Functional Near-infrared Spectroscopy Data Research Articles

Using multivariate partial least squares on fNIRS data to examine load-dependent brain-behaviour relationships in aging.

Researchers implementing non-invasive neuroimaging have reported distinct load-dependent brain activity patterns in older adults compared with younger adults. Although findings are mixed, these age-related patterns are often associated with compensatory mechanisms of cognitive decline even in the absence of direct comparisons between brain activity and cognitive performance. This study investigated the effects of cognitive load on brain-behavior relationships in younger and older adults using a data-driven, multivariate partial least squares (PLS) analysis of functional near-infrared spectroscopy (fNIRS) data. We measured bilateral prefrontal brain activity in 31 older and 27 younger adults while they performed single and dual 2-back tasks. Behavioral PLS analysis was used to determine relationships between performance metrics (reaction time and error rate) and brain oxygenation (HbO) and deoxygenation (HbR) patterns across groups and task loads. Results revealed significant age-group differences in brain-behavior relationships. In younger adults, increased brain activity (i.e., increased HbO and decreased HbR) was associated with faster reaction times and better accuracy in the single task, indicating sufficient neural capacity. Conversely, older adults showed a negative correlation between HbR and error rates in the single task; however, in the dual task, they demonstrated a positive relationship between HbO and performance, indicative of compensatory mechanisms under the higher cognitive load. Overall, older adults' showed relationships with either HbR or HbO, but not both, indicating that the robustness of the relationship between brain activity and behavior varies across task load conditions. Our PLS approach revealed distinct load-dependent brain activity between age groups, providing further insight into neurocognitive aging patterns, such as compensatory mechanisms, by emphasizing the variability and complexity of brain-behavior relationships. Our findings also highlight the importance of considering task complexity and cognitive demands in interpreting age-related brain activity patterns.

Comparing different motion correction approaches for resting-state functional connectivity analysis with functional near-infrared spectroscopy data.

Motion artifacts are a notorious challenge in the functional near-infrared spectroscopy (fNIRS) field. However, little is known about how to deal with them in resting-state data. We assessed the impact of motion artifact correction approaches on assessing functional connectivity, using semi-simulated datasets with different percentages and types of motion artifact contamination. Thirty-five healthy adults underwent a 15-min resting-state acquisition. Semi-simulated datasets were generated by adding spike-like and/or baseline-shift motion artifacts to the real dataset. Fifteen pipelines, employing various correction approaches, were applied to each dataset, and the group correlation matrix was computed. Three metrics were used to test the performance of each approach. When motion artifact contamination was low, various correction approaches were effective. However, with increased contamination, only a few pipelines were reliable. For datasets mostly free of baseline-shift artifacts, discarding contaminated frames after pre-processing was optimal. Conversely, when both spike and baseline-shift artifacts were present, discarding contaminated frames before pre-processing yielded the best results. This study emphasizes the need for customized motion correction approaches as the effectiveness varies with the specific type and amount of motion artifacts present.

A Spatial-Temporal Analysis of Brain Activation to Explain the Take-Over Failure in Conditionally Automated Driving.

This study aims to investigate the causes of take-over failures in conditional automated driving with spatial-temporal analysis of brain zone activation. Take-over requires a human driver to resume the control of the vehicle when its automation system disengages. Existing studies have found that take-over failures occur frequently on some drivers, but the causes have not been thoroughly studied. In a driving simulator experiment, 40 drivers took over in critical freeway cut-in situations. Functional near-infrared spectroscopy (fNIRS) data were collected before and during the take-over process to evaluate brain zone activation. Successful and failed take-overs were compared with changes in fNIRS data based on spatial-temporal comparisons and cluster analysis. The results suggested a significant difference in temporal brain activation between take-over failure and success conditions. Take-over failure conditions are mostly related to earlier and longer brain activation in most brain zones and repeated activation of the cognition brain zones. Drivers' attention switches, steering, and braking patterns are also related to different brain zone activation orders. The results indicate the need to reduce the mental workload caused by the sudden system disengagement to prevent take-over failure. Future research and implementation should focus on earlier warnings of upcoming hazards and driver education in dealing with sudden system disengagement.

