can be informative and crucial for patient with brain complications such as traumatic brain injuries. To bridge the gap, this study explored the feasibility of simultaneously extracting skull thickness and flow in the cortex layer using DCS. We validated a two-layer analytical model that assumed the skull as top layer with a finite thickness and the brain cortex as bottom layer with semi-infinite geometry. The model fitted for thickness of the top layer and flow of the bottom layer, while assumed other parameters as constant. The accuracy of the two-layer model was tested against the conventional single-layer model using measurements from custom made two-layer phantoms mimicking skull and brain. We found that the fitted top layer thickness at each source detector (SD) distance is correlated with the expected thickness. For the fitted bottom layer flow, the two-layer model fits relatively consistent flow across all top layer thicknesses. In comparison, the conventional one-layer model increasingly underestimates the bottom layer flow as top layer thickness increases. The overall accuracy of estimating first layer thickness and flow depends on the SD distance in relationship to first layer thickness. Lastly, we quantified the influence of uncertainties in the optical properties of each layer. We found that uncertainties in the optical properties only mildly influence the fitted thickness and flow. In this work we demonstrate the feasibility of simultaneously extracting of layer thickness and flow using a two-layer DCS model. Findings from this work may introduce a robust and cost-effective approach towards simultaneous bedside assessment of skull thickness and cerebral blood flow.

Differentiation of different stages of brain tumor infiltration using optical coherence tomography: Comparison of two systems and histology

The discrimination of tumor-infiltrated tissue from non-tumorous brain tissue during neurosurgical tumor excision is a major challenge in neurosurgery. It is critical to achieve full tumor removal since it directly correlates with the survival rate of the patient. Optical coherence tomography (OCT) might be an additional imaging method in the field of neurosurgery that enables the classification of different levels of tumor infiltration and non-tumorous tissue. This work investigated two OCT systems with different imaging wavelengths (930 nm/1310 nm) and different resolutions (axial (air): 4.9 μm/16 μm, lateral: 5.2 μm/22 μm) in their ability to identify different levels of tumor infiltration based on freshly excised ex vivo brain samples. A convolutional neural network was used for the classification. For both systems, the neural network could achieve classification accuracies above 91% for discriminating between healthy white matter and highly tumor infiltrated white matter (tumor infiltration >60%) .This work shows that both OCT systems with different optical properties achieve similar results regarding the identification of different stages of brain tumor infiltration.

Model-based prediction of optogenetic sound encoding in the human cochlea by future optical cochlear implants

When hearing fails, electrical cochlear implants (eCIs) partially restore hearing by direct stimulation of spiral ganglion neurons (SGNs). As light can be better confined in space than electrical current, optical CIs (oCIs) provide more spectral information promising a fundamental improvement of hearing restoration by cochlear implants. Here, we turned to computer modelling for predicting the outcome of optogenetic hearing restoration by future oCIs in humans. We combined three-dimensional reconstruction of the human cochlea with ray-tracing simulation of emission from LED or laser-coupled waveguide emitters of the oCI. Irradiance was read out at the somata of SGNs. The irradiance values reached with waveguides were about 14 times higher than with LEDs, at the same radiant flux of the emitter. Moreover, waveguides outperformed LEDs regarding spectral selectivity. oCIs with either emitter type showed greater spectral selectivity when compared to eCI. In addition, modeling the effects of the source-to-SGN distance, orientation of the sources and impact of scar tissue further informs the development of optogenetic hearing restoration.

Head Phantom Optical Properties Validation for Near-Infrared Measurements: A Comparison with Animal Tissue

The interest in optical healthcare technologies has increased significantly over the recent years. The innovation of new optical technologies such as Near Infrared Spectroscopy (NIRS), used for the monitoring of brain perfusion, demands a comprehensive understanding and knowledge of the light tissue interaction. Phantoms can provide a rigorous, reproducible and convenient approach for evaluating an optical sensor's performance. However, up to date literature does not provide a detailed description of a complete head model that involves the human anatomy, physiological changes, and the tissue optical properties. The latter is key for the design, development and testing of optical sensors, such as NIRS technologies. This paper compared the optical properties of the materials chosen to build a head phantom, against the optical properties of real brain and skull tissues extracted from animal models. The spectra of a silicone brain and resin skull samples were compared with the spectra of the respective tissues extracted from pigs and mice. The results of this study demonstrated that both phantom materials have similar optical properties to mice and pigs' tissues. The morphology of the phantom's spectra were very similar to the respective animal tissue comparator.

Dimensional label learning contributes to the development of executive functions

A key to understanding how the brain develops is to understand how learning can change brain function. One index of learning that takes place in early childhood involves the comprehension and production of labels describing the shape and color features of objects, a process known as dimensional label learning (DLL). DLL requires integrating auditory and visual stimuli to form a system of mappings that link label representations (e.g. “red” and “color”) and visual feature representations (e.g. “red” and the hue red). Children gain expertise with these labels between the ages of 2 and 5 years, and at the same time they begin to demonstrate skills in using labels to guide cognitive function in other domains. For example, one of the hallmark measures of executive function development requires children to use verbally instructed rules to guide attention to visual dimensions. The broader impact of DLL, however, has not yet been explored. Here, we examine how the neural processes associated with the comprehension and production of labels for visual features predicts later performance on executive function tasks. Specifically, we show that left frontal cortex is activated during comprehension and production tasks at 33 months of age. Moreover, we find that neural activation in this region during label production at 33 months is associated with dimensional attention, but not spatial selective attention, at 45 months. These results shed new light on the role of label learning in developmental changes in brain and behavior. Moreover, these data suggest that dimensional label learning generalizes beyond the learned information to influence other aspects of cognition. We anticipate that these results may serve as a starting point for future work to implement label training as an intervention to influence later cognition.

Smartphone-based photogrammetry provides improved localization and registration of scalp-mounted neuroimaging sensors

Functional near infrared spectroscopy and electroencephalography are non-invasive techniques that rely on sensors placed over the scalp. The spatial localization of the measured brain activity requires the precise individuation of sensor positions and, when individual anatomical information is not available, the accurate registration of these sensor positions to a head atlas. Both these issues could be successfully addressed using a photogrammetry-based method. In this study we demonstrate that sensor positions can be accurately detected from a video recorded with a smartphone, with a median localization error of 0.7 mm, comparable if not lower, to that of conventional approaches. Furthermore, we demonstrate that the additional information of the shape of the participant’s head can be further exploited to improve the registration of the sensor’s positions to a head atlas, reducing the median sensor localization error of 31% compared to the standard registration approach.

