Limitations on visual information processing in the sleep-deprived brain and their underlying mechanisms

Does sleep quality affect balance? The perspective from the somatosensory, vestibular, and visual systems

OBJECTIVE

Previous studies have focused on the balance system's involvement in sleep deprivation or disorders. This study investigated how daily routine sleep quality affects the balance system of people without sleep deprivation or diagnosed sleep disorders.

METHODS

The study included 45 participants with a BMI score of <25. The PSQI was used to determine sleep quality. The SOT, HS-SOT, and ADT evaluated the vestibular system's functionality.

RESULTS

In SOT, condition 3, 4, 5, and 6 composite scores, VIS and VEST composite balance scores, and HS-SOT 5 scores were lower in the HPSQI group. At the same time, there is a statistically significant negative correlation between these scores and PSQI scores.

CONCLUSION

Poor sleep quality may be a factor influencing the balance system. Sleep quality affects the visual and vestibular systems rather than the somatosensory system. The population should be made aware of this issue, and clinicians should consider the potential impact of sleep quality when evaluating the balance system.

Accurate detection of acute sleep deprivation using a metabolomic biomarker-A machine learning approach

Sleep deprivation enhances risk for serious injury and fatality on the roads and in workplaces. To facilitate future management of these risks through advanced detection, we developed and validated a metabolomic biomarker of sleep deprivation in healthy, young participants, across three experiments. Bi-hourly plasma samples from 2 × 40-hour extended wake protocols (for train/test models) and 1 × 40-hour protocol with an 8-hour overnight sleep interval were analyzed by untargeted liquid chromatography-mass spectrometry. Using a knowledge-based machine learning approach, five consistently important variables were used to build predictive models. Sleep deprivation (24 to 38 hours awake) was predicted accurately in classification models [versus well-rested (0 to 16 hours)] (accuracy = 94.7%/AUC 99.2%, 79.3%/AUC 89.1%) and to a lesser extent in regression (R2 = 86.1 and 47.8%) models for within- and between-participant models, respectively. Metabolites were identified for replicability/future deployment. This approach for detecting acute sleep deprivation offers potential to reduce accidents through "fitness for duty" or "post-accident analysis" assessments.

Impact of sleep disruption on BDD symptoms and treatment response

BACKGROUND

Body dysmorphic disorder (BDD) is severe, undertreated, and relatively common. Although gold-standard cognitive behavioral therapy (CBT) for BDD has strong empirical support, a significant number of patients do not respond. More work is needed to understand BDD's etiology and modifiable barriers to treatment response. Given its high prevalence and impact on the development, maintenance, and treatment of related, frequently comorbid disorders, sleep disruption is a compelling, but not-yet studied factor.

METHODS

Data were drawn from a randomized controlled trial of guided smartphone app-based CBT for BDD. Included participants were offered 12-weeks of treatment, immediately (n = 40) or after a 12-week waitlist (n = 37). Sleep disruption and BDD symptom severity were assessed at baseline, week-6, and week-12.

RESULTS

Hypotheses and analysis plan were pre-registered. Two-thirds of patients reported significant insomnia symptoms at baseline. Baseline severity of sleep disruption and BDD symptoms were not related (r = 0.02). Pre-treatment sleep disruption did not predict BDD symptom reduction across treatment, nor did early sleep improvements predict greater BDD symptom improvement. Early BDD symptom improvement also did not predict later improvements in sleep.

LIMITATIONS

Limitations include the small sample, restricted ranges of BDD symptom severity and treatment response, and few metrics of sleep disruption.

CONCLUSIONS

Although insomnia was disproportionately high in this sample and both BDD symptoms and sleep improved in treatment, results suggest sleep and BDD symptoms may function largely independent of one another. More work is encouraged to replicate and better understand findings as well as potential challenges and benefits of addressing sleep in BDD.

Effect of homeostatic pressure and circadian rhythm on the task-switching: Evidence from drift diffusion model and ERP

The effect of diurnal fluctuations on cognitive functions is widely studied, yet rare research has attempted to separate the role of two crucial processes underlying diurnal fluctuations: homeostatic pressure and circadian rhythm. The present study aimed to dissociate their effects by conducting a task-switching task in the morning, napping afternoon, and no-napping afternoon, respectively. Additionally, DDM and ERP were utilized to explore how these two processes differentially affect cognitive processes involved in task-switching. By a within-participant design, 35 healthy adults (20.03 ± 2.01 year-old, 14 males) with an intermediate-type chronotype were recruited in the current study. The results demonstrated that accumulated homeostatic pressure caused reduced accuracy, drift rate, and decision threshold. In the no-napping afternoon, P1 and P2 amplitudes were also decreased due to homeostatic pressure, whereas an afternoon nap could partially restore performance and neural activity. Conversely, the upward circadian rhythm in the afternoon exerted a compensatory effect, resulting in increases in N2 and P3 amplitudes. The findings highlight the disassociated impacts of homeostatic pressure and circadian rhythm on the cognitive processes involved in task-switching and further underscore the importance of considering diurnal variation in both scientific research and accident prevention.