Synchronization processes in fNIRS visibility networks

We employ Kuramoto model to assess the presence of synchronization in individuals who fulfill a cooperation task. Our input data is a couple of signals obtained from functional Near-Infrared Spectroscopy Data Acquisition and Pre-processing technology that is used to capture the brain activity of an individual by measuring the oxyhemoglobin (HbO) level. We consider 1 min signal for individuals in three distinct states: (i) rest; (ii) before a disturb happens; (iii) after the disturbance. We estimate global and local order parameters synchronization with the purpose to compare the conditions of reaching a synchronous state in the networks corresponding to different states for distinct individuals and hemispheres of the prefrontal cortices of same individual. Experimental results confirmed once more that coherent state is reached not for same conditions in both individuals and hemispheres of the prefrontal cortices. Furthermore, condition changes even for different events. The computation of the effective frequencies for each degree class indicates clearly the network difference in rest, before and after disturb. Finally, we investigate the dynamic connectivity matrix and consider the similarity between distinct prefrontal cortices over time.

VR rehabilitation system evaluator: A fNIRS-based and LLM-enabled evaluation paradigm for Mild Cognitive Impairment

Virtual Reality (VR) has significant potential for Mild Cognitive Impairment (MCI) rehabilitation by offering dynamic and engaging tasks designed to stimulate various cognitive domains. However, how efficiently evaluating the VR tasks is always challenging. The current effectiveness evaluation methods for task evaluation and optimisation typically rely on post-training metrics like game performance or subjective questionnaires, which cannot directly reflect the comprehensiveness and intensity of cognitive stimulation. To bridge these gaps, we propose a novel approach integrating functional near-infrared spectroscopy (fNIRS) data and a Large Language Model (LLM) for VR rehabilitation task evaluation and optimisation. Firstly, we leverage fundamental cognitive theories to propose a systematic evaluation paradigm for assessing the scope of the cognitive domains stimulated by VR tasks. Secondly, we propose fNIRS-derived graph parameters to objectively evaluate the cognitive stimulation with high-time resolution. Finally, we develop a prompt-generating strategy to facilitate Large Language Model (LLM)-based analysis, which transforms fNIRS-derived metrics combined with the scope of stimulated cognitive domains into easy-to-understand evaluation reports and actionable recommendations for task optimisation. We demonstrate this approach with two VR rehabilitation tasks. Several experts evaluated the LLM-based task evaluation results and task optimisation recommendation and agreed that the LLM-generated results are meaningful and applicable. This approach offers a rapid, objective method to evaluate VR task effectiveness and improves their design, marking a significant step in developing more effective MCI interventions.

Resting-State Functional Connectivity Predicts Cochlear-Implant Speech Outcomes.

Cochlear implants (CIs) have revolutionized hearing restoration for individuals with severe or profound hearing loss. However, a substantial and unexplained variability persists in CI outcomes, even when considering subject-specific factors such as age and the duration of deafness. In a pioneering study, we use resting-state functional near-infrared spectroscopy to predict speech-understanding outcomes before and after CI implantation. Our hypothesis centers on resting-state functional connectivity (FC) reflecting brain plasticity post-hearing loss and implantation, specifically targeting the average clustering coefficient in resting FC networks to capture variation among CI users. Twenty-three CI candidates participated in this study. Resting-state functional near-infrared spectroscopy data were collected preimplantation and at 1 month, 3 months, and 1 year postimplantation. Speech understanding performance was assessed using consonant-nucleus-consonant words in quiet and Bamford-Kowal-Bench sentences in noise 1-year postimplantation. Resting-state FC networks were constructed using regularized partial correlation, and the average clustering coefficient was measured in the signed weighted networks as a predictive measure for implantation outcomes. Our findings demonstrate a significant correlation between the average clustering coefficient in resting-state functional networks and speech understanding outcomes, both pre- and postimplantation. This approach uses an easily deployable resting-state functional brain imaging metric to predict speech-understanding outcomes in implant recipients. The results indicate that the average clustering coefficient, both pre- and postimplantation, correlates with speech understanding outcomes.

Adjusting effective multiplicity ( ) for family-wise error rate in functional near-infrared spectroscopy data with a small sample size.

