In 2022, in the Netherlands, among individuals aged 18 to 24 years, **** percent of men reported being affected by sleep deprivation, whereas **** percent of women reported to experience the same issue. This statistic depicts the percentage of young population affected by sleep deprivation in the Netherlands in 2022, by gender and age

Introduction

Sleep Statistics: Sleep is a fundamental component of overall health, yet a significant portion of the adult population fails to obtain the recommended amount. Adults are advised to sleep between seven and nine hours per night. However, only 31% manage to achieve this duration for at least five nights each week. In the United States, approximately 35% of adults report sleeping less than seven hours per night.

The consequences of insufficient sleep are profound. Chronic sleep deprivation is linked to an increased risk of cardiovascular diseases, including heart attacks and strokes. It also elevates the likelihood of developing type 2 diabetes, obesity, and mental health disorders such as depression and anxiety. Moreover, sleep deficiency impairs cognitive functions, leading to decreased attention, memory lapses, and poor decision-making.

The economic impact is equally alarming. In the United States alone, insufficient sleep is estimated to cost over USD 411 billion annually due to lost productivity, increased healthcare expenses, and accidents.

Given these statistics, it is imperative to prioritize quality sleep as a cornerstone of health and well-being. Sleep deprivation can lead to both physical and mental health issues, a higher risk of mortality, and an increased likelihood of accidents. Let's delve deeper into sleep statistics in this article.

This statistic depicts the percentage of adults in select countries worldwide who experienced select impacts from sleeplessness as of 2018. It was found that 46 percent of respondents stated that they looked tired after sleeping less than 7 to 9 hours.

This research demonstrated that although new memories for individual items can be acquired rapidly, the process of discovering regularities across individual items to permit generalisation requires a period of overnight memory consolidation. The aim of the present research project is to discover whether sleep is the critical factor in the acquisition of general linguistic knowledge, and further, to describe the neural processes arising during sleep that facilitate this form of learning. This collection contains stimuli, data, and analysis code for the article: Tamminen, J. et al. (2020). Generalisation in language learning can withstand total sleep deprivation. Neurobiology of Learning and Memory, 173, 107274. Data and data documentation are housed on the Open Science Framework (https://osf.io/2kyrd/).One remarkable aspect of human learning is our ability to build general knowledge from individual experiences. This general knowledge is central to virtually all cognitive functions, but is particularly important in language, as it allows us to use new words, phrases, and sentences that have not been communicated previously. For example, we understand the novel word 'untweetable' because we have general knowledge of the functions of affixes {un} and {able}. However, despite the significance of this form of knowledge for human communication, we know remarkably little about how it is acquired. Recent research provides strong clues that sleep may play a vital role in the acquisition of general linguistic knowledge. In a previous ESRC project, we developed a laboratory analogue of language learning to track how general knowledge is built through multiple experiences with individual words. This research demonstrated that although new memories for individual items can be acquired rapidly, the process of discovering regularities across individual items to permit generalisation requires a period of overnight memory consolidation. The aim of the present research project is to discover whether sleep is the critical factor in the acquisition of general linguistic knowledge, and further, to describe the neural processes arising during sleep that facilitate this form of learning. This proposal describes three work packages that combine methods at the leading edge of sleep science with our laboratory analogue of language learning to uncover how sleep impacts on the development of item-specific and general knowledge. In the first work package, we track the acquisition of item-specific and general knowledge when there is a delay between training and testing, and assess whether it matters if that delay consists of overnight sleep as opposed to daytime wake. In the second work package, we investigate how sleep deprivation before or after training impacts on the acquisition of item-specific and general linguistic knowledge. In the third work package, we use an olfactory cuing technique to reactivate memories of newly-learned information during sleep, and measure whether this reactivation enhances the acquisition of linguistic knowledge. We then take this experimental paradigm one step further to ask whether we can bias the course of long-term learning by selectively reactivating particular memories. In all experiments involving sleep, we use polysomnography to assess the importance of particular sleep stages or neural events during sleep for different forms of learning. We also assess learning in all experiments after one week to draw conclusions about the stability of new knowledge over the longer term. International research has shown that the UK has one of the largest proportions of children who are sleep deprived, and that more than a quarter of the UK population gets on average less than five hours of sleep nightly. Given these statistics, it is of vital importance to understand what the consequences of poor sleep are for learning and memory. This research programme will address profound questions about how the brain continues to process new memories during sleep, how these sleep-related neural mechanisms shape the acquisition of long-term linguistic knowledge, and how sleep prepares the brain for new learning. Our findings will be transformative for theoretical models of learning, particularly as these apply to language, and will provide a range of new opportunities for creating substantive impacts within educational settings. We have developed a full programme of engagement with academic and non-academic stakeholders to realise this potential. Laboratory study of language learning in adults while manipulating access to overnight sleep.

