Fronto-parietal activity changes associated with changes in working memory load: Evidence from simultaneous electroencephalography and functional near-infrared spectroscopy analysis.

Working memory (WM) involves the capacity to maintain and manipulate information over short periods. Previous research has suggested that fronto-parietal activities play a crucial role in WM. However, there remains no agreement on the effect of working memory load (WML) on neural activities and haemodynamic responses. Here, our study seeks to examine the effect of WML through simultaneous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). In this study, a delay change detection task was conducted on 23 healthy volunteers. The task included three levels: one item, three items and five items. The EEG and fNIRS were simultaneously recorded during the task. Neural activities and haemodynamic responses at prefrontal and parietal regions were analysed using time-frequency analysis and weighted phase-lag index (wPLI). We observed a significant enhancement in prefrontal and parietal ß suppression as WML increased. Furthermore, as WML increased, there was a notable enhancement in fronto-parietal connectivity (FPC), as evidenced by both EEG and fNIRS. Correlation analysis indicated that as WML increased, there was a potential for enhancement of neurovascular coupling (NVC) of FPC.

Cortical response characteristics of passive, active, and resistance movements: a multi-channel fNRIS study.

Objective: This study aimed to explore the impact of exercise training modes on sensory and motor-related cortex excitability using functional near-infrared spectroscopy technology (fNIRS) and reveal specific cortical effects. Materials and methods: Twenty participants with no known health conditions took part in a study involving passive, active, and resistance tasks facilitated by an upper-limb robot, using a block design. The participants wore functional near-infrared spectroscopy (fNIRS) devices throughout the experiment to monitor changes in cortical blood oxygen levels during the tasks. The fNIRS optode coverage primarily targeted key areas of the brain cortex, including the primary motor cortex (M1), primary somatosensory cortex (S1), supplementary motor area (SMA), and premotor cortex (PMC) on both hemispheres. The study evaluated cortical activation areas, intensity, and lateralization values. Results: Passive movement primarily activates M1 and part of S1, while active movement mainly activates contralateral M1 and S1. Resistance training activates brain regions in both hemispheres, including contralateral M1, S1, SMA, and PMC, as well as ipsilateral M1, S1, SMA, and PMC. Resistance movement also activates the ipsilateral sensorimotor cortex (S1, SMA, PMC) more than active or passive movement. Active movement has higher contralateral activation in M1 compared to passive movement. Resistance and active movements increase brain activity more than passive movement. Different movements activate various cortical areas equally on both sides, but lateralization differs. The correlation between lateralization of brain regions is significant in the right cortex but not in the left cortex during three movement patterns. Conclusion: All types of exercise boost motor cortex excitability, but resistance exercise activates both sides of the motor cortex more extensively. The PMC is crucial for intense workouts. The right cortex shows better coordination during motor tasks than the left. fNIRS findings can help determine the length of treatment sessions.

ninjaCap: a fully customizable and 3D printable headgear for functional near-infrared spectroscopy and electroencephalography brain imaging.

Accurate sensor placement is vital for non-invasive brain imaging, particularly for functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT), which lack standardized layouts such as those in electroencephalography (EEG). Custom, manually prepared probe layouts on textile caps are often imprecise and labor intensive. We introduce a method for creating personalized, 3D-printed headgear, enabling the accurate translation of 3D brain coordinates to 2D printable panels for custom fNIRS and EEG sensor layouts while reducing costs and manual labor. Our approach uses atlas-based or subject-specific head models and a spring-relaxation algorithm for flattening 3D coordinates onto 2D panels, using 10-5 EEG coordinates for reference. This process ensures geometrical fidelity, crucial for accurate probe placement. Probe geometries and holder types are customizable and printed directly on the cap, making the approach agnostic to instrument manufacturers and probe types. Our ninjaCap method offers 2.7 ± 1.8 mm probe placement accuracy. Over the last five years, we have developed and validated this approach with over 50 cap models and 500 participants. A cloud-based ninjaCap generation pipeline along with detailed instructions is now available at openfnirs.org. The ninjaCap marks a significant advancement in creating individualized neuroimaging caps, reducing costs and labor while improving probe placement accuracy, thereby reducing variability in research.

Understanding the Effect of Listening to Music, Playing Music, and Singing on Brain Function: A Scoping Review of fNIRS Studies.

Music is integrated into daily life when listening to it, playing it, and singing, uniquely modulating brain activity. Functional near-infrared spectroscopy (fNIRS), celebrated for its ecological validity, has been used to elucidate this music-brain interaction. This scoping review synthesizes 22 empirical studies using fNIRS to explore the intricate relationship between music and brain function. This synthesis of existing evidence reveals that diverse musical activities, such as listening to music, singing, and playing instruments, evoke unique brain responses influenced by individual traits and musical attributes. A further analysis identifies five key themes, including the effect of passive and active music experiences on relevant human brain areas, lateralization in music perception, individual variations in neural responses, neural synchronization in musical performance, and new insights fNIRS has revealed in these lines of research. While this review highlights the limited focus on specific brain regions and the lack of comparative analyses between musicians and non-musicians, it emphasizes the need for future research to investigate the complex interplay between music and the human brain.

EEG-fNIRS-Based Emotion Recognition Using Graph Convolution and Capsule Attention Network.

Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) can objectively reflect a person's emotional state and have been widely studied in emotion recognition. However, the effective feature fusion and discriminative feature learning from EEG-fNIRS data is challenging. In order to improve the accuracy of emotion recognition, a graph convolution and capsule attention network model (GCN-CA-CapsNet) is proposed. Firstly, EEG-fNIRS signals are collected from 50 subjects induced by emotional video clips. And then, the features of the EEG and fNIRS are extracted; the EEG-fNIRS features are fused to generate higher-quality primary capsules by graph convolution with the Pearson correlation adjacency matrix. Finally, the capsule attention module is introduced to assign different weights to the primary capsules, and higher-quality primary capsules are selected to generate better classification capsules in the dynamic routing mechanism. We validate the efficacy of the proposed method on our emotional EEG-fNIRS dataset with an ablation study. Extensive experiments demonstrate that the proposed GCN-CA-CapsNet method achieves a more satisfactory performance against the state-of-the-art methods, and the average accuracy can increase by 3-11%.

Long COVID patients' brain activation is suppressed during walking and severer symptoms lead to stronger suppression.

This research aims to study the factors contributing to Long COVID and its effects on motor and cognitive brain regions using population surveys and brain imaging. The goal is to provide new insights into the neurological effects of the illness and establish a basis for addressing neuropsychiatric symptoms associated with Long COVID. Study 1 used a cross-sectional design to collect data on demographic characteristics and factors related to Long COVID symptoms in 551 participants. In Study 2, subjects with Long COVID and SARS-CoV-2 uninfected individuals underwent fNIRS monitoring while performing various tasks. Study 1 found that gender, age, BMI, Days since the first SARS-CoV-2 infection, and Symptoms at first onset influenced Long COVID performance. Study 2 demonstrated that individuals in the SARS-CoV-2 uninfected group exhibited greater activation of cognitive function-related brain regions than those in the Long COVID group while performing a level walking task. Furthermore, individuals in the Long COVID group without functional impairment displayed higher activation of brain regions associated with motor function during a weight-bearing walking task than those with functional impairment. Among individuals with Long COVID, those with mild symptoms at onset exhibited increased activation of brain regions linked to motor and cognitive function relative to those with moderate symptoms at onset. Individuals with Long COVID exhibited decreased activation in brain regions associated with cognitive and motor function compared to SARS-CoV-2 uninfected individuals. Moreover, those with more severe initial symptoms or functional impairment displayed heightened inhibition in these brain regions.

Spontaneous movement synchrony as an exogenous source for interbrain synchronization in cooperative learning.

