Applications of functional near-infrared spectroscopy (fNIRS) to Neurorehabilitation of cognitive disabilities

EEG biomarkers analysis in different cognitive impairment after stroke: an exploration study

Stroke is a cerebrovascular illness that brings about the demise of brain tissue. It is the third most prevalent cause of mortality worldwide and a significant contributor to physical impairment. Generally, stroke is triggered by blood clots obstructing the brain's blood vessels, or when these vessels rupture. And, the cognitive impairment's evaluation and detection after stroke is crucial research issue and significant project. Thus, the objective of this work is to explore an potential neuroimage tool and find their EEG biomarkers to evaluate and detect four cognitive impairment levels after stroke. In this study, power density spectrum (PSD), functional connectivity map, and one-way ANOVA methods were proposed to analyze the EEG biomarker differences, and the number of patient participants were thirty-two human including eight healthy control, mild, moderate, severe cognitive impairment levels, respectively. Finally, healthy control has significant PSD differences compared to mid, moderate and server cognitive impairment groups. And, the theta and alpha bands of severe cognitive impairment groups have presented consistent superior PSD power at the right frontal cortex, and the theta and beta bands of mild, moderated cognitive impairment (MoCI) groups have shown significant similar superior PSD power tendency at the parietal cortex. The significant gamma PSD power difference has presented at the left-frontal cortex in the mild cognitive impairment (MCI) groups, and severe cognitive impairment (SeCI) group has shown the significant PSD power at the gamma band of parietal cortex. At the point of functional connectivity map, the SeCI group appears to have stronger functional connectivity compared to the other groups. In conclusion, EEG biomarkers can be applied to classify different cognitive impairment groups after stroke. These findings provide a new approach for early detection and diagnosis of cognitive impairment after stroke and also for the development of new treatment options.

Disentangling the effects of near-infrared light stimulation and exercise on cognitive function in fNIRS studies

Functional near-infrared spectroscopy (fNIRS) studies often aim to measure changes in the brain's hemodynamic response in relation to a specific intervention. We recently showed how a fNIRS device could induce photobiomodulatory effects on cognition by using its near-infrared (NIR) light. However, so far, fNIRS research has overlooked the stimulatory potential intrinsic to this technique. The work by Kuwamizu et al. (2023) on pupil dynamics during exercise is no exception. Here, we suggest a fix to their experimental design, which could be taken into account in other fNIRS studies, to guarantee an adequate level of control for possible unconsidered photobiomodulatory effects.

From functional neuroimaging to neurostimulation: fNIRS devices as cognitive enhancers

Functional near-infrared spectroscopy (fNIRS) relies on near-infrared (NIR) light for changes in tissue oxygenation. For decades, this technique has been used in neuroscience to measure cortical activity. However, recent research suggests that NIR light directed to neural populations can modulate their activity through "photobiomodulation" (PBM). Yet, fNIRS is being used exclusively as a measurement tool. By adopting cognitive tests sensitive to prefrontal functioning, we show that a 'classical' fNIRS device, placed in correspondence of the prefrontal cortices of healthy participants, induces faster RTs and better accuracy in some of the indexes considered. A well-matched control group, wearing the same but inactive device, did not show any improvement. Hence, our findings indicate that the 'standard' use of fNIRS devices generates PBM impacting cognition. The neuromodulatory power intrinsic in that technique has been so far completely overlooked, and future studies will need to take this into account.

Unlocking the neural mechanisms of consumer loan evaluations: an fNIRS and ML-based consumer neuroscience study

Introduction

This study conducts a comprehensive exploration of the neurocognitive processes underlying consumer credit decision-making using cutting-edge techniques from neuroscience and machine learning (ML). Employing functional Near-Infrared Spectroscopy (fNIRS), the research examines the hemodynamic responses of participants while evaluating diverse credit offers.

Methods

The experimental phase of this study investigates the hemodynamic responses collected from 39 healthy participants with respect to different loan offers. This study integrates fNIRS data with advanced ML algorithms, specifically Extreme Gradient Boosting, CatBoost, Extra Tree Classifier, and Light Gradient Boosted Machine, to predict participants' credit decisions based on prefrontal cortex (PFC) activation patterns.

Results

Findings reveal distinctive PFC regions correlating with credit behaviors, including the dorsolateral prefrontal cortex (dlPFC) associated with strategic decision-making, the orbitofrontal cortex (OFC) linked to emotional valuations, and the ventromedial prefrontal cortex (vmPFC) reflecting brand integration and reward processing. Notably, the right dorsomedial prefrontal cortex (dmPFC) and the right vmPFC contribute to positive credit preferences.

Discussion

This interdisciplinary approach bridges neuroscience, machine learning and finance, offering unprecedented insights into the neural mechanisms guiding financial choices regarding different loan offers. The study's predictive model holds promise for refining financial services and illuminating human financial behavior within the burgeoning field of neurofinance. The work exemplifies the potential of interdisciplinary research to enhance our understanding of human financial decision-making.

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.

Effect of the VR-guided grasping task on the brain functional network

Virtual reality (VR) technology has been demonstrated to be effective in rehabilitation training with the assistance of VR games, but its impact on brain functional networks remains unclear. In this study, we used functional near-infrared spectroscopy imaging to examine the brain hemodynamic signals from 18 healthy participants during rest and grasping tasks with and without VR game intervention. We calculated and compared the graph theory-based topological properties of the brain networks using phase locking values (PLV). The results revealed significant differences in the brain network properties when VR games were introduced compared to the resting state. Specifically, for the VR-guided grasping task, the modularity of the brain network was significantly higher than the resting state, and the average clustering coefficient of the motor cortex was significantly lower compared to that of the resting state and the simple grasping task. Correlation analyses showed that a higher clustering coefficient, local efficiency, and modularity were associated with better game performance during VR game participation. This study demonstrates that a VR game task intervention can better modulate the brain functional network compared to simple grasping movements and may be more beneficial for the recovery of grasping abilities in post-stroke patients with hand paralysis.

