Functional Near Infrared Spectroscopy (fNIRS): An Emerging Neuroimaging Technology with Important Applications for the Study of Brain Disorders

Future Applications of Real-World Neuroimaging to Clinical Psychology

Clinical neuroimaging has largely been limited to examining the neurophysiological outcomes of treatments for psychiatric conditions rather than the neurocognitive mechanisms by which these outcomes are brought about as a function of clinical strategies, and the cognitive neuroscientific research aiming to investigate these mechanisms in nonclinical and clinical populations has been ecologically challenged by the extent to which tasks represent and generalize to intervention strategies. However, recent technological and methodological advancements to neuroimaging techniques such as functional near-infrared spectroscopy and functional near-infrared spectroscopy-based hyperscanning provide novel opportunities to investigate the mechanisms of change in more naturalistic and interactive settings, representing a unique prospect for improving our understanding of the intra- and interbrain systems supporting the recogitation of dysfunctional cognitive operations.

See, Hear, or Feel - to Speak: A Versatile Multiple-Choice Functional Near-Infrared Spectroscopy-Brain-Computer Interface Feasible With Visual, Auditory, or Tactile Instructions

Severely motor-disabled patients, such as those suffering from the so-called "locked-in" syndrome, cannot communicate naturally. They may benefit from brain-computer interfaces (BCIs) exploiting brain signals for communication and therewith circumventing the muscular system. One BCI technique that has gained attention recently is functional near-infrared spectroscopy (fNIRS). Typically, fNIRS-based BCIs allow for brain-based communication via voluntarily modulation of brain activity through mental task performance guided by visual or auditory instructions. While the development of fNIRS-BCIs has made great progress, the reliability of fNIRS-BCIs across time and environments has rarely been assessed. In the present fNIRS-BCI study, we tested six healthy participants across three consecutive days using a straightforward four-choice fNIRS-BCI communication paradigm that allows answer encoding based on instructions using various sensory modalities. To encode an answer, participants performed a motor imagery task (mental drawing) in one out of four time periods. Answer encoding was guided by either the visual, auditory, or tactile sensory modality. Two participants were tested outside the laboratory in a cafeteria. Answers were decoded from the time course of the most-informative fNIRS channel-by-chromophore combination. Across the three testing days, we obtained mean single- and multi-trial (joint analysis of four consecutive trials) accuracies of 62.5 and 85.19%, respectively. Obtained multi-trial accuracies were 86.11% for visual, 80.56% for auditory, and 88.89% for tactile sensory encoding. The two participants that used the fNIRS-BCI in a cafeteria obtained the best single- (72.22 and 77.78%) and multi-trial accuracies (100 and 94.44%). Communication was reliable over the three recording sessions with multi-trial accuracies of 86.11% on day 1, 86.11% on day 2, and 83.33% on day 3. To gauge the trade-off between number of optodes and decoding accuracy, averaging across two and three promising fNIRS channels was compared to the one-channel approach. Multi-trial accuracy increased from 85.19% (one-channel approach) to 91.67% (two-/three-channel approach). In sum, the presented fNIRS-BCI yielded robust decoding results using three alternative sensory encoding modalities. Further, fNIRS-BCI communication was stable over the course of three consecutive days, even in a natural (social) environment. Therewith, the developed fNIRS-BCI demonstrated high flexibility, reliability and robustness, crucial requirements for future clinical applicability.

Refined prefrontal working memory network as a neuromarker for Alzheimer's disease

Detecting Alzheimer's disease (AD) is an important step in preventing pathological brain damage. Working memory (WM)-related network modulation can be a pathological feature of AD, but is usually modulated by untargeted cognitive processes and individual variance, resulting in the concealment of this key information. Therefore, in this study, we comprehensively investigated a new neuromarker, named "refined network," in a prefrontal cortex (PFC) that revealed the pathological features of AD. A refined network was acquired by removing unnecessary variance from the WM-related network. By using a functional near-infrared spectroscopy (fNIRS) device, we evaluated the reliability of the refined network, which was identified from the three groups classified by AD progression: healthy people (N=31), mild cognitive impairment (N=11), and patients with AD (N=18). As a result, we identified edges with significant correlations between cognitive functions and groups in the dorsolateral PFC. Moreover, the refined network achieved a significantly correlating metric with neuropsychological test scores, and a remarkable three-class classification accuracy (95.0%). These results implicate the refined PFC WM-related network as a powerful neuromarker for AD screening.

Effects of Acute Dance and Aerobic Exercise on Drug Craving and Food Reward in Women with Methamphetamine Dependence

INTRODUCTION

Drug dependence causes an overestimation of drug-related stimuli and an underestimation of non-drug-related stimuli, such as food. The purpose of this study was to investigate the effects of acute moderate-intensity dance and aerobic exercise on drug craving, appetite, prefrontal neural activation to food cues, and food reward in women with methamphetamine MA dependence.

METHODS

Thirty-nine women who met the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition MA dependence criteria participated in the experiment and were randomly assigned to either a dance (n = 20) or exercise (n = 19) group. A moderate-intensity (65%-75% maximum heart rate) 35-min dance or treadmill intervention counterbalanced with a reading control session was conducted. After the intervention or control, subjective drug craving was measured before and after exposure to drug-related cues. Visual analog scales were used to measure subjective feelings of appetite. Participants then completed a visual food cue paradigm while using functional near-infrared spectroscopy to monitor prefrontal blood oxygen changes. Finally, the Leeds Food Preference Questionnaire was used to measure reward responses to different categories of food.

RESULTS

The results showed that the dance and exercise interventions reduced subjective craving for drugs after being exposed to drug cues (P = 0.019). Implicit wanting (P < 0.001) and relative preferences (P = 0.001) for high-calorie savory foods were all increased after interventions relative to control. Compared with the control session, the left dorsolateral prefrontal cortex (P = 0.020) was activated when viewing high-calorie foods after moderate-intensity aerobic exercise.

CONCLUSIONS

The current results support the use of moderate-intensity exercise as a therapeutic intervention to restore the balance between drug and nondrug rewards by decreasing cue-induced MA craving and increasing food reward.

Task Complexity and Image Clarity Facilitate Motor and Visuo-Motor Activities in Mirror Therapy in Post-stroke Patients

Introduction: Mirror therapy is effective in the recovery of upper-limb function among post-stroke patients. An important component of mirror therapy is imagining finger movements. This study aimed to determine the influence of finger movement complexity and mirror image clarity on facilitating motor and visuo-motor activities in post-stroke patients.

Methods: Fifteen post-stroke patients and 18 right-handed healthy participants performed simple or complex finger tapping while viewing mirror images of these movements at varying levels of clarity. The physical setup was identical to typical mirror therapy. Functional near infrared spectroscopy (fNIRS) was used to capture the brain activities elicited in the bilateral primary motor cortices (M1) and the precuneus using a block experimental design.

Results: In both study groups, the “complex finger-tapping task with blurred mirror image” condition resulted in lower intensity (p < 0.01) and authenticity (p < 0.01) of the kinesthetic mirror illusion, and higher levels of perceived effort in generating the illusion (p < 0.01), relative to the “simple finger-tapping with clear mirror image” condition. Greater changes in the oxygenated hemoglobin (HbO) concentration were recorded at the ipsilesional and ipsilateral M1 in the “complex finger-tapping task with blurred mirror image” condition relative to that recorded in the “simple finger-tapping task with clear mirror image” condition (p = 0.03). These HbO concentration changes were not significant in the precuneus. Post-stroke patients showed greater changes than their healthy counterparts at the ipsilesional M1 (F = 5.08; p = 0.03; partial eta squared = 0.14) and the precuneus (F = 7.71; p < 0.01; partial eta squared = 0.20).

Conclusion: The complexity and image clarity of the finger movements increased the neural activities in the ipsilesional motor cortex in the post-stroke patients. These findings suggest plausible roles for top-down attention and working memory in the treatment effects of mirror therapy. Future research can aim to corroborate these findings by using a longitudinal design to examine the use of mirror therapy to promote upper limb motor recovery in post-stroke patients.

Investigating Language and Domain-General Processing in Neurotypicals and Individuals With Aphasia - A Functional Near-Infrared Spectroscopy Pilot Study

Brain reorganization patterns associated with language recovery after stroke have long been debated. Studying mechanisms of spontaneous and treatment-induced language recovery in post-stroke aphasia requires a network-based approach given the potential for recruitment of perilesional left hemisphere language regions, homologous right hemisphere language regions, and/or spared bilateral domain-general regions. Recent hardware, software, and methodological advances in functional near-infrared spectroscopy (fNIRS) make it well-suited to examine this question. fNIRS is cost-effective with minimal contraindications, making it a robust option to monitor treatment-related brain activation changes over time. Establishing clear activation patterns in neurotypical adults during language and domain-general cognitive processes via fNIRS is an important first step. Some fNIRS studies have investigated key language processes in healthy adults, yet findings are challenging to interpret in the context of methodological limitations. This pilot study used fNIRS to capture brain activation during language and domain-general processing in neurotypicals and individuals with aphasia. These findings will serve as a reference when interpreting treatment-related changes in brain activation patterns in post-stroke aphasia in the future. Twenty-four young healthy controls, seventeen older healthy controls, and six individuals with left hemisphere stroke-induced aphasia completed two language tasks (i.e., semantic feature, picture naming) and one domain-general cognitive task (i.e., arithmetic) twice during fNIRS. The probe covered bilateral frontal, parietal, and temporal lobes and included short-separation detectors for scalp signal nuisance regression. Younger and older healthy controls activated core language regions during semantic feature processing (e.g., left inferior frontal gyrus pars opercularis) and lexical retrieval (e.g., left inferior frontal gyrus pars triangularis) and domain-general regions (e.g., bilateral middle frontal gyri) during hard versus easy arithmetic as expected. Consistent with theories of post-stroke language recovery, individuals with aphasia activated areas outside the traditional networks: left superior frontal gyrus and left supramarginal gyrus during semantic feature judgment; left superior frontal gyrus and right precentral gyrus during picture naming; and left inferior frontal gyrus pars opercularis during arithmetic processing. The preliminary findings in the stroke group highlight the utility of using fNIRS to study language and domain-general processing in aphasia.

An Examination of the Relationship Between Perfectionism and Neurological Functioning

Clinical perfectionism is the rigid pursuit of high standards, interfering with functioning. Little research has explored neural patterns in clinical perfectionism. The present study explores neural correlates of clinical perfectionism, before and after receiving ten 50-minute, weekly sessions of acceptance and commitment therapy (ACT), as compared to low-perfectionist controls, in specific cortical structures: the dorsolateral prefrontal cortex (DLPFC), medial prefrontal cortex (MPFC), right inferior parietal lobule (IPL). Participants in the perfectionist condition (n = 43) were from a randomized controlled trial evaluating ACT for clinical perfectionism and low-perfectionist controls were undergraduate students (n = 12). Participants completed three tasks (editing a passage, mirror image tracing, circle tracing) using functional near-infrared spectroscopy (fNIRS) to measure neural activation. Results indicate that ḥin the DLPFC and MPFC of the perfectionists whereas activation in the other tasks were relatively similar. There were no differences were observed in the right DLPFC, MPFC, and right IPL between the posttreatment perfectionist and nonperfectionist control groups. Our findings suggest an unclear relationship between neural activation and perfectionism.

A systematic review of studies that used NIRS to measure neural activation during emotion processing in healthy individuals

Functional neuroimaging provides an avenue for earlier diagnosis and tailored treatment of psychological disorders characterised by emotional impairment. Near-infrared spectroscopy (NIRS) offers ecological advantages compared to other neuroimaging techniques and suitability of measuring regions involved in emotion functions. A systematic review was conducted to evaluate the capacity of NIRS to detect activation during emotion processing and to provide recommendations for future research. Following a comprehensive literature search, we reviewed 85 journal articles, which compared activation during emotional experience, regulation or perception with either a neutral condition or baseline period among healthy participants. The quantitative synthesis of outcomes was limited to thematical analysis, owing to the lack of standardisation between studies. Although most studies found increased prefrontal activity during emotional experience and regulation, the findings were more inconsistent for emotion perception. Some researchers reported increased activity during the task, some reported decreases, some no significant changes, and some reported mixed findings depending on the valence and region. We propose that variations in the cognitive task and stimuli, recruited sample, and measurement and analysis of data are the primary causes of inconsistency. Recommendations to improve consistency in future research by carefully considering the choice of population, cognitive task and analysis approach are provided.

fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases

Significance: Clinical use of fNIRS-derived features has always suffered low sensitivity and specificity due to signal contamination from background systemic physiological fluctuations. We provide an algorithm to extract cognition-related features by eliminating the effect of background signal contamination, hence improving the classification accuracy. Aim: The aim in this study is to investigate the classification accuracy of an fNIRS-derived biomarker based on global efficiency (GE). To this end, fNIRS data were collected during a computerized Stroop task from healthy controls and patients with migraine, obsessive compulsive disorder, and schizophrenia. Approach: Functional connectivity (FC) maps were computed from [HbO] time series data for neutral (N), congruent (C), and incongruent (I) stimuli using the partial correlation approach. Reconstruction of FC matrices with optimal choice of principal components yielded two independent networks: cognitive mode network (CM) and default mode network (DM). Results: GE values computed for each FC matrix after applying principal component analysis (PCA) yielded strong statistical significance leading to a higher specificity and accuracy. A new index, neurocognitive ratio (NCR), was computed by multiplying the cognitive quotients (CQ) and ratio of GE of CM to GE of DM. When mean values of NCR ( N C R ¯ ) over all stimuli were computed, they showed high sensitivity (100%), specificity (95.5%), and accuracy (96.3%) for all subjects groups. Conclusions: N C R ¯ can reliable be used as a biomarker to improve the classification of healthy to neuropsychiatric patients.

Recognition of Flexion and Extension Imagery Involving the Right and Left Arms Based on Deep Belief Network and Functional Near-Infrared Spectroscopy

Brain-computer interaction based on motor imagery (MI) is an important brain-computer interface (BCI). Most methods for MI classification are based on electroencephalogram (EEG), and few studies have investigated signal processing based on MI-Functional Near-Infrared Spectroscopy (fNIRS). In addition, there is a need to improve the classification accuracy for MI fNIRS methods. In this study, a deep belief network (DBN) based on a restricted Boltzmann machine (RBM) was used to classify fNIRS signals of flexion and extension imagery involving the left and right arms. fNIRS signals from 16 channels covering the motor cortex area were recorded for each of 10 subjects executing or imagining flexion and extension involving the left and right arms. Oxygenated hemoglobin (HbO) concentration was used as a feature to train two RBMs that were subsequently stacked with an additional softmax regression output layer to construct DBN. We also explored the DBN model classification accuracy for the test dataset from one subject using training dataset from other subjects. The average DBN classification accuracy for flexion and extension movement and imagery involving the left and right arms was 84.35 ± 3.86% and 78.19 ± 3.73%, respectively. For a given DBN model, better classification results are obtained for test datasets for a given subject when the model is trained using dataset from the same subject than when the model is trained using datasets from other subjects. The results show that the DBN algorithm can effectively identify flexion and extension imagery involving the right and left arms using fNIRS. This study is expected to serve as a reference for constructing online MI-BCI systems based on DBN and fNIRS.

Understanding Mental Health and Cognitive Restructuring With Ecological Neuroscience

Neuroimaging and neuropsychological methods have contributed much toward an understanding of the information processing systems of the human brain in the last few decades, but to what extent do cognitive neuroscientific findings represent and generalize to the inter- and intra-brain dynamics engaged in adapting to naturalistic situations? If it is not marked, and experimental designs lack ecological validity, then this stands to potentially impact the practical applications of a paradigm. In no other domain is this more important to acknowledge than in human clinical neuroimaging research, wherein reduced ecological validity could mean a loss in clinical utility. One way to improve the generalizability and representativeness of findings is to adopt a more "real-world" approach to the development and selection of experimental designs and neuroimaging techniques to investigate the clinically-relevant phenomena of interest. For example, some relatively recent developments to neuroimaging techniques such as functional near-infrared spectroscopy (fNIRS) make it possible to create experimental designs using naturalistic tasks that would otherwise not be possible within the confines of a conventional laboratory. Mental health, cognitive interventions, and the present challenges to investigating the brain during treatment are discussed, as well as how the ecological use of fNIRS might be helpful in bridging the explanatory gaps to understanding the cultivation of mental health.

Correcting physiological noise in whole-head functional near-infrared spectroscopy

BACKGROUND

Functional near-infrared spectroscopy (fNIRS) has been increasingly employed to monitor cerebral hemodynamics in normal and diseased conditions. However, fNIRS suffers from its susceptibility to superficial activity and systemic physiological noise. The objective of the study was to establish a noise reduction method for fNIRS in a whole-head montage.

NEW METHOD

We have developed an automated denoising method for whole-head fNIRS. A high-density montage consisting of 109 long-separation channels and 8 short-separation channels was used for recording. Auxiliary sensors were also used to measure motion, respiration and pulse simultaneously. The method incorporates principal component analysis and general linear model to identify and remove a globally uniform superficial component. Our denoising method was evaluated in experimental data acquired from a group of healthy human subjects during a visually cued motor task and further compared with a minimal preprocessing method and three established denoising methods in the literature. Quantitative metrics including contrast-to-noise ratio, within-subject standard deviation and adjusted coefficient of determination were evaluated.

RESULTS

After denoising, whole-head topography of fNIRS revealed focal activations concurrently in the primary motor and visual areas.

COMPARISON WITH EXISTING METHODS

Analysis showed that our method improves upon the four established preprocessing methods in the literature.

CONCLUSIONS

An automatic, effective and robust preprocessing pipeline was established for removing physiological noise in whole-head fNIRS recordings. Our method can enable fNIRS as a reliable tool in monitoring large-scale, network-level brain activities for clinical uses.

Pain Induced Changes in Brain Oxyhemoglobin: A Systematic Review and Meta-Analysis of Functional NIRS Studies

BACKGROUND

Neuroimaging studies show that nociceptive stimuli elicit responses in an extensive cortical network. Functional near-infrared spectroscopy (fNIRS) allows for functional assessment of changes in oxyhemoglobin (HbO), an indirect index for cortical activity. Unlike functional magnetic resonance imaging (fMRI), fNIRS is portable, relatively inexpensive, and allows subjects greater function. No systematic review or meta-analysis has drawn together the data from existing literature of fNIRS studies on the effects of experimental pain on oxyhemoglobin changes in the superficial areas of the brain.

OBJECTIVES

To investigate the effects of experimental pain on brain fNIRS measures in the prefrontal-cortex and the sensory-motor-area; to determine whether there is a difference in oxyhemodynamics between the prefrontal-cortex and sensory-motor-area during pain processing; to determine if there are differences in HbO between patients with centralized persistent pain and healthy controls.

METHODS

Studies that used fNIRS to record changes in oxyhemodynamics in prefrontal-cortex or sensory-motor-cortex in noxious and innoxious conditions were included. In total, 13 studies were included in the meta-analysis.

RESULTS

Pain has a significantly greater effect on pre-frontal-cortex and sensory-motor areas than nonpainful stimulation on oxyhemodynamics. The effect of pain on sensory-motor areas was greater than the effect of pain on the prefrontal-cortex. There was an effect of centralized pain in the CPP group on oxyhemodynamics from a noxious stimulus compared to control's response to pain.

CONCLUSIONS

Pain affects the prefrontal and sensory-motor cortices of the brain and can be measured using fNIRS. Implications of this study may lead to a simple and readily accessible objective measure of pain.

Shedding light on neuroscience: Two decades of functional near-infrared spectroscopy applications and advances from a bibliometric perspective

Functional near-infrared spectroscopy (fNIRS) is a noninvasive optical brain-imaging technique that detects changes in hemoglobin concentration in the cerebral cortex. fNIRS devices are safe, silent, portable, robust against motion artifacts, and have good temporal resolution. fNIRS is reliable and trustworthy, as well as an alternative and a complement to other brain-imaging modalities, such as electroencephalography or functional magnetic resonance imaging. Given these advantages, fNIRS has become a well-established tool for neuroscience research, used not only for healthy cortical activity but also as a biomarker during clinical assessment in individuals with schizophrenia, major depressive disorder, bipolar disease, epilepsy, Alzheimer's disease, vascular dementia, and cancer screening. Owing to its wide applicability, studies on fNIRS have increased exponentially over the last two decades. In this study, scientific publications indexed in the Web of Science databases were collected and a bibliometric-type methodology was developed. For this purpose, a comprehensive science mapping analysis, including top-ranked authors, journals, institutions, countries, and co-occurring keywords network, was conducted. From a total of 2310 eligible documents, 6028 authors and 531 journals published fNIRS-related papers, Fallgatter published the highest number of articles and was the most cited author. University of Tübingen in Germany has produced the most trending papers since 2000. USA was the most prolific country with the most active institutions, followed by China, Japan, Germany, and South Korea. The results also revealed global trends in emerging areas of research, such as neurodevelopment, aging, and cognitive and emotional assessment.

Data Processing in Functional Near-Infrared Spectroscopy (fNIRS) Motor Control Research

FNIRS pre-processing and processing methodologies are very important-how a researcher chooses to process their data can change the outcome of an experiment. The purpose of this review is to provide a guide on fNIRS pre-processing and processing techniques pertinent to the field of human motor control research. One hundred and twenty-three articles were selected from the motor control field and were examined on the basis of their fNIRS pre-processing and processing methodologies. Information was gathered about the most frequently used techniques in the field, which included frequency cutoff filters, wavelet filters, smoothing filters, and the general linear model (GLM). We discuss the methodologies of and considerations for these frequently used techniques, as well as those for some alternative techniques. Additionally, general considerations for processing are discussed.

Evidence of Neurovascular Un-Coupling in Mild Alzheimer's Disease through Multimodal EEG-fNIRS and Multivariate Analysis of Resting-State Data

Alzheimer’s disease (AD) is associated with modifications in cerebral blood perfusion and autoregulation. Hence, neurovascular coupling (NC) alteration could become a biomarker of the disease. NC might be assessed in clinical settings through multimodal electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Multimodal EEG-fNIRS was recorded at rest in an ambulatory setting to assess NC and to evaluate the sensitivity and specificity of the methodology to AD. Global NC was evaluated with a general linear model (GLM) framework by regressing whole-head EEG power envelopes in three frequency bands (theta, alpha and beta) with average fNIRS oxy- and deoxy-hemoglobin concentration changes in the frontal and prefrontal cortices. NC was lower in AD compared to healthy controls (HC) with significant differences in the linkage of theta and alpha bands with oxy- and deoxy-hemoglobin, respectively (p = 0.028 and p = 0.020). Importantly, standalone EEG and fNIRS metrics did not highlight differences between AD and HC. Furthermore, a multivariate data-driven analysis of NC between the three frequency bands and the two hemoglobin species delivered a cross-validated classification performance of AD and HC with an Area Under the Curve, AUC = 0.905 (p = 2.17 × 10⁻⁵). The findings demonstrate that EEG-fNIRS may indeed represent a powerful ecological tool for clinical evaluation of NC and early identification of AD.

Systemic Review on Transcranial Electrical Stimulation Parameters and EEG/fNIRS Features for Brain Diseases

Background

Brain disorders are gradually becoming the leading cause of death worldwide. However, the lack of knowledge of brain disease’s underlying mechanisms and ineffective neuropharmacological therapy have led to further exploration of optimal treatments and brain monitoring techniques.

Objective

This study aims to review the current state of brain disorders, which utilize transcranial electrical stimulation (tES) and daily usable noninvasive neuroimaging techniques. Furthermore, the second goal of this study is to highlight available gaps and provide a comprehensive guideline for further investigation.

Method

A systematic search was conducted of the PubMed and Web of Science databases from January 2000 to October 2020 using relevant keywords. Electroencephalography (EEG) and functional near-infrared spectroscopy were selected as noninvasive neuroimaging modalities. Nine brain disorders were investigated in this study, including Alzheimer’s disease, depression, autism spectrum disorder, attention-deficit hyperactivity disorder, epilepsy, Parkinson’s disease, stroke, schizophrenia, and traumatic brain injury.

Results

Sixty-seven studies (1,385 participants) were included for quantitative analysis. Most of the articles (82.6%) employed transcranial direct current stimulation as an intervention method with modulation parameters of 1 mA intensity (47.2%) for 16–20 min (69.0%) duration of stimulation in a single session (36.8%). The frontal cortex (46.4%) and the cerebral cortex (47.8%) were used as a neuroimaging modality, with the power spectrum (45.7%) commonly extracted as a quantitative EEG feature.

Conclusion

An appropriate stimulation protocol applying tES as a therapy could be an effective treatment for cognitive and neurological brain disorders. However, the optimal tES criteria have not been defined; they vary across persons and disease types. Therefore, future work needs to investigate a closed-loop tES with monitoring by neuroimaging techniques to achieve personalized therapy for brain disorders.

Diffuse optical tomography in the human brain: A briefly review from the neurophysiology to its applications

The present work describes the use of noninvasive diffuse optical tomography (DOT) technology to measure hemodynamic changes, providing relevant information which helps to understand the basis of neurophysiology in the human brain. Advantages such as portability, direct measurements of hemoglobin state, temporal resolution, non‐restricted movements as occurs in magnetic resonance imaging (MRI) devices mean that DOT technology can be used in research and clinical fields. In this review we covered the neurophysiology, physical principles underlying optical imaging during tissue‐light interactions, and technology commonly used during the construction of a DOT device including the source‐detector requirements to improve the image quality. DOT provides 3D cerebral activation images due to complex mathematical models which describe the light propagation inside the tissue head. Moreover, we describe briefly the use of Bayesian methods for raw DOT data filtering as an alternative to linear filters widely used in signal processing, avoiding common problems such as the filter selection or a false interpretation of the results which is sometimes due to the interference of background physiological noise with neural activity.

Quantitative Assessment of Resting-State for Mild Cognitive Impairment Detection: A Functional Near-Infrared Spectroscopy and Deep Learning Approach

BACKGROUND

Mild cognitive impairment (MCI) is considered a prodromal stage of Alzheimer's disease. Early diagnosis of MCI can allow for treatment to improve cognitive function and reduce modifiable risk factors.

OBJECTIVE

This study aims to investigate the feasibility of individual MCI detection from healthy control (HC) using a minimum duration of resting-state functional near-infrared spectroscopy (fNIRS) signals.

METHODS

In this study, nine different measurement durations (i.e., 30, 60, 90, 120, 150, 180, 210, 240, and 270 s) were evaluated for MCI detection via the graph theory analysis and traditional machine learning approach, such as linear discriminant analysis, support vector machine, and K-nearest neighbor algorithms. Moreover, feature representation- and classification-based transfer learning (TL) methods were applied to identify MCI from HC through the input of connectivity maps with 30 and 90 s duration.

RESULTS

There was no significant difference among the nine various time windows in the machine learning and graph theory analysis. The feature representation-based TL showed improved accuracy in both 30 and 90 s cases (i.e., 30 s: 81.27% and 90 s: 76.73%). Notably, the classification-based TL method achieved the highest accuracy of 95.81% using the pre-trained convolutional neural network (CNN) model with the 30 s interval functional connectivity map input.

CONCLUSION

The results indicate that a 30 s measurement of the resting-state with fNIRS could be used to detect MCI. Moreover, the combination of neuroimaging (e.g., functional connectivity maps) and deep learning methods (e.g., CNN and TL) can be considered as novel biomarkers for clinical computer-assisted MCI diagnosis.

Finger orthoses for management of joint hypermobility disorders: Relative effects on hand function and cognitive load

BACKGROUND

Joint hypermobility refers to joints that move beyond their normal limits. Individuals with hypermobility of the fingers experience difficulties in activities of daily living. Finger orthoses are available for managing hypermobility of the fingers, but their effectiveness has received little attention in scholarly literature.

OBJECTIVES

To determine if use of custom fit finger orthoses leads to improvements in time needed to perform standardised hand function tests, and attentional demand required to perform these tests, in individuals with joint hypermobility syndrome, Hypermobile Ehlers-Danlos syndrome or Classical Ehlers-Danlos syndrome.

STUDY DESIGN

Repeated-measures study.

METHODS

Fourteen participants performed three different hand function tests (target box and block test, writing and picking up coins), with and without their finger orthoses. Time to complete each test was recorded as a measure of functional performance. Brain activity was recorded in the pre-frontal cortices as a measure of attentional demand.

RESULTS

Functional performance significantly improved for all but one test (picking up coins with non-dominant hand) when participants wore finger orthoses (p < 0.05). Activity in the pre-frontal cortex was lower when using the orthosis to perform the coin test (dominant hand; p < 0.05). No differences were observed in other tests (p > 0.05).

CONCLUSIONS

Results suggested that finger orthoses improved hand function and provided limited evidence to suggest that they may also affect attentional demand. While the limited sample does not provide conclusive evidence supporting the use of finger orthosis in this clinical population, results warrant further investigation in large scale longitudinal studies or randomised controlled trials.

Identification of autism spectrum disorder based on short-term spontaneous hemodynamic fluctuations using deep learning in a multi-layer neural network

OBJECTIVE

To classify children with autism spectrum disorder (ASD) and typical development (TD) using short-term spontaneous hemodynamic fluctuations and to explore the abnormality of inferior frontal gyrus and temporal lobe in ASD.

METHODS

25 ASD children and 22 TD children were measured with functional near-infrared spectroscopy located on the inferior frontal gyrus and temporal lobe. To extract features used to classify ASD and TD, a multi-layer neural network was applied, combining with a three-layer convolutional neural network, a layer of long and short-term memory network (LSTM) and a layer of LSTM with Attention mechanism. In order to shorten the time of data collection and get more information from limited samples, a sliding window with 3.5 s width was utilized after comparisons, and numerous short (3.5 s) fNIRS time series were then obtained and used as the input of the multi-layer neural network.

RESULTS

A good classification between ASD and TD was obtained with considerably high accuracy by using a multi-layer neural network in different brain regions, especially in the left temporal lobe, where sensitivity of 90.6% and specificity of 97.5% achieved.

CONCLUSIONS

The "CLAttention" multi-layer neural network has the potential to excavate more meaningful features to distinguish between ASD and TD. Moreover, the temporal lobe may be worth further study.

SIGNIFICANCE

The findings in this study may have implications for rapid diagnosis of children with ASD and provide a new perspective for future medical diagnosis.

Accurate hemodynamic response estimation by removal of stimulus-evoked superficial response in fNIRS signals

ObjectiveWe address the problem of hemodynamic response estimation when task-evoked extra-cerebral components are present in functional near-infrared spectroscopy (fNIRS) signals. These components might bias the hemodynamic response estimation, therefore careful and accurate denoising of data is needed.ApproachWe propose a dictionary-based algorithm to process every single event-related segment of the acquired signal for both long separation and short separation channels. Stimulus-evoked components and physiological noise are modeled by means of two distinct waveform dictionaries. For each segment, after removal of the physiological noise component in each channel, a template is employed to estimate stimulus-evoked responses in both channels. Then, the estimate from the short-separation channel is employed to correct for the evoked superficial response and refine the hemodynamic response estimate from the long-separation channel.Main resultsAnalysis of simulated, semi-simulated and real data shows that, by averaging single-segment estimates over multiple trials in an experiment, reliable results and improved accuracy compared to other methods can be obtained. The average estimation error of the proposed method for the semi-simulated data set is 34% for HbO and 78% for HbR, considering 40 trials. The proposed method outperforms the results of the methods proposed in the literature. While still far from the possibility of single-trial hemodynamic response estimation, a significant reduction in the number of averaged trials can also be obtained.SignificanceThis work proves that dedicated dictionaries can be successfully employed to model all different components of fNIRS signals. It demonstrates the effectiveness of a specifically designed algorithm structure in dealing with a complex denoising problem, enhancing the possibilities of fNIRS-based hemodynamic response analysis.

Clinical Utility of Functional Near-Infrared Spectroscopy for Assessment and Prediction of Suicidality: A Systematic Review

Introduction: Suicide is a pressing psychiatric concern worldwide with no established biomarker. While there is some evidence of the clinical utility of functional near-infrared spectroscopy (fNIRS) in assessing and predicting suicidality, no systematic review of such evidence has been conducted to date. Therefore, this review aimed to systematically review and gather evidence from existing studies that used fNIRS signals to assess suicidality and its associated changes in the brain, and those that examined how such signals correlated with suicide symptomatology. Methods: PubMed, EMBASE, and Cochrane Library databases were used in a systematic literature search for English-language articles published between 2000 and December 19, 2020 that focused on the utility of fNIRS for (i) assessing suicidality and its associated changes in the brain, and (ii) correlating with suicide symptomatology. Studies were included if they utilised fNIRS to evaluate variations in fNIRS-measured cerebral hemodynamic responses in patients with different mental disorders (e.g., major depressive disorder, schizophrenia), as well as in healthy controls, of any age group. Quality of evidence was assessed using the Newcastle-Ottawa quality assessment scale. Results: A total of 7 cross-sectional studies were included in this review, all of which had acceptable quality. Across all studies, fNIRS demonstrated reduced cerebral hemodynamic changes in suicidal individuals when compared to non-suicidal individuals. One study also demonstrated the potential of fNIRS signals in correlating with the severity of suicidality. Conclusions: This review provides a comprehensive, updated review of evidence supporting the clinical utility of fNIRS in the assessment and prediction of suicidality. Further studies involving larger sample sizes, standardised methodology, and longitudinal follow-ups are needed.

Design and validation of a mechanically flexible and ultra-lightweight high-density diffuse optical tomography system for functional neuroimaging of newborns

Significance: Neonates are a highly vulnerable population. The risk of brain injury is greater during the first days and weeks after birth than at any other time of life. Functional neuroimaging that can be performed longitudinally and at the cot-side has the potential to improve our understanding of the evolution of multiple forms of neurological injury over the perinatal period. However, existing technologies make it very difficult to perform repeated and/or long-duration functional neuroimaging experiments at the cot-side. Aim: We aimed to create a modular, high-density diffuse optical tomography (HD-DOT) technology specifically for neonatal applications that is ultra-lightweight, low profile and provides high mechanical flexibility. We then sought to validate this technology using an anatomically accurate dynamic phantom. Approach: An advanced 10-layer rigid-flexible printed circuit board technology was adopted as the basis for the DOT modules, which allows for a compact module design that also provides the flexibility needed to conform to the curved infant scalp. Two module layouts were implemented: dual-hexagon and triple-hexagon. Using in-built board-to-board connectors, the system can be configured to provide a vast range of possible layouts. Using epoxy resin, thermochromic dyes, and MRI-derived 3D-printed moulds, we constructed an electrically switchable, anatomically accurate dynamic phantom. This phantom was used to quantify the imaging performance of our flexible, modular HD-DOT system. Results: Using one particular module configuration designed to cover the infant sensorimotor system, the device provided 36 source and 48 detector positions, and over 700 viable DOT channels per wavelength, ranging from 10 to ∼ 45    mm over an area of approximately 60    cm 2 . The total weight of this system is only 70 g. The signal changes from the dynamic phantom, while slow, closely simulated real hemodynamic response functions. Using difference images obtained from the phantom, the measured 3D localization error provided by the system at the depth of the cortex was in the of range 3 to 6 mm, and the lateral image resolution at the depth of the neonatal cortex is estimated to be as good as 10 to 12 mm. Conclusions: The HD-DOT system described is ultra-low weight, low profile, can conform to the infant scalp, and provides excellent imaging performance. It is expected that this device will make functional neuroimaging of the neonatal brain at the cot-side significantly more practical and effective.

Decreased connection density and modularity of functional brain networks during n-back working memory paradigm

INTRODUCTION

Investigating how the brain adapts to increased mental workload through large-scale functional reorganization appears as an important research question. Functional connectivity (FC) aims at capturing how disparate regions of the brain dynamically interact, while graph theory provides tools for the topological characterization of the reconstructed functional networks. Although numerous studies investigated how FC is altered in response to increased working memory (WM) demand, current results are still contradictory as few studies confirmed the robustness of these findings in a low-density setting.

METHODS

In this study, we utilized the n-back WM paradigm, in which subjects were presented stimuli (single digits) sequentially, and their task was to decide for each given stimulus if it matched the one presented n-times earlier. Electroencephalography recordings were performed under a control (0-back) and two task conditions of varying difficulty (2- and 3-back). We captured the characteristic connectivity patterns for each difficulty level by performing FC analysis and described the reconstructed functional networks with various graph theoretical measures.

RESULTS

We found a substantial decrease in FC when transitioning from the 0- to the 2- or 3-back conditions, however, no differences relating to task difficulty were identified. The observed changes in brain network topology could be attributed to the dissociation of two (frontal and occipitotemporal) functional modules that were only present during the control condition. Furthermore, behavioral and performance measures showed both positive and negative correlations to connectivity indices, although only in the higher frequency bands.

CONCLUSION

The marked decrease in FC may be due to temporarily abandoned connections that are redundant or irrelevant in solving the specific task. Our results indicate that FC analysis is a robust tool for investigating the response of the brain to increased cognitive workload.

Probing Neurovisceral Integration via Functional Near-Infrared Spectroscopy and Heart Rate Variability

The neurovisceral integration model (NVM) proposes that an organism’s ability to flexibly adapt to its environment is related to biological flexibility within the central autonomic network (CAN). One important aspect of this flexibility is behavioral inhibition (Thayer and Friedman, 2002). During a behavioral inhibition task, the CAN, which comprises a series of feedback loops, must be able to integrate information and react to these inputs flexibly to facilitate optimal performance. The functioning of the CAN is shown to be associated with respiratory sinus arrhythmia (RSA), as the vagus nerve is part of this feedback system. Although the NVM has been examined through neural imaging and RSA, only a few studies have examined these measures simultaneously during the neuroimaging procedure. Furthermore, these studies were done at rest or used tasks that were not targeted at processes associated with the NVM, such as behavioral inhibition and cognitive flexibility. For this reason, the present study assessed RSA and neural activation in the pre-frontal cortex simultaneously while participants completed a behavior inhibition task. RSA and functional near-infrared spectroscopy were collected in 38 adults, and resting levels of pre-frontal activation were negatively related to RSA, but pre-frontal activation during the behavior inhibition task was not. The negative relationship between RSA and oxygenated hemoglobin is consistent with previous functional magnetic resonance imaging work examining the NVM at baseline and should be further studied. Additional research investigating how this relationship may change based on task demands or environmental contexts would help clarify the applicability of the model.

NIRS measures in pain and analgesia: Fundamentals, features, and function

Current pain assessment techniques based only on clinical evaluation and self-reports are not objective and may lead to inadequate treatment. Having a functional biomarker will add to the clinical fidelity, diagnosis, and perhaps improve treatment efficacy in patients. While many approaches have been deployed in pain biomarker discovery, functional near-infrared spectroscopy (fNIRS) is a technology that allows for non-invasive measurement of cortical hemodynamics. The utility of fNIRS is especially attractive given its ability to detect specific changes in the somatosensory and high-order cortices as well as its ability to measure (1) brain function similar to functional magnetic resonance imaging, (2) graded responses to noxious and innocuous stimuli, (3) analgesia, and (4) nociception under anesthesia. In this review, we evaluate the utility of fNIRS in nociception/pain with particular focus on its sensitivity and specificity, methodological advantages and limitations, and the current and potential applications in various pain conditions. Everything considered, fNIRS technology could enhance our ability to evaluate evoked and persistent pain across different age groups and clinical populations.

Use of functional near-infrared spectroscopy to evaluate cognitive change when using healthcare simulation tools

Background

The use of brain imaging techniques in healthcare simulation is relatively rare. However, the use of mobile, wireless technique, such as functional near-infrared spectroscopy (fNIRS), is becoming a useful tool for assessing the unique demands of simulation learning. For this study, this imaging technique was used to evaluate cognitive load during simulation learning events.

Methods

This study took place in relation to six simulation activities, paired for similarity, and evaluated comparative cognitive change between the three task pairs. The three paired tasks were: receiving a (1) face-to-face and (2) video patient handover; observing a simulated scene in (1) two dimensions and (2) 360° field of vision; and on a simulated patient (1) taking a pulse and (2) taking a pulse and respiratory rate simultaneously. The total number of participants was n=12.

Results

In this study, fNIRS was sensitive to variations in task difficulty in common simulation tools and scenarios, showing an increase in oxygenated haemoglobin concentration and a decrease in deoxygenated haemoglobin concentration, as tasks increased in cognitive load.

Conclusion

Overall, findings confirmed the usefulness of neurohaemoglobin concentration markers as an evaluation tool of cognitive change in healthcare simulation. Study findings suggested that cognitive load increases in more complex cognitive tasks in simulation learning events. Task performance that increased in complexity therefore affected cognitive markers, with increase in mental effort required.

The Effects of Fast and Slow Yoga Breathing on Cerebral and Central Hemodynamics

Background:

Yoga breathing has shown to impose significant cardiovascular and psychological health benefits.

Objective:

The mechanism (s) responsible for these health benefits remain unclear. The aim of the present study was to assess the differences in cerebral and central hemodynamic responses following fast breathing (FB) and slow breathing (SB) protocols compared to breathing awareness (BA) as a control.

Methods:

Twenty healthy participants (10 males and 10 females) volunteered to take part in the study. Participants were between ages 18–55 years (group mean: 24 ± 5 years), with a height of 168.7 ± 9.8 cm and a weight of 70.16 ± 10.9 kg. A familiarization trial including FB and SB protocols were performed by each participant at least 24 h before the testing day. The breathing protocols were designed to achieve 6 breath/min for SB and ~ 120 breaths/min for FB.

Results:

FB resulted in an increase in both right prefrontal cortex (RPFC) and left prefrontal cortex (LPFC) hemoglobin difference (Hbdiff) (brain oxygenation) compared to BA (P < 0.05). FB resulted in an increased Hbdiff in LPFC compared to RPFC SB (P < 0.05). FB resulted in an increased Hbdiff in LPFC compared to SB (P < 0.05).

Conclusion:

FB may be an effective yoga breathing technique for eliciting cerebral brain oxygenation indicated by increased Hbdiff. These results may be applicable to both healthy and clinical populations.

Role of kinaesthetic motor imagery in mirror-induced visual illusion as intervention in post-stroke rehabilitation

Mirror-induced visual illusion obtained through mirror therapy is widely used to facilitate motor recovery after stroke. Activation of primary motor cortex (M1) ipsilateral to the moving limb has been reported during mirror-induced visual illusion. However, the mechanism through which the mirror illusion elicits motor execution processes without movements observed in the mirrored limb remains unclear. This study aims to review evidence based on brain imaging studies for testing the hypothesis that neural processes associated with kinaesthetic motor imagery are attributed to ipsilateral M1 activation. Four electronic databases were searched. Studies on functional brain imaging, investigating the instant effects of mirror-induced visual illusion among stroke survivors and healthy participants were included. Thirty-five studies engaging 78 stroke survivors and 396 healthy participants were reviewed. Results of functional brain scans (n = 20) indicated that half of the studies (n = 10, 50%) reported significant changes in the activation of ipsilateral M1, which mediates motor preparation and execution. Other common neural substrates included primary somatosensory cortex (45%, kinaesthesia), precuneus (40%, image generation and self-processing operations) and cerebellum (20%, motor control). Similar patterns of ipsilateral M1 activations were observed in the two groups. These neural substrates mediated the generation, maintenance, and manipulation of motor-related images, which were the key processes in kinaesthetic motor imagery. Relationships in terms of shared neural substrates and mental processes between mirror-induced visual illusion and kinaesthetic motor imagery generate new evidence on the role of the latter in mirror therapy. Future studies should investigate the imagery processes in illusion training for post-stroke patients.

A Systematic Review of Cerebral Functional Near-Infrared Spectroscopy in Chronic Neurological Diseases-Actual Applications and Future Perspectives

BACKGROUND

The management of people affected by age-related neurological disorders requires the adoption of targeted and cost-effective interventions to cope with chronicity. Therapy adaptation and rehabilitation represent major targets requiring long-term follow-up of neurodegeneration or, conversely, the promotion of neuroplasticity mechanisms. However, affordable and reliable neurophysiological correlates of cerebral activity to be used throughout treatment stages are often lacking. The aim of this systematic review is to highlight actual applications of functional Near-Infrared Spectroscopy (fNIRS) as a versatile optical neuroimaging technology for investigating cortical hemodynamic activity in the most common chronic neurological conditions.

METHODS

We reviewed studies investigating fNIRS applications in Parkinson's Disease (PD), Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) as those focusing on motor and cognitive impairment in ageing and Multiple Sclerosis (MS) as the most common chronic neurological disease in young adults. The literature search was conducted on NCBI PubMed and Web of Science databases by PRISMA guidelines.

RESULTS

We identified a total of 63 peer-reviewed articles. The AD spectrum is the most investigated pathology with 40 articles ranging from the traditional monitoring of tissue oxygenation to the analysis of functional resting-state conditions or cognitive functions by means of memory and verbal fluency tasks. Conversely, applications in PD (12 articles) and MS (11 articles) are mainly focused on the characterization of motor functions and their association with dual-task conditions. The most investigated cortical area is the prefrontal cortex, since reported to play an important role in age-related compensatory mechanism and neurofunctional changes associated to these chronic neurological conditions. Interestingly, only 9 articles applied a longitudinal approach.

CONCLUSION

The results indicate that fNIRS is mainly employed for the cross-sectional characterization of the clinical phenotypes of these pathologies, whereas data on its utility for longitudinal monitoring as surrogate biomarkers of disease progression and rehabilitation effects are promising but still lacking.

Functional Near-Infrared Spectroscopy in the Study of Speech and Language Impairment Across the Life Span: A Systematic Review

Purpose Functional brain imaging is playing an increasingly important role in the diagnosis and treatment of communication disorders, yet many populations and settings are incompatible with functional magnetic resonance imaging and other commonly used techniques. We conducted a systematic review of neuroimaging studies using functional near-infrared spectroscopy (fNIRS) with individuals with speech or language impairment across the life span. We aimed to answer the following question: To what extent has fNIRS been used to investigate the neural correlates of speech-language impairment? Method This systematic review was preregistered with PROSPERO, the international prospective register of systematic reviews (CRD42019136464). We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol for preferred reporting items for systematic reviews. The database searches were conducted between February and March of 2019 with the following search terms: (a) fNIRS or functional near-infrared spectroscopy or NIRS or near-infrared spectroscopy, (b) speech or language, and (c) disorder or impairment or delay. Results We found 34 fNIRS studies that involved individuals with speech or language impairment across nine categories: (a) autism spectrum disorders; (b) developmental speech and language disorders; (c) cochlear implantation and deafness; (d) dementia, dementia of the Alzheimer's type, and mild cognitive impairment; (e) locked-in syndrome; (f) neurologic speech disorders/dysarthria; (g) stroke/aphasia; (h) stuttering; and (i) traumatic brain injury. Conclusions Though it is not without inherent challenges, fNIRS may have advantages over other neuroimaging techniques in the areas of speech and language impairment. fNIRS has clinical applications that may lead to improved early and differential diagnosis, increase our understanding of response to treatment, improve neuroprosthetic functioning, and advance neurofeedback.

A 119dB Dynamic Range Charge Counting Light-to-Digital Converter For Wearable PPG/NIRS Monitoring Applications

This paper presents a low power, high dynamic range (DR), reconfigurable light-to-digital converter (LDC) for photoplethysmogram (PPG), and near-infrared spectroscopy (NIRS) sensor readouts. The proposed LDC utilizes a current integration and a charge counting operation to directly convert the photocurrent to a digital code, reducing the noise contributors in the system. This LDC consists of a latched comparator, a low-noise current reference, a counter, and a multi-function integrator, which is used in both signal amplification and charge counting based data quantization. Furthermore, a current DAC is used to further increase the DR by canceling the baseline current. The LDC together with LED drivers and auxiliary digital circuitry are implemented in a standard 0.18 μm CMOS process and characterized experimentally. The LDC and LED drivers consume a total power of 196 μW while achieving a maximum 119 dB DR. The charge counting clock, and the pulse repetition frequency of the LED driver can be reconfigured, providing a wide range of power-resolution trade-off. At a minimum power consumption of 87 μW, the LDC still achieves 95 dB DR. The LDC is also validated with on-body PPG and NIRS measurement by using a photodiode (PD) and a silicon photomultiplier (SIPM), respectively.

Random Subspace Ensemble Learning for Functional Near-Infrared Spectroscopy Brain-Computer Interfaces

The feasibility of the random subspace ensemble learning method was explored to improve the performance of functional near-infrared spectroscopy-based brain-computer interfaces (fNIRS-BCIs). Feature vectors have been constructed using the temporal characteristics of concentration changes in fNIRS chromophores such as mean, slope, and variance to implement fNIRS-BCIs systems. The mean and slope, which are the most popular features in fNIRS-BCIs, were adopted. Linear support vector machine and linear discriminant analysis were employed, respectively, as a single strong learner and multiple weak learners. All features in every channel and available time window were employed to train the strong learner, and the feature subsets were selected at random to train multiple weak learners. It was determined that random subspace ensemble learning is beneficial to enhance the performance of fNIRS-BCIs.

