fNIRS Data Research Articles

Functional Near‐Infrared Spectroscopy‐Based Computer‐Aided Diagnosis of Major Depressive Disorder Using Convolutional Neural Network with a New Channel Embedding Layer Considering Inter‐Hemispheric Asymmetry in Prefrontal Hemodynamic Responses

Background. Functional near‐infrared spectroscopy (fNIRS) is being extensively explored as a potential primary screening tool for major depressive disorder (MDD) because of its portability, cost‐effectiveness, and low susceptibility to motion artifacts. However, the fNIRS‐based computer‐aided diagnosis (CAD) of MDD using deep learning methods has rarely been studied. In this study, we propose a novel deep learning framework based on a convolutional neural network (CNN) for the fNIRS‐based CAD of MDD with high accuracy. Materials and Methods. The fNIRS data of participants—48 patients with MDD and 68 healthy controls (HCs)—were obtained while they performed a Stroop task. The hemodynamic responses calculated from the preprocessed fNIRS data were used as inputs to the proposed CNN model with an ensemble CNN architecture, comprising three 1D depth‐wise convolutional layers specifically designed to reflect interhemispheric asymmetry in hemodynamic responses between patients with MDD and HCs, which is known to be a distinct characteristic in previous MDD studies. The performance of the proposed model was evaluated using a leave‐one‐subject‐out cross‐validation strategy and compared with those of conventional machine learning and CNN models. Results. The proposed model exhibited a high accuracy, sensitivity, and specificity of 84.48%, 83.33%, and 85.29%, respectively. The accuracies of conventional machine learning algorithms—shrinkage linear discriminator analysis, regularized support vector machine, EEGNet, and ShallowConvNet—were 73.28%, 74.14%, 62.93%, and 62.07%, respectively. Conclusions. In conclusion, the proposed deep learning model can differentiate between the patients with MDD and HCs more accurately than the conventional models, demonstrating its applicability in fNIRS‐based CAD systems.

HuBar: A Visual Analytics Tool to Explore Human Behavior based on fNIRS in AR Guidance Systems.

The concept of an intelligent augmented reality (AR) assistant has significant, wide-ranging applications, with potential uses in medicine, military, and mechanics domains. Such an assistant must be able to perceive the environment and actions, reason about the environment state in relation to a given task, and seamlessly interact with the task performer. These interactions typically involve an AR headset equipped with sensors which capture video, audio, and haptic feedback. Previous works have sought to facilitate the development of intelligent AR assistants by visualizing these sensor data streams in conjunction with the assistant's perception and reasoning model outputs. However, existing visual analytics systems do not focus on user modeling or include biometric data, and are only capable of visualizing a single task session for a single performer at a time. Moreover, they typically assume a task involves linear progression from one step to the next. We propose a visual analytics system that allows users to compare performance during multiple task sessions, focusing on non-linear tasks where different step sequences can lead to success. In particular, we design visualizations for understanding user behavior through functional near-infrared spectroscopy (fNIRS) data as a proxy for perception, attention, and memory as well as corresponding motion data (acceleration, angular velocity, and gaze). We distill these insights into embedding representations that allow users to easily select groups of sessions with similar behaviors. We provide two case studies that demonstrate how to use these visualizations to gain insights about task performance using data collected during helicopter copilot training tasks. Finally, we evaluate our approach by conducting an in-depth examination of a think-aloud experiment with five domain experts.

Within-session Reliability of fNIRS in Robot-assisted Upper-limb Training.

