Potential Of Functional Near-infrared Spectroscopy Research Articles

Use of cortical hemodynamic responses in digital therapeutics for upper limb rehabilitation in patients with stroke

BackgroundStroke causes long-term disabilities, highlighting the need for innovative rehabilitation strategies for reducing residual impairments. This study explored the potential of functional near-infrared spectroscopy (fNIRS) for monitoring cortical activation during rehabilitation using digital therapeutics.MethodsThis cross-sectional study included 18 patients with chronic stroke, of whom 13 were men. The mean age of the patients was 67.0 ± 7.1 years. Motor function was evaluated through various tests, including the Fugl–Meyer assessment for upper extremity (FMA-UE), grip and pinch strength test, and box and block test. All the patients completed the digital rehabilitation program (MotoCog®, Cybermedic Co., Ltd., Republic of Korea) while being monitored using fNIRS (NIRScout®, NIRx Inc., Germany). Statistical parametric mapping (SPM) was employed to analyze the cortical activation patterns from the fNIRS data. Furthermore, the K-nearest neighbor (K-NN) algorithm was used to analyze task performance and fNIRS data to classify the severity of motor impairment.ResultsThe participants showed diverse task performances in the digital rehabilitation program, demonstrating distinct patterns of cortical activation that correlated with different motor function levels. Significant activation was observed in the ipsilesional primary motor area (M1), primary somatosensory area (S1), and contralateral prefrontal cortex. The activation patterns varied according to the FMA-UE scores. Positive correlations were observed between the FMA-UE scores and SPM t-values in the ipsilesional M1, whereas negative correlations were observed in the ipsilesional S1, frontal lobe, and parietal lobe. The incorporation of cortical hemodynamic responses with task scores in a digital rehabilitation program substantially improves the accuracy of the K-NN algorithm in classifying upper limb functional levels in patients with stroke. The accuracy for tasks, such as the gas stove-operation task, increased from 44.4% using only task scores to 83.3% when these scores were combined with oxy-Hb t-values from the ipsilesional M1.ConclusionsThe results advocated the development of tailored digital rehabilitation strategies by combining the behavioral and cerebral hemodynamic data of patients with stroke. This approach aligns with the evolving paradigm of personalized rehabilitation in stroke recovery, highlighting the need for further extensive research to optimize rehabilitation outcomes.

Unraveling the Potential of Functional Near-Infrared Spectroscopy (fNIRS) in Spatial Design - Literature Review and Future Research Agenda -

Unraveling the Potential of Functional Near-Infrared Spectroscopy (fNIRS) in Spatial Design - Literature Review and Future Research Agenda -

Comparing Multi-Dimensional fNIRS Features Using Bayesian Optimization-Based Neural Networks for Mild Cognitive Impairment (MCI) Detection.

The diagnosis of mild cognitive impairment (MCI), a prodromal stage of Alzheimer's disease (AD), is essential for initiating timely treatment to delay the onset of AD. Previous studies have shown the potential of functional near-infrared spectroscopy (fNIRS) for diagnosing MCI. However, preprocessing fNIRS measurements requires extensive experience to identify poor-quality segments. Moreover, few studies have explored how proper multi-dimensional fNIRS features influence the classification results of the disease. Thus, this study outlined a streamlined fNIRS preprocessing method to process fNIRS measurements and compared multi-dimensional fNIRS features with neural networks in order to explore how temporal and spatial factors affect the classification of MCI and cognitive normality. More specifically, this study proposed using Bayesian optimization-based auto hyperparameter tuning neural networks to evaluate 1D channel-wise, 2D spatial, and 3D spatiotemporal features of fNIRS measurements for detecting MCI patients. The highest test accuracies of 70.83%, 76.92%, and 80.77% were achieved for 1D, 2D, and 3D features, respectively. Through extensive comparisons, the 3D time-point oxyhemoglobin feature was proven to be a more promising fNIRS feature for detecting MCI by using an fNIRS dataset of 127 participants. Furthermore, this study presented a potential approach for fNIRS data processing, and the designed models required no manual hyperparameter tuning, which promoted the general utilization of fNIRS modality with neural network-based classification to detect MCI.

Using fNIRS to Identify Transparency- and Reliability-Sensitive Markers of Trust Across Multiple Timescales in Collaborative Human-Human-Agent Triads.