Applications of flexible electronics related to cardiocerebral vascular system

Ensuring accessible and high-quality healthcare worldwide requires field-deployable and affordable clinical diagnostic tools with high performance. In recent years, flexible electronics with wearable and implantable capabilities have garnered significant attention from researchers, which functioned as vital clinical diagnostic-assisted tools by real-time signal transmission from interested targets in vivo. As the most crucial and complex system of human body, cardiocerebral vascular system together with heart-brain network attracts researchers inputting profuse and indefatigable efforts on proper flexible electronics design and materials selection, trying to overcome the impassable gulf between vivid organisms and rigid inorganic units. This article reviews recent breakthroughs in flexible electronics specifically applied to cardiocerebral vascular system and heart-brain network. Relevant sensor types and working principles, electronics materials selection and treatment methods are expounded. Applications of flexible electronics related to these interested organs and systems are specially highlighted. Through precedent great working studies, we conclude their merits and point out some limitations in this emerging field, thus will help to pave the way for revolutionary flexible electronics and diagnosis assisted tools development.

Development of Immersive Virtual Reality-Based Hand Rehabilitation System Using a Gesture-Controlled Rhythm Game With Vibrotactile Feedback: An fNIRS Pilot Study

Recently, virtua reality (VR) has been widely utilized with rehabilitation to promote user engagement, which has been shown to induce brain plasticity. In this study, we developed a VR-based hand rehabilitation system consisting of a personalized gesture-controlled rhythm game with vibrotactile feedback and investigated the cortical activation pattern induced by our system using functional near-infrared spectroscopy (fNIRS). Our system provides vibrotactile feedback as the user matches their hand gestures to VR targets customized to their pre-recorded hand gestures. Cortical activation was measured via fNIRS during 420 seconds of alternating gameplay and rest in 11 healthy subjects and one stroke survivor. Regions of interest (ROI) were the prefrontal cortex (PFC), the premotor cortex & the supplementary motor area (PMC&SMA), the primary sensorimotor cortex (SM1), and the somatosensory association cortex (SAC). The mean success rate of gesture matching among healthy subjects was 90 % with a standard deviation of 10.7 %, and the success rate of the stroke survivor was 79.6 %. The averaged cortical activation map for the 11 healthy subjects and the individual cortical activation map for the single stroke survivor showed increased hemodynamic responses of oxygenated hemoglobin (HbO) during the VR-based hand rehabilitation compared to the resting condition. Paired t-test analysis demonstrated a significant increase in HbO activation values in 19 out of 51 channels, corresponding to all ROIs except the left PFC and PMC&SMA, which exhibited high subject variability. The experimental results indicate that the proposed system successfully activated brain areas related to motor planning/execution, multisensory integration, and attention.

The underpinning of meaningful activities by brain correlates: a systematic review

Introduction

Engaging in meaningful activities contributes to health and wellbeing. Research identifies meaningfulness by analysing retrospective and subjective data such as personal experiences in activities. Objectively measuring meaningful activities by registering the brain (fNIRS, EEG, PET, fMRI) remains poorly investigated.

Methods

A systematic review using PubMed, Web of Science, CINAHL, and Cochrane Library.

Findings

Thirty-one studies investigating the correlations between daily activities in adults, their degree of meaningfulness for the participant, and the brain areas involved, were identified. The activities could be classified according to the degree of meaningfulness, using the attributes of meaningfulness described in the literature. Eleven study activities contained all attributes, which means that these can be assumed to be meaningful for the participant. Brain areas involved in these activities were generally related to emotional and affective processing, motivation, and reward.

Conclusion

Although it is demonstrated that neural correlates of meaningful activities can be measured objectively by neurophysiological registration techniques, "meaning" as such has not yet been investigated explicitly. Further neurophysiological research for objective monitoring of meaningful activities is recommended.

Predicting poststroke dyskinesia with resting-state functional connectivity in the motor network

SIGNIFICANCE

Motor function evaluation is essential for poststroke dyskinesia rehabilitation. Neuroimaging techniques combined with machine learning help decode a patient's functional status. However, more research is needed to investigate how individual brain function information predicts the dyskinesia degree of stroke patients.

AIM

We investigated stroke patients' motor network reorganization and proposed a machine learning-based method to predict the patients' motor dysfunction.

APPROACH

Near-infrared spectroscopy (NIRS) was used to measure hemodynamic signals of the motor cortex in the resting state (RS) from 11 healthy subjects and 31 stroke patients, 15 with mild dyskinesia (Mild), and 16 with moderate-to-severe dyskinesia (MtS). The graph theory was used to analyze the motor network characteristics.

RESULTS

The small-world properties of the motor network were significantly different between groups: (1) clustering coefficient, local efficiency, and transitivity: MtS > Mild > Healthy and (2) global efficiency: MtS < Mild < Healthy. These four properties linearly correlated with patients' Fugl-Meyer Assessment scores. Using the small-world properties as features, we constructed support vector machine (SVM) models that classified the three groups of subjects with an accuracy of 85.7%.

CONCLUSIONS

Our results show that NIRS, RS functional connectivity, and SVM together constitute an effective method for assessing the poststroke dyskinesia degree at the individual level.

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.

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.

Functional Near-Infrared Spectroscopy as Promising Method for Studying Cognitive Functions in Children

The description of new promising method of functional neuroimaging, functional near-infrared spectroscopy (fNIRS), is presented. General information on functional tomography and its features in children are given. Brief description on the history of fNIRS development, the method itself, its advantages and disadvantages are covered. fNIRS implementation areas in science and clinical practice are clarified. fNIRS features are described, and the role of this method among others in functional tomography is determined. It was noted that fNIRS significantly complements other research and diagnostic methods, including functional magnetic resonance imaging, electroencephalography, induced potentials, thereby expanding the range of scientific and clinical issues that can be solved by functional neuroimaging.

