Quantitative investigation of the effect of the extra-cerebral vasculature in diffuse optical imaging: a simulation study

Boundary-layer structures arising in linear transport theory

We consider boundary-layer structures that arise in connection with the transport of neutral particles (e.g., photons or neutrons) through a participating medium. Such boundary-layer structures were previously identified by the authors in certain particular cases [Phys. Rev. E 104, L032801 (2021)2470-004510.1103/PhysRevE.104.L032801]. Extending the previous work to anisotropic scattering and general Fresnel boundary conditions, this contribution presents computational algorithms which (1) resolve the aforementioned layers as well as previously unreported boundary layers associated with Fresnel boundary transmission and reflection, and (2) yield accurate simulations at fixed computational cost for transport under phase functions with arbitrarily strong anisotropy. The present paper additionally includes (3) Mathematical proofs which justify the numerical methods proposed for resolution of boundary-layer structures. The impact of the new theory on algorithmic performance is demonstrated through a series of 1D computational benchmarks that emulate typical photon- and neutron-transport applications such as, e.g., optical tomography, and nuclear reactor analysis and design. Experimental results for transmission of photons through turbid media are presented, exhibiting close agreement between simulated and experimental data. As illustrated by means of a variety of numerical results, the proposed boundary-layer-based approach tackles transport problems with unprecedented accuracy and efficiency.

Prefrontal NIRS signal is unaffected by forehead Doppler flux during incremental cycling exercise

Near-infrared spectroscopy (NIRS) is used to measure tissue concentrations of oxyhemoglobin (O2 Hb) and deoxyhemoglobin (HHb). In the context of exercise, NIRS confers a higher signal-to-noise ratio than other neuroimaging techniques. However, part of the signal may be influenced by thermoregulatory hyperemia in the superficial cutaneous capillaries of the forehead. The degree to which NIRS signals during exercise reflect cerebral or extracerebral hemodynamic changes is a continuing source of controversy. However, the influence of skin blood flow may be attenuated depending on the NIRS technique (e.g., frequency domain machines with maximal optode separation distances >3.5 cm). The purpose of this study was to compare the changes in forehead skin blood flow and cerebral hemoglobin concentration during incremental exercise versus direct vasodilation of the forehead skin induced by gradual local heating. Thirty participants (12 females, 18 males; age: 20.8 ± 3.2 years; body mass index: 23.8 ± 3.7 kg·m-2 ) participated in the study. Forehead skin blood flow was quantified laser Doppler flux and absolute concentrations of cerebral O2 Hb and HHb were measured by NIRS. Local heating significantly increased the Doppler flux signal across time and these changes were significantly correlated with skin temperature. During incremental exercise, skin temperature, Doppler flux, O2 Hb and HHb increased however, the only significant change that was consistently correlated with Doppler flux was skin temperature. Therefore, a significant change in forehead skin blood flow may not significantly the NIRS hemoglobin data, depending on the type of NIRS device used.

Use of Hypoxic Respiratory Challenge for Differentiating Alzheimer's Disease and Wild-Type Mice Non-Invasively: A Diffuse Optical Spectroscopy Study

Alzheimer's disease is one of the most critical brain diseases. The prevalence of the disease keeps rising due to increasing life spans. This study aims to examine the use of hemodynamic signals during hypoxic respiratory challenge for the differentiation of Alzheimer's disease (AD) and wild-type (WT) mice. Diffuse optical spectroscopy, an optical system that can non-invasively monitor transient changes in deoxygenated (ΔRHb) and oxygenated (ΔOHb) hemoglobin concentrations, was used to monitor hemodynamic reactivity during hypoxic respiratory challenges in an animal model. From the acquired signals, 13 hemodynamic features were extracted from each of ΔRHb and -ΔOHb (26 features total) for more in-depth analyses of the differences between AD and WT. The hemodynamic features were statistically analyzed and tested to explore the possibility of using machine learning (ML) to differentiate AD and WT. Among the twenty-six features, two features of ΔRHb and one feature of -ΔOHb showed statistically significant differences between AD and WT. Among ML techniques, a naive Bayes algorithm achieved the best accuracy of 84.3% when whole hemodynamic features were used for differentiation. While further works are required to improve the approach, the suggested approach has the potential to be an alternative method for the differentiation of AD and WT.