Quantifying tissue optical properties of human heads in vivo using continuous-wave near-infrared spectroscopy and subject-specific three-dimensional Monte Carlo models

SIGNIFICANCE

Quantifying subject-specific optical properties (OPs) including absorption and transport scattering coefficients of tissues in the human head could improve the modeling of photon propagation for the analysis of functional near-infrared spectroscopy (fNIRS) data and dosage quantification in therapeutic applications. Current methods employ diffuse approximation, which excludes a low-scattering cerebrospinal fluid compartment and causes errors.

AIM

This work aims to quantify OPs of the scalp, skull, and gray matter in vivo based on accurate Monte Carlo (MC) modeling.

APPROACH

Iterative curve fitting was applied to quantify tissue OPs from multidistance continuous-wave NIR reflectance spectra. An artificial neural network (ANN) was trained using MC-simulated reflectance values based on subject-specific voxel-based tissue models to replace MC simulations as the forward model in curve fitting. To efficiently generate sufficient data for training the ANN, the efficiency of MC simulations was greatly improved by white MC simulations, increasing the detectors' acceptance angle, and building a lookup table for interpolation.

RESULTS

The trained ANN was six orders of magnitude faster than the original MC simulations. OPs of the three tissue compartments were quantified from NIR reflectance spectra measured at the forehead of five healthy subjects and their uncertainties were estimated.

CONCLUSIONS

This work demonstrated an MC-based iterative curve fitting method to quantify subject-specific tissue OPs in-vivo, with all OPs except for scattering coefficients of scalp within the ranges reported in the literature, which could aid the modeling of photon propagation in human heads.

Optical properties of human brain and tumour tissue: An ex vivo study spanning the visible range to beyond the second near-infrared window

Neuro-oncology surgery would benefit from detailed intraoperative tissue characterization provided by noncontact, contrast-agent-free, noninvasive optical imaging methods. In-depth knowledge of target tissue optical properties across a wide-wavelength spectrum could inform the design of optical imaging and computational methods to enable robust tissue analysis during surgery. We adapted a dual-beam integrating sphere to analyse small tissue samples and investigated ex vivo optical properties of five types of human brain tumour (meningioma, pituitary adenoma, schwannoma, low- and high-grade glioma) and nine different types of healthy brain tissue across a wavelength spectrum of 400 to 1800 nm. Fresh and frozen tissue samples were analysed. All tissue types demonstrated similar absorption spectra, but the reduced scattering coefficients of tumours show visible differences in the obtained optical spectrum compared to those of surrounding normal tissue. These results underline the potential of optical imaging technologies for intraoperative tissue characterization.

Photonic Nanojet-Mediated Optogenetics

Optogenetics has become a widely used technique in neuroscience research, capable of controlling neuronal activity with high spatiotemporal precision and cell-type specificity. Expressing exogenous opsins in the selected cells can induce neuronal activation upon light irradiation, and the activation depends on the power of incident light. However, high optical power can also lead to off-target neuronal activation or even cell damage. Limiting the incident power, but enhancing power distribution to the targeted neurons, can improve optogenetic efficiency and reduce off-target effects. Here, the use of optical lenses made of polystyrene microspheres is demonstrated to achieve effective focusing of the incident light of relatively low power to neighboring neurons via photonic jets. The presence of microspheres significantly localizes and enhances the power density to the target neurons both in vitro and ex vivo, resulting in increased inward current and evoked action potentials. In vivo results show optogenetic stimulation with microspheres that can evoke significantly more motor behavior and neuronal activation at lowered power density. In all, a proof-of-concept of a strategy is demonstrated to increase the efficacy of optogenetic neuromodulation using pulses of reduced optical power.

Remote dose imaging from cherenkov light using spatially-resolved CT calibration in breast radiotherapy

PURPOSE

Imaging Cherenkov light during radiotherapy allows the visualization and recording of frame-by-frame relative maps of the dose being delivered to the tissue at each control point used throughout treatment, providing one of the most complete real-time means of treatment quality assurance. In non-turbid media, the intensity of Cherenkov light is linear with surface dose deposited, however the emission from patient tissue is well-known to be reduced by absorbing tissue components such as hemoglobin, fat, water and melanin, and diffused by the scattering components of tissue. Earlier studies have shown that bulk correction could be achieved by using the patient planning CT scan for attenuation correction.

METHODS

In this study, CT maps were used for correction of spatial variations in emissivity. Testing was completed on Cherenkov images from radiotherapy treatments of post-lumpectomy breast cancer patients (n = 13), combined with spatial renderings of the patient radiodensity (CT number) from their planning CT scan.

RESULTS

The correction technique was shown to provide a pixel-by-pixel correction that suppressed many of the inter- and intra-patient differences in the Cherenkov light emitted per unit dose. This correction was established from a calibration curve that correlated Cherenkov light intensity to surface-rendered CT number (R_6MV ² = 0.70 and R_10MV ² = 0.72). The corrected Cherenkov intensity per unit dose standard error was reduced by nearly half (from ∼30% to ∼17%).

CONCLUSIONS

This approach provides evidence that the planning CT scan can mitigate some of the tissue-specific attenuation in Cherenkov images, allowing them to be translated into near surface dose images. This article is protected by copyright. All rights reserved.

Imaging affective and non-affective touch processing in two-year-old children

Touch is an important component of early parent-child interaction and plays a critical role in the socio-emotional development of children. However, there are limited studies on touch processing amongst children in the age range from one to three years. The present study used frequency-domain diffuse optical tomography (DOT) to investigate the processing of affective and non-affective touch over left frontotemporal brain areas contralateral to the stimulated forearm in two-year-old children. Affective touch was administered by a single stroke with a soft brush over the child's right dorsal forearm at 3 cm/s, while non-affective touch was provided by multiple brush strokes at 30 cm/s. We found that in the insula, the total haemoglobin (HbT) response to slow brushing was significantly greater than the response to fast brushing (slow > fast). Additionally, a region in the postcentral gyrus, Rolandic operculum and superior temporal gyrus exhibited greater response to fast brushing than slow brushing (fast > slow). These findings confirm that an adult-like pattern of haemodynamic responses to affective and non-affective touch can be recorded in two-year-old subjects using DOT. To improve the accuracy of modelling light transport in the two-year-old subjects, we used a published age-appropriate atlas and deformed it to match the exterior shape of each subject's head. We estimated the combined scalp and skull, and grey matter (GM) optical properties by fitting simulated data to calibrated and coupling error corrected phase and amplitude measurements. By utilizing a two-compartment cerebrospinal fluid (CSF) model, the accuracy of estimation of GM optical properties and the localization of activation in the insula was improved. The techniques presented in this paper can be used to study neural development of children at different ages and illustrate that the technology is well-tolerated by most two-year-old children and not excessively sensitive to subject movement. The study points the way towards exciting possibilities in functional imaging of deeper functional areas near sulci in small children.