Intelligent agents are rapidly evolving from assistants into teammates as they perform increasingly complex tasks. Successful human-agent teams leverage the computational power and sensory capabilities of automated agents while keeping the human operator's expectation consistent with the agent's ability. This helps prevent over-reliance on and under-utilization of the agent to optimize its effectiveness. Research at the intersection of human-computer interaction, social psychology, and neuroergonomics has identified trust as a governing factor of human-agent interactions that can be modulated to maintain an appropriate expectation. To achieve this calibration, trust can be monitored continuously and unobtrusively using neurophysiological sensors. While prior studies have demonstrated the potential of functional near-infrared spectroscopy (fNIRS), a lightweight neuroimaging technology, in the prediction of social, cognitive, and affective states, few have successfully used it to measure complex social constructs like trust in artificial agents. Even fewer studies have examined the dynamics of hybrid teams of more than 1 human or 1 agent. We address this gap by developing a highly collaborative task that requires knowledge sharing within teams of 2 humans and 1 agent. Using brain data obtained with fNIRS sensors, we aim to identify brain regions sensitive to changes in agent behavior on a long- and short-term scale. We manipulated agent reliability and transparency while measuring trust, mental demand, team processes, and affect. Transparency and reliability levels are found to significantly affect trust in the agent, while transparency explanations do not impact mental demand. Reducing agent communication is shown to disrupt interpersonal trust and team cohesion, suggesting similar dynamics as human-human teams. Contrasts of General Linear Model analyses identify dorsal medial prefrontal cortex activation specific to assessing the agent's transparency explanations and characterize increases in mental demand as signaled by dorsal lateral prefrontal cortex and frontopolar activation. Short scale event-level data is analyzed to show that predicting whether an individual will trust the agent, with data from 15 s before their decision, is feasible with fNIRS data. Discussing our results, we identify targets and directions for future neuroergonomics research as a step toward building an intelligent trust-modulation system to optimize human-agent collaborations in real time.

The functional near-infrared spectroscopy in the diagnosis of schizophrenia

Background and objectivesObtaining an objective schizophrenia diagnosis is difficult given current practices. This study aims to evaluate the potential of functional near-infrared spectroscopy (NIRS) in diagnosing individuals with schizophrenia. MethodsThis study recruited 100 subjects with schizophrenia (45 males; 55 females) and 70 healthy controls (36 males; 34 females), and analyzed the spectrum of NIRS signals from ETG-4000 Optical Tomography System while subjects took the Verbal Fluency Test (VFT). Two professional psychiatrists performed the wave analysis. ResultsThere was no statistical difference in the number of words produced during the VFT detected between individuals with schizophrenia and the control group. Results demonstrated that NIRS had an 89.0% sensitivity and 88.6% specificity in identifying individuals with schizophrenia. ConclusionThe results of this study indicated that NIRS spectrums could play a significant role in the diagnosis of schizophrenia by providing an objective index for clinical screening and diagnostic assistance.

Functional near-infrared spectroscopy in the evaluation of urban rail transit drivers’ mental workload under simulated driving conditions

The objective of this study is to investigate the potential of functional near-infrared spectroscopy (fNIRS) combined with heart rate variability indices, for the evaluation of the mental workload of urban rail transit drivers under simulated driving conditions, particularly during task engagement and disengagement. Experienced metro drivers wearing fNIRS monitoring systems were asked to drive for 90 min in a professional metro driving simulator. Workload stimulus tasks were added and an n-back task (n = 3) was implemented to induce workload in the simulated driving experiment. Experimental results indicate that fNIRS are sensitive to mental workload and reliable for discriminating the degree of mental workload. Research findings demonstrate the feasibility and reliability of fNIRS as a tool for real-time evaluating and monitoring driver mental workload along with task factors from a perspective of brain activations during simulated or actual driving.Practitioner Summary: This study provides evidence for the potential of functional near-infrared spectroscopy (fNIRS) for the evaluation of the mental workload of urban rail transit drivers under simulated driving conditions. The first fNIRS application to mental workload evaluation in the field of urban rail transportation helps companies develop reasonable shiftwork schedule and ensure operation safety.Abbreviations: fNIRS: functional near-infrared spectroscopy; oxy-Hb: Oxy-hemoglobin; NASA-TLX: National Aeronautics and Space Administration Task Load Index; EEG: electroencephalogram; ECG: electrocardiogram; HRV: variability; LF: low-frequency power; HF: high-frequency power; PFC: prefrontal cortex; NIRS: near-infrared spectroscopy; DWT: discrete wavelet transform; EMG: electromyography; DT: determination test; TP: total power; LFnorm: standardized LF; HFnorm: standardized HF; VLF: very low frequency; deoxy-Hb: deoxy-hemoglobin.

From the Laboratory to the Classroom: The Potential of Functional Near-Infrared Spectroscopy in Educational Neuroscience.