Broadband diffuse optical spectroscopy of two-layered scattering media containing oxyhemoglobin, deoxyhemoglobin, water, and lipids

We investigated the relationship between chromophore concentrations in two-layered scattering media and the apparent chromophore concentrations measured with broadband optical spectroscopy in conjunction with commonly used homogeneous medium inverse models. We used diffusion theory togenerate optical data from a two-layered distribution of relevant tissue absorbers, namely, oxyhemoglobin, deoxyhemoglobin, water, and lipids, with a top-layer thickness in the range 1-15 mm. The generated data consisted of broadband continuous-wave (CW) diffuse reflectance in the wavelength range 650-1024 nm, and frequency-domain (FD) diffuse reflectance at 690 and 830 nm; two source-detector distances of 25 and 35 mm were used to simulate a dual-slope technique. The data were inverted using diffusion theory for a semi-infinite homogeneous medium to generate reduced scattering coefficients at 690 and 830 nm (from FD data) and effective absorption spectra in the range 650-1024 nm (from CW data). The absorption spectra were then converted into effective total concentration and oxygen saturation of hemoglobin, as well as water and lipid concentrations. For absolute values, it was found that the effective hemoglobin parameters are typically representative of the bottom layer, whereas water and lipid represent some average of the respective concentrations in the two layers. For concentration changes, lipid showed a significant cross-talk with other absorber concentrations, thus indicating that lipid dynamics obtained in these conditions may not be reliable. These systematic simulations of broadband spectroscopy of two-layered media provide guidance on how to interpret effective optical properties measured with similar instrumental setups under the assumption of medium homogeneity.

Design of a source-detector array for dual-slope diffuse optical imaging

We recently proposed a dual-slope technique for diffuse optical spectroscopy and imaging of scattering media. This technique requires a special configuration of light sources and optical detectors to create dual-slope sets. Here, we present methods for designing, optimizing, and building an optical imaging array that features m dual-slope sets to image n voxels. After defining the m × n matrix (S) that describes the sensitivity of the m dual-slope measurements to absorption perturbations in each of the n voxels, we formulate the inverse imaging problem in terms of the Moore-Penrose pseudoinverse matrix of S (S+). This approach allows us to introduce several measures of imaging performance: reconstruction accuracy (correct spatial mapping), crosstalk (incorrect spatial mapping), resolution (point spread function), and localization (offset between actual and reconstructed point perturbations). Furthermore, by considering the singular value decomposition formulation, we show the significance of visualizing the first m right singular vectors of S, whose linear combination generates the reconstructed map. We also describe methods to build a physical array using a three-layer mesh structure (two polyethylene films and polypropylene hook-and-loop fabric) embedded in silicone (PDMS). Finally, we apply these methods to design two arrays and choose one to construct. The chosen array consists of 16 illumination fibers, 10 detection fibers, and 27 dual-slope sets for dual-slope imaging optimized for the size of field of view and localization of absorption perturbations. This particular array is aimed at functional near-infrared spectroscopy of the human brain, but the methods presented here are of general applicability to a variety of devices and imaging scenarios.