Regional Haemodynamic and Metabolic Coupling in Infants

Metabolic pathways underlying brain function remain largely unexplored during neurodevelopment, predominantly due to the lack of feasible techniques for use with awake infants. Broadband near-infrared spectroscopy (bNIRS) provides the opportunity to explore the relationship between cerebral energy metabolism and blood oxygenation/haemodynamics through the measurement of changes in the oxidation state of mitochondrial respiratory chain enzyme cytochrome-c-oxidase (ΔoxCCO) alongside haemodynamic changes. We used a bNIRS system to measure ΔoxCCO and haemodynamics during functional activation in a group of 42 typically developing infants aged between 4 and 7 months. bNIRS measurements were made over the right hemisphere over temporal, parietal and central cortical regions, in response to social and non-social visual and auditory stimuli. Both ΔoxCCO and Δ[HbO2] displayed larger activation for the social condition in comparison to the non-social condition. Integration of haemodynamic and metabolic signals revealed networks of stimulus-selective cortical regions that were not apparent from analysis of the individual bNIRS signals. These results provide the first spatially resolved measures of cerebral metabolic activity alongside haemodynamics during functional activation in infants. Measuring synchronised changes in metabolism and haemodynamics have the potential for uncovering the development of cortical specialisation in early infancy.

OCT-Guided Surgery for Gliomas: Current Concept and Future Perspectives

Optical coherence tomography (OCT) has been recently suggested as a promising method to obtain in vivo and real-time high-resolution images of tissue structure in brain tumor surgery. This review focuses on the basics of OCT imaging, types of OCT images and currently suggested OCT scanner devices and the results of their application in neurosurgery. OCT can assist in achieving intraoperative precision identification of tumor infiltration within surrounding brain parenchyma by using qualitative or quantitative OCT image analysis of scanned tissue. OCT is able to identify tumorous tissue and blood vessels detection during stereotactic biopsy procedures. The combination of OCT with traditional imaging such as MRI, ultrasound and 5-ALA fluorescence has the potential to increase the safety and accuracy of the resection. OCT can improve the extent of resection by offering the direct visualization of tumor with cellular resolution when using microscopic OCT contact probes. The theranostic implementation of OCT as a part of intelligent optical diagnosis and automated lesion localization and ablation could achieve high precision, automation and intelligence in brain tumor surgery. We present this review for the increase of knowledge and formation of critical opinion in the field of OCT implementation in brain tumor surgery.

Multi-scattering software part II: experimental validation for the light intensity distribution

This article, Part II of an article series on GPU-accelerated Monte Carlo simulation of photon transport through turbid media, focuses on the validation of the online software Multi-Scattering. While Part I detailed the implementation of the computational model, simulated and experimental results are now compared for the distribution of the scattered light intensity. The scattering phantoms prepared here are aqueous dispersions of polystyrene microspheres of diameter D = 0.5, 2 and 5 μm and at various concentrations, resulting in optical depth ranging from OD = 1 to 17.5. The Lorenz-Mie scattering phase functions used in the simulations have been verified experimentally at low particle concentrations by analyzing the angular light intensity distribution at the Fourier plane of a collecting lens. The validation approach herein accounts for the specific light collection and image formation by the camera. The front and side surfaces of the medium are imaged and the corresponding light intensity distributions are compared qualitatively and quantitatively. It is concluded that the model enables reliable simulations over the tested parameters, offering predictive simulations of transmitted intensities with a mean relative error ≤~19% over the full range. The online software is available at: https://multi-scattering.com/.

Imaging Cerebral Energy Metabolism in Healthy Infants

Broadband near-infrared spectroscopy (bNIRS) has the potential to provide non-invasive measures of cerebral haemodynamic changes alongside changes in cellular oxygen utilisation through the measurement of mitochondrial enzyme cytochrome-c-oxidase (oxCCO). It therefore provides the opportunity to explore brain function and specialisation, which remains largely unexplored in infancy. We used bNIRS to measure changes in haemodynamics and changes in oxCCO in 4-to-7-month-old infants over the occipital and right temporal and parietal cortices in response to social and non-social visual and auditory stimuli. Changes in concentration of oxygenated-haemoglobin (Δ[HbO₂]), deoxygenated haemoglobin (Δ[HHb]) and change in the oxidation state of oxCCO (Δ[oxCCO]) were calculated using changes in attenuation of light at 120 wavelengths between 780 and900 nm, using the UCLn algorithm. For 4 infants, the attenuation changes in a subset of wavelengths were used to perform image reconstruction, in an age-matched infant model, for channels over the right parietal and temporal cortices, using a multispectral approach which allows direct reconstruction of concentration change data. The volumetric reconstructed images were mapped onto the cortical surface to visualise the reconstructed changes in concentration of HbO₂ and HHb and changes in metabolism for both social and non-social stimuli. Spatially localised activation was observed for Δ[oxCCO] and Δ[HbO₂] over the temporo-parietal region, in response to the social stimulus. This study provides the first reconstructed images of changes in metabolism in healthy, awake infants.

Interrelation between Spectral Online Monitoring and Postoperative T1-Weighted MRI in Interstitial Photodynamic Therapy of Malignant Gliomas

Simple Summary

Treatment monitoring is highly important for the delivery and control of brain tumor therapy. For interstitial photodynamic therapy (iPDT), an intraoperative spectral online monitoring (SOM) setup was established in former studies to monitor photosensitizer fluorescence and treatment light transmission during therapy. In this work, data from patients treated with iPDT as the initial treatment for newly diagnosed glioblastoma (n = 11) were retrospectively analyzed. Observed changes in treatment light transmission were assessed, and changes in optical tissue absorption were calculated out of these. In addition, magnetic resonance imaging (MRI) data were recorded within 48 h after therapy and showed intrinsic T1 hyperintensity in the treated area in non-contrast-enhanced T1-weighted sequences. A 3D co-registration of intrinsic T1 hyperintensity lesions and the light transmission zones between cylindrical diffuser fiber pairs showed that reduction in treatment light transmission corresponding to increased light absorption had a spatial correlation with post-therapeutic intrinsic T1 hyperintensity (p ≤ 0.003).

