Functional Near-infrared Spectroscopy Research Articles

Supplementary and Premotor Cortical Activation During Manual Dexterity Involving Motor Imagery in Multiple Sclerosis: A Functional Near-Infrared Spectroscopy Study

Background Investigating brain activation during motor imagery (MI) tasks in people with multiple sclerosis (pwMS) can increase the knowledge of the neural mechanisms underlying motor dysfunction in MS and, hopefully, aid in developing improved rehabilitation strategies. Objective To investigate brain activation in the supplementary motor area and premotor cortex via functional near-infrared spectroscopy (fNIRS) during a hand manipulation task, and comparing MI with actual practice (AP) in pwMS. Methods Each subject completed a sequence of 4 consecutive manual dexterity trials wearing an fNIRS device. The tasks included the following conditions: AP dominant hand, MI dominant hand, AP non-dominant hand, and MI non-dominant hand. Results Twenty pwMS (mean Expanded Disability Status Scale = 4.75 [3.0-6.5]) and 20 healthy controls (HC) participated in the study. According to the fNIRS timeline course, a similar increase (compared with baseline) was observed in the relative oxygenated hemoglobin (HbO) concentration during the MI and AP tasks, which was immediately followed by a decrease (for either hand) in the pwMS and the HC groups. A difference in the relative HbO concentration between the HC and pwMS was detected solely when the 2 groups mentally replicated the manual dexterity task movements in the MI condition (dominant hand). The increase was higher in the HC group ( P = .030). Conclusions Despite exhibiting manual dexterity difficulties, pwMS demonstrated comparable neural activation patterns as the HCs during MI tasks in regions associated with motor planning and complex movement control, thus, suggesting that deficits in manual dexterity among pwMS may not solely originate from impairments in the motor planning processes.

Exploring feasibility of fNIRS to assess delayed inhibition effect of prefrontal cortex for acute stress by acupuncture on GV20: a pilot study

IntroductionAcupuncture on the acupuncture point GV20 has been considered effective for managing psychoneurological disorders since ancient times, and GV20 is now regularly used to treat stress-related diseases (SRDs), including psychoneurological disorders. Although reports indicating that acupuncture stimulation of GV20 alone regulates cerebral blood flow (CBF) in brain regions associated with SRDs have been scattered, from the perspective of the need for earlier action, the effects on subjective stress as self-reported in the stress state of healthy individuals and CBF changes in related prefrontal cortex (PFC) subregions, which is based as underlying mechanism, remain unclear. Therefore, there is a need to explore effective methods for analyzing such effects.MethodsMultiple consecutive mental arithmetic tasks were performed to induce sufficient stress in healthy university students. Real acupuncture or sham acupuncture was performed on GV20, and subjective stress and CBF changes in PFC subregions were observed before and after acupuncture using functional near-infrared spectroscopy, which were analyzed based on the general linear model method and correlation analysis.ResultsSubjective stress was suppressed and recovered significantly faster in the true acupuncture group than in the sham acupuncture group. Furthermore, significant negative correlations were found between subjective stress and CBF in several PFC subregions during and after the tasks, with more correlated regions on the left side than on the right side of the PFC. Among them, the CBF of the left ventrolateral PFC (vl-PFC) was suggested to be maintained in the true acupuncture group under repeated tasks load, which was inferred to be correlated with delayed decreases in subjective stress after acupuncture.DiscussionThis pilot study showed that fNIRS is feasible for assessing delayed PFC inhibition in acute stress by acupuncture on GV20. Acupuncture treatment on GV20 resulted in early suppression of subjective stress and early recovery. The neuroscientific rationale for this finding may lie in its effect on maintaining CBF in the left vl-PFC related to delayed inhibition of subjective stress, and would make it reasonable to apply acupuncture to GV20 in healthy individuals. Larger studies are needed to corroborate these findings and obtain reliable conclusions.

Individualized post-operative prediction of cochlear implantation outcomes in children with prelingual deafness using functional near-infrared spectroscopy.