General information

The dataset provides resting-state EEG data (eyes open,partially eyes closed) from 71 participants who underwent two experiments involving normal sleep (NS---session1) and sleep deprivation(SD---session2) .The dataset also provides information on participants' sleepiness and mood states. （Please note here Session 1 (NS) and Session 2 (SD) is not the time order, the time order is counterbalanced across participants and is listed in metadata.）

Dataset

Presentation

The data collection was initiated in March 2019 and was terminated in December 2020. The detailed description of the dataset is currently under working by Chuqin Xiang,Xinrui Fan,Duo Bai,Ke Lv and Xu Lei, and will submit to Scientific Data for publication.

EEG acquisition

• EEG system (Brain Products GmbH, Steing- rabenstr, Germany, 61 electrodes)
• Sampling frequency: 500Hz
• Impedances were kept below 5k

Contact

 * If you have any questions or comments, please contact:
 * Xu Lei: xlei@swu.edu.cn   

Article

Xiang, C., Fan, X., Bai, D. et al. A resting-state EEG dataset for sleep deprivation. Sci Data 11, 427 (2024). https://doi.org/10.1038/s41597-024-03268-2

Data for this manuscript.

The data here include one large, multi-paradigm study on the impact of sleep deprivation on mentalizing and cognition and a series of studies on the impact of anxiety on belief and desire reasoning. The rationale for the broader project was to consider the hypothesis that anxiety and sleep deprivation impact mentalizing in distinct ways for distinct reasons. Mentalizing (also known as theory of mind) is our ability to understand other people’s mental states. There are good reasons to expect that sleep deprivation might impact mentalizing. Sleep deprivation impacts processes associated with mentalizing, including executive functioning, and other social processes, such as emotion recognition. We wanted to provide the first detailed consideration of whether sleep deprivation impacted mentalizing itself.

Participants took part in paradigms when rested and when sleep deprived. These included three mentalizing tasks: the reading the mind in the eyes task, the belief-desire reasoning task and the emotional egocentricity touch paradigm. These allowed us to test the impact of sleep deprivation on mentalizing ability, egocentrism and self-other distinction. Sleep deprivation only negatively impacted overall performance. We concluded that sleep deprivation may impact overall processing performance at mentalizing, rather than specific processes, such as the ability to inhibit egocentrism or distinguish between self and other perspectives.

It has been proposed that anxiety makes us more egocentric, to overcome uncertainty. We wished to examine this in belief reasoning. In study one, participants completed a belief-desire reasoning task when anxious (manipulated with an autobiographical writing task) and when relaxed. There was no impact of anxiety on performance. To understand this, we conducted two follow-up studies. The first examined the impact of the independent variable, by using a threat-of-shock paradigm. The second, the impact of the dependent variable, through requiring participants to infer the belief of the character. Both replicated the original findings.

The data set is an output of the Track and Know project to be shared with the scientific community. The data set contains the output of a monitoring app recorded during a series of journeys organised in two sub sets. The first subset of data was recorded from journeys made on different days around a circular route by a single driver. On different iterations of the journey the driver determined to drive either; as carefully as possible, normally or poorly. The intention of the poor driving was to imitate sleepy driving with harsh breaking, cornering and acceleration and deliberate lane drifting. The data set is designed to allow the development of algorithms to detect different driving behaviours The second data set was generated by 3 volunteers who were engaged in shift work. The journeys consist of trips to work and home at different times of day and other journeys not related to work. The intention of the data set is to allow comparisons to be made between journeys undertaken by the drivers when sleep replete and sleep deprived (after working a night shift). The data are enriched with weather information pertaining to the date, time and location of each journey.

This repository contains data and supporting materials for the manuscript titled "Impact of Sleep Deprivation on Monocyte Subclasses and Function in Obese Individuals." This cross-sectional study investigates the relationship between sleep efficiency, obesity, and systemic inflammation, focusing on the role of monocyte subclasses.