Abstract

In a former study, interstitial photodynamic therapy (iPDT) was performed on patients suffering from newly diagnosed glioblastoma (n = 11; 8/3 male/female; median age: 68, range: 40–76). The procedure includes the application of 5-ALA to selectively metabolize protoporphyrin IX (PpIX) in tumor cells and illumination utilizing interstitially positioned optical cylindrical diffuser fibers (CDF) (2–10 CDFs, 2–3 cm diffusor length, 200 mW/cm, 635 nm, 60 min irradiation). Intraoperative spectral online monitoring (SOM) was employed to monitor treatment light transmission and PpIX fluorescence during iPDT. MRI was used for treatment planning and outcome assessment. Case-dependent observations included intraoperative reduction of treatment light transmission and local intrinsic T1 hyperintensity in non-contrast-enhanced T1-weighted MRI acquired within one day after iPDT. Intrinsic T1 hyperintensity was observed and found to be associated with the treatment volume, which indicates the presence of methemoglobin, possibly induced by iPDT. Based on SOM data, the optical absorption coefficient and its change during iPDT were estimated for the target tissue volumes interjacent between evaluable CDF-pairs at the treatment wavelength of 635 nm. By spatial comparison and statistical analysis, it was found that observed increases of the absorption coefficient during iPDT were larger in or near regions of intrinsic T1 hyperintensity (p = 0.003). In cases where PpIX-fluorescence was undetectable before iPDT, the increase in optical absorption and intrinsic T1 hyperintensity tended to be less. The observations are consistent with in vitro experiments and indicate PDT-induced deoxygenation of hemoglobin and methemoglobin formation. Further investigations are needed to provide more data on the time course of the observed changes, thus paving the way for optimized iPDT irradiation protocols.

Ultrafast and Ultrahigh-Resolution Diffuse Optical Tomography for Brain Imaging with Sensitivity Equation based Noniterative Sparse Optical Reconstruction (SENSOR)

We introduce a novel image reconstruction method for time-resolved diffuse optical tomography (DOT) that yields submillimeter resolution in less than a second. This opens the door to high-resolution real-time DOT in imaging of the brain activity. We call this approach the sensitivity equation based noniterative sparse optical reconstruction (SENSOR) method. The high spatial resolution is achieved by implementing an asymptotic l ₀-norm operator that guarantees to obtain sparsest representation of reconstructed targets. The high computational speed is achieved by employing the nontruncated sensitivity equation based noniterative inverse formulation combined with reduced sensing matrix and parallel computing. We tested the new method with numerical and experimental data. The results demonstrate that the SENSOR algorithm can achieve 1 mm³ spatial-resolution optical tomographic imaging at depth of ∼60 mean free paths (MFPs) in 20∼30 milliseconds on an Intel Core i9 processor.

A scalable, multi-wavelength, broad bandwidth frequency-domain near-infrared spectroscopy platform for real-time quantitative tissue optical imaging

Frequency-domain near-infrared spectroscopy (FD-NIRS) provides quantitative noninvasive measurements of tissue optical absorption and scattering, as well as a safe and accurate method for characterizing tissue composition and metabolism. However, the poor scalability and high complexity of most FD-NIRS systems assembled to date have contributed to its limited clinical impact. To address these shortcomings, we present a scalable, digital-based FD-NIRS platform capable of measuring optical properties and tissue chromophore concentrations in real-time. The system provides single-channel FD-NIRS amplitude/phase, optical property, and chromophore data at a maximum display rate of 36.6 kHz, 17.9 kHz, and 10.2 kHz, respectively, and can be scaled to multiple channels as well as integrated into a handheld format. The entire system is enabled by several innovations including an ultra-high-speed k-nearest neighbor lookup table method (maximum of 250,000 inversions/s for a large 2500x700 table of absorption and reduced scattering coefficients), embedded FPGA and CPU high-speed co-processing, and high-speed data transfer (due to on-board processing). We show that our 6-wavelength, broad modulation bandwidth (1-400 MHz) system can be used to perform 2D high-density spatial mapping of optical properties and high speed quantification of hemodynamics.

Frontotemporal activation differs between perception of simulated cochlear implant speech and speech in background noise: An image-based fNIRS study

In this study we used functional near-infrared spectroscopy (fNIRS) to investigate neural responses in normal-hearing adults as a function of speech recognition accuracy, intelligibility of the speech stimulus, and the manner in which speech is distorted. Participants listened to sentences and reported aloud what they heard. Speech quality was distorted artificially by vocoding (simulated cochlear implant speech) or naturally by adding background noise. Each type of distortion included high and low-intelligibility conditions. Sentences in quiet were used as baseline comparison. fNIRS data were analyzed using a newly developed image reconstruction approach. First, elevated cortical responses in the middle temporal gyrus (MTG) and middle frontal gyrus (MFG) were associated with speech recognition during the low-intelligibility conditions. Second, activation in the MTG was associated with recognition of vocoded speech with low intelligibility, whereas MFG activity was largely driven by recognition of speech in background noise, suggesting that the cortical response varies as a function of distortion type. Lastly, an accuracy effect in the MFG demonstrated significantly higher activation during correct perception relative to incorrect perception of speech. These results suggest that normal-hearing adults (i.e., untrained listeners of vocoded stimuli) do not exploit the same attentional mechanisms of the frontal cortex used to resolve naturally degraded speech and may instead rely on segmental and phonetic analyses in the temporal lobe to discriminate vocoded speech.

Investigation of effect of modulation frequency on high-density diffuse optical tomography image quality

Significance: By incorporating multiple overlapping functional near-infrared spectroscopy (fNIRS) measurements, high-density diffuse optical tomography (HD-DOT) images human brain function with fidelity comparable to functional magnetic resonance imaging (fMRI). Previous work has shown that frequency domain high-density diffuse optical tomography (FD-HD-DOT) may further improve image quality over more traditional continuous wave (CW) HD-DOT. Aim: The effects of modulation frequency on image quality as obtainable with FD-HD-DOT is investigated through simulations with a realistic noise model of functional activations in human head models, arising from 11 source modulation frequencies between CW and 1000 MHz. Approach: Simulations were performed using five representative head models with an HD regular grid of 158 light sources and 166 detectors and an empirically derived noise model. Functional reconstructions were quantitatively assessed with multiple image quality metrics including the localization error (LE), success rate, full width at half maximum, and full volume at half maximum (FVHM). All metrics were evaluated against CW-based models. Results: Compared to CW, localization accuracy is improved by >40% throughout brain depths of 13 to 25 mm below the surface with 300 to 500 MHz modulation frequencies. Additionally, the reliable field of view in brain tissue is enlarged by 35% to 48% within an optimal frequency of 300 MHz after considering realistic noise, depending on the dynamic range of the system. Conclusions: These results point to the tremendous opportunities in further development of high bandwidth FD-HD-DOT system hardware for applications in human brain mapping.

The Optical Coherence Tomography and Raman Spectroscopy for Sensing of the Bone Demineralization Process

The presented research was intended to seek new optical methods to investigate the demineralization process of bones. Optical examination of the bone condition could facilitate clinical trials and improve the safety of patients. The authors used a set of complementary methods: polarization-sensitive optical coherence tomography (PS-OCT) and Raman spectroscopy. Chicken bone samples were used in this research. To stimulate in laboratory conditions the process of demineralization and gradual removal of the hydroxyapatite, the test samples of bones were placed into 10% acetic acid. Measurements were carried out in two series. The first one took two weeks with data acquired every day. In the second series, the measurements were made during one day at an hourly interval (after 1, 2, 3, 5, 7, 10, and 24 h). The relation between the content of hydroxyapatite and images recorded using OCT was analyzed and discussed. Moreover, the polarization properties of the bones, including retardation angles of the bones, were evaluated. Raman measurement confirmed the disappearance of the hydroxyapatite and the speed of this process. This work presents the results of the preliminary study on the possibility of measuring changes in bone mineralization by means of the proposed methods and confirms their potential for practical use in the future.

High Resolution, Deep Imaging Using Confocal Time-of-Flight Diffuse Optical Tomography

Light scattering by tissue severely limits how deep beneath the surface one can image, and the spatial resolution one can obtain from these images. Diffuse optical tomography (DOT) is one of the most powerful techniques for imaging deep within tissue - well beyond the conventional  ∼ 10-15 mean scattering lengths tolerated by ballistic imaging techniques such as confocal and two-photon microscopy. Unfortunately, existing DOT systems are limited, achieving only centimeter-scale resolution. Furthermore, they suffer from slow acquisition times and slow reconstruction speeds making real-time imaging infeasible. We show that time-of-flight diffuse optical tomography (ToF-DOT) and its confocal variant (CToF-DOT), by exploiting the photon travel time information, allow us to achieve millimeter spatial resolution in the highly scattered diffusion regime ( mean free paths). In addition, we demonstrate two additional innovations: focusing on confocal measurements, and multiplexing the illumination sources allow us to significantly reduce the measurement acquisition time. Finally, we rely on a novel convolutional approximation that allows us to develop a fast reconstruction algorithm, achieving a 100× speedup in reconstruction time compared to traditional DOT reconstruction techniques. Together, we believe that these technical advances serve as the first step towards real-time, millimeter resolution, deep tissue imaging using DOT.

Photoacoustic computed tomography for functional human brain imaging [Invited]

The successes of magnetic resonance imaging and modern optical imaging of human brain function have stimulated the development of complementary modalities that offer molecular specificity, fine spatiotemporal resolution, and sufficient penetration simultaneously. By virtue of its rich optical contrast, acoustic resolution, and imaging depth far beyond the optical transport mean free path (∼1 mm in biological tissues), photoacoustic computed tomography (PACT) offers a promising complementary modality. In this article, PACT for functional human brain imaging is reviewed in its hardware, reconstruction algorithms, in vivo demonstration, and potential roadmap.

Quantitative evaluation of frequency domain measurements in high density diffuse optical tomography

SIGNIFICANCE

High density diffuse optical tomography (HD-DOT) as applied in functional near-infrared spectroscopy (fNIRS) is largely limited to continuous wave (CW) data. Using a single modulation frequency, frequency domain (FD) HD-DOT has recently demonstrated better localization of focal activation as compared to CW data. We show that combining CW and FD measurements and multiple modulation frequencies increases imaging performance in fNIRS.

AIM

We evaluate the benefits of multiple modulation frequencies, combining different frequencies as well as CW data in fNIRS HD-DOT.

APPROACH

A layered model was used, with activation occurring within a cortex layer. CW and FD measurements were simulated at 78, 141, and 203 MHz with and without noise. The localization error, full width half maximum, and effective resolution were evaluated.

RESULTS

Across the average of the three metrics, at 141 MHz, FD performed 8.4% better than CW, and the combination of CW and FD was 21.7% better than CW. FD measurements at 203 MHz performed 5% better than 78 MHz. Moreover, the three combined modulation frequencies of FD and CW performed up to 3.92% better than 141 MHz alone.

CONCLUSIONS

We show that combining CW and FD measurements offers better performance than FD alone, with higher modulation frequencies increasing accuracy. Combining CW and FD measurements at multiple modulation frequencies yields the best overall performance.

Wireless and battery-free platforms for collection of biosignals

Recent progress in biosensors have quantitively expanded current capabilities in exploratory research tools, diagnostics and therapeutics. This rapid pace in sensor development has been accentuated by vast improvements in data analysis methods in the form of machine learning and artificial intelligence that, together, promise fantastic opportunities in chronic sensing of biosignals to enable preventative screening, automated diagnosis, and tools for personalized treatment strategies. At the same time, the importance of widely accessible personal monitoring has become evident by recent events such as the COVID-19 pandemic. Progress in fully integrated and chronic sensing solutions is therefore increasingly important. Chronic operation, however, is not truly possible with tethered approaches or bulky, battery-powered systems that require frequent user interaction. A solution for this integration challenge is offered by wireless and battery-free platforms that enable continuous collection of biosignals. This review summarizes current approaches to realize such device architectures and discusses their building blocks. Specifically, power supplies, wireless communication methods and compatible sensing modalities in the context of most prevalent implementations in target organ systems. Additionally, we highlight examples of current embodiments that quantitively expand sensing capabilities because of their use of wireless and battery-free architectures.

Evaluating a new generation of wearable high-density diffuse optical tomography technology via retinotopic mapping of the adult visual cortex

Significance: High-density diffuse optical tomography (HD-DOT) has been shown to approach the resolution and localization accuracy of blood oxygen level dependent-functional magnetic resonance imaging in the adult brain by exploiting densely spaced, overlapping samples of the probed tissue volume, but the technique has to date required large and cumbersome optical fiber arrays. Aim: To evaluate a wearable HD-DOT system that provides a comparable sampling density to large, fiber-based HD-DOT systems, but with vastly improved ergonomics. Approach: We investigated the performance of this system by replicating a series of classic visual stimulation paradigms, carried out in one highly sampled participant during 15 sessions to assess imaging performance and repeatability. Results: Hemodynamic response functions and cortical activation maps replicate the results obtained with larger fiber-based systems. Our results demonstrate focal activations in both oxyhemoglobin and deoxyhemoglobin with a high degree of repeatability observed across all sessions. A comparison with a simulated low-density array explicitly demonstrates the improvements in spatial localization, resolution, repeatability, and image contrast that can be obtained with this high-density technology. Conclusions: The system offers the possibility for minimally constrained, spatially resolved functional imaging of the human brain in almost any environment and holds particular promise in enabling neuroscience applications outside of the laboratory setting. It also opens up new opportunities to investigate populations unsuited to traditional imaging technologies.

Processing pipeline for image reconstructed fNIRS analysis using both MRI templates and individual anatomy

Significance: Image reconstruction of fNIRS data is a useful technique for transforming channel-based fNIRS into a volumetric representation and managing spatial variance based on optode location. We present an innovative integrated pipeline for image reconstruction of fNIRS data using either MRI templates or individual anatomy. Aim: We demonstrate a pipeline with accompanying code to allow users to clean and prepare optode location information, prepare and standardize individual anatomical images, create the light model, run the 3D image reconstruction, and analyze data in group space. Approach: We synthesize a combination of new and existing software packages to create a complete pipeline, from raw data to analysis. Results: This pipeline has been tested using both templates and individual anatomy, and on data from different fNIRS data collection systems. We show high temporal correlations between channel-based and image-based fNIRS data. In addition, we demonstrate the reliability of this pipeline with a sample dataset that included 74 children as part of a longitudinal study taking place in Scotland. We demonstrate good correspondence between data in channel space and image reconstructed data. Conclusions: The pipeline presented here makes a unique contribution by integrating multiple tools to assemble a complete pipeline for image reconstruction in fNIRS. We highlight further issues that may be of interest to future software developers in the field.

Optical investigation of bovine grey and white matters in visible and near-infrared ranges

Introduction: Due to enormous interests for laser in medicine and biology, optical properties characterization of different tissue have be affecting in development processes. In addition, the optical properties of biological tissues could be influenced by storage methods. Thus, optical properties of bovine white and grey tissues preserved by formalin have been characterized over a wide wavelength spectrum varied between 440 nm and 1000 nm.

Materials and Methods: To that end, a single integrating sphere system was assembled for spectroscopic characterization and an inverse adding-doubling algorithm was used to retrieve optical coefficients, i.e. reduced scattering and absorption coefficients.

Results: White matter has shown a strong scattering property in comparison to grey matter. On the other hand, the grey matter has absorbed light extensively. In comparison, the reduced scattering profile for both tissue types turned out to be consistent with prior works that characterized optical coefficients in vivo. On the contrary, absorption coefficient behavior has a different feature.

Conclusion: Formalin could change the tissue’s optical properties because of the alteration of tissue’s structure and components. The absence of hemoglobin that seeps out due to the use of a formalin could reduce the absorption coefficient over the visible range. Both the water replacement by formalin could reduce the refractive index of a stored tissue and the absence of hemoglobin that scatters light over the presented wavelength range should diminish the reduced scattering coefficients over that wavelength range.

Targeting brain regions of interest in functional near-infrared spectroscopy-Scalp-cortex correlation using subject-specific light propagation models

Targeting specific brain regions of interest by the accurate positioning of optodes (emission and detection probes) on the scalp remains a challenge for functional near‐infrared spectroscopy (fNIRS). Since fNIRS data does not provide any anatomical information on the brain cortex, establishing the scalp‐cortex correlation (SCC) between emission‐detection probe pairs on the scalp and the underlying brain regions in fNIRS measurements is extremely important. A conventional SCC is obtained by a geometrical point‐to‐point manner and ignores the effect of light scattering in the head tissue that occurs in actual fNIRS measurements. Here, we developed a sensitivity‐based matching (SBM) method that incorporated the broad spatial sensitivity of the probe pair due to light scattering into the SCC for fNIRS. The SCC was analyzed between head surface fiducial points determined by the international 10–10 system and automated anatomical labeling brain regions for 45 subject‐specific head models. The performance of the SBM method was compared with that of three conventional geometrical matching (GM) methods. We reveal that the light scattering and individual anatomical differences in the head affect the SCC, which indicates that the SBM method is compulsory to obtain the precise SCC. The SBM method enables us to evaluate the activity of cortical regions that are overlooked in the SCC obtained by conventional GM methods. Together, the SBM method could be a promising approach to guide fNIRS users in designing their probe arrangements and in explaining their measurement data.

Soft, Wireless and subdermally implantable recording and neuromodulation tools

Progress in understanding neuronal interaction and circuit behavior of the central and peripheral nervous system strongly relies on the advancement of tools that record and stimulate with high fidelity and specificity. Currently, devices used in exploratory research predominantly utilize cables or tethers to provide pathways for power supply, data communication, stimulus delivery and recording, which constrains the scope and use of such devices. In particular, the tethered connection, mechanical mismatch to surrounding soft tissues and bones frustrate the interface leading to irritation and limitation of motion of the subject, which in the case of fundamental and preclinical studies, impacts naturalistic behaviors of animals and precludes the use in experiments involving social interaction and ethologically relevant three-dimensional environments, limiting the use of current tools to mostly rodents and exclude species such as birds and fish. This review explores the current state-of-the-art in wireless, subdermally implantable tools that quantitively expand capabilities in analysis and perturbation of the central and peripheral nervous system by removing tethers and externalized features of implantable neuromodulation and recording tools. Specifically, the review explores power harvesting strategies, wireless communication schemes, and soft materials and mechanics that enable the creation of such devices and discuss their capabilities in the context of freely-behaving subjects. Highlights of this class of devices includes wireless battery-free and fully implantable operation with capabilities in cell specific recording, multimodal neural stimulation and electrical, optogenetic and pharmacological neuromodulation capabilities. We conclude with discussion on translation of such technologies which promises routes towards broad dissemination.