The goal of this study was to develop an objective measure and predictor of cochlear implantation (CI) outcomes using functional near-infrared spectroscopy (fNIRS) for young children with prelingual deafness. Sound-evoked hemodynamic responses were recorded from auditory and language-related cortical regions of 47 child CI recipients (35.47 ± 17.24 months of age) using fNIRS shortly after CI activation (0.26 ± 0.30 months). There were four sound conditions (natural speech, instrumental music, multi-speaker babble noise, and speech-in-noise). Post-CI auditory and verbal communication performance was evaluated using clinical questionnaires with caretakers. Both classification and individualized regression models were constructed to predict post-CI behavioral improvement from fNIRS data using support vector machine (SVM) learning algorithms. Auditory cortical responses shortly after CI hearing onset yielded highly accurate prediction of behavioral development in young CI children. For classification models, optimal prediction was achieved using cortical responses to two or more sound conditions, with the highest accuracy of 98.20% (precision = 98.17%, sensitivity = 98.96%, area under the curve of the receiver operating characteristic curve = 99.61%) obtained with the combination of speech, noise, and music stimuli. Similarly, for regression models, best prediction of individual development was achieved using three (highest r = 0.919) or four (r = 0.966) sound conditions. The predictability of cortical responses far outperformed (Cohen's d: 18.56) that of the collection of audiological and demographic parameters (classification accuracy: 0.62) under the same SVM algorithms and could not benefit from the inclusion of the latter. Machine learning models using auditory cortical hemodynamic responses shortly after CI activation were able to predict individualized post-CI behavioral improvement in children with prelingual deafness. Level 5.

Functional near-infrared spectroscopy and vagus somatosensory evoked potentials add to the power of established parameters such as poor cognitive performance, dsyosmia and APOe genotype to predict cognitive decline over 8 years in the elderly.

Alzheimer's dementia is the main cause of cognitive impairment in people over the age of 65, with Alzheimer's disease starting presumably 10-15 years before the onset of clinical symptoms. It is therefore important to recognize dementia at an early stage and identify possible predictors. The existing methods, like different parameters of ß-Amyloid and Tau quantification in cerebrospinal fluid (CSF) or the living brain by measure of PET, are invasive and expensive. Therefore, the present study investigates the predictive value of a battery of clinical, neuropsychological, and blood parameters as well as two neurophysiological methods (functional near-infrared spectroscopy [fNIRS] and vagus somatosensory evoked potentials [VSEP]) which are easy to perform, less invasive and cost-efficient, for developing cognitive impairments in the elderly.In this longitudinal, prospective study, we enrolled 604 healthy participants between 70 and 77 years of age. The participants were invited back after a mean time interval of 3 years and 11 months, and after 7 years and 8 months, and their cognitive impairments were determined.Here we show that the development of cognitive impairments after approximately 8 years can be predicted not only by previously known risk factors such as ApoE4 risk alleles, dysosmia, or poor cognitive performance at baseline but that latency prolongation in the VSEP and altered functional activation patterns measured by NIRS at baseline also provide additional predictive value.We therefore suggest that both neurophysiological parameters, VSEP and NIRS, should be included in future studies, investigating the prediction of dementia. Dementia ClinicalTrials.gov Identifier: NCT02224326.

Hybrid Functional Near-Infrared Spectroscopy System and Electromyography for Prosthetic Knee Control

The increasing number of individuals with limb loss worldwide highlights the need for advancements in prosthetic knee technology. To improve control and quality of life, integrating brain–computer communication with motor imagery offers a promising solution. This study introduces a hybrid system that combines electromyography (EMG) and functional near-infrared spectroscopy (fNIRS) to address these limitations and enhance the control of knee movements for individuals with above-knee amputations. The study involved an experiment with nine healthy male participants, consisting of two sessions: real execution and imagined execution using motor imagery. The OpenBCI Cyton board collected EMG signals corresponding to the desired movements, while fNIRS monitored brain activity in the prefrontal and motor cortices. The analysis of the simultaneous measurement of the muscular and hemodynamic responses demonstrated that combining these data sources significantly improved the classification accuracy compared to using each dataset alone. The results showed that integrating both the EMG and fNIRS data consistently achieved a higher classification accuracy. More specifically, the Support Vector Machine performed the best during the motor imagery tasks, with an average accuracy of 49.61%, while the Linear Discriminant Analysis excelled in the real execution tasks, achieving an average accuracy of 89.67%. This research validates the feasibility of using a hybrid approach with EMG and fNIRS to enable prosthetic knee control through motor imagery, representing a significant advancement potential in prosthetic technology.