Learning through cooperation with conspecifics-'cooperative learning'-is critical to cultural evolution and survival. Recent progress has established that interbrain synchronization (IBS) between individuals predicts success in cooperative learning. However, the likely sources of IBS during learning interactions remain poorly understood. To address this dearth of knowledge, we tested whether movement synchrony serves as an exogenous factor that drives IBS, taking an embodiment perspective. We formed dyads of individuals with varying levels of prior knowledge (high-high (HH), high-low (HL), low-low (LL) dyads) and instructed them to collaboratively analyse an ancient Chinese poem. During the task, we simultaneously recorded their brain activity using functional near-infrared spectroscopy and filmed the entire experiment to parse interpersonal movement synchrony using the computer-vision motion energy analysis. Interestingly, the homogeneous groups (HH and/or LL) exhibited stronger movement synchrony and IBS compared with the heterogeneous group. Importantly, mediation analysis revealed that spontaneous and synchronized body movements between individuals contribute to IBS, hence facilitating learning. This study therefore fills a critical gap in our understanding of how interpersonal transmission of information between individual brains, associated with behavioural entrainment, shapes social learning. This article is part of the theme issue 'Minds in movement: embodied cognition in the age of artificial intelligence'.

The effects of real vs simulated high altitude on associative memory for emotional stimuli.

INTRODUCTION: This study aimed to investigate the effects of normobaric hypoxia (NH) and hypobaric hypoxia (HH) on associative memory performance for emotionally valenced stimuli. METHODS: Two experiments were conducted. In Study 1, n=18 undergraduates performed an associative memory task under three NH conditions (FiO2= 20.9%, 15.1%, 13.6%) using a tent with a hypoxic generator. In Study 2, n=20 participants were assessed in a field study at various altitudes on the Himalayan mountains, including the Pyramid Laboratory (5,000 meters above sea level), using functional Near-Infrared Spectroscopy (fNIRS) and behavioral assessments. RESULTS: Study 1 revealed no significant differences in recognition accuracy across NH conditions. However, Study 2 showed a complex relationship between altitude and memory for emotionally valenced stimuli. At lower altitudes, participants more accurately recognized emotional stimuli compared to neutral ones, a trend that reversed at higher altitudes. Brain oxygenation varied with altitude, indicating adaptive cognitive processing, as revealed by fNIRS measurements. CONCLUSIONS: These findings suggest that hypoxia affects associative memory and emotional processing in an altitude-dependent manner, highlighting adaptive cognitive mechanisms. Understanding the effects of hypobaric hypoxia on cognition and memory can help develop strategies to mitigate its impact in high-altitude and hypoxic environments.

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)].

Preschoolers' deception related to prefrontal cortex activation: An fNIRS study.

Deception is an essential part of children's moral development. Previous developmental studies have shown that children start to deceive at the age of 3 years, and as age increased to 5 years, almost all children were able to deceive for their own benefit. Although behavioral studies have indicated that the emergence and development of deception are related to cognitive abilities, their neural correlates remain poorly understood. Therefore, the present study examined the neural correlates underlying deception in preschool-aged children (N = 89, 44 % boys, age 3.13 to 5.96 years, Han Chinese) using functional near-infrared spectroscopy. A modified hide-and-seek paradigm was applied to elicit deceptive and truth-telling behaviors. The results showed that activation of bilateral dorsolateral prefrontal cortex was positively associated with the tendency to deceive an opponent in a competitive game in the 3-year-olds. In addition, 3-year-olds who showed a high tendency to deceive showed the same brain activation in the frontopolar area as 5-year-olds did when engaged in deception, whereas no such effect was found in 3-year-olds who never engaged in deception. These findings underscore the link between preschoolers' deception and prefrontal cortex function.

Spinal cord electrical stimulation for severe disturbance of consciousness after traumatic brain injury: A case report.

