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

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

Significance

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

Aim

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

Approach

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

Results

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

Conclusions

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

Stress estimation by the prefrontal cortex asymmetry: Study on fNIRS signals

BACKGROUND

Functional near-infrared spectroscopy (fNIRS) is a non-invasive technique frequently used to measure the brain hemodynamic activity in applications to evaluate affective disorders and stress. Using two wavelengths of light, it is possible to monitor relative changes in the concentrations of oxyhemoglobin and deoxyhemoglobin. Besides, the spatial asymmetry in the prefrontal cortex activity has been correlated with the brain response to stressful situations.

METHODS

We measured prefrontal cortex activity with a NIRS multi-distance device during a baseline period, under stressful conditions (e.g., social stress), and after a recovery phase. We calculated a laterality index for the contaminated brain signal and for the brain signal where we removed the influence of extracerebral hemodynamic activity by using a short channel.

RESULTS

There was a significant right lateralization during stress when using the contaminated signals, consistent with previous investigations, but this significant difference disappeared using the corrected signals. Indeed, exploration of the susceptibility to contamination of the different channels showed non-homogeneous spatial patterns, which would hint at detection of stress from extracerebral activity from the forehead.

LIMITATIONS

There was no recovery phase between the social and the arithmetic stressor, a cumulative effect was not considered.

CONCLUSIONS

Extracerebral hemodynamic activity provided insights into the pertinence of short channel corrections in fNIRS studies dealing with emotions. It is important to consider this issue in clinical applications including modern monitoring systems based on fNIRS technique to assess emotional states in affective disorders.

Assessment of dynamic cerebral autoregulation in near-infrared spectroscopy using short channels: A feasibility study in acute ischemic stroke patients

Introduction

In acute ischemic stroke, progressive impairment of cerebral autoregulation (CA) is frequent and associated with unfavorable outcomes. Easy assessment of cerebral blood flow and CA in stroke units bedside tools like near-infrared spectroscopy (NIRS) might improve early detection of CA deterioration. This study aimed to assess dynamic CA with multichannel CW-NIRS in acute ischemic stroke (AIS) patients compared to agematched healthy controls.

Methods

CA reaction was amplified by changes in head of bed position. Long- and short channels were used to monitor systemic artery pressure- and intracranial oscillations simultaneously. Gain and phase shift in spontaneous low- and very low-frequency oscillations (LFO, VLFO) of blood pressure were assessed.

Results

A total of 54 participants, 27 with AIS and 27 age-matched controls were included. Gain was significantly lower in the AIS group in the LFO range (i) when the upper body was steadily elevated to 30. and (ii) after its abrupt elevation to 30°. No other differences were found between groups.

Discussion

This study demonstrates the feasibility of NIRS short channels to measure CA in AIS patients in one single instrument. A lower gain in AIS might indicate decreased CA activity in this pilot study, but further studies investigating the role of NIRS short channels in AIS are needed.

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.

Young female participants show blunted placebo effects associated with blunted responses to a cue predicting a safe stimulus in the right dorsolateral prefrontal cortex

Discrimination of cues predicting non-nociceptive/nociceptive stimuli is essential for predicting whether a non-painful or painful stimulus will be administered and for eliciting placebo/nocebo (pain reduction/pain enhancement) effects. Dysfunction of the neural system involved in placebo effects has been implicated in the pathology of chronic pain, while female sex is one of the important risk factors for development of chronic pain in young adults. The dorsolateral prefrontal cortex (dl-PFC) is suggested to be involved in placebo effects and is sensitive to sex and age. In this study, to examine the neural mechanisms by which sex and age alter placebo and nocebo effects, we analyzed cerebral hemodynamic activities in the dl-PFC in different sex and age groups during a differential conditioning task. During the training session, two different sounds were followed by low- and high-intensity electrical shocks. In the following recording session, electrical shocks, the intensity of which was mismatched to the sounds, were occasionally administered to elicit placebo and nocebo effects. In young female participants, both placebo effects and hemodynamic responses to the conditioned sounds in the right dl-PFC were significantly lower than those in elderly female participants, while there were no age differences in male participants. The hemodynamic responses to the sound paired with the safe stimulus in the right dl-PFC were significantly correlated with placebo effects, except in the young female group. These results suggest that blunted placebo effects in the young female participants are ascribed to blunted responses to the sound associated with the safe stimulus in the right dl-PFC, and that sex- and age-related factors may alter the responsiveness of the right dl-PFC to associative cues predicting a safe stimulus.

Deep learning-based motion artifact removal in functional near-infrared spectroscopy

Significance: Functional near-infrared spectroscopy (fNIRS), a well-established neuroimaging technique, enables monitoring cortical activation while subjects are unconstrained. However, motion artifact is a common type of noise that can hamper the interpretation of fNIRS data. Current methods that have been proposed to mitigate motion artifacts in fNIRS data are still dependent on expert-based knowledge and the post hoc tuning of parameters. Aim: Here, we report a deep learning method that aims at motion artifact removal from fNIRS data while being assumption free. To the best of our knowledge, this is the first investigation to report on the use of a denoising autoencoder (DAE) architecture for motion artifact removal. Approach: To facilitate the training of this deep learning architecture, we (i) designed a specific loss function and (ii) generated data to mimic the properties of recorded fNIRS sequences. Results: The DAE model outperformed conventional methods in lowering residual motion artifacts, decreasing mean squared error, and increasing computational efficiency. Conclusion: Overall, this work demonstrates the potential of deep learning models for accurate and fast motion artifact removal in fNIRS data.

Correction of global physiology in resting-state functional near-infrared spectroscopy

Significance: Resting-state functional connectivity (RSFC) analyses of functional near-infrared spectroscopy (fNIRS) data reveal cortical connections and networks across the brain. Motion artifacts and systemic physiology in evoked fNIRS signals present unique analytical challenges, and methods that control for systemic physiological noise have been explored. Whether these same methods require modification when applied to resting-state fNIRS (RS-fNIRS) data remains unclear. Aim: We systematically examined the sensitivity and specificity of several RSFC analysis pipelines to identify the best methods for correcting global systemic physiological signals in RS-fNIRS data. Approach: Using numerically simulated RS-fNIRS data, we compared the rates of true and false positives for several connectivity analysis pipelines. Their performance was scored using receiver operating characteristic analysis. Pipelines included partial correlation and multivariate Granger causality, with and without short-separation measurements, and a modified multivariate causality model that included a non-traditional zeroth-lag cross term. We also examined the effects of pre-whitening and robust statistical estimators on performance. Results: Consistent with previous work on bivariate correlation models, our results demonstrate that robust statistics and pre-whitening are effective methods to correct for motion artifacts and autocorrelation in the fNIRS time series. Moreover, we found that pre-filtering using principal components extracted from short-separation fNIRS channels as part of a partial correlation model was most effective in reducing spurious correlations due to shared systemic physiology when the two signals of interest fluctuated synchronously. However, when there was a temporal lag between the signals, a multivariate Granger causality test incorporating the short-separation channels was better. Since it is unknown if such a lag exists in experimental data, we propose a modified version of Granger causality that includes the non-traditional zeroth-lag term as a compromising solution. Conclusions: A combination of pre-whitening, robust statistical methods, and partial correlation in the processing pipeline to reduce autocorrelation, motion artifacts, and global physiology are suggested for obtaining statistically valid connectivity metrics with RS-fNIRS. Further studies should validate the effectiveness of these methods using human data.