Characteristic Length and Time Scales of the Highly Forward Scattering of Photons in Random Media

Background: Elucidation of the highly forward scattering of photons in random media such as biological tissue is crucial for further developments of optical imaging using photon transport models. We evaluated length and time scales of the photon scattering in three-dimensional media. Methods: We employed analytical solutions of the time-dependent radiative transfer, M-th order delta-Eddington, and photon diffusion equations (RTE, dEM, and PDE). We calculated the fluence rates at different source-detector distances and optical properties. Results: We found that the zeroth order dEM and PDE, which approximate the highly forward scattering to the isotropic scattering, are valid in longer length and time scales than approximately 10 / μ t ′ and 40 / μ t ′ v , respectively, where μ t ′ is the reduced transport coefficient and v the speed of light in a medium. The first and second order dEM, which approximate the highly forward-peaked phase function by the first two and three Legendre moments, are valid in the longer scales than approximately 4.0 / μ t ′ and 6.3 / μ t ′ v ; 2.8 / μ t ′ and 3.5 / μ t ′ v , respectively. The boundary conditions less influence the length scales, while they reduce the times scales from those for bulk at the longer length scale than approximately 4.0 / μ t ′ . Conclusion: Our findings are useful for constructions of accurate and efficient photon transport models. We evaluated length and time scales of the highly forward scattering of photons in various kinds of three-dimensional random media by analytical solutions of the radiative transfer, M-th order delta-Eddington, and photon diffusion equations.

Perspective: Prospects of non-invasive sensing of the human brain with diffuse optical imaging

Since the initial demonstration of near-infrared spectroscopy (NIRS) for noninvasive measurements of brain perfusion and metabolism in the 1970s, and its application to functional brain studies (fNIRS) in the 1990s, the field of noninvasive optical studies of the brain has been continuously growing. Technological developments, data analysis advances, and novel areas of application keep advancing the field. In this article, we provide a view of the state of the field of cerebral NIRS, starting with a brief historical introduction and a description of the information content of the NIRS signal. We argue that NIRS and fNIRS studies should always report data of both oxy- and deoxyhemoglobin concentrations in brain tissue, as they complement each other to provide more complete functional and physiological information, and may help identify different types of confounds. One significant challenge is the assessment of absolute tissue properties, be them optical or physiological, so that relative measurements account for the vast majority of NIRS and fNIRS applications. However, even relative measurements of hemodynamics or metabolic changes face the major problem of a potential contamination from extracerebral tissue layers. Accounting for extracerebral contributions to fNIRS signals is one of the most critical barriers in the field. We present some of the approaches that were proposed to tackle this challenge in the study of cerebral hemodynamics and functional connectivity. Finally, we critically compare fNIRS and functional magnetic resonance imaging (fMRI) by relating their measurements in terms of signal and noise, and by commenting on their complementarity.

Decoding human mental states by whole-head EEG+fNIRS during category fluency task performance

OBJECTIVE

Concurrent scalp electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), which we refer to as EEG+fNIRS, promises greater accuracy than the individual modalities while remaining nearly as convenient as EEG. We sought to quantify the hybrid system's ability to decode mental states and compare it with its unimodal components.

APPROACH

We recorded from healthy volunteers taking the category fluency test and applied machine learning techniques to the data.

MAIN RESULTS

EEG+fNIRS's decoding accuracy was greater than that of its subsystems, partly due to the new type of neurovascular features made available by hybrid data.

SIGNIFICANCE

Availability of an accurate and practical decoding method has potential implications for medical diagnosis, brain-computer interface design, and neuroergonomics.

Comparison of source localization techniques in diffuse optical tomography for fNIRS application using a realistic head model