Paralleling two decades of growth in the emergent field known as educational neuroscience is an increasing concern that educational practices and programs should be evidence-based, however, the idea that neuroscience could potentially influence education is controversial. One of the criticisms, regarding applications of the findings produced in this discipline, concerns the artificiality of neuroscientific experiments and the oversimplified nature of the tests used to investigate cognitive processes in educational contexts. The simulations may not account for all of the variables present in real classroom activities. In this study, we aim to get a step closer to the formation of data-supported classroom methodologies by employing functional near-infrared spectroscopy in various experimental paradigms. First, we present two hyperscanning scenarios designed to explore realistic interdisciplinary contexts, i.e., the classroom. In a third paradigm, we present a case study of a single student evaluated with functional near-infrared spectroscopy and mobile eye-tracking glasses. These three experiments are performed to provide proofs of concept for the application of functional near-infrared spectroscopy in scenarios that more closely resemble authentic classroom routines and daily activities. The goal of our study is to explore the potential of this technique in hopes that it offers insights in experimental design to investigate teaching-learning processes during teacher-student interactions.

Near-Infrared Spectroscopy Applied to Complex Systems and Human Hyperscanning Networking

Concepts and techniques developed to investigate complex systems have found practical implications in the study of many complex physical, biological, and social phenomena. Social neuroscience is coherently moving to new investigation and analysis approaches to properly explore social dynamics and to qualify neural processes that mediate and define them. The present paper aims to sketch a global picture of the application of the concept of synchronization to study complex social systems and the neural signatures of interpersonal coupling during interaction dynamics. We then focus on an innovative experimental paradigm—hyperscanning—that allows researchers to sample, compare, and integrate information flows related to the bodily activities of two (or more) individuals involved in a shared naturalistic or experimentally-controlled task, thus giving the opportunity to explore inter-individual synchronization and inter-brain coupling. In particular, the potential of functional Near-Infrared Spectroscopy (fNIRS) as a primary investigation tool in the field is discussed. Finally, we introduce the most-used quantification and analysis methods for hyperscanning applications.

Decoding Answers to Four-Choice Questions Using Functional near Infrared Spectroscopy

This paper presents a functional near infrared (NIR) spectroscopy-based paradigm that can be used to decode answers to four-choice questions. Ten healthy subjects were asked to perform one of the four different brain activities, that is, right-hand motor imagery (RMI), left-hand motor imagery (LMI), mental arithmetic (MA) and mental counting (MC), to answer the given four-choice questions. In selecting the A, B, C or D choices, the subjects were asked to perform RMI, LMI, MA or MC, respectively. Signals from the primary motor and prefrontal cortices were acquired simultaneously using a continuous-wave functional NIR spectroscopy system. The four activities were classified using multiclass linear discriminant analysis to decode the answers to an average accuracy of 73.3% across the 10 subjects. The results demonstrate the potential of functional NIR spectroscopy to decode answers to four-choice questions using four different intentionally generated brain activities as control signals.

Differences in anxiety sensitivity and sensation-seeking in relation to risk-taking on the Balloon Analogue Risk Task (BART)

Risk is an important factor impacting financial decisions. Risk can be processed objectively, e.g. as variance across possible outcomes of a choice option or subjectively, e.g. as value of that variance to a given individual. The aim of the present study was to test the potential of functional near-infrared spectroscopy (fNIRS) in assessing these different ways of processing risk while subjects decided between either high or low risk financial options or a safe (risk-free) option. For comparison we simultaneously measured electrodermal activity (EDA), a well-established method in decision-making research and a core measure of affective processes. FNIRS showed that lateral prefrontal cortex responses to high risk were enhanced relative to low risk only in risk-seeking individuals but reduced relative to low risk in risk-averse individuals. This is in-line with individual-specific risk processing reflecting the subjective value of risk. By contrast, EDA showed enhanced responses to high risk, independent of individual risk attitude, in-line with the notion of objective risk processing. The dissociation between the two measures arose even though they overall were equally sensitive to detect individual risk-related differences and even though there was an increased, risk attitude-independent, temporal coherence between the two measures during high-risk conditions. Our results suggest that hemodynamic responses in lateral prefrontal cortex as measured by fNIRS reflect the subjective value of risk, whereas EDA may index the objective amount of risk people are presented with. The findings suggest that fNIRS could be a useful method for studying risk behavior in financial decisions.

Unleashing the future potential of functional near-infrared spectroscopy in brain sciences.