Systemic physiology augmented functional near-infrared spectroscopy: a powerful approach to study the embodied human brain

In this Outlook paper, we explain why an accurate physiological interpretation of functional near-infrared spectroscopy (fNIRS) neuroimaging signals is facilitated when systemic physiological activity (e.g., cardiorespiratory and autonomic activity) is measured simultaneously by employing systemic physiology augmented functional near-infrared spectroscopy (SPA-fNIRS). The rationale for SPA-fNIRS is twofold: (i) SPA-fNIRS enables a more complete interpretation and understanding of the fNIRS signals measured at the head since they contain components originating from neurovascular coupling and from systemic physiological sources. The systemic physiology signals measured with SPA-fNIRS can be used for regressing out physiological confounding components in fNIRS signals. Misinterpretations can thus be minimized. (ii) SPA-fNIRS enables to study the embodied brain by linking the brain with the physiological state of the entire body, allowing novel insights into their complex interplay. We envisage the SPA-fNIRS approach will become increasingly important in the future.

Frequency-domain analysis of fNIRS fluctuations induced by rhythmic mental arithmetic

Functional near‐infrared spectroscopy (fNIRS) is an increasingly used technology for imaging neural correlates of cognitive processes. However, fNIRS signals are commonly impaired by task‐evoked and spontaneous hemodynamic oscillations of non‐cerebral origin, a major challenge in fNIRS research. In an attempt to isolate the task‐evoked cortical response, we investigated the coupling between hemodynamic changes arising from superficial and deep layers during mental effort. For this aim, we applied a rhythmic mental arithmetic task to induce cyclic hemodynamic fluctuations suitable for effective frequency‐resolved measurements. Twenty university students aged 18–25 years (eight males) underwent the task while hemodynamic changes were monitored in the forehead using a newly developed NIRS device, capable of multi‐channel and multi‐distance recordings. We found significant task‐related fluctuations for oxy‐ and deoxy‐hemoglobin, highly coherent across shallow and deep tissue layers, corroborating the strong influence of surface hemodynamics on deep fNIRS signals. Importantly, after removing such surface contamination by linear regression, we show that the frontopolar cortex response to a mental math task follows an unusual inverse oxygenation pattern. We confirm this finding by applying for the first time an alternative method to estimate the neural signal, based on transfer function analysis and phasor algebra. Altogether, our results demonstrate the feasibility of using a rhythmic mental task to impose an oscillatory state useful to separate true brain functional responses from those of non‐cerebral origin. This separation appears to be essential for a better understanding of fNIRS data and to assess more precisely the dynamics of the neuro‐visceral link.

We proposed the use of rhythmic mental arithmetic tasks to induce cyclic oscillations in multi‐distance fNIRS signals measured on the forehead, suitable for effective frequency‐domain analysis to better identify the actual neural functional response. We confirm the impairment of deep signals by task‐evoked non‐cerebral confounds, while providing evidence for an inverse oxygenation response in the frontopolar cortex.

Characterizing reproducibility of cerebral hemodynamic responses when applying short-channel regression in functional near-infrared spectroscopy

Significance: Functional near-infrared spectroscopy (fNIRS) enables the measurement of brain activity noninvasively. Optical neuroimaging with fNIRS has been shown to be reproducible on the group level and hence is an excellent research tool, but the reproducibility on the single-subject level is still insufficient, challenging the use for clinical applications. Aim: We investigated the effect of short-channel regression (SCR) as an approach to obtain fNIRS measurements with higher reproducibility on a single-subject level. SCR simultaneously considers contributions from long- and short-separation channels and removes confounding physiological changes through the regression of the short-separation channel information. Approach: We performed a test-retest study with a hand grasping task in 15 healthy subjects using a wearable fNIRS device, optoHIVE. Relevant brain regions were localized with transcranial magnetic stimulation to ensure correct placement of the optodes. Reproducibility was assessed by intraclass correlation, correlation analysis, mixed effects modeling, and classification accuracy of the hand grasping task. Further, we characterized the influence of SCR on reproducibility. Results: We found a high reproducibility of fNIRS measurements on a single-subject level ( ICC single = 0.81 and correlation r = 0.81 ). SCR increased the reproducibility from 0.64 to 0.81 ( ICC single ) but did not affect classification (85% overall accuracy). Significant intersubject variability in the reproducibility was observed and was explained by Mayer wave oscillations and low raw signal strength. The raw signal-to-noise ratio (threshold at 40 dB) allowed for distinguishing between persons with weak and strong activations. Conclusions: We report, for the first time, that fNIRS measurements are reproducible on a single-subject level using our optoHIVE fNIRS system and that SCR improves reproducibility. In addition, we give a benchmark to easily assess the ability of a subject to elicit sufficiently strong hemodynamic responses. With these insights, we pave the way for the reliable use of fNIRS neuroimaging in single subjects for neuroscientific research and clinical applications.

Evaluation of fNIRS signal components elicited by cognitive and hypercapnic stimuli

Functional near infrared spectroscopy (fNIRS) measurements are confounded by signal components originating from multiple physiological causes, whose activities may vary temporally and spatially (across tissue layers, and regions of the cortex). Furthermore, the stimuli can induce evoked effects, which may lead to over or underestimation of the actual effect of interest. Here, we conducted a temporal, spectral, and spatial analysis of fNIRS signals collected during cognitive and hypercapnic stimuli to characterize effects of functional versus systemic responses. We utilized wavelet analysis to discriminate physiological causes and employed long and short source-detector separation (SDS) channels to differentiate tissue layers. Multi-channel measures were analyzed further to distinguish hemispheric differences. The results highlight cardiac, respiratory, myogenic, and very low frequency (VLF) activities within fNIRS signals. Regardless of stimuli, activity within the VLF band had the largest contribution to the overall signal. The systemic activities dominated the measurements from the short SDS channels during cognitive stimulus, but not hypercapnic stimulus. Importantly, results indicate that characteristics of fNIRS signals vary with type of the stimuli administered as cognitive stimulus elicited variable responses between hemispheres in VLF band and task-evoked temporal effect in VLF, myogenic and respiratory bands, while hypercapnic stimulus induced a global response across both hemispheres.

High-resolution in vivo imaging of rhesus cerebral cortex with ultrafast portable photoacoustic microscopy

Revealing the structural and functional change of microvasculature is essential to match vascular response with neuronal activities in the investigation of neurovascular coupling. The increasing use of rhesus models in fundamental and clinical studies of neurovascular coupling presents an emerging need for a new imaging modality. Here we report a structural and functional cerebral vascular study of rhesus monkeys using an ultrafast, portable, and high resolution photoacoustic microscopic system with a long working distance and a special scanning mechanism to eliminate the relative displacement between the imaging interface and samples. We derived the structural and functional response of the cerebral vasculature to the alternating normoxic and hypoxic conditions by calculating the vascular diameter and functional connectivity. Both vasodilatation and vasoconstriction were observed in hypoxia. In addition to the change of vascular diameter, the decrease of functional connectivity is also an important phenomenon induced by the reduction of oxygen ventilatory. These results suggest that photoacoustic microscopy is a promising method to study the neurovascular coupling and cerebral vascular diseases due to the advanced features of high spatiotemporal resolution, excellent sensitivity to hemoglobin, and label-free imaging capability of observing hemodynamics.

