Two-detector Corrected Near Infrared Spectroscopy (C-NIRS) detects hemodynamic activation responses more robustly than single-detector NIRS

Identifying and quantifying main components of physiological noise in functional near infrared spectroscopy on the prefrontal cortex

Functional Near-Infrared Spectroscopy (fNIRS) is a promising method to study functional organization of the prefrontal cortex. However, in order to realize the high potential of fNIRS, effective discrimination between physiological noise originating from forehead skin haemodynamic and cerebral signals is required. Main sources of physiological noise are global and local blood flow regulation processes on multiple time scales. The goal of the present study was to identify the main physiological noise contributions in fNIRS forehead signals and to develop a method for physiological de-noising of fNIRS data. To achieve this goal we combined concurrent time-domain fNIRS and peripheral physiology recordings with wavelet coherence analysis (WCA). Depth selectivity was achieved by analyzing moments of photon time-of-flight distributions provided by time-domain fNIRS. Simultaneously, mean arterial blood pressure (MAP), heart rate (HR), and skin blood flow (SBF) on the forehead were recorded. WCA was employed to quantify the impact of physiological processes on fNIRS signals separately for different time scales. We identified three main processes contributing to physiological noise in fNIRS signals on the forehead. The first process with the period of about 3 s is induced by respiration. The second process is highly correlated with time lagged MAP and HR fluctuations with a period of about 10 s often referred as Mayer waves. The third process is local regulation of the facial SBF time locked to the task-evoked fNIRS signals. All processes affect oxygenated haemoglobin concentration more strongly than that of deoxygenated haemoglobin. Based on these results we developed a set of physiological regressors, which were used for physiological de-noising of fNIRS signals. Our results demonstrate that proposed de-noising method can significantly improve the sensitivity of fNIRS to cerebral signals.

Preserved foot motor cortex in patients with complete spinal cord injury: a functional near-infrared spectroscopic study

BACKGROUND

Since the brain is intact, persons with a spinal cord injury (SCI) might benefit from a brain-computer interface (BCI) to improve mobility by making use of functional near-infrared spectroscopy (fNIRS).

OBJECTIVE

We aimed to use fNIRS to detect contralateral primary motor cortex activity during attempted foot movements in participants with complete SCI.

METHODS

A 6-channel fNIRS, including 2 reference channels, measured relative concentration changes of oxy- (HbO) and deoxy-hemoglobin (HbR) in the contralateral motor cortex for the right foot. Seven subjects, studied within 18 months after injury, performed 12 trials of attempted right foot and real hand movements.

RESULTS

T tests revealed significant HbO and HbR responses of the left motor cortex for attempted foot movements, but not for right hand movements. A 2-way repeated-measures analysis of variance revealed a larger decrease in HbR for attempted foot movements compared to hand movements. Individual results show major interindividual differences in (number of) channels activated and the sensitive chromophore (HbR or HbO).

CONCLUSIONS

On group level, activity in the motor cortex of the foot can be measured with fNIRS in patients with complete SCI during attempted foot movements and might in principle be used in future BCI studies and applications.

Test-retest reliability of graph metrics in functional brain networks: a resting-state fNIRS study

Recent research has demonstrated the feasibility of combining functional near-infrared spectroscopy (fNIRS) and graph theory approaches to explore the topological attributes of human brain networks. However, the test-retest (TRT) reliability of the application of graph metrics to these networks remains to be elucidated. Here, we used resting-state fNIRS and a graph-theoretical approach to systematically address TRT reliability as it applies to various features of human brain networks, including functional connectivity, global network metrics and regional nodal centrality metrics. Eighteen subjects participated in two resting-state fNIRS scan sessions held ∼20 min apart. Functional brain networks were constructed for each subject by computing temporal correlations on three types of hemoglobin concentration information (HbO, HbR, and HbT). This was followed by a graph-theoretical analysis, and then an intraclass correlation coefficient (ICC) was further applied to quantify the TRT reliability of each network metric. We observed that a large proportion of resting-state functional connections (∼90%) exhibited good reliability (0.6< ICC <0.74). For global and nodal measures, reliability was generally threshold-sensitive and varied among both network metrics and hemoglobin concentration signals. Specifically, the majority of global metrics exhibited fair to excellent reliability, with notably higher ICC values for the clustering coefficient (HbO: 0.76; HbR: 0.78; HbT: 0.53) and global efficiency (HbO: 0.76; HbR: 0.70; HbT: 0.78). Similarly, both nodal degree and efficiency measures also showed fair to excellent reliability across nodes (degree: 0.52∼0.84; efficiency: 0.50∼0.84); reliability was concordant across HbO, HbR and HbT and was significantly higher than that of nodal betweenness (0.28∼0.68). Together, our results suggest that most graph-theoretical network metrics derived from fNIRS are TRT reliable and can be used effectively for brain network research. This study also provides important guidance on the choice of network metrics of interest for future applied research in developmental and clinical neuroscience.