Background: Currently, the use of spinal cord electrical stimulations for patients with severe disorders of consciousness after traumatic brain injury remains limited, and long-term follow-up studies are even scarcer. To date, there have been few reports using near-infrared spectroscopy to evaluate the clinical effects and optimal parameters of spinal cord electrical stimulation for severe consciousness disorders. This report describes a case of a patient with severe disturbance of consciousness after traumatic brain injury who underwent spinal cord electrical stimulation implantation. Advanced near-infrared spectroscopy was employed to monitor and evaluate postoperative efficacy. The findings of this case report will provide a reference for the clinical treatment of severe consciousness disturbances. Methods: A patient diagnosed with a severe disturbance of consciousness following traumatic brain injury presented symptoms of coma and lack of voluntary activity. The treatment regimen included conventional approaches (medication combined with rehabilitation training) and adjustments to the spinal cord electrical stimulation parameters. Advanced functional near-infrared spectroscopy (fNIRS) was used to explore changes in brain functional connectivity strength and assess clinical efficacy. Results: The integration of conventional treatment and continuous modification of spinal cord electrical stimulation parameters, combined with fNIRS monitoring, demonstrated that conventional treatment and spinal cord electrical stimulation displayed a positive effect on increasing brain functional strength connection. The Glasgow Coma Scale(GCS) score significantly improved from the baseline. Optimal results were observed with spinal cord stimulation settings at 4.5 V amplitude, 210 µs pulse width, and 70 Hz frequency, operating from 8:00-20:00 in a cycling mode of 15 min on and 15 min off, where improvements in consciousness were markedly evident. Conclusions: Patients with severe disturbances of consciousness after traumatic brain injury recover slowly. Conventional treatment combined with spinal cord electrical stimulation can improve the degree of disturbance of consciousness and promote recovery from the condition.

Functional near-infrared spectroscopy: A novel tool for detecting consciousness after acute severe brain injury.

Recent advancements in functional neuroimaging have demonstrated that some unresponsive patients in the intensive care unit retain a level of consciousness that is inconsistent with their behavioral diagnosis of awareness. Functional near-infrared spectroscopy (fNIRS) is a portable optical neuroimaging method that can be used to measure neural activity with good temporal and spatial resolution. However, the reliability of fNIRS for detecting the neural correlates of consciousness remains to be established. In a series of studies, we evaluated whether fNIRS can record sensory, perceptual, and command-driven neural processing in healthy participants and in behaviorally nonresponsive patients. At the individual healthy subject level, we demonstrate that fNIRS can detect commonly studied resting state networks, sensorimotor processing, speech-specific auditory processing, and volitional command-driven brain activity to a motor imagery task. We then tested fNIRS with three acutely brain injured patients and found that one could willfully modulate their brain activity when instructed to imagine playing a game of tennis-providing evidence of preserved consciousness despite no observable behavioral signs of awareness. The successful application of fNIRS for detecting preserved awareness among behaviorally nonresponsive patients highlights its potential as a valuable tool for uncovering hidden cognitive states in critical care settings.

Different neural correlates of deception: Crafting high and low creative scams.

Deception is a complex social behavior that manifests in various forms, including scams. To successfully deceive victims, liars have to continually devise novel scams. This ability to create novel scams represents one kind of malevolent creativity, referred to as lying. This study aimed to explore different neural substrates involved in the generation of high and low creative scams. A total of 40 participants were required to design several creative scams, and their cortical activity was recorded by functional near-infrared spectroscopy. The results revealed that the right frontopolar cortex (FPC) was significantly active in scam generation. This region associated with theory of mind may be a key region for creating novel and complex scams. Moreover, creativity-related regions were positively involved in creative scams, while morality-related areas showed negative involvement. This suggests that individuals might attempt to use malevolent creativity while simultaneously minimizing the influence of moral considerations. The right FPC exhibited increased coupling with the right precentral gyrus during the design of high-harmfulness scams, suggesting a diminished control over immoral thoughts in the generation of harmful scams. Additionally, the perception of the victim's emotions (related to right pre-motor cortex) might diminish the quality of highly original scams. Furthermore, an efficient and cohesive neural coupling state appears to be a key factor in generating high-creativity scams. These findings suggest that the right FPC was crucial in scam creation, highlighting a neural basis for balancing malevolent creativity against moral considerations in high-creativity deception.