Functional near-infrared spectroscopy (fNIRS) seems opportune for neurofeedback in robot-assisted rehabilitation training due to its noninvasive, less physical restriction, and no electromagnetic disturbance. Previous research has proved the cross-session reliability of fNIRS responses to non-motor tasks (e.g., visual stimuli) and fine-motor tasks (e.g., finger tapping). However, it is still unknown whether fNIRS responses remain reliable 1) in gross-motor tasks, 2) within a training session, and 3) for different training parameters. Hence, this study aimed to investigate the within-session reliability of fNIRS responses to gross-motor tasks for different training parameters. Ten healthy participants were recruited to conduct right elbow extension-flexion in three robot-assisted modes. The Passive mode was fully motor-actuated, while Active1 and Active2 modes involved active engagement with different resistance levels. FNIRS data of three identical runs were used to assess the within-session reliability in terms of the map- (R2) and cluster-wise (Roverlap) spatial reproducibility and the intraclass correlation (ICC) of temporal features. The results revealed good spatial reliability (R2 up to 0.69, Roverlap up to 0.68) at the subject level. Besides, the within-session temporal reliabilities of Slope, Max/Min, and Mean were between good and excellent (0.60 < ICC < 0.86). We also found that the within-session reliability was positively correlated with the intensity of the training mode, except for the temporal reliability of HbO in Active2 mode. Overall, our results demonstrated good within-session reliability of fNIRS responses, suggesting fNIRS as reliable neurofeedback for constructing closed-loop robot-assisted rehabilitation systems.

Effects of individual research practices on fNIRS signal quality and latent characteristics.

Functional near-infrared spectroscopy (fNIRS) is an increasingly popular tool for cross-cultural neuroimaging studies. However, the reproducibility and comparability of fNIRS studies is still an open issue in the scientific community. The paucity of experimental practices and the lack of clear guidelines regarding fNIRS use contribute to undermining the reproducibility of results. For this reason, much effort is now directed at assessing the impact of heterogeneous experimental practices in creating divergent fNIRS results. The current work aims to assess differences in fNIRS signal quality in data collected by two different labs in two different cohorts: Singapore (N=74) and Italy (N=84). Random segments of 20s were extracted from each channel in each participant's NIRScap and 1280 deep features were obtained using a deep learning model trained to classify the quality of fNIRS data. Two datasets were generated: ALL dataset (segments with bad and good data quality) and GOOD dataset (segments with good quality). Each dataset was divided into train and test partitions, which were used to train and evaluate the performance of a Support Vector Machine (SVM) model in classifying the co-horts from signal quality features. Results showed that the SG cohort had significantly higher occurrences of bad signal quality in the majority of the fNIRS channels. Moreover, the SVM correctly classified the cohorts when using the ALL dataset. However, the performance dropped almost completely (except for five channels) when the SVM had to classify the cohorts using data from the GOOD dataset. These results suggest that fNIRS raw data obtained by different labs might possess different levels of quality as well as different latent characteristics beyond quality per se. The current study highlights the importance of defining clear guidelines in the conduction of fNIRS experiments in the reporting of data quality in fNIRS manuscripts.

INVESTIGATING THE NEURAL CORRELATES OF SOCIAL AND INDIVIDUAL SINGING IN PERSONS WITH DEMENTIA

Abstract Music interventions for persons with Alzheimer’s Disease and related dementias (PwADRD) have documented psychological benefits; however, the neurological correlates underlying these advantages remain less certain. Using functional near infrared spectroscopy (fNIRS), the present study i) investigated within-person patterns of cortical oxygenation during choral vs. individual singing; ii) explored how singing context (choral vs. individual) modulated patterns of functional connectivity (FC) within and across frontal and parietal cortices; and iii) leveraged these functional activations as predictors of cognitive status (degree of impairment) in a series of discriminant function analyses (DFA). Participants were 13 PwADRD who volunteered from a larger, ongoing social-cognitive choir intervention. fNIRS data were collected using a TechEn Cw6 system during both choral and individual singing conditions. Paired sample t-tests evaluated differences in activation patterns between singing conditions, with DFA used to determine whether these activations and neuropsychological function were predictive of between-person differences in cognitive impairment. Significant differences in cerebral oxygenation were identified in the right anterior PFC, with individual singing associated with significantly greater cortical oxygenation relative to group singing. Although not significant, individual singing was also associated with bilateral increases in cortical oxygenation across the majority of fNIRS channels, as well as increased FC, relative to group singing. The DFA analyses were not significant. This novel study is the first to examine differences in music-cognition correlates across environmental contexts for PwADRD. Patterns of functional activation suggest that choral singing in particular may represent an optimal lifestyle activity, placing comparatively fewer demands on cognitive function.

