sleep deprivation research article

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Effects of Sleep Deprivation on Performance: A Meta-Analysis

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June J. Pilcher, Allen I. Huffcutt, Effects of Sleep Deprivation on Performance: A Meta-Analysis, Sleep , Volume 19, Issue 4, June 1996, Pages 318–326, https://doi.org/10.1093/sleep/19.4.318

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To quantitatively describe the effects of sleep loss, we used meta-analysis, a technique relatively new to the sleep research field, to mathematically summarize data from 19 original research studies. Results of our analysis of 143 study coefficients and a total sample size of 1,932 suggest that overall sleep deprivation strongly impairs human functioning. Moreover, we found that mood is more affected by sleep deprivation than either cognitive or motor performance and that partial sleep deprivation has a more profound effect on functioning than either long-term or short-term sleep deprivation. In general, these results indicate that the effects of sleep deprivation may be underestimated in some narrative reviews, particularly those concerning the effects of partial sleep deprivation.

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Peer-reviewed

Research Article

Neurophysiological Effects of Sleep Deprivation in Healthy Adults, a Pilot Study

* E-mail: [email protected]

Affiliations Department of Psychiatry, VU University Medical Center, Amsterdam, The Netherlands, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands

Affiliation Neuroimaging Center University Medical Center, Groningen, The Netherlands

Affiliations Department of Nuclear Medicine & PET Research, VU University Medical Center, Amsterdam, The Netherlands, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands

Current address: Department of Psychiatry, Erasmus Medical Center, Rotterdam, The Netherlands

• Ursula M. H. Klumpers, 
• Dick J. Veltman, 
• Marie-Jose van Tol, 
• Reina W. Kloet, 
• Ronald Boellaard, 
• Adriaan A. Lammertsma, 
• Witte J. G. Hoogendijk

• Published: January 21, 2015
• https://doi.org/10.1371/journal.pone.0116906
• Reader Comments

Total sleep deprivation (TSD) may induce fatigue, neurocognitive slowing and mood changes, which are partly compensated by stress regulating brain systems, resulting in altered dopamine and cortisol levels in order to stay awake if needed. These systems, however, have never been studied in concert. At baseline, after a regular night of sleep, and the next morning after TSD, 12 healthy subjects performed a semantic affective classification functional magnetic resonance imaging (fMRI) task, followed by a [ 11 C]raclopride positron emission tomography (PET) scan. Saliva cortisol levels were acquired at 7 time points during both days. Affective symptoms were measured using Beck Depression Inventory (BDI), Spielberger State Trait Anxiety Index (STAI) and visual analogue scales. After TSD, perceived energy levels, concentration, and speed of thought decreased significantly, whereas mood did not. During fMRI, response speed decreased for neutral words and positive targets, and accuracy decreased trendwise for neutral words and for positive targets with a negative distracter. Following TSD, processing of positive words was associated with increased left dorsolateral prefrontal activation. Processing of emotional words in general was associated with increased insular activity, whereas contrasting positive vs. negative words showed subthreshold increased activation in the (para)hippocampal area. Cortisol secretion was significantly lower after TSD. Decreased voxel-by-voxel [ 11 C]raclopride binding potential (BP ND ) was observed in left caudate. TSD induces widespread cognitive, neurophysiologic and endocrine changes in healthy adults, characterized by reduced cognitive functioning, despite increased regional brain activity. The blunted HPA-axis response together with altered [ 11 C]raclopride binding in the basal ganglia indicate that sustained wakefulness requires involvement of additional adaptive biological systems.

Citation: Klumