Functional connectivity analysis using fNIRS in healthy subjects during prolonged simulated driving

Mental fatigue causes significant activation of the prefrontal cortex: A systematic review and meta-analysis of fNIRS studies

Mental fatigue, a psychobiological prevalent and underestimated condition, is defined by increased lethargy and impaired concentration. This condition is not restricted by age and is exacerbated by various predisposing factors. Prolonged mental fatigue in occupational environments raises the probability of accidents or fatalities. Its fundamental mechanism is largely obscure and inherently subjective, thus there is no universally accepted parameter for its detection. Recently, there has been an increase in research that focuses on the use of functional near-infrared spectroscopy (fNIRS) to observe changes in brain hemoglobin during mental fatigue. Thus, this study assessed the reliability of oxygenhemoglobin and deoxyhemoglobin as fatigue biomarkers and conducted a systematic review and meta-analysis of studies which used fNIRS to monitor mental fatigue. The findings revealed significant activation of the prefrontal lobe under mental fatigue, and its activation level is intricately associated with the monitoring of diverse states during mental fatigue. Importantly, the type of induced mental fatigue and whether pre-trial training was provided to subjects were independent of the prefrontal lobe activation level. Overall, fNIRS proves to be an effective tool in tracking brain activity during mental fatigue, with a highly active prefrontal cortex acting as a dependable indicator for early identification of mental fatigue.

Characteristic brain functional activation and connectivity during actual and imaginary right-handed grasp

We used 34-channel functional near infrared spectroscopy to investigate and compare changes in oxyhemoglobin concentration of brain networks in bilateral prefrontal cortex, sensorimotor cortex, and occipital lobe of 22 right-handed healthy adults during executive right-handed grasp (motor execution task) and imagined right-handed grasp (motor imagery task). Then calculated lateral index and functional contribution degree, and measured functional connectivity strength between the regions of interest. In the motor executive block task, there was a significant increase in oxyhemoglobin concentration in regions of interest except for right occipital lobe (P<0.05), while in the motor imagery task, all left regions of interest's oxyhemoglobin concentration increased significantly (P<0.05). Except the prefrontal cortex in motor executive task, the left side of the brain was dominant. Left sensorimotor cortex played a major role in these two tasks, followed by right sensorimotor cortex. Among all functional contribution degree, left sensorimotor cortex, right sensorimotor cortex and left occipital lobe ranked top three during these tasks. In continuous acquisition tasks, functional connectivity on during motor imagery task was stronger than that during motor executive task. Brain functions during two tasks of right-hand grasping movement were partially consistent. However, the excitability of brain during motor imagery was lower, and it was more dependent on the participation of left prefrontal cortex, and its synchronous activity of the whole brain was stronger. The trend of functional contribution degree was basically consistent with oxyhemoglobin concentration and lateral index, and can be used as a novel index to evaluate brain function. [ChiCTR2200063792 (2022-09-16)].

Clustering for mitigating subject variability in driving fatigue classification using electroencephalography source-space functional connectivity features

Objective.While Electroencephalography (EEG)-based driver fatigue state classification models have demonstrated effectiveness, their real-world application remains uncertain. The substantial variability in EEG signals among individuals poses a challenge in developing a universal model, often necessitating retraining with the introduction of new subjects. However, obtaining sufficient data for retraining, especially fatigue data for new subjects, is impractical in real-world settings.Approach.In response to these challenges, this paper introduces a hybrid solution for fatigue detection that combines clustering with classification. Unsupervised clustering groups subjects based on their EEG functional connectivity (FC) in an alert state, and classification models are subsequently applied to each cluster for predicting alert and fatigue states.Main results. Results indicate that classification on clusters achieves higher accuracy than scenarios without clustering, suggesting successful grouping of subjects with similar FC characteristics through clustering, thereby enhancing the classification process.Significance.Furthermore, the proposed hybrid method ensures a practical and realistic retraining process, improving the adaptability and effectiveness of the fatigue detection system in real-world applications.

Phase coherence-A time-localized approach to studying interactions

Coherence measures the similarity of progression of phases between oscillations or waves. When applied to multi-scale, nonstationary dynamics with time-varying amplitudes and frequencies, high values of coherence provide a useful indication of interactions, which might otherwise go unnoticed. However, the choice of analyzing coherence based on phases and amplitudes (amplitude-weighted phase coherence) vs only phases (phase coherence) has long been seen as arbitrary. Here, we review the concept of coherence and focus on time-localized methods of analysis, considering both phase coherence and amplitude-weighted phase coherence. We discuss the importance of using time-localized analysis and illustrate the methods and their practicalities on both numerically modeled and real time-series. The results show that phase coherence is more robust than amplitude-weighted phase coherence to both noise perturbations and movement artifacts. The results also have wider implications for the analysis of real data and the interpretation of physical systems.

Effects of transcranial magnetic stimulation on dynamic functional networks in stroke patients as assessed by functional near-infrared spectroscopy: a randomized controlled clinical trial

Studies have shown that there is heterogeneity in the efficacy bewteen the low-frequency (LF) and high-frequency (HF) repetitive transcranial magnetic stimulation (rTMS), but the neural mechanisms underlying the differences in efficacy remain unclear. This study aimed to investigate the specific effects of LF- and HF-rTMS on cortial functional network and the process of neural regulation. A total of sixty-eight patients with hemiplegic motor impairment after stroke were randomly allocated to one of three groups: the LF-rTMS, HF-rTMS, and sham groups. Tissue concentrations of oxyhaemoglobin and deoxyhaemoglobin oscillations in cerebral cortex regions were measured by functional near-infrared spectroscopy (fNIRS) in the resting and rTMS states. Four specific time-windows were divided from the trial duration to observe dynamic changes in cortical haemodynamic responses. Compared with sham, LF-rTMS significantly induced the activation of the contralesional superior frontal cortex and premotor cortex, and continuously regulated ipsilesional hemisphere functional networks in stroke patients. However, HF-rTMS did not induce a significant neurovascular coupling response. Our study provided evidence that LF- and HF-rTMS interventions induced different neurovascular coupling responses and demonstrated the cortical functional network change process of rTMS in specific time-windows. These findings may help to understand the differences in the efficacy of rTMS modalities.

Assessing the development of mental fatigue during simulated flights with concurrent EEG-fNIRS measurement

Mental fatigue (MF) can impair pilots' performance and reactions to unforeseen events and is therefore an important concept within aviation. The physiological measurement of MF, especially with EEG and, in recent years, fNIRS, has gained much attention. However, a systematic investigation and comparison of the measurements is seldomly done. We induced MF via time on task during a 90-min simulated flight task and collected concurrent EEG-fNIRS, performance and self-report data from 31 participants. While their subjective MF increased linearly, the participants were able to keep their performance stable over the course of the experiment. EEG data showed an early increase and levelling in parietal alpha power and a slower, but steady increase in frontal theta power. No consistent trend could be observed in the fNIRS data. Thus, more research on fNIRS is needed to understand its possibilities and limits for MF assessment, and a combination with EEG is advisable to compare and validate results. Until then, EEG remains the better choice for continuous MF assessment in cockpit applications because of its high sensitivity to a transition from alert to fatigued, even before performance is impaired.

Emergency Braking Evoked Brain Activities during Distracted Driving

Electroencephalogram (EEG) was used to analyze the mechanisms and differences in brain neural activity of drivers in visual, auditory, and cognitive distracted vs. normal driving emergency braking conditions. A pedestrian intrusion emergency braking stimulus module and three distraction subtasks were designed in a simulated experiment, and 30 subjects participated in the study. The common activated brain regions during emergency braking in different distracted driving states included the inferior temporal gyrus, associated with visual information processing and attention; the left dorsolateral superior frontal gyrus, related to cognitive decision-making; and the postcentral gyrus, supplementary motor area, and paracentral lobule associated with motor control and coordination. When performing emergency braking under different driving distraction states, the brain regions were activated in accordance with the need to process the specific distraction task. Furthermore, the extent and degree of activation of cognitive function-related prefrontal regions increased accordingly with the increasing task complexity. All distractions caused a lag in emergency braking reaction time, with 107.22, 67.15, and 126.38 ms for visual, auditory, and cognitive distractions, respectively. Auditory distraction had the least effect and cognitive distraction the greatest effect on the lag.

A functional near-infrared spectroscopy study of the effects of video game-based bilateral upper limb training on brain cortical activation and functional connectivity

Video game-based therapies are widely used in rehabilitation. Compared with conventional bilateral upper limb training (CBULT), the effects of video game-based bilateral upper limb training (VGBULT) on brain cortical activation and functional connectivity, still not fully clear. We have developed a VGBULT system, and measured the brain activity of 20 elderly subjects (10 male, mean age = 62.4 ± 5.8) while performing CBULT and VGBULT tasks by using functional near infrared spectroscopy (fNIRS). The results showed that the cerebral cortex of the two groups both showed significant activation (p < 0.05), compared with the baseline; In the VGBLUT group, the activation of motor cortex (MC) and prefrontal cortex (PFC) was stronger, and the functional connectivity between PFC and MC was also enhanced. This study showed that VGBULT is potentially more beneficial for the elderly neural activities and cognitive control, and provides a theoretical basis for future research and development of such rehabilitation products. Moreover, fNIRS is a reliable tool for tracking brain activation in the evaluation of retraining regimens.

Brain Functional Connectivity in Middle-Aged Hong Chuan Tai Chi Players in Resting State

Tai Chi is an effective strategy for slowing cognitive decline, although the underlying mechanism remains unclear. We designed a cross-sectional study to examine brain functional connectivity in middle-aged Hong Chuan Tai Chi practitioners. Eighteen middle-aged Hong Chuan Tai Chi practitioners and 22 age-matched Tai Chi-naïve controls completed functional near-infrared spectroscopy (fNIRS) tests to evaluate oxyhemoglobin changes in the prefrontal cortex (PFC), motor cortex (MC), and occipital cortex (OC) in five frequency intervals (I, 0.6-2 Hz; II, 0.145-0.6 Hz; III, 0.052-0.145 Hz; IV, 0.021-0.052 Hz; V, 0.0095-0.021 Hz). Wavelet phase coherence was used to analyze the match between the instantaneous phases of the two signals to accurately measure brain functional connectivity. Global cognition was measured using the Montreal Cognitive Assessment scale. Compared with the control group, Hong Chuan Tai Chi practitioners had better global cognition (p < 0.01) and showed higher functional connectivity of the PFC, MC, and OC in intervals I, III, VI, and V in the resting state within the same brain hemispheres or between the left and right hemispheres. Our findings revealed that middle-aged Hong Chuan Tai Chi practitioners had higher functional connectivity of the PFC, MC, and OC across both brain hemispheres in cardiac activity, myogenic activity, sympathetic nervous system, and endothelial cell metabolic activities which may contribute to higher global cognition.

Effects of robot-assisted task-oriented upper limb motor training on neuroplasticity in stroke patients with different degrees of motor dysfunction: A neuroimaging motor evaluation index

Introduction

Although robot-assisted task-oriented upper limb (UL) motor training had been shown to be effective for UL functional rehabilitation after stroke, it did not improve UL motor function more than conventional therapy. Due to the lack of evaluation of neurological indicators, it was difficult to confirm the robot treatment parameters and clinical efficacy in a timely manner. This study aimed to explore the changes in neuroplasticity induced by robot-assisted task-oriented UL motor training in different degrees of dysfunction patients and extract neurological evaluation indicators to provide the robot with additional parameter information.

Materials and methods

A total of 33 adult patients with hemiplegic motor impairment after stroke were recruited as participants in this study, and a manual muscle test divided patients into muscle strength 0–1 level (severe group, n = 10), 2–3 level (moderate group, n = 14), and 4 or above level (mild group, n = 9). Tissue concentration of oxyhemoglobin and deoxyhemoglobin oscillations in the bilateral prefrontal cortex, dorsolateral prefrontal cortex (DLPFC), superior frontal cortex (SFC), premotor cortex, primary motor cortex (M1), primary somatosensory cortex (S1), and occipital cortex were measured by functional near-infrared spectroscopy (fNIRS) in resting and motor training state. The phase information of a 0.01 −0.08 Hz signal was identified by the wavelet transform method. The wavelet amplitude, lateralization index, and wavelet phase coherence (WPCO) were calculated to describe the frequency-specific cortical changes.

Results

Compared with the resting state, significant increased cortical activation was observed in ipsilesional SFC in the mild group and bilateral SFC in the moderate group during UL motor training. Patients in the mild group demonstrated significantly decreased lateralization of activation in motor training than resting state. Moreover, the WPCO value of motor training between contralesional DLPFC and ipsilesional SFC, bilateral SFC, contralesional, S1, and ipsilesional M1 showed a significant decrease compared with the resting state in the mild group.

Conclusion

Robot-assisted task-oriented UL motor training could modify the neuroplasticity of SFC and contribute to control movements and continuous learning motor regularity for patients. fNIRS could provide a variety of real-time sensitive neural evaluation indicators for the robot, which was beneficial to formulating more reasonable and effective personalized prescriptions during motor training.

Effects of simultaneous use of m-NMES and language training on brain functional connectivity in stroke patients with aphasia: A randomized controlled clinical trial

Introduction

The m-NMES had been demonstrated to redistribute brain resources and induce plastic changes in the stroke patients. However, the physiological mechanism and clinical efficacy of m-NMES combination with existing clinical rehabilitation programs remains unclear in patients with aphasia after stroke. This study aimed to investigate the effects of simultaneous use of m-NMES and language training (m-NMES-LT) with on cerebral oscillations and brain connection, as well as the effect on clinical efficacy.

Materials and methods

Total 21 right–handed adult patients with aphasia were randomly assigned to language training (LT) group and m-NMES-LT group, and tissue concentration of oxyhemoglobin and deoxyhemoglobin oscillations were measured by functional near-infrared spectroscopy in resting and treatment state during three consecutive weeks. Five characteristic frequency signals (I, 0.6–2 Hz; II, 0.145–0.6 Hz; III, 0.052–0.145 Hz; IV, 0.021–0.052 Hz; and V, 0.0095–0.021 Hz) were identified using the wavelet method. The wavelet amplitude (WA) and wavelet phase coherence (WPCO) were calculated to describe the frequency-specific cortical activities.

Results

The m-NMES-LT induced significantly higher WA values in contralesional PFC in intervals I, II, and V, and ipsilesional MC in intervals I-V than the resting state. The WPCO values between ipsilesional PFC-MC in interval III-IV, and between bilateral MC in interval III-IV were significantly higher than resting state. In addition, there was a significant positive correlation between WPCO and Western Aphasia Battery in m-NMES-LT group.

Conclusion

The language training combined with neuromuscular electrical stimulation on median nerve could improve and achieve higher clinical efficacy for aphasia. This is attributed to the m-NMES-LT could enhance cortical activation and brain functional connectivity in patients with aphasia, which was derived from myogenic, neurogenic, and endothelial cell metabolic activities.

Different Cortex Activation and Functional Connectivity in Executive Function Between Young and Elder People During Stroop Test: An fNIRS Study

Objective

The objective of this study was to examine the activation and functional connectivity of the prefrontal and temporal lobe in young and elder people during the Stroop test using functional near-infrared spectroscopy (fNIRS).

Methods

A total of 33 healthy volunteers (20 young people, mean age: 23.7 ± 3.9 years; 13 elder people, mean age: 63.9 ± 4.0 years) participated in the study. All subjects were asked to finish the Stroop Color Word Test. The oxygenated hemoglobin concentration (Delta [HbO₂]) signals and the deoxygenated hemoglobin (Delta [HbR]) signals were recorded from temporopolar area (TA), pars triangularis Broca's area (Broca), dorsolateral prefrontal cortex (DLPFC), and frontopolar area (FA) by fNIRS. The coherence between the left and right frontotemporal lobe delta [HbO₂] oscillations in four frequency intervals (I, 0.6–2 Hz; II, 0.145–0.6 Hz; III, 0.052–0.145 Hz; and IV, 0.021–0.052 Hz) was analyzed using wavelet coherence analysis and wavelet phase coherent.

Results

In the Stroop test, the young group was significantly better than the elder group at the responses time, whether at congruent tasks or at incongruent tasks (congruent: F = 250.295, p < 0.001; incongruent: p < 0.001). The accuracy of the two groups differed significantly when performing incongruent tasks but not when performing congruent tasks (incongruent: F = 9.498, p = 0.001; congruent: p = 0.254). Besides, only elders show significant activation in DLPFC, Broca, FA, and TA (p < 0.05) during the Stroop test, but young people did not show significant differences. In the functional connectivity of task states, younger people had stronger connections between different brain regions in both the left and right brain compared with the elderly (p < 0.05). In particular, the left and right DLPFC showed stronger connection strength in most of the brain areas. The result suggested that younger people had stronger functional connectivity of brain areas than older people when completing the task.

Conclusion

According to these results, although the cortical activation in the elder people was higher than the young people, the young showed stronger connectivity in most of the brain areas than the elders. Both sides of DLPFC and right Broca area were the most significant cortical activation in Stroop test. It was suggested that the decrease in functional connectivity in the elder people resulted in the atrophy of white matter, to which we should pay more attention.

Predicting the Degree of Distracted Driving Based on fNIRS Functional Connectivity: A Pilot Study

Distracted driving is one of the main causes of traffic accidents. By predicting the attentional state of drivers, it is possible to prevent distractions and promote safe driving. In this study, we developed a model that could predict the degree of distracted driving based on brain activity. Changes in oxyhemoglobin concentrations were measured in drivers while driving a real car using functional near-infrared spectroscopy (fNIRS). A regression model was constructed for each participant using functional connectivity as an explanatory variable and brake reaction time to random beeps while driving as an objective variable. As a result, we were able to construct a prediction model with the mean absolute error of 5.58 × 102 ms for the BRT of the 12 participants. Furthermore, the regression model with the highest prediction accuracy for each participant was analyzed to gain a better understanding of the neural basis of distracted driving. The 11 of 12 models that showed significant accuracy were classified into five clusters by hierarchical clustering based on their functional connectivity edges used in each cluster. The results showed that the combinations of the dorsal attention network (DAN)-sensory-motor network (SMN) and DAN-ventral attention network (VAN) connections were common in all clusters and that these networks were essential to predict the degree of distraction in complex multitask driving. They also confirmed the existence of multiple types of prediction models with different within- and between-network connectivity patterns. These results indicate that it is possible to predict the degree of distracted driving based on the driver's brain activity during actual driving. These results are expected to contribute to the development of safe driving systems and elucidate the neural basis of distracted driving.

Evoking stress reactivity in virtual reality: A systematic review and meta-analysis

BACKGROUND

Virtual reality (VR) research probes stress environments that are infeasible to create in the real world. However, because research simulations are applied to narrow populations, it remains unclear if VR simulations can stimulate a broadly applicable stress-response. This systematic review and meta-analysis was conducted on studies using VR stress tasks and biomarkers.

METHODS

Included papers (N = 52) measured cortisol, heart rate (HR), galvanic skin response (GSR), systolic blood pressure (SBP), diastolic blood pressure (DBP), respiratory sinus arrhythmia (RSA), parasympathetic activity (RMSSD), sympathovagal balance (LF/HF), and/or salivary alpha-amylase (sAA). Effect sizes (ES) and confidence intervals (CI) were calculated based on standardized mean change of baseline-to-peak biomarker levels.

RESULTS

From baseline-to-peak (ES, CI), analyses showed a statistically significant change in cortisol (0.56, 0.28-0.83), HR (0.68, 0.53-0.82), GSR (0.59, 0.36-0.82), SBP (.55, 0.19-0.90), DBP (.64, 0.23-1.05), RSA (-0.59, -0.88 to -0.30), and sAA (0.27, 0.092-0.45). There was no effect for RMSSD and LF/HF.

CONCLUSION

VR stress tasks elicited a varied magnitude of physiological stress reactivity. VR may be an effective tool in stress research.

EEG-Based Index for Timely Detecting User's Drowsiness Occurrence in Automotive Applications

Human errors are widely considered among the major causes of road accidents. Furthermore, it is estimated that more than 90% of vehicle crashes causing fatal and permanent injuries are directly related to mental tiredness, fatigue, and drowsiness of the drivers. In particular, driving drowsiness is recognized as a crucial aspect in the context of road safety, since drowsy drivers can suddenly lose control of the car. Moreover, the driving drowsiness episodes mostly appear suddenly without any prior behavioral evidence. The present study aimed at characterizing the onset of drowsiness in car drivers by means of a multimodal neurophysiological approach to develop a synthetic electroencephalographic (EEG)-based index, able to detect drowsy events. The study involved 19 participants in a simulated scenario structured in a sequence of driving tasks under different situations and traffic conditions. The experimental conditions were designed to induce prominent mental drowsiness in the final part. The EEG-based index, so-called “MDrow index”, was developed and validated to detect the driving drowsiness of the participants. The MDrow index was derived from the Global Field Power calculated in the Alpha EEG frequency band over the parietal brain sites. The results demonstrated the reliability of the proposed MDrow index in detecting the driving drowsiness experienced by the participants, resulting also more sensitive and timely sensible with respect to more conventional autonomic parameters, such as the EyeBlinks Rate and the Heart Rate Variability, and to subjective measurements (self-reports).

Research on the Identification of Pilots’ Fatigue Status Based on Functional Near-Infrared Spectroscopy

Fatigue can lead to sluggish responses, misjudgments, flight illusions and other problems for pilots, which could easily bring about serious flight accidents. In this paper, a wearable functional near-infrared spectroscopy (fNIRS) device was used to record the changes of hemoglobin concentration of pilots during flight missions. The data was pre-processed, and 1080 valid samples were determined. Then, mean value, variance, standard deviation, kurtosis, skewness, coefficient of variation, peak value, and range of oxyhemoglobin (HbO2) in each channel were extracted. These indexes were regarded as the input of a stacked denoising autoencoder (SDAE) and were used to train the identification model of pilots’ fatigue state. The identification model of pilots’ fatigue status was established. The identification accuracy of the SDAE model was 91.32%, which was 23.26% and 15.97% higher than that of linear discriminant analysis (LDA) models and support vector machines (SVM) models, respectively. Results show that the SDAE model established in our study has high identification accuracy, which can accurately identify different fatigue states of pilots. Identification of pilots’ fatigue status based on fNIRS has important practical significance for reducing flight accidents caused by pilot fatigue.

