Mapping distributed brain function and networks with diffuse optical tomography

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.

Spread spectrum time-resolved diffuse optical measurement system for enhanced sensitivity in detecting human brain activity

Diffuse optical spectroscopy (DOS) and imaging methods have been widely applied to noninvasive detection of brain activity. We have designed and implemented a low cost, portable, real-time one-channel time-resolved DOS system for neuroscience studies. Phantom experiments were carried out to test the performance of the system. We further conducted preliminary human experiments and demonstrated that enhanced sensitivity in detecting neural activity in the cortex could be achieved by the use of late arriving photons.

Neural correlates of conflict between gestures and words: A domain-specific role for a temporal-parietal complex

The interpretation of social cues is a fundamental function of human social behavior, and resolution of inconsistencies between spoken and gestural cues plays an important role in successful interactions. To gain insight into these underlying neural processes, we compared neural responses in a traditional color/word conflict task and to a gesture/word conflict task to test hypotheses of domain-general and domain-specific conflict resolution. In the gesture task, recorded spoken words ("yes" and "no") were presented simultaneously with video recordings of actors performing one of the following affirmative or negative gestures: thumbs up, thumbs down, head nodding (up and down), or head shaking (side-to-side), thereby generating congruent and incongruent communication stimuli between gesture and words. Participants identified the communicative intent of the gestures as either positive or negative. In the color task, participants were presented the words "red" and "green" in either red or green font and were asked to identify the color of the letters. We observed a classic "Stroop" behavioral interference effect, with participants showing increased response time for incongruent trials relative to congruent ones for both the gesture and color tasks. Hemodynamic signals acquired using functional near-infrared spectroscopy (fNIRS) were increased in the right dorsolateral prefrontal cortex (DLPFC) for incongruent trials relative to congruent trials for both tasks consistent with a common, domain-general mechanism for detecting conflict. However, activity in the left DLPFC and frontal eye fields and the right temporal-parietal junction (TPJ), superior temporal gyrus (STG), supramarginal gyrus (SMG), and primary and auditory association cortices was greater for the gesture task than the color task. Thus, in addition to domain-general conflict processing mechanisms, as suggested by common engagement of right DLPFC, socially specialized neural modules localized to the left DLPFC and right TPJ including adjacent homologous receptive language areas were engaged when processing conflicting communications. These findings contribute to an emerging view of specialization within the TPJ and adjacent areas for interpretation of social cues and indicate a role for the region in processing social conflict.

Optical neuroimaging of spoken language

In this review I introduce the historical context and methods of optical neuroimaging, leading to the modern use of functional near-infrared spectroscopy (fNIRS) and high-density diffuse optical tomography (HD-DOT) to study human brain function. In its most frequent application, optical neuroimaging measures a hemodynamically-mediated signal indirectly related to neural processing, similar to that captured by fMRI. Compared to other approaches to measuring human brain function, optical imaging has many advantages: it is noninvasive, frequently portable, acoustically silent, robust to motion and muscle movement, and appropriate in many situations in which fMRI is not possible (for example, due to implanted medical devices). Challenges include producing a full-brain field of view, homogenous spatial resolution, and accurate source localization. Experimentally, optical neuroimaging has been used to study phoneme, word, and sentence processing in a variety of paradigms. With continuing technical and methodological improvements the future of optical neuroimaging is increasingly bright.

Functional connectomics from a "big data" perspective

In the last decade, explosive growth regarding functional connectome studies has been observed. Accumulating knowledge has significantly contributed to our understanding of the brain's functional network architectures in health and disease. With the development of innovative neuroimaging techniques, the establishment of large brain datasets and the increasing accumulation of published findings, functional connectomic research has begun to move into the era of "big data", which generates unprecedented opportunities for discovery in brain science and simultaneously encounters various challenging issues, such as data acquisition, management and analyses. Big data on the functional connectome exhibits several critical features: high spatial and/or temporal precision, large sample sizes, long-term recording of brain activity, multidimensional biological variables (e.g., imaging, genetic, demographic, cognitive and clinic) and/or vast quantities of existing findings. We review studies regarding functional connectomics from a big data perspective, with a focus on recent methodological advances in state-of-the-art image acquisition (e.g., multiband imaging), analysis approaches and statistical strategies (e.g., graph theoretical analysis, dynamic network analysis, independent component analysis, multivariate pattern analysis and machine learning), as well as reliability and reproducibility validations. We highlight the novel findings in the application of functional connectomic big data to the exploration of the biological mechanisms of cognitive functions, normal development and aging and of neurological and psychiatric disorders. We advocate the urgent need to expand efforts directed at the methodological challenges and discuss the direction of applications in this field.

Cerebral functional connectivity and Mayer waves in mice: Phenomena and separability

Resting-state functional connectivity is a growing neuroimaging approach that analyses the spatiotemporal structure of spontaneous brain activity, often using low-frequency (<0.08 Hz) hemodynamics. In addition to these fluctuations, there are two other low-frequency hemodynamic oscillations in a nearby spectral region (0.1-0.4 Hz) that have been reported in the brain: vasomotion and Mayer waves. Despite how close in frequency these phenomena exist, there is little research on how vasomotion and Mayer waves are related to or affect resting-state functional connectivity. In this study, we analyze spontaneous hemodynamic fluctuations over the mouse cortex using optical intrinsic signal imaging. We found spontaneous occurrence of oscillatory hemodynamics ∼0.2 Hz consistent with the properties of Mayer waves reported in the literature. Across a group of mice (n = 19), there was a large variability in the magnitude of Mayer waves. However, regardless of the magnitude of Mayer waves, functional connectivity patterns could be recovered from hemodynamic signals when filtered to the lower frequency band, 0.01-0.08 Hz. Our results demonstrate that both Mayer waves and resting-state functional connectivity patterns can co-exist simultaneously, and that they can be separated by applying bandpass filters.