Sleep-like changes in neural processing emerge during sleep deprivation in early auditory cortex

Insufficient sleep is commonplace in modern lifestyle and can lead to grave outcomes, yet the changes in neuronal activity accumulating over hours of extended wakefulness remain poorly understood. Specifically, which aspects of cortical processing are affected by sleep deprivation (SD), and whether they also affect early sensory regions, remain unclear. Here, we recorded spiking activity in the rat auditory cortex along with polysomnography while presenting sounds during SD followed by recovery sleep. We found that frequency tuning, onset responses, and spontaneous firing rates were largely unaffected by SD. By contrast, SD decreased entrainment to rapid (≥20 Hz) click trains, increased population synchrony, and increased the prevalence of sleep-like stimulus-induced silent periods, even when ongoing activity was similar. Recovery NREM sleep was associated with similar effects as SD with even greater magnitude, while auditory processing during REM sleep was similar to vigilant wakefulness. Our results show that processes akin to those in NREM sleep invade the activity of cortical circuits during SD, even in the early sensory cortex.

Effects of partially automated driving on the development of driver sleepiness

The objective of this study was to compare the development of sleepiness during manual driving versus level 2 partially automated driving, when driving on a motorway in Sweden. The hypothesis was that partially automated driving will lead to higher levels of fatigue due to underload. Eighty-nine drivers were included in the study using a 2 × 2 design with the conditions manual versus partially automated driving and daytime (full sleep) versus night-time (sleep deprived). The results showed that night-time driving led to markedly increased levels of sleepiness in terms of subjective sleepiness ratings, blink durations, PERCLOS, pupil diameter and heart rate. Partially automated driving led to slightly higher subjective sleepiness ratings, longer blink durations, decreased pupil diameter, slower heart rate, and higher EEG alpha and theta activity. However, elevated levels of sleepiness mainly arose from the night-time drives when the sleep pressure was high. During daytime, when the drivers were alert, partially automated driving had little or no detrimental effects on driver fatigue. Whether the negative effects of increased sleepiness during partially automated driving can be compensated by the positive effects of lateral and longitudinal driving support needs to be investigated in further studies.

Driver sleepiness detection with deep neural networks using electrophysiological data

OBJECTIVE

The objective of this paper is to present a driver sleepiness detection model based on electrophysiological data and a neural network consisting of Convolutional Neural Networks and a Long Short Term Memory architecture.

APPROACH

The model was developed and evaluated on data from 12 different experiments with 269 drivers and 1187 driving sessions during daytime (low sleepiness condition) and night-time (high sleepiness condition), collected during naturalistic driving conditions on real roads in Sweden or in an advanced moving-base driving simulator. Electrooculographic and electroencephalographic time series data, split up in 16634 2.5-minute data segments was used as input to the deep neural network. This probably constitutes the largest labelled driver sleepiness dataset in the world. The model outputs a binary decision as alert (defined as ≤6 on the Karolinska Sleepiness Scale, KSS) or sleepy (KSS≥8) or a regression output corresponding to KSS ϵ [1-5,6,7,8,9].

MAIN RESULTS

The subject-independent mean absolute error (MAE) was 0.78. Binary classification accuracy for the regression model was 82.6% as compared to 82.0% for a model that was trained specifically for the binary classification task. Data from the eyes were more informative than data from the brain. A combined input improved performance for some models, but the gain was very limited.

SIGNIFICANCE

Improved classification results were achieved with the regression model compared to the classification model. This suggests that the implicit order of the KSS ratings, i.e. the progression from alert to sleepy, provides important information for robust modelling of driver sleepiness, and that class labels should not simply be aggregated into an alert and a sleepy class. Furthermore, the model consistently showed better results than a model trained on manually extracted features based on expert knowledge, indicating that the model can detect sleepiness that is not covered by traditional algorithms.