Cortical activity associated with focal muscle vibration applied directly to the affected forearm flexor muscle in post-stroke patients: an fNIRS study.

The purpose of this study was to utilize functional near-infrared spectroscopy (fNIRS) to identify changes in cortical activity caused by focal muscle vibration (FMV), which was directly administered to the affected forearm flexor muscles of hemiplegic stroke patients. Additionally, the study aimed to investigate the correlation between these changes and the clinical characteristics of the patients, thereby expanding the understanding of potential neurophysiological mechanisms linked to these effects. Twenty-two stroke patients with right hemiplegia who were admitted to our ward for rehabilitation were selected for this study. The fNIRS data were collected from subjects using a block-design paradigm. Subsequently, the collected data were analyzed using the NirSpark software to determine the mean Oxyhemoglobin (Hbo) concentrations for each cortical region of interest (ROI) in the task and rest states for every subject. The stimulation task was FMV (frequency 60 Hz, amplitude 6 mm) directly applied to belly of the flexor carpi radialis muscle (FCR) on the affected side. Hbo was measured in six regions of interest (ROIs) in the cerebral cortex, which included the bilateral prefrontal cortex (PFC), sensorimotor cortex (SMC), and occipital cortex (OC). The clinical characteristics of the patients were assessed concurrently, including Lovett's 6-level muscle strength assessment, clinical muscle tone assessment, the upper extremity function items of the Fugl-Meyer Assessment (FMA-UE), Bruunstrom staging scale (BRS), and Modified Barthel index (MBI). Statistical analyses were conducted to determine the activation in the ROIs and to comprehend its correlation with the clinical characteristics of the patients. Statistical analysis revealed that, except for right OC, there were statistically significant differences between the mean Hbo in the task state and rest state for bilateral SMC, PFC, and left OC. A positive correlation was observed between the muscle strength of the affected wrist flexor group and the change values of Hbo (Hbo-CV), as well as the beta values in the left SMC, PFC, and OC. However, no statistical correlation was found between muscle strength and Hbo-CV or beta values in the right SMC, PFC, and OC. The BRS of the affected upper limb exhibited a positive correlation with the Hbo-CV or beta values in the left SMC and PFC. In contrast, no statistical correlation was observed in the right SMC, PFC, and bilateral OC. No significant correlation was found between the muscle tone of the affected wrist flexor group, FMA-UE, MBI, and Hbo-CV or beta values of cortical ROIs. FMV-evoked sensory stimulation applied directly to the FCR belly on the paralyzed side activated additional brain cortices, including bilateral PFC and ipsilesional OC, along with bilateral SMC in stroke patients. However, the clinical characteristics of the patients were only correlated with the intensity of ipsilesional SMC and PFC activation. The results of this study provide neurophysiological theoretical support for the expanded clinical application of FMV.

Distinct age-related brain activity patterns in the prefrontal cortex when increasing cognitive load: A functional near-infrared spectroscopy study

Researchers have long observed distinct brain activity patterns in older adults compared with younger adults that correlate with cognitive performance. Mainly, older adults tend to show over-recruitment of bilateral brain regions during lower task loads and improved performance interpreted as compensation, but not observed at higher loads. However, there are discrepancies about whether increases in activity are compensatory and whether older adults can show compensation at higher loads. Our aim was to examine age-related differences in prefrontal cortex (PFC) activity and cognitive performance using functional near-infrared spectroscopy (fNIRS) during single and dual N-back tasks. Twenty-seven young adults (18–27 years) and 31 older adults (64–84 yrs) took part in the study. We used a robust fNIRS data methodology consisting of channel and region of interest analyses. Results showed differences in performance between task load conditions and age-related differences in reaction times but no age-group effects for accuracy. Older adults exhibited more bilateral PFC activation compared with young adults across all tasks and showed increases in brain activity in high compared to low load conditions. Our findings further support previous reports showing that older adults use compensatory recruitment of additional brain regions in PFC to maintain cognitive performance but go against the notion that such compensation is not present at higher cognitive loads. Additionally, our results indicate that fNIRS is a sensitive tool that can characterize adaptive cortical changes in healthy aging.