Are the neural correlates of subitizing and estimation dissociable? An fNIRS investigation

Human performance in visual enumeration tasks typically shows two distinct patterns as a function of set size. For small sets, usually up to 4 items, numerosity judgments are extremely rapid, precise and confident, a phenomenon known as subitizing. When this limit is exceeded and serial counting is precluded, exact enumeration gives way to estimation: performance becomes error-prone and more variable. Surprisingly, despite the importance of subitizing and estimation in numerical cognition, only few neuroimaging studies have examined whether the neural activity related to these two phenomena can be dissociated. In the present work, we used multi-channel near-infrared spectroscopy (fNIRS) to measure hemodynamic activity of the bilateral parieto-occipital cortex during a visual enumeration task. Participants had to judge the numerosity of dot arrays and indicate it by means of verbal response. We observed a different hemodynamic pattern in the parietal cortex, both in terms of amplitude modulation and temporal profile, for numerosities below and beyond the subitizing range. Crucially, the neural dissociation between subitizing and estimation was strongest at the level of right IPS. The present findings confirm that fNIRS can be successfully used to detect subtle temporal differences in hemodynamic activity and to produce inferences on the neural mechanisms underlying cognitive functions.

Depth-compensated diffuse optical tomography enhanced by general linear model analysis and an anatomical atlas of human head

One of the main challenges in functional diffuse optical tomography (DOT) is to accurately recover the depth of brain activation, which is even more essential when differentiating true brain signals from task-evoked artifacts in the scalp. Recently, we developed a depth-compensated algorithm (DCA) to minimize the depth localization error in DOT. However, the semi-infinite model that was used in DCA deviated significantly from the realistic human head anatomy. In the present work, we incorporated depth-compensated DOT (DC-DOT) with a standard anatomical atlas of human head. Computer simulations and human measurements of sensorimotor activation were conducted to examine and prove the depth specificity and quantification accuracy of brain atlas-based DC-DOT. In addition, node-wise statistical analysis based on the general linear model (GLM) was also implemented and performed in this study, showing the robustness of DC-DOT that can accurately identify brain activation at the correct depth for functional brain imaging, even when co-existing with superficial artifacts.

Influence of probe pressure on the diffuse correlation spectroscopy blood flow signal: extra-cerebral contributions

A pilot study explores relative contributions of extra-cerebral (scalp/skull) versus brain (cerebral) tissues to the blood flow index determined by diffuse correlation spectroscopy (DCS). Microvascular DCS flow measurements were made on the head during baseline and breath-holding/hyperventilation tasks, both with and without pressure. Baseline (resting) data enabled estimation of extra-cerebral flow signals and their pressure dependencies. A simple two-component model was used to derive baseline and activated cerebral blood flow (CBF) signals, and the DCS flow indices were also cross-correlated with concurrent Transcranial Doppler Ultrasound (TCD) blood velocity measurements. The study suggests new pressure-dependent experimental paradigms for elucidation of blood flow contributions from extra-cerebral and cerebral tissues.