Analysis of functional network asymmetry in major depressive disorder under four fNIRS tasks.

BACKGROUND: Research in functional asymmetry of Major Depressive Disorder (MDD) under different tasks is crucial for clinical diagnose. METHODS: Fifty individuals with MDD and twenty healthy controls (HCS) were recruited for hemodynamic data collection under four fNIRS tasks (Emotional picture, Verbal fluency, Fingering and Negative emotional picture description task). Integral values and functional connectivity strength were employed to probe neural activation and functional connectivity in frontal and temporal lobes in MDD. Following, asymmetry characteristic of the frontal cortex between MDD and HCS under four tasks were carefully analyzed and compared. RESULTS: Individuals with MDD demonstrated heightened connectivity between the frontal and right temporal lobes and reduced connectivity between the frontal and left temporal lobes compared to HCS in all tasks. Additionally, MDD exhibited attenuated activation in the left frontal lobes and exaggerated activation in the right frontal lobes, diverging from HCS. Furthermore, the disparities in left-right asymmetry characteristic of frontal cortex activation between MDD and HCS were more pronounced during the combined task. LIMITATIONS: Further research is required to grasp the neurophysiological mechanisms governing left-right asymmetry across various tasks and the influence of task-induced brain fatigue on cerebral cortex hemodynamics in MDD. CONCLUSION: The left-right asymmetry feature provides valuable neurophysiological insights for diagnosing MDD clinically. Variations in activation patterns and functional connectivity features between MDD and HCS are closely tied to the task chosen. Thus, in clinical practice, carefully selecting appropriate fNIRS tasks and relevant features can significantly improve the diagnostic accuracy of MDD.

Examining the relationship between functional connectivity and broader autistic traits in non-autistic children.

In the current study, we used functional near-infrared spectroscopy (fNIRS) to examine functional connectivity (FC) in relation to measures of cognitive flexibility and autistic features in non-autistic children. Previous research suggests that disruptions in FC between brain regions may underlie the cognitive and behavioral traits of autism. Moreover, research has identified a broader autistic phenotype (BAP), which refers to a set of behavioral traits that fall along a continuum of behaviors typical for autism but which do not cross a clinically relevant threshold. Thus, by examining FC in relation to the BAP in non-autistic children, we can better understand the spectrum of behaviors related to this condition and their neural basis. Results indicated age-related differences in performance across three measures of cognitive flexibility, as expected given the rapid development of this skill within this time period. Additionally, results showed that across the flexibility tasks, measures of autistic traits were associated with weaker FC along the executive control network, though task performance was not associated with FC. These results suggest that behavioral scores may be less sensitive than neural measures to autistic traits. Further, these results corroborate the use of broader autistic traits and the BAP to better understand disruptions to neural function associated with autism.

Deep learning networks based decision fusion model of EEG and fNIRS for classification of cognitive tasks.