Diffuse optical tomography reconstruction method using ultrasound images as prior for regularization matrix

Ultrasound-guided diffuse optical tomography (DOT) is a promising imaging technique that maps hemoglobin concentrations of breast lesions to assist ultrasound (US) for cancer diagnosis and treatment monitoring. The accurate recovery of breast lesion optical properties requires an effective image reconstruction method. We introduce a reconstruction approach in which US images are encoded as prior information for regularization of the inversion matrix. The framework of this approach is based on image reconstruction package “NIRFAST.” We compare this approach to the US-guided dual-zone mesh reconstruction method, which is based on Born approximation and conjugate gradient optimization developed in our laboratory. Results were evaluated using phantoms and clinical data. This method improves classification of malignant and benign lesions by increasing malignant to benign lesion absorption contrast. The results also show improvements in reconstructed lesion shapes and the spatial distribution of absorption maps.

Light in diagnosis, therapy and surgery

Light and optical techniques have made profound impacts on modern medicine, with numerous lasers and optical devices being currently used in clinical practice to assess health and treat disease. Recent advances in biomedical optics have enabled increasingly sophisticated technologies — in particular those that integrate photonics with nanotechnology, biomaterials and genetic engineering. In this Review, we revisit the fundamentals of light–matter interactions, describe the applications of light in imaging, diagnosis, therapy and surgery, overview their clinical use, and discuss the promise of emerging light-based technologies.

Wearable near-infrared optical probe for continuous monitoring during breast cancer neoadjuvant chemotherapy infusions

We present a new continuous-wave wearable diffuse optical probe aimed at investigating the hemodynamic response of locally advanced breast cancer patients during neoadjuvant chemotherapy infusions. The system consists of a flexible printed circuit board that supports an array of six dual wavelength surface-mount LED and photodiode pairs. The probe is encased in a soft silicone housing that conforms to natural breast shape. Probe performance was evaluated using tissue-simulating phantoms and in vivo normal volunteer measurements. High SNR (71 dB), low source-detector crosstalk ( ? 60 ?? dB ), high measurement precision (0.17%), and good thermal stability (0.22% V rms / ° C ) were achieved in phantom studies. A cuff occlusion experiment was performed on the forearm of a healthy volunteer to demonstrate the ability to track rapid hemodynamic changes. Proof-of-principle normal volunteer measurements were taken to demonstrate the ability to collect continuous in vivo breast measurements. This wearable probe is a first of its kind tool to explore prognostic hemodynamic changes during chemotherapy in breast cancer patients.

Functional optoacoustic neuro-tomography for scalable whole-brain monitoring of calcium indicators

Non-invasive observation of spatiotemporal activity of large neural populations distributed over entire brains is a longstanding goal of neuroscience. We developed a volumetric multispectral optoacoustic tomography platform for imaging neural activation deep in scattering brains. It can record 100 volumetric frames per second across scalable fields of view ranging between 50 and 1000 mm³ with respective spatial resolution of 35-200 μm. Experiments performed in immobilized and freely swimming larvae and in adult zebrafish brains expressing the genetically encoded calcium indicator GCaMP5G demonstrate, for the first time, the fundamental ability to directly track neural dynamics using optoacoustics while overcoming the longstanding penetration barrier of optical imaging in scattering brains. The newly developed platform thus offers unprecedented capabilities for functional whole-brain observations of fast calcium dynamics; in combination with optoacoustics' well-established capacity for resolving vascular hemodynamics, it could open new vistas in the study of neural activity and neurovascular coupling in health and disease.

Printed optics: phantoms for quantitative deep tissue fluorescence imaging

Three-dimensional (3D) printing allows for complex or physiologically realistic phantoms, useful, for example, in developing biomedical imaging methods and for calibrating measured data. However, available 3D printing materials provide a limited range of static optical properties. We overcome this limitation with a new method using stereolithography that allows tuning of the printed phantom's optical properties to match that of target tissues, accomplished by printing a mixture of polystyrene microspheres and clear photopolymer resin. We show that Mie theory can be used to design the optical properties, and demonstrate the method by fabricating a mouse phantom and imaging it using fluorescence optical diffusion tomography.

Time-domain diffuse optical tomography using silicon photomultipliers: feasibility study

Silicon photomultipliers (SiPMs) have been very recently introduced as the most promising detectors in the field of diffuse optics, in particular due to the inherent low cost and large active area. We also demonstrate the suitability of SiPMs for time-domain diffuse optical tomography (DOT). The study is based on both simulations and experimental measurements. Results clearly show excellent performances in terms of spatial localization of an absorbing perturbation, thus opening the way to the use of SiPMs for DOT, with the possibility to conceive a new generation of low-cost and reliable multichannel tomographic systems.

Lawrence K. Subtraction-based approach for enhancing the depth sensitivity of time-resolved NIRS

The aim of this study was to evaluate enhancing of the depth sensitivity of time-resolved near-infrared spectroscopy with a subtraction-based approach. Due to the complexity of light propagation in a heterogeneous media, and to prove the validity of the proposed method in a heterogeneous turbid media we conducted a broad analysis taking into account a number of parameters related to the method as well as various parameters of this media. The results of these experiments confirm that the depth sensitivity of the subtraction-based approach is better than classical approaches using continuous-wave or time-resolved methods. Furthermore, the results showed that the subtraction-based approach has a unique, selective sensitivity to a layer at a specific depth. In vivo application of the proposed method resulted in a greater magnitude of the hemodynamic changes during functional activation than with the standard approach.

An accelerated photo-magnetic imaging reconstruction algorithm based on an analytical forward solution and a fast Jacobian assembly method

We previously introduced photo-magnetic imaging (PMI), an imaging technique that illuminates the medium under investigation with near-infrared light and measures the induced temperature increase using magnetic resonance thermometry (MRT). Using a multiphysics solver combining photon migration and heat diffusion, PMI models the spatiotemporal distribution of temperature variation and recovers high resolution optical absorption images using these temperature maps. In this paper, we present a new fast non-iterative reconstruction algorithm for PMI. This new algorithm uses analytic methods during the resolution of the forward problem and the assembly of the sensitivity matrix. We validate our new analytic-based algorithm with the first generation finite element method (FEM) based reconstruction algorithm previously developed by our team. The validation is performed using, first synthetic data and afterwards, real MRT measured temperature maps. Our new method accelerates the reconstruction process 30-fold when compared to a single iteration of the FEM-based algorithm.

Long-term continuous monitoring of the preterm brain with diffuse optical tomography and electroencephalography: a technical note on cap manufacturing

Diffuse optical tomography (DOT) has recently proved useful for detecting whole-brain oxygenation changes in preterm and term newborns' brains. The data recording phase in prior explorations was limited up to a maximum of a couple of hours, a time dictated by the need to minimize skin damage caused by the protracted contact with optode holders and interference with concomitant clinical/nursing procedures. In an attempt to extend the data recording phase, we developed a new custom-made cap for multimodal DOT and electroencephalography acquisitions for the neonatal population. The cap was tested on a preterm neonate (28 weeks gestation) for a 7-day continuous monitoring period. The cap was well tolerated by the neonate, who did not suffer any evident discomfort and/or skin damage. Montage and data acquisition using our cap was operated by an attending nurse with no difficulty. DOT data quality was remarkable, with an average of 92% of reliable channels, characterized by the clear presence of the heartbeat in most of them.

Functional imaging of the human brain using a modular, fibre-less, high-density diffuse optical tomography system

We present the first three-dimensional, functional images of the human brain to be obtained using a fibre-less, high-density diffuse optical tomography system. Our technology consists of independent, miniaturized, silicone-encapsulated DOT modules that can be placed directly on the scalp. Four of these modules were arranged to provide up to 128, dual-wavelength measurement channels over a scalp area of approximately 60 × 65 mm². Using a series of motor-cortex stimulation experiments, we demonstrate that this system can obtain high-quality, continuous-wave measurements at source-detector separations ranging from 14 to 55 mm in adults, in the presence of hair. We identify robust haemodynamic response functions in 5 out of 5 subjects, and present diffuse optical tomography images that depict functional haemodynamic responses that are well-localized in all three dimensions at both the individual and group levels. This prototype modular system paves the way for a new generation of wearable, wireless, high-density optical neuroimaging technologies.

Probe-hosted silicon photomultipliers for time-domain functional near-infrared spectroscopy: phantom and in vivo tests

We report the development of a compact probe for time-domain (TD) functional near-infrared spectroscopy (fNIRS) based on a fast silicon photomultiplier (SiPM) that can be put directly in contact with the sample without the need of optical fibers for light collection. We directly integrated an avalanche signal amplification stage close to the SiPM, thus reducing the size of the detection channel and optimizing the signal immunity to electromagnetic interferences. The whole detection electronics was placed in a plastic screw holder compatible with the electroencephalography standard cap for measurement on brain or with custom probe holders. The SiPM is inserted into a transparent and insulating resin to avoid the direct contact of the scalp with the 100-V bias voltage. The probe was integrated in an instrument for TD fNIRS spectroscopy. The system was characterized on tissue phantoms in terms of temporal resolution, responsivity, linearity, and capability to detect deep absorption changes. Preliminary in vivo tests on adult volunteers were performed to monitor hemodynamic changes in the arm during a cuff occlusion and in the brain cortex during a motor task.

Automated voxel classification used with atlas-guided diffuse optical tomography for assessment of functional brain networks in young and older adults

Atlas-guided diffuse optical tomography (atlas-DOT) is a computational means to image changes in cortical hemodynamic signals during human brain activities. Graph theory analysis (GTA) is a network analysis tool commonly used in functional neuroimaging to study brain networks. Atlas-DOT has not been analyzed with GTA to derive large-scale brain connectivity/networks based on near-infrared spectroscopy (NIRS) measurements. We introduced an automated voxel classification (AVC) method that facilitated the use of GTA with atlas-DOT images by grouping unequal-sized finite element voxels into anatomically meaningful regions of interest within the human brain. The overall approach included volume segmentation, AVC, and cross-correlation. To demonstrate the usefulness of AVC, we applied reproducibility analysis to resting-state functional connectivity measurements conducted from 15 young adults in a two-week period. We also quantified and compared changes in several brain network metrics between young and older adults, which were in agreement with those reported by a previous positron emission tomography study. Overall, this study demonstrated that AVC is a useful means for facilitating integration or combination of atlas-DOT with GTA and thus for quantifying NIRS-based, voxel-wise resting-state functional brain networks.

Quantification in time-domain diffuse optical tomography using Mellin-Laplace transforms

Simulations and phantom measurements are used to evaluate the ability of time-domain diffuse optical tomography using Mellin-Laplace transforms to quantify the absorption perturbation of centimetric objects immersed at depth 1-2 cm in turbid media. We find that the estimated absorption coefficient varies almost linearly with the absorption change in the range of 0-0.15 cm^-1 but is underestimated by a factor that depends on the inclusion depth (~2, 3 and 6 for depths of 1.0, 1.5 and 2.0 cm respectively). For larger absorption changes, the variation is sublinear with ~20% decrease for δμ_a = 0.37 cm^-1. By contrast, constraining the absorption change to the actual volume of the inclusion may considerably improve the accuracy and linearity of the reconstructed absorption.

New frontiers in time-domain diffuse optics, a review

The recent developments in time-domain diffuse optics that rely on physical concepts (e.g., time-gating and null distance) and advanced photonic components (e.g., vertical cavity source-emitting laser as light sources, single photon avalanche diode, and silicon photomultipliers as detectors, fast-gating circuits, and time-to-digital converters for acquisition) are focused. This study shows how these tools could lead on one hand to compact and wearable time-domain devices for point-of-care diagnostics down to the consumer level and on the other hand to powerful systems with exceptional depth penetration and sensitivity.

Overview of diffuse optical tomography and its clinical applications

Near-infrared diffuse optical tomography (DOT), one of the most sophisticated optical imaging techniques for observations through biological tissue, allows 3-D quantitative imaging of optical properties, which include functional and anatomical information. With DOT, it is expected to be possible to overcome the limitations of conventional near-infrared spectroscopy (NIRS) as well as offering the potential for diagnostic optical imaging. However, DOT has been under development for more than 30 years, and the difficulties in development are attributed to the fact that light is strongly scattered and that diffusive photons are used for the image reconstruction. The DOT algorithm is based on the techniques of inverse problems. The radiative transfer equation accurately describes photon propagation in biological tissue, while, because of its high computation load, the diffusion equation (DE) is often used as the forward model. However, the DE is invalid in low-scattering and/or highly absorbing regions and in the vicinity of light sources. The inverse problem is inherently ill-posed and highly undetermined. Here, we first summarize NIRS and then describe various approaches in the efforts to develop accurate and efficient DOT algorithms and present some examples of clinical applications. Finally, we discuss the future prospects of DOT.

Optical methods for quantitative and label-free sensing in living human tissues: principles, techniques, and applications

We present an overview of quantitative and label-free optical methods used to characterize living biological tissues, with an emphasis on emerging applications in clinical tissue diagnostics. Specifically, this review focuses on diffuse optical spectroscopy, imaging, and tomography, optical coherence-based techniques, and non-linear optical methods for molecular imaging. The potential for non- or minimally-invasive assessment, quantitative diagnostics, and continuous monitoring enabled by these tissue-optics technologies provides significant promise for continued clinical translation.

Roadmap on neurophotonics

Mechanistic understanding of how the brain gives rise to complex behavioral and cognitive functions is one of science's grand challenges. The technical challenges that we face as we attempt to gain a systems-level understanding of the brain are manifold. The brain's structural complexity requires us to push the limit of imaging resolution and depth, while being able to cover large areas, resulting in enormous data acquisition and processing needs. Furthermore, it is necessary to detect functional activities and 'map' them onto the structural features. The functional activity occurs at multiple levels, using electrical and chemical signals. Certain electrical signals are only decipherable with sub-millisecond timescale resolution, while other modes of signals occur in minutes to hours. For these reasons, there is a wide consensus that new tools are necessary to undertake this daunting task. Optical techniques, due to their versatile and scalable nature, have great potentials to answer these challenges. Optical microscopy can now image beyond the diffraction limit, record multiple types of brain activity, and trace structural features across large areas of tissue. Genetically encoded molecular tools opened doors to controlling and detecting neural activity using light in specific cell types within the intact brain. Novel sample preparation methods that reduce light scattering have been developed, allowing whole brain imaging in rodent models. Adaptive optical methods have the potential to resolve images from deep brain regions. In this roadmap article, we showcase a few major advances in this area, survey the current challenges, and identify potential future needs that may be used as a guideline for the next steps to be taken.

Improving the quantitative accuracy of cerebral oxygen saturation in monitoring the injured brain using atlas based Near Infrared Spectroscopy models

The application of Near Infrared Spectroscopy (NIRS) for the monitoring of the cerebral oxygen saturation within the brain is well established, albeit using temporal data that can only measure relative changes of oxygenation state of the brain from a baseline. The focus of this investigation is to demonstrate that hybridisation of existing near infrared probe designs and reconstruction techniques can pave the way to produce a system and methods that can be used to monitor the absolute oxygen saturation in the injured brain. Using registered Atlas models in simulation, a novel method is outlined by which the quantitative accuracy and practicality of NIRS for specific use in monitoring the injured brain, can be improved, with cerebral saturation being recovered to within 10.1 ± 1.8% of the expected values.

Functional Near-Infrared Spectroscopy (fNIRS) for Assessing Cerebral Cortex Function During Human Behavior in Natural/Social Situations: A Concise Review

Upon adequate stimulation, real-time maps of cortical hemodynamic responses can be obtained by functional near-infrared spectroscopy (fNIRS), which noninvasively measures changes in oxygenated and deoxygenated hemoglobin after positioning multiple sources and detectors over the human scalp. This review is aimed at giving a concise and simple overview of the basic principles of fNIRS including features, strengths, advantages, limitations, and utility for evaluating human behavior. The transportable/wireless commercially available fNIRS systems have a time resolution of 1 to 10 Hz, a depth sensitivity of about 1.5 cm, and a spatial resolution up to 1 cm. fNIRS has been found suitable for many applications on human beings, either adults or infants/children, in the field of social sciences, neuroimaging basic research, and medicine. Some examples of present and future prospects of fNIRS for assessing cerebral cortex function during human behavior in different situations (in natural and social situations) will be provided. Moreover, the most recent fNIRS studies for investigating interpersonal interactions by adopting the hyperscanning approach, which consists of the measurement of brain activity simultaneously on two or more people, will be reported.

Diffuse optical tomography using multi-directional sources and detectors

Diffuse optical tomography (DOT) is an advanced imaging method used to visualize the internal state of biological tissues as 3D images. However, current continuous-wave DOT requires high-density probe arrays for measurement (less than 15-mm interval) to gather enough information for 3D image reconstruction, which makes the experiment time-consuming. In this paper, we propose a novel DOT measurement system using multi-directional light sources and multi-directional photodetectors instead of high-density probe arrays. We evaluated this system's multi-directional DOT through computer simulation and a phantom experiment. From the results, we achieved DOT with less than 5-mm localization error up to a 15-mm depth with low-density probe arrays (30-mm interval), indicating that the multi-directional measurement approach allows DOT without requiring high-density measurement.

Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissues

Light propagating in tissue attains a spectrum that varies with location due to wavelength-dependent fluence attenuation, an effect that causes spectral corruption. Spectral corruption has limited the quantification accuracy of optical and optoacoustic spectroscopic methods, and impeded the goal of imaging blood oxygen saturation (sO2) deep in tissues; a critical goal for the assessment of oxygenation in physiological processes and disease. Here we describe light fluence in the spectral domain and introduce eigenspectra multispectral optoacoustic tomography (eMSOT) to account for wavelength-dependent light attenuation, and estimate blood sO2 within deep tissue. We validate eMSOT in simulations, phantoms and animal measurements and spatially resolve sO2 in muscle and tumours, validating our measurements with histology data. eMSOT shows substantial sO2 accuracy enhancement over previous optoacoustic methods, potentially serving as a valuable tool for imaging tissue pathophysiology.

Spatio-temporal visualization of light transport in complex photonic structures

Spatio-temporal imaging of light propagation is very important in photonics because it provides the most direct tool available to study the interaction between light and its host environment. Sub-ps time resolution is needed to investigate the fine and complex structural features that characterize disordered and heterogeneous structures, which are responsible for a rich array of transport physics that have not yet been fully explored. A newly developed wide-field imaging system enables us to present a spatio-temporal study on light transport in various disordered media, revealing properties that could not be properly assessed using standard techniques. By extending our investigation to an almost transparent membrane, a configuration that has been difficult to characterize until now, we unveil the peculiar physics exhibited by such thin scattering systems with transport features that go beyond mainstream diffusion modeling, despite the occurrence of multiple scattering.

Hemodynamic signals in fNIRS

Near-infrared spectroscopy (NIRS) was originally designed for clinical monitoring of tissue oxygenation, and it has also been developed into a useful tool in neuroimaging studies, with the so-called functional NIRS (fNIRS). With NIRS, cerebral activation is detected by measuring the cerebral hemoglobin (Hb), where however, the precise correlation between NIRS signal and neural activity remains to be fully understood. This can in part be attributed to the situation that NIRS signals are inherently subject to contamination by signals arising from extracerebral tissue. In recent years, several approaches have been investigated to distinguish between NIRS signals originating in cerebral tissue and signals originating in extracerebral tissue. Selective measurements of cerebral Hb will enable a further evolution of fNIRS. This chapter is divided into six sections: first a summary of the basic theory of NIRS, NIRS signals arising in the activated areas, correlations between NIRS signals and fMRI signals, correlations between NIRS signals and neural activities, and the influence of a variety of extracerebral tissue on NIRS signals and approaches to this issue are reviewed. Finally, future prospects of fNIRS are described.

Combining energy and Laplacian regularization to accurately retrieve the depth of brain activity of diffuse optical tomographic data

Diffuse optical tomography (DOT) provides data about brain function using surface recordings. Despite recent advancements, an unbiased method for estimating the depth of absorption changes and for providing an accurate three-dimensional (3-D) reconstruction remains elusive. DOT involves solving an ill-posed inverse problem, requiring additional criteria for finding unique solutions. The most commonly used criterion is energy minimization (energy constraint). However, as measurements are taken from only one side of the medium (the scalp) and sensitivity is greater at shallow depths, the energy constraint leads to solutions that tend to be small and superficial. To correct for this bias, we combine the energy constraint with another criterion, minimization of spatial derivatives (Laplacian constraint, also used in low resolution electromagnetic tomography, LORETA). Used in isolation, the Laplacian constraint leads to solutions that tend to be large and deep. Using simulated, phantom, and actual brain activation data, we show that combining these two criteria results in accurate (error <2 mm) absorption depth estimates, while maintaining a two-point spatial resolution of <24 mm up to a depth of 30 mm. This indicates that accurate 3-D reconstruction of brain activity up to 30 mm from the scalp can be obtained with DOT.

Lock-in-photon-counting-based highly-sensitive and large-dynamic imaging system for continuous-wave diffuse optical tomography

We implemented a novel lock-in photon-counting detection architecture that combines the ultra-high sensitivity of the photon-counting detection and the measurement parallelism of the lock-in technique. Based on this technique, a dual-wavelength simultaneous measurement continuous wave diffuse optical tomography system was developed with a configuration of 16 sources and 16 detectors that works in a tandem serial-to-parallel fashion. Methodology validation and performance assessment of the system were conducted using phantom experiments that demonstrate excellent measurement linearity, moderate-term system stability, robustness to noise and negligible inter-wavelength crosstalk. 2-D imaging experiments further validate high sensitivity of the lock-in photon-counting methodology as well as high reliability of the proposed system. The advanced detection principle can be adapted to achieving a fully parallelized instrumentation for the extended applications.

Medical near Infrared Spectroscopy: A Prestigious History and a Bright Future

This article provides an overview of the present and the bright future of near infrared (NIR) spectroscopy applications in the medical field with special regard to brain oximetry and functional NIR spectroscopy (fNIRS).

Fabrication and application of heterogeneous printed mouse phantoms for whole animal optical imaging

This work demonstrates the usefulness of 3D printing for optical imaging applications. Progress in developing optical imaging for biomedical applications requires customizable and often complex objects for testing and evaluation. There is therefore high demand for what have become known as tissue-simulating "phantoms." We present a new optical phantom fabricated using inexpensive 3D printing methods with multiple materials, allowing for the placement of complex inhomogeneities in complex or anatomically realistic geometries, as opposed to previous phantoms, which were limited to simple shapes formed by molds or machining. We use diffuse optical imaging to reconstruct optical parameters in 3D space within a printed mouse to show the applicability of the phantoms for developing whole animal optical imaging methods. This phantom fabrication approach is versatile, can be applied to optical imaging methods besides diffusive imaging, and can be used in the calibration of live animal imaging data.

Reverse-Time Migration Based Optical Imaging

We theoretically demonstrated a new optical imaging technique based on reverse-time migration (RTM) for reconstructing optical structures in homogeneous media for the first time. RTM is a powerful wave-equation-based method to reconstruct the image of the structure by modeling the wave propagation inside the media with both forward modeling and reverse-time extrapolation. While RTM is commonly used with acoustic seismic waves, this paper represents the first effort to develop optical RTM imaging method for biomedical research. To refine the image quality, we further developed new methods to suppress the low-wavenumber artifact (LWA). When compared with the conventional means for LWA suppression such as Laplacian filtering, illumination normalization, and the ratio method, our new derivative-based and power-image methods are able to significantly reduce LWA, resulting in high-quality reconstructed images with sufficient contrasts and spatial resolutions for structure identification. The optical RTM imaging technique may provide a new platform for non-invasive optical imaging of structures in deep layers of tissues for biomedical applications.

Experimental validation of a high-resolution diffuse optical imaging modality: photomagnetic imaging

We present experimental results that validate our imaging technique termed photomagnetic imaging (PMI). PMI illuminates the medium under investigation with a near-infrared light and measures the induced temperature increase using magnetic resonance imaging. A multiphysics solver combining light and heat propagation is used to model spatiotemporal distribution of temperature increase. Furthermore, a dedicated PMI reconstruction algorithm has been developed to reveal high-resolution optical absorption maps from temperature measurements. Being able to perform measurements at any point within the medium, PMI overcomes the limitations of conventional diffuse optical imaging. We present experimental results obtained on agarose phantoms mimicking biological tissue with inclusions having either different sizes or absorption contrasts, located at various depths. The reconstructed images show that PMI can successfully resolve these inclusions with high resolution and recover their absorption coefficient with high-quantitative accuracy. Even a 1-mm inclusion located 6-mm deep is recovered successfully and its absorption coefficient is underestimated by only 32%. The improved PMI system presented here successfully operates under the maximum skin exposure limits defined by the American National Standards Institute, which opens up the exciting possibility of its future clinical use for diagnostic purposes.

Evaluating real-time image reconstruction in diffuse optical tomography using physiologically realistic test data

In diffuse optical tomography (DOT), real-time image reconstruction of oxy- and deoxy-haemoglobin changes occurring in the brain could give valuable information in clinical care settings. Although non-linear reconstruction techniques could provide more accurate results, their computational burden makes them unsuitable for real-time applications. Linear techniques can be employed under the assumption that the expected change in absorption is small. Several approaches exist, differing primarily in their handling of regularization and the noise statistics. In real experiments, it is impossible to compute the true noise statistics, because of the presence of physiological oscillations in the measured data. This is even more critical in real-time applications, where no off-line filtering and averaging can be performed to reduce the noise level. Therefore, many studies substitute the noise covariance matrix with the identity matrix. In this paper, we examined two questions: does using the noise model with realistic, imperfect data yield an improvement in image quality compared to using the identity matrix; and what is the difference in quality between online and offline reconstructions. Bespoke test data were created using a novel process through which simulated changes in absorption were added to real resting-state DOT data. A realistic multi-layer head model was used as the geometry for the reconstruction. Results validated our assumptions, highlighting the validity of computing the noise statistics from the measured data for online image reconstruction, which was performed at 2 Hz. Our results can be directly extended to a real application where real-time imaging is required.

Fast and efficient image reconstruction for high density diffuse optical imaging of the human brain

Real-time imaging of human brain has become an important technique within neuroimaging. In this study, a fast and efficient sensitivity map generation based on Finite Element Models (FEM) is developed which utilises a reduced sensitivitys matrix taking advantage of sparsity and parallelisation processes. Time and memory efficiency of these processes are evaluated and compared with conventional method showing that for a range of mesh densities from 50000 to 320000 nodes, the required memory is reduced over tenfold and computational time fourfold allowing for near real-time image recovery.

Optimization of the method for assessment of brain perfusion in humans using contrast-enhanced reflectometry: multidistance time-resolved measurements

The aim of the study was to determine optimal measurement conditions for assessment of brain perfusion with the use of optical contrast agent and time-resolved diffuse reflectometry in the near-infrared wavelength range. The source-detector separation at which the distribution of time of flights (DTOF) of photons provided useful information on the inflow of the contrast agent to the intracerebral brain tissue compartments was determined. Series of Monte Carlo simulations was performed in which the inflow and washout of the dye in extra- and intracerebral tissue compartments was modeled and the DTOFs were obtained at different source-detector separations. Furthermore, tests on diffuse phantoms were carried out using a time-resolved setup allowing the measurement of DTOFs at 16 source-detector separations. Finally, the setup was applied in experiments carried out on the heads of adult volunteers during intravenous injection of indocyanine green. Analysis of statistical moments of the measured DTOFs showed that the source-detector separation of 6 cm is recommended for monitoring of inflow of optical contrast to the intracerebral brain tissue compartments with the use of continuous wave reflectometry, whereas the separation of 4 cm is enough when the higher-order moments of DTOFs are available.