Prefrontal Inter-brain Synchronization Reflects Convergence and Divergence of Flow Dynamics in Collaborative Learning: A Pilot Study

Flow is a highly motivated and affectively positive state in which a person is deeply engaged in an activity and feeling enjoyment from it. In collaborative activities, it would be optimal if all participants were in a state of flow. However, flow states fluctuate amongst individuals due to differences in the dynamics of motivation and cognition. To explore the possibility that inter-brain synchronization can provide a quantitative measure of the convergence and divergence of collective motivational dynamics, we conducted a pilot study to investigate the relationship between inter-brain synchronization and the interpersonal similarity of flow state dynamics during the collaborative learning process. In two English as a Foreign Language (EFL) classes, students were divided into groups of three-four and seated at desks facing each other while conducting a 60-min group work. In both classes, two groups with four members were randomly selected, and their medial prefrontal neural activities were measured simultaneously using wireless functional near-infrared spectroscopy (fNIRS) devices. Later the participants observed their own activities on recorded videos and retrospectively rated their subjective degree of flow state on a seven-point scale for each 2-min period. For the pairs of students whose neural activities were measured, the similarity of their flow experience dynamics was evaluated by the temporal correlation between their flow ratings. Prefrontal inter-brain synchronization of the same student pairs during group work was evaluated using wavelet transform coherence. Statistical analyses revealed that: (1) flow dynamics were significantly more similar for the student pairs within the same group compared to the pairs of students assigned across different groups; (2) prefrontal inter-brain synchronization in the relatively short time scale (9.3-13.9 s) was significantly higher for the within-group pairs than for the cross-group pairs; and (3) the prefrontal inter-brain synchronization at the same short time scale was significantly and positively correlated with the similarity of flow dynamics, even after controlling for the effects of within- vs. cross-group pair types from the two variables. These suggest that inter-brain synchronization can indeed provide a quantitative measure for converging and diverging collective motivational dynamics during collaborative learning, with higher inter-brain synchronization corresponding to a more convergent flow experience.

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

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

Comparison of short-channel separation and spatial domain filtering for removal of non-neural components in functional near-infrared spectroscopy signals

Significance: With the increasing popularity of functional near-infrared spectroscopy (fNIRS), the need to determine localization of the source and nature of the signals has grown. Aim: We compare strategies for removal of non-neural signals for a finger-thumb tapping task, which shows responses in contralateral motor cortex and a visual checkerboard viewing task that produces activity within the occipital lobe. Approach: We compare temporal regression strategies using short-channel separation to a spatial principal component (PC) filter that removes global signals present in all channels. For short-channel temporal regression, we compare non-neural signal removal using first and combined first and second PCs from a broad distribution of short channels to limited distribution on the forehead. Results: Temporal regression of non-neural information from broadly distributed short channels did not differ from forehead-only distribution. Spatial PC filtering provides results similar to short-channel separation using the temporal domain. Utilizing both first and second PCs from short channels removes additional non-neural information. Conclusions: We conclude that short-channel information in the temporal domain and spatial domain regression filtering methods remove similar non-neural components represented in scalp hemodynamics from fNIRS signals and that either technique is sufficient to remove non-neural components.

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

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

A Review of Cerebral Hemodynamics During Sleep Using Near-Infrared Spectroscopy

Investigating cerebral hemodynamic changes during regular sleep cycles and sleep disorders is fundamental to understanding the nature of physiological and pathological mechanisms in the regulation of cerebral oxygenation during sleep. Although sleep neuroimaging methods have been studied and have been well-reviewed, they have limitations in terms of technique and experimental design. Neurologists are convinced that Near-infrared spectroscopy (NIRS) provides essential information and can be used to assist the assessment of cerebral hemodynamics, and numerous studies regarding sleep have been carried out based on NIRS. Thus, a brief historical overview of the sleep studies using NIRS will be helpful for the biomedical students, academicians, and engineers to better understand NIRS from various perspectives. In this study, the existing literature on sleep studies is reviewed, and an overview of the NIRS applications is synthesized and provided. The paper first reviews the application scenarios, as well as the patterns of fluctuation of NIRS, which includes the investigation in regular sleep and sleep-disordered breathing. Various factors such as different sleep stages, populations, and degrees of severity were considered. Furthermore, the experimental design and signal processing, as well as the regulation mechanisms involved in regular and pathological sleep, are investigated and discussed. The strengths and weaknesses of the existing NIRS applications are addressed and presented, which can direct further NIRS analysis and utilization.

Performance Improvement for Detecting Brain Function Using fNIRS: A Multi-Distance Probe Configuration With PPL Method

To improve the spatial resolution of imaging and get more effective brain function information, a multi-distance probe configuration with three distances (28.2, 40, and 44.7 mm) and 52 channels is designed. At the same time, a data conversion method of modified Beer–Lambert law (MBLL) with partial pathlength (PPL) is proposed. In the experiment, three kinds of tasks, grip of left hand, grip of right hand, and rest, are performed with eight healthy subjects. First, with a typical single-distance probe configuration (30 mm, 24 channels), the feasibility of the proposed MBLL with PPL is preliminarily validated. Further, the characteristic of the proposed method is evaluated with the multi-distance probe configuration. Compared with MBLL with differential pathlength factor (DPF), the proposed MBLL with PPL is able to acquire more obvious concentration change and can achieve higher classification accuracy of the three tasks. Then, with the proposed method, the performance of the multi-distance probe configuration is discussed. Results show that, compared with a single distance, the combination of the three distances has better spatial resolution and could explore more accurate brain activation information. Besides, the classification accuracy of the three tasks obtained with the combination of three distances is higher than that of any combination of two distances. Also, with the combination of the three distances, the two-class classification between different tasks is carried out. Both theory and experimental results demonstrate that, using multi-distance probe configuration and the MBLL with PPL method, the performance of brain function detected by NIRS can be improved.

Reliability of fNIRS for noninvasive monitoring of brain function and emotion in sheep

The aim of this work was to critically assess if functional near infrared spectroscopy (fNIRS) can be profitably used as a tool for noninvasive recording of brain functions and emotions in sheep. We considered an experimental design including advances in instrumentation (customized wireless multi-distance fNIRS system), more accurate physical modelling (two-layer model for photon diffusion and 3D Monte Carlo simulations), support from neuroanatomical tools (positioning of the fNIRS probe by MRI and DTI data of the very same animals), and rigorous protocols (motor task, startling test) for testing the behavioral response of freely moving sheep. Almost no hemodynamic response was found in the extra-cerebral region in both the motor task and the startling test. In the motor task, as expected we found a canonical hemodynamic response in the cerebral region when sheep were walking. In the startling test, the measured hemodynamic response in the cerebral region was mainly from movement. Overall, these results indicate that with the current setup and probe positioning we are primarily measuring the motor area of the sheep brain, and not probing the too deeply located cortical areas related to processing of emotions.

Short-channel regression in functional near-infrared spectroscopy is more effective when considering heterogeneous scalp hemodynamics

Significance: The reliability of functional near-infrared spectroscopy (fNIRS) measurements is reduced by systemic physiology. Short-channel regression algorithms aim at removing systemic "noise" by subtracting the signal measured at a short source-detector separation (mainly scalp hemodynamics) from the one of a long separation (brain and scalp hemodynamics). In literature, incongruent approaches on the selection of the optimal regressor signal are reported based on different assumptions on scalp hemodynamics properties. Aim: We investigated the spatial and temporal distribution of scalp hemodynamics over the sensorimotor cortex and evaluated its influence on the effectiveness of short-channel regressions. Approach: We performed hand-grasping and resting-state experiments with five subjects, measuring with 16 optodes over sensorimotor areas, including eight 8-mm channels. We performed detailed correlation analyses of scalp hemodynamics and evaluated 180 hand-grasping and 270 simulated (overlaid on resting-state measurements) trials. Five short-channel regressor combinations were implemented with general linear models. Three were chosen according to literature, and two were proposed based on additional physiological assumptions [considering multiple short channels and their Mayer wave (MW) oscillations]. Results: We found heterogeneous hemodynamics in the scalp, coming on top of a global close-to-homogeneous behavior (correlation 0.69 to 0.92). The results further demonstrate that short-channel regression always improves brain activity estimates but that better results are obtained when heterogeneity is assumed. In particular, we highlight that short-channel regression is more effective when combining multiple scalp regressors and when MWs are additionally included. Conclusion: We shed light on the selection of optimal regressor signals for improving the removal of systemic physiological artifacts in fNIRS. We conclude that short-channel regression is most effective when assuming heterogeneous hemodynamics, in particular when combining spatial- and frequency-specific information. A better understanding of scalp hemodynamics and more effective short-channel regression will promote more accurate assessments of functional brain activity in clinical and research settings.