Optical characterization of two-layered turbid media for non-invasive, absolute oximetry in cerebral and extracerebral tissue

We introduce a multi-distance, frequency-domain, near-infrared spectroscopy (NIRS) method to measure the optical coefficients of two-layered media and the thickness of the top layer from diffuse reflectance measurements. This method features a direct solution based on diffusion theory and an inversion procedure based on the Levenberg-Marquardt algorithm. We have validated our method through Monte Carlo simulations, experiments on tissue-like phantoms, and measurements on the forehead of three human subjects. The Monte Carlo simulations and phantom measurements have shown that, in ideal two-layered samples, our method accurately recovers the top layer thickness (L), the absorption coefficient (µ a ) and the reduced scattering coefficient (µ' s ) of both layers with deviations that are typically less than 10% for all parameters. Our method is aimed at absolute measurements of hemoglobin concentration and saturation in cerebral and extracerebral tissue of adult human subjects, where the top layer (layer 1) represents extracerebral tissue (scalp, skull, dura mater, subarachnoid space, etc.) and the bottom layer (layer 2) represents cerebral tissue. Human subject measurements have shown a significantly greater total hemoglobin concentration in cerebral tissue (82±14 µM) with respect to extracerebral tissue (30±7 µM). By contrast, there was no significant difference between the hemoglobin saturation measured in cerebral tissue (56%±10%) and extracerebral tissue (62%±6%). To our knowledge, this is the first time that an inversion procedure in the frequency domain with six unknown parameters with no other prior knowledge is used for the retrieval of the optical coefficients and top layer thickness with high accuracy on two-layered media. Our absolute measurements of cerebral hemoglobin concentration and saturation are based on the discrimination of extracerebral and cerebral tissue layers, and they can enhance the impact of NIRS for cerebral hemodynamics and oxygenation assessment both in the research arena and clinical practice.

Atlas-based head modeling and spatial normalization for high-density diffuse optical tomography: in vivo validation against fMRI

Diffuse optical imaging (DOI) is increasingly becoming a valuable neuroimaging tool when fMRI is precluded. Recent developments in high-density diffuse optical tomography (HD-DOT) overcome previous limitations of sparse DOI systems, providing improved image quality and brain specificity. These improvements in instrumentation prompt the need for advancements in both i) realistic forward light modeling for accurate HD-DOT image reconstruction, and ii) spatial normalization for voxel-wise comparisons across subjects. Individualized forward light models derived from subject-specific anatomical images provide the optimal inverse solutions, but such modeling may not be feasible in all situations. In the absence of subject-specific anatomical images, atlas-based head models registered to the subject's head using cranial fiducials provide an alternative solution. In addition, a standard atlas is attractive because it defines a common coordinate space in which to compare results across subjects. The question therefore arises as to whether atlas-based forward light modeling ensures adequate HD-DOT image quality at the individual and group level. Herein, we demonstrate the feasibility of using atlas-based forward light modeling and spatial normalization methods. Both techniques are validated using subject-matched HD-DOT and fMRI data sets for visual evoked responses measured in five healthy adult subjects. HD-DOT reconstructions obtained with the registered atlas anatomy (i.e. atlas DOT) had an average localization error of 2.7mm relative to reconstructions obtained with the subject-specific anatomical images (i.e. subject-MRI DOT), and 6.6mm relative to fMRI data. At the group level, the localization error of atlas DOT reconstruction was 4.2mm relative to subject-MRI DOT reconstruction, and 6.1mm relative to fMRI. These results show that atlas-based image reconstruction provides a viable approach to individual head modeling for HD-DOT when anatomical imaging is not available.

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.

Further improvement in reducing superficial contamination in NIRS using double short separation measurements

Near-Infrared Spectroscopy (NIRS) allows the recovery of the evoked hemodynamic response to brain activation. In adult human populations, the NIRS signal is strongly contaminated by systemic interference occurring in the superficial layers of the head. An approach to overcome this difficulty is to use additional NIRS measurements with short optode separations to measure the systemic hemodynamic fluctuations occurring in the superficial layers. These measurements can then be used as regressors in the post-experiment analysis to remove the systemic contamination and isolate the brain signal. In our previous work, we showed that the systemic interference measured in NIRS is heterogeneous across the surface of the scalp. As a consequence, the short separation measurement used in the regression procedure must be located close to the standard NIRS channel from which the evoked hemodynamic response of the brain is to be recovered. Here, we demonstrate that using two short separation measurements, one at the source optode and one at the detector optode, further increases the performance of the short separation regression method compared to using a single short separation measurement. While a single short separation channel produces an average reduction in noise of 33% for HbO, using a short separation channel at both source and detector reduces noise by 59% compared to the standard method using a general linear model (GLM) without short separation. For HbR, noise reduction of 3% is achieved using a single short separation and this number goes to 47% when two short separations are used. Our work emphasizes the importance of integrating short separation measurements both at the source and at the detector optode of the standard channels from which the hemodynamic response is to be recovered. While the implementation of short separation sources presents some difficulties experimentally, the improvement in noise reduction is significant enough to justify the practical challenges.