Effects of the Interaction Between Time-on-Task and Task Load on Response Lapses

To investigate the interaction effects of prolonged working periods and different task loads on response lapses, focusing on the mechanisms of delayed responses and error lapses. Professionals such as pilots, truck drivers, and nurses often face extended work hours and fluctuating task loads. While these factors individually affect performance, their interaction and its impact on response lapses remain unclear. Twenty participants completed the Uchida–Kraepelin (U–K) Psychological Test and a dual-task version with functional near-infrared spectroscopy. Independent variables were time-on-task and task load. Dependent variables included measures of fatigue, arousal, workload, task performance (delayed and error rates), and brain functional connectivity. Both time-on-task and task load significantly affected cerebral connectivity, response lapses, workload (frustration level), fatigue, and arousal. Arousal levels significantly decreased and reaction times increased after 60 min of work. Cognitive resource regulation became challenging after 90 min under high task load levels. A decline in the connection between the prefrontal and occipital cortex during high-load tasks was observed. The findings provide insight into the mechanisms of response lapses under different task load levels and can inform strategies to mitigate these lapses during extended work periods. This study’s findings can be applied to improve work schedules and fatigue management in industries like aviation, transportation, and healthcare, helping reduce response lapses and errors during extended work periods under high task load conditions.

Brain functional connectivity under teleoperation latency: a fNIRS study

IntroductionLong-distance robot teleoperation faces high latencies that pose cognitive challenges to human operators. Latency between command, execution, and feedback in teleoperation can impair performance and affect operators’ mental state. The neural underpinnings of these effects are not well understood.MethodsThis study aims to understand the cognitive impact of latency in teleoperation and the related mitigation methods, using functional Near-Infrared Spectroscopy (fNIRS) to analyze functional connectivity. A human subject experiment (n = 41) of a simulated remote robot manipulation task was performed. Three conditions were tested: no latency, with visual and haptic latency, with visual latency and no haptic latency. fNIRS and performance data were recorded and analyzed.ResultsThe presence of latency in teleoperation significantly increased functional connectivity within and between prefrontal and motor cortexes. Maintaining visual latency while providing real-time haptic feedback reduced the average functional connectivity in all cortical networks and showed a significantly different connectivity ratio within prefrontal and motor cortical networks. The performance results showed the worst performance in the all-delayed condition and best performance in no latency condition, which echoes the neural activity patterns.ConclusionThe study provides neurological evidence that latency in teleoperation increases cognitive load, anxiety, and challenges in motion planning and control. Real-time haptic feedback, however, positively influences neural pathways related to cognition, decision-making, and sensorimotor processes. This research can inform the design of ergonomic teleoperation systems that mitigate the effects of latency.

Role of the dorsolateral prefrontal cortex in processing temporal anomalies retained in working memory

IntroductionTime is a crucial abstract construct, allowing us to perceive the duration of events. Working memory (WM) plays an important role in manipulating and storing the different features of environmental stimuli, including temporal features. Different brain structures, including the dorsolateral prefrontal cortex, are involved in time processing.MethodsHere we investigated the functional aspects of time processing by using functional near-infrared spectroscopy (fNIRS) to assess changes in DLPFC activity. A modified version of the “Times Squares Sequences” (TSS) task was used, in which participants are required to match sequences of squares that have fixed or variable durations.ResultsFindings showed that the DLPFC activates when information necessary for later comparison needs to be maintained online, as is common in visuo-spatial WM tasks. Importantly, the DLPFC deactivates when a temporal anomaly is detected.DiscussionThis deactivation occurs because the temporal anomaly does not require ongoing maintenance for later comparison, thus demanding fewer cognitive resources from the DLPFC. This seemingly counterintuitive effect can be attributed to the temporal aspects being irrelevant to the primary task goals. This finding highlights the crucial role of implicit temporal interference and establishes a strong connection between timing and executive cognitive processes.

It takes two to empathize: Interbrain coupling contributes to distress regulation.