Functional near-infrared spectroscopy (fNIRS) is a non-invasive imaging technique that elicits growing interest for research and clinical applications. In the last decade, efforts have been made to develop a mathematical framework in order to image the effective sources of hemoglobin variations in brain tissues. Different approaches can be used to impose additional information or constraints when reconstructing the cerebral images of an ill-posed problem. The goal of this study is to compare the performance and limitations of several source localization techniques in the context of fNIRS tomography using individual anatomical magnetic resonance imaging (MRI) to model light propagation. The forward problem is solved using a Monte Carlo simulation of light propagation in the tissues. The inverse problem has been linearized using the Rytov approximation. Then, Tikhonov regularization applied to least squares, truncated singular value decomposition, back-projection, L1-norm regularization, minimum norm estimates, low resolution electromagnetic tomography and Bayesian model averaging techniques are compared using a receiver operating characteristic analysis, blurring and localization error measures. Using realistic simulations (n = 450) and data acquired from a human participant, this study depicts how these source localization techniques behave in a human head fNIRS tomography. When compared to other methods, Bayesian model averaging is proposed as a promising method in DOT and shows great potential to improve specificity, accuracy, as well as to reduce blurring and localization error even in presence of noise and deep sources. Classical reconstruction methods, such as regularized least squares, offer better sensitivity but higher blurring; while more novel L1-based method provides sparse solutions with small blurring and high specificity but lower sensitivity. The application of these methods is also demonstrated experimentally using visual fNIRS experiment with adult participant.

Modeling of light propagation in the human neck for diagnoses of thyroid cancers by diffuse optical tomography

Diffuse optical tomography using near-infrared light in a wavelength range from 700 to 1000 nm has the potential to enable non-invasive diagnoses of thyroid cancers; some of which are difficult to detect by conventional methods such as ultrasound tomography. Diffuse optical tomography needs to be based on a physically accurate model of light propagation in the neck, because it reconstructs tomographic images of the optical properties in the human neck by inverse analysis. Our objective here was to investigate the effects of three factors on light propagation in the neck using the 2D time-dependent radiative transfer equation: (1) the presence of the trachea, (2) the refractive-index mismatch at the trachea-tissue interface, and (3) the effect of neck organs other than the trachea (spine, spinal cord, and blood vessels). There was a significant influence of reflection and refraction at the trachea-tissue interface on the light intensities in the region between the trachea and the front of the neck surface. Organs other than the trachea showed little effect on the light intensities measured at the front of the neck surface although these organs affected the light intensities locally. These results indicated the necessity of modeling the refractive-index mismatch at the trachea-tissue interface and the possibility of modeling other neck organs simply as a homogeneous medium when the source and detectors were far from large blood vessels.

Quantification of extra-cerebral and cerebral hemoglobin concentrations during physical exercise using time-domain near infrared spectroscopy

Fitness is known to have beneficial effects on brain anatomy and function. However, the understanding of mechanisms underlying immediate and long-term neurophysiological changes due to exercise is currently incomplete due to the lack of tools to investigate brain function during physical activity. In this study, we used time-domain near infrared spectroscopy (TD-NIRS) to quantify and discriminate extra-cerebral and cerebral hemoglobin concentrations and oxygen saturation (SO₂) in young adults at rest and during incremental intensity exercise. In extra-cerebral tissue, an increase in deoxy-hemoglobin (HbR) and a decrease in SO₂ were observed while only cerebral HbR increased at high intensity exercise. Results in extra-cerebral tissue are consistent with thermoregulatory mechanisms to dissipate excess heat through skin blood flow, while cerebral changes are in agreement with cerebral blood flow (CBF) redistribution mechanisms to meet oxygen demand in activated regions during exercise. No significant difference was observed in oxy- (HbO₂) and total hemoglobin (HbT). In addition HbO₂, HbR and HbT increased with subject's peak power output (equivalent to the maximum oxygen volume consumption; VO₂ peak) supporting previous observations of increased total mass of red blood cells in trained individuals. Our results also revealed known gender differences with higher hemoglobin in men. Our approach in quantifying both extra-cerebral and cerebral absolute hemoglobin during exercise may help to better interpret past and future continuous-wave NIRS studies that are prone to extra-cerebral contamination and allow a better understanding of acute cerebral changes due to physical exercise.