Hemodynamic responses during standing and sitting activities: a study toward fNIRS-BCI

In this paper, we utilized functional near-infrared spectroscopy (fNIRS) technology to examine the hemodynamic responses in the motor cortex for two conditions, namely standing and sitting tasks. Nine subjects performed five trials of standing and sitting (SAS) tasks with both real movements and imagery thinking of SAS. A group level of statistical parametric mapping (SPM) analysis during these tasks showed bilateral activation of oxy-hemoglobin for both real movements and imagery experiments. Interestingly, the SPM analysis clearly revealed that the sitting tasks induced a higher oxy-hemoglobin level activation compared to the standing task. Remarkably, this finding is persistent across the 22 measured channels at the individual and group levels for both experiments. Furthermore, six features were extracted from pre-processed HbO signals and the performance of four different classifiers was examined in order to test the viability of using SAS tasks in future fNIRS-brain-computer interface (fNIRS-BCI) systems. In particular, two features-combination tests revealed that the signal slope with signal variance represents one of the three best two-combined features for its consistency in providing high accuracy results for both real and imagery experiments. This study shows the potential of implementing such tasks into the fNIRS-BCI system. In the future, the results of this work could pave the way towards the application of fNIRS-BCI in lower limb rehabilitation.

Prefrontal Cortical Activation While Doing Mindfulness Task: a Pilot Functional Near-Infrared Spectroscopy Study

Mindfulness interventions are increasingly used in clinical settings. Neurophysiological mechanisms underlying mindfulness offer objective evidence that can help us evaluate the efficacy of mindfulness. Recent advances in technology have facilitated the use of functional Near-Infrared Spectroscopy (fNIRS) as a light weight, portable, and relatively lower cost neuroimaging device as compared to functional Magnetic Resonance Imaging (fMRI). In contrast to numerous fMRI studies, there are scanty investigations using fNIRS to study mindfulness. Hence, this study was done to investigate the feasibility of using a continuous-wave multichannel fNIRS system to study cerebral cortex activations on a mindfulness task versus a baseline task. NIRS data from 14 healthy Asian subjects were collected. A statistical parametric mapping toolbox specific for statistical analysis of NIRS signal called NIRS_SPM was used to study the activations. The results from group analysis performed on the contrast of the mindfulness versus baseline tasks showed foci of activations on the left and central parts of the prefrontal cortex. The findings are consistent with prevailing fMRI studies and show promise of using fNIRS system for studying real-time neurophysiological cortical activations during mindfulness practice.

Near-Infrared Spectroscopy in Bio-Applications

Near-infrared (NIR) spectroscopy occupies a specific spot across the field of bioscience and related disciplines. Its characteristics and application potential differs from infrared (IR) or Raman spectroscopy. This vibrational spectroscopy technique elucidates molecular information from the examined sample by measuring absorption bands resulting from overtones and combination excitations. Recent decades brought significant progress in the instrumentation (e.g., miniaturized spectrometers) and spectral analysis methods (e.g., spectral image processing and analysis, quantum chemical calculation of NIR spectra), which made notable impact on its applicability. This review aims to present NIR spectroscopy as a matured technique, yet with great potential for further advances in several directions throughout broadly understood bio-applications. Its practical value is critically assessed and compared with competing techniques. Attention is given to link the bio-application potential of NIR spectroscopy with its fundamental characteristics and principal features of NIR spectra.

Using Functional Near-Infrared Spectroscopy to Assess Brain Activation Evoked by Guilt and Shame

Functional near-infrared spectroscopy (fNIRS) is a promising brain imaging modality for studying the neural substrates of moral emotions. However, the feasibility of using fNIRS to measure moral emotions has not been established. In the present study, we used fNIRS to detect the brain activation evoked by two typical moral emotions-guilt and shame. We presented the participants with guilt and shame context to evoke emotional responses and measured the brain activity by using fNIRS. The univariate general linear model analysis showed significant activations for both emotions in the orbitofrontal cortex, dorsolateral prefrontal cortex, and middle temporal gyrus, and specific activation for guilt in the right temporoparietal junction. The multivariate classification analysis showed an overall recognition accuracy of 52.50%, which was significantly higher than the chance level in classifying the guilt, shame, and neutral emotions. These results suggested the feasibility of using fNIRS to assess the brain activation evoked by guilt and shame and demonstrated the potentials of fNIRS in studying the neural correlates of moral emotions.

New Directions in Exercise Prescription: Is There a Role for Brain-Derived Parameters Obtained by Functional Near-Infrared Spectroscopy?

In the literature, it is well established that regular physical exercise is a powerful strategy to promote brain health and to improve cognitive performance. However, exact knowledge about which exercise prescription would be optimal in the setting of exercise–cognition science is lacking. While there is a strong theoretical rationale for using indicators of internal load (e.g., heart rate) in exercise prescription, the most suitable parameters have yet to be determined. In this perspective article, we discuss the role of brain-derived parameters (e.g., brain activity) as valuable indicators of internal load which can be beneficial for individualizing the exercise prescription in exercise–cognition research. Therefore, we focus on the application of functional near-infrared spectroscopy (fNIRS), since this neuroimaging modality provides specific advantages, making it well suited for monitoring cortical hemodynamics as a proxy of brain activity during physical exercise.

Transforming treatments for schizophrenia: Virtual reality, brain stimulation and social cognition

Schizophrenia is characterised by delusions, hallucinations, anhedonia and apathy; while impairments in social cognition are often less recognised. Poor social cognition can lead to difficulties in obtaining and maintaining employment, academic progression, interpersonal relationships, and community functioning. Current interventions are highly intensive, require significant resources and have only modest effects on functional outcomes. Virtual reality (VR) and non-invasive brain stimulation (NIBS) may have a role in addressing these limitations. VR allows treatments that are potentially more accessible, less delivery intensive, and have higher ecological validity. While NIBS is able to directly modulate activity in social brain areas in order to promote neuroplasticity, strengthen neural connections and enhance brain function related to social cognitive behaviours. Therefore, the combination of VR and NIBS may allow for more efficient and transferrable interventions than those currently available. This review will explore the potential role of these technologies in the treatment of social cognitive impairment.

Detection of Mild Cognitive Impairment Using Convolutional Neural Network: Temporal-Feature Maps of Functional Near-Infrared Spectroscopy

Mild cognitive impairment (MCI) is the clinical precursor of Alzheimer's disease (AD), which is considered the most common neurodegenerative disease in the elderly. Some MCI patients tend to remain stable over time and do not evolve to AD. It is essential to diagnose MCI in its early stages and provide timely treatment to the patient. In this study, we propose a neuroimaging approach to identify MCI using a deep learning method and functional near-infrared spectroscopy (fNIRS). For this purpose, fifteen MCI subjects and nine healthy controls (HCs) were asked to perform three mental tasks: N-back, Stroop, and verbal fluency (VF) tasks. Besides examining the oxygenated hemoglobin changes (ΔHbO) in the region of interest, ΔHbO maps at 13 specific time points (i.e., 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, and 65 s) during the tasks and seven temporal feature maps (i.e., two types of mean, three types of slope, kurtosis, and skewness) in the prefrontal cortex were investigated. A four-layer convolutional neural network (CNN) was applied to identify the subjects into either MCI or HC, individually, after training the CNN model with ΔHbO maps and temporal feature maps above. Finally, we used the 5-fold cross-validation approach to evaluate the performance of the CNN. The results of temporal feature maps exhibited high classification accuracies: The average accuracies for the N-back task, Stroop task, and VFT, respectively, were 89.46, 87.80, and 90.37%. Notably, the highest accuracy of 98.61% was achieved from the ΔHbO slope map during 20-60 s interval of N-back tasks. Our results indicate that the fNIRS imaging approach based on temporal feature maps is a promising diagnostic method for early detection of MCI and can be used as a tool for clinical doctors to identify MCI from their patients.