While extant research on empathy has made significant progress in uncovering the mechanisms underlying the responses of an observer (empathizer) to the distress of another (target), it remains unclear how the interaction between the empathizer and the target contributes to distress regulation in the target. Here, we propose that behavioral and neural coupling during empathic interactions contribute to diminished distress. From November 2020 to November 2022, we recruited 37 pairs of previously unacquainted participants (N = 74) from multicultural backgrounds. They engaged in a 5 min face-to-face emotional sharing task, where one participant shared a distressing biographical experience with the other participant. We used functional near-infrared spectroscopy (fNIRS) to measure interbrain coupling in the emotion regulation system, specifically the dorsolateral prefrontal cortex (dlPFC), and the observation execution system, specifically the inferior frontal gyrus (IFG). Results indicate that during emotional sharing the target and the empathizer emotionally converge, such that the empathizer becomes sadder. Moreover, the levels of empathizers' empathy predicted both emotional convergence and target distress relief. The neuroimaging findings indicate that interbrain coupling in the dlPFC, IFG, and premotor cortex, predicted distress relief in the target, and more critically that interbrain coupling in the dlPFC played a mediating role in the relationship between distress relief and the levels of empathy of the empathizer. Considering the role of the dlPFC in emotion regulation, we conclude that interbrain coupling in this region during emotional sharing plays a key role in dyadic coregulation of distress. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Metaverse-based distance learning as a transactional distance mitigator and memory retrieval stimulant

This present study explores Metaverse-based Distance Learning (MDL) as a mitigative strategy of transactional distance (TD) and an enhancer of memory retrieval in an educational setting. We conducted two experimental studies. In the first study (n = 367 participants), we found that MDL significantly reduced perceived TD, leading to positive learner attitudes and increased intentions for repeat learning. The second study utilized functional Near-Infrared Spectroscopy (fNIRS) to assess hemodynamic responses in the prefrontal cortex of 30 participants, comparing brain activity during lectures in MDL and e-learning environments. Results indicated that MDL elicited higher oxy-Hb activation in the prefrontal cortex, particularly during cognitively challenging tasks, correlating with improved memory retrieval. Grounded in both Transactional Distance Theory (TDT) and context-dependent memory (CDM) frameworks, we found that the technological and educational potential of MDL not only reduces psychological barriers in distance learning but also shows how it can improve cognitive engagement and retention. These findings underscore the potential of MDL in distance education and suggest pathways for future research to explore its implications further, particularly in conjunction with other emerging technologies.

Research on the Advantages and Challenges of Replacing LEDs with Lasers in Functional Near-Infrared Spectroscopy Systems Based on Advanced Signal Processing Algorithms

Abstract: Functional near infrared spectroscopy (fNIRS) has received increasing attention as a non-invasive, portable brain hemodynamic monitoring tool due to its potential for use in natural Settings. Traditional fNIRS systems commonly use light-emitting diodes (LEDs) as light sources, which have become the mainstream choice because of their low cost, easy integration, low heat output, and availability of a variety of near-infrared wavelengths. However, LEDs are limited by their low optical output power and broad wavelength range, which restrict their effectiveness in deep tissue penetration and signal quality. In contrast, Laser Diodes (LD) have the advantages of good monochromaticity and high optical output power, which can provide deeper tissue penetration and higher signal stability. Therefore, this study explores the feasibility of using lasers instead of leds in fNIRS systems and their impact on signal quality and imaging depth. By comparing the performance of LED and laser-based fNIRS systems, it is evident that lasers provide significant improvements in signal quality and imaging depth, although they face challenges in terms of cost, thermal management, and safety. The research in this paper provides an important reference for the future design of fNIRS system, especially in the application scenarios requiring high precision imaging.

The impact of acupuncture on the brain function of patients with mild to moderate major depressive disorder: a randomized controlled trial protocol