Hemodynamic correlates of spontaneous neural activity measured by human whole-head resting state EEG+fNIRS

The brains of awake, resting human subjects display spontaneously occurring neural activity patterns whose magnitude is typically many times greater than those triggered by cognitive or perceptual performance. Evoked and resting state activations affect local cerebral hemodynamic properties through processes collectively referred to as neurovascular coupling. Its investigation calls for an ability to track both the neural and vascular aspects of brain function. We used scalp electroencephalography (EEG), which provided a measure of the electrical potentials generated by cortical postsynaptic currents. Simultaneously we utilized functional near-infrared spectroscopy (NIRS) to continuously monitor hemoglobin concentration changes in superficial cortical layers. The multi-modal signal from 18 healthy adult subjects allowed us to investigate the association of neural activity in a range of frequencies over the whole-head to local changes in hemoglobin concentrations. Our results verified the delayed alpha (8-16Hz) modulation of hemodynamics in posterior areas known from the literature. They also indicated strong beta (16-32Hz) modulation of hemodynamics. Analysis revealed, however, that beta modulation was likely generated by the alpha-beta coupling in EEG. Signals from the inferior electrode sites were dominated by scalp muscle related activity. Our study aimed to characterize the phenomena related to neurovascular coupling observable by practical, cost-effective, and non-invasive multi-modal techniques.

Tutorial on platform for optical topography analysis tools

Optical topography/functional near-infrared spectroscopy (OT/fNIRS) is a functional imaging technique that noninvasively measures cerebral hemoglobin concentration changes caused by neural activities. The fNIRS method has been extensively implemented to understand the brain activity in many applications, such as neurodisorder diagnosis and treatment, cognitive psychology, and psychiatric status evaluation. To assist users in analyzing fNIRS data with various application purposes, we developed a software called platform for optical topography analysis tools (POTATo). We explain how to handle and analyze fNIRS data in the POTATo package and systematically describe domain preparation, temporal preprocessing, functional signal extraction, statistical analysis, and data/result visualization for a practical example of working memory tasks. This example is expected to give clear insight in analyzing data using POTATo. The results specifically show the activated dorsolateral prefrontal cortex is consistent with previous studies. This emphasizes analysis robustness, which is required for validating decent preprocessing and functional signal interpretation. POTATo also provides a self-developed plug-in feature allowing users to create their own functions and incorporate them with established POTATo functions. With this feature, we continuously encourage users to improve fNIRS analysis methods. We also address the complications and resolving opportunities in signal analysis.

Influences of blood flow changes in cerebrospinal fluid and skin layers on optical mapping

In optical mapping for imaging brain activity, the effect of blood flow changes in superficial tissues such as the cerebrospinal fluid (CSF) and skin layers should be considered. However, it is difficult to know those changes in in vivo experiments. To investigate the influence of blood flow changes in CSF and skin layers on optical mapping, we perform numerical simulations of optical mapping by solving the photon diffusion equation for layered-models simulating human heads using the finite element method (FEM). The results show that mapping images of activated region in the gray matter layer are affected by the existence of blood vessels in CSF layer and by the blood flow changes in the skin layer. The increases in both the vessel size and vessel absorption coefficient reduce the sensitivity of the mapping images to the brain activity in the gray matter. On the other hand, the increase in the vessel volume fraction in the skin layer increases the sensitivity of the mapping images.

How short is short? Optimum source-detector distance for short-separation channels in functional near-infrared spectroscopy

In recent years, it has been demonstrated that using functional near-infrared spectroscopy (fNIRS) channels with short separations to explicitly sample extra-cerebral tissues can provide a significant improvement in the accuracy and reliability of fNIRS measurements. The aim of these short-separation channels is to measure the same superficial hemodynamics observed by standard fNIRS channels while also being insensitive to the brain. We use Monte Carlo simulations of photon transport in anatomically informed multilayer models to determine the optimum source-detector distance for short-separation channels in adult and newborn populations. We present a look-up plot that provides (for an acceptable value of short-separation channel brain sensitivity relative to standard channel brain sensitivity) the optimum short-separation distance. Though values vary across the scalp, when the acceptable ratio of the short-separation channel brain sensitivity to standard channel brain sensitivity is set at 5%, the optimum short-separation distance is 8.4 mm in the typical adult and 2.15 mm in the term-age infant.

