Search Results (120)

Background: Understanding the behavioral and neural underpinnings of the post-concussion recovery of working memory function is critically important for improving clinical outcomes and adequately planning return-to-activity decisions. Previous studies provided inconsistent results due to small sample sizes and the use of a mixed population of participants who were at different post-injury time points. We aimed to examine working memory recovery during the first 6 months post-concussion in adolescents. Methods: We used functional magnetic resonance imaging (fMRI) to scan 45 concussed adolescents [CONCs] at baseline (<10 days post-concussion) and at 6 months post-concussion. Healthy control adolescents [HCs; n = 32] without a history of concussion were scanned once. During the scans, participants performed one-back and two-back working memory tasks with letters as the stimuli and angry, happy, neutral, and sad faces as distractors. Results: All affected adolescents were asymptomatic and cleared to return to activity 6 months after concussion. Working memory recovery was associated with faster and more accurate responses at 6 months vs. baseline (p-values < 0.05). It was also characterized by significant difficulty-related activation increases in the left inferior frontal gyrus (LIFG) and the left orbitofrontal cortex (LOFC) at 6 months vs. baseline. Although the activation differences between one-back and two-back were comparable between HCs and CONCs at 6 months, HCs had more pronounced activation in the LIFG than concussed adolescents. Conclusions: Post-concussion recovery is associated with significant performance improvements in speed and accuracy, as well as the normalization of brain responses in the LIFG and LOFC during the n-back task. The observed patterns of LOFC activation might reflect compensatory strategies to distribute neural processing and reduce neural fatigue post-concussion. Full article

Guarana (GUA), a Brazilian seed extract, contains caffeine and other bioactive compounds that may have psychoactive effects. To assess the acute effects of GUA compared to a low dose of caffeine (CAF) on cognitive and mood parameters, twenty participants completed a double-blind, crossover experiment where they ingested capsules containing the following: (1) 100 mg CAF, (2) 500 mg GUA containing 130 mg caffeine, or (3) placebo (PLA). Cognitive tests (Simon and 2N-Back Task) were performed at the baseline (pre-ingestion) and 60 min after ingestion. The response time for the cognitive tests and heart rate variability were unaffected (p > 0.05) by treatment, although 2N-Back was overall faster (p = 0.001) across time. The accuracy in the 2N-Back Task showed a significant interaction effect (p = 0.029) due to higher post-ingestion versus pre-ingestion levels (p = 0.033), but only with the PLA. The supplements also had no effect on cognitive measures following physical fatigue (n = 11). There was an interaction effect on perceived mental energy, where the pre-ingestion of GUA had lower mental pep ratings compared to post-ingestion (p = 0.006) and post-exercise (p = 0.018) levels. Neither the acute ingestion of GUA nor low dose of CAF influenced cognitive performance or provided consistent benefit on mood or mental workload through vagal modulation. Additional investigations are beneficial to determining the lowest effective dose for CAF or GUA to influence mood and/or cognitive performance. Full article

While unsupervised denoising models have shown progress in recent years, their noise reduction capabilities still lag behind those of supervised denoising models. This limitation can be attributed to the lack of effective constraints during training, which only utilizes noisy images and hinders further performance improvements In this work, we propose a novel dual-branch self-boosting network called DBSNet, which offers a straightforward and effective approach to image denoising. By leveraging task-dependent features, we exploit the intrinsic relationships between the two branches to enhance the effectiveness of our proposed model. Initially, we extend the classic Noise2Noise (N2N) architecture by adding a new branch for noise component prediction to the existing single-branch network designed for content prediction. This expansion creates a dual-branch structure, enabling us to simultaneously decompose a given noisy image into its content (clean) and noise components. This enhancement allows us to establish stronger constraint conditions and construct more powerful loss functions to guide the training process. Furthermore, we replace the UNet structure in the N2N network with the proven DnCNN (Denoising Convolutional Neural Network) sequential network architecture, which enhances the nonlinear mapping capabilities of the DBSNet. This modification enables our dual-branch network to effectively map a noisy image to its content (clean) and noise components simultaneously. To further improve the stability and effectiveness of training, and consequently enhance the denoising performance, we introduce a feedback mechanism where the network’s outputs, i.e., content and noise components, are fed back into the dual-branch network. This results in an enhanced loss function that ensures our model possesses excellent decomposition ability and further boosts the denoising performance. Extensive experiments conducted on both synthetic and real-world images demonstrate that the proposed DBSNet outperforms the unsupervised N2N denoising model as well as mainstream supervised models trained with supervised methods. Moreover, the evaluation results on real-world noisy images highlight the desirable generalization ability of DBSNet for practical denoising applications. Full article