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.

Decoding visual information from high-density diffuse optical tomography neuroimaging data

Background:

Neural decoding could be useful in many ways, from serving as a neuroscience research tool to providing a means of augmented communication for patients with neurological conditions. However, applications of decoding are currently constrained by the limitations of traditional neuroimaging modalities. Electrocorticography requires invasive neurosurgery, magnetic resonance imaging (MRI) is too cumbersome for uses like daily communication, and alternatives like functional near-infrared spectroscopy (fNIRS) offer poor image quality. High-density diffuse optical tomography (HD-DOT) is an emerging modality that uses denser optode arrays than fNIRS to combine logistical advantages of optical neuroimaging with enhanced image quality. Despite the resulting promise of HD-DOT for facilitating field applications of neuroimaging, decoding of brain activity as measured by HD-DOT has yet to be evaluated.

Objective:

To assess the feasibility and performance of decoding with HD-DOT in visual cortex.

Methods and Results:

To establish the feasibility of decoding at the single-trial level with HD-DOT, a template matching strategy was used to decode visual stimulus position. A receiver operating characteristic (ROC) analysis was used to quantify the sensitivity, specificity, and reproducibility of binary visual decoding. Mean areas under the curve (AUCs) greater than 0.97 across 10 imaging sessions in a highly sampled participant were observed. ROC analyses of decoding across 5 participants established both reproducibility in multiple individuals and the feasibility of inter-individual decoding (mean AUCs > 0.7), although decoding performance varied between individuals. Phase-encoded checkerboard stimuli were used to assess more complex, non-binary decoding with HD-DOT. Across 3 highly sampled participants, the phase of a 60° wide checkerboard wedge rotating 10° per second through 360° was decoded with a within-participant error of 25.8±24.7°. Decoding between participants was also feasible based on permutation-based significance testing.

Conclusions:

Visual stimulus information can be decoded accurately, reproducibly, and across a range of detail (for both binary and non-binary outcomes) at the single-trial level (without needing to block-average test data) using HD-DOT data. These results lay the foundation for future studies of more complex decoding with HD-DOT and applications in clinical populations.

A comparison between plausible models in layered turbid media with geometrical variations applying a Bayesian selection criterion

One possible application of Near Infrared techniques is to analyze human brain metabolic activity. Currently used models take into account the layered structure of the human head but, usually, they do not consider the non-planar surface of some of the boundaries, i.e. gray matter, which results in a much more complex structure, thus leading to more sophisticated models and longer calculation times. The main objective of this work is to determine if it is worth to replace a planar layered structure by a non-planar one. To this end we implement a Bayesian-based quantitative methodology for choosing between two competitive models describing light propagation in layered turbid media. Experiments of time-resolved diffuse reflectance measurements are performed in layered phantoms and complemented with numerical calculations. The resulting Distributions of Time of Flight of both models are compared using Bayesian model selection analysis. The non-planar interface was introduced in the simulations by a simple surface parametrization. Results suggest that, under certain conditions, a multilayer model with planar boundaries is good enough.

Wavelength weightings in machine learning for ovine joint tissue differentiation using diffuse reflectance spectroscopy (DRS)

Objective: To investigate the DRS of ovine joint tissue to determine the optimal optical wavelengths for tissue differentiation and relate these wavelengths to the biomolecular composition of tissues. In this study, we combine machine learning with DRS for tissue classification and then look further at the weighting matrix of the classifier to further understand the key differentiating features. Methods: Supervised machine learning was used to analyse DRS data. After normalising the data, dimension reduction was achieved through multiclass Fisher's linear discriminant analysis (Multiclass FLDA) and classified with linear discriminant analysis (LDA). The classifier was first run with all the tissue types and the wavelength range 190 nm - 1081 nm. We analysed the weighting matrix of the classifier and then ran the classifier again, the first time using the ten highest weighted wavelengths and the second using only the single highest. Our method was applied to a dataset containing ovine joint tissue including cartilage, cortical and subchondral bone, fat, ligament, meniscus, and muscle. Results: It achieved a classification accuracy of 100% using the wavelength 190 nm - 1081 nm (2048 attributes) with an accuracy of 90% being present for 10 attributes with the exception of those with comparable compositions such as ligament and meniscus. An accuracy greater than 70% was achieved using a single wavelength, with the same exceptions. Conclusion: Multiclass FLDA combined with LDA is a viable technique for tissue identification from DRS data. The majority of differentiating features existed within the wavelength ranges 370-470 and 800-1010 nm. Focusing on key spectral regions means that a spectrometer with a narrower range can potentially be used, with less computational power needed for subsequent analysis.

Tissue Phantoms for Biomedical Applications in Raman Spectroscopy: A Review

Raman spectroscopy is a group of analytical techniques, currently applied in several research fields, including clinical diagnostics. Tissue-mimicking optical phantoms have been established as an essential intermediate stage for medical applications with their employment from spectroscopic techniques to be constantly growing. This review outlines the types of tissue phantoms currently employed in different biomedical applications of Raman spectroscopy, focusing on their composition and optical properties. It is therefore an attempt to present an informed range of options for potential use to the researchers.

A wide field-of-view, modular, high-density diffuse optical tomography system for minimally constrained three-dimensional functional neuroimaging

The ability to produce high-quality images of human brain function in any environment and during unconstrained movement of the subject has long been a goal of neuroimaging research. Diffuse optical tomography, which uses the intensity of back-scattered near-infrared light from multiple source-detector pairs to image changes in haemoglobin concentrations in the brain, is uniquely placed to achieve this goal. Here, we describe a new generation of modular, fibre-less, high-density diffuse optical tomography technology that provides exceptional sensitivity, a large dynamic range, a field-of-view sufficient to cover approximately one-third of the adult scalp, and also incorporates dedicated motion sensing into each module. Using in-vivo measures, we demonstrate a noise-equivalent power of 318 fW, and an effective dynamic range of 142 dB. We describe the application of this system to a novel somatomotor neuroimaging paradigm that involves subjects walking and texting on a smartphone. Our results demonstrate that wearable high-density diffuse optical tomography permits three-dimensional imaging of the human brain function during overt movement of the subject; images of somatomotor cortical activation can be obtained while subjects move in a relatively unconstrained manner, and these images are in good agreement with those obtained while the subjects remain stationary. The scalable nature of the technology we described here paves the way for the routine acquisition of high-quality, three-dimensional, whole-cortex diffuse optical tomography images of cerebral haemodynamics, both inside and outside of the laboratory environment, which has profound implications for neuroscience.