Functional Near-Infrared Spectroscopy Indicates That Asymmetric Right Hemispheric Activation in Mental Rotation of a Jigsaw Puzzle Decreases With Task Difficulty

Mental rotation (MR) is a cognitive skill whose neural dynamics are still a matter of debate as previous neuroimaging studies have produced controversial results. In order to investigate the underlying neurophysiology of MR, hemodynamic responses from the prefrontal cortex of 14 healthy subjects were recorded with functional near-infrared spectroscopy (fNIRS) during a novel MR task that had three categorical difficulty levels. Hemodynamic activity strength (HAS) parameter, which reflects the ratio of brain activation during the task to the baseline activation level, was used to assess the prefrontal cortex activation localization and strength. Behavioral data indicated that the MR requiring conditions are more difficult than the condition that did not require MR. The right dorsolateral prefrontal cortex (DLPFC) was found to be active in all conditions and to be the dominant region in the easiest task while more complex tasks showed widespread bilateral prefrontal activation. A significant increase in left DLPFC activation was observed with increasing task difficulty. Significantly higher right DLPFC activation was observed when the incongruent trials were contrasted against the congruent trials, which implied the possibility of a robust error or conflict-monitoring process during the incongruent trials. Our results showed that the right DLPFC is a core region for the processing of MR tasks regardless of the task complexity and that the left DLPFC is involved to a greater extent with increasing task complexity, which is consistent with the previous neuroimaging literature.

Cognitive and neural underpinnings of goal maintenance in young children

Active maintenance of goal representations is an integral part of our mental regulatory processes. Previous developmental studies have highlighted goal neglect, which is the phenomenon caused by a failure to maintain goal representations, and demonstrated developmental changes of the ability to maintain goal representations among preschoolers. Yet, few studies have explored the cognitive mechanisms underlying preschoolers' development of goal maintenance. The first aim of this study was to test whether working memory capacity and inhibitory control contribute to goal maintenance using a paradigm for measuring goal neglect. Moreover, although recent studies have shown that preschoolers recruit lateral prefrontal regions in performing executive functions tasks, they could not specify the neural underpinnings of goal maintenance. Thus, the second aim was to examine whether lateral prefrontal regions played a key role in maintaining goal representations using functional near-infrared spectroscopy. Our results showed that developmental differences in inhibitory control predicted the degree of goal neglect. It was also demonstrated that activation in the right prefrontal region was associated with children's successful avoidance of goal neglect. These findings offer important insights into the cognitive and neural underpinnings of goal maintenance in preschoolers.

Exoskeleton-assisted gait in chronic stroke: An EMG and functional near-infrared spectroscopy study of muscle activation patterns and prefrontal cortex activity

OBJECTIVES

Gait impairment dramatically affects stroke patients' functional independence. The Ekso™ is a wearable powered exoskeleton able to improve over-ground gait abilities, but the relationship between the cortical gait control mechanisms and lower limbs kinematics is still unclear. Our aims are: to assess whether the Ekso™ induces an attention-demanding process with prefrontal cortex activation during a gait task; to describe the relationship between the gait-induced muscle activation pattern and the prefrontal cortex activity.

METHODS

We enrolled 22 chronic stroke patients and 15 matched controls. We registered prefrontal cortex (PFC) activity with functional Near-Infrared Spectroscopy (fNIRS) and muscle activation with surface-electromyography (sEMG) during an over-ground gait task, performed with and without the Ekso™.

RESULTS

We observed prefrontal cortex activation during normal gait and a higher activation during Ekso-assisted walking among stroke patients. Furthermore, we found that muscle hypo-activation and co-activation of non-paretic limb are associated to a high prefrontal metabolism.

CONCLUSIONS

Among stroke patients, over-ground gait is an attention-demanding task. Prefrontal activity is modulated both by Ekso-assisted tasks and muscle activation patterns of non-paretic lower limb. Further studies are needed to elucidate if other Ekso™ settings induce different cortical and peripheral effects.

SIGNIFICANCE

This is the first study exploring the relationship between central and peripheral mechanisms during an Ekso-assisted gait task.

An Augmented-Reality fNIRS-Based Brain-Computer Interface: A Proof-of-Concept Study

Augmented reality (AR) enhances the user's environment by projecting virtual objects into the real world in real-time. Brain-computer interfaces (BCIs) are systems that enable users to control external devices with their brain signals. BCIs can exploit AR technology to interact with the physical and virtual world and to explore new ways of displaying feedback. This is important for users to perceive and regulate their brain activity or shape their communication intentions while operating in the physical world. In this study, twelve healthy participants were introduced to and asked to choose between two motor-imagery tasks: mental drawing and interacting with a virtual cube. Participants first performed a functional localizer run, which was used to select a single fNIRS channel for decoding their intentions in eight subsequent choice-encoding runs. In each run participants were asked to select one choice of a six-item list. A rotating AR cube was displayed on a computer screen as the main stimulus, where each face of the cube was presented for 6 s and represented one choice of the six-item list. For five consecutive trials, participants were instructed to perform the motor-imagery task when the face of the cube that represented their choice was facing them (therewith temporally encoding the selected choice). In the end of each run, participants were provided with the decoded choice based on a joint analysis of all five trials. If the decoded choice was incorrect, an active error-correction procedure was applied by the participant. The choice list provided in each run was based on the decoded choice of the previous run. The experimental design allowed participants to navigate twice through a virtual menu that consisted of four levels if all choices were correctly decoded. Here we demonstrate for the first time that by using AR feedback and flexible choice encoding in form of search trees, we can increase the degrees of freedom of a BCI system. We also show that participants can successfully navigate through a nested menu and achieve a mean accuracy of 74% using a single motor-imagery task and a single fNIRS channel.

Impaired hemodynamic activity in the right dorsolateral prefrontal cortex is associated with impairment of placebo analgesia and clinical symptoms in postherpetic neuralgia

The dorsolateral prefrontal cortex (dlPFC) is functionally linked to the descending pain modulation system and has been implicated in top down pain inhibition, including placebo analgesia. Therefore, functions of the dlPFC may be impaired in patients with chronic pain. Postherpetic neuralgia (PHN) is one of several syndromes with chronic neuropathic pain. In the present study, we investigated possible dysfunction of the dlPFC in chronic pain using patients with PHN. In a conditioning phase, heathy controls (n = 15) and patients with PHN (n = 7) were exposed to low (LF) and high (HF) frequency tones associated with noxious stimuli: weak (WS) and strong (SS) electrical stimulation, respectively. After the conditioning, cerebral hemodynamic activity was recorded from the bilateral dlPFC while the subjects were subjected to the cue tone-noxious electrical stimulation paradigm, in which incorrectly cued noxious stimuli were sometimes delivered to induce placebo and nocebo effects. The results indicated that hemodynamic responses to the LF tone in the right dlPFC was significantly lower in patients with PHN compared to the healthy controls. Furthermore, the same hemodynamic responses in the right dlPFC were correlated with placebo effects. In addition, clinical symptoms of PHN were negatively correlated to cerebral hemodynamic responses in the right dlPFC and magnitudes of the placebo effects. The results suggest that the right dlPFC, which is closely associated with the descending pain modulation system, is disturbed in PHN.