Acute effects of (-)-gallocatechin gallate-rich green tea extract on the cerebral hemodynamic response of the prefrontal cortex in healthy humans.

The benefits of long-term consumption of green tea on the brain are well known. However, among many ingredients of green tea, the acute effects of (-)-gallocatechin gallate-rich green tea extract (GCG-GTE), have received comparatively less attention. Herein, we investigated the acute effects of oral ingestion of green tea with GCG-GTE, which contains close replicas of the ingredients of hot green tea, on task-dependent hemodynamics in the prefrontal cortex of healthy adult human brains. In this randomized, double-blind, placebo-controlled, parallel group trial, 35 healthy adults completed computerized cognitive tasks that demand activation of the prefrontal cortex at baseline and 1 h after consumption of placebo and 900 mg of GCG-GTE extract supplement. During cognitive testing, hemodynamic responses (change in HbO2 concentration) in the prefrontal cortex were assessed using functional near-infrared spectroscopy (fNIRS). In fNIRS data, significant group x session interactions were found in the left (p = 0.035) and right (p = 0.036) dorsolateral prefrontal cortex (DLPFC). In behavioral data, despite the numerical increase in the GCG-GTE group and the numerical decrease in the Placebo group, no significant differences were observed in the cognitive performance measure between the groups. The result suggests a single dose of orally administered GCG-GTE can reduce DLPFC activation in healthy humans even with increased task demand. GCG-GTE is a promising functional material that can affect neural efficiency to lower mental workload during cognitively demanding tasks. However, further studies are needed to verify this.

A study on the role of information cues in E-commerce live streaming: evidence from self-reported and fNIRS data

A study on the role of information cues in E-commerce live streaming: evidence from self-reported and fNIRS data

Audiovisual integration in children with cochlear implants revealed through EEG and fNIRS

Sensory deprivation can offset the balance of audio versus visual information in multimodal processing. Such a phenomenon could persist for children born deaf, even after they receive cochlear implants (CIs), and could potentially explain why one modality is given priority over the other. Here, we recorded cortical responses to a single speaker uttering two syllables, presented in audio-only (A), visual-only (V), and audio-visual (AV) modes. Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were successively recorded in seventy-five school-aged children. Twenty-five were children with normal hearing (NH) and fifty wore CIs, among whom 26 had relatively high language abilities (HL) comparable to those of NH children, while 24 others had low language abilities (LL). In EEG data, visual-evoked potentials were captured in occipital regions, in response to V and AV stimuli, and they were accentuated in the HL group compared to the LL group (the NH group being intermediate). Close to the vertex, auditory-evoked potentials were captured in response to A and AV stimuli and reflected a differential treatment of the two syllables but only in the NH group. None of the EEG metrics revealed any interaction between group and modality. In fNIRS data, each modality induced a corresponding activity in visual or auditory regions, but no group difference was observed in A, V, or AV stimulation. The present study did not reveal any sign of abnormal AV integration in children with CI. An efficient multimodal integrative network (at least for rudimentary speech materials) is clearly not a sufficient condition to exhibit good language and literacy.

Compressibility Analysis of Functional Near-Infrared Spectroscopy Signals in Children With Attention-Deficit/Hyperactivity Disorder.