IntroductionMajor depressive disorder (MDD) is a common mental disease that significantly impact the daily lives of those affected. Acupuncture has shown promising therapeutic effects in the treatment of MDD, but its underlying mechanisms remain unclear. This study aims to evaluate the influence of acupuncture on the brain function of patients with MDD and it’s therapeutic effects on MDD.MethodsThis study plans to recruit 112 participants, who will be randomly allocated to either the acupuncture group or the sham acupuncture group in a 1:1 ratio. Both groups will undergo 8 weeks of treatment, consisting of three sessions per week, with each session lasting for 30 min. A follow-up period of 3 months will be conducted after the treatment. Assessments will be conducted at weeks 0, 8, 12, 16, and 20 in this study. The primary outcome measure will be functional near-infrared spectroscopy (fNIRS) brain imaging, while secondary outcome measures will include Beck Depression Inventory II (BDI-II), Self-Rating Anxiety Scale (SAS), 36-Item Short Form Survey Instrument (SF-36), Traditional Chinese Medicine (TCM) syndrome score scale, and symptom elimination rate.DiscussionWhile acupuncture has been confirmed to have a certain degree of therapeutic efficacy in the treatment of MDD, research on the mechanisms underlying acupuncture’s treatment of MDD remains limited, particularly with regards to its impact on the brain function of individuals with MDD. This study aims to explore both the clinical effectiveness and treatment mechanisms of acupuncture for MDD, with a specific focus on its influence on brain function. This research endeavors to provide new insights into the potential of acupuncture as a therapeutic approach for the treatment of MDD in the future.Trial Registrationwww.chictr.org.cn, ID: ChiCTR2300076292. Registered on 28 September 2023.

NiReject: toward automated bad channel detection in functional near-infrared spectroscopy.

The increasing sample sizes and channel densities in functional near-infrared spectroscopy (fNIRS) necessitate precise and scalable identification of signals that do not permit reliable analysis to exclude them. Despite the relevance of detecting these "bad channels," little is known about the behavior of fNIRS detection methods, and the potential of unsupervised and semi-supervised machine learning remains unexplored. We developed three novel machine learning-based detectors, unsupervised, semi-supervised, and hybrid NiReject, and compared them with existing approaches. We conducted a systematic literature search and demonstrated the influence of bad channel detection. Based on 29,924 signals from two independently rated datasets and a simulated scenario space of diverse phenomena, we evaluated the NiReject models, six of the most established detection methods in fNIRS, and 11 prominent methods from other domains. Although the results indicated that a lack of proper detection can strongly bias findings, detection methods were reported in only 32% of the included studies. Semi-supervised models, specifically semi-supervised NiReject, outperformed both established thresholding-based and unsupervised detectors. Hybrid NiReject, utilizing a human feedback loop, addressed the practical challenges of semi-supervised methods while maintaining precise detection and low rating effort. This work contributes toward more automated and reliable fNIRS signal quality control by comprehensively evaluating existing and introducing novel machine learning-based techniques and outlining practical considerations for bad channel detection.

Machine Learning-based Filtering system for fNIRS Signals’ Analysis Purpose

This paper presents a preliminary study delving into the application of machine learning-based methods for optimising parameter selection in filtering techniques. The authors focus on exploring the efficacy of two prominent filtering methods: smoothing and cascade filters, known for their profound impact on enhancing the quality of brain signals. The study specifically examines signals acquired through functional near-infrared spectroscopy (fNIRS), a non-invasive neuroimaging modality offering valuable insights into brain activity. Through meticulous analysis, the research underscores the potential of machine learning approaches in discerning optimal parameters for filtering, thereby leading to a significant enhancement in the quality and reliability of fNIRS-derived signals. The results demonstrate the effectiveness of machine learning-based methods in optimizing parameter selection for filtering techniques, particularly in the context of fNIRS signals. By leveraging these approaches, the study achieves notable improvements in the quality and reliability of brain signal data. This work sheds light on promising avenues for refining neuroimaging methodologies and advancing the field of signal processing in neuroscience. The successful application of machine learning-based techniques highlights their potential for optimizing neuroimaging data processing, ultimately contributing to a deeper understanding of brain function.

Effect of habitual physical activity on motor performance and prefrontal cortex activity during implicit motor learning.

Acute bouts of exercise have been shown to improve motor learning. However, whether these benefits can be observed from habitual physical activity (PA) levels remains unclear and has important implications around PA guidelines to promote motor learning across the lifespan. This study investigated the effect of habitual PA levels on brain activity within the dorsolateral prefrontal cortex (DLPFC) during procedural motor skill acquisition. Twenty-six right-handed healthy young adults had physical activity levels quantified by calculating the metabolic equivalent of task (METs) in minutes per week, derived from the International Physical Activity Questionnaire (IPAQ). Functional near-infrared spectroscopy (fNIRS) over the DLPFC was recorded to measure neural activation during a serial reaction time task (SRTT). Behavioural indicators of procedural motor skill acquisition were quantified as reaction time and accuracy of correct trials during the SRTT. DLPFC activation was characterised as task-related changes in oxyhaemoglobin (∆[HbO2]). Findings showed that higher PA levels were associated with improvements in reaction time during procedural motor skill acquisition (p = 0.03). However, no significant effects of PA levels on accuracy or ∆[HbO2] during procedural motor skill acquisition were observed. These findings show that while habitual PA may promote motor performance in young adults, this is not reflected by changes in the DLPFC area of the brain.