An Adaptive EEG Feature Extraction Method Based on Stacked Denoising Autoencoder for Mental Fatigue Connectivity

Mental fatigue is a common psychobiological state elected by prolonged cognitive activities. Although, the performance and the disadvantage of the mental fatigue have been well known, its connectivity among the multiareas of the brain has not been thoroughly studied yet. This is important for the clarification of the mental fatigue mechanism. However, the common method of connectivity analysis based on EEG cannot get rid of the interference from strong noise. In this paper, an adaptive feature extraction model based on stacked denoising autoencoder has been proposed. The signal to noise ratio of the extracted feature has been analyzed. Compared with principal component analysis, the proposed method can significantly improve the signal to noise ratio and suppress the noise interference. The proposed method has been applied on the analysis of mental fatigue connectivity. The causal connectivity among the frontal, motor, parietal, and visual areas under the awake, fatigue, and sleep deprivation conditions has been analyzed, and different patterns of connectivity between conditions have been revealed. The connectivity direction under awake condition and sleep deprivation condition is opposite. Moreover, there is a complex and bidirectional connectivity relationship, from the anterior areas to the posterior areas and from the posterior areas to the anterior areas, under fatigue condition. These results imply that there are different brain patterns on the three conditions. This study provides an effective method for EEG analysis. It may be favorable to disclose the underlying mechanism of mental fatigue by connectivity analysis.

Evaluation of the postural balance and visual perception in young adults with acute sleep deprivation

BACKGROUND/OBJECTIVE

Few studies have suggested a relationship between vestibular system and sleep deprivation. The aim of the present study is to investigate the effects of acute sleep deprivation lasting 24 hours or more on the postural balance and the visual abilities related to the vestibular system in healthy young adults.

METHODS

Thirty-one healthy young adults (8 males, 23 female; ages 18- 36 years) who had experienced at least 24 hours of sleep deprivation were included in the study. Subjects made two visits to the test laboratory. One visit was scheduled during a sleep deprivation (SD) condition, and the other was scheduled during a daily life (DL) condition. Five tests- the Sensory Organization Test (SOT), Static Visual Acuity Test (SVA), Minimum Perception Time Test (mPT), Dynamic Visual Acuity Test (DVA), and Gaze Stabilization Test (GST)- were performed using a Computerized Dynamic Posturography System.

RESULTS

A statistically significant difference was found between SD and DL measurements in somatosensorial (p = 0.003), visual (p = 0.037), vestibular (p = 0.008) ratios, and composite scores (p = 0.001) in SOT. The mPT results showed a statistically significant difference between SD and DL conditions (p = 0.001). No significant difference was found between SD and DL conditions in the comparison of the mean SVA (p = 0.466), DVA (p = 0.192), and GST head velocity values (p = 0.160).

CONCLUSIONS

Sleep deprivation has a considerable impact on the vestibular system and visual perception time in young adults. Increased risk of accidents and performance loss after SD were thought to be due to the postural control and visual processing parameters rather than dynamic visual parameters of the vestibular system.

The impact of driver sleepiness on fixation-related brain potentials

The effects of driver sleepiness are often quantified as deteriorated driving performance, increased blink durations and high levels of subjective sleepiness. Driver sleepiness has also been associated with increasing levels of electroencephalogram (EEG) power, especially in the alpha range. The present exploratory study investigated a new measure of driver sleepiness, the EEG fixation-related lambda response. Thirty young male drivers (23.6 ± 1.7 years old) participated in a driving simulator experiment in which they drove on rural and suburban roads in simulated daylight versus darkness during both the daytime (full sleep) and night-time (sleep deprived). The results show lower lambda responses during night driving and with longer time on task, indicating that sleep deprivation and time on task cause a general decrement in cortical responsiveness to incoming visual stimuli. Levels of subjective sleepiness and line crossings were higher under the same conditions. Furthermore, results of a linear mixed-effects model showed that low lambda responses are associated with high subjective sleepiness and more line crossings. We suggest that the fixation-related lambda response can be used to investigate driving impairment induced by sleep deprivation while driving and that, after further refinement, it may be useful as an objective measure of driver sleepiness.

Sleep and performance in Eathletes: for the win!

Over the last decade, Esports, defined as a form of organized video game competition, has emerged as a global phenomenon. The professional players who compete in Esports, namely, Eathletes, share many similarities with their traditional athlete counterparts. However, in sharp contrast to traditional athletes, there is a paucity of research investigating the factors that influence the performance of Eathletes. This gap in the literature is problematic because Eathletes are unable to make informed and empirically supported decisions about their performance management, unlike traditional athletes. Sleep is an important factor that influences athletic performance in traditional sports, particularly those that require a high level of cognitive demand. Research is yet to examine whether sleep also plays an important function in optimal performance and success of Eathletes in Esports. Accordingly, the aim of this opinion piece is to review the broader sleep and sports medicine literature and provide theoretically grounded suggestions as to how existing findings may apply to Eathletes competing professionally in Esports. Overall, it appears that Eathlete performance may be vulnerable to the deleterious effects of sleep restriction. Furthermore, Eathletes are likely at risk of sleep disturbances due to the unique situations and conditions that characterize Esports.