Functional near- infrared spectroscopy (fNIRS) as an emerging optical neuroimaging technique has attracted the interest and attention of many investigators. With the growth of fNIRS data volume, effective data compression methods are urgent. Compressive sensing (CS) has been demonstrated a promising tool to deal with biomedical data. However, whether the compressibility of fNIRS data can discriminate different brain states is unclear. In this study, the fNIRS signals from fifteen attention-deficit/hyperactivity disorder (ADHD) children and fifteen typically developing (TD) children were recorded during an N-back task and a Go/NoGo task respectively. A block sparse Bayesian learning-based CS method was used to reconstruct the compressed fNIRS data. To assess the performance of the CS method, we adopted two metrics, structural similarity index (SSIM) and mean squared error (MSE), both of them effective in evaluating the compressibility of fNIRS data. Then, the two metrics were analyzed to discriminate the brain states of ADHD children and TD children during the two tasks using the multivariate pattern analysis (MVPA) method. As indicated by the results, the CS method could reconstruct the compressed fNIRS data with a high reconstruction quality at different compression ratio ([Formula: see text] and [Formula: see text]). Furthermore, the MVPA method could distinguish different brain states with high accuracy, and identify that the prefrontal cortex is a key brain region for distinguishing ADHD vs. TD or N-back vs. Go/NoGo. These findings indicated that CS is very promising for the storage and transmission of massive fNIRS data, and the compressibility of fNIRS data is a potential biomarker for the diagnosis of ADHD.

65 Neuroscience in the Everyday World: Lateralization of Brain Activity During Dual-Task Walking

Objective:Functional near-infrared spectroscopy (fNIRS) is a non-invasive functional neuroimaging method that takes advantage of the optical properties of hemoglobin to provide an indirect measure of brain activation via task-related relative changes in oxygenated hemoglobin (HbO). Its advantage over fMRI is that fNIRS is portable and can be used while walking and talking. In this study, we used fNIRS to measure brain activity in prefrontal and motor region of interests (ROIs) during single- and dual-task walking, with the goal of identifying neural correlates.Participants and Methods:Nineteen healthy young adults [mean age=25.4 (SD=4.6) years; 14 female] engaged in five tasks: standing single-task cognition (serial-3 subtraction); single-task walking at a self-selected comfortable speed on a 24.5m oval-shaped course (overground walking) and on a treadmill; and dual-task cognition+walking on the same overground course and treadmill (8 trials/condition: 20 seconds standing rest, 30 seconds task). Performance on the cognitive task was quantified as the number of correct subtractions, number of incorrect subtractions, number of self-corrected errors, and percent accuracy over the 8 trials. Walking speed (m/sec) was recorded for all walking conditions. fNIRS data were collected on a system consisting of 16 sources, 15 detectors, and 8 short-separation detectors in the following ROIs: right and left lateral frontal (RLF, LLF), right and left medial frontal (RMF, LMF), right and left medial superior frontal (RMSF, LMSF), and right and left motor (RM, LM). Lateral and medial refer to ROIs’ relative positions on lateral prefrontal cortex. fNIRS data were analyzed in Homer3 using a spline motion correction and the iterative weighted least squares method in the general linear model. Correlations between the cognitive/speed variables and ROI HbO data were applied using a Bonferroni adjustment for multiple comparisons.Results:Subjects with missing cognitive data were excluded from analyses, resulting in sample sizes of 18 for the single-task cognition, dual-task overground walking, and dual-task treadmill walking conditions. During dual-task overground walking, there was a significant positive correlation between walking speed and relative change in HbO in RMSF [r(18)=.51, p&lt;.05] and RM [r(18)=.53, p&lt;.05)]. There was a significant negative correlation between total number of correct subtractions and relative change in HbO in LMSF ([r(18)=-.75, p&lt;.05] and LM [r(18)=-.52, p&lt;.05] during dual-task overground walking. No other significant correlations were identified.Conclusions:These results indicate that there is lateralization of the cognitive and motor components of overground dual-task walking. The right hemisphere appears to be more active the faster people walk during the dual-task. By contrast, the left hemisphere appears to be less active when people are working faster on the cognitive task (i.e., serial-3 subtraction). The latter results suggest that automaticity of the cognitive task (i.e., more total correct subtractions) is related to decreased brain activity in the left hemisphere. Future research will investigate whether there is a change in cognitive automaticity over trials and if there are changes in lateralization patterns in neurodegenerative disorders that are known to differentially affect the hemispheres (e.g., Parkinson’s disease).

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels.