Functional Connectivity Analysis and Detection of Mental Fatigue Induced by Different Tasks Using Functional Near-Infrared Spectroscopy

Objectives

The objective of this study was to investigate common functional near-infrared spectroscopy (fNIRS) features of mental fatigue induced by different tasks. In addition to distinguishing fatigue from non-fatigue state, the early signs of fatigue were also studied so as to give an early warning of fatigue.

Methods

fNIRS data from 36 participants were used to investigate the common character of functional connectivity network corresponding to mental fatigue, which was induced by psychomotor vigilance test (PVT), cognitive work, or simulated driving. To analyze the network reorganizations quantitatively, clustering coefficient, characteristic path length, and small worldness were calculated in five sub-bands (0.6-2.0, 0.145-0.600, 0.052-0.145, 0.021-0.052, and 0.005-0.021 Hz). Moreover, we applied a random forest method to classify three fatigue states.

Results

In a moderate fatigue state: the functional connectivity strength between brain regions increased overall in 0.021-0.052 Hz, and an asymmetrical pattern of connectivity (right hemisphere > left hemisphere) was presented. In 0.052-0.145 Hz, the connectivity strength decreased overall, the clustering coefficient decreased, and the characteristic path length increased significantly. In severe fatigue state: in 0.021-0.052 Hz, the brain network began to deviate from a small-world pattern. The classification accuracy of fatigue and non-fatigue was 85.4%. The classification accuracy of moderate fatigue and severe fatigue was 82.8%.

Conclusion

The preliminary research demonstrates the feasibility of detecting mental fatigue induced by different tasks, by applying the functional network features of cerebral hemoglobin signal. This universal and robust method has the potential to detect early signs of mental fatigue and prevent relative human error in various working environments.

The Exercising Brain: An Overlooked Factor Limiting the Tolerance to Physical Exertion in Major Cardiorespiratory Diseases?

“Exercise starts and ends in the brain”: this was the title of a review article authored by Dr. Bengt Kayser back in 2003. In this piece of work, the author highlights that pioneer studies have primarily focused on the cardiorespiratory-muscle axis to set the human limits to whole-body exercise tolerance. In some circumstances, however, exercise cessation may not be solely attributable to these players: the central nervous system is thought to hold a relevant role as the ultimate site of exercise termination. In fact, there has been a growing interest relative to the “brain” response to exercise in chronic cardiorespiratory diseases, and its potential implication in limiting the tolerance to physical exertion in patients. To reach these overarching goals, non-invasive techniques, such as near-infrared spectroscopy and transcranial magnetic stimulation, have been successfully applied to get insights into the underlying mechanisms of exercise limitation in clinical populations. This review provides an up-to-date outline of the rationale for the “brain” as the organ limiting the tolerance to physical exertion in patients with cardiorespiratory diseases. We first outline some key methodological aspects of neuromuscular function and cerebral hemodynamics assessment in response to different exercise paradigms. We then review the most prominent studies, which explored the influence of major cardiorespiratory diseases on these outcomes. After a balanced summary of existing evidence, we finalize by detailing the rationale for investigating the “brain” contribution to exercise limitation in hitherto unexplored cardiorespiratory diseases, an endeavor that might lead to innovative lines of applied physiological research.

Is There a Difference in Brain Functional Connectivity between Chinese Coal Mine Workers Who Have Engaged in Unsafe Behavior and Those Who Have Not?

(1) Background: As a world-recognized high-risk occupation, coal mine workers need various cognitive functions to process the surrounding information to cope with a large number of perceived hazards or risks. Therefore, it is necessary to explore the connection between coal mine workers’ neural activity and unsafe behavior from the perspective of cognitive neuroscience. This study explored the functional brain connectivity of coal mine workers who have engaged in unsafe behaviors (EUB) and those who have not (NUB). (2) Methods: Based on functional near-infrared spectroscopy (fNIRS), a total of 106 workers from the Hongliulin coal mine of Shaanxi North Mining Group, one of the largest modern coal mines in China, completed the test. Pearson’s Correlation Coefficient (COR) analysis, brain network analysis, and two-sample t-test were used to investigate the difference in brain functional connectivity between the two groups. (3) Results: The results showed that there were significant differences in functional brain connectivity between EUB and NUB among the frontopolar area (p = 0.002325), orbitofrontal area (p = 0.02102), and pars triangularis Broca’s area (p = 0.02888). Small-world properties existed in the brain networks of both groups, and the dorsolateral prefrontal cortex had significant differences in clustering coefficient (p = 0.0004), nodal efficiency (p = 0.0384), and nodal local efficiency (p = 0.0004). (4) Conclusions: This study is the first application of fNIRS to the field of coal mine safety. The fNIRS brain functional connectivity analysis is a feasible method to investigate the neuropsychological mechanism of unsafe behavior in coal mine workers in the view of brain science.

In search of a naturalistic neuroimaging approach: Exploration of general feasibility through the case of VR-fMRI and application in the domain of episodic memory

Virtual Reality (VR) is an increasingly widespread tool for research as it allows the creation of experiments taking place in multimodal and daily-life-like environments, while keeping a strong experimental control. Adding neuroimaging to VR leads to a better understanding of the underlying brain networks activated during a naturalistic task, whether for research purposes or rehabilitation. The present paper focuses on the specific use of concurrent VR and fMRI and its technical challenges and feasibility, with a brief examination of the general existing solutions. Following the PRISMA guidelines, the review investigates the particular case of how VR-fMRI has explored episodic memory so far, with a comparison of object- and place-based episodic memory. This review confirms the involvement of cerebral regions well-known to be implicated in episodic memory and unravels other regions devoted to bodily and narrative aspects of the self, promoting new avenues of research in the domain of naturalistic episodic memory. Future studies should develop more immersive and interactive virtual neuroimaging features to increase ecological and embodied neurocognition aspects.

Asymmetry of Regional Phase Synchrony Cortical Networks Under Cognitive Alertness and Vigilance Decrement States

This study investigates intra-regional connectivity and regional hemispheric asymmetry under two vigilance states: alertness and vigilance decrement. The vigilance states were induced on nine healthy subjects while performing 30 min in-congruent Stroop color-word task (I-SCWT). We measured brain activity using Electroencephalography (EEG) signals with 64-channels. We quantified the regional network connectivity using the phase-locking value (PLV) with graph theory analysis (GTA) and Support Vector Machines (SVM). Results showed that the vigilance decrement state was associated with impaired information processing within the frontal and central regions in delta and theta frequency bands. Meanwhile, the hemispheric asymmetry results showed that the laterality shifted to the right-temporal in delta, right-central, parietal, and left frontal in theta, right-frontal and left-central, temporal and parietal in alpha, and right-parietal and left temporal in beta frequency bands. These findings represent the first demonstration of intra-regional connectivity and hemispheric asymmetry changes as a function of cognitive vigilance states. The overall results showed that vigilance decrement is region and frequency band-specific. Our SVM model achieved the highest classification accuracy of 99.73% in differentiating between the two vigilance states based on the frontal and central connectivity networks measures.