Shift work, long work hours, and operational tasks contribute to sleep and circadian disruption in defence personnel, with profound impacts on cognition. To address this, a digital technology, the SleepSync app, was designed for use in defence. A pre-post design study was undertaken to examine whether four weeks app use improved sleep and cognitive fitness (high performance neurocognition) in a cohort of shift workers from the Royal Australian Air Force. In total, 13 of approximately 20 shift-working personnel from one base volunteered for the study. Sleep outcomes were assessed using the Insomnia Severity Index (ISI), the Patient-Reported Outcomes Measurement Information System (PROMIS), Sleep Disturbance and Sleep-Related Impairment Scales, the Glasgow Sleep Effort Scale, the Sleep Hygiene Index, and mental health was assessed using the Depression, Anxiety, and Stress Scale-21. Sustained attention was measured using the 3-min Psychomotor Vigilance Task (PVT) and controlled response using the NBack. Results showed significant improvements in insomnia (ISI scores 10.31 at baseline and 7.50 after app use), sleep-related impairments (SRI T-scores 53.03 at baseline to 46.75 post-app use), and healthy sleep practices (SHI scores 21.61 at baseline to 18.83 post-app use; all p < 0.001). Trends for improvement were recorded for depression. NBack incorrect responses reduced significantly (9.36 at baseline; reduced by −3.87 at last week of app use, p < 0.001), but no other objective measures improved. These findings suggest that SleepSync may improve sleep and positively enhance cognitive fitness but warrants further investigation in large samples. Randomised control trials with other cohorts of defence personnel are needed to confirm the utility of this intervention in defence settings. Full article

Analyzing brain activity during mental arithmetic tasks provides insight into psychological disorders such as ADHD, dyscalculia, and autism. While most research is conducted on the static functional connectivity of the brain while performing a cognitive task, the dynamic changes of the brain, which provide meaningful information for diagnosing individual differences in cognitive tasks, are often ignored. This paper aims to classify electroencephalogram (EEG) signals for rest vs. mental arithmetic task performance, using Bayesian functional connectivity features in the sensor space as inputs into a graph convolutional network. The subject-specific (intrasubject) classification performed on 36 subjects for rest vs. mental arithmetic task performance achieved the highest subject-specific classification accuracy of 98% and an average accuracy of 91% in the beta frequency band, outperforming state-of-the-art methods. In addition, statistical analysis confirms the consistency of Bayesian functional connectivity features compared to traditional functional connectivity features. Furthermore, the graph-theoretical analysis of functional connectivity networks reveals that good-performance subjects had higher global efficiency, betweenness centrality, and closeness centrality than bad-performance subjects. The ablation study on the classification of three cognitive states (subtraction, music, and memory) achieved a classification accuracy of 97%, and visual working memory (n-back task) achieved a classification accuracy of 94%, confirming the consistency and reliability of the proposed methodology. Full article

Children with disabilities have higher prevalence estimates of obesity than typically developing children. The Healthy Caregivers–Healthy Children Phase 3 (HC3) project implemented an obesity prevention program adapted for children with special health care needs (CSHCN) that includes dietary intake and physical activity (PA) components. The primary outcome was a change in dietary intake, PA, and the body mass index (BMI) percentile. Ten childcare centers (CCCs) serving low-resource families with ≥30 2- to 5-year-olds attending were randomized to either the intervention (n = 5) or control (n = 5). The HC3 CCCs received (1) snack, beverage, PA, and screen time policies via weekly technical assistance; (2) adapted lesson plans for CSHCN; and (3) parent curricula. The control CCCs received a behavioral health attention curriculum. HC3 was delivered over three school years, with data collected at five different timepoints. It was delivered weekly for six months in year one. To ensure capacity building, the HC3 tasks were scaled back, with quarterly intervention delivery in year 2 and annually in year 3. Adaptations were made to the curriculum to ensure appropriate access for CSHCN. Given that the program was being delivered during the COVID-19 pandemic, special modifications were made to follow CDC safety standards. The primary outcome measures included the Environment and Policy Assessment and Observation (EPAO) tool, standardized dietary intake and PA assessments, and the child BMI percentile. CCCs are an ideal setting for targeting CSHCN for obesity prevention efforts as they provide an opportunity to address modifiable risk factors. Full article