According to a survey from 2024, around eight percent of college students in the United States had extremely difficulty falling asleep for seven of the last seven days. This statistic shows the percentage of college students in the U.S. who had an extremely hard time falling asleep within the past seven days as of fall 2024.

Adequate sleep is of paramount importance for relieving stress and restoring mental vigor. However, the adverse physiological and pathological responses resulting from sleep insufficiency or sleep deprivation (SD) are becoming increasingly prevalent. Currently, the impact of sleep deficiency on gut microbiota and microbiota-associated human diseases, especially cardiac diseases, remains controversial. Here, we employed the following methods: constructed an experimental sleep-deprivation model in mice; conducted 16S rRNA sequencing to investigate the changes in gut microbiota; through fecal microbiota transplantation (FMT) experiments, transplanted fecal microbiota from sleep-deprived mice to other mice; established an environment with a 30% oxygen concentration to explore the therapeutic effects of oxygen therapy on gut microbiota-associated cardiac fibrosis and dysfunction; and utilized transcriptome data to study the underlying mechanisms of oxygen therapy. The results revealed that: sleep-deprived mice exhibited weakness, depression-like behaviors, and dysfunction in multiple organs. Pathogenic cardiac hypertrophy and fibrosis occurred in sleep-deprived mice, accompanied by poor ejection fraction and fractional shortening. 16S rRNA sequencing indicated that sleep deprivation induced pathogenic effects on gut microbiota, and similar phenomena were also observed in mice that received fecal microbiota from sleep-deprived mice in the FMT experiments. The environment with a 30% oxygen concentration effectively alleviated the pathological impacts on cardiac function. Transcriptome data showed that oxygen therapy targeted several hypoxia-dependent pathways and inhibited the production of cardiac collagen. In conclusion, these results demonstrate the significance of sufficient sleep for gut microbiota and may represent a potential therapeutic strategy, where the oxygen environment exerts a protective effect on insomniacs through gut microbiota.

Microbiome data (Project Chronic sleep deprivation and Precocious puberty) including raw fastq files, metadata files, and tables of OTU relative abundance (male and female rats separately)

Between 2013 and 2022, the share of adults in the U.S. not getting enough sleep fluctuated between roughly 33 and 37 percent. In 2022, this figure reached 36.8 percent, the highest share in the given period. This statistic displays the share of adults getting insufficient sleep in the U.S. between 2013 and 2022.

The research that produced this data tested how sleep loss impacted the phenomena of reactivation and replay, which occurs when recently-learned information is reactivated/replayed during post-learning sleep/rest.

How does knowing another team member is cognitively impaired or enhanced affect people’s motivation to contribute to the team’s performance? Building on the Effects of Grouping on Impairments and Enhancements (GIE) framework, we conducted two between-subjects experiments (total N=2,352) with participants from a representative, nationwide sample of the German working population. We found that both another group member’s impairment (due to sleep deprivation) and enhancement (due to taking enhancement drugs) lowered participants’ intention to contribute to the team’s performance. These effects were mediated by lowered perceived competence (enhancement and impairment) and warmth (only enhancement) of the other group member. The other group member´s reason for being impaired or enhanced (altruistic vs. egoistic reasons) only moderated the mediation between impairment and warmth on intended effort. Our results illustrate that people’s work motivation is influenced by the psychophysiological states of other group members. Hence, enhancement of one group member can have the paradoxical effect of impairing the work performance of another.

This dataset explores the effects of sleep deprivation on cognitive performance and emotional regulation, based on a 2024 study conducted in the Middle East. It includes 60 participants from diverse backgrounds, capturing data on sleep duration, sleep quality, daytime sleepiness, cognitive function (reaction times, memory accuracy), and emotional stability. Additionally, it records demographic factors such as age, gender, BMI, and lifestyle influences like caffeine intake, physical activity levels, and stress levels.

The study was conducted using standardized cognitive performance tests, including the Stroop Task, N-Back Test, and Psychomotor Vigilance Task (PVT), commonly used in neuroscience and psychology research. This dataset is structured to support statistical analysis, machine learning applications, and behavioral research. It provides valuable insights for sleep research, mental health studies, and cognitive performance analysis, particularly in the context of Middle Eastern populations and lifestyle factors in 2024.