Temporal attention fusion network with custom loss function for EEG--fNIRS classification.

Objective.Methods that can detect brain activities accurately are crucial owing to the increasing prevalence of neurological disorders. In this context, a combination of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) offers a powerful approach to understanding normal and pathological brain functions, thereby overcoming the limitations of each modality, such as susceptibility to artefacts of EEG and limited temporal resolution of fNIRS. However, challenges such as class imbalance and inter-class variability within multisubject data hinder their full potential.Approach.To address this issue, we propose a novel temporal attention fusion network (TAFN) with a custom loss function. The TAFN model incorporates attention mechanisms to its long short-term memory and temporal convolutional layers to accurately capture spatial and temporal dependencies in the EEG--fNIRS data. The custom loss function combines class weights and asymmetric loss terms to ensure the precise classification of cognitive and motor intentions, along with addressing class imbalance issues.Main resultsRigorous testing demonstrated the exceptional cross-subject accuracy of the TAFN, exceeding 99% for cognitive tasks and 97% for motor imagery (MI) tasks. Additionally, the ability of the model to detect subtle differences in epilepsy was analyzed using scalp topography in MI tasks.Significance.This study presents a technique that outperforms traditional methods for detecting high-precision brain activity with subtle differences in the associated patterns. This makes it a promising tool for applications such as epilepsy and seizure detection, in which discerning subtle pattern differences is of paramount importance.

The Role of the Bilateral Inferior Frontal Gyrus in Non-suicidal Self-injury (NSSI) among Depressed Adolescents: A Functional Near-infrared Spectroscopy (fNIRS) Study during Verbal Fluency Tasks

BackgroundThe diagnosis and treatment of adolescents engaging in non-suicidal self-injury (NSSI) have been a clinical focus. We examined the hemodynamic changes in verbal fluency tasks (VFT) of adolescents with depression and NSSI, and its association with NSSI indexes. MethodsSeventy-three adolescents with depression and NSSI and sixty-nine healthy controls (HCs) were employed. The VFT was performed by functional near-infrared spectroscopy (fNIRS). Independent-sample t-test, correlation analysis, and liner regression were measured. ResultsIn the VFT, reduced hemodynamic changes were found in the NSSI group, including thirteen channels. Totally seven ROIs were defined according to which brain region these channels had the largest coverage and other channels that also belonged to it. They are the right inferior frontal gyrus (RIFG, t = -2.53, FDR corrected p = 0.02), right middle frontal gyrus (RMFG, t = -3.47, FDR corrected p = 0.002), right superior frontal gyrus (RSFG, t = -2.98, FDR corrected p = 0.005), left middle frontal gyrus (LMFG, t = -3.26, FDR corrected p = 0.002), left inferior frontal gyrus (LIFG, t = -2.80, FDR corrected p = 0.001), left precentral gyrus (LPrCG, t = -2.22, FDR corrected p = 0.03), and left supramarginal gyrus (LSMG, t = -2.20, FDR corrected p = 0.03). Negative correlations were found between the frequency of NSSI and the bilateral IFG (RIFG, r = -0.28, p = 0.01; LIFG, r = -0.26, p = 0.03). BDI and BAI have positive correlations with the frequency of NSSI (BDI: r = 0.42, FDR corrected p < 0.001; BAI: r = 0.41, FDR corrected p < 0.001), but results of liner regression showed that both of them do not affect the association between the frequency of NSSI and hemodynamic changes in bilateral IFG (RIFG, p = 0.01; LIFG, p = 0.04). ConclusionAdolescents with depression and NSSI have worse performance in the VFT, and lower activation in the bilateral IFG may represent a higher frequency of NSSI. These results help physicians enhance the understanding of adolescents with depression and NSSI.