Extended hierarchical Bayesian diffuse optical tomography for removing scalp artifact

Functional near-infrared spectroscopy (fNIRS) can non-invasively measure hemodynamic responses in the cerebral cortex with a portable apparatus. However, the observation signal in fNIRS measurements is contaminated by the artifact signal from the hemodynamic response in the scalp. In this paper, we propose a method to separate the signals from the cortex and the scalp by estimating both hemodynamic changes by diffuse optical tomography (DOT). In the inverse problem of DOT, we introduce smooth regularization to the hemodynamic change in the scalp and sparse regularization to that in the cortex based on the nature of the hemodynamic responses. These appropriate regularization models, with the spatial information of optical paths of many measurement channels, allow three-dimensional reconstruction of both hemodynamic changes. We validate our proposed method through two-layer phantom experiments and MRI-based head-model simulations. In both experiments, the proposed method simultaneously estimates the superficial smooth activity in the scalp area and the deep localized activity in the cortical area.

Method for the discrimination of superficial and deep absorption variations by time domain fNIRS

A method for the discrimination of superficial and deep absorption variations by time domain functional near infrared spectroscopy is presented. The method exploits the estimate of the photon time-dependent pathlength in different domains of the sampled medium and makes use of an approach based on time-gating of the photon distribution of time-of-flights. Validation of the method is performed in the two-layer geometry to focus on muscle and head applications. Numerical simulations varied the thickness of the upper layer, the interfiber distance, the shape of the instrument response function and the photon counts. Preliminary results from in vivo data are also shown.

Separation of fNIRS signals into functional and systemic components based on differences in hemodynamic modalities

In conventional functional near-infrared spectroscopy (fNIRS), systemic physiological fluctuations evoked by a body's motion and psychophysiological changes often contaminate fNIRS signals. We propose a novel method for separating functional and systemic signals based on their hemodynamic differences. Considering their physiological origins, we assumed a negative and positive linear relationship between oxy- and deoxyhemoglobin changes of functional and systemic signals, respectively. Their coefficients are determined by an empirical procedure. The proposed method was compared to conventional and multi-distance NIRS. The results were as follows: (1) Nonfunctional tasks evoked substantial oxyhemoglobin changes, and comparatively smaller deoxyhemoglobin changes, in the same direction by conventional NIRS. The systemic components estimated by the proposed method were similar to the above finding. The estimated functional components were very small. (2) During finger-tapping tasks, laterality in the functional component was more distinctive using our proposed method than that by conventional fNIRS. The systemic component indicated task-evoked changes, regardless of the finger used to perform the task. (3) For all tasks, the functional components were highly coincident with signals estimated by multi-distance NIRS. These results strongly suggest that the functional component obtained by the proposed method originates in the cerebral cortical layer. We believe that the proposed method could improve the reliability of fNIRS measurements without any modification in commercially available instruments.