Functional near-infrared spectroscopy (fNIRS) is a portable neuroimaging methodology, more robust to motion and more cost-effective than functional magnetic resonance imaging (fMRI), which makes it highly suitable for conducting naturalistic studies of brain function and for use with developmental and clinical populations. Both fNIRS and fMRI methodologies detect changes in cerebral blood oxygenation during functional brain activation, and prior studies have shown high spatial and temporal correspondence between the two signals. There is, however, no quantitative comparison of the two signals collected simultaneously from the same subjects with whole-head fNIRS coverage. This comparison is necessary to comprehensively validate area-level activations and functional connectivity against the fMRI gold standard, which in turn has the potential to facilitate comparisons of the two signals across the lifespan. We address this gap by describing a protocol for simultaneous data collection of fMRIand fNIRS signals that: i) provides whole-head fNIRS coverage; ii) includes short-distance measurements for regression of the non-cortical, systemic physiological signal; and iii) implements two different methods for optode-to-scalp co-registration of fNIRS measurements. fMRI and fNIRS data from three subjects are presented, and recommendations for adapting the protocol to test developmental and clinical populations are discussed. The current setup with adults allows scanning sessions for an average of approximately 40 min, which includes both functional and structural scans. The protocol outlines the steps required to adapt the fNIRS equipment for use in the magnetic resonance (MR) environment, provides recommendations for both data recording and optode-to-scalp co-registration, and discusses potential modifications of the protocol to fit the specifics of the available MR-safe fNIRS system. Representative subject-specific responses from a flashing-checkerboard task illustrate the feasibility of the protocol to measure whole-head fNIRS signals in the MR environment. This protocol will be particularly relevant for researchers interested in validating fNIRS signals against fMRI across the lifespan.

Obese people are more likely to exhibit unhealthy food decisions when sated

Food decision-making plays a key role in weight loss and obesity prevention. It involves the value and integration of food attributes, such as health and taste. However, how homeostatic status affects the value and integration of food attributes, especially in obese people, remains unknown. To address this question, we investigated how metabolic states (sated vs. fasting) may affect food decision-making and brain response. This study encompassed a multifaceted approach, investigating hormone levels, decision-making processes relating to food, and fNIRS data. We recruited 20 obese and 20 normal-weight subjects. We found that there were no differences between obese and normal weights in health and taste ratings. However, the weight of health ratings was higher in sated normal weight people than sated obese people. The dlPFC activity showed a similar pattern with the weight of health ratings. The functional connectivity between the vmPFC and dlPFC was lower in obese people when sated than during fasting, while this connectivity remained relatively constant in counterparts. Sated normal weight people had stronger associations between ghrelin levels and the dlPFC activation than sated obese people. Our results highlight the role of the dlPFC with regard to the homeostatic signaling regulation of food decision-making and suggest that obese people are more likely to exhibit unhealthy eating behavior when sated, which may be related to the reduced ability of the dlPFC to downregulate the vmPFC.

Friend or foe: classifying collaborative interactions using fNIRS.

To succeed, effective teams depend on both cooperative and competitive interactions between individual teammates. Depending on the context, cooperation and competition can amplify or neutralize a team's problem solving ability. Therefore, to assess successful collaborative problem solving, it is first crucial to distinguish competitive from cooperative interactions. We investigate the feasibility of using lightweight brain sensors to distinguish cooperative from competitive interactions in pairs of participants (N=84) playing a decision-making game involving uncertain outcomes. We measured brain activity using functional near-infrared spectroscopy (fNIRS) from social, motor, and executive areas during game play alone and in competition or cooperation with another participant. To distinguish competitive, cooperative, and alone conditions, we then trained support vector classifiers using combinations of features extracted from fNIRS data. We find that features from social areas of the brain outperform other features for discriminating competitive, cooperative, and alone conditions in cross-validation. Comparing the competitive and alone conditions, social features yield a 5% improvement over motor and executive features. Social features show promise as means of distinguishing competitive and cooperative environments in problem solving settings. Using fNIRS data provides a real-time measure of subjective experience in an ecologically valid environment. These results have the potential to inform intelligent team monitoring to provide better real-time feedback and improve team outcomes in naturalistic settings.

B - 61 Increased Cortical Efficiency in the Absence of Behavioral Improvement on Working Memory Task Revealed by Functional Near-Infrared Spectroscopy.