Tracking differential activation of primary and supplementary motor cortex across timing tasks: An fNIRS validation study

Functional near-infrared spectroscopy (fNIRS) provides an alternative to functional magnetic resonance imaging (fMRI) for assessing changes in cortical hemodynamics. To establish the utility of fNIRS for measuring differential recruitment of the motor network during the production of timing-based actions, we measured cortical hemodynamic responses in 10 healthy adults while they performed two versions of a finger-tapping task. The task, used in an earlier fMRI study (Jantzen et al., 2004), was designed to track the neural basis of different timing behaviors. Participants paced their tapping to a metronomic tone, then continued tapping at the established pace without the tone. Initial tapping was either synchronous or syncopated relative to the tone. This produced a 2 × 2 design: synchronous or syncopated tapping and pacing the tapping with or continuing without a tone. Accuracy of the timing of tapping was tracked while cortical hemodynamics were monitored using fNIRS. Hemodynamic responses were computed by canonical statistical analysis across trials in each of the four conditions. Task-induced brain activation resulted in significant increases in oxygenated hemoglobin concentration (oxy-Hb) in a broad region in and around the motor cortex. Overall, syncopated tapping was harder behaviorally and produced more cortical activation than synchronous tapping. Thus, we observed significant changes in oxy-Hb in direct relation to the complexity of the task.

Reported Side Effects, Weight and Blood Pressure, After Repeated Sessions of Transcranial Photobiomodulation

Background: Transcranial photobiomodulation (t-PBM) consists in the delivery of near-infrared light (NIR) to the scalp, directed to cortical areas of the brain. NIR t-PBM recently emerged as a potential therapy for depression, although safety of repeated treatments has not been adequately explored. Objective: This study assessed incidence of side effects, including weight and blood pressure changes, during repeated sessions of NIR t-PBM using a light-emitting diode source. Methods: We performed a secondary analysis of a double-blind clinical trial on t-PBM for major depressive disorder. Eighteen individuals received NIR t-PBM (n = 9) or sham (n = 9) twice weekly for 8 weeks. Side effects were assessed using the Systematic Assessment for Treatment-Emergent Effects-Specific Inquiry. In 14 individuals (n_NIR = 6 vs. n_sham = 8), body weight and systemic blood pressure were recorded at baseline and end-point. Results: More subjects in the NIR t-PBM group experienced side effects compared to sham, but only a trend for statistical significance was observed (χ² = 3.60; df = 1; p = 0.058). The rate of side effects described by participants as "severe" in intensity was low and similar between the treatment groups (χ² = 0.4; df = 1; p = 0.53), with no serious adverse events. Most side effects resolved during the study and treatment interruption were not required. Changes in weight and systolic blood pressure across groups were neither significant nor approached significance. In the NIR t-PBM group, diastolic blood pressure increased and reached statistical-however not clinical-significance (5.67 ± 7.26 vs. -6.13 ± 6.88; z = -2.40, p = 0.016). Conclusions: This small-sample, exploratory study indicates repeated sessions of NIR t-PBM might be associated with treatment-emergent side effects. The systemic metabolic and hemodynamic profile of repeated t-PBM appeared benign. Future studies with larger samples and longer follow-up are needed to more accurately determine the side-effect profile and safety of NIR t-PBM.

Frequency-Domain Techniques for Cerebral and Functional Near-Infrared Spectroscopy

This article reviews the basic principles of frequency-domain near-infrared spectroscopy (FD-NIRS), which relies on intensity-modulated light sources and phase-sensitive optical detection, and its non-invasive applications to the brain. The simpler instrumentation and more straightforward data analysis of continuous-wave NIRS (CW-NIRS) accounts for the fact that almost all the current commercial instruments for cerebral NIRS have embraced the CW technique. However, FD-NIRS provides data with richer information content, which complements or exceeds the capabilities of CW-NIRS. One example is the ability of FD-NIRS to measure the absolute optical properties (absorption and reduced scattering coefficients) of tissue, and thus the absolute concentrations of oxyhemoglobin and deoxyhemoglobin in brain tissue. This article reviews the measured values of such optical properties and hemoglobin concentrations reported in the literature for animal models and for the human brain in newborns, infants, children, and adults. We also review the application of FD-NIRS to functional brain studies that focused on slower hemodynamic responses to brain activity (time scale of seconds) and faster optical signals that have been linked to neuronal activation (time scale of 100 ms). Another example of the power of FD-NIRS data is related to the different regions of sensitivity featured by intensity and phase data. We report recent developments that take advantage of this feature to maximize the sensitivity of non-invasive optical signals to brain tissue relative to more superficial extracerebral tissue (scalp, skull, etc.). We contend that this latter capability is a highly appealing quality of FD-NIRS, which complements absolute optical measurements and may result in significant advances in the field of non-invasive optical sensing of the brain.

Does task complexity influence motor facilitation and visuo-motor memory during mirror therapy in post-stroke patients?

Stroke is one of the most common causes of mortality and reduced disability-adjusted life years worldwide. Hemiparesis due to reduced skeletal-muscle power is an effect of brain lesions. Mirror therapy can significantly improve motor performance among post-stroke patients. To determine if altering the complexity of the mirror task in the mirror therapy paradigm would enhance top-down motor facilitation and visuo-motor memory demand, we conducted a pilot study on four post-stroke patients. Our preliminary results showed that performing complex finger tapping task resulted in enhanced activities in the primary motor cortex and precuneus, ipsilateral to the moving hand in the mirror therapy paradigm. We hypothesise the following: (a) complex finger tapping would result in stronger top-down motor facilitation and higher demand on visuo-motor memory than simple finger tapping in the mirror therapy paradigm, and (b) observing a blurred mirror image would result in increased top-down motor facilitation and higher demand on visuo-motor memory than a clear mirror image. To confirm these hypotheses, we propose a cross-sectional observational study on a large sample of post-stroke patients. This paper reports the findings of the pilot study, the rationale for testing the hypotheses, the experimental set-up, the task design and the assessment protocol for functional near-infrared spectroscopy.

Multi-time-point analysis: A time course analysis with functional near-infrared spectroscopy

In the data analysis of functional near-infrared spectroscopy (fNIRS), linear model frameworks, in particular mass univariate analysis, are often used when researchers consider examining the difference between conditions at each sampled time point. However, some statistical issues, such as assumptions of linearity, autocorrelation and multiple comparison problems, influence statistical inferences when mass univariate analysis is used on fNIRS time course data. In order to address these issues, the present study proposes a novel perspective, multi-time-point analysis (MTPA), to discriminate signal differences between conditions by combining temporal information from multiple time points in fNIRS. In addition, MTPA adopts the random forest algorithm from the statistical learning domain, followed by a series of cross-validation procedures, providing reasonable power for detecting significant time points and ensuring generalizability. Using a real fNIRS data set, the proposed MTPA outperformed mass univariate analysis in detecting more time points, showing significant differences between experimental conditions. Finally, MTPA was also able to make comparisons between different areas, leading to a novel viewpoint of fNIRS time course analysis and providing additional theoretical implications for future fNIRS studies. The data set and all source code are available for researchers to replicate the analyses and to adapt the program for their own needs in future fNIRS studies.

Impaired Frontal Brain Activity in Patients With Heart Failure Assessed by Near-Infrared Spectroscopy

Background The prevalence of depression and/or anxiety disorders is reported to be higher in patients with heart failure (HF) than in the general population, and patients with HF also have coexisting cognitive problems. Recently, the development of near-infrared spectroscopy (NIRS) has enabled noninvasive measurements of regional cerebral blood volume and brain activity, in terms of cerebral oxyhemoglobin in the cerebral cortex, with a high time resolution. The aim of the current study was to determine the associations between frontal brain activity and depressive symptoms, anxiety status, and cognitive function in patients with HF. Methods and Results We measured and compared frontal brain activity determined by NIRS during a verbal fluency task in patients with HF (n=35) and control subjects (n=28). The Center for Epidemiologic Studies Depression Scale for assessment of depressive symptoms, State-Trait Anxiety Inventory for assessment of anxiety status, Mini-Mental State Examination for assessment of cognitive function, and NIRS were simultaneously conducted. NIRS showed that frontal brain activity was significantly lower in the HF group than in the control subjects (28.5 versus 88.0 mM·mm; P<0.001). Next, we examined the associations between frontal brain activity and the findings of Center for Epidemiologic Studies Depression Scale, State-Trait Anxiety Inventory, Mini-Mental State Examination, and verbal fluency task. There were significant correlations between frontal brain activity and State-Trait Anxiety Inventory (R=-0.228, P=0.046), Mini-Mental State Examination (R=0.414, P=0.017), and verbal fluency task (R=0.338, P=0.007), but not with Center for Epidemiologic Studies Depression Scale (R=-0.160, P=0.233). Conclusions Frontal brain activity assessed by NIRS is reduced and is associated with high anxiety status and low cognitive function in patients with HF.