Neuroimaging Modalities in Alzheimer’s Disease: Diagnosis and Clinical Features

Alzheimer’s disease (AD) is a neurodegenerative disease causing progressive cognitive decline until eventual death. AD affects millions of individuals worldwide in the absence of effective treatment options, and its clinical causes are still uncertain. The onset of dementia symptoms indicates severe neurodegeneration has already taken place. Therefore, AD diagnosis at an early stage is essential as it results in more effective therapy to slow its progression. The current clinical diagnosis of AD relies on mental examinations and brain imaging to determine whether patients meet diagnostic criteria, and biomedical research focuses on finding associated biomarkers by using neuroimaging techniques. Multiple clinical brain imaging modalities emerged as potential techniques to study AD, showing a range of capacity in their preciseness to identify the disease. This review presents the advantages and limitations of brain imaging modalities for AD diagnosis and discusses their clinical value.

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.

Characterization of cortical hemodynamic changes following sensory, visual, and speech activation by intraoperative optical imaging utilizing phase-based evaluation methods

Alterations within cerebral hemodynamics are the intrinsic signal source for a wide variety of neuroimaging techniques. Stimulation of specific functions leads due to neurovascular coupling, to changes in regional cerebral blood flow, oxygenation and volume. In this study, we investigated the temporal characteristics of cortical hemodynamic responses following electrical, tactile, visual, and speech activation for different stimulation paradigms using Intraoperative Optical Imaging (IOI). Image datasets from a total of 22 patients that underwent surgical resection of brain tumors were evaluated. The measured reflectance changes at different light wavelength bands, representing alterations in regional cortical blood volume (CBV), and deoxyhemoglobin (HbR) concentration, were assessed by using Fourier-based evaluation methods. We found a decrease of CBV connected to an increase of HbR within the contralateral primary sensory cortex (SI) in patients that were prolonged (30 s/15 s) electrically stimulated. Additionally, we found differences in amplitude as well as localization of activated areas for different stimulation patterns. Contrary to electrical stimulation, prolonged tactile as well as prolonged visual stimulation are provoking increases in CBV within the corresponding activated areas (SI, visual cortex). The processing of the acquired data from awake patients performing speech tasks reveals areas with increased, as well as areas with decreased CBV. The results lead us to the conclusion, that the CBV decreases in connection with HbR increases in SI are associated to processing of nociceptive stimuli and that stimulation type, as well as paradigm have a nonnegligible impact on the temporal characteristics of the following hemodynamic response.

Reduced prefrontal cortex and sympathetic nervous system activity correlate with fatigue after aHSCT

Long-term fatigue and cognitive dysfunction affects 35% of allogeneic haematopoietic stem cell transplantation (aHSCT) survivors, suggesting a dysfunctional prefrontal cortex. In this study, we assessed prefrontal cortex and sympathetic nervous system activity in aHSCT patients with fatigue (n = 12), non-fatigued patients (n = 12) and healthy controls (n = 27). Measurement of near-infrared spectroscopy and electrodermal activity was carried out at rest and during cognitive performance (Stroop, verbal fluency and emotion regulation tasks). Prefrontal cortex and sympathetic nervous system activity were also analyzed in response to dopamine and noradrenaline increase after a single dose of methylphenidate. Baseline cognitive performance was similar in the two patient groups. However, after methylphenidate, only non-fatigued patients improved in Stroop accuracy and had better verbal fluency task performance compared to the fatigued group. Task-related activation of prefrontal cortex in fatigued patients was lower compared to non-fatigued patients during all cognitive tests, both before and after methylphenidate administration. During the Stroop task, reaction time, prefrontal cortex activation, and sympathetic nervous system activity were all lower in fatigued patients compared to healthy controls, but similar in non-fatigued patients and healthy controls.Reduced prefrontal cortex activity and sympathetic arousal suggests novel treatment targets to improve fatigue after aHSCT.

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

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

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

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

A Narrative Review on Clinical Applications of fNIRS

Functional near-infrared spectroscopy (fNIRS) is a relatively new imaging modality in the functional neuroimaging research arena. The fNIRS modality non-invasively investigates the change of blood oxygenation level in the human brain utilizing the transillumination technique. In the last two decades, the interest in this modality is gradually evolving for its real-time monitoring, relatively low-cost, radiation-less environment, portability, patient-friendliness, etc. Including brain-computer interface and functional neuroimaging research, this technique has some important application of clinical perspectives such as Alzheimer's disease, schizophrenia, dyslexia, Parkinson's disease, childhood disorders, post-neurosurgery dysfunction, attention, functional connectivity, and many more can be diagnosed as well as in some form of assistive modality in clinical approaches. Regarding the issue, this review article presents the current scopes of fNIRS in medical assistance, clinical decision making, and future perspectives. This article also covers a short history of fNIRS, fundamental theories, and significant outcomes reported by a number of scholarly articles. Since this review article is hopefully the first one that comprehensively explores the potential scopes of the fNIRS in a clinical perspective, we hope it will be helpful for the researchers, physicians, practitioners, current students of the functional neuroimaging field, and the related personnel for their further studies and applications.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Age-related prefrontal cortex activation in associative memory: An fNIRS pilot study

Older adults typically perform more poorly than younger adults in free recall memory tests. This age-related deficit has been linked to decline of brain activation and brain prefrontal lateralization, which may be the result of compensatory mechanisms. In the present pilot study, we investigated the effect of age on prefrontal cortex (PFC) activation during performance of a task that requires memory associations (temporal vs. spatial clustering), using functional Near-Infrared Spectroscopy (fNIRS). Ten younger adults, ten cognitively high-performing older individuals, and ten low-performing older individuals completed a free recall task, where either a temporal or spatial strategy (but not both simultaneously) could be employed to retrieve groups of same-category stimuli, whilst changes in PFC hemodynamics were recorded by means of a 12-channel fNIRS system. The results suggest PFC activation, and right lateralization specific to younger adults. Moreover, age did not affect use of memory organization, given that temporal clustering was preferred over spatial clustering in all groups. These findings are in line with previous literature on the aging brain and on temporal organization of memory. Our results also suggest that the PFC may be specifically involved in memory for temporal associations. Future research may consider whether age-related deficits in temporal organization may be an early sign of PFC pathology and possible neurodegeneration.