Functional near-infrared spectroscopy (fNIRS) is a non-invasive functional neuroimaging method that indirectly measures cortical activation via task-related changes in oxygenated hemoglobin (HbO). We used fNIRS during a working memory task to assess learning effect over time by assessing brain activity (fNIRS signal) and task performance. We hypothesized that in later blocks of the task, learning (better accuracy) would be correlated to less increase in HbO in prefrontal regions, indicating improved cognitive efficiency. Eighteen healthy adults [mean age = 24.9 (SD = 4.2); 14 female] engaged in 8 blocks of serial-3 subtraction for 30seconds each followed by 20seconds of rest. fNIRS data were collected in 8 cortical regions of interest (ROI) broadly covering the frontal lobe. fNIRS signal in each ROI and task-performance data were compared for the first 4 and last 4 blocks to examine learning. fNIRS signal was significantly greater for the first 4 than last 4 blocks (z = -2.1, p < 0.05) in only the right dorsolateral prefrontal cortex ROI. No learning effects appeared for any task-performance variables. These results indicate a dissociation between brain activity and task performance during a working memory task in healthy adults. There was less activity in the right dorsolateral prefrontal cortex during later than earlier trials, indicating an increase in this region's efficiency, without a change in task performance. The results suggest that fNIRS may be sensitive to change in brain activity before it appears clinically, which may be useful in studying people with conditions such as preclinical Alzheimer's disease, and in assessing subtle effects of interventions.

B - 66 Functional Near-Infrared Spectroscopy as a Sensitive Marker of Neuropsychological Deficits in Executive Functioning Tasks in Young Adults with a History of Child Abuse.

Research has shown the utility of imaging measures of neural activity in identifying deficits in cognitive functioning in individuals with a history of child abuse. The purpose of this study was to measure differences that may exist between individuals who reported physical, emotional, or sexual abuse as children vs. those who did not using Functional Near Infrared Spectroscopy (fNIRS) during the completion of executive function tasks. The present study recruited 84 students from the University of North Dakota. The participants were administered several prescreening measures, including a measures of child abuse. Based on responses, participants were assigned to either a "no child abuse" or "child abuse" group. Next, the Wisconsin Card Sorting Task, the Operation Span Task, and the Connors Continuous Performance Task were administered while functional near-infrared spectroscopy (fNIRS) data was collected. The results showed a significantly higher rate and number of errors of commission on the Conners CPT in the child abuse group compared to the control group (F(1,57) = 6.604, p = 0.013 partial eta2 = 0.104). Neurological data revealed a statistically significant effect of group (F(1, 69) = 2.934, p = 0.043) across all executive functioning tasks. Additional tests showed these differences exist only during measures of decision-making (t(71) = 2.063, p = 0.043). The results suggest that the child abuse group may show subtle neurological deficits that persist into adulthood that may not manifest on traditional measures of cognitive function. These findings have implications for the development of remediation and treatment strategies in this population.

Feasibility of Acquiring Neuroimaging Data from Adults with Acquired Brain Injuries before and after a Yoga Intervention.

To date, no one has prospectively evaluated yoga intervention-induced changes in brain structure or function in adults with acquired brain injuries (ABI). Thus, this study was conducted to test the feasibility of acquiring neuroimaging data from adults with ABI before and after a yoga intervention. This was a single-arm intervention feasibility study that included 12 adults with chronic (i.e., greater than 6 months post-injury) ABI and self-reported limitations in balance. Neuroimaging data were acquired before and after yoga. The yoga intervention was completed once per week for eight weeks. Feasibility objectives and benchmarks were established a priori. Most feasibility objectives and benchmarks were achieved. The goal of recruiting 12 participants was successfully achieved, and 75% of participants were retained throughout the study (goal of 80%). All imaging feasibility benchmarks were met; rs-fMRI and fNIRS data were acquired safely, data were of acceptable quality, and data pre-processing procedures were successful. Additionally, improvements were detected in balance after yoga, as group-level balance was significantly better post-yoga compared to pre-yoga, p = 0.043. These findings indicate it is feasible to acquire neuroimaging data from adults with ABI before and after a yoga intervention. Thus, future prospective studies are warranted.