Nocturnal smartphone use emits blue light, which can adversely affect sleep, leading to a variety of negative effects, particularly in children. Therefore, the present study aimed to determine the effect of acute (AC) (one night) and repeated (RC) (five nights) nocturnal smartphone exposure on sleep, cortisol, and next-day performance in Tunisian children. Thirteen participants (seven girls and six boys, age 9 ± 0.6, height 1.32 ± 0.06, weight 34.47 ± 4.41) attended six experimental nights. The experiment started with a baseline night (BL) with no smartphone exposure, followed by repeated sessions of nocturnal smartphone exposure lasting 90 minutes (08:00 pm–09:30 pm). Actigraphy; salivary cortisol; the Stroop test (selective attention); choice reaction time (CRT); N-back (working memory); counter-movement jump (CMJ), composed of flight time (time spent in the CMJ flight phase) and jump height; and a 30 m sprint were assessed the morning after each condition. Both AC and RC shortened total sleep time (TST) (p < 0.01), with a greater decrease with RC (−46.7 min, ∆% = −9.46) than AC (−28.8 min, ∆% = −5.8) compared to BL. AC and RC significantly increased waking after sleep onset (3.5 min, ∆% = 15.05, to 9.9 min, ∆% = 43.11%) and number of errors made on the Stroop test (1.8 error, ∆% = 74.23, to 3.07 error, ∆% = 97.56%). Children made 0.15 and 0.8 more errors (∆% = 6.2 to 57.61%) and spent 46.9 s and 71.6 s more time on CRT tasks (∆% = 7.22 to 11.11%) with AC and RC, respectively, compared to BL. The high-interference index of the Stroop task, CMJ performance, and 30 m sprint speed were only altered (p < 0.01) following RC (0.36, Δ% = 41.52%; −34 s, Δ% = −9.29%, for flight time and −1.23 m, −8.72%, for jump height; 0.49 s, Δ% = 6.48, respectively) when compared to BL. In conclusion, one- or five-night exposure to smartphones disturbed the children’s sleep quality and their performance, with more pronounced effects following RC. Full article

The main contribution of this study was the concurrent application of EEG and eye tracking techniques during n-back tasks as part of the methodology for addressing the problem of mental workload classification through machine learning algorithms. The experiments involved 15 university students, consisting of 7 women and 8 men. Throughout the experiments, the researchers utilized the n-back memory task and the NASA-Task Load Index (TLX) subjective rating scale to assess various levels of mental workload. The results indicating the relationship between EEG and eye tracking measures and mental workload are consistent with previous research. Regarding the four-class classification task, mental workload level could be predicted with 76.59% accuracy using 34 selected features. This study makes a significant contribution to the literature by presenting a four-class mental workload estimation model that utilizes different machine learning algorithms. Full article

The Attentional Control Theory (ACT) posits that, while trait anxiety may not directly impact performance, it can influence processing efficiency by prompting the use of compensatory mechanisms. The specific nature of these mechanisms, which might be reflective, is not detailed by the ACT. In a study involving 110 students (M = 20.12; SD = 2.10), surveys were administered to assess the students’ metacognitive beliefs, trait anxiety, and emotion regulation strategies (ERSs). The participants engaged in two working memory exercises: the digit span task from the WAIS-IV and an emotional n-back task. The findings indicated that anxiety, metacognitive beliefs, and maladaptive ERSs did not affect task performance but were correlated with increased response times. Several regression analyses demonstrated that a lack of confidence in one’s cognitive abilities and maladaptive ERSs predict higher reaction times (RT) in the n-back task. Additionally, maladaptive ERSs also predict an increased use of strategies in the digit span task. Finally, two mediation analyses revealed that anxiety increases processing efficiency, and this relation is mediated by the use of maladaptive ERSs. These results underscore the importance of the reflective level in mediating the effects of trait anxiety on efficiency. They highlight the necessity of incorporating metacognitive beliefs and maladaptive emotion regulation strategies for a thorough comprehension of the Attentional Control Theory. Recognizing these factors offers valuable perspectives for enhancing cognitive capabilities and fostering academic achievement. Full article