Investigation of the Effect of the Skull in Transcranial Photoacoustic Imaging: A Preliminary Ex Vivo Study

Although transcranial photoacoustic imaging (TCPAI) has been used in small animal brain imaging, in animals with thicker skull bones or in humans both light illumination and ultrasound propagation paths are affected. Hence, the PA image is largely degraded and in some cases completely distorted. This study aims to investigate and determine the maximum thickness of the skull through which photoacoustic imaging is feasible in terms of retaining the imaging target structure without incorporating any post processing. We identify the effect of the skull on both the illumination path and acoustic propagation path separately and combined. In the experimental phase, the distorting effect of ex vivo sheep skull bones with thicknesses in the range of 0.7~1.3 mm are explored. We believe that the findings in this study facilitate the clinical translation of TCPAI.

Fast and accurate Monte Carlo simulations of subdiffusive spatially resolved reflectance for a realistic optical fiber probe tip model aided by a deep neural network

In this work, we introduce a framework for efficient and accurate Monte Carlo (MC) simulations of spatially resolved reflectance (SRR) acquired by optical fiber probes that account for all the details of the probe tip including reflectivity of the stainless steel and the properties of the epoxy fill and optical fibers. While using full details of the probe tip is essential for accurate MC simulations of SRR, the break-down of the radial symmetry in the detection scheme leads to about two orders of magnitude longer simulation times. The introduced framework mitigates this performance degradation, by an efficient reflectance regression model that maps SRR obtained by fast MC simulations based on a simplified probe tip model to SRR simulated using the full details of the probe tip. We show that a small number of SRR samples is sufficient to determine the parameters of the regression model. Finally, we use the regression model to simulate SRR for a stainless steel optical probe with six linearly placed fibers and experimentally validate the framework through the use of inverse models for estimation of absorption and reduced scattering coefficients and subdiffusive scattering phase function quantifiers.

Beyond diffuse correlations: deciphering random flow in time-of-flight resolved light dynamics

Diffusing wave spectroscopy (DWS) and diffuse correlation spectroscopy (DCS) can assess blood flow index (BFI) of biological tissue with multiply scattered light. Though the main biological function of red blood cells (RBCs) is advection, in DWS/DCS, RBCs are assumed to undergo Brownian motion. To explain this discrepancy, we critically examine the cumulant approximation, a major assumption in DWS/DCS. We present a precise criterion for validity of the cumulant approximation, and in realistic tissue models, identify conditions that invalidate it. We show that, in physiologically relevant scenarios, the first cumulant term for random flow and second cumulant term for Brownian motion alone can cancel each other. In such circumstances, assuming pure Brownian motion of RBCs and the first cumulant approximation, a routine practice in DWS/DCS of BFI, can yield good agreement with data, but only because errors due to two incorrect assumptions cancel out. We conclude that correctly assessing random flow from scattered light dynamics requires going beyond the cumulant approximation and propose a more accurate model to do so.

Predictors and Limitations of the Penetration Depth of Photodynamic Effects in the Rodent Brain

Fluorescence-guided surgery (FGS) is routinely utilized in clinical centers around the world, whereas the combination of FGS and photodynamic therapy (PDT) has yet to reach clinical implementation and remains an active area of translational investigations. Two significant challenges to the clinical translation of PDT for brain cancer are as follows: (1) Limited light penetration depth in brain tissues and (2) Poor selectivity and delivery of the appropriate photosensitizers. To address these shortcomings, we developed nanoliposomal protoporphyrin IX (Nal-PpIX) and nanoliposomal benzoporphyrin derivative (Nal-BPD) and then evaluated their photodynamic effects as a function of depth in tissue and light fluence using rat brains. Although red light penetration depth (defined as the depth at which the incident optical energy drops to 1/e, ~37%) is typically a few millimeters in tissues, we demonstrated that the remaining optical energy could induce PDT effects up to 2 cm within brain tissues. Photobleaching and singlet oxygen yield studies between Nal-BPD and Nal-PpIX suggest that deep-tissue PDT (>1 cm) is more effective when using Nal-BPD. These findings indicate that Nal-BPD-PDT is more likely to generate cytotoxic effects deep within the brain and allow for the treatment of brain invading tumor cells centimeters away from the main, resectable tumor mass.

Portable, field-based neuroimaging using high-density diffuse optical tomography

Behavioral and cognitive tests in individuals who were malnourished as children have revealed malnutrition-related deficits that persist throughout the lifespan. These findings have motivated recent neuroimaging investigations that use highly portable functional near-infrared spectroscopy (fNIRS) instruments to meet the demands of brain imaging experiments in low-resource environments and enable longitudinal investigations of brain function in the context of long-term malnutrition. However, recent studies in healthy subjects have demonstrated that high-density diffuse optical tomography (HD-DOT) can significantly improve image quality over that obtained with sparse fNIRS imaging arrays. In studies of both task activations and resting state functional connectivity, HD-DOT is beginning to approach the data quality of fMRI for superficial cortical regions. In this work, we developed a customized HD-DOT system for use in malnutrition studies in Cali, Colombia. Our results evaluate the performance of the HD-DOT instrument for assessing brain function in a cohort of malnourished children. In addition to demonstrating portability and wearability, we show the HD-DOT instrument's sensitivity to distributed brain responses using a sensory processing task and measurements of homotopic functional connectivity. Task-evoked responses to the passive word listening task produce activations localized to bilateral superior temporal gyrus, replicating previously published work using this paradigm. Evaluating this localization performance across sparse and dense reconstruction schemes indicates that greater localization consistency is associated with a dense array of overlapping optical measurements. These results provide a foundation for additional avenues of investigation, including identifying and characterizing a child's individual malnutrition burden and eventually contributing to intervention development.