Prefrontal dysfunction in post-COVID-19 hyposmia: an EEG/fNIRS study.

Subtle cognitive dysfunction and mental fatigue are frequent after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, characterizing the so-called long COVID-19 syndrome. This study aimed to correlate cognitive, neurophysiological, and olfactory function in a group of subjects who experienced acute SARS-CoV-2 infection with persistent hyposmia at least 12 weeks before the observation. For each participant (32 post-COVID-19 patients and 16 controls), electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) data were acquired using an integrated EEG-fNIRS system during the execution of a P300 odd-ball task and a Stroop test. The Sniffin' Sticks test was conducted to assess subjects' olfactory performance. The Montreal Cognitive Assessment (MoCA) and the Frontal Assessment Battery (FAB) were also administered. The post-COVID-19 group consisted of 32 individuals (20 women and 12 men) with an average education level of 12.9 ± 3.12 years, while the control group consisted of 16 individuals (10 women and 6 men) with an average education level of 14.9 ± 3.2 years. There were no significant differences in gender (X2 = 0, p = 1) or age between the two groups (age 44.81 ± 13.9 vs. 36.62 ± 11.4, p = 0.058). We identified a lower concentration of oxyhemoglobin (p < 0.05) at the prefrontal cortical level in post-COVID-19 subjects during the execution of the Stroop task, as well as a reduction in the amplitude of the P3a response. Moreover, we found that post-COVID-19 subjects performed worst at the MoCA screening test (p = 0.001), Sniffin's Sticks test (p < 0.001), and Stroop task response latency test (p < 0.001). This study showed that post-COVID-19 patients with persistent hyposmia present mild deficits in prefrontal function, even 4 months after the end of the infection. These deficits, although subtle, could have long-term implications for quality of life and cognitive wellbeing. It is essential to continue monitoring and evaluating these patients to better understand the extent and duration of cognitive impairments associated with long COVID-19.

Assessment of learning in simulator-based arthroscopy training with the diagnostic arthroscopy skill score (DASS) and neurophysiological measures.

Virtual arthroscopic training has become increasingly popular. However, there is a lack of efficiency-based tracking of the trainee, which may be critical for determining the specifics of training programs and adapting them for the needs of each trainee. This study aims to evaluate and compare the measures obtained with a non-invasive neurophysiological method with The Diagnostic Arthroscopy Skill Score (DASS), a commonly used assessment tool for evaluating arthroscopic skills. The study collected simulator performance scores, consisting of "Triangulation Right Hand", "Triangulation Left Hand", "Catch the Stars" and "Three Rings" and DASS scores from 22 participants (11 novices, 11 experts). These scores were obtained while participants underwent a structured program of exercises for the fundamentals of arthroscopic surgery training (FAST) and knee module using a simulator-based arthroscopy device. During the evaluation, data on oxy-hemoglobin and deoxy-hemoglobin levels in the prefrontal cortex were collected using the Functional Near-Infrared Spectroscopy (fNIRS) imaging system. Performance scores, DASS scores, and fNIRS data were subsequently analyzed to determine any correlation between performance and cortex activity. The simulator performance scores and the DASSPart2 scores were significantly higher in the expert group compared to the novice group (200.1 ± 28.5 vs 172.5 ± 48.9, p = 0.04 and 9.4 ± 5.6 vs. 5.4 ± 5.6 p = 0.02). In the expert group, fNIRS data showed a significantly lower prefrontal cortex activation during fundamental tasks in the FAST module, indicating significantly more efficient mental resource use. The analysis of cognitive workload changes during simulation-based arthroscopy training revealed a significant correlation between the trainees' DASS scores and fNIRS data. This correlation suggests the potential use of fNIRS data and DASS scores as additional metrics to create adaptive training protocols for each participant. By incorporating these metrics, the training process can be optimized, leading to more efficient arthroscopic training and better preparedness for clinical operations. III.