Multiregional functional near-infrared spectroscopy reveals globally symmetrical and frequency-specific patterns of superficial interference

Linear regression with short source-detector separation channels (S-channels) as references is an efficient way to overcome significant physiological interference from the superficial layer for functional near-infrared spectroscopy (fNIRS). However, the co-located configuration of S-channels and long source-detector separation channels (L-channels) is difficult to achieve in practice. In this study, we recorded superficial interference with S-channels in multiple scalp regions. We found that superficial interference has overall frequency-specific and globally symmetrical patterns. The performance of linear regression is also dependent on these patterns, indicating the possibility of simplifying the S-channel configurations for multiregional fNIRS imaging.

XNAT Central: Open sourcing imaging research data

XNAT Central is a publicly accessible medical imaging data repository based on the XNAT open-source imaging informatics platform. It hosts a wide variety of research imaging data sets. The primary motivation for creating XNAT Central was to provide a central repository to host and provide access to a wide variety of neuroimaging data. In this capacity, XNAT Central hosts a number of data sets from research labs and investigative efforts from around the world, including the OASIS Brains imaging studies, the NUSDAST study of schizophrenia, and more. Over time, XNAT Central has expanded to include imaging data from many different fields of research, including oncology, orthopedics, cardiology, and animal studies, but continues to emphasize neuroimaging data. Through the use of XNAT's DICOM metadata extraction capabilities, XNAT Central provides a searchable repository of imaging data that can be referenced by groups, labs, or individuals working in many different areas of research. The future development of XNAT Central will be geared towards greater ease of use as a reference library of heterogeneous neuroimaging data and associated synthetic data. It will also become a tool for making data available supporting published research and academic articles.

Evaluation of rigid registration methods for whole head imaging in diffuse optical tomography

Functional brain imaging has become an important neuroimaging technique for the study of brain organization and development. Compared to other imaging techniques, diffuse optical tomography (DOT) is a portable and low-cost technique that can be applied to infants and hospitalized patients using an atlas-based light model. For DOT imaging, the accuracy of the forward model has a direct effect on the resulting recovered brain function within a field of view and so the accuracy of the spatially normalized atlas-based forward models must be evaluated. Herein, the accuracy of atlas-based DOT is evaluated on models that are spatially normalized via a number of different rigid registration methods on 24 subjects. A multileveled approach is developed to evaluate the correlation of the geometrical and sensitivity accuracies across the full field of view as well as within specific functional subregions. Results demonstrate that different registration methods are optimal for recovery of different sets of functional brain regions. However, the "nearest point to point" registration method, based on the EEG 19 landmark system, is shown to be the most appropriate registration method for image quality throughout the field of view of the high-density cap that covers the whole of the optically accessible cortex.

Pressure modulation algorithm to separate cerebral hemodynamic signals from extracerebral artifacts

We introduce and validate a pressure measurement paradigm that reduces extracerebral contamination from superficial tissues in optical monitoring of cerebral blood flow with diffuse correlation spectroscopy (DCS). The scheme determines subject-specific contributions of extracerebral and cerebral tissues to the DCS signal by utilizing probe pressure modulation to induce variations in extracerebral blood flow. For analysis, the head is modeled as a two-layer medium and is probed with long and short source-detector separations. Then a combination of pressure modulation and a modified Beer-Lambert law for flow enables experimenters to linearly relate differential DCS signals to cerebral and extracerebral blood flow variation without a priori anatomical information. We demonstrate the algorithm's ability to isolate cerebral blood flow during a finger-tapping task and during graded scalp ischemia in healthy adults. Finally, we adapt the pressure modulation algorithm to ameliorate extracerebral contamination in monitoring of cerebral blood oxygenation and blood volume by near-infrared spectroscopy.

Fast silicon photomultiplier improves signal harvesting and reduces complexity in time-domain diffuse optics

We present a proof of concept prototype of a time-domain diffuse optics probe exploiting a fast Silicon PhotoMultiplier (SiPM), featuring a timing resolution better than 80 ps, a fast tail with just 90 ps decay time-constant and a wide active area of 1 mm². The detector is hosted into the probe and used in direct contact with the sample under investigation, thus providing high harvesting efficiency by exploiting the whole SiPM numerical aperture and also reducing complexity by avoiding the use of cumbersome fiber bundles. Our tests also demonstrate high accuracy and linearity in retrieving the optical properties and suitable contrast and depth sensitivity for detecting localized inhomogeneities. In addition to a strong improvement in both instrumentation cost and size with respect to legacy solutions, the setup performances are comparable to those of state-of-the-art time-domain instrumentation, thus opening a new way to compact, low-cost and high-performance time-resolved devices for diffuse optical imaging and spectroscopy.

Mapping cortical responses to speech using high-density diffuse optical tomography

The functional neuroanatomy of speech processing has been investigated using positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) for more than 20years. However, these approaches have relatively poor temporal resolution and/or challenges of acoustic contamination due to the constraints of echoplanar fMRI. Furthermore, these methods are contraindicated because of safety concerns in longitudinal studies and research with children (PET) or in studies of patients with metal implants (fMRI). High-density diffuse optical tomography (HD-DOT) permits presenting speech in a quiet acoustic environment, has excellent temporal resolution relative to the hemodynamic response, and provides noninvasive and metal-compatible imaging. However, the performance of HD-DOT in imaging the brain regions involved in speech processing is not fully established. In the current study, we use an auditory sentence comprehension task to evaluate the ability of HD-DOT to map the cortical networks supporting speech processing. Using sentences with two levels of linguistic complexity, along with a control condition consisting of unintelligible noise-vocoded speech, we recovered a hierarchically organized speech network that matches the results of previous fMRI studies. Specifically, hearing intelligible speech resulted in increased activity in bilateral temporal cortex and left frontal cortex, with syntactically complex speech leading to additional activity in left posterior temporal cortex and left inferior frontal gyrus. These results demonstrate the feasibility of using HD-DOT to map spatially distributed brain networks supporting higher-order cognitive faculties such as spoken language.