Data used for a prospective study on occupational stress, sleep problems and metabolic syndrome in a sample of police officers, Italy, 2009-2014. Paper submitted to PLoS One, waiting for a decision.

Sleep deprivation is a global health problem adversely affecting health as well as causing decrements in learning and performance. Sleep deprivation induces significant changes in gene transcription in many brain regions, with the hippocampus particularly susceptible to acute sleep deprivation. However, less is known about the impacts of sleep deprivation on post-transcriptional gene regulation. To identify the effects of sleep deprivation on the translatome, we took advantage of the RiboTag mouse line to express HA-labeled Rpl22 in CaMKII? neurons to selectively isolate and sequence mRNA transcripts associated with ribosomes in excitatory neurons. We found 198 differentially expressed genes in the ribosome-associated mRNA subset after sleep deprivation. In comparison with previously published data on gene expression in the hippocampus after sleep deprivation, we found that the subset of genes affected by sleep deprivation was considerably different in the translatome compared with the transcriptome, with only 49 genes regulated similarly. Interestingly, we found 478 genes differentially regulated by sleep deprivation in the transcriptome that were not significantly regulated in the translatome of excitatory neurons. Conversely, there were 149 genes differentially regulated by sleep deprivation in the translatome but not in the whole transcriptome. Pathway analysis revealed differences in the biological functions of genes exclusively regulated in the transcriptome or translatome, with protein deacetylase activity and small GTPase binding regulated in the transcriptome and unfolded protein binding, kinase inhibitor activity, neurotransmitter receptors and circadian rhythms regulated in the translatome. These results indicate that sleep deprivation induces significant changes affecting the pool of actively translated mRNAs.

Impaired sleep is a common aspect of aging and often precedes the onset of Alzheimer's disease. Here, we compare the effects of sleep deprivation in young wild-type mice and their APP/PS1 littermates, a murine model of Alzheimer's disease. After 7 h of sleep deprivation, both genotypes exhibit an increase in EEG slow-wave activity. However, only the wild-type mice demonstrate an increase in the power of infraslow norepinephrine oscillations, which are characteristic of healthy non-rapid eye movement sleep. Notably, the APP/PS1 mice fail to enhance norepinephrine oscillations 24 h after sleep deprivation, coinciding with an accumulation of cerebral amyloid-β protein. Proteome analysis of cerebrospinal fluid and extracellular fluid further supports these findings by showing altered protein clearance in APP/PS1 mice. We propose that the suppression of infraslow norepinephrine oscillations following sleep deprivation contributes to increased vulnerability to sleep loss and heightens the risk of developing amyloid pathology in early stages of Alzheimer's disease.