High mental workload reduces human performance and the ability to correctly carry out complex tasks. In particular, aircraft pilots enduring high mental workloads are at high risk of failure, even with catastrophic outcomes. Despite progress, there is still a lack of knowledge about the interrelationship between mental workload and brain functionality, and there is still limited data on flight-deck scenarios. Although recent emerging deep-learning (DL) methods using physiological data have presented new ways to find new physiological markers to detect and assess cognitive states, they demand large amounts of properly annotated datasets to achieve good performance. We present a new dataset of electroencephalogram (EEG) recordings specifically collected for the recognition of different levels of mental workload. The data were recorded from three experiments, where participants were induced to different levels of workload through tasks of increasing cognition demand. The first involved playing the N-back test, which combines memory recall with arithmetical skills. The second was playing Heat-the-Chair, a serious game specifically designed to emphasize and monitor subjects under controlled concurrent tasks. The third was flying in an Airbus320 simulator and solving several critical situations. The design of the dataset has been validated on three different levels: (1) correlation of the theoretical difficulty of each scenario to the self-perceived difficulty and performance of subjects; (2) significant difference in EEG temporal patterns across the theoretical difficulties and (3) usefulness for the training and evaluation of AI models. Full article

Assessing drivers’ mental workload is crucial for reducing road accidents. This study examined drivers’ mental workload in a simulated auditory-based dual-task driving scenario, with driving tasks as the main task, and auditory-based N-back tasks as the secondary task. A total of three levels of mental workload (i.e., low, medium, high) were manipulated by varying the difficulty levels of the secondary task (i.e., no presence of secondary task, 1-back, 2-back). Multimodal measures, including a set of subjective measures, physiological measures, and behavioral performance measures, were collected during the experiment. The results showed that an increase in task difficulty led to increased subjective ratings of mental workload and a decrease in task performance for the secondary N-back tasks. Significant differences were observed across the different levels of mental workload in multimodal physiological measures, such as delta waves in EEG signals, fixation distance in eye movement signals, time- and frequency-domain measures in ECG signals, and skin conductance in EDA signals. In addition, four driving performance measures related to vehicle velocity and the deviation of pedal input and vehicle position also showed sensitivity to the changes in drivers’ mental workload. The findings from this study can contribute to a comprehensive understanding of effective measures for mental workload assessment in driving scenarios and to the development of smart driving systems for the accurate recognition of drivers’ mental states. Full article

Background: Previous investigations on healthy humans showed conflicting evidence regarding the impact of mood on working memory performance. A systematic investigation of how mood affects apathy levels in healthy participants is currently missing. Methods: We administered a visuospatial (VS) and a numerical (N) n-back task to a sample of 120 healthy individuals. In these participants, using a series of questionnaires, we also evaluated apathy, mood, working memory, perceived stress, PTSD symptoms caused by the COVID-19 pandemic outbreak, and general psychiatric symptoms. Successively, we investigated their performance in the n-back task as a function of scores to these questionnaires. Results: Participants performed better in the N block than in the VS one. Their accuracy decreased as a function of the n-back difficulty. We reported no differences in working memory performance or apathy as a function of mood, stress, or PTSD symptoms. We found that phobic anxiety negatively predicted accuracy to the numerical n-back task and that subjects with greater anxiety and difficulty in regulating emotions also showed higher levels of withdrawal from the task. Conclusion: The study’s results suggest that while mood did not significantly affect working memory performance, strong associations were found between WMQ scores and working memory capabilities. Full article