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.

Diffuse correlation spectroscopy for non-invasive, micro-vascular cerebral blood flow measurement

Diffuse correlation spectroscopy (DCS) uses the temporal fluctuations of near-infrared (NIR) light to measure cerebral blood flow (CBF) non-invasively. Here, we provide a brief history of DCS applications in the brain with an emphasis on the underlying physical ideas, common instrumentation and validation. Then we describe recent clinical research that employs DCS-measured CBF as a biomarker of patient well-being, and as an indicator of hemodynamic and metabolic responses to functional stimuli.

Quantitative evaluation of deep and shallow tissue layers' contribution to fNIRS signal using multi-distance optodes and independent component analysis

To quantify the effect of absorption changes in the deep tissue (cerebral) and shallow tissue (scalp, skin) layers on functional near-infrared spectroscopy (fNIRS) signals, a method using multi-distance (MD) optodes and independent component analysis (ICA), referred to as the MD-ICA method, is proposed. In previous studies, when the signal from the shallow tissue layer (shallow signal) needs to be eliminated, it was often assumed that the shallow signal had no correlation with the signal from the deep tissue layer (deep signal). In this study, no relationship between the waveforms of deep and shallow signals is assumed, and instead, it is assumed that both signals are linear combinations of multiple signal sources, which allows the inclusion of a "shared component" (such as systemic signals) that is contained in both layers. The method also assumes that the partial optical path length of the shallow layer does not change, whereas that of the deep layer linearly increases along with the increase of the source-detector (S-D) distance. Deep- and shallow-layer contribution ratios of each independent component (IC) are calculated using the dependence of the weight of each IC on the S-D distance. Reconstruction of deep- and shallow-layer signals are performed by the sum of ICs weighted by the deep and shallow contribution ratio. Experimental validation of the principle of this technique was conducted using a dynamic phantom with two absorbing layers. Results showed that our method is effective for evaluating deep-layer contributions even if there are high correlations between deep and shallow signals. Next, we applied the method to fNIRS signals obtained on a human head with 5-, 15-, and 30-mm S-D distances during a verbal fluency task, a verbal working memory task (prefrontal area), a finger tapping task (motor area), and a tetrametric visual checker-board task (occipital area) and then estimated the deep-layer contribution ratio. To evaluate the signal separation performance of our method, we used the correlation coefficients of a laser-Doppler flowmetry (LDF) signal and a nearest 5-mm S-D distance channel signal with the shallow signal. We demonstrated that the shallow signals have a higher temporal correlation with the LDF signals and with the 5-mm S-D distance channel than the deep signals. These results show the MD-ICA method can discriminate between deep and shallow signals.

The influence of frontal sinus in brain activation measurements by near-infrared spectroscopy analyzed by realistic head models

Adequate modeling of light propagation in the head is important to predict the sensitivity of NIRS signal and the spatial sensitivity profile of source-detector pairs. The 3D realistic head models of which the geometry is based upon the anatomical images acquired by magnetic resonance imaging and x-ray computed tomography are constructed to investigate the influence of the frontal sinus on the NIRS signal and spatial sensitivity. Light propagation in the head is strongly affected by the presence of the frontal sinus. The light tends to propagate around the frontal sinus. The influence of the frontal sinus on the sensitivity of the NIRS signal to the brain activation is not consistent and depends on the depth of the frontal sinus, the optical properties of the superficial tissues and the relative position between the source-detector pair and the frontal sinus. The frontal sinus located in the shallow region of the skull tends to reduce the sensitivity of the NIRS signal while the deep frontal sinus can increase the sensitivity of the NIRS signal.