Sleep is broadly conserved across the animal kingdom but can vary widely between species. It is currently unclear which selective pressures and regulatory mechanisms influence differences in sleep between species. The fruit fly Drosophilamelanogaster has become a successful model system for examining sleep regulation and function, but little is known about the sleep patterns in many related fly species. Here, we find that fly species with adaptations to extreme desert environments, including D. mojavensis, exhibit strong increases in baseline sleep compared to D. melanogaster. Long-sleeping D. mojavensis show intact homeostasis, indicating that desert flies carry an elevated drive for sleep. In addition, D. mojavensis exhibit altered abundance or distribution of several sleep/wake related neuromodulators and neuropeptides that are consistent with their reduced locomotor activity and increased sleep. Finally, we find that in a nutrient-deprived environment, the sleep patterns of individual D. mojavensis are strongly correlated with their survival time and that disrupting sleep via constant light stimulation renders D. mojavensis more sensitive to starvation. Our results demonstrate that D. mojavensis is a novel model for studying organisms with high sleep drive, and for exploring sleep strategies that provide resilience in extreme environments. Methods Behavior 4-8 day old female flies were housed individually in borosilicate glass tubes (65mm length, 5mm diameter) containing fly food coated with paraffin wax at one end and a foam plug in the other. Locomotor activity was recorded using DAM5M or DAM5H multibeam Drosophila Activity Monitors from Trikinetics Inc. (Waltham MA, USA) and sleep was analyzed in Matlab (MathWorks Inc) with the SCAMP script package90. Locomotor activity was measured as the number of movements between beams per one-minute bins. Periods of sleep were defined by at least 5 minutes with no change in position within the multibeam activity monitors. Sleep time courses display 30-min time bins. Sleep Deprivation and Arousability Sleep deprivations were performed mechanically by mounting DAM5M activity monitors onto platform vortexers (VWR 58816-115). Individual tubes were plugged with food at one end and 3D-printed PLA plastic caps at the other. Monitors were vortexed at an intensity of 2.5g for 3-second pulses every minute through the duration of the 12-hour dark period. Arousability was tested in a darkened incubator with 60 seconds of blue light (luminance 0.048 Lv) every hour for 24 hours following sleep deprivation. Food- and Water- Deprivation Assays All flies were put in DAM5H activity monitors on standard food for baseline recording. After 2-3 days, control flies were transferred to tubes containing fresh food, food-deprived flies to tubes containing a 1% agar gel, and food-and-water-deprived flies to empty tubes plugged with foam at both ends. Flies immobile for at least 24 hours were defined as dead and data subsequent to their last full day alive was removed from sleep analysis. Pharmacological Microinjections 4-8 day old female flies were loaded into behavior tubes and monitored in DAM5M Activity Monitors to obtain baseline sleep and locomotor activity under 12h light: 12h dark (25˚C). After 1-2 days of baseline in DAM5M monitors, flies housed in borosilicate tubes were placed on ice for anesthetization prior to injection using Drummond Nanoject II. For injection of exogenous neuromodulators, the anteriormost ocelli of D. mojavensis baja were injected with 18.4nl of 20mg/mL of Octopamine (Sigma-Aldrich, Catalog # O0250). For each round of injections, new OA is solubilized using Schneider’s Drosophila Medium with L-Glutamine (Genesee Scientific, Catalog # 25-515). Following each individual injection, flies are returned back into individual borosilicate tubes, and placed in respective DAM5M Activity Monitors to continue sleep and activity surveillance for >48h. Neurochemical Quantifications Sample preparation protocol Fly brain samples were stored at -80°C then treated with 99.9/1 Water/Formic Acid. An internal standard (IS) of each targeted compound was added to every sample to account for compound loss during sample processing. The samples are vortexed, homogenized for 30 sec in a bead beater using 2.0 mm zirconia beads, and centrifuged at 16.000xg for 5 min. The supernatant is transferred to new microcentrifuge test tubes and dried in a vacuum concentrator. The samples are reconstituted in 40 µl of water, vortexed, and centrifuged. The supernatant is transferred to HPLC vials and 10 µl is injected to an HPLC - triple quadrupole mass spectrometer system for analysis. Liquid Chromatography-Tandem Mass Spectrometry LC-MS A targeted LC-MS/MS assay was developed for each compound using the multiple reaction monitoring (MRM) acquisition method on a triple quadrupole mass spectrometer (6460, Agilent Technologies) coupled to an HPLC system (1290 Infinity, Agilent Technologies) with an analytical reversed phase column (GL Sciences, Phenyl 2 µm 150 x 2.1 mm UP). The HPLC method utilized a mobile phase constituted of solvent A (100/0.1, v/v, Water/Formic Acid) and solvent B (100/0.1, v/v, Acetonitrile/Formic Acid) and a gradient was used for the elution of the compounds (min/%B: 0/0, 10/0, 25/75, 27/0, 35/0). The mass spectrometer was operated in positive ion mode and fragment ions originating from each compound was monitored at specific LC retention times to ensure specificity and accurate quantification in the complex biological samples (Octopamine OA 159-136, Histamine HA 112-95, Dopamine DA 154-137, Serotonin 5HT 177-160). The standard curve was made by plotting the known concentration for each analyte of interest (CDN Isotopes) against the ratio of measured chromatographic peak areas corresponding to the analyte over that of the labeled standards. The trendline equation was then used to calculate the absolute concentrations of each compound in fly brain tissue. QUANTIFICATION AND STATISTICAL ANALYSIS Statistical Analysis Statistical tests were completed as described in the figure legends using Prism 9 (GraphPad Software, Boston MA, USA). Statistical comparisons primarily consist of one- or two-way ANOVAs followed by pairwise Holm-Sidak’s multiple comparisons test when experiments include at least three experimental groups or two-tailed Student’s T-test for experiments that include two groups; specific tests used are described in each figure legend. All data figures pool individual data points from at least two independent replicates.