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

Diagnostic machine learning applications on clinical populations using functional near infrared spectroscopy: a review

Functional near-infrared spectroscopy (fNIRS) and its interaction with machine learning (ML) is a popular research topic for the diagnostic classification of clinical disorders due to the lack of robust and objective biomarkers. This review provides an overview of research on psychiatric diseases by using fNIRS and ML. Article search was carried out and 45 studies were evaluated by considering their sample sizes, used features, ML methodology, and reported accuracy. To our best knowledge, this is the first review that reports diagnostic ML applications using fNIRS. We found that there has been an increasing trend to perform ML applications on fNIRS-based biomarker research since 2010. The most studied populations are schizophrenia (n = 12), attention deficit and hyperactivity disorder (n = 7), and autism spectrum disorder (n = 6) are the most studied populations. There is a significant negative correlation between sample size (>21) and accuracy values. Support vector machine (SVM) and deep learning (DL) approaches were the most popular classifier approaches (SVM = 20) (DL = 10). Eight of these studies recruited a number of participants more than 100 for classification. Concentration changes in oxy-hemoglobin (ΔHbO) based features were used more than concentration changes in deoxy-hemoglobin (ΔHb) based ones and the most popular ΔHbO-based features were mean ΔHbO (n = 11) and ΔHbO-based functional connections (n = 11). Using ML on fNIRS data might be a promising approach to reveal specific biomarkers for diagnostic classification.

Lights on music cognition: A systematic and critical review of fNIRS applications and future perspectives

Research investigating the neural processes related to music perception and production constitutes a well-established field within the cognitive neurosciences. While most neuroimaging tools have limitations in studying the complexity of musical experiences, functional Near-Infrared Spectroscopy (fNIRS) represents a promising, relatively new tool for studying music processes in both laboratory and ecological settings, which is also suitable for both typical and pathological populations across development. Here we systematically review fNIRS studies on music cognition, highlighting prospects and potentialities. We also include an overview of fNIRS basic theory, together with a brief comparison to characteristics of other neuroimaging tools. Fifty-nine studies meeting inclusion criteria (i.e., using fNIRS with music as the primary stimulus) are presented across five thematic sections. Critical discussion of methodology leads us to propose guidelines of good practices aiming for robust signal analyses and reproducibility. A continuously updated world map is proposed, including basic information from studies meeting the inclusion criteria. It provides an organized, accessible, and updatable reference database, which could serve as a catalyst for future collaborations within the community. In conclusion, fNIRS shows potential for investigating cognitive processes in music, particularly in ecological contexts and with special populations, aligning with current research priorities in music cognition.

Discriminative analysis of schizophrenia and major depressive disorder using fNIRS

BACKGROUND

Research into the shared and distinct brain dysfunctions in patients with schizophrenia (SCZ) and major depressive disorder (MDD) has been increasing. However, few studies have explored the application of functional near-infrared spectroscopy (fNIRS) in investigating brain dysfunction and enhancing diagnostic methodologies in these two conditions.

METHODS

A general linear model was used for analysis of brain activation following task-state fNIRS from 131 patients with SCZ, 132 patients with MDD and 130 healthy controls (HCs). Subsequently, seventy-seven time-frequency analysis methods were used to construct new features of fNIRS, followed by the implementation of five machine learning algorithms to develop a differential diagnosis model for the three groups. This model was evaluated by comparing it to both a diagnostic model relying on traditional fNIRS features and assessments made by two psychiatrists.

RESULTS

Brain activation analysis revealed significantly lower activation in Broca's area, the dorsolateral prefrontal cortex, and the middle temporal gyrus for both the SCZ and MDD groups compared to HCs. Additionally, the SCZ group exhibited notably lower activation in the superior temporal gyrus and the subcentral gyrus compared to the MDD group. When distinguishing among the three groups using independent validation datasets, the models utilizing new fNIRS features achieved an accuracy of 85.90 % (AUC = 0.95). In contrast, models based on traditional fNIRS features reached an accuracy of 52.56 % (AUC = 0.66). The accuracies of the two psychiatrists were 42.00 % (AUC = 0.60) and 38.00 % (AUC = 0.50), respectively.

CONCLUSION

This investigation brings to light the shared and distinct neurobiological abnormalities present in SCZ and MDD, offering potential enhancements for extant diagnostic systems.

Novel Feature Generation for Classification of Motor Activity from Functional Near-Infrared Spectroscopy Signals Using Machine Learning

Recent research in the field of cognitive motor action decoding focuses on data acquired from Functional Near-Infrared Spectroscopy (fNIRS) and its analysis. This research aims to classify two different motor activities, namely, mental drawing (MD) and spatial navigation (SN), using fNIRS data from non-motor baseline data and other motor activities. Accurate activity detection in non-stationary signals like fNIRS is challenging and requires complex feature descriptors. As a novel framework, a new feature generation by fusion of wavelet feature, Hilbert, symlet, and Hjorth parameters is proposed for improving the accuracy of the classification. This new fused feature has statistical descriptor elements, time-localization in the frequency domain, edge feature, texture features, and phase information to detect and locate the activity accurately. Three types of independent component analysis, including FastICA, Picard, and Infomax were implemented for preprocessing which removes noises and motion artifacts. Two independent binary classifiers are designed to handle the complexity of classification in which one is responsible for mental drawing (MD) detection and the other one is spatial navigation (SN). Four different types of algorithms including nearest neighbors (KNN), Linear Discriminant Analysis (LDA), light gradient-boosting machine (LGBM), and Extreme Gradient Boosting (XGBOOST) were implemented. It has been identified that the LGBM classifier gives high accuracies-98% for mental drawing and 97% for spatial navigation. Comparison with existing research proves that the proposed method gives the highest classification accuracies. Statistical validation of the proposed new feature generation by the Kruskal-Wallis H-test and Mann-Whitney U non-parametric test proves the reliability of the proposed mechanism.

Remote Wearable Neuroimaging Devices for Health Monitoring and Neurophenotyping: A Scoping Review

Digital health tracking is a source of valuable insights for public health research and consumer health technology. The brain is the most complex organ, containing information about psychophysical and physiological biomarkers that correlate with health. Specifically, recent developments in electroencephalogram (EEG), functional near-infra-red spectroscopy (fNIRS), and photoplethysmography (PPG) technologies have allowed the development of devices that can remotely monitor changes in brain activity. The inclusion criteria for the papers in this review encompassed studies on self-applied, remote, non-invasive neuroimaging techniques (EEG, fNIRS, or PPG) within healthcare applications. A total of 23 papers were reviewed, comprising 17 on using EEGs for remote monitoring and 6 on neurofeedback interventions, while no papers were found related to fNIRS and PPG. This review reveals that previous studies have leveraged mobile EEG devices for remote monitoring across the mental health, neurological, and sleep domains, as well as for delivering neurofeedback interventions. With headsets and ear-EEG devices being the most common, studies found mobile devices feasible for implementation in study protocols while providing reliable signal quality. Moderate to substantial agreement overall between remote and clinical-grade EEGs was found using statistical tests. The results highlight the promise of portable brain-imaging devices with regard to continuously evaluating patients in natural settings, though further validation and usability enhancements are needed as this technology develops.

Reliability Evaluation for Continuous-Wave Functional Near-Infrared Spectroscopy Systems: Comprehensive Testing from Bench Characterization to Human Test

In recent years, biomedical optics technology has developed rapidly. The current widespread use of biomedical optics was made possible by the invention of optical instruments. The advantages of being non-invasive, portable, effective, low cost, and less susceptible to system noise have led to the rapid development of functional near-infrared spectroscopy (fNIRS) technology for hemodynamics detection, especially in the field of functional brain imaging. At the same time, laboratories and companies have developed various fNIRS-based systems. The safety, stability, and efficacy of fNIRS systems are key performance indicators. However, there is still a lack of comprehensive and systematic evaluation methods for fNIRS instruments. This study uses the fNIRS system developed in our laboratory as the test object. The test method established in this study includes system validation and performance testing to comprehensively assess fNIRS systems' reliability. These methods feature low cost and high practicality. Based on this study, existing or newly developed systems can be comprehensively and easily evaluated in the laboratory or workspace.

Artificial neural network models: implementation of functional near-infrared spectroscopy-based spontaneous lie detection in an interactive scenario

Deception is an inevitable occurrence in daily life. Various methods have been used to understand the mechanisms underlying brain deception. Moreover, numerous efforts have been undertaken to detect deception and truth-telling. Functional near-infrared spectroscopy (fNIRS) has great potential for neurological applications compared with other state-of-the-art methods. Therefore, an fNIRS-based spontaneous lie detection model was used in the present study. We interviewed 10 healthy subjects to identify deception using the fNIRS system. A card game frequently referred to as a bluff or cheat was introduced. This game was selected because its rules are ideal for testing our hypotheses. The optical probe of the fNIRS was placed on the subject's forehead, and we acquired optical density signals, which were then converted into oxy-hemoglobin and deoxy-hemoglobin signals using the Modified Beer-Lambert law. The oxy-hemoglobin signal was preprocessed to eliminate noise. In this study, we proposed three artificial neural networks inspired by deep learning models, including AlexNet, ResNet, and GoogleNet, to classify deception and truth-telling. The proposed models achieved accuracies of 88.5%, 88.0%, and 90.0%, respectively. These proposed models were compared with other classification models, including k-nearest neighbor, linear support vector machines (SVM), quadratic SVM, cubic SVM, simple decision trees, and complex decision trees. These comparisons showed that the proposed models performed better than the other state-of-the-art methods.

Changes in cerebral cortex activity during a simple motor task after MRgFUS treatment in patients affected by essential tremor and Parkinson's disease: a pilot study using functional NIRS

Objective.Magnetic resonance imaging-guided focused ultrasound surgery (MRgFUS) is a non-invasive thermal ablation method that involves high-intensity focused ultrasound surgery (FUS) and Magnetic Resonance Imaging for anatomical imaging and real-time thermal mapping. This technique is widely employed for the treatment of patients affected by essential tremor (ET) and Parkinson's disease (PD). In the current study, functional near-infrared spectroscopy (fNIRS) was used to highlight hemodynamics changes in cerebral cortex activity, during a simple hand motor task, i.e. unimanual left and right finger-tapping, in ET and PD patients.Approach.All patients were evaluated before, one week and one month after MRgFUS treatment.Main results.fNIRS revealed cerebral hemodynamic changes one week and one month after MRgFUS treatment, especially in the ET group, that showed a significant clinical improvement in tremor clinical scores.Significance.To our knowledge, our study is the first that showed the use of fNIRS system to measure the cortical activity changes following unilateral ventral intermediate nucleus thalamotomy after MRgFUS treatment. Our findings showed that therapeutic MRgFUS promoted the remodeling of neuronal networks and changes in cortical activity in association with symptomatic improvements.

Cortical activity and spatiotemporal parameters during gait termination and walking: A preliminary study

Gait termination requires an interaction between the biomechanical and neuromuscular systems to arrest forward momentum. Currently, the biomechanical characteristics of gait termination have been demonstrated; however, the neural mechanism of gait termination remains unclear. This study aimed to investigate cortical activity during gait termination using functional near-infrared spectroscopy (fNIRS). Thirteen healthy younger adults (mean age:24.0 ± 1.7) participated in this study. All participants performed three experimental sessions: planned gait termination (PGT), unplanned gait termination (UGT), and walking. Each experimental session comprised a block paradigm design (three cycles; 20 s resting, 45 s task). Cortical activity in the dorsolateral prefrontal cortex (DLPFC), supplementary motor area (SMA), and primary motor cortex (M1) and spatiotemporal parameters were measured. We compared the cortical activities and spatiotemporal parameters among PGT, UGT, and walking sessions. In addition, we performed Pearson correlations between hemodynamic responses and spatiotemporal parameters. The PGT was activated in the right DLPFC, whereas the UGT and walking were activated in the left SMA (p < 0.05). Comparing cortical activation between sessions, both the PGT and UGT showed significantly higher activation in the right DLPFC than during walking (p < 0.05). There were no significant differences in cortical activity between PGT and UGT (p > 0.05). In addition, the gait termination time revealed moderate positive correlation with hemodynamic responses in the right DLPFC (p < 0.05). Our results indicate that the right DLPFC is associated with gait termination, regardless of gait termination type. Our findings provide the potential implication that the hemodynamic response in the right DLPFC would be a biomarker to evaluate the ability of gait termination.

Risk, Trust, and Emotion in Online Pharmacy Medication Purchases: Multimethod Approach Incorporating Customer Self-Reports, Facial Expressions, and Neural Activation

BACKGROUND

Online pharmacies are used less than other e-commerce sites in Germany. Shopping behavior does not correspond to consumption behavior, as online purchases are predominantly made for over-the-counter (OTC) medications.

OBJECTIVE

The objective of this study was to understand the purchasing experiences of online pharmacy customers in terms of critical factors for online pharmacy adoption.

METHODS

This study examined the perceived risk, perceived trust, and emotions related to purchasing medications online and, consequently, the purchase intention toward online pharmacies. In a within-subjects design (N=37 participants), 2 German online pharmacies with different perceptions of risk and trust were investigated for their main business, namely OTC and prescription drugs. The results of a preliminary study led to 1 online pharmacy with high and 1 with significantly low self-reported risk by the prestudy sample. Emotions were measured with a multimethod approach during and after the purchase situation as follows: (1) neural evaluation processes using functional near-infrared spectroscopy, (2) the automated direct motor response during the use of the online pharmacy via facial expression analysis (FaceReader), and (3) subjective evaluations through self-reports. Following the shopping experiences at both pharmacies for both product types, risk, trust, and purchase intention toward the pharmacies were assessed using self-assessments.

RESULTS

The 2 online pharmacies were rated differently in terms of risk, trust, emotions, and purchase intention. The high-risk pharmacy was also perceived as having lower trust and vice versa. Significantly stronger negative emotional expressions on customers' faces and different neural activations in the ventromedial prefrontal cortex and dorsomedial prefrontal cortex were measured when purchasing prescription drugs from the high-risk pharmacy than from the low-risk pharmacy, combined with OTC medications. In line with this, customers' self-ratings indicated higher negative emotions for the high-risk pharmacy and lower negative emotions for the low-risk pharmacy. Moreover, the ratings showed lower purchase intention for the high-risk pharmacy.

CONCLUSIONS

Using multimethod measurements, we showed that the preceding neural activation and subsequent verbal evaluation of online pharmacies are reflected in the customers' immediate emotional facial expressions. High-risk online pharmacies and prescription drugs lead to stronger negative emotional facial expressions and trigger neural evaluation processes that imply perceived loss. Low-risk online pharmacies and OTC medications lead to weaker negative emotional facial expressions and trigger neural evaluation processes that signify certainty and perceived reward. The results may provide an explanation for why OTC medications are purchased online more frequently than prescription medications.

Effect of hyperbaric oxygen therapy combined with repetitive transcranial magnetic stimulation on vascular cognitive impairment: a randomised controlled trial protocol

INTRODUCTION

Vascular cognitive impairment (VCI) has an increasing prevalence worldwide, accounting for at least 20%-40% of all diagnoses of dementia. The decline in cognitive function seriously impairs patients' activities of daily living and social participation and reduces their quality of life. However, there is still a lack of advanced, definitive rehabilitation programmes for VCI. Hyperbaric oxygen therapy (HBOT) and repetitive transcranial magnetic stimulation (rTMS) are recognised treatments for improving cognitive impairment. The former can restore oxygen supply in the brain by increasing oxygen partial pressure in brain tissue, while the latter can enhance neuronal excitability and promote synaptic plasticity. However, no studies have explored the effect of HBO combined with rTMS on VCI.

METHODS AND ANALYSIS

This study is designed as a single-centre, assessor-blind, randomised controlled clinical trial with four parallel arms. A total of 72 participants will be recruited and randomly assigned to the control group, HBOT group, rTMS group and HBOT combined with rTMS group at a ratio of 1:1:1:1. All enrolled participants will receive conventional treatment. The entire intervention period is 4 weeks, with a 3-week follow-up. Outcomes will be measured at baseline (T0), after a 4-week intervention (T1) and after an additional 3-week follow-up period (T2). The primary endpoint is the Montreal Cognitive Assessment score. The secondary endpoints are Mini-Mental State Examination score, Modified Barthel Index score, latency and amplitude of P300, cerebral cortical oxygenated haemoglobin (HbO2) and deoxygenated haemoglobin (HbR) concentrations as measured by task-state functional near-infrared spectroscopy.

ETHICS AND DISSEMINATION

Ethics approval was obtained from the West China Hospital Clinical Trials and Biomedical Ethics Committee of Sichuan University (ethics reference: 2022 (1972)). The findings will be published in peer-reviewed journals and disseminated through scientific conferences and seminars.

TRIAL REGISTRATION NUMBER

ChiCTR2300068242.

Consumer-Grade Electroencephalogram and Functional Near-Infrared Spectroscopy Neurofeedback Technologies for Mental Health and Wellbeing

Neurofeedback, utilizing an electroencephalogram (EEG) and/or a functional near-infrared spectroscopy (fNIRS) device, is a real-time measurement of brain activity directed toward controlling and optimizing brain function. This treatment has often been attributed to improvements in disorders such as ADHD, anxiety, depression, and epilepsy, among others. While there is evidence suggesting the efficacy of neurofeedback devices, the research is still inconclusive. The applicability of the measurements and parameters of consumer neurofeedback wearable devices has improved, but the literature on measurement techniques lacks rigorously controlled trials. This paper presents a survey and literary review of consumer neurofeedback devices and the direction toward clinical applications and diagnoses. Relevant devices are highlighted and compared for treatment parameters, structural composition, available software, and clinical appeal. Finally, a conclusion on future applications of these systems is discussed through the comparison of their advantages and drawbacks.

Neural Plasticity Changes Induced by Motor Robotic Rehabilitation in Stroke Patients: The Contribution of Functional Neuroimaging

Robotic rehabilitation is one of the most advanced treatments helping people with stroke to faster recovery from motor deficits. The clinical impact of this type of treatment has been widely defined and established using clinical scales. The neurofunctional indicators of motor recovery following conventional rehabilitation treatments have already been identified by previous meta-analytic investigations. However, a clear definition of the neural correlates associated with robotic neurorehabilitation treatment has never been performed. This systematic review assesses the neurofunctional correlates (fMRI, fNIRS) of cutting-edge robotic therapies in enhancing motor recovery of stroke populations in accordance with PRISMA standards. A total of 7, of the initial yield of 150 articles, have been included in this review. Lessons from these studies suggest that neural plasticity within the ipsilateral primary motor cortex, the contralateral sensorimotor cortex, and the premotor cortices are more sensitive to compensation strategies reflecting upper and lower limbs' motor recovery despite the high heterogeneity in robotic devices, clinical status, and neuroimaging procedures. Unfortunately, the paucity of RCT studies prevents us from understanding the neurobiological differences induced by robotic devices with respect to traditional rehabilitation approaches. Despite this technology dating to the early 1990s, there is a need to translate more functional neuroimaging markers in clinical settings since they provide a unique opportunity to examine, in-depth, the brain plasticity changes induced by robotic rehabilitation.

Hemodynamic signal changes and functional connectivity in acute stroke patients with dysphagia during volitional swallowing: a pilot study

BACKGROUND

Dysphagia is one of the major post-stroke complications, understanding post-stroke changes in cortical excitability and promoting early remodeling of swallowing-related cortical areas to enable accurate treatment is essential for recovery of patients.

OBJECTIVES

We aimed to investigate hemodynamic signal changes and functional connectivity in acute stroke patients with dysphagia compared to age-matched healthy participants in response to volitional swallowing using functional near-infrared spectroscopy (fNIRS) in this pilot study.

METHODS

Patients with first-ever post-stroke dysphagia having an onset of 1-4 weeks and age-matched right-handed healthy subjects were recruited in our study. fNIRS with 47 channels was utilized to detect the oxyhemoglobin (HbO2 ) and reduced hemoglobin (HbR) concentration changes when volitional swallowing. Cohort analysis was performed by a one-sample t-test. Two-sample t-test was utilized to compare the difference in cortical activation between patients with post-stroke dysphagia and healthy subjects. Furthermore, the relative changes in the concentration of the HbO2 throughout the experimental procedure were extracted for the functional connectivity analysis. The Pearson correlation coefficients of the HbO2 concentration of each channel were analyzed on a time series, and then a Fisher Z transformation was then performed, and the transformed values were defined as the functional connection strengths between the channels.

RESULTS

In this present study, a total of nine patients with acute post-stroke dysphagia were enrolled in the patient group and nine age-matched healthy participants in the healthy control group. Our study observed that the extensive regions of the cerebral cortex were activated in the healthy control group, while the activation area of patient group's cortical regions was quite limited. The mean functional connectivity strength of participants was 0.485 ± 0.105 in the healthy control group, and 0.252 ± 0.146 in the patient group, with a significant difference between the two groups (p = 0.001).

CONCLUSION

Compared to the healthy individuals, cerebral cortex regions of acute stroke patients were only marginally activated during volitional swallowing task, and the average functional connectivity strength of cortical network in patients was relatively weaker.

Dyslexia Data Consortium Repository: A Data Sharing and Delivery Platform for Research

Specific learning disability of reading, or dyslexia, affects 5-17% of the population in the United States. Research on the neurobiology of dyslexia has included studies with relatively small sample sizes across research sites, thus limiting inference and the application of novel methods, such as deep learning. To address these issues and facilitate open science, we developed an online platform for data-sharing and advanced research programs to enhance opportunities for replication by providing researchers with secondary data that can be used in their research (https://www.dyslexiadata.org). This platform integrates a set of well-designed machine learning algorithms and tools to generate secondary datasets, such as cortical thickness, as well as regional brain volume metrics that have been consistently associated with dyslexia. Researchers can access shared data to address fundamental questions about dyslexia and development, replicate research findings, apply new methods, and educate the next generation of researchers. The overarching goal of this platform is to advance our understanding of a disorder that has significant academic, social, and economic impacts on children, their families, and society.

Hierarchical Classification Strategy for Mitigating the Impact of The Presence of Pain in fNIRS-based BCIs

Brain computer interfaces (BCIs) can find applications in assistive systems for patients who experience conditions that impede their motor abilities. A BCI uses signals acquired from the brain to control external devices. As physical pain influences cortical signals, the presence of pain can negatively impact the performance of the BCI. In this work, we propose a strategy to mitigate this negative impact. Cortical signals are acquired from test subjects while they performed two mental arithmetic tasks, in the presence and the absence of painful stimuli. The task of the BCI is to reliably classify the two mental arithmetic tasks from the cortical recordings, irrespective of the presence or the absence of pain. We propose to do this classification, hierarchically, in two levels. In the first level, the data is classified into those captured in the presence and the absence of pain. Depending on the results of the classification from the first level, in the second level, the BCI performs the classification of tasks using a classifier trained either in the presence or the absence of pain. A 1-dimensional convolutional neural network (1D-CNN) is used for classification at both levels. It is observed that using this hierarchical strategy, the BCI is able to classify the tasks with an accuracy greater than 90%, irrespective of the presence or the absence of pain. Given that the presence of physical pain has shown previously to reduce the classification accuracy of a BCI to almost chance levels, this mitigation strategy will be a significant step towards enhancing the performance of BCIs when they are used in assistive systems for patients.

High-Speed Time-Domain Diffuse Optical Tomography with a Sensitivity Equation-based Neural Network

Steady progress in time-domain diffuse optical tomography (TD-DOT) technology is allowing for the first time the design of low-cost, compact, and high-performance systems, thus promising more widespread clinical TD-DOT use, such as for recording brain tissue hemodynamics. TD-DOT is known to provide more accurate values of optical properties and physiological parameters compared to its frequency-domain or steady-state counterparts. However, achieving high temporal resolution is still difficult, as solving the inverse problem is computationally demanding, leading to relatively long reconstruction times. The runtime is further compromised by processes that involve 'nontrivial' empirical tuning of reconstruction parameters, which increases complexity and inefficiency. To address these challenges, we present a new reconstruction algorithm that combines a deep-learning approach with our previously introduced sensitivity-equation-based, non-iterative sparse optical reconstruction (SENSOR) code. The new algorithm (called SENSOR-NET) unfolds the iterations of SENSOR into a deep neural network. In this way, we achieve high-resolution sparse reconstruction using only learned parameters, thus eliminating the need to tune parameters prior to reconstruction empirically. Furthermore, once trained, the reconstruction time is not dependent on the number of sources or wavelengths used. We validate our method with numerical and experimental data and show that accurate reconstructions with 1 mm spatial resolution can be obtained in under 20 milliseconds regardless of the number of sources used in the setup. This opens the door for real-time brain monitoring and other high-speed DOT applications.

Utilizing EEG and fNIRS for the detection of sleep-deprivation-induced fatigue and its inhibition using colored light stimulation

Drowsy driving is a common, but underestimated phenomenon in terms of associated risks as it often results in crashes causing fatalities and serious injuries. It is a challenging task to alert or reduce the driver's drowsy state using non-invasive techniques. In this study, a drowsiness reduction strategy has been developed and analyzed using exposure to different light colors and recording the corresponding electrical and biological brain activities. 31 subjects were examined by dividing them into 2 classes, a control group, and a healthy group. Fourteen EEG and 42 fNIRS channels were used to gather neurological data from two brain regions (prefrontal and visual cortices). Experiments shining 3 different colored lights have been carried out on them at certain times when there is a high probability to get drowsy. The results of this study show that there is a significant increase in HbO of a sleep-deprived participant when he is exposed to blue light. Similarly, the beta band of EEG also showed an increased response. However, the study found that there is no considerable increase in HbO and beta band power in the case of red and green light exposures. In addition to that, values of other physiological signals acquired such as heart rate, eye blinking, and self-reported Karolinska Sleepiness Scale scores validated the findings predicted by the electrical and biological signals. The statistical significance of the signals achieved has been tested using repeated measures ANOVA and t-tests. Correlation scores were also calculated to find the association between the changes in the data signals with the corresponding changes in the alertness level.

Dorsolateral prefrontal activation in depressed young adults with and without suicidal ideation during an emotional autobiographical memory task: A fNIRS study

OBJECTIVE

Previous studies have proved that there is a strong association between dorsolateral prefrontal cortex and mood symptoms. This study aimed at using functional near-infrared spectroscopy technology to invest brain activity in dlPFC of depressed individuals with and without suicidal ideation during emotional autobiographical memory test, and to understand their differences in brain cognitive mechanisms. It is helpful to improve our ability to predict and subsequently to prevent suicide.

METHODS

85 young adults participated in the study by a simple random sampling method, with health control (34participants), depression with suicidal ideation (17participants), and depression without suicidal ideation (34participants). The average oxyhemoglobin in dlPFC of subjects during EAMT was collected by a 53-channel fNIRS imaging device.

RESULTS

A marginal significant difference was found between three groups in left dlPFC and right dlPFC. Post hoc analysis revealed that: (1) under negative emotion, depression without suicidal ideation group had higher activation than healthy control group in left dlPFC. (2) under positive emotion, depression with suicidal ideation group had lower activation than healthy control in right dlPFC.

CONCLUSIONS

Results indicated that the depressed individuals with suicidal ideation had some deficits in executive function in right dlPFC, while the depressed adults without suicidal ideation may have mechanism of resource compensatory recruitment in left dlPFC and the dlPFC abnormality involved in the pathophysiology, may localize within left hemisphere. The depressed individuals with and without suicidal ideation had the different mechanisms in dlPFC and fNIRS can be a neuroimaging biomarker characterizing or predicting suicidality in depressed individuals.

Resting-state coupling between HbO and Hb measured by fNIRS in autism spectrum disorder

To distinguish between children with autism spectrum disorder (ASD) and typically developing (TD) children, we have uncovered a new discriminative feature, hemoglobin coupling. Functional near-infrared spectroscopy (fNIRS) was used to record resting-state hemodynamic fluctuations in the bilateral temporal lobes in 25 children with ASD and 22 TD children, in which the coupling between low frequency oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (Hb) fluctuations was evaluated by Pearson correlation coefficient. The results showed significantly weak coupling in children with ASD in both the left and right, and throughout the whole temporal cortex. To explain this observation, a simulation study was performed using a balloon model, in which we found four related parameters could impact the coupling. This study suggested that hemoglobin coupling might be applied as a new cerebral hemodynamic characteristic for ASD screening or diagnostics.

Interdisciplinary views of fNIRS: Current advancements, equity challenges, and an agenda for future needs of a diverse fNIRS research community

Functional Near-Infrared Spectroscopy (fNIRS) is an innovative and promising neuroimaging modality for studying brain activity in real-world environments. While fNIRS has seen rapid advancements in hardware, software, and research applications since its emergence nearly 30 years ago, limitations still exist regarding all three areas, where existing practices contribute to greater bias within the neuroscience research community. We spotlight fNIRS through the lens of different end-application users, including the unique perspective of a fNIRS manufacturer, and report the challenges of using this technology across several research disciplines and populations. Through the review of different research domains where fNIRS is utilized, we identify and address the presence of bias, specifically due to the restraints of current fNIRS technology, limited diversity among sample populations, and the societal prejudice that infiltrates today's research. Finally, we provide resources for minimizing bias in neuroscience research and an application agenda for the future use of fNIRS that is equitable, diverse, and inclusive.

Impact of Anatomical Variability on Sensitivity Profile in fNIRS-MRI Integration

Functional near-infrared spectroscopy (fNIRS) is an important non-invasive technique used to monitor cortical activity. However, a varying sensitivity of surface channels vs. cortical structures may suggest integrating the fNIRS with the subject-specific anatomy (SSA) obtained from routine MRI. Actual processing tools permit the computation of the SSA forward problem (i.e., cortex to channel sensitivity) and next, a regularized solution of the inverse problem to map the fNIRS signals onto the cortex. The focus of this study is on the analysis of the forward problem to quantify the effect of inter-subject variability. Thirteen young adults (six males, seven females, age 29.3 ± 4.3) underwent both an MRI scan and a motor grasping task with a continuous wave fNIRS system of 102 measurement channels with optodes placed according to a 10/5 system. The fNIRS sensitivity profile was estimated using Monte Carlo simulations on each SSA and on three major atlases (i.e., Colin27, ICBM152 and FSAverage) for comparison. In each SSA, the average sensitivity curves were obtained by aligning the 102 channels and segmenting them by depth quartiles. The first quartile (depth < 11.8 (0.7) mm, median (IQR)) covered 0.391 (0.087)% of the total sensitivity profile, while the second one (depth < 13.6 (0.7) mm) covered 0.292 (0.009)%, hence indicating that about 70% of the signal was from the gyri. The sensitivity bell-shape was broad in the source-detector direction (20.953 (5.379) mm FWHM, first depth quartile) and steeper in the transversal one (6.082 (2.086) mm). The sensitivity of channels vs. different cortical areas based on SSA were analyzed finding high dispersions among subjects and large differences with atlas-based evaluations. Moreover, the inverse cortical mapping for the grasping task showed differences between SSA and atlas based solutions. In conclusion, integration with MRI SSA can significantly improve fNIRS interpretation.

Interferometric diffusing wave spectroscopy imaging with an electronically variable time-of-flight filter

Diffuse optics (DO) is a light-based technique used to study the human brain, but it suffers from low brain specificity. Interferometric diffuse optics (iDO) promises to improve the quantitative accuracy and depth specificity of DO, and particularly, coherent light fluctuations (CLFs) arising from blood flow. iDO techniques have alternatively achieved either time-of-flight (TOF) discrimination or highly parallel detection, but not both at once. Here, we break this barrier with a single iDO instrument. Specifically, we show that rapid tuning of a temporally coherent laser during the sensor integration time increases the effective linewidth seen by a highly parallel interferometer. Using this concept to create a continuously variable and user-specified TOF filter, we demonstrate a solution to the canonical problem of DO, measuring optical properties. Then, with a deep TOF filter, we reduce scalp sensitivity of CLFs by 2.7 times at 1 cm source-collector separation. With this unique combination of desirable features, i.e., TOF-discrimination, spatial localization, and highly parallel CLF detection, we perform multiparametric imaging of light intensities and CLFs via the human forehead.

Explainable artificial intelligence model to predict brain states from fNIRS signals

Objective: Most Deep Learning (DL) methods for the classification of functional Near-Infrared Spectroscopy (fNIRS) signals do so without explaining which features contribute to the classification of a task or imagery. An explainable artificial intelligence (xAI) system that can decompose the Deep Learning mode's output onto the input variables for fNIRS signals is described here. Approach: We propose an xAI-fNIRS system that consists of a classification module and an explanation module. The classification module consists of two separately trained sliding window-based classifiers, namely, (i) 1-D Convolutional Neural Network (CNN); and (ii) Long Short-Term Memory (LSTM). The explanation module uses SHAP (SHapley Additive exPlanations) to explain the CNN model's output in terms of the model's input. Main results: We observed that the classification module was able to classify two types of datasets: (a) Motor task (MT), acquired from three subjects; and (b) Motor imagery (MI), acquired from 29 subjects, with an accuracy of over 96% for both CNN and LSTM models. The explanation module was able to identify the channels contributing the most to the classification of MI or MT and therefore identify the channel locations and whether they correspond to oxy- or deoxy-hemoglobin levels in those locations. Significance: The xAI-fNIRS system can distinguish between the brain states related to overt and covert motor imagery from fNIRS signals with high classification accuracy and is able to explain the signal features that discriminate between the brain states of interest.

Language reorganization patterns in global aphasia-evidence from fNIRS

Background

Exploring the brain reorganization patterns associated with language recovery would promote the treatment of global aphasia. While functional near-infrared spectroscopy (fNIRS) has been widely used in the study of speech and language impairment, its application in the field of global aphasia is still limited.

Aims

We aimed to identify cortical activation patterns of patients with global aphasia during naming and repetition tasks.

Methods and procedures

We recruited patients with post-stroke aphasia from the Department of Rehabilitation Medicine at Huashan Hospital. These individuals were diagnosed with global aphasia without cognitive impairments, as assessed by speech-language pathology evaluations. Age- and sex-matched healthy controls were recruited from the greater Shanghai area. During fNIRS measurement, patients and healthy controls completed the picture-naming and phrase repetition task. Cortical activation patterns on each of these language tasks were then compared between groups.

Outcomes and results

A total of nine patients with global aphasia and 14 healthy controls were included in this study. Compared with the healthy subjects, patients with global aphasia showed increased activation in the left Broca's area, middle temporal gyrus (MTG), superior temporal gyrus (STG), and pre-motor and supplementary motor cortex (SMA) (p < 0.05) in the picture-naming task. Furthermore, the latency of the oxyhemoglobin (HbO) concentration in the left supramarginal gyrus (SMG) region had a strong negative correlation with their score of the naming task (p < 0.01). In the phrase repetition task, decreased activation was detected in the left SMA and SMG (p < 0.05) of patients relative to controls.

Conclusion

The left SMG plays a critical role in the language function of patients with global aphasia, especially in their abilities to name and repeat. fNIRS is a promising approach to revealing the changes in brain activities in patients with aphasia, and we believe it will contribute to a deeper understanding of the neurological mechanisms and the establishment of a novel treatment approach for global aphasia.

Systems Thinking in an era of climate change: Does cognitive neuroscience hold the key to improving environmental decision making? A perspective on Climate-Smart Agriculture

Systems Thinking (ST) can be defined as a mental construct that recognises patterns and connections in a particular complex system to make the "best decision" possible. In the field of sustainable agriculture and climate change, higher degrees of ST are assumed to be associated with more successful adaptation strategies under changing conditions, and "better" environmental decision making in a number of environmental and cultural settings. Future climate change scenarios highlight the negative effects on agricultural productivity worldwide, particularly in low-income countries (LICs) situated in the Global South. Alongside this, current measures of ST are limited by their reliance on recall, and are prone to possible measurement errors. Using Climate-Smart Agriculture (CSA), as an example case study, in this article we explore: (i) ST from a social science perspective; (ii) cognitive neuroscience tools that could be used to explore ST abilities in the context of LICs; (iii) an exploration of the possible correlates of systems thinking: observational learning, prospective thinking/memory and the theory of planned behaviour and (iv) a proposed theory of change highlighting the integration of social science frameworks and a cognitive neuroscience perspective. We find, recent advancements in the field of cognitive neuroscience such as Near-Infrared Spectroscopy (NIRS) provide exciting potential to explore previously hidden forms of cognition, especially in a low-income country/field setting; improving our understanding of environmental decision-making and the ability to more accurately test more complex hypotheses where access to laboratory studies is severely limited. We highlight that ST may correlate with other key aspects involved in environmental decision-making and posit motivating farmers via specific brain networks would: (a) enhance understanding of CSA practices (e.g., via the frontoparietal network extending from the dorsolateral prefrontal cortex (DLPFC) to the parietal cortex (PC) a control hub involved in ST and observational learning) such as tailoring training towards developing improved ST abilities among farmers and involving observational learning more explicitly and (b) motivate farmers to use such practices [e.g., via the network between the DLPFC and nucleus accumbens (NAc)] which mediates reward processing and motivation by focussing on a reward/emotion to engage farmers. Finally, our proposed interdisciplinary theory of change can be used as a starting point to encourage discussion and guide future research in this space.

Feasibility of using fNIRS to explore motor-related regional haemodynamic signal changes in patients with sensorimotor impairment and healthy controls: A pilot study

BACKGROUND

While functional near-infrared spectroscopy (fNIRS) can provide insight into cortical brain activity during motor tasks in healthy and diseased populations, the feasibility of using fNIRS to assess haemoglobin-evoked responses to reanimated upper limb motor function in patients with tetraplegia remains unknown.

OBJECTIVE

The primary objective of this pilot study is to determine the feasibility of using fNIRS to assess cortical signal intensity changes during upper limb motor tasks in individuals with surgically restored grip functions. The secondary objectives are: 1) to collect pilot data on individuals with tetraplegia to determine any trends in the cortical signal intensity changes as measured by fNIRS and 2) to compare cortical signal intensity changes in affected individuals versus age-appropriate healthy volunteers. Specifically, patients presented with tetraplegia, a type of paralysis resulting from a cervical spinal cord injury causing loss of movement and sensation in both lower and upper limbs. All patients have their grip functions restored by surgical tendon transfer, a procedure which constitutes a unique, focused stimulus for brain plasticity.

METHOD

fNIRS is used to assess changes in cortical signal intensity during the performance of two motor tasks (isometric elbow and thumb flexion). Six individuals with tetraplegia and six healthy controls participate in the study. A block paradigm is utilized to assess contralateral and ipsilateral haemodynamic responses in the premotor cortex (PMC) and primary motor cortex (M1). We assess the amplitude of the optical signal and spatial features during the paradigms. The accuracy of channel locations is maximized through 3D digitizations of channel locations and co-registering these locations to template atlas brains. A general linear model approach, with short-separation regression, is used to extract haemodynamic response functions at the individual and group levels.

RESULTS

Peak oxyhaemoglobin (oxy-Hb) changes in PMC appear to be particularly bilateral in nature in the tetraplegia group during both pinch and elbow trials whereas for controls, a bilateral PMC response is not especially evident. In M1 / primary sensory cortex (S1), the oxy-Hb responses to the pinch task are mainly contralateral in both groups, while for the elbow flexion task, lateralization is not particularly clear.

CONCLUSIONS

This pilot study shows that the experimental setup is feasible for assessing brain activation using fNIRS during volitional upper limb motor tasks in individuals with surgically restored grip functions. Cortical signal changes in brain regions associated with upper extremity sensorimotor processing appear to be larger and more bilateral in nature in the tetraplegia group than in the control group. The bilateral hemispheric response in the tetraplegia group may reflect a signature of adaptive brain plasticity mechanisms. Larger studies than this one are needed to confirm these findings and draw reliable conclusions.

Hotspots and trends in fNIRS disease research: A bibliometric analysis

Objective

To summarize the general information and hotspots of functional near-infrared spectroscopy (fNIRS)-based clinical disease research over the past 10 years and provide some references for future research.

Methods

The related literature published between 1 January 2011 and 31 January 2022 was retrieved from the Web of Science core database (WoS). Bibliometric visualization analysis of countries/regions, institutions, authors, journals, keywords and references were conducted by using CiteSpace 6.1.R3.

Results

A total of 467 articles were included, and the annual number of articles published over nearly a decade showed an upward trend year-by-year. These articles mainly come from 39 countries/regions and 280 institutions. The representative country and institution were the USA and the University of Tubingen. We identified 266 authors, among which Andreas J Fallgatter and Ann-Christine Ehlis were the influential authors. Neuroimage was the most co-cited journal. The major topics in fNIRS disease research included activation, prefrontal cortex, working memory, cortex, and functional magnetic resonance imaging (fMRI). In recent years, the Frontier topics were executive function, functional connectivity, performance, diagnosis, Alzheimer's disease, children, and adolescents. Based on the burst of co-cited references, gait research has received much attention.

Conclusion

This study conducted a comprehensive, objective, and visual analysis of publications, and revealed the status of relevant studies, hot topics, and trends concerning fNIRS disease research from 2011 to 2022. It is hoped that this work would help researchers to identify new perspectives on potential collaborators, important topics, and research Frontiers.

Cortical Activation of Swallowing Using fNIRS: A Proof of Concept Study with Healthy Adults

The purpose of this study was to determine whether functional near-infrared spectroscopy (fNIRS) could reliably identify cortical activation patterns as healthy adults engaged in single sip and continuous swallowing tasks. Thirty-three right-handed adults completed two functional swallowing tasks, one control jaw movement task, and one rest task while being imaged with fNIRS. Swallowing tasks included a single sip of 5 mL of water via syringe and continuous straw drinking. fNIRS patches for acquisition of neuroimaging data were placed parallel over left and right hemispheres. Stimuli presentation was controlled with set time intervals and audio instructions. Using a series of linear mixed effect models, results demonstrated clear cortical activation patterns during swallowing. The continuous swallowing task demonstrated significant differences in blood oxygenation and deoxygenation concentration values across nearly all regions examined, but most notably M1 in both hemispheres. Of note is that there were areas of greater activation, particularly on the right hemisphere, when comparing the single sip swallow to the jaw movement control and rest tasks. Results from the current study support the use of fNIRS during investigation of swallowing. The utilization of healthy adults as a method for acquiring normative data is vital for comparison purposes when investigating individuals with disorders, but also in the development of rehabilitation techniques. Identifying activation areas that pertain to swallowing will have important implications for individuals requiring dysphagia therapy.

The analgesic effect of different interactive modes of virtual reality: A prospective functional near-infrared spectroscopy (fNIRS) study

Virtual reality has demonstrated its analgesic effectiveness. However, its optimal interactive mode for pain relief is yet unclear, with rare objective measurements that were performed to explore its neural mechanism.

OBJECTIVE

This study primarily aimed at investigating the analgesic effect of different VR interactive modes via functional near-infrared spectroscopy (fNIRS) and exploring its correlations with the subjectively reported VR experience through a self-rating questionnaire.

METHODS

Fifteen healthy volunteers (Age: 21.93 ± 0.59 years, 11 female, 4 male) were enrolled in this prospective study. Three rounds of interactive mode, including active mode, motor imagery (MI) mode, and passive mode, were successively facilitated under consistent noxious electrical stimuli (electrical intensity: 23.67 ± 5.69 mA). Repeated-measures of analysis of variance (ANOVA) was performed to examine its pain relief status and cortical activation, with post hoc analysis after Bonferroni correction performed. Spearman's correlation test was conducted to explore the relationship between VR questionnaire (VRQ) items and cortical activation.

RESULTS

A larger analgesic effect on the active (-1.4(95%CI, -2.23 to -0.57), p = 0.001) and MI modes (-0.667(95%CI, -1.165 to -0.168), p = 0.012) was observed compared to the passive mode in the self-rating pain score, with no significant difference reported between the two modes (-0.733(95%CI, -1.631 to.165), p = 0.131), associated with diverse activated cortical region of interest (ROI) in charge of motor and cognitive functions, including the left primary motor cortex (LM1), left dorsal-lateral prefrontal cortex (LDLPFC), left primary somatosensory cortex (LS1), left visual cortex at occipital lobe (LOL), and left premotor cortex (LPMC). On the other hand, significant correlations were found between VRQ items and different cortical ROIs (r = -0.629 to 0.722, p < 0.05) as well as its corresponding channels (r = -0.599 to 0.788, p < 0.05).

CONCLUSION

Our findings suggest that VR can be considered as an effective non-invasive approach for pain relief by modulating cortical pain processing. A better analgesic effect can be obtained by exciting and integrating cortical ROIs in charge of motor and cognitive functions. The interactive mode can be easily tailored to be in line with the client's characteristics, in spite of the diverse cortical activation status when an equivalent analgesic effect can be obtained.

Non-invasive assessment of cerebral hemoglobin parameters in intracranial dural arteriovenous fistula using functional near-infrared spectroscopy-A feasibility study

PURPOSE

The purpose of this study was to assess the feasibility of non-invasive assessment of cerebral hemodynamics using functional near-infrared spectroscopy (fNIRS) in patients with intracranial dural arteriovenous fistula (DAVF) and to correlate the hemodynamic changes with definitive endovascular treatment.

METHODOLOGY

Twenty-seven DAVF patients and 23 healthy controls underwent 20-mins task-based functional near-infrared spectroscopy and neuropsychology evaluation. The mean change in the hemoglobin concentrations obtained from the prefrontal cortex was assessed for oxyhemoglobin, deoxyhemoglobin, and oxygen saturation (HbO, HbR, and SO2, respectively). The fNIRS data were analyzed and correlated with improvement in neuropsychology scores at 1-month follow-up.

RESULTS

There was a significant reduction in HbO in the patient group, while it increased in controls (-2.57E-05 vs. 1.09E-04 mM, p < 0.001). The reduced HbO significantly improved after embolization (-2.1E-04 vs. 9.9E-04, p = 0.05, q = 0.05). In patients with aggressive DAVF (Cognard 2B and above), the change was highly significant (p < 0.001; q = 0.001). A moderate correlation was observed between MMSE scores and HbO changes (ρ = 0.4).

CONCLUSION

fNIRS is a useful non-invasive modality for the assessment of DAVF, and could potentially assist in bedside monitoring of treatment response.

Non-invasive near-infrared spectroscopy assessment of the spinal neurovascular response in a patient with transverse myelitis: a case report

BACKGROUND

Transverse myelitis (TM) is characterized by acute development of motor, sensory and autonomic dysfunctions due to horizontally diffused inflammation in one or more segments of the spinal cord in the absence of a compressive lesion. The not well-known inflammation process induces demyelination resulting in neurological dysfunction.

CASE PRESENTATION

In this case report we used a functional Near-Infrared Spectroscopy (fNIRS) technique to evaluate changes in the peri-spinal vascular response induced by a peripheral median nerve electrical stimulation in a patient with chronic transverse myelitis (TM). fNIRS showed drastically reduced signal amplitude in the peri-spinal vascular response, compared to that obtained from a healthy control group throughout most of the C7-T1 and T10-L2 spinal cord segments.

CONCLUSION

The potential use of this relatively non-invasive fNIRS technology support the potential clinical application of this method for functional test of the spinal cord through the assessment of the spinal neurovascular response.

Deep neural network to differentiate brain activity between patients with euthymic bipolar disorders and healthy controls during verbal fluency performance: A multichannel near-infrared spectroscopy study

In this study, we aimed to differentiate between euthymic bipolar disorder (BD) patients and healthy controls (HC) based on frontal activity measured by fNIRS that were converted to spectrograms with Convolutional Neural Networks (CNN). And also, we investigated brain regions that cause this distinction. In total, 29 BD patients and 28 HCs were recruited. Their brain cortical activities were measured using fNIRS while performing letter versions of VFT. Each one of the 24 fNIRS channels was converted to a 2D spectrogram on which a CNN architecture was designed and utilized for classification. We found that our CNN algorithm using fNIRS activity during a VFT is able to differentiate subjects with BD from healthy controls with 90% accuracy, 80% sensitivity, and 100% specificity. Moreover, validation performance reached an AUC of 94%. From our individual channel analyses, we observed channels corresponding to the left inferior frontal gyrus (left-IFC), medial frontal cortex (MFC), right dorsolateral prefrontal cortex (DLPFC), Broca area, and right premotor have considerable activity variation to distinguish patients from HC. fNIRS activity during VFT can be used as a potential marker to classify euthymic BD patients from HCs. Activity particularly in the MFC, left-IFC, Broca's area, and DLPFC have a considerable variation to distinguish patients from healthy controls.

Executive control of freestyle skiing aerials athletes in different training conditions

Introduction

Due to the actual limitation of training conditions, the freestyle skiing aerials winter training term is short. Training tasks such as adaptability training and developing new skills are needed in summer training. When facing different training environments, freestyle skiing aerial athletes’ executive control over their abilities could be affected, which can affect their performance. Therefore, we want to research the effect of training conditions on executive control in freestyle skiing aerials athletes and its neural mechanism.

Materials and methods

Thirty-two freestyle skiing aerials athletes were recruited. We evaluated their executive control and used fNIRS to measure oxygenated hemoglobin concentration changes in the prefrontal cortex during a rapid event-related design go/nogo task with different training condition-activated materials.

Results

Athletes’ behavior control in the summer condition has a lower accuracy than it is in the control condition. Athletes’ behavior control in the summer and winter training conditions had a longer reaction time than that in the control condition. The activation of the bilateral dlPFC and orbitofrontal cortex had a significant main effect across training conditions when freestyle skiing aerial athletes completed executive control tasks. The activation of athletes’ bilateral vlPFC and left dlPFC had an interaction between training conditions and behavioral control.

Conclusion

Different training conditions can lead to freestyle skiing aerial athletes executive control ability to drop, players in different training conditions show less activation on both sides of the vlPFC and orbitofrontal. The bilateral vlPFC and left dlPFC have an integrated effect on behavior inhibition across training conditions.

Brain augmentation and neuroscience technologies: current applications, challenges, ethics and future prospects

Ever since the dawn of antiquity, people have strived to improve their cognitive abilities. From the advent of the wheel to the development of artificial intelligence, technology has had a profound leverage on civilization. Cognitive enhancement or augmentation of brain functions has become a trending topic both in academic and public debates in improving physical and mental abilities. The last years have seen a plethora of suggestions for boosting cognitive functions and biochemical, physical, and behavioral strategies are being explored in the field of cognitive enhancement. Despite expansion of behavioral and biochemical approaches, various physical strategies are known to boost mental abilities in diseased and healthy individuals. Clinical applications of neuroscience technologies offer alternatives to pharmaceutical approaches and devices for diseases that have been fatal, so far. Importantly, the distinctive aspect of these technologies, which shapes their existing and anticipated participation in brain augmentations, is used to compare and contrast them. As a preview of the next two decades of progress in brain augmentation, this article presents a plausible estimation of the many neuroscience technologies, their virtues, demerits, and applications. The review also focuses on the ethical implications and challenges linked to modern neuroscientific technology. There are times when it looks as if ethics discussions are more concerned with the hypothetical than with the factual. We conclude by providing recommendations for potential future studies and development areas, taking into account future advancements in neuroscience innovation for brain enhancement, analyzing historical patterns, considering neuroethics and looking at other related forecasts.

Frontal lobe hemodynamics detected by functional near-infrared spectroscopy during head-up tilt table tests in patients with electrical burns

Significance

Electrical burns can cause severe damage to the nervous system, resulting in autonomic dysfunction with reduced cerebral perfusion. However, few studies have investigated these consequences.

Aim

To elucidate changes in prefrontal cerebral hemodynamics using functional near-infrared spectroscopy (fNIRS) during the head-up tilt table test (HUT) for patients with electrical burns.

Approach

We recruited 17 patients with acute electrical burns within 1 week after their accidents and 10 healthy volunteers. The NIRS parameters acquired using an fNIRS device attached to the forehead were analyzed in five distinct HUT phases.

Results

Based on their HUT response patterns, patients with electrical burns were classified into the group with abnormal HUT results (APG, n = 4) or normal HUT results (NPG, n = 13) and compared with the healthy control (HC, n = 10) participants. We found trends in hemodynamic changes during the HUT that distinguished HC, NPG, and APG. Reduced cerebral perfusion and decreased blood oxygenation during the HUT were found in both the NPG and APG groups. Patients with electrical burns had autonomic dysfunction compared to the HC participants.

Conclusions

Using fNIRS, we observed that acute-stage electrical burn injuries could affect cerebral perfusion.

Hemodynamic signal changes and swallowing improvement of repetitive transcranial magnetic stimulation on stroke patients with dysphagia: A randomized controlled study

Objective

Our study aims to measure the cortical correlates of swallowing execution in patients with dysphagia after repetitive transcranial magnetic stimulation (rTMS) therapy using functional near-infrared spectroscopy (fNIRS), and observe the change of pattern of brain activation in stroke patients with dysphagia after rTMS intervention. In addition, we tried to analyze the effect of rTMS on brain activation in dysphagia patients with different lesion sides. This study also concentrated on the effect of stimulating the affected mylohyoid cortical region by 5 Hz rTMS, providing clinical evidence for rTMS therapy of dysphagia in stroke patients.

Methods

This study was a sham-controlled, single-blind, randomized controlled study with a blinded observer. A total of 49 patients completed the study, which was randomized to the rTMS group (n = 23) and sham rTMS group (n = 26) by the random number table method. The rTMS group received 5 Hz rTMS stimulation to the affected mylohyoid cortical region of the brain and the sham rTMS group underwent rTMS using the same parameters as the rTMS group, except for the position of the coil. Each patient received 2 weeks of stimulation followed by conventional swallowing therapy. Standardized Swallowing Assessment (SSA), Fiberoptic Endoscopic Dysphagia Severity Scale (FEDSS), Penetration-Aspiration Scale (PAS), and functional oral intake status were assessed at two times: baseline (before treatment) and 2 weeks (after intervention). Meanwhile, we use the fNIRS system to measure the cerebral hemodynamic changes during the experimental procedure.

Results

The rTMS group exhibited significant improvement in the SSA scale, FEDSS scale, and PAS scale after rTMS therapy (all P &lt; 0.001). The sham rTMS group had the same analysis on the same scales (all P &lt; 0.001). There was no significant difference observed in clinical assessments at 2 weeks after baseline between the rTMS group and sham rTMS group (all P &gt; 0.05). However, there were statistically significant differences between the two groups in the rate of change in the FEDSS score (P = 0.018) and PAS score (P = 0.004), except for the SSA score (P = 0.067). As for the removal rate of the feeding tube, there was no significant difference between the rTMS group and sham rTMS group (P = 0.355), but there was a significant difference compared with the baseline characteristics in both groups (PrTMS &lt; 0.001, PshamrTMS = 0.002). In fNIRS analysis, the block average result showed differences in brain areas RPFC (right prefrontal cortex) and RMC (right motor cortex) significantly between the rTMS group and sham rTMS group after intervention (Pchannel30 = 0.046, Pchannel16 = 0.006). In the subgroup analysis, rTMS group was divided into left-rTMS group and right-rTMS group and sham rTMS group was divided into sham left-rTMS group and sham right-rTMS group. The fNIRS results showed no significance in block average and block differential after intervention between the left-rTMS group and sham left-rTMS group, but differences were statistically significant between the right-rTMS group and sham right-rTMS group in block average: channel 30 (T = −2.34, P = 0.028) in LPFC (left prefrontal cortex) and 16 (T = 2.54, P = 0.018) in RMC. After intervention, there was no significance in left-rTMS group compared with baseline, but in right-rTMS group, channel 27 (T = 2.18, P = 0.039) in LPFC and 47 (T = 2.17, P = 0.039) in RPFC had significance in block differential. In the sham rTMS group, neither sham left-rTMS group and sham right-rTMS group had significant differences in block average and block differential in each brain area after intervention (P &gt; 0.05).