Antidepressant Monotherapy and Combination Therapy with Acupuncture in Depressed Patients: A Resting-State Functional Near-Infrared Spectroscopy (fNIRS) Study

Depression is a common psychiatric illness affecting over 300 million people globally. Acupuncture has been reported to be a safe complementary treatment for depression. This study is aimed to investigate the efficacy and mechanism of combining acupuncture with antidepressants in treating depression compared to the sole use of antidepressants. Seventy depression patients were randomly assigned to the treatment group (n = 50) and control group (n = 20). The treatment group received acupuncture combined antidepressants treatment for 3 weeks, while the control group took antidepressants monotherapy for 3 weeks. Among the 70 patients, 40 participants (20 control; 20 treatment) were randomized for studying functional connectivity (FC) of the dorsolateral prefrontal cortex (DLPFC) measured by the functional near-infrared spectroscopy. The primary outcome was HAMD-17 and secondary outcomes were PHQ-9, and the relationships of resting-state FC (rsFC) with the depression severity. PHQ-9 and HAMD-17 scores in the treatment group were significantly lower than those in the control group at Week 3 (p = 0.01) with effect sizes of -0.4 and -0.61 respectively. The rsFC in F1, F3, AF3, AF7, FC3, FC5 (left DLPFC, 10-20 system), AF8, and F6 (right DLPFC) in the treatment group had significant temporal correlation (p < 0.05, FDR corrected) in DLPFC compared to the channels in the control group. No significant correlation was found between the changes of rsFC and depression severity. In conclusion, depressed patients receiving acupuncture combined with antidepressants have improvement of depressive symptoms and the stronger rsFC in the DLPFC compared to those using antidepressants alone.

Brain Function Changes Induced by Intermittent Sequential Pneumatic Compression in Patients With Stroke as Assessed by Functional Near-Infrared Spectroscopy

OBJECTIVE

Intermittent sequential pneumatic compression (ISPC) can effectively promote cerebral perfusion and collateral blood supply in patients with stroke. However, the effects of ISPC on cerebral oscillations are still unclear.

METHODS

The tissue concentration of oxyhemoglobin and deoxyhemoglobin oscillations were measured by functional near-infrared spectroscopy under resting and ISPC conditions in 27 right-handed adult patients with stroke. Five characteristic frequency signals (I, 0.6-2 Hz; II, 0.145-0.6 Hz; III, 0.052-0.145 Hz; IV, 0.021-0.052 Hz; and V, 0.0095-0.021 Hz) were identified using the wavelet method. The wavelet amplitude (WA) and laterality index (LI) were calculated to describe the frequency-specific cortical activities.

RESULTS

The ISPC state of patients with ischemic stroke showed significantly increased WA values of the ipsilesional motor cortex (MC) in the frequency intervals III (F37 = 8.017), IV (F37 = 6.347), and V (F37 = 5.538). There was no significant difference in the WA values in the ISPC state compared with the resting state in patients with hemorrhagic stroke. Also, the LI values of the prefrontal cortex and MC in patients decreased more obviously in the ISPC state than in the resting state despite no significant difference.

CONCLUSION

The significantly increased WA values in the frequency intervals III, IV, and V in the MC of patients with ischemic stroke might be related to cortical activity in the MC in addition to increased cerebral perfusion. The decreased LI values in the prefrontal cortex and MC indicated that the ISPC may have had a positive effect on the functional rehabilitation of these regions.

IMPACT

This study provides a method for assessing the effects of ISPC on cerebral oscillations, and the results benefit the optimization of ISPC parameters in personalized treatment for the functional recovery of patients with stroke.

Usability and Effectiveness of Immersive Virtual Grocery Shopping for Assessing Cognitive Fatigue in Healthy Controls: Protocol for a Randomized Controlled Trial

BACKGROUND

Cognitive fatigue (CF) is a human response to stimulation and stress and is a common comorbidity in many medical conditions that can result in serious consequences; however, studying CF under controlled conditions is difficult. Immersive virtual reality provides an experimental environment that enables the precise measurement of the response of an individual to complex stimuli in a controlled environment.

OBJECTIVE

We aim to examine the development of an immersive virtual shopping experience to measure subjective and objective indicators of CF induced by instrumental activities of daily living.

METHODS

We will recruit 84 healthy participants (aged 18-75 years) for a 2-phase study. Phase 1 is a user experience study for testing the software functionality, user interface, and realism of the virtual shopping environment. Phase 2 uses a 3-arm randomized controlled trial to determine the effect that the immersive environment has on fatigue. Participants will be randomized into 1 of 3 conditions exploring fatigue response during a typical human activity (grocery shopping). The level of cognitive and emotional challenges will change during each activity. The primary outcome of phase 1 is the experience of user interface difficulties. The primary outcome of phase 2 is self-reported CF. The core secondary phase 2 outcomes include subjective cognitive load, change in task performance behavior, and eye tracking. Phase 2 uses within-subject repeated measures analysis of variance to compare pre- and postfatigue measures under 3 conditions (control, cognitive challenge, and emotional challenge).

RESULTS

This study was approved by the scientific review committee of the National Institute of Nursing Research and was identified as an exempt study by the institutional review board of the National Institutes of Health. Data collection will begin in spring 2021.

CONCLUSIONS

Immersive virtual reality may be a useful research platform for simulating the induction of CF associated with the cognitive and emotional challenges of instrumental activities of daily living.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04883359; http://clinicaltrials.gov/ct2/show/NCT04883359.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/28073.

Toward practical driving fatigue detection using three frontal EEG channels: a proof-of-concept study

Objective. Although various driving fatigue detection strategies have been introduced, the limited practicability is still an obstacle for the real application of these technologies. This study is based on the newly proposed non-hair-bearing (NHB) method to achieve practical driving fatigue detection with fewer channels from NHB areas and more efficient electroencephalogram (EEG) features.Approach. EEG data were recorded from 20 healthy subjects (15 males, age = 22.2 ± 3.2 years) in a 90 min simulated driving task using a remote wireless cap. Behaviorally, subjects demonstrated a salient fatigue effect, as reflected by a monotonic increase in reaction time. Using a sliding-window approach, we determined the vigilant and fatigued states at individual level to reduce the inter-subject differences in behavioral impairment and brain activity. Multiple EEG features, including power-spectrum density (PSD), functional connectivity (FC), and entropy, were estimated in a pairwise manner, which were set as input for fatigue classification.Main results. Intriguingly, this data-driven approach showed that the best classification performance was achieved using three EEG channel pairs located in the NHB area. The mixed features of the frontal NHB area lead to the high within-subject detection rate of driving fatigue (92.7% ± 0.92%) with satisfactory generalizability for fatigue classification across different subjects (77.13% ± 0.85%). Moreover, we found the most prominent contributing features were PSD of different frequency bands within the frontal NHB area and FC within the frontal NHB area and between frontal and parietal areas.Significance. In summary, the current work provided objective evidence to support the effectiveness of the NHB method and further improved the performance, thereby moving a step forward towards practical driving fatigue detection in real-world scenarios.

A Methodological Review of fNIRS in Driving Research: Relevance to the Future of Autonomous Vehicles

As automobile manufacturers have begun to design, engineer, and test autonomous driving systems of the future, brain imaging with functional near-infrared spectroscopy (fNIRS) can provide unique insights about cognitive processes associated with evolving levels of autonomy implemented in the automobile. Modern fNIRS devices provide a portable, relatively affordable, and robust form of functional neuroimaging that allows researchers to investigate brain function in real-world environments. The trend toward "naturalistic neuroscience" is evident in the growing number of studies that leverage the methodological flexibility of fNIRS, and in doing so, significantly expand the scope of cognitive function that is accessible to observation via functional brain imaging (i.e., from the simulator to on-road scenarios). While more than a decade's worth of study in this field of fNIRS driving research has led to many interesting findings, the number of studies applying fNIRS during autonomous modes of operation is limited. To support future research that directly addresses this lack in autonomous driving research with fNIRS, we argue that a cogent distillation of the methods used to date will help facilitate and streamline this research of tomorrow. To that end, here we provide a methodological review of the existing fNIRS driving research, with the overarching goal of highlighting the current diversity in methodological approaches. We argue that standardization of these approaches will facilitate greater overlap of methods by researchers from all disciplines, which will, in-turn, allow for meta-analysis of future results. We conclude by providing recommendations for advancing the use of such fNIRS technology in furthering understanding the adoption of safe autonomous vehicle technology.

Thoughts on neurophysiological signal analysis and classification

Neurophysiological signals are crucial intermediaries, through which brain activity can be quantitatively measured and brain mechanisms are able to be revealed. In particular, non‐invasive neurophysiological signals, such as electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), are welcomed and frequently utilised in various studies since these signals can be non‐invasively recorded without harming the human brain while they convey abundant information pertaining to brain activity. The recorded neurophysiological signals are analysed to mine meaningful information for the understanding of brain mechanisms or are classified to distinguish different patterns (e.g., different cognitive states, brain diseases versus healthy controls). To date, remarkable progress has been made in both the analysis and classification of neurophysiological signals, but scholars are not feeling complacent. Consistent effort ought to be paid to advance the research of analysis and classification based on neurophysiological signals. In this paper, I express my thoughts regarding promising future directions in neurophysiological signal analysis and classification based on current developments and accomplishments. I will elucidate the thoughts after brief summaries of relevant backgrounds, accomplishments, and tendencies. According to my personal selection and preference, I mainly focus on brain connectivity, multidimensional array (tensor), multi‐modality, multiple task classification, deep learning, big data, and naturalistic experiment. Hopefully, my thoughts could give a little help to inspire new ideas and contribute to the research of the analysis and classification of neurophysiological signals in some way.

Shedding light on the prefrontal correlates of mental workload in simulated driving: a functional near-infrared spectroscopy study

Optimal mental workload plays a key role in driving performance. Thus, driver-assisting systems that automatically adapt to a drivers current mental workload via brain-computer interfacing might greatly contribute to traffic safety. To design economic brain computer interfaces that do not compromise driver comfort, it is necessary to identify brain areas that are most sensitive to mental workload changes. In this study, we used functional near-infrared spectroscopy and subjective ratings to measure mental workload in two virtual driving environments with distinct demands. We found that demanding city environments induced both higher subjective workload ratings as well as higher bilateral middle frontal gyrus activation than less demanding country environments. A further analysis with higher spatial resolution revealed a center of activation in the right anterior dorsolateral prefrontal cortex. The area is highly involved in spatial working memory processing. Thus, a main component of drivers' mental workload in complex surroundings might stem from the fact that large amounts of spatial information about the course of the road as well as other road users has to constantly be upheld, processed and updated. We propose that the right middle frontal gyrus might be a suitable region for the application of powerful small-area brain computer interfaces.

The Role of the Prefrontal Cortex and Functional Connectivity during Maritime Operations: An fNIRS study

INTRODUCTION

Watchkeeping is a significant activity during maritime operations, and failures of sustained attention and decision-making can increase the likelihood of a collision.

METHODS

A study was conducted in a ship bridge simulator where 40 participants (20 experienced/20 inexperienced) performed: (1) a 20-min period of sustained attention to locate a target vessel and (2) a 10-min period of decision-making/action selection to perform an evasive maneuver. Half of the participants also performed an additional task of verbally reporting the position of their vessel. Activation of the prefrontal cortex (PFC) was captured via a 15-channel functional near-infrared spectroscopy (fNIRS) montage, and measures of functional connectivity were calculated frontal using graph-theoretic measures.

RESULTS

Neurovascular activation of right lateral area of the PFC decreased during sustained attention and increased during decision-making. The graph-theoretic analysis revealed that density declined during decision-making in comparison with the previous period of sustained attention, while local clustering declined during sustained attention and increased when participants prepared their evasive maneuver. A regression analysis revealed an association between network measures and behavioral outcomes, with respect to spotting the target vessel and making an evasive maneuver.

CONCLUSIONS

The right lateral area of the PFC is sensitive to watchkeeping and decision-making during operational performance. Graph-theoretic measures allow us to quantify patterns of functional connectivity and were predictive of safety-critical performance.

Functional brain connectivity related to surgical skill dexterity in physical and virtual simulation environments

Significance: Surgical simulators, both virtual and physical, are increasingly used as training tools for teaching and assessing surgical technical skills. However, the metrics used for assessment in these simulation environments are often subjective and inconsistent. Aim: We propose functional activation metrics, derived from brain imaging measurements, to objectively assess the correspondence between brain activation with surgical motor skills for subjects with varying degrees of surgical skill. Approach: Cortical activation based on changes in the oxygenated hemoglobin (HbO) of 36 subjects was measured using functional near-infrared spectroscopy at the prefrontal cortex (PFC), primary motor cortex, and supplementary motor area (SMA) due to their association with motor skill learning. Inter-regional functional connectivity metrics, namely, wavelet coherence (WCO) and wavelet phase coherence were derived from HbO changes to correlate brain activity to surgical motor skill levels objectively. Results: One-way multivariate analysis of variance found a statistically significant difference in the inter-regional WCO metrics for physical simulator based on Wilk's Λ for expert versus novice, F ( 10,1 ) = 7495.5 , p < 0.01 . Partial eta squared effect size for the inter-regional WCO metrics was found to be highest between the central prefrontal cortex (CPFC) and SMA, CPFC-SMA ( η 2 = 0.257 ). Two-tailed Mann-Whitney U tests with a 95% confidence interval showed baseline equivalence and a statistically significant ( p < 0.001 ) difference in the CPFC-SMA WPCO metrics for the physical simulator training group ( 0.960 ± 0.045 ) versus the untrained control group ( 0.735 ± 0.177 ) following training for 10 consecutive days in addition to the pretest and posttest days. Conclusion: We show that brain functional connectivity WCO metric corresponds to surgical motor skills in the laparoscopic physical simulators. Functional connectivity between the CPFC and the SMA is lower for subjects that exhibit expert surgical motor skills than untrained subjects in laparoscopic physical simulators.

Effects of three different rehabilitation games' interaction on brain activation using functional near-infrared spectroscopy

OBJECTIVE

This study reveals the changes in brain activation due to different game interaction states based on functional near-infrared spectroscopy signals and discusses their significance for stroke rehabilitation.

APPROACH

The oxygenated hemoglobin concentration (Delta [HbO₂]) signals and the deoxygenated hemoglobin (Delta [HbR]) signals were recorded from the prefrontal cortex (PFC), the motor cortex (MC), the occipital lobe (OL) and the temporal lobe of 21 subjects (mean age: 24.6 ± 1.9 years old) in three game interaction states: physical, motion-sensing, and button-push training. The subjects were also asked to complete user-satisfaction survey scales after the experiment.

MAIN RESULTS

Compared with the button-training state, several channels in the PFC and MC region of the physical-training state were significantly altered as were several channels in the RMC region of the motion-sensing training state (P < 0.05 after adjustment). The motion-sensing state of the PFC had a significant correlation with that of the MC and the OL. The subjective scale results show that the acceptability of the physical and motion-sensing states was greater than the acceptability of the button-push training state.