The wondrous innovations bound to the introduction of functional near-infrared spectroscopy in cognitive neuroscience are characterized by a multifaceted nature, ranging from technological improvements to sophisticated signal processing methods; the outstanding progress enabled scientists to investigate a variety of hard-to-test clinical populations and to successfully employ optical imaging in fields that were almost unimaginable twenty years ago. Here we illustrate how the emerging use of fNIRS methodologies might represent a drawing power in a variety of challenging experimental and medical contexts; we expect in the near future a wide increase of the use of wireless fNIRS, especially in children and in particular clinical populations, as well as a striking progress of fNIRS-BCI and hybrid BCI systems for neurofeedback and neurorehabilitation. These emerging trends might dramatically foster the future potential of fNIRS in brain sciences, provided that they are properly supported by a significant progress in signal processing and cognitive neuroscience.

Hemodynamic and affective correlates assessed during performance on the Columbia Card Task (CCT)

The study aimed to test the potential of functional near-infrared spectroscopy (fNIRS) in combination with electrodermal activity (EDA) in a decision paradigm by means of the Columbia card task (CCT). The CCT is a dynamic decision task characterized by assessing subjects' risk-taking via eliciting voluntary stopping points in a series of incrementally increasingly risky choices. Using the combined fNIRS-EDA approach, we aim to examine the hemodynamic and affective correlates of both decision and outcome responses during performance on the CCT. Twenty healthy subjects completed the Cold and Hot CCT version while fNIRS over prefrontal cortex and EDA were recorded. Results showed that (1) in the decision phase fNIRS revealed larger total hemoglobin concentration changes [tHb] in the Cold as compared to the Hot CCT, whereas EDA revealed an opposite pattern with larger skin conductance responses (SCRs) to the Hot as compared to the Cold CCT. (2) No significant [tHb] signals or SCRs were found in the outcome phase. (3) Coherence calculations between fNIRS and EDA in the heart rate frequency showed a significant increase during the Hot as compared to the Cold CCT. Our findings designate fNIRS as suitable tool for monitoring decision-making processes. The combination of fNIRS and EDA demonstrates the potential of simultaneously assessing the interaction between hemodynamic and affective responses which can provide additional information concerning the relationship between these two physiological systems for various research areas.

Comparison of functional near-infrared spectroscopy and electrodermal activity in assessing objective versus subjective risk during risky financial decisions

Risk is an important factor impacting financial decisions. Risk can be processed objectively, e.g. as variance across possible outcomes of a choice option or subjectively, e.g. as value of that variance to a given individual. The aim of the present study was to test the potential of functional near-infrared spectroscopy (fNIRS) in assessing these different ways of processing risk while subjects decided between either high or low risk financial options or a safe (risk-free) option. For comparison we simultaneously measured electrodermal activity (EDA), a well-established method in decision-making research and a core measure of affective processes. FNIRS showed that lateral prefrontal cortex responses to high risk were enhanced relative to low risk only in risk-seeking individuals but reduced relative to low risk in risk-averse individuals. This is in-line with individual-specific risk processing reflecting the subjective value of risk. By contrast, EDA showed enhanced responses to high risk, independent of individual risk attitude, in-line with the notion of objective risk processing. The dissociation between the two measures arose even though they overall were equally sensitive to detect individual risk-related differences and even though there was an increased, risk attitude-independent, temporal coherence between the two measures during high-risk conditions. Our results suggest that hemodynamic responses in lateral prefrontal cortex as measured by fNIRS reflect the subjective value of risk, whereas EDA may index the objective amount of risk people are presented with. The findings suggest that fNIRS could be a useful method for studying risk behavior in financial decisions.

Classification of frontal cortex haemodynamic responses during cognitive tasks using wavelet transforms and machine learning algorithms

Recent advances in neuroimaging demonstrate the potential of functional near-infrared spectroscopy (fNIRS) for use in brain–computer interfaces (BCIs). fNIRS uses light in the near-infrared range to measure brain surface haemoglobin concentrations and thus determine human neural activity. Our primary goal in this study is to analyse brain haemodynamic responses for application in a BCI. Specifically, we develop an efficient signal processing algorithm to extract important mental-task-relevant neural features and obtain the best possible classification performance. We recorded brain haemodynamic responses due to frontal cortex brain activity from nine subjects using a 19-channel fNIRS system. Our algorithm is based on continuous wavelet transforms (CWTs) for multi-scale decomposition and a soft thresholding algorithm for de-noising. We adopted three machine learning algorithms and compared their performance. Good performance can be achieved by using the de-noised wavelet coefficients as input features for the classifier. Moreover, the classifier performance varied depending on the type of mother wavelet used for wavelet decomposition. Our quantitative results showed that CWTs can be used efficiently to extract important brain haemodynamic features at multiple frequencies if an appropriate mother wavelet function is chosen. The best classification results were obtained by a specific combination of input feature type and classifier.