A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy

Near-infrared spectroscopy (NIRS) is susceptible to signal artifacts caused by relative motion between NIRS optical fibers and the scalp. These artifacts can be very damaging to the utility of functional NIRS, particularly in challenging subject groups where motion can be unavoidable. A number of approaches to the removal of motion artifacts from NIRS data have been suggested. In this paper we systematically compare the utility of a variety of published NIRS motion correction techniques using a simulated functional activation signal added to 20 real NIRS datasets which contain motion artifacts. Principle component analysis, spline interpolation, wavelet analysis, and Kalman filtering approaches are compared to one another and to standard approaches using the accuracy of the recovered, simulated hemodynamic response function (HRF). Each of the four motion correction techniques we tested yields a significant reduction in the mean-squared error (MSE) and significant increase in the contrast-to-noise ratio (CNR) of the recovered HRF when compared to no correction and compared to a process of rejecting motion-contaminated trials. Spline interpolation produces the largest average reduction in MSE (55%) while wavelet analysis produces the highest average increase in CNR (39%). On the basis of this analysis, we recommend the routine application of motion correction techniques (particularly spline interpolation or wavelet analysis) to minimize the impact of motion artifacts on functional NIRS data.

Task-related component analysis for functional neuroimaging and application to near-infrared spectroscopy data

Reproducibility of experimental results lies at the heart of scientific disciplines. Here we propose a signal processing method that extracts task-related components by maximizing the reproducibility during task periods from neuroimaging data. Unlike hypothesis-driven methods such as general linear models, no specific time courses are presumed, and unlike data-driven approaches such as independent component analysis, no arbitrary interpretation of components is needed. Task-related components are constructed by a linear, weighted sum of multiple time courses, and its weights are optimized so as to maximize inter-block correlations (CorrMax) or covariances (CovMax). Our analysis method is referred to as task-related component analysis (TRCA). The covariance maximization is formulated as a Rayleigh-Ritz eigenvalue problem, and corresponding eigenvectors give candidates of task-related components. In addition, a systematic statistical test based on eigenvalues is proposed, so task-related and -unrelated components are classified objectively and automatically. The proposed test of statistical significance is found to be independent of the degree of autocorrelation in data if the task duration is sufficiently longer than the temporal scale of autocorrelation, so TRCA can be applied to data with autocorrelation without any modification. We demonstrate that simple extensions of TRCA can provide most distinctive signals for two tasks and can integrate multiple modalities of information to remove task-unrelated artifacts. TRCA was successfully applied to synthetic data as well as near-infrared spectroscopy (NIRS) data of finger tapping. There were two statistically significant task-related components; one was a hemodynamic response, and another was a piece-wise linear time course. In summary, we conclude that TRCA has a wide range of applications in multi-channel biophysical and behavioral measurements.

High-density diffuse optical tomography of term infant visual cortex in the nursery

Advancements in antenatal and neonatal medicine over the last few decades have led to significant improvement in the survival rates of sick newborn infants. However, this improvement in survival has not been matched by a reduction in neurodevelopmental morbidities with increasing recognition of the diverse cognitive and behavioral challenges that preterm infants face in childhood. Conventional neuroimaging modalities, such as cranial ultrasound and magnetic resonance imaging, provide an important definition of neuroanatomy with recognition of brain injury. However, they fail to define the functional integrity of the immature brain, particularly during this critical developmental period. Diffuse optical tomography methods have established success in imaging adult brain function; however, few studies exist to demonstrate their feasibility in the neonatal population. We demonstrate the feasibility of using recently developed high-density diffuse optical tomography (HD-DOT) to map functional activation of the visual cortex in healthy term-born infants. The functional images show high contrast-to-noise ratio obtained in seven neonates. These results illustrate the potential for HD-DOT and provide a foundation for investigations of brain function in more vulnerable newborns, such as preterm infants.

Multi-channel NIRS of the primary motor cortex to discriminate hand from foot activity

The poor spatial resolution of near-infrared spectroscopy (NIRS) makes it difficult to distinguish two closely located cortical areas from each other. Here, a combination of multi-channel NIRS and a centre of gravity (CoG) approach (widely accepted in the field of transcranial magnetic stimulation; TMS) was used to discriminate between closely located cortical areas activated during hand and foot movements. Similarly, the possibility of separating the more anteriorly represented discrete movements from rhythmic movements was studied. Thirteen healthy right-handed subjects performed rhythmic or discrete ('task') hand or foot ('extremity') tapping. Hemodynamic responses were measured using an 8-channel NIRS setup. For oxyhemoglobin (OHb) and deoxyhemoglobin (HHb), a CoG was determined for each condition using the mean hemodynamic responses and the coordinates of the channels. Significant hemodynamic responses were found for hand and foot movements. Based on the HHb responses, the NIRS-CoG of hand movements was located 0.6 cm more laterally compared to the NIRS-CoG of foot movements. For OHb responses no difference in NIRS-CoG was found for 'extremity' nor for 'task'. This is the first NIRS study showing hemodynamic responses for isolated foot movements. Furthermore, HHb responses have the potential to be used in multi-channel NIRS experiments requiring differential activation of motor cortex areas linked to either hand or foot movements.