SIGNIFICANCE

The results show that the brain regions responded more strongly when activated by the physical and motion-sensing states compared with the button-push training state, and the physical and motion-sensing states are more conducive to the rehabilitation of the nervous system. The design of rehabilitation products for stroke patients is discussed and valuable insights are offered to support the selection of better interactive training methods.

Coupling neuroscience and driving simulation: A systematic review of studies on crash-risk behaviors in young drivers

OBJECTIVE

Motor vehicle crashes are a leading cause of death for adolescents and young adults. The aim of this study is to examine and discuss the state-of-the-art literature which uses neuroscience methods in the context of driving simulation to study adolescent and young adult drivers.

METHODS

We conducted a systematic English-language literature search of Ovid MEDLINE (1946-2020), PsycINFO (1967-2020), PubMed, Web of Science, SCOPUS, and CINAHL using keywords and MeSH terms. Studies were excluded if participants were not within the ages of 15-25, if the driving simulator did not include a visual monitor/computer monitor/projection screen and steering wheel and foot pedals, or brain data (specifically EEG [electroencephalogram], fNIRS [functional near-infrared spectroscopy], or fMRI [functional magnetic resonance imaging]) was not collected at the same time as driving simulation data.

RESULTS

Seventy-six full text articles of the 736 studies that met inclusion criteria were included in the final review. The 76 articles used one of the following neuroscience methods: electrophysiology, functional near-infrared spectroscopy, or functional magnetic resonance imaging. In the identified studies, there were primarily two areas of investigation pursued; driving impairment and distraction in driving. Impairment studies primarily explored the areas of drowsy/fatigued driving or alcohol-impaired driving. Studies of distracted driving primarily focused on cognitive load and auditory and visual distractors.

CONCLUSIONS

Our state of the science systematic review highlights the feasibility for coupling neuroscience with driving simulation to study the neurocorrelates of driving behaviors in the context of young drivers and neuromaturation. Findings show that, to date, most research has focused on examining brain correlates and driving behaviors related to contributing factors for fatal motor vehicle crashes. However, there remains a considerable paucity of research designed to understand underlying brain mechanisms that might otherwise facilitate greater understanding of individual variability of normative and risky driving behavior within the young driving population.

Mid-Task Physical Exercise Keeps Your Mind Vigilant: Evidences From Behavioral Performance and EEG Functional Connectivity

Accumulating efforts have been made to discover effective solutions for fatigue recovery with the ultimate aim of reducing adverse consequences of mental fatigue in real life. The previously-reported behavioral benefits of physical exercise on mental fatigue recovery prompted us to investigate the restorative effect and reveal the underlying neural mechanisms. Specifically, we introduced an empirical method to investigate the beneficial effect of physical exercise on the reorganization of EEG functional connectivity (FC) in a two-session experiment where one session including a successive 30-min psychomotor vigilance task (PVT) (No-intervention session) compared to an insertion of a mid-task 15-min cycling exercise (Intervention session). EEG FC was obtained from 21 participants and quantitatively assessed via graph theoretical analysis and a classification framework. The findings demonstrated the effectiveness of exercise intervention on behavioral performance as shown in improved reaction time and response accuracy. Although we found significantly altered network alterations towards the end of experiment in both sessions, no significant differences between the two sessions and no interaction between session and time were found in EEG network topology. Further interrogation of functional connectivity through classification analysis showed decreased FC in distributed brain areas, which may lead to the significant reduction of network efficiency in both sessions. Moreover, we showed distinct patterns of FC alterations between the two sessions, indicating different information processing strategies adopted in the intervention session. In sum, these results provide some of the first quantitative insights into the complex neural mechanism of exercise intervention for fatigue recovery and lead a new direction for further application research in real-world situations.

Effects of physical training on brain functional connectivity of methamphetamine dependencies as assessed using functional near-infrared spectroscopy

OBJECTIVE

This study aims to assess the effects of physical training based on the functional near-infrared spectroscopy (fNIRS) and heart rate signals.

METHODS

The oxygenated hemoglobin concentration (Delta [HbO₂]) signals were recorded from the left prefrontal cortex (LPFC), right prefrontal cortex (RPFC), left motor cortex (LMC) and right motor cortex (RMC) of 23 subjects with methamphetamine (METH) dependencies at resting, spinning training and strength training states. The wavelet phase coherence (WPCO) values were calculated in four frequency intervals: I, 0.6-2; II, 0.145-0.6; III, 0.052-0.145; and IV, 0.021-0.052 Hz. During the spinning training and strength training states, heart rate signals were recorded at 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10 min, respectively.

RESULTS

After physical training, the brain regions of LPFC, RPFC and LMC showed different degrees of activation in the subjects with METH dependencies (p <  0.05). The WPCO values between the brain regions significantly altered after spinning training and strength training (p <  0.05) in frequency intervals I, II, III and IV.

CONCLUSIONS

The altered WPCO values indicated physical training could affect brain functional connectivity (FC) to a certain extent in the subjects with METH dependencies. These findings provide a method for the assessment of the effects of physical training in FC and will contribute to the development of drug rehabilitation methods in subjects with METH dependencies.

Tai Chi Chuan exercise related change in brain function as assessed by functional near-infrared spectroscopy

Early studies have shown that Tai Chi Chuan (TCC) contributes to the rehabilitation of cognitive disorders and increases blood oxygen concentration levels in the parietal and occipital brain areas; however, the mechanism of TCC training on brain function remains poorly understood. This study hypothesize that TCC has altered brain function and aims to explore the effects of TCC on functional connection and effective connection of the prefrontal cortex (PFC), motor cortex (MC), and occipital cortex (OC). The participants were 23 experienced Chen-style TCC practitioners (TCC group), and 32 demographically matched TCC-naive healthy controls (control group). Functional and effective connections were calculated using wavelet-based coherence analysis and dynamic Bayesian inference method, respectively. Results showed that beyond the intensity of activity in a particular cortical region induced by TCC, significant differences in brain activity and dynamic configuration of connectivity were observed between the TCC and control groups during resting and movement states. These findings suggested that TCC training improved the connection of PFC, MC and OC in myogenic activity, sympathetic nervous system, and endothelial cell metabolic activities; enhanced brain functional connections and relayed the ability of TCC to improve cognition and the anti-memory decline potential.

Exploring the Brain Responses to Driving Fatigue Through Simultaneous EEG and fNIRS Measurements

Fatigue is one problem with driving as it can lead to difficulties with sustaining attention, behavioral lapses, and a tendency to ignore vital information or operations. In this research, we explore multimodal physiological phenomena in response to driving fatigue through simultaneous functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) recordings with the aim of investigating the relationships between hemodynamic and electrical features and driving performance. Sixteen subjects participated in an event-related lane-deviation driving task while measuring their brain dynamics through fNIRS and EEGs. Three performance groups, classified as Optimal, Suboptimal, and Poor, were defined for comparison. From our analysis, we find that tonic variations occur before a deviation, and phasic variations occur afterward. The tonic results show an increased concentration of oxygenated hemoglobin (HbO₂) and power changes in the EEG theta, alpha, and beta bands. Both dynamics are significantly correlated with deteriorated driving performance. The phasic EEG results demonstrate event-related desynchronization associated with the onset of steering vehicle in all power bands. The concentration of phasic HbO₂ decreased as performance worsened. Further, the negative correlations between tonic EEG delta and alpha power and HbO₂ oscillations suggest that activations in HbO₂ are related to mental fatigue. In summary, combined hemodynamic and electrodynamic activities can provide complete knowledge of the brain's responses as evidence of state changes during fatigue driving.