Daily variation of visual sensitivity to luminance contrast: Effects of time of measurement and circadian typology

This study analyzed the fluctuation of the achromatic visual contrast sensitivity (CS) of adult males (M = 23.42 ± 2.6 years) during a daily period. Twenty-eight volunteers were divided into three groups according to circadian typology (CT): moderate morning (MM; n = 8); intermediate (I; n = 10) and moderate evening (ME; n = 10). The Pittsburgh Sleep Quality Index was used to evaluate sleep quality, and the Horne and Ostberg Morningness-Eveningness Questionnaire was used to measure CT. To measure CS, we used Metropsis software version 11.0 with vertical sinusoidal grids of 0.2, 0.6, 1, 3.1, 6.1, 8.8, 13.2 and 15.6 cycles per degree of visual angle (cpd). The stimuli were presented on a cathode ray tube (CRT) color video monitor with a 19-inch flat screen, a 1024 × 786 pixel resolution, a 100 Hz refresh rate and a photopic luminance of 39.6 cd/m². It was inferred that there is a tendency for visual contrast to vary according to daily rhythmicity and CT, mainly for the median spatial frequencies (1.0 cpd, χ² = 9.93, p < 0.05 and 3.1 cpd, χ² = 10.33, p < 0.05) and high spatial frequencies (13.2 cpd, χ² = 11.54, p < 0.05) of ME participants. ME participants had minimal visual contrast sensitivity during the morning shift and a progressive increase from afternoon to night.

Sleep Deprivation Diminishes Attentional Control Effectiveness and Impairs Flexible Adaptation to Changing Conditions

Insufficient sleep is a global public health problem resulting in catastrophic accidents, increased mortality, and hundreds of billions of dollars in lost productivity. Yet the effect of sleep deprivation (SD) on decision making and performance is often underestimated by fatigued individuals and is only beginning to be understood by scientists. The deleterious impact of SD is frequently attributed to lapses in vigilant attention, but this account fails to explain many SD-related problems, such as loss of situational awareness and perseveration. Using a laboratory study protocol, we show that SD individuals can maintain information in the focus of attention and anticipate likely correct responses, but their use of such a top-down attentional strategy is less effective at preventing errors caused by competing responses. Moreover, when the task environment requires flexibility, performance under SD suffers dramatically. The impairment in flexible shifting of attentional control we observed is distinct from lapses in vigilant attention, as corroborated by the specificity of the influence of a genetic biomarker, the dopaminergic polymorphism DRD2 C957T. Reduced effectiveness of top-down attentional control under SD, especially when conditions require flexibility, helps to explain maladaptive performance that is not readily explained by lapses in vigilant attention.

Cognitive Performance, Sleepiness, and Mood in Partially Sleep Deprived Adolescents: The Need for Sleep Study

STUDY OBJECTIVES

To investigate the effects of sleep restriction (7 nights of 5 h time in bed [TIB]) on cognitive performance, subjective sleepiness, and mood in adolescents.

METHODS

A parallel-group design was adopted in the Need for Sleep Study. Fifty-six healthy adolescents (25 males, age = 15-19 y) who studied in top high schools and were not habitual short sleepers were randomly assigned to Sleep Restriction (SR) or Control groups. Participants underwent a 2-w protocol consisting of 3 baseline nights (TIB = 9 h), 7 nights of sleep opportunity manipulation (TIB = 5 h for the SR and 9 h for the control groups), and 3 nights of recovery sleep (TIB = 9 h) at a boarding school. A cognitive test battery was administered three times each day.

RESULTS

During the manipulation period, the SR group demonstrated incremental deterioration in sustained attention, working memory and executive function, increase in subjective sleepiness, and decrease in positive mood. Subjective sleepiness and sustained attention did not return to baseline levels even after 2 recovery nights. In contrast, the control group maintained baseline levels of cognitive performance, subjective sleepiness, and mood throughout the study. Incremental improvement in speed of processing, as a result of repeated testing and learning, was observed in the control group but was attenuated in the sleep-restricted participants, who, despite two recovery sleep episodes, continued to perform worse than the control participants.

CONCLUSIONS

A week of partial sleep deprivation impairs a wide range of cognitive functions, subjective alertness, and mood even in high-performing high school adolescents. Some measures do not recover fully even after 2 nights of recovery sleep.

COMMENTARY

A commentary on this article appears in this issue on page 497.