The purpose of the present study was to develop an experimental model for the study of intra-articular knee movements depending on the function of the knee joint and involved muscle groups under isometric stretching conditions with different loads. The experimental procedure included an ultrasound examination of a knee joint after isometric stretching in healthy men (n = 32). The changes (in millimeters) in the distances between the femur and tibia were measured using an ultrasound sonographer at three stages. The first stage was performed on ten (n = 10) healthy men in five different sitting and upright positions. In the second and third experimental model stages, lower limbs loading was applied to 22 participants. Our hypothesis, which was confirmed, was that as a result of increased loads on the participant’s back, an intra-articular decrease in the femur–tibia cartilage surface distance would be observed. The accuracy of the created experimental model was improved over its three stages from 30% to 9%. Quantitative model data can help to create a mathematical model of the mechanical effects during the deformation of knee joint bone cartilage and it can also help outline some future tasks: increasing loading weights, enlarging participant groups, performing comparisons of men and women, and performing comparisons of healthy and pathological individuals. Full article

Background: Opioids are used in pharmacotherapy for chronic pain. The phenomenon of their influence on the oxidative–antioxidant balance is poorly understood. Additionally, little is known about the oxidative status in patients receiving chronic opioid noncancer pain therapy. Methods: The primary goal was to explore oxidative status using the total oxidative capacity (TOC) and total antioxidative capacity (TAC) in patients with chronic lower back pain (LBP) treated with opioids. The secondary task was to present the risk factors connected with the duration of therapy or anthropometric parameters. Plasma TOC and TAC were analyzed in the study group (n = 28), i.e., patients with chronic LBP treated with opioids, and in the control group (n = 11), i.e., healthy volunteers. Results: The TAC was significantly lower in the study group compared to the control group (p < 0.05), while the TOC did not differ significantly. A statistically lower TOC for buprenorphine compared to oxycodone (p = 0.019) and tramadol (p = 0.036) was observed. The TOC did not differ between tramadol and oxycodone. The highest TAC was described for oxycodone, while the TAC for buprenorphine and tramadol was significantly lower in comparison with oxycodone (p = 0.007 and p = 0.016). The TOC/TAC ratio was higher in patients with nicotinism in both groups.Conclusions: Patients receiving chronic opioid therapy presented a lower antioxidative capacity. There were differences in opioid-induced oxidative imbalance, which is very important clinically. Nicotinism increases the oxidative–antioxidative imbalance. The least oxidative capacity was associated with buprenorphine, while oxycodone showed the greatest antioxidant activity. The most favorable TOC/TAC ratio was observed for buprenorphine. It is suggested that buprenorphine or oxycodone has the best profile, and there is no correlation with the duration of opioid therapy or the opioid dose. However, all opioid substances can potentially enhance the oxidative–antioxidative status. Full article

The aim of this study was to assess the effects of guayusa extract and Nordic Lion’s Mane (LM) on cognition. Using a randomized, double-blind, placebo-controlled, crossover design, we examined the effects of a single dose of 650 mg guayusa extract (AMT: AmaTea^® Max) vs. 1 g Nordic-grown Lion’s Mane (LM) vs. placebo (PL). Participants attended three testing visits consisting of neuropsychological tests (Go/No-go, N-Back, and Serial 7 s tasks) assessing performance, subjective assessments of cognitive perception, and vital signs. Each assessment was measured at baseline (pre-ingestion) and 1 and 2 h post ingestion. AMT significantly (p ≤ 0.05) improved the number of attempts during Serial 7s, total score, number of correct responses, total number of responses, and reaction time during N-Back and improved Go stimulus reaction time, but it reduced the percentage of correct responses in the No-go stimulus response during Go/No-go. LM significantly (p ≤ 0.05) improved the number of attempts during Serial 7s and reaction time during N-Back and improved Go stimulus reaction time in Go/No-go. AMT improved mental clarity, focus, concentration, mood, and productivity at 1 and 2 h (p < 0.05); the ability to tolerate stress at 1 h; and had greater ratings than LM and PL for mental clarity, focus, concentration, and productivity. PL improved focus and concentration at 1 h from baseline (p ≤ 0.05). AMT and LM improved subjective ratings of “happiness compared to peers” and “getting the most out of everything” (p < 0.05); however, this occurred earlier in LM (i.e., 1 h post ingestion). AMT uniquely elevated blood pressure from baseline. AMT significantly improved cognitive performance and self-perceived cognitive indices of affect over a 2 h period and perceptions of happiness 2 h post ingestion. In comparison, LM helped improve working memory, complex attention, and reaction time 2 h post ingestion and perceptions of happiness over a 2 h period. Full article