The advancement of multichannel functional near-infrared spectroscopy (fNIRS) has enabled measurements across a wide range of brain regions. This increase in multiplicity necessitates the control of family-wise errors in statistical hypothesis testing. To address this issue, the effective multiplicity ( ) method designed for channel-wise analysis, which considers the correlation between fNIRS channels, was developed. However, this method loses reliability when the sample size is smaller than the number of channels, leading to a rank deficiency in the eigenvalues of the correlation matrix and hindering the accuracy of calculations. We aimed to reevaluate the effectiveness of the method for fNIRS data with a small sample size. In experiment 1, we used resampling simulations to explore the relationship between sample size and values. Based on these results, experiment 2 employed a typical exponential model to investigate whether valid could be predicted from a small sample size. Experiment 1 revealed that the values were underestimated when the sample size was smaller than the number of channels. However, an exponential pattern was observed. Subsequently, in experiment 2, we found that valid values can be derived from sample sizes of 30 to 40 in datasets with 44 and 52 channels using a typical exponential model. The findings from these two experiments indicate the potential for the effective application of correction in fNIRS studies with sample sizes smaller than the number of channels.

Convolutional neural networks can detect orthostatic hypotension in Parkinson's disease using resting-state functional near-infrared spectroscopy data.

Neurological disorders such as Parkinson's disease (PD) often adversely affect the vascular system, leading to alterations in blood flow patterns. Functional near-infrared spectroscopy (fNIRS) is used to monitor hemodynamic changes via signal measurement. This study investigated the potential of using resting-state fNIRS data through a convolutional neural network (CNN) to evaluate PD with orthostatic hypotension. The CNN demonstrated significant efficacy in analyzing fNIRS data, and it outperformed the other machine learning methods. The results indicate that judicious input data selection can enhance accuracy by over 85%, while including the correlation matrix as an input further improves the accuracy to more than 90%. This study underscores the promising role of CNN-based fNIRS data analysis in the diagnosis and management of the PD. This approach enhances diagnostic accuracy, particularly in resting-state conditions, and can reduce the discomfort and risks associated with current diagnostic methods, such as the head-up tilt test.

Connecting the dots: Sensory cueing enhances functional connectivity between pre-motor and supplementary motor areas in Parkinson's disease.

People with Parkinson's disease often exhibit improvements in motor tasks when exposed to external sensory cues. While the effects of different types of sensory cues on motor functions in Parkinson's disease have been widely studied, the underlying neural mechanism of these effects and the potential of sensory cues to alter the motor cortical activity patterns and functional connectivity of cortical motor areas are still unclear. This study aims to compare changes in oxygenated haemoglobin, deoxygenated haemoglobin and correlations among different cortical regions of interest during wrist movement under different external stimulus conditions between people with Parkinson's disease and controls. Ten Parkinson's disease patients and 10 age- and sex-matched neurologically healthy individuals participated, performing repetitive wrist flexion and extension tasks under auditory and visual cues. Changes in oxygenated and deoxygenated haemoglobin in motor areas were measured using functional near-infrared spectroscopy, along with electromyograms from wrist muscles and wrist movement kinematics. The functional near-infrared spectroscopy data revealed significantly higher neural activity changes in the Parkinson's disease group's pre-motor area compared to controls (p = 0.006), and functional connectivity between the supplementary motor area and pre-motor area was also significantly higher in the Parkinson's disease group when external sensory cues were present (p = 0.016). These results indicate that external sensory cues' beneficial effects on motor tasks are linked to changes in the functional connectivity between motor areas responsible for planning and preparation of movements.

Cortical activation for adolescent-onset minor depression and major depressive disorder: an fNIRS study

BackgroundWhile depression is increasing worldwide, some patients are diagnosed as having Major Depressive Disorder (MDD), but others are diagnosed with minor depression, however, the potential neuro mechanism is unknown.MethodsSixty-two patients with minor depression, 44 adolescents with MDD and 54 healthy adolescents participated in this study. Functional near-infrared spectroscopy (fNIRS), both HAMD and HAMA data were collected from all of the participants.ResultsThe result indicates the pervasively decreased activation of BA, 11, 21, 45 and 46 were observed in the MDD group and reduced activation of BA 45 was observed in the minor depression group. However, cortical activation was not observed between the minor depression or MDD groups. Cortical activation was also not correlated with the depressive/anxious score in the minor and MDD groups separately.ConclusionsCortical activation was pervasively decreased in the MDD group and slightly reduced in the minor depression group, which may be a potential neural mechanism. As reduced cortical activation in minor depression, interventions in the early stages of minor depression may help slow or even modify the development of the illness.

Efficacy and executive function of solution-focused brief therapy on adolescent depression.