Effect of Mastication Muscle Activity on Prefrontal Cortex NIRS Measurement: A Pilot Study

Changes in NIRS signals are related to changes in local cerebral blood flow or oxy-Hb concentration. On the other hand, recent studies have revealed the effect of chewing gum on cognitive performance, stress control etc. which accompanied brain activity in the prefrontal cortex (PFC). However, these relationships are still controversial. To evaluate the chewing effect on PFC, NIRS seems to be a suitable method of imaging such results. When measuring NIRS on PFC, blood volume in superficial tissues (scalp, skin, muscle) might have some affect. The aim of the present study was to clarify the effect of the anterior temporal muscle on NIRS signals during gum chewing. Eight healthy volunteers participated. Two-channel NIRS (HOT-1000, NeU, Japan), which can distinguish total-Hb concentrations in deep tissue and superficial tissue layers, was used. In addition to a conventional optode separation distance of 3.0 cm, Hot 1000 has a short distance of 1.0 cm (NEAR channel) to measure NIRS signals that originate exclusively from surface tissues. NIRS probes were placed at Fp1 and Fp2 in the normal probe setting. The headset was displaced to the left in order to allow the left probe to be placed over the left anterior temporal muscle. In the normal setting, the superficial signal curve shows no notable change; however, the neural (calculated and defined in HOT-1000) and deep curves show an increase during the gum chewing task. At the deviated setting, all three signals show marked changes during the task. Total-Hb concentration in the deviated probe setting is significantly large (p < 0.05) than that of in the normal probe setting. When using gum chewing as a task, it would be better to consider a probe position carefully so that the influence of muscle activity on NIRS signal can be distinguished.

Analgesic Effects of Compression at Trigger Points Are Associated With Reduction of Frontal Polar Cortical Activity as Well as Functional Connectivity Between the Frontal Polar Area and Insula in Patients With Chronic Low Back Pain: A Randomized Trial

Background

Compression of myofascial trigger points (MTrPs) in muscles is reported to reduce chronic musculoskeletal pain. Although the prefrontal cortex (PFC) is implicated in development of chronic pain, the mechanisms of how MTrP compression at low back regions affects PFC activity remain under debate. In this study, we investigated effects of MTrP compression on brain hemodynamics and EEG oscillation in subjects with chronic low back pain.

Methods

The study was a prospective, randomized, parallel-group trial and an observer and subject-blinded clinical trial. Thirty-two subjects with chronic low back pain were divided into two groups: subjects with compression at MTrPs (n = 16) or those with non-MTrPs (n = 16). Compression at MTrP or non-MTrP for 30 s was applied five times, and hemodynamic activity (near-infrared spectroscopy; NIRS) and EEGs were simultaneously recorded during the experiment.

Results

The results indicated that compression at MTrPs significantly (1) reduced subjective pain (P < 0.05) and increased the pressure pain threshold (P < 0.05), (2) decreased the NIRS hemodynamic activity in the frontal polar area (pPFC) (P < 0.05), and (3) increased the current source density (CSD) of EEG theta oscillation in the anterior part of the PFC (P < 0.05). CSD of EEG theta oscillation was negatively correlated with NIRS hemodynamic activity in the pPFC (P < 0.05). Furthermore, functional connectivity in theta bands between the medial pPFC and insula cortex was significantly decreased in the MTrP group (P < 0.05). The functional connectivity between those regions was positively correlated with subjective low back pain (P < 0.05).

Discussion

The results suggest that MTrP compression at the lumbar muscle modulates pPFC activity and functional connectivity between the pPFC and insula, which may relieve chronic musculoskeletal pain.

Trial registration

This trial was registered at University Hospital Medical Information Network Clinical Trials Registry (UMIN000033913) on 27 August 2018, at https://upload.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000038660.

Simulation and in vivo investigation of light-emitting diode, near infrared Gaussian beam profiles

Near infrared spectroscopy is an optical imaging technique which offers a non-invasive, portable, and low-cost method for continuously measuring the oxygenation of tissues. In particular, it can provide the brain activation through measuring the blood oxygenation and blood volume in the cortex. Understanding and then improving the spatial and depth sensitivity of near infrared spectroscopy measurements to brain tissue are essential for designing experiments as well as interpreting research findings. In this study, we investigate the effect of applying two common light beam profiles including Uniform and Gaussian on the penetration depth of an LED-based near infrared spectroscopy. In this regard, two Gaussian profiles were produced by adjusting plano-convex and bi-convex lenses and the Uniform profile was provided by applying a flat lens. Two experiments were conducted in this study. First, a simulation experiment was carried out based on scanning the intra space of a liquid phantom by using static and pulsating absorbers to compare the penetration depth of the configurations applied on the LED-based near infrared spectroscopy with that of a laser-based near infrared spectroscopy. Second, to show the feasibility of the best proposed configuration applied, an in vivo experiment of stress assessment has been performed and its results have been compared with that results obtained by laser one. The results showed that the LED-based near infrared spectroscopy equipped with bi-convex lens provides a penetration depth and hence quality measurements of near infrared spectroscopy and its extracted heart rate variability signals as well as laser-based near infrared spectroscopy especially in the application of stress assessment.

Assessment of age-related decline of neurovascular coupling responses by functional near-infrared spectroscopy (fNIRS) in humans

Preclinical studies provide strong evidence that age-related impairment of neurovascular coupling (NVC) plays a causal role in the pathogenesis of vascular cognitive impairment (VCI). NVC is a critical homeostatic mechanism in the brain, responsible for adjustment of local cerebral blood flow to the energetic needs of the active neuronal tissue. Recent progress in geroscience has led to the identification of critical cellular and molecular mechanisms involved in neurovascular aging, identifying these pathways as targets for intervention. In order to translate the preclinical findings to humans, there is a need to assess NVC in geriatric patients as an endpoint in clinical studies. Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technique that enables the investigation of local changes in cerebral blood flow, quantifying task-related changes in oxygenated and deoxygenated hemoglobin concentrations. In the present overview, the basic principles of fNIRS are introduced and the application of this technique to assess NVC in older adults with implications for the design of studies on the mechanistic underpinnings of VCI is discussed.

Comparison of Feature Vector Compositions to Enhance the Performance of NIRS-BCI-Triggered Robotic Hand Orthosis for Post-Stroke Motor Recovery

Recently, brain–computer interfaces, combined with feedback systems and goal-oriented training, have been investigated for their capacity to promote functional recovery after stroke. Accordingly, we developed a brain–computer interface-triggered robotic hand orthosis that assists hand-closing and hand-opening for post-stroke patients without sufficient motor output. In this system, near-infrared spectroscopy is used to monitor the affected motor cortex, and a linear discriminant analysis-based binary classifier estimates hand posture. The estimated posture then wirelessly triggers the robotic hand orthosis. For better performance of the brain–computer interface, we tested feature windows of different lengths and varying feature vector compositions with motor execution data from seven neurologically intact participants. The interaction between a feature window and a delay in the hemodynamic response significantly affected both classification accuracy (Matthew Correlation Coefficient) and detection latency. The ‘preserving channels’ feature vector was able to increase accuracy by 13.14% and decrease latency by 29.48%, relative to averaging. Oxyhemoglobin combined with deoxyhemoglobin improved accuracy by 3.71% and decreased latency by 6.01% relative to oxyhemoglobin alone. Thus, the best classification performance resulted in an accuracy of 0.7154 and a latency of 2.8515 s. The hand rehabilitation system was successfully implemented using this feature vector composition, which yielded better classification performance.