Conclusions

The present study confirmed that a 5-Hz rTMS is feasible at the affected mylohyoid cortical region in post-stroke patients with dysphagia and rTMS therapy can alter cortical excitability. Based on previous studies, there is a dominant hemisphere in swallowing and the results of our fNIRS analysis seemed to show a better increase in cortical activation on the right side than on the left after rTMS of the affected mylohyoid cortical region. However, there was no difference between the left and right hemispheres in the subgroup analysis. Nevertheless, the present study provides a novel and feasible method of applying fNIRS to assessment in stroke patients with dysphagia.

Systematic Bibliometric and Visualized Analysis of Research Hotspots and Trends on Autism Spectrum Disorder Neuroimaging

Background

Autism spectrum disorder (ASD) is a chronic developmental disability caused by differences in the brain. The gold standard for the diagnosis of this condition is based on behavioral science, but research on the application of neurological detection to diagnose the atypical nervous system of ASD is ongoing. ASD neuroimaging research involves the examination of the brain's structure, functional connections, and neurometabolic. However, limited medical resource and the unique heterogeneity of ASD have resulted in many challenges when neuroimaging is utilized.

Objective

This bibliometric study is aimed at summarizing themes and trends in research on autism spectrum disorder neuroimaging and at proposing potential directions for future inquiry.

Methods

Citations were downloaded from the Web of Science Core Collection database on neuroimaging published from January 1, 2012, to December 31, 2021. The retrieved information was analyzed using Bibliometric.com, CiteSpace.5.8. R3, and VOS viewer.

Results

A total of 1,363 papers were published across 58 regions. The United States was the leading source of publications. The League of European Research Universities published the largest number of articles (171). Burst keywords from 2018 to 2021 include identification and network. The clusters of references that continued into 2020 included graph theory, functional connectivity, and classification, which represent key research topics.

Conclusions

Imaging data is being used to identify neuro-network models with higher accuracy for ASD discrimination. Functional near-infrared imaging is advantageous compared to other neuroimaging. In the future, research on systematic and accurate computer-aided diagnosis technology should be encouraged. Moreover, the study of neuroimaging of ASD in different psychological and behavioral states can inspire new ideas about the diagnosis and intervention training of ASD and should be explored.

An Approach to Neuroimaging Interpersonal Interactions in Mental Health Interventions

BACKGROUND

Conventional paradigms in clinical neuroscience tend to be constrained in terms of ecological validity, raising several challenges to studying the mechanisms mediating treatments and outcomes in clinical settings. Addressing these issues requires real-world neuroimaging techniques that are capable of continuously collecting data during free-flowing interpersonal interactions and that allow for experimental designs that are representative of the clinical situations in which they occur.

METHODS

In this work, we developed a paradigm that fractionates the major components of human-to-human verbal interactions occurring in clinical situations and used functional near-infrared spectroscopy to assess the brain systems underlying clinician-client discourse (N = 30).

RESULTS

Cross-brain neural coupling between people was significantly greater during clinical interactions compared with everyday life verbal communication, particularly between the prefrontal cortex (e.g., inferior frontal gyrus) and inferior parietal lobule (e.g., supramarginal gyrus). The clinical tasks revealed extensive increases in activity across the prefrontal cortex, especially in the rostral prefrontal cortex (area 10), during periods in which participants were required to silently reason about the dysfunctional cognitions of the other person.

CONCLUSIONS

This work demonstrates a novel experimental approach to investigating the neural underpinnings of interpersonal interactions that typically occur in clinical settings, and its findings support the idea that particular prefrontal systems might be critical to cultivating mental health.

Impaired brain activity in patients with persistent atrial fibrillation assessed by near-infrared spectroscopy and its changes after catheter ablation

Although the prevalence of cognitive impairment and depression is higher in patients with atrial fibrillation (AF) than in the general population, the mechanism has not been fully examined and impact of catheter ablation (CA) of AF also remains unclear. 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. We assessed brain activities by NIRS, depressive symptoms by the Center for Epidemiologic Studies Depression Scale (CES-D) and cognitive function by Mini-Mental State Examination (MMSE). We then compared the results between AF patients (paroxysmal AF n = 18 and persistent AF n = 14) and control subjects (n = 29). Next, we also followed up persistent AF patients who kept sinus rhythm at 3 months after CA (n = 8) and measured their brain activities using NIRS, CES-D and MMSE after CA to investigate the associations of changes in brain activities with changes in both CES-D and MMSE. Our results showed that (1) frontal and temporal brain activities were lower in patients with persistent AF than both in control subjects and paroxysmal AF patients (P < 0.01), (2) frontal and temporal brain activities were improved in more than half of the persistent AF patients who kept sinus rhythm at 3 months after CA, especially in those who presented impaired brain activity before CA, and (3) improvement of frontal brain activity was associated with improvement of CES-D (R =  − 0.793, P = 0.019), whereas improvement of temporal brain activity was associated with improvement of MMSE (R = 0.749, P = 0.033). NIRS measurement showed reduced frontal and temporal brain activities in the persistent AF patients, CA improved frontal and temporal brain activities in some of these patients, and associated with improvement of depressive state and/or improvement of cognitive function.

Medial Prefrontal Activity During Self-Other Judgments is Modulated by Relationship Need Fulfillment

The medial prefrontal cortex (MPFC) plays an important role in representing semantic self-knowledge. Studies comparing semantic self-judgments with judgments of close others suggest that interpersonal closeness may influence the degree to which the MPFC differentiates self and other. We used optical neuroimaging to examine if support for competence, relatedness, and autonomy from relationship partners moderates MPFC activity during a personality judgement task. Participants (N = 109) were asked to judge the descriptive accuracy of trait adjectives for both themselves and a friend. Participants who reported lower need fulfillment with their friend showed elevated activity only in the self-judgment condition; in contrast, participants who reported higher need fulfillment with their friend showed similarly high levels of MPFC activity across the conditions. These results are consistent with the idea that the MPFC differentially represents others on the basis of the need fulfillment experienced within the relationship.

Deep Learning-Based Multilevel Classification of Alzheimer’s Disease Using Non-invasive Functional Near-Infrared Spectroscopy

The timely diagnosis of Alzheimer’s disease (AD) and its prodromal stages is critically important for the patients, who manifest different neurodegenerative severity and progression risks, to take intervention and early symptomatic treatments before the brain damage is shaped. As one of the promising techniques, functional near-infrared spectroscopy (fNIRS) has been widely employed to support early-stage AD diagnosis. This study aims to validate the capability of fNIRS coupled with Deep Learning (DL) models for AD multi-class classification. First, a comprehensive experimental design, including the resting, cognitive, memory, and verbal tasks was conducted. Second, to precisely evaluate the AD progression, we thoroughly examined the change of hemodynamic responses measured in the prefrontal cortex among four subject groups and among genders. Then, we adopted a set of DL architectures on an extremely imbalanced fNIRS dataset. The results indicated that the statistical difference between subject groups did exist during memory and verbal tasks. This presented the correlation of the level of hemoglobin activation and the degree of AD severity. There was also a gender effect on the hemoglobin changes due to the functional stimulation in our study. Moreover, we demonstrated the potential of distinguished DL models, which boosted the multi-class classification performance. The highest accuracy was achieved by Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) using the original dataset of three hemoglobin types (0.909 ± 0.012 on average). Compared to conventional machine learning algorithms, DL models produced a better classification performance. These findings demonstrated the capability of DL frameworks on the imbalanced class distribution analysis and validated the great potential of fNIRS-based approaches to be further contributed to the development of AD diagnosis systems.

Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains

Occupational disorders considerably impact workers’ quality of life and organizational productivity, and even affect mortality worldwide. Such health issues are related to mental health and ergonomics risk factors. In particular, mental health may be affected by cognitive strain caused by unexpected interruptions and other attention compromising factors. Risk factors assessment associated with cognitive strain in office environments, namely related to attention states, still suffers from the lack of scientifically validated tools. In this work, we aim to develop a series of classification models that can classify attention during pre-defined cognitive tasks based on the acquisition of biosignals to create a ground truth of attention. Biosignals, such as electrocardiography, electroencephalography, and functional near-infrared spectroscopy, were acquired from eight subjects during standard cognitive tasks inducing attention. Individually tuned machine learning models trained with those biosignals allowed us to successfully detect attention on the individual level, with results in the range of 70–80%. The electroencephalogram and electrocardiogram were revealed to be the most appropriate sensors in this context, and the combination of multiple sensors demonstrated the importance of using multiple sources. These models prove to be relevant for the development of attention identification tools by providing ground truth to determine which human–computer interaction variables have strong associations with attention.