Quantitative assessment of diffuse optical tomography sensitivity to the cerebral cortex using a whole-head probe

We quantify the variability in diffuse optical tomography (DOT) sensitivity over the cortical surface in eight young adult subjects. We use the 10/5 electroencephalography system as a basis for our whole-head optical high-density probe design. The contrast-to-noise ratio (CNR) is calculated along with the percentage of the cortex that is above a CNR = 0 dB threshold. We also quantify the effect of including vasculature on the forward model and list our assumptions that allow us to estimate light penetration depth in the head. We show that using the 10/5 system for the optical probe design allows for the measurement of 37% of the cortical surface on average, with a mean CNR in the visible region of 5.5 dB. Certain anatomical regions, such as the lateral occipital cortex, had a very high percentage above the CNR threshold, while other regions such as the cingulate cortex were not measurable. Vasculature blocked optical sensitivity over 1% of the cortex. Cortical coverage was positively correlated with intracranial volume and relative cerebrospinal fluid volume, and negatively correlated with relative scalp volume and skull volume. These contributions allow experimenters to understand how anatomical variation in a subject population may impact DOT or functional near-infrared spectroscopy measurements.

Quantitative effect of the neonatal fontanel on synthetic near infrared spectroscopy measurements

Near infrared spectroscopy (NIRS) is a functional imaging technique allowing measurement of local cerebral oxygenation. This modality is particularly adapted to critically ill neonates, as it can be used at the bedside and is a suitable and noninvasive tool for carrying out longitudinal studies. However, NIRS is sensitive to the imaged medium and consequently to the optical properties of biological tissues in which photons propagate. In this study, the effect of the neonatal fontanel was investigated by predicting photon propagation using a probabilistic Monte Carlo approach. Two anatomical newborn head models were created from computed tomography and magnetic resonance images: (1) a realistic model including the fontanel tissue and (2) a model in which the fontanel was replaced by skull tissue. Quantitative change in absorption due to simulated activation was compared for the two models for specific regions of activation and optical arrays simulated in the temporal area. A correction factor was computed to quantify the effect of the fontanel and defined by the ratio between the true and recovered change. The results show that recovered changes in absorption were more precise when determined with the anatomical model including the fontanel. The results suggest that the fontanel should be taken into account in quantification of NIRS responses to avoid misinterpretation in experiments involving temporal areas, such as language or auditory studies.

Quantification of the cortical contribution to the NIRS signal over the motor cortex using concurrent NIRS-fMRI measurements

Near-Infrared Spectroscopy (NIRS) measures the functional hemodynamic response occurring at the surface of the cortex. Large pial veins are located above the surface of the cerebral cortex. Following activation, these veins exhibit oxygenation changes but their volume likely stays constant. The back-reflection geometry of the NIRS measurement renders the signal very sensitive to these superficial pial veins. As such, the measured NIRS signal contains contributions from both the cortical region as well as the pial vasculature. In this work, the cortical contribution to the NIRS signal was investigated using (1) Monte Carlo simulations over a realistic geometry constructed from anatomical and vascular MRI and (2) multimodal NIRS-BOLD recordings during motor stimulation. A good agreement was found between the simulations and the modeling analysis of in vivo measurements. Our results suggest that the cortical contribution to the deoxyhemoglobin signal change (ΔHbR) is equal to 16-22% of the cortical contribution to the total hemoglobin signal change (ΔHbT). Similarly, the cortical contribution of the oxyhemoglobin signal change (ΔHbO) is equal to 73-79% of the cortical contribution to the ΔHbT signal. These results suggest that ΔHbT is far less sensitive to pial vein contamination and therefore, it is likely that the ΔHbT signal provides better spatial specificity and should be used instead of ΔHbO or ΔHbR to map cerebral activity with NIRS. While different stimuli will result in different pial vein contributions, our finger tapping results do reveal the importance of considering the pial contribution.