The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy

A major methodological challenge of functional near-infrared spectroscopy (fNIRS) is its high sensitivity to haemodynamic fluctuations in the scalp. Superficial fluctuations contribute on the one hand to the physiological noise of fNIRS, impairing the signal-to-noise ratio, and may on the other hand be erroneously attributed to cerebral changes, leading to false positives in fNIRS experiments. Here we explore the localisation, time course and physiological origin of task-evoked superficial signals in fNIRS and present a method to separate them from cortical signals. We used complementary fNIRS, fMRI, MR-angiography and peripheral physiological measurements (blood pressure, heart rate, skin conductance and skin blood flow) to study activation in the frontal lobe during a continuous performance task. The General Linear Model (GLM) was applied to analyse the fNIRS data, which included an additional predictor to account for systemic changes in the skin. We found that skin blood volume strongly depends on the cognitive state and that sources of task-evoked systemic signals in fNIRS are co-localized with veins draining the scalp. Task-evoked superficial artefacts were mainly observed in concentration changes of oxygenated haemoglobin and could be effectively separated from cerebral signals by GLM analysis. Based on temporal correlation of fNIRS and fMRI signals with peripheral physiological measurements we conclude that the physiological origin of the systemic artefact is a task-evoked sympathetic arterial vasoconstriction followed by a decrease in venous volume. Since changes in sympathetic outflow accompany almost any cognitive and emotional process, we expect scalp vessel artefacts to be present in a wide range of fNIRS settings used in neurocognitive research. Therefore a careful separation of fNIRS signals originating from activated brain and from scalp is a necessary precondition for unbiased fNIRS brain activation maps.

Dynamic phantom with two stage-driven absorbers for mimicking hemoglobin changes in superficial and deep tissues

In near-infrared spectroscopy (NIRS) for monitoring brain activity and cerebral functional connectivity, the effect of superficial tissue on NIRS signals needs to be considered. Although some methods for determining the effect of scalp and brain have been proposed, direct validation of the methods has been difficult because the actual absorption changes cannot be known. In response to this problem, we developed a dynamic phantom that mimics hemoglobin changes in superficial and deep tissues, thus allowing us to experimentally validate the methods. Two absorber layers are independently driven with two one-axis automatic stages. We can use the phantom to design any type of waveform (e.g., brain activity or systemic fluctuation) of absorption change, which can then be reproducibly measured. To determine the effectiveness of the phantom, we used it for a multiple source-detector distance measurement. We also investigated the performance of a subtraction method with a short-distance regressor. The most accurate lower-layer change was obtained when a shortest-distance channel was used. Furthermore, when an independent component analysis was applied to the same data, the extracted components were in good agreement with the actual signals. These results demonstrate that the proposed phantom can be used for evaluating methods of discriminating the effects of superficial tissue.

Questioning the questions that have been asked about the infant brain using near-infrared spectroscopy

Near-infrared spectroscopy (NIRS) is a noninvasive diffuse optical-imaging technique that can measure local metabolic demand in the surface of the cortex due to differential absorption of light by oxygenated and deoxygenated blood. Over the past decade, NIRS has become increasingly used as a complement to other neuroimaging techniques, such as electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI), particularly in paediatric populations who cannot easily be tested using fMRI and MEG. In this review of empirical findings from human infants, ranging in age from birth to 12 months of age, a number of interpretive concerns are raised about what can be concluded from NIRS data. In addition, inconsistencies across studies are highlighted, and strategies are proposed for enhancing the reliability of NIRS data gathered from infants. Finally, a variety of new and promising advances in NIRS techniques are highlighted.