Evaluating prefrontal changes in depression using functional near-infrared spectroscopy utilizing Stroop test: A comparison with healthy controls

Background: Functional near-infrared spectroscopy (fNIRS) is being increasingly utilized to visualize the brain areas involved in cognitive activity to understand the human brain better. Its portability and easy setup give it an advantage over other functional brain imaging tools. The current study utilizes fNIRS while performing a Stroop test, which is commonly used to assess the impairment of information selection in depression. Aim: To compare cortical activation during the Stroop test in depressed individuals to healthy controls. Methods: This cross-sectional study compared oxyhemoglobin (OxyHb) concentration changes in 39 individuals with depression to 40 healthy individuals. The Stroop test was construed in an event-related design with an intertrial interval of 2 seconds with jitter. A continuous wave fNIRS system was used for recording the cortical activity at 17 locations. Analysis of fNIRS data was done using statistical parametric mapping (SPM) for estimating general linear model (GLM) coefficients. Further analysis of the mean change of OxyHb concentrations during the 2 seconds after the presentation of congruent and incongruent stimuli was done between the groups using Mann–Whitney U test corrected for multiple comparisons with Bonferroni correction. Results: While the number of errors and correct responses were similar between the groups, the reaction time for correct responses was more in the depression group in comparison to healthy individuals (t = -2.39, P = 0.01). For both healthy and depressive individuals in incongruent versus congruent task contrast, deactivation was seen in the region between the left middle frontal sulcus and frontopolar area of the brain (t = 0.41 and t = 0.21, respectively, P &lt; 0.05). The depressed group had a higher mean change in OxyHb concentration following incongruent stimuli in comparison to the HCs (mean rank: HC = 32.63, depression = 47.56). Conclusion: Our findings indicate that individuals with depression exhibited prolonged reaction times and distinct activation patterns of the frontal cortex compared to healthy individuals. The observed pattern of brain activation for congruent and incongruent tasks among both healthy and depressed individuals aligns with the findings of the prior studies, emphasizing the utility of fNIRS as a valuable instrument for assessing brain activity.

Prior self-control exertion decreases pre-frontal cortex oxygenation during a CO2rebreathing challenge but does not affect perceptions of dyspnoea or tolerance time.

Dyspnoea perception is influenced by a complex interplay of physiological, psychological, and environmental factors. Recently, we showed that males with high trait self-control experience less dyspnoea and persist for longer in a carbon dioxide (CO2) rebreathing challenge than males with low trait self-control. As self-control can also vary within individuals (state self-control), the primary aim of the present study was to investigate whether prior self-control exertion influenced perceptions of dyspnoea and tolerance of a CO2 rebreathing challenge in healthy young males. We also used functional near-infrared spectroscopy (fNIRS) to assess haemodynamic activity of the pre-frontal cortex (PFC) which is a region of interest (ROI) in dyspnoea research, and the primary brain region associated with exertion of self-control. In a within-subjects design, fifteen healthy young males completed an easy (congruent) Stroop task (control condition) and a difficult (incongruent) Stroop task (prior self-control exertion, experimental condition) followed by a CO2 rebreathing challenge until the limit of tolerance. Changes in oxyhaemoglobin (ΔO2Hb) and deoxyhaemoglobin (ΔHHb) were assessed continuously in the Stroop task and CO2 rebreathing challenge. During the CO2 rebreathing challenge, dyspnoea intensity and unpleasantness were rated every 30s. Prior self-control exertion did not affect perceptions of dyspnoea or tolerance time in the CO2 rebreathing challenge (all P > 0.05). ΔO2Hb from baseline was higher in the left (+38%) and right (+44%) pre-frontal cortices during the difficult Stroop task than the easy Stroop task (both P < 0.05). During the subsequent CO2 rebreathing challenge, ΔO2Hb was attenuated following prior self-control exertion in the left PFC. Although prior self-control exertion decreased pre-frontal cortex oxygenation during a subsequent CO2 rebreathing challenge, there was no change in tolerance time or perceptions of dyspnoea.

Network-based statistics reveals an enhanced subnetwork in prefrontal cortex in mild cognitive impairment: a functional near-infrared spectroscopy study

Network-based statistics reveals an enhanced subnetwork in prefrontal cortex in mild cognitive impairment: a functional near-infrared spectroscopy study