To investigate the efficacy and impact on executive function of Solution-Focused Brief Therapy (SFBT) in treating Major Depressive Disorder (MDD) in adolescents. A total of 129 adolescents diagnosed with MDD were enrolled in the study. Out of these, 28 adolescents were assigned to the SFBT group, while 25 were part of the Active Control group (AC group), receiving psychodynamic psychotherapy. Executive function, depressive and anxiety symptoms were assessed at baseline, at the time of the third intervention, the sixth intervention, and the 10th intervention. After the third intervention, the scores of the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) of the participants in the SFBT group decreased significantly, which had the cumulative effect at the 6th and 10th interventions. The verbal fluency task (VFT) performances of the SFBT group participants yielded significantly higher scores after the third intervention and remained increasing at the 6th and 10th interventions. The AC group steadily decreased after the intervention. Analysis of functional near-infrared spectroscopy (fNIRS) data revealed a progressive and significant increase in the average oxyhemoglobin (oxy-Hb) levels in the dorsolateral prefrontal cortex (DLPFC) in the SFBT group compared to the AC group after the 10th intervention. SFBT might improve depressive and anxiety symptoms as well as executive function of adolescent depression. https://www.chictr.org.cn, identifier ChiCTR2300067909.

Abnormal prefrontal cortical activation during the GO/NOGO and verbal fluency tasks in adult patients with comorbid generalized anxiety disorder and attention-deficit/hyperactivity disorder: An fNIRS study

Generalized anxiety disorder (GAD) and adult attention-deficit/hyperactivity disorder (ADHD) are commonly reported comorbidities. Adult GAD patients with comorbid ADHD are often underdiagnosed and undertreated. To explore the clinical value of functional near-infrared spectroscopy (fNIRS) data for assisting in the accurate diagnosis of ADHD in individuals with GAD, haemoglobin (HbO) concentration changes in the prefrontal cortex (PFC) were detected via fNIRS in 49 patients with both GAD and ADHD, 46 patients with GAD, and 34 healthy controls (HCs) during a verbal fluency task (VFT) and a GO/NOGO task. The correlations between PFC fNIRS data and the severity of inattention and hyperactivity symptoms assessed using the adult ADHD Self-Report Scale (ASRS) were analyzed. Our results showed that, during the GO/NOGO task, channels in the left dorsolateral PFC (channels 28 and 29) were hyperactivated, while channels in the medial PFC (channels 36, 37, and 47) were hypoactivated in participants with ADHD and GAD compared with those with GAD alone. During the VFT, compared with the HC group, both the ADHD + GAD group and the GAD group exhibited significantly decreased HbO activation in the medial PFC (channels 37, 38, and 48) and in the left ventrolateral PFC (channel 39); moreover, no difference was found between the ADHD + GAD group and the GAD group. Activation in the left dorsolateral PFC (channels 28 and 29) during the GO/NOGO task showed a significant positive correlation with ASRS-inattention scores. Our results indicated that fNIRS data collected during the GO/NOGO task may help to distinguish patients with comorbid GAD and ADHD from those with GAD alone.

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.

Distinct Time-Resolved Brain-Wide Coactivations in Oxygenated and Deoxygenated Hemoglobin.

Human resting-state networks (RSNs) estimated from oxygenated (HbO) and deoxygenated hemoglobin (HbR) data exhibit strong similarities, while task-based studies show different dynamics in HbR and HbO responses. Such a discrepancy might be explained due to time-averaged estimations of RSNs. Our study investigated differences between HbO and HbR on time-resolved brain-wide coactivation patterns (CAPs). Diffuse optical tomography was reconstructed from resting-state whole-head functional near-infrared spectroscopy data of HbR and HbO in individual healthy participants. Time-averaged RSNs were obtained using the group-level independent component analysis. Time-resolved CAPs were estimated using a clustering approach on the time courses of all obtained RSNs. Characteristics of the RSNs and CAPs from HbR and HbO were compared. Spatial patterns of HbR and HbO RSNs exhibited significant similarities. Meanwhile, HbR CAPs revealed much more organized spatial and dynamic characteristics than HbO CAPs. The entire set of HbR CAPs suggests a superstructure resulted from brain-wide neuronal dynamics, which is less evident in the set of HbO CAPs. These differences between HbO and HbR CAPs were consistently replicated in individual session data. Our results suggest that human resting brain-wide neuronal activations are preserved better in time-resolved brain-wide patterns, i.e., CAPs, from HbR than those from HbO, while such a difference is lost between time-averaged HbR and HbO RSNs. Our results reveal, for the first time, HbR concentration fluctuations are more directly coupled with resting dynamics of brain-wide neuronal activations in human brains.