Effect of motion artifacts and their correction on near-infrared spectroscopy oscillation data: a study in healthy subjects and stroke patients

Functional near-infrared spectroscopy is prone to contamination by motion artifacts (MAs). Motion correction algorithms have previously been proposed and their respective performance compared for evoked rain activation studies. We study instead the effect of MAs on "oscillation" data which is at the basis of functional connectivity and autoregulation studies. We use as our metric of interest the interhemispheric correlation (IHC), the correlation coefficient between symmetrical time series of oxyhemoglobin oscillations. We show that increased motion content results in a decreased IHC. Using a set of motion-free data on which we add real MAs, we find that the best motion correction approach consists of discarding the segments of MAs following a careful approach to minimize the contamination due to band-pass filtering of data from "bad" segments spreading into adjacent "good" segments. Finally, we compare the IHC in a stroke group and in a healthy group that we artificially contaminated with the MA content of the stroke group, in order to avoid the confounding effect of increased motion incidence in the stroke patients. After motion correction, the IHC remains lower in the stroke group in the frequency band around 0.1 and 0.04 Hz, suggesting a physiological origin for the difference. We emphasize the importance of considering MAs as a confounding factor in oscillation-based functional near-infrared spectroscopy studies.

Towards next-generation time-domain diffuse optics for extreme depth penetration and sensitivity

Light is a powerful tool to non-invasively probe highly scattering media for clinical applications ranging from oncology to neurology, but also for molecular imaging, and quality assessment of food, wood and pharmaceuticals. Here we show that, for a paradigmatic case of diffuse optical imaging, ideal yet realistic time-domain systems yield more than 2-fold higher depth penetration and many decades higher contrast as compared to ideal continuous-wave systems, by adopting a dense source-detector distribution with picosecond time-gating. Towards this aim, we demonstrate the first building block made of a source-detector pair directly embedded into the probe based on a pulsed Vertical-Cavity Surface-Emitting Laser (VCSEL) to allow parallelization for dense coverage, a Silicon Photomultiplier (SiPM) to maximize light harvesting, and a Single-Photon Avalanche Diode (SPAD) to demonstrate the time-gating capability on the basic SiPM element. This paves the way to a dramatic advancement in terms of increased performances, new high impact applications, and availability of devices with orders of magnitude reduction in size and cost for widespread use, including quantitative wearable imaging.

A kurtosis-based wavelet algorithm for motion artifact correction of fNIRS data

Movements are a major source of artifacts in functional Near-Infrared Spectroscopy (fNIRS). Several algorithms have been developed for motion artifact correction of fNIRS data, including Principal Component Analysis (PCA), targeted Principal Component Analysis (tPCA), Spline Interpolation (SI), and Wavelet Filtering (WF). WF is based on removing wavelets with coefficients deemed to be outliers based on their standardized scores, and it has proven to be effective on both synthetized and real data. However, when the SNR is high, it can lead to a reduction of signal amplitude. This may occur because standardized scores inherently adapt to the noise level, independently of the shape of the distribution of the wavelet coefficients. Higher-order moments of the wavelet coefficient distribution may provide a more diagnostic index of wavelet distribution abnormality than its variance. Here we introduce a new procedure that relies on eliminating wavelets that contribute to generate a large fourth-moment (i.e., kurtosis) of the coefficient distribution to define "outliers" wavelets (kurtosis-based Wavelet Filtering, kbWF). We tested kbWF by comparing it with other existing procedures, using simulated functional hemodynamic responses added to real resting-state fNIRS recordings. These simulations show that kbWF is highly effective in eliminating transient noise, yielding results with higher SNR than other existing methods over a wide range of signal and noise amplitudes. This is because: (1) the procedure is iterative; and (2) kurtosis is more diagnostic than variance in identifying outliers. However, kbWF does not eliminate slow components of artifacts whose duration is comparable to the total recording time.

Use of resting-state functional MRI to study brain development and injury in neonates

Advances in methodology have led to expanded application of resting-state functional MRI (rs-fMRI) to the study of term and prematurely born infants during the first years of life, providing fresh insight into the earliest forms of functional cerebral development. In this review, we detail our evolving understanding of the use of rs-fMRI for studying neonates. We initially focus on the biological processes of cortical development related to resting-state network development. We then review technical issues principally affecting neonatal investigations, including the effects of subject motion during acquisition and image distortions related to magnetic susceptibility effects. We next summarize the literature in which rs-fMRI is used to study normal brain development during the early postnatal period, the effects of prematurity, and the effects of cerebral injury. Finally, we review potential future directions for the field, such as the use of complementary imaging modalities and advanced analysis techniques.

Dynamic causal modelling for functional near-infrared spectroscopy

Functional near-infrared spectroscopy (fNIRS) is an emerging technique for measuring changes in cerebral hemoglobin concentration via optical absorption changes. Although there is great interest in using fNIRS to study brain connectivity, current methods are unable to infer the directionality of neuronal connections. In this paper, we apply Dynamic Causal Modelling (DCM) to fNIRS data. Specifically, we present a generative model of how observed fNIRS data are caused by interactions among hidden neuronal states. Inversion of this generative model, using an established Bayesian framework (variational Laplace), then enables inference about changes in directed connectivity at the neuronal level. Using experimental data acquired during motor imagery and motor execution tasks, we show that directed (i.e., effective) connectivity from the supplementary motor area to the primary motor cortex is negatively modulated by motor imagery, and this suppressive influence causes reduced activity in the primary motor cortex during motor imagery. These results are consistent with findings of previous functional magnetic resonance imaging (fMRI) studies, suggesting that the proposed method enables one to infer directed interactions in the brain mediated by neuronal dynamics from measurements of optical density changes.