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

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

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

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

An EEG-fNIRS hybridization technique in the four-class classification of alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is projected to become one of the most expensive diseases in modern history, and yet diagnostic uncertainties exist that can only be confirmed by postmortem brain examination. Machine Learning (ML) algorithms have been proposed as a feasible alternative to the diagnosis of several neurological diseases and disorders, such as AD. An ideal ML-derived diagnosis should be inexpensive and noninvasive while retaining the accuracy and versatility that make ML techniques desirable for medical applications.

NEW METHODS

Two portable modalities, Electroencephalography (EEG) and functional Near-Infrared Spectroscopy (fNIRS) have been widely employed in constructing hybrid classification models to compensate for each other's weaknesses. In this study, we present a hybrid EEG-fNIRS model for classifying four classes of subjects including one healthy control (HC) group, one mild cognitive impairment (MCI) group, and, two AD patient groups. A concurrent EEG-fNIRS setup was used to record data from 29 subjects during a random digit encoding-retrieval task. EEG-derived and fNIRS-derived features were sorted using a Pearson correlation coefficient-based feature selection (PCCFS) strategy and then fed into a linear discriminant analysis (LDA) classifier to evaluate their performance.

RESULTS

The hybrid EEG-fNIRS feature set was able to achieve a higher accuracy (79.31 %) by integrating their complementary properties, compared to using EEG (65.52 %) or fNIRS alone (58.62 %). Moreover, our results indicate that the right prefrontal and left parietal regions are associated with the progression of AD.

COMPARISON WITH EXISTING METHODS

Our hybrid and portable system provided enhanced classification performance in multi-class classification of AD population.

CONCLUSIONS

These findings suggest that hybrid EEG-fNIRS systems are a promising tool that may enhance the AD diagnosis and assessment process.

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

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

Towards a Unified Framework for De-noising Neural Signals

Neural signals provide key information for decision-making processes in multiple disciplines including medicine, engineering, and neuroscience. The correct interpretation of these signals, however, requires substantial processing, especially when the signals exhibit low Signal to Noise Ratio (SNR). Electroencephalographic (EEG) signals are considered among this group and require effective handling of multiple types of artifactual components. Unfortunately, most available de-noising tools are suitable only for offline signal processing. For some artifacts (e.g., EEG motion artifacts), no established method of effective denoising exists for offline or real-time applications. Thus, there is a critical need for methods that can handle artifacts in neural signals with high performance, reliability and real-time capability. Here, we propose novel methods for handling some of the most challenging artifacts that exhibit highly complex dynamics, including motion artifacts. Having the same core sample-adaptive processing tool used for handling different types of artifacts, we present our efforts towards a unified framework for neural data artifact denoising with real-time compatibility.

Performance Monitoring via Functional Near Infrared Spectroscopy for Virtual Reality Based Basic Life Support Training

The use of serious game tools in training of medical professions is steadily growing. However, there is a lack of reliable performance assessment methods to evaluate learner’s outcome. The aim of this study is to determine whether functional near infrared spectroscopy (fNIRS) can be used as an additional tool for assessing the learning outcome of virtual reality (VR) based learning modules. The hypothesis is that together with an improvement in learning outcome there would be a decrease in the participants’ cerebral oxygenation levels measured from the prefrontal cortex (PFC) region and an increase of participants’ serious gaming results. To test this hypothesis, the subjects were recruited and divided into four groups with different combinations of prior virtual reality experience and prior Basic Life Support (BLS) knowledge levels. A VR based serious gaming module for teaching BLS and 16-Channel fNIRS system were used to collect data from the participants. Results of the participants’ scores acquired from the serious gaming module were compared with fNIRS measures on the initial and final training sessions. Kruskal Wallis test was run to determine any significant statistical difference between the groups and Mann–Whitney U test was utilized to obtain pairwise comparisons. BLS training scores of the participants acquired from VR based serious game’s the learning management system and fNIRS measurements revealed decrease in use of resources from the PFC, but increase in behavioral performance. Importantly, brain-based measures can provide an additional quantitative metric for trainee’s expertise development and can assist the medical simulation instructors.

Validating attentive locomotion training using interactive treadmill: an fNIRS study

BACKGROUND

Existing treadmill-based locomotion training, which has been used for gait function recovery, still has limitations, such as less attentive training. Interactive treadmills (ITMs) were developed to overcome these limitations, but it has not yet been verified that ITMs can make the user pay closer attention to walk training.

METHODS

An experimental comparison between ITMs and conventional treadmills was conducted by measuring the level of the user's attention using functional near-infrared spectroscopy (fNIRS). To consider the effect of task complexity on the subject's attention, we provided two (slow and fast) speed conditions for walking on both treadmills.

RESULTS

Both the cortical activity images and oxygenated hemoglobin (oxyHb) changes showed that the level of attention to walking induced by the ITM was significantly higher than that induced by the conventional treadmill. We found that the walking speed on the ITM also affected the level of attention.

CONCLUSION

ITM-based locomotion training would be a promising solution to the limitations of existing treadmill-based locomotion training currently used to improve gait function recovery.

TRIAL REGISTRATION

DGIST-HR-150309-03-02 . Registered 01 March 2015.

Cognitive Involvement in Balance, Gait and Dual-Tasking in Aging: A Focused Review From a Neuroscience of Aging Perspective

A substantial corpus of evidence suggests that the cognitive involvement in postural control and gait increases with aging. A large portion of such studies were based on dual-task experimental designs, which typically use the simultaneous performance of a motor task (e.g., static or dynamic balancing, walking) and a continuous cognitive task (e.g., mental arithmetic, tone detection). This focused review takes a cognitive neuroscience of aging perspective in interpreting cognitive motor dual-task findings. Specifically, we consider the importance of identifying the neural circuits that are engaged by the cognitive task in relation to those that are engaged during motor task performance. Following the principle of neural overlap, dual-task interference should be greatest when the cognitive and motor tasks engage the same neural circuits. Moreover, the literature on brain aging in general, and models of dedifferentiation and compensation, in particular, suggest that in cognitive motor dual-task performance, the cognitive task engages different neural substrates in young as compared to older adults. Also considered is the concept of multisensory aging, and the degree to which the age-related decline of other systems (e.g., vision, hearing) contribute to cognitive load. Finally, we discuss recent work on focused cognitive training, exercise and multimodal training of older adults and their effects on postural and gait outcomes. In keeping with the principle of neural overlap, the available cognitive training research suggests that targeting processes such as dividing attention and inhibition lead to improved balance and gait in older adults. However, more studies are needed that include functional neuroimaging during actual, upright performance of gait and balance tasks, in order to directly test the principle of neural overlap, and to better optimize the design of intervention studies to improve gait and posture.

Timing of cortical activation during spontaneous swallowing

Saliva accumulation in the oropharynx generates an automatic pattern of swallowing in the brainstem in animals. Previous fMRI studies have found that spontaneous saliva and water swallows in humans evoked activation following swallow onset in both precentral motor and postcentral somatosensory cortical regions. Using event-related averaging of continuous functional near infrared spectroscopy (fNIRS), we examined cortical hemodynamic responses (HDR) from 5 s before to 35 s after spontaneous reflexive saliva swallow onset in the lateral postcentral somatosensory and precentral motor regions in both hemispheres in healthy volunteers. Three HDR changes from baseline were detected. First, the onset of HDR occurred 2 s before swallow onset in the left postcentral somatosensory area and 0.67 s before swallow onset in the right postcentral somatosensory area. Second, an early HDR peak amplitude occurred 3-4 s after swallow onset in all four regions. Z scores relative to baseline pre-swallow cortical activity levels averaged 20 and 22.7 s in the right and left somatosensory regions and 10 and 15.8 s in left and right motor areas, respectively. Finally, a late HDR peak occurring between 22 and 23 s after swallow onset in the somatosensory regions and 17-19 s in the motor areas likely resulted from esophageal peristalsis. Overall, cortical activation timing relative to swallow onsets showed activation began before the pharyngeal phase of swallowing in the somatosensory areas. This indicates that somatosensory triggering of swallowing occurs not only in the brainstem but also in the cortex for reflexive saliva swallowing in awake humans.

Multichannel wearable fNIRS-EEG system for long-term clinical monitoring

Continuous brain imaging techniques can be beneficial for the monitoring of neurological pathologies (such as epilepsy or stroke) and neuroimaging protocols involving movement. Among existing ones, functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) have the advantage of being noninvasive, nonobstructive, inexpensive, yield portable solutions, and offer complementary monitoring of electrical and local hemodynamic activities. This article presents a novel system with 128 fNIRS channels and 32 EEG channels with the potential to cover a larger fraction of the adult superficial cortex than earlier works, is integrated with 32 EEG channels, is light and battery-powered to improve portability, and can transmit data wirelessly to an interface for real-time display of electrical and hemodynamic activities. A novel fNIRS-EEG stretchable cap, two analog channels for auxiliary data (e.g., electrocardiogram), eight digital triggers for event-related protocols and an internal accelerometer for movement artifacts removal contribute to improve data acquisition quality. The system can run continuously for 24 h. Following instrumentation validation and reliability on a solid phantom, performance was evaluated on (1) 12 healthy participants during either a visual (checkerboard) task at rest or while pedalling on a stationary bicycle or a cognitive (language) task and (2) 4 patients admitted either to the epilepsy (n = 3) or stroke (n = 1) units. Data analysis confirmed expected hemodynamic variations during validation recordings and useful clinical information during in-hospital testing. To the best of our knowledge, this is the first demonstration of a wearable wireless multichannel fNIRS-EEG monitoring system in patients with neurological conditions. Hum Brain Mapp 39:7-23, 2018. © 2017 Wiley Periodicals, Inc.

A pilot study on the optimal speeds for passive wrist movements by a rehabilitation robot of stroke patients: A functional NIRS study

The optimal conditions inducing proper brain activation during performance of rehabilitation robots should be examined to enhance the efficiency of robot rehabilitation based on the concept of brain plasticity. In this study, we attempted to investigate differences in cortical activation according to the speeds of passive wrist movements performed by a rehabilitation robot for stroke patients. 9 stroke patients with right hemiparesis participated in this study. Passive movements of the affected wrist were performed by the rehabilitation robot at three different speeds: 0.25 Hz; slow, 0.5Hz; moderate and 0.75 Hz; fast. We used functional near-infrared spectroscopy to measure the brain activity during the passive movements performed by a robot. Group-average activation map and the relative changes in oxy-hemoglobin (ΔOxyHb) in two regions of interest: the primary sensory-motor cortex (SM1); premotor area (PMA) and region of all channels were measured. In the result of group-averaged activation map, the contralateral SM1, PMA and somatosensory association cortex (SAC) showed the greatest significant activation according to the movements at 0.75 Hz, while there is no significantly activated area at 0.5 Hz. Regarding ΔOxyHb, no significant diiference was observed among three speeds regardless of region. In conclusion, the contralateral SM1, PMA and SAC showed the greatest activation by a fast speed (0.75 Hz) rather than slow (0.25 Hz) and moderate (0. 5 Hz) speed. Our results suggest an optimal speed for execution of the wrist rehabilitation robot. Therefore, we believe that our findings might point to several promising applications for future research regarding useful and empirically-based robot rehabilitation therapy.

Force related hemodynamic responses during execution and imagery of a hand grip task: A functional near infrared spectroscopy study

We examined force related hemodynamic changes during the performance of a motor execution (ME) and motor imagery (MI) task by means of multichannel functional near infrared spectroscopy (fNIRS). The hemodynamic responses of fourteen healthy participants were measured while they performed a hand grip execution or imagery task with low and high grip forces. We found an overall higher increase of [oxy-Hb] concentration changes during ME for both grip forces but with a delayed peak maximum for the lower grip force. During the MI task with lower grip force, the [oxy-Hb] level increases are stronger compared to the MI with higher grip force. The facilitation in performing MI with higher grip strength might thus indicate less inhibition of the actual motor act which could also explain the later increase onset of [oxy-Hb] in the ME task with the lower grip force. Our results suggest that execution and imagery of a hand grip task with high and low grip forces, leads to different cortical activation patterns. Since impaired control of grip forces during object manipulation in particular is one aspect of fine motor control deficits after stroke, our study will contribute to future rehabilitation programs enhancing patient's grip force control.

The Optimal Speed for Cortical Activation of Passive Wrist Movements Performed by a Rehabilitation Robot: A Functional NIRS Study

Objectives: To advance development of rehabilitation robots, the conditions to induce appropriate brain activation during rehabilitation performed by robots should be optimized, based on the concept of brain plasticity. In this study, we examined differences in cortical activation according to the speed of passive wrist movements performed by a rehabilitation robot. Methods: Twenty three normal subjects participated in this study. Passive movements of the right wrist were performed by the wrist rehabilitation robot at three different speeds: 0.25 Hz; slow, 0.5 Hz; moderate and 0.75 Hz; fast. We used functional near-infrared spectroscopy to measure the brain activity accompanying the passive movements performed by a robot. The relative changes in oxy-hemoglobin (HbO) were measured in two regions of interest (ROI): the primary sensory-motor cortex (SM1) and premotor area (PMA). Results: In the left SM1 the HbO value was significantly higher at 0.5 Hz, compared with movements performed at 0.25 Hz and 0.75 Hz (p < 0.05), while no significant differences were observed in the left PMA (p > 0.05). In the group analysis, the left SM1 was activated during passive movements at three speeds (uncorrected p < 0.05) and the greatest activation in the SM1 was observed at 0.5 Hz. Conclusions: In conclusion, the contralateral SM1 showed the greatest activation by a moderate speed (0.5 Hz) rather than slow (0.25 Hz) and fast (0.75 Hz) speed. Our results suggest an ideal speed for execution of the wrist rehabilitation robot. Therefore, our results might provide useful data for more effective and empirically-based robot rehabilitation therapy.

Comparable Cerebral Oxygenation Patterns in Younger and Older Adults during Dual-Task Walking with Increasing Load

The neuroimaging literature on dual-task gait clearly demonstrates increased prefrontal cortex (PFC) involvement when performing a cognitive task while walking. However, findings from direct comparisons of the cerebral oxygenation patterns of younger (YA) and older (OA) adults during dual-task walking are mixed and it is unclear how YA and OA respond to increasing cognitive load (difficulty) while walking. This functional near infra-red (fNIRS) study examined cerebral oxygenation of YA and OA during self-paced dual-task treadmill walking at two different levels of cognitive load (auditory n-back). Changes in accuracy (%) as well as oxygenated (HbO) and deoxygenated (HbR) hemoglobin were examined. For the HbO and HbR measures, eight regions of interest (ROIs) were assessed: the anterior and posterior dorsolateral and ventrolateral PFC (aDLPFC, pDLPFC, aVLPFC, pVLPFC) in each hemisphere. Nineteen YA (M = 21.83 years) and 14 OA (M = 66.85 years) walked at a self-selected pace while performing auditory 1-back and 2-back tasks. Walking alone (single motor: SM) and performing the cognitive tasks alone (single cognitive: SC) were compared to dual-task walking (DT = SM + SC). In the behavioural data, participants were more accurate in the lowest level of load (1-back) compared to the highest (2-back; p < 0.001). YA were more accurate than OA overall (p = 0.009), and particularly in the 2-back task (p = 0.048). In the fNIRS data, both younger and older adults had task effects (SM < DT) in specific ROIs for ΔHbO (three YA, one OA) and ΔHbR (seven YA, eight OA). After controlling for walk speed differences, direct comparisons between YA and OA did not reveal significant age differences, but did reveal a difficulty effect in HbO in the left aDLPFC (p = 0.028) and significant task effects (SM < DT) in HbR for six of the eight ROIs. Findings suggest that YA and OA respond similarly to manipulations of cognitive load when walking on a treadmill at a self-selected pace.

A Proof of Concept Study of Function-Based Statistical Analysis of fNIRS Data: Syntax Comprehension in Children with Specific Language Impairment Compared to Typically-Developing Controls

Functional near infrared spectroscopy (fNIRS) is a neuroimaging technology that enables investigators to indirectly monitor brain activity in vivo through relative changes in the concentration of oxygenated and deoxygenated hemoglobin. One of the key features of fNIRS is its superior temporal resolution, with dense measurements over very short periods of time (100 ms increments). Unfortunately, most statistical analysis approaches in the existing literature have not fully utilized the high temporal resolution of fNIRS. For example, many analysis procedures are based on linearity assumptions that only extract partial information, thereby neglecting the overall dynamic trends in fNIRS trajectories. The main goal of this article is to assess the ability of a functional data analysis (FDA) approach for detecting significant differences in hemodynamic responses recorded by fNIRS. Children with and without SLI wore two, 3 × 5 fNIRS caps situated over the bilateral parasylvian areas as they completed a language comprehension task. FDA was used to decompose the high dimensional hemodynamic curves into the mean function and a few eigenfunctions to represent the overall trend and variation structures over time. Compared to the most popular GLM, we did not assume any parametric structure and let the data speak for itself. This analysis identified significant differences between the case and control groups in the oxygenated hemodynamic mean trends in the bilateral inferior frontal and left inferior posterior parietal brain regions. We also detected significant group differences in the deoxygenated hemodynamic mean trends in the right inferior posterior parietal cortex and left temporal parietal junction. These findings, using dramatically different approaches, experimental designs, data sets, and foci, were consistent with several other reports, confirming group differences in the importance of these two areas for syntax comprehension. The proposed FDA was consistent with the temporal characteristics of fNIRS, thus providing an alternative methodology for fNIRS analyses.

Dynamic filtering improves attentional state prediction with fNIRS

Brain activity can predict a person's level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise - thereby increasing such state prediction accuracy - remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% ± 6% versus 72% ± 15%).

Effect of mechanical optical clearing on near-infrared spectroscopy

Near-infrared Spectroscopy (NIRS) is a broadly utilized technology with many emerging applications including clinical diagnostics, sports medicine, and functional neuroimaging, to name a few. For functional brain imaging NIR light is delivered at multiple wavelengths through the scalp and skull to the brain to enable spatial oximetry measurements. Dynamic changes in brain oxygenation are highly correlated with neural stimulation, activation, and function. Unfortunately, NIRS is currently limited by its low spatial resolution, shallow penetration depth, and, perhaps most importantly, signal corruption due to light interactions with superficial non-target tissues such as scalp and skull. In response to these issues, we have combined the non-invasive and rapidly reversible method of mechanical tissue optical clearing (MOC) with a commercially available NIRS system. MOC utilizes a compressive loading force on tissue, causing the lateral displacement of blood and water, while simultaneously thinning the tissue. A MOC-NIRS Breath Hold Test displayed a ∼3.5-fold decrease in the time-averaged standard deviation between channels, consequentially promoting greater channel agreement. A Skin Pinch Test was implemented to negate brain and muscle activity from affecting the recorded signal. These results displayed a 2.5-3.0 fold increase in raw signal amplitude. Existing NIRS instrumentation has been further integrated within a custom helmet device to provide a uniform force distribution across the NIRS sensor array. These results showed a gradual decrease in time-averaged standard deviation among channels with an increase in applied pressure. Through these experiments, and the development of the MOC-NIRS helmet device, MOC appears to provide enhancement of NIRS technology beyond its current limitations.

Separating heart and brain: on the reduction of physiological noise from multichannel functional near-infrared spectroscopy (fNIRS) signals

OBJECTIVE

Functional near-infrared spectroscopy (fNIRS) is an emerging technique for the in vivo assessment of functional activity of the cerebral cortex as well as in the field of brain-computer interface (BCI) research. A common challenge for the utilization of fNIRS in these areas is a stable and reliable investigation of the spatio-temporal hemodynamic patterns. However, the recorded patterns may be influenced and superimposed by signals generated from physiological processes, resulting in an inaccurate estimation of the cortical activity. Up to now only a few studies have investigated these influences, and still less has been attempted to remove/reduce these influences. The present study aims to gain insights into the reduction of physiological rhythms in hemodynamic signals (oxygenated hemoglobin (oxy-Hb), deoxygenated hemoglobin (deoxy-Hb)).

APPROACH

We introduce the use of three different signal processing approaches (spatial filtering, a common average reference (CAR) method; independent component analysis (ICA); and transfer function (TF) models) to reduce the influence of respiratory and blood pressure (BP) rhythms on the hemodynamic responses.

MAIN RESULTS

All approaches produce large reductions in BP and respiration influences on the oxy-Hb signals and, therefore, improve the contrast-to-noise ratio (CNR). In contrast, for deoxy-Hb signals CAR and ICA did not improve the CNR. However, for the TF approach, a CNR-improvement in deoxy-Hb can also be found.

SIGNIFICANCE

The present study investigates the application of different signal processing approaches to reduce the influences of physiological rhythms on the hemodynamic responses. In addition to the identification of the best signal processing method, we also show the importance of noise reduction in fNIRS data.

Exploring diazepam's effect on hemodynamic responses of mouse brain tissue by optical spectroscopic imaging

In this study, a simple duel-optical spectroscopic imaging apparatus capable of simultaneously determining relative changes in brain oxy-and deoxy-hemoglobin concentrations was used following administration of the anxiolytic compound diazepam in mice with strong dominant (Dom) and submissive (Sub) behavioral traits. Three month old mice (n = 30) were anesthetized and after 10 min of baseline imaging, diazepam (1.5 mg/kg) was administered and measurements were taken for 80 min. The mouse head was illuminated by white light based LED's and diffused reflected light passing through different channels, consisting of a bandpass filter and a CCD camera, respectively, was collected and analyzed to measure the hemodynamic response. This work's major findings are threefold: first, Dom and Sub animals showed statistically significant differences in hemodynamic response to diazepam administration. Secondly, diazepam was found to more strongly affect the Sub group. Thirdly, different time-series profiles were observed post-injection, which can serve as a possible marker for the groups' differentiation. To the best of our knowledge, this is the first report on the effects of an anxiolytic drug on brain hemodynamic responses in mice using diffused light optical imaging.