Longitudinal assessment of the effects of passive training on stroke rehabilitation using fNIRS technology

For patients with severe conditions such as stroke, passive exercise is commonly used in the early stages of their rehabilitation training. This study aimed to assess the effect of passive training on stroke rehabilitation and track brain function changes and the patient's rehabilitation progress, by employing a robot glove to perform a passive flexion-extension task. Specifically, functional near-infrared spectroscopy data from 26 patients under resting and right-hand passive interactions (task state) were recorded before and after a one-week interval, respectively, to explore the variations in the brain network of the patients. The results showed a stronger functional connectivity in both states of the two experiments in the left and right homotopic brain regions (p < 0.01). The effective connectivity network between brain regions maintained a consistent trend during the same test. Changes in functional connectivity and effective connectivity networks may represent the advantages of motor rehabilitation tasks and patient treatment duration, providing a perspective for an easy understanding of the mechanisms of stroke-related brain changes. This study demonstrates the feasibility of passive training strategies in the field of intelligent rehabilitation by applying neuroscientific diagnostic methods based on physiological data to the field of human-computer interaction.

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.

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.

Regional activity and effective connectivity within the frontoparietal network during precision walking with visual cueing: an fNIRS study.

Precision walking (PW) incorporates precise step adjustments into regular walking patterns to navigate challenging surroundings. However, the brain processes involved in PW control, which encompass cortical regions and interregional interactions, are not fully understood. This study aimed to investigate the changes in regional activity and effective connectivity within the frontoparietal network associated with PW. Functional near-infrared spectroscopy data were recorded from adult subjects during treadmill walking tasks, including normal walking (NOR) and PW with visual cues, wherein the intercue distance was either fixed (FIX) or randomly varied (VAR) across steps. The superior parietal lobule (SPL), dorsal premotor area (PMd), supplementary motor area (SMA), and dorsolateral prefrontal cortex (dlPFC) were specifically targeted. The results revealed higher activities in SMA and left PMd, as well as left-to-right SPL connectivity, in VAR than in FIX. Activities in SMA and right dlPFC, along with dlPFC-to-SPL connectivity, were higher in VAR than in NOR. Overall, these findings provide insights into the roles of different brain regions and connectivity patterns within the frontoparietal network in facilitating gait control during PW, providing a useful baseline for further investigations into brain networks involved in locomotion.

Classification of fNIRS signals from adolescents with MDD in suicide high- and low-risk groups using machine learning

Prefrontal cortex activation is attenuated during cognitive tasks in patients with suicidal ideation or major depressive disorder (MDD). However, the apparent relationship between patients with MDD, especially suicide high-risk (SHR) adolescents, and the characteristics of their hemodynamic responses has not yet been elucidated. To investigate this relationship, we recruited 30 patients with MDD aged 13−19. Functional near-infrared spectroscopy (fNIRS) data were collected for all patients during a Stroop test. Through a ten-time iterative leave-one-out cross-validation via 1000 iterative random search-based feature selections, we achieved a generalized classification accuracy of 70.3±5.0 % (from min. 63.3 % to max. 76.7 %). From the results of random search-based feature selection, Ch08oxy and Ch09deoxy were identified as the two most relevant fNIRS channels. This finding implies that these fNIRS channels can be used as neurological biomarkers to distinguish SHR adolescents with MDD from suicide low-risk (SLR) adolescents. In addition, we determined the oxy-Hb channels of the SHR group, except for Ch01oxy, Ch02oxy, Ch11oxy, and Ch14oxy, were hyperactivated compared to the SLR group.

Partner's emotions are associated with preschoolers' prefrontal activation under joint attention: An fNIRS evidence.

Emotions and joint attention are highly associated and mutually influenced during preschool, the critical period for early emotional and cognitive development. However, few studies have explored the neuropsychological mechanism of joint attention with preschoolers and their partners under different emotions. This study has examined the prefrontal activation under a comprehensive emotional joint attention task in 45 preschoolers (25 boys, Mage = 58 ± 9.02 months) to compare the different influences of partners' positive, neutral, and negative emotions. Analysis of the functional near-infrared spectroscopy data indicated that the participants' prefrontal activation triggered by joint attention in positive and negative emotions was significantly higher than in neutral emotions. Moreover, their brain synchronization intensity was significantly higher in positive emotions of joint attention than in negative emotions. These findings advance our understanding of the neural mechanism of early childhood emotional processing under joint attention and provide a neural perspective to explain the effects of different emotions on preschoolers' social cognition.