Prior physical synchrony enhances rapport and inter-brain synchronization during subsequent educational communication

Physical synchrony has been suggested to have positive effects on not only concurrent but also subsequent communication, but the underlying neural processes are unclear. Using functional near-infrared spectroscopy (fNIRS) hyperscanning, we tested the effects of preceding physical synchrony on subsequent dyadic teaching-learning communication. Thirty-two pairs of participants performed two experimental sessions. In each session, they underwent a rhythmic arm movement block with synchronous or asynchronous conditions, and then taught/learned unknown words to/from each other according to a given scenario. Neural activities in their medial and left lateral prefrontal cortex (PFC) were measured and inter-brain synchronization (IBS) during the teaching-learning blocks was evaluated. Participants rated their subjective rapport during the teaching-learning blocks, and took a word memory test. The analyses revealed that (1) prior physical synchrony enhanced teacher-learner rapport; (2) prior physical synchrony also enhanced IBS in the lateral PFC; and (3) IBS changes correlated positively with rapport changes. Physical synchrony did however not affect word memory performance. These results suggest that IBS can be useful to measure the effects of social-bonding facilitation activities for educational communication.

An Information-Theoretic Approach to Quantitative Analysis of the Correspondence Between Skin Blood Flow and Functional Near-Infrared Spectroscopy Measurement in Prefrontal Cortex Activity

Effect of Skin blood flow (SBF) on functional near-infrared spectroscopy (fNIRS) measurement of cortical activity proves to be an illusive subject matter with divided stances in the neuroscientific literature on its extent. Whereas, some reports on its non-significant influence on fNIRS time series of cortical activity, others consider its impact misleading, even detrimental, in analysis of the brain activity as measured by fNIRS. This situation is further escalated by the fact that almost all analytical studies are based on comparison with functional Magnetic Resonance Imaging (fMRI). In this article, we pinpoint the lack of perspective in previous studies on preservation of information content of resulting fNIRS time series once the SBF is attenuated. In doing so, we propose information-theoretic criteria to quantify the necessary and sufficient conditions for SBF attenuation such that the information content of frontal brain activity in resulting fNIRS times series is preserved. We verify these criteria through evaluation of their utility in comparative analysis of principal component (PCA) and independent component (ICA) SBF attenuation algorithms. Our contributions are 2-fold. First, we show that mere reduction of SBF influence on fNIRS time series of frontal activity is insufficient to warrant preservation of cortical activity information. Second, we empirically justify a higher fidelity of PCA-based algorithm in preservation of the fontal activity's information content in comparison with ICA-based approach. Our results suggest that combination of the first two principal components of PCA-based algorithm results in most efficient SBF attenuation while preserving maximum frontal activity's information. These results contribute to the field by presenting a systematic approach to quantification of the SBF as an interfering process during fNIRS measurement, thereby drawing an informed conclusion on this debate. Furthermore, they provide evidence for a reliable choice among existing SBF attenuation algorithms and their inconclusive number of components, thereby ensuring minimum loss of cortical information during SBF attenuation process.

Stride-time variability is related to sensorimotor cortical activation during forward and backward walking

Previous research has used functional near-infrared spectroscopy (fNIRS) to show that motor areas of the cortex are activated more while walking backward compared to walking forward. It is also known that head movement creates motion artifacts in fNIRS data. The aim of this study was to investigate cortical activation during forward and backward walking, while also measuring head movement. We hypothesized that greater activation in motor areas while walking backward would be concurrent with increased head movement. Participants performed forward and backward walking on a treadmill. Participants wore motion capture markers on their head to quantify head movement and pressure sensors on their feet to calculate stride-time. fNIRS was placed over motor areas of the cortex to measure cortical activation. Measurements were compared for forward and backward walking conditions. No significant differences in body movement or head movement were observed between forward and backward walking conditions, suggesting that conditional differences in movement did not influence fNIRS results. Stride-time was significantly shorter during backward walking than during forward walking, but not more variable. There were no differences in activation for motor areas of the cortex when outliers were removed. However, there was a positive correlation between stride-time variability and activation in the primary motor cortex. This positive correlation between motor cortex activation and stride-time variability suggests that forward walking variability may be represented in the primary motor cortex.

Denoising of neuronal signal from mixed systemic low-frequency oscillation using peripheral measurement as noise regressor in near-infrared imaging

Functional near-infrared spectroscopy (fNIRS) is a noninvasive functional imaging technique measuring hemodynamic changes including oxygenated ( O 2 Hb ) and deoxygenated (HHb) hemoglobin. Low frequency (LF; 0.01 to 0.15 Hz) band is commonly analyzed in fNIRS to represent neuronal activation. However, systemic physiological artifacts (i.e., nonneuronal) likely occur also in overlapping frequency bands. We measured peripheral photoplethysmogram (PPG) signal concurrently with fNIRS (at prefrontal region) to extract the low-frequency oscillations (LFOs) as systemic noise regressors. We investigated three main points in this study: (1) the relationship between prefrontal fNIRS and peripheral PPG signals; (2) the denoising potential using these peripheral LFOs, and (3) the innovative ways to avoid the false-positive result in fNIRS studies. We employed spatial working memory (WM) and control tasks (e.g., resting state) to illustrate these points. Our results showed: (1) correlation between signals from prefrontal fNIRS and peripheral PPG is region-dependent. The high correlation with peripheral ear signal (i.e., O 2 Hb ) occurred mainly in frontopolar regions in both spatial WM and control tasks. This may indicate the finding of task-dependent effect even in peripheral signals. We also found that the PPG recording at the ear has a high correlation with prefrontal fNIRS signal than the finger signals. (2) The systemic noise was reduced by 25% to 34% on average across regions, with a maximum of 39% to 58% in the highly correlated frontopolar region, by using these peripheral LFOs as noise regressors. (3) By performing the control tasks, we confirmed that the statistically significant activation was observed in the spatial WM task, not in the controls. This suggested that systemic (and any other) noises unlikely violated the major statistical inference. (4) Lastly, by denoising using the task-related signals, the significant activation of region-of-interest was still observed suggesting the manifest task-evoked response in the spatial WM task.

Adaptive filtering of physiological noises in fNIRS data

The study presents a recursive least-squares estimation method with an exponential forgetting factor for noise removal in functional near-infrared spectroscopy data and extraction of hemodynamic responses (HRs) from the measured data. The HR is modeled as a linear regression form in which the expected HR, the first and second derivatives of the expected HR, a short-separation measurement data, three physiological noises, and the baseline drift are included as components in the regression vector. The proposed method is applied to left-motor-cortex experiments on the right thumb and little finger movements in five healthy male participants. The algorithm is evaluated with respect to its performance improvement in terms of contrast-to-noise ratio in comparison with Kalman filter, low-pass filtering, and independent component method. The experimental results show that the proposed model achieves reductions of 77% and 99% in terms of the number of channels exhibiting higher contrast-to-noise ratios in oxy-hemoglobin and deoxy-hemoglobin, respectively. The approach is robust in obtaining consistent HR data. The proposed method is applied for both offline and online noise removal.

Applications of Functional Near-Infrared Spectroscopy (fNIRS) Neuroimaging in Exercise⁻Cognition Science: A Systematic, Methodology-Focused Review

For cognitive processes to function well, it is essential that the brain is optimally supplied with oxygen and blood. In recent years, evidence has emerged suggesting that cerebral oxygenation and hemodynamics can be modified with physical activity. To better understand the relationship between cerebral oxygenation/hemodynamics, physical activity, and cognition, the application of state-of-the art neuroimaging tools is essential. Functional near-infrared spectroscopy (fNIRS) is such a neuroimaging tool especially suitable to investigate the effects of physical activity/exercises on cerebral oxygenation and hemodynamics due to its capability to quantify changes in the concentration of oxygenated hemoglobin (oxyHb) and deoxygenated hemoglobin (deoxyHb) non-invasively in the human brain. However, currently there is no clear standardized procedure regarding the application, data processing, and data analysis of fNIRS, and there is a large heterogeneity regarding how fNIRS is applied in the field of exercise⁻cognition science. Therefore, this review aims to summarize the current methodological knowledge about fNIRS application in studies measuring the cortical hemodynamic responses during cognitive testing (i) prior and after different physical activities interventions, and (ii) in cross-sectional studies accounting for the physical fitness level of their participants. Based on the review of the methodology of 35 as relevant considered publications, we outline recommendations for future fNIRS studies in the field of exercise⁻cognition science.