Demonstrating Brain-Level Interactions Between Visuospatial Attentional Demands and Working Memory Load While Driving Using Functional Near-Infrared Spectroscopy

Driving is a complex task concurrently drawing on multiple cognitive resources. Yet, there is a lack of studies investigating interactions at the brain-level among different driving subtasks in dual-tasking. This study investigates how visuospatial attentional demands related to increased driving difficulty interacts with different working memory load (WML) levels at the brain level. Using multichannel whole-head high density functional near-infrared spectroscopy (fNIRS) brain activation measurements, we aimed to predict driving difficulty level, both separate for each WML level and with a combined model. Participants drove for approximately 60 min on a highway with concurrent traffic in a virtual reality driving simulator. In half of the time, the course led through a construction site with reduced lane width, increasing visuospatial attentional demands. Concurrently, participants performed a modified version of the n-back task with five different WML levels (from 0-back up to 4-back), forcing them to continuously update, memorize, and recall the sequence of the previous 'n' speed signs and adjust their speed accordingly. Using multivariate logistic ridge regression, we were able to correctly predict driving difficulty in 75.0% of the signal samples (1.955 Hz sampling rate) across 15 participants in an out-of-sample cross-validation of classifiers trained on fNIRS data separately for each WML level. There was a significant effect of the WML level on the driving difficulty prediction accuracies [range 62.2-87.1%; χ2(4) = 19.9, p < 0.001, Kruskal-Wallis H test] with highest prediction rates at intermediate WML levels. On the contrary, training one classifier on fNIRS data across all WML levels severely degraded prediction performance (mean accuracy of 46.8%). Activation changes in the bilateral dorsal frontal (putative BA46), bilateral inferior parietal (putative BA39), and left superior parietal (putative BA7) areas were most predictive to increased driving difficulty. These discriminative patterns diminished at higher WML levels indicating that visuospatial attentional demands and WML involve interacting underlying brain processes. The changing pattern of driving difficulty related brain areas across WML levels could indicate potential changes in the multitasking strategy with level of WML demand, in line with the multiple resource theory.

Mind over motor mapping: Driver response to changing vehicle dynamics

Improvements in vehicle safety require understanding of the neural systems that support the complex, dynamic task of real-world driving. We used functional near infrared spectroscopy (fNIRS) and pupilometry to quantify cortical and physiological responses during a realistic, simulated driving task in which vehicle dynamics were manipulated. Our results elucidate compensatory changes in driver behavior in response to changes in vehicle handling. We also describe associated neural and physiological responses under different levels of mental workload. The increased cortical activation we observed during the late phase of the experiment may indicate motor learning in prefrontal-parietal networks. Finally, relationships among cortical activation, steering control, and individual personality traits suggest that individual brain states and traits may be useful in predicting a driver's response to changes in vehicle dynamics. Results such as these will be useful for informing the design of automated safety systems that facilitate safe and supportive driver-car communication.

Assessing bimanual motor skills with optical neuroimaging

Measuring motor skill proficiency is critical for the certification of highly skilled individuals in numerous fields. However, conventional measures use subjective metrics that often cannot distinguish between expertise levels. We present an advanced optical neuroimaging methodology that can objectively and successfully classify subjects with different expertise levels associated with bimanual motor dexterity. The methodology was tested by assessing laparoscopic surgery skills within the framework of the fundamentals of a laparoscopic surgery program, which is a prerequisite for certification in general surgery. We demonstrate that optical-based metrics outperformed current metrics for surgical certification in classifying subjects with varying surgical expertise. Moreover, we report that optical neuroimaging allows for the successful classification of subjects during the acquisition of these skills.

Graph analysis of functional brain network topology using minimum spanning tree in driver drowsiness

A large number of traffic accidents due to driver drowsiness have been under more attention of many countries. The organization of the functional brain network is associated with drowsiness, but little is known about the brain network topology that is modulated by drowsiness. To clarify this problem, in this study, we introduce a novel approach to detect driver drowsiness. Electroencephalogram (EEG) signals have been measured during a simulated driving task, in which participants are recruited to undergo both alert and drowsy states. The filtered EEG signals are then decomposed into multiple frequency bands by wavelet packet transform. Functional connectivity between all pairs of channels for multiple frequency bands is assessed using the phase lag index (PLI). Based on this, PLI-weighted networks are subsequently calculated, from which minimum spanning trees are constructed-a graph method that corrects for comparison bias. Statistical analyses are performed on graph-derived metrics as well as on the PLI connectivity values. The major finding is that significant differences in the delta frequency band for three graph metrics and in the theta frequency band for five graph metrics suggesting network integration and communication between network nodes are increased from alertness to drowsiness. Together, our findings also suggest a more line-like configuration in alert states and a more star-like topology in drowsy states. Collectively, our findings point to a more proficient configuration in drowsy state for lower frequency bands. Graph metrics relate to the intrinsic organization of functional brain networks, and these graph metrics may provide additional insights on driver drowsiness detection for reducing and preventing traffic accidents and further understanding the neural mechanisms of driver drowsiness.

Wavelet Transform Analysis the Recognizing Brain Activities for Development the Palm-Size and Simplification Near-Infrared Spectroscopy Prototype System by Using Arduino

Functional near-infrared spectroscopy (fNIRS) and brain computer interface (BCI) have become indispensable tools for recording and monitoring brain activity, comprising a non-invasive and safe technique that allows researchers to monitor blood flow in the front part of the brain. Although some medical device manufacturers developed complex fNIRS systems, downsized fNIRS systems are important for other uses, such as in portable (palm-sized) and wearable healthcare devices. This paper proposes a downsized compact fNIRS prototype that detects hemodynamics in the frontal lobe. The aim is to develop a compact fNIRS system, which is reliable and easy to integrate into portable (palm-sized) BCI devices. Through practical experiments with human subjects, our proposed system showed an ability to detect and monitor the start and end time of human brain activities when participants were solving a calculation table.

Functional connectivity analysis of distracted drivers based on the wavelet phase coherence of functional near-infrared spectroscopy signals

The present study aimed to evaluate the functional connectivity (FC) in relevant cortex areas during simulated driving with distraction based on functional near-infrared spectroscopy (fNIRS) method. Twelve subjects were recruited to perform three types of driving tasks, namely, straight driving, straight driving with secondary auditory task, and straight driving with secondary visual vigilance task, on a driving simulator. The wavelet amplitude (WA) and wavelet phase coherence (WPCO) of the fNIRS signals were calculated in six frequency intervals: I, 0.6-2 Hz; II, 0.145-0.6 Hz; III, 0.052-0.145 Hz; IV, 0.021-0.052 Hz; and V, 0.0095-0.021 Hz, VI, 0.005-0.0095Hz. Results showed that secondary tasks during driving led to worse driving performance, brain activity changes, and dynamic configuration of the connectivity. The significantly lower WA value in the right motor cortex in interval IV, and higher WPCO values in intervals II, V, and VI were found with additional auditory task. Significant standard deviation of speed and lower WA values in the left prefrontal cortex and right prefrontal cortex in interval VI, and lower WPCO values in intervals I, IV, V, and VI were found under the additional visual vigilance task. The results suggest that the changed FC levels in intervals IV, V, and VI were more likely to reflect the driver's distraction condition. The present study provides new insights into the relationship between distracted driving behavior and brain activity. The method may be used for the evaluation of drivers' attention level.