Short separation channel location impacts the performance of short channel regression in NIRS

Near-Infrared Spectroscopy (NIRS) allows the recovery of cortical oxy- and deoxyhemoglobin changes associated with evoked brain activity. NIRS is a back-reflection measurement making it very sensitive to the superficial layers of the head, i.e. the skin and the skull, where systemic interference occurs. As a result, the NIRS signal is strongly contaminated with systemic interference of superficial origin. A recent approach to overcome this problem has been the use of additional short source-detector separation optodes as regressors. Since these additional measurements are mainly sensitive to superficial layers in adult humans, they can be used to remove the systemic interference present in longer separation measurements, improving the recovery of the cortical hemodynamic response function (HRF). One question that remains to answer is whether or not a short separation measurement is required in close proximity to each long separation NIRS channel. Here, we show that the systemic interference occurring in the superficial layers of the human head is inhomogeneous across the surface of the scalp. As a result, the improvement obtained by using a short separation optode decreases as the relative distance between the short and the long measurement is increased. NIRS data was acquired on 6 human subjects both at rest and during a motor task consisting of finger tapping. The effect of distance between the short and the long channel was first quantified by recovering a synthetic hemodynamic response added over the resting-state data. The effect was also observed in the functional data collected during the finger tapping task. Together, these results suggest that the short separation measurement must be located as close as 1.5 cm from the standard NIRS channel in order to provide an improvement which is of practical use. In this case, the improvement in Contrast-to-Noise Ratio (CNR) compared to a standard General Linear Model (GLM) procedure without using any small separation optode reached 50% for HbO and 100% for HbR. Using small separations located farther than 2 cm away resulted in mild or negligible improvements only.

Optical scanning system for light-absorption measurement of deep biological tissue

A noncontact near-infrared scanning system for multi-distance absorption measurement of deep biological tissue was developed. An 808-nm laser, whose focal point on the surface of biological tissue is controlled by a galvano scanner, is used as a light source. A phosphor is placed at a detection focal point on the tissue surface. The light that propagates through tissue and exits from the tissue surface beneath the phosphor excites the phosphor. The fluorescence emitted from the phosphor is detected by an avalanche photodiode. The system is used to measure 20 points on tissue surface at which source-detector (S-D) distances are 7-45 mm (with 2-mm intervals). Neither the light source nor the detector contacts the tissue surface. The system was validated by using it to measure the absorption change of an absorber (which is embedded in a deep layer of a tissue-simulating phantom) while the surface-layer thickness of the phantom was changed from 1 to 12 mm. It was demonstrated that both the relative absorption change of the absorber and the absolute thickness of the surface layer can be estimated from the measured optical-density change (ΔOD) and the dependence of ΔOD on S-D distance, respectively.

A methodology to improve estimation of stimulus-evoked hemodynamic response from fNIRS measurements

Functional near-infrared spectroscopy (fNIRS) is a non-invasive optical neuroimaging method used to investigate functional activity of the cerebral cortex evoked by cognitive, visual, auditory and motor tasks, detecting regional changes of oxy- and deoxy-hemoglobin concentration. Accurate estimation of the stimulus-evoked hemodynamic response (HR) from fNIRS signals in order to quantitatively investigate cognitive functions requires to cope with several noise components. Some of them appear as random disturbances (typically tackled through averaging techniques), while others are due to physiological sources, such as heart beat, respiration, vasomotor waves, and are particularly challenging to be dealt with because they lie in the same frequency band of HR. In this work we present a new two-steps methodology for the HR estimation from fNIRS data. The first step is a pre-processing stage where physiological trends in fNIRS data are reduced by exploiting a mathematical model identified from the signal of a reference channel. In the second step, the pre-processed data of the other channels are filtered with a recently presented non-parametric Bayesian approach (Scarpa et al., Optics Express, 2010). The presented method for HR estimation is compared with widely used methods: conventional averaging, band-pass filtering and principal component analysis (PCA). Results on simulated data reveal the ability of the proposed method to improve the accuracy of the estimates of the functional hemodynamic response, as well as the estimate of peak amplitude and latency. Encouraging preliminary results in a representative real data set showing an improvement of contrast to noise ratio are also reported.