Directional changes in information flow between human brain cortical regions after application of anodal transcranial direct current stimulation (tDCS) over Broca's area

Little work has been done on the information flow in functional brain imaging and none so far in fNIRS. In this work, alterations in the directionality of net information flow induced by a short-duration, low-current (2 min 40 s; 0.5 mA) and a longer-duration, high-current (8 min; 1 mA) anodal tDCS applied over the Broca's area of the dominant language hemisphere were studied by fNIRS. The tDCS-induced patterns of information flow, quantified by a novel directed phase transfer entropy (dPTE) analysis, were distinct for different hemodynamic frequency bands and were qualitatively similar between low and high-current tDCS. In the endothelial band (0.003-0.02 Hz), the stimulated Broca's area became the strongest hub of outgoing information flow, whereas in the neurogenic band (0.02-0.04 Hz) the contralateral homologous area became the strongest information outflow source. In the myogenic band (0.04-0.15 Hz), only global patterns were seen, independent of tDCS stimulation that were interpreted as Mayer waves. These findings showcase dPTE analysis in fNIRS as a novel, complementary tool for studying cortical activity reorganization after an intervention.

Wavelet-based method for removing global physiological noise in functional near-infrared spectroscopy

Functional near-infrared spectroscopy (fNIRS) is a fast-developing non-invasive functional brain imaging technology widely used in cognitive neuroscience, clinical research and neural engineering. However, it is a challenge to effectively remove the global physiological noise in the fNIRS signal. The global physiological noise in fNIRS arises from multiple physiological origins in both superficial tissues and the brain. It has complex temporal, spatial and frequency characteristics, casting significant influence on the results. In the present study, we developed a novel wavelet-based method for fNIRS global physiological noise removal. The method is data-driven and does not rely on any additional hardware or subjective noise component selection procedure. It consists of two steps. Firstly, we use wavelet transform coherence to automatically detect the time-frequency points contaminated by the global physiological noise. Secondly, we decompose the fNIRS signal by using the wavelet transform, and then suppress the wavelet energy of the contaminated time-frequency points. Finally, we transform the signal back to a time series. We validated the method by using simulation and real data at both task- and resting-state. The results showed that our method can effectively remove the global physiological noise from the fNIRS signal and improve the spatial specificity of the task activation and the resting-state functional connectivity pattern.

Critical bounds on noise and SNR for robust estimation of real-time brain activity from functional near infra-red spectroscopy

The robust characterization of real-time brain activity carries potential for many applications. However, the contamination of measured signals by various instrumental, environmental, and physiological sources of noise introduces a substantial amount of signal variance and, consequently, challenges real-time estimation of contributions from underlying neuronal sources. Functional near infra-red spectroscopy (fNIRS) is an emerging imaging modality whose real-time potential is yet to be fully explored. The objectives of the current study are to (i) validate a time-dependent linear model of hemodynamic responses in fNIRS, and (ii) test the robustness of this approach against measurement noise (instrumental and physiological) and mis-specification of the hemodynamic response basis functions (amplitude, latency, and duration). We propose a linear hemodynamic model with time-varying parameters, which are estimated (adapted and tracked) using a dynamic recursive least square algorithm. Owing to the linear nature of the activation model, the problem of achieving robust convergence to an accurate estimation of the model parameters is recast as a problem of parameter error stability around the origin. We show that robust convergence of the proposed method is guaranteed in the presence of an acceptable degree of model misspecification and we derive an upper bound on noise under which reliable parameters can still be inferred. We also derived a lower bound on signal-to-noise-ratio over which the reliable parameters can still be inferred from a channel/voxel. Whilst here applied to fNIRS, the proposed methodology is applicable to other hemodynamic-based imaging technologies such as functional magnetic resonance imaging.

Automated Processing of fNIRS Data-A Visual Guide to the Pitfalls and Consequences

With the rapid increase in new fNIRS users employing commercial software, there is a concern that many studies are biased by suboptimal processing methods. The purpose of this study is to provide a visual reference showing the effects of different processing methods, to help inform researchers in setting up and evaluating a processing pipeline. We show the significant impact of pre- and post-processing choices and stress again how important it is to combine data from both hemoglobin species in order to make accurate inferences about the activation site.

Real-Time Reduction of Task-Related Scalp-Hemodynamics Artifact in Functional Near-Infrared Spectroscopy with Sliding-Window Analysis

Functional near-infrared spectroscopy (fNIRS) is an effective non-invasive neuroimaging technique for measuring hemoglobin concentration in the cerebral cortex. Owing to the nature of fNIRS measurement principles, measured signals can be contaminated with task-related scalp blood flow (SBF), which is distributed over the whole head and masks true brain activity. Aiming for fNIRS-based real-time application, we proposed a real-time task-related SBF artifact reduction method. Using a principal component analysis, we estimated a global temporal pattern of SBF from few short-channels, then we applied a general linear model for removing it from long-channels that were possibly contaminated by SBF. Sliding-window analysis was applied for both signal steps for real-time processing. To assess the performance, a semi-real simulation was executed with measured short-channel signals in a motor-task experiment. Compared with conventional techniques with no elements of SBF, the proposed method showed significantly higher estimation performance for true brain activation under a task-related SBF artifact environment.

Frequency-specific Effective Connectivity in Subjects with Cerebral Infarction as Revealed by NIRS Method

A connectivity-based approach can highlight the network reorganization in the chronic phases after stroke and contributes to the development of therapeutic interventions. Using dynamic Bayesian inference, this study aimed to assess the effective connectivity (EC) in various frequency bands through the near-infrared spectroscopy (NIRS) method in subjects with cerebral infarction (CI). A phase-coupling model was established based on phase information extracted using the wavelet transform of NIRS signals. Coupling strength and the main coupling direction were estimated using dynamic Bayesian inference. Wilcoxon test and chi-square test were used to determine the significant difference in EC between two groups. Results showed that the coupling strength of the EC in the CI group significantly decreased relative to that in the healthy group. The decrease was most significant in the frequency intervals IV (0.021 Hz-0.052 Hz; p = 0.0006) and VI (0.005 Hz-0.095 Hz; p = 0.0028). The main coupling direction changed from the right prefrontal cortex to the right motor cortex and left motor cortex in the frequency intervals IV (p₁ = 0.041, p₂ = 0.047) and II (p₁ = 0.0017, p₂ = 0.0036), respectively. The EC decreased or was even lost significantly in the EC map of the CI group. Experimental results indicated that information propagation was blocked in the CI group than in the healthy group and resulted in the decreased coupling strength and connectivity loss. The main coupling direction of the motor section changed from driving into being driven because of the degradation of limb movement function.