Cortical activation change induced by neuromuscular electrical stimulation during hand movements: a functional NIRS study

OBJECTIVES

Neuromuscular electrical stimulation (NMES) has been used in the field of rehabilitation for a long time. Previous studies on NMES have focused on the peripheral effect, in contrast, relatively little is known about the effect on the cerebral cortex. In the current study, we attempted to investigate the change of cortical activation pattern induced by NMES during execution of hand movements in normal subjects, using functional near infrared spectroscopy (fNIRS).

METHODS

Twelve healthy normal subjects were randomly assigned to the NMES group (six subjects) and the sham group (six subjects). We measured oxy-hemoglobin (HbO) in six regions of interest (ROI) during pre-NMES and post-NMES motor phase; the left dorsolateral and ventrolateral prefrontal cortex, premotor cortex, primary sensory-motor cortex (SM1), hand somatotopic area of SM1, and posterior parietal cortex. Between the pre-NMES and the post-NMES motor phases, real or sham NMES was applied on finger and wrist extensors of all subjects during a period of 5 minutes.

RESULTS

In all groups, during the pre-NMES motor phase, the HbO value in the hand somatotopic area of the left SM1 was higher than those of other ROIs. In the NMES group, during the post-NMES motor phase, HbO value variation in the hand somatotopic area of the left SM1 showed a significant decrease, compared with that of sham group (p < 0.05). However, in the sham group, similar aspect of results in HbO values of all ROIs was observed between pre-NMES and post-NMES motor phases (p > 0.05).

CONCLUSIONS

Results of this study showed that NMES induced a decrease of cortical activation during execution of hand movements. This finding appears to indicate that application of NMES can increase the efficiency of the cerebral cortex during execution of motor tasks.

The cortical activation pattern by a rehabilitation robotic hand: a functional NIRS study

Introduction: Clarification of the relationship between external stimuli and brain response has been an important topic in neuroscience and brain rehabilitation. In the current study, using functional near infrared spectroscopy (fNIRS), we attempted to investigate cortical activation patterns generated during execution of a rehabilitation robotic hand.

Methods: Ten normal subjects were recruited for this study. Passive movements of the right fingers were performed using a rehabilitation robotic hand at a frequency of 0.5 Hz. We measured values of oxy-hemoglobin (HbO), deoxy-hemoglobin (HbR) and total-hemoglobin (HbT) in five regions of interest: the primary sensory-motor cortex (SM1), hand somatotopy of the contralateral SM1, supplementary motor area (SMA), premotor cortex (PMC), and prefrontal cortex (PFC).

Results: HbO and HbT values indicated significant activation in the left SM1, left SMA, left PMC, and left PFC during execution of the rehabilitation robotic hand (uncorrected, p < 0.01). By contrast, HbR value indicated significant activation only in the hand somatotopic area of the left SM1 (uncorrected, p < 0.01).

Conclusions: Our results appear to indicate that execution of the rehabilitation robotic hand could induce cortical activation.

Exploring frontal asymmetry using functional near-infrared spectroscopy: a preliminary study of the effects of social anxiety during interaction and performance tasks

Preliminary studies examining brain function associated with social anxiety suggest the possibility of right-sided prefrontal activation associated with phobic stimulation. Although most existing neuroimaging techniques preclude participants from engaging in ecologically valid social tasks during assessment, functional near-infrared spectroscopy (fNIRS) is a promising new technique that permits such assessment. The present study investigated the utility of the fNIRS procedure and explored frontal asymmetry during in vivo social challenge tasks among female undergraduate students who scored in top and bottom percentiles on a social anxiety screening measure. Results revealed that participants in both groups experienced a significant increase in concentration of blood volume and oxygenated hemoglobin in the right hemisphere compared to the left hemisphere while giving a speech. Non-hemispheric effects were also observed. In addition, the high anxiety group showed a non-significant trend toward greater right frontal activity than the low anxiety group. This study highlights the utility of the fNIRS device in successfully assessing real-time changes in cerebrovascular response as a function of naturalistic social behavior, and supports the potential utility of this technology in the study of the neurophysiology of social anxiety.

Cortical effects of user training in a motor imagery based brain-computer interface measured by fNIRS and EEG

The present study aims to gain insights into the effects of training with a motor imagery (MI)-based brain-computer interface (BCI) on activation patterns of the sensorimotor cortex. We used functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) to investigate long-term training effects across 10 sessions using a 2-class (right hand and feet) MI-based BCI in fifteen subjects. In the course of the training a significant enhancement of activation pattern emerges, represented by an [oxy-Hb] increase in fNIRS and a stronger event-related desynchronization in the upper β-frequency band in the EEG. These effects were only visible in participants with relatively low BCI performance (mean accuracy ≤ 70%). We found that training with an MI-based BCI affects cortical activation patterns especially in users with low BCI performance. Our results may serve as a valuable contribution to the field of BCI research and provide information about the effects that training with an MI-based BCI has on cortical activation patterns. This might be useful for clinical applications of BCI which aim at promoting and guiding neuroplasticity.

NIRS in clinical neurology - a 'promising' tool?

Near-infrared spectroscopy (NIRS) has become a relevant research tool in neuroscience. In special populations such as infants and for special tasks such as walking, NIRS has asserted itself as a low resolution functional imaging technique which profits from its ease of application, portability and the option to co-register other neurophysiological and behavioral data in a 'near natural' environment. For clinical use in neurology this translates into the option to provide a bed-side oximeter for the brain, broadly available at comparatively low costs. However, while some potential for routine brain monitoring during cardiac and vascular surgery and in neonatology has been established, NIRS is largely unknown to clinical neurologists. The article discusses some of the reasons for this lack of use in clinical neurology. Research using NIRS in three major neurologic diseases (cerebrovascular disease, epilepsy and headache) is reviewed. Additionally the potential to exploit the established position of NIRS as a functional imaging tool with regard to clinical questions such as preoperative functional assessment and neurorehabilitation is discussed.