Observation and motor imagery balance tasks evaluation: An fNIRS feasibility study

In this study, we aimed at exploring the feasibility of functional near-infrared spectroscopy (fNIRS) for studying the observation and/or motor imagination of various postural tasks. Thirteen healthy adult subjects followed five trials of static and dynamic standing balance tasks, throughout three different experimental setups of action observation (AO), a combination of action observation and motor imagery (AO+MI), and motor imagery (MI). During static and dynamic standing tasks, both the AO+MI and MI experiments revealed that many channels in prefrontal or motor regions are significantly activated while the AO experiment showed almost no significant increase in activations in most of the channels. The contrast between static and dynamic standing tasks showed that with more demanding balance tasks, relative higher activation patterns were observed, particularly during AO and in AO+MI experiments in the frontopolar area. Moreover, the AO+MI experiment revealed a significant difference in premotor and supplementary motor cortices that are related to balance control. Furthermore, it has been observed that the AO+MI experiment induced relatively higher activation patterns in comparison to AO or MI alone. Remarkably, the results of this work match its counterpart from previous functional magnetic resonance imaging studies. Therefore, they may pave the way for using the fNIRS as a diagnostic tool for evaluating the performance of the non-physical balance training during the rehabilitation period of temporally immobilized patients.

Cortical Oxygenation during a Motor Task to Evaluate Recovery in Subacute Stroke Patients: A Study with Near-Infrared Spectroscopy

In subacute stroke patients we studied cortical oxygenation changes by near-infrared spectroscopy (NIRS) during a motor task performed with the hemiparetic arm (15 s of reaching and grasping, 45 s of rest, repeated 6 times). Twenty-three subjects were included at baseline, compared with six healthy subjects, and restudied after 6 weeks of rehabilitation. Motor/premotor cortical changes in oxyhemoglobin detected by NIRS were quantified as the area under the curve (AUC) for the total cortex (TOT-AUC) and for both affected (AFF-AUC) and unaffected hemispheres (UN-AUC). The ratio between AUC and the number of task repetitions performed identified the cortical metabolic cost (CMC) or the oxygenation increase for a single movement. Fugl−Meyer assessment of the upper extremity (FMA-UE) was also performed. At baseline, both total and hemispheric CMC were significantly higher in stroke patients than in healthy subjects and inversely correlated with FMA-UE. After rehabilitation, changes in total-CMC and unaffected-CMC, but not Affected-CMC, were inversely correlated with variations in the FMA-UE score. A value > 5000 a.u. for the ratio baseline TOT-CMC/days since stroke was associated with not reaching the clinically important difference for FMA-UE after rehabilitation. In subacute stroke the CMC, a biomarker assessed by NIRS during a motor task with the hemiparetic arm, may describe cortical time/treatment reorganization and favor patient selection for rehabilitation.

A Comprehensive Survey on the Detection, Classification, and Challenges of Neurological Disorders

Simple Summary

This study represents a resourceful review article that can deliver resources on neurological diseases and their implemented classification algorithms to reveal the future direction of researchers. Researchers interested in studying neurological diseases and previously implemented techniques in this field can follow this article. Various challenges occur in detecting different stages of the disorders. A limited amount of labeled and unlabeled datasets and other limitations is represented in this article to assist them in finding out the directions. The authors’ purpose for composing this article is to make a straightforward and concrete path for researchers to quickly find the way and the scope in this field for implementing future research on neurological disease detection.

Abstract

Neurological disorders (NDs) are becoming more common, posing a concern to pregnant women, parents, healthy infants, and children. Neurological disorders arise in a wide variety of forms, each with its own set of origins, complications, and results. In recent years, the intricacy of brain functionalities has received a better understanding due to neuroimaging modalities, such as magnetic resonance imaging (MRI), magnetoencephalography (MEG), and positron emission tomography (PET), etc. With high-performance computational tools and various machine learning (ML) and deep learning (DL) methods, these modalities have discovered exciting possibilities for identifying and diagnosing neurological disorders. This study follows a computer-aided diagnosis methodology, leading to an overview of pre-processing and feature extraction techniques. The performance of existing ML and DL approaches for detecting NDs is critically reviewed and compared in this article. A comprehensive portion of this study also shows various modalities and disease-specified datasets that detect and records images, signals, and speeches, etc. Limited related works are also summarized on NDs, as this domain has significantly fewer works focused on disease and detection criteria. Some of the standard evaluation metrics are also presented in this study for better result analysis and comparison. This research has also been outlined in a consistent workflow. At the conclusion, a mandatory discussion section has been included to elaborate on open research challenges and directions for future work in this emerging field.

Identifying Individuals by fNIRS-Based Brain Functional Network Fingerprints

Individual identification based on brain functional network (BFN) has attracted a lot of research interest in recent years, since it provides a novel biometric for identity authentication, as well as a feasible way of exploring the brain at an individual level. Previous studies have shown that an individual can be identified by its BFN fingerprint estimated from functional magnetic resonance imaging, electroencephalogram, or magnetoencephalography data. Functional near-infrared spectroscopy (fNIRS) is an emerging imaging technique that, by measuring the changes in blood oxygen concentration, can respond to cerebral activities; in this paper, we investigate whether fNIRS-based BFN could be used as a “fingerprint” to identify individuals. In particular, Pearson's correlation is first used to calculate BFN based on the preprocessed fNIRS signals, and then the nearest neighbor scheme is used to match the estimated BFNs between different individuals. Through the experiments on an open-access fNIRS dataset, we have two main findings: (1) under the cases of cross-task (i.e., resting, right-handed, left-handed finger tapping, and foot tapping), the BFN fingerprints generally work well for the individual identification, and, more interestingly, (2) the accuracy under cross-task is well above the accuracy under cross-view (i.e., oxyhemoglobin and de-oxyhemoglobin). These findings indicate that fNIRS-based BFN fingerprint is a potential biometric for identifying individual.

The Effects of Virtual Reality Nonphysical Mental Training on Balance Skills and Functional Near-Infrared Spectroscopy Activity in Healthy Adults

CONTEXT

Athletic skills such as balance are considered physical skills. However, these skills may not just improve by physical training, but also by mental training. The purpose of this study was to investigate the effects of mental training programs on balance skills and hemodynamic responses of the prefrontal cortex.

DESIGN

Randomized controlled trial.

METHODS

Fifty-seven healthy adults (28 females, 29 males), aged between 18-25 years, participated in this study. Participants were randomly assigned to 3 groups: virtual reality mental training (VRMT) group, conventional mental training (CMT) group, and control group. The training program included action observation and motor imagery practices with balance exercise videos. The VRMT group trained with a VR head-mounted display, while the CMT group trained with a non-immersive computer screen, for 30 minutes, 3 days per week for 4 weeks. At baseline and after 4 weeks of training, balance was investigated with stabilometry and Star Excursion Balance Test (SEBT). Balance tests were performed with simultaneous functional near-infrared spectroscopy (fNIRS) imaging to measure prefrontal cortex oxygenation.

RESULTS

For the stabilometry test, at least 1 variable improved significantly in both VRMT and CMT groups but not in the control group. For SEBT, composite reach distance significantly increased in both VRMT and CMT groups but significantly decreased in the control group. For separate directional scores, reach distance was significantly increased in both mental training groups for nondominant leg posterolateral and posteromedial directions, and dominant leg posterolateral direction, while nondominant posteromedial score was significantly increased only in the VRMT group. Between-group comparisons showed that dominant leg posteromedial and posterolateral score improvements were significantly higher than control group for both mental training groups, while nondominant leg improvements were significantly higher than control group only for the VRMT group. The fNIRS oxyhemoglobin levels were not significantly changed during stabilometry tests. However, oxyhemoglobin levels significantly reduced only in the control group during SEBT.

CONCLUSIONS

Our findings suggest that both mental training interventions can significantly improve balance test results. Additionally, VRMT may have some advantages over CMT. These findings are promising for the use of mental training in prevention and rehabilitation for special populations such as athletes and older adults.

Increased both cortical activation and functional connectivity after transcranial direct current stimulation in patients with post-stroke: A functional near-infrared spectroscopy study

BACKGROUND

Previous studies have shown that cognitive impairment is common after stroke. Transcranial direct current stimulation (tDCS) is a promising tool for rehabilitating cognitive impairment. This study aimed to investigate the effects of tDCS on the rehabilitation of cognitive impairment in patients with stroke.

METHODS

Twenty-two mild-moderate post-stroke patients with cognitive impairments were treated with 14 tDCS sessions. A total of 14 healthy individuals were included in the control group. Cognitive function was assessed using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). Cortical activation was assessed using functional near-infrared spectroscopy (fNIRS) during the verbal fluency task (VFT).

RESULTS

The cognitive function of patients with stroke, as assessed by the MMSE and MoCA scores, was lower than that of healthy individuals but improved after tDCS. The cortical activation of patients with stroke was lower than that of healthy individuals in the left superior temporal cortex (lSTC), right superior temporal cortex (rSTC), right dorsolateral prefrontal cortex (rDLPFC), right ventrolateral prefrontal cortex (rVLPFC), and left ventrolateral prefrontal cortex (lVLPFC) cortical regions. Cortical activation increased in the lSTC cortex after tDCS. The functional connectivity (FC) between the cerebral hemispheres of patients with stroke was lower than that of healthy individuals but increased after tDCS.

CONCLUSION

The cognitive and brain functions of patients with mild-to-moderate stroke were damaged but recovered to a degree after tDCS. Increased cortical activation and increased FC between the bilateral cerebral hemispheres measured by fNIRS are promising biomarkers to assess the effectiveness of tDCS in stroke.