Signal Processing in Functional Near-Infrared Spectroscopy (fNIRS): Methodological Differences Lead to Different Statistical Results

Even though research in the field of functional near-infrared spectroscopy (fNIRS) has been performed for more than 20 years, consensus on signal processing methods is still lacking. A significant knowledge gap exists between established researchers and those entering the field. One major issue regularly observed in publications from researchers new to the field is the failure to consider possible signal contamination by hemodynamic changes unrelated to neurovascular coupling (i.e., scalp blood flow and systemic blood flow). This might be due to the fact that these researchers use the signal processing methods provided by the manufacturers of their measurement device without an advanced understanding of the performed steps. The aim of the present study was to investigate how different signal processing approaches (including and excluding approaches that partially correct for the possible signal contamination) affect the results of a typical functional neuroimaging study performed with fNIRS. In particular, we evaluated one standard signal processing method provided by a commercial company and compared it to three customized approaches. We thereby investigated the influence of the chosen method on the statistical outcome of a clinical data set (task-evoked motor cortex activity). No short-channels were used in the present study and therefore two types of multi-channel corrections based on multiple long-channels were applied. The choice of the signal processing method had a considerable influence on the outcome of the study. While methods that ignored the contamination of the fNIRS signals by task-evoked physiological noise yielded several significant hemodynamic responses over the whole head, the statistical significance of these findings disappeared when accounting for part of the contamination using a multi-channel regression. We conclude that adopting signal processing methods that correct for physiological confounding effects might yield more realistic results in cases where multi-distance measurements are not possible. Furthermore, we recommend using manufacturers' standard signal processing methods only in case the user has an advanced understanding of every signal processing step performed.

Effect of cerebral vasomotion during physical exercise on associative memory, a near-infrared spectroscopy study

Regular physical exercise has been shown to benefit neurocognitive functions, especially enhancing neurogenesis in the hippocampus. However, the effects of a single exercise session on cognitive functions are controversial. To address this issue, we measured hemodynamic changes in the brain during physical exercise using near-infrared spectroscopy (NIRS) and investigated related effects on memory consolidation processes. Healthy young participants underwent two experimental visits. During each visit, they performed an associative memory task in which they first encoded a series of pictures, then spent 30-min exercising or resting, and finally were asked to recall the picture associations. We used NIRS to track changes in oxygenated hemoglobin concentration over the prefrontal cortex during exercise and rest. To characterize local tissue oxygenation and perfusion, we focused on low frequency oscillations in NIRS, also called vasomotion. We report a significant increase in associative memory consolidation after exercise, as compared to after rest, along with an overall increase in vasomotion. Additionally, performance improvement after exercise correlated positively with power in the neurogenic component (0.02 to 0.04 Hz) and negatively with power in the endothelial component (0.003 to 0.02 Hz). Overall, these results suggest that changes in vasomotion over the prefrontal cortex during exercise may promote memory consolidation processes.

Functional near-infrared spectroscopy in movement science: a systematic review on cortical activity in postural and walking tasks

Safe locomotion is a crucial aspect of human daily living that requires well-functioning motor control processes. The human neuromotor control of daily activities such as walking relies on the complex interaction of subcortical and cortical areas. Technical developments in neuroimaging systems allow the quantification of cortical activation during the execution of motor tasks. Functional near-infrared spectroscopy (fNIRS) seems to be a promising tool to monitor motor control processes in cortical areas in freely moving subjects. However, so far, there is no established standardized protocol regarding the application and data processing of fNIRS signals that limits the comparability among studies. Hence, this systematic review aimed to summarize the current knowledge about application and data processing in fNIRS studies dealing with walking or postural tasks. Fifty-six articles of an initial yield of 1420 publications were reviewed and information about methodology, data processing, and findings were extracted. Based on our results, we outline the recommendations with respect to the design and data processing of fNIRS studies. Future perspectives of measuring fNIRS signals in movement science are discussed.

The Temporal Muscle of the Head Can Cause Artifacts in Optical Imaging Studies with Functional Near-Infrared Spectroscopy

Background: Extracranial signals are the main source of noise in functional near-infrared spectroscopy (fNIRS) as light is penetrating the cortex but also skin and muscles of the head. Aim: Here we performed three experiments to investigate the contamination of fNIRS measurements by temporal muscle activity. Material and methods: For experiment 1, we provoked temporal muscle activity by instructing 31 healthy subjects to clench their teeth three times. We measured fNIRS signals over left temporal and frontal channels with an interoptode distance of 3 cm, in one short optode distance (SOD) channel (1 cm) and electromyography (EMG) over the edge of the temporal muscle. In experiment 2, we screened resting state fNIRS-fMRI (functional magnetic resonance imaging) data of one healthy subject for temporal muscle artifacts. In experiment 3, we screened a dataset of sound-evoked activity (n = 33) using bi-temporal probe-sets and systematically contrasted subjects presenting vs. not presenting artifacts and blocks/events contaminated or not contaminated with artifacts. Results: In experiment 1, we could demonstrate a hemodynamic-response-like increase in oxygenated (O2Hb) and decrease in deoxygenated (HHb) hemoglobin with a large amplitude and large spatial extent highly exceeding normal cortical activity. Correlations between EMG, SOD, and fNIRS artifact activity showed only limited evidence for associations on a group level with rather clear associations in a sub-group of subjects. The fNIRS-fMRI experiment showed that during the temporal muscle artifact, fNIRS is completely saturated by muscle oxygenation. Experiment 3 showed hints for contamination of sound-evoked oxygenation by the temporal muscle artifact. This was of low relevance in analyzing the whole sample. Discussion: Temporal muscle activity e.g., by clenching the teeth induces a large hemodynamic-like artifact in fNIRS measurements which should be avoided by specific subject instructions. Data should be screened for this artifact might be corrected by exclusion of contaminated blocks/events. The usefulness of established artifact correction methods should be evaluated in future studies. Conclusion: Temporal muscle activity, e.g., by clenching the teeth is one major source of noise in fNIRS measurements.

Effect of Resting-State fNIRS Scanning Duration on Functional Brain Connectivity and Graph Theory Metrics of Brain Network

As an emerging brain imaging technique, functional near infrared spectroscopy (fNIRS) has attracted widespread attention for advancing resting-state functional connectivity (FC) and graph theoretical analyses of brain networks. However, it remains largely unknown how the duration of the fNIRS signal scanning is related to stable and reproducible functional brain network features. To answer this question, we collected resting-state fNIRS signals (10-min duration, two runs) from 18 participants and then truncated the hemodynamic time series into 30-s time bins that ranged from 1 to 10 min. Measures of nodal efficiency, nodal betweenness, network local efficiency, global efficiency, and clustering coefficient were computed for each subject at each fNIRS signal acquisition duration. Analyses of the stability and between-run reproducibility were performed to identify optimal time length for each measure. We found that the FC, nodal efficiency and nodal betweenness stabilized and were reproducible after 1 min of fNIRS signal acquisition, whereas network clustering coefficient, local and global efficiencies stabilized after 1 min and were reproducible after 5 min of fNIRS signal acquisition for only local and global efficiencies. These quantitative results provide direct evidence regarding the choice of the resting-state fNIRS scanning duration for functional brain connectivity and topological metric stability of brain network connectivity.

Effectiveness Evaluation of Real-Time Scalp Signal Separating Algorithm on Near-Infrared Spectroscopy Neurofeedback

Near-infrared spectroscopy (NIRS), one of the candidates to be used in a neurofeedback system or brain-computer interface (BCI), measures the brain activity by monitoring the changes in cerebral hemoglobin concentration. However, hemodynamic changes in the scalp may affect the NIRS signals. In order to remove the superficial signals when NIRS is used in a neurofeedback system or BCI, real-time processing is necessary. Real-time scalp signal separating (RT-SSS) algorithm, which is capable of separating the scalp-blood signals from NIRS signals obtained in real-time, may thus be applied. To demonstrate its effectiveness, two separate neurofeedback experiments were conducted. In the first experiment, the feedback signal was the raw NIRS signal recorded while in the second experiment, deep signal extracted using RT-SSS algorithm was used as the feedback signal. In both experiments, participants were instructed to control the feedback signal to follow a predefined track. Accuracy scores were calculated based on the differences between the trace controlled by feedback signal and the targeted track. Overall, the second experiment yielded better performance in terms of accuracy scores. These findings proved that RT-SSS algorithm is beneficial for neurofeedback.