The detection of the cognitive tasks performed by a subject during data acquisition of a neuroimaging method has a wide range of applications: functioning of brain-computer interface (BCI), detection of neuronal disorders, neurorehabilitation for disabled patients, and many others. Recent studies show that the combination or fusion of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) demonstrates improved classification and detection performance compared to sole-EEG and sole-fNIRS. Deep learning (DL) networks are suitable for the classification of large volume time-series data like EEG and fNIRS. This study performs the decision fusion of EEG and fNIRS. The classification of EEG, fNIRS, and decision-fused EEG-fNIRSinto cognitive task labels is performed by DL networks. Two different open-source datasets of simultaneously recorded EEG and fNIRS are examined in this study. Dataset 01 is comprised of 26 subjects performing 3 cognitive tasks: n-back, discrimination or selection response (DSR), and word generation (WG). After data acquisition, fNIRS is converted to oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (HbR) in Dataset 01. Dataset 02 is comprised of 29 subjects who performed 2 tasks: motor imagery and mental arithmetic. The classification procedure of EEG and fNIRS (or HbO2, HbR) are carried out by 7 DL classifiers: convolutional neural network (CNN), long short-term memory network (LSTM), gated recurrent unit (GRU), CNN-LSTM, CNN-GRU, LSTM-GRU, and CNN-LSTM-GRU. After the classification of single modalities, their prediction scores or decisions are combined to obtain the decision-fused modality. The classification performance is measured by overall accuracy and area under the ROC curve (AUC). The highest accuracy and AUC recorded in Dataset 01 are 96% and 100% respectively; both by the decision fusion modality using CNN-LSTM-GRU. For Dataset 02, the highest accuracy and AUC are 82.76% and 90.44% respectively; both by the decision fusion modality using CNN-LSTM. The experimental result shows that decision-fused EEG-HbO2-HbR and EEG-fNIRSdeliver higher performances compared to their constituent unimodalities in most cases. For DL classifiers, CNN-LSTM-GRU in Dataset 01 and CNN-LSTM in Dataset 02 yield the highest performance.

Exploring cognitive load through neuropsychological features: an analysis using fNIRS-eye tracking.

Cognition is crucial to brain function, and accurately classifying cognitive load is essential for understanding the psychological processes across tasks. This paper innovatively combines functional near-infrared spectroscopy (fNIRS) with eye tracking technology to delve into the classification of cognitive load at the neurocognitive level. This integration overcomes the limitations of a single modality, addressing challenges such as feature selection, high dimensionality, and insufficient sample capacity. We employ fNIRS-eye tracking technology to collect neural activity and eye tracking data during various cognitive tasks, followed by preprocessing. Using the maximum relevance minimum redundancy algorithm, we extract the most relevant features and evaluate their impact on the classification task. We evaluate the classification performance by building models (naive Bayes, support vector machine, K-nearest neighbors, and random forest) and employing cross-validation. The results demonstrate the effectiveness of fNIRS-eye tracking, the maximum relevance minimum redundancy algorithm, and machine learning techniques in discriminating cognitive load levels. This study emphasizes the impact of the number of features on performance, highlighting the need for an optimal feature set to improve accuracy. These findings advance our understanding of neuroscientific features related to cognitive load, propelling neural psychology research to deeper levels and holding significant implications for future cognitive science.

Corrigendum: Motor learning in developmental coordination disorder: behavioral and neuroimaging study.

[This corrects the article DOI: 10.3389/fnins.2023.1187790.].

Hybrid Integrated Wearable Patch for Brain EEG-fNIRS Monitoring.

Synchronous monitoring electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have received significant attention in brain science research for their provision of more information on neuro-loop interactions. There is a need for an integrated hybrid EEG-fNIRS patch to synchronously monitor surface EEG and deep brain fNIRS signals. Here, we developed a hybrid EEG-fNIRS patch capable of acquiring high-quality, co-located EEG and fNIRS signals. This patch is wearable and provides easy cognition and emotion detection, while reducing the spatial interference and signal crosstalk by integration, which leads to high spatial-temporal correspondence and signal quality. The modular design of the EEG-fNIRS acquisition unit and optimized mechanical design enables the patch to obtain EEG and fNIRS signals at the same location and eliminates spatial interference. The EEG pre-amplifier on the electrode side effectively improves the acquisition of weak EEG signals and significantly reduces input noise to 0.9 µVrms, amplitude distortion to less than 2%, and frequency distortion to less than 1%. Detrending, motion correction algorithms, and band-pass filtering were used to remove physiological noise, baseline drift, and motion artifacts from the fNIRS signal. A high fNIRS source switching frequency configuration above 100 Hz improves crosstalk suppression between fNIRS and EEG signals. The Stroop task was carried out to verify its performance; the patch can acquire event-related potentials and hemodynamic information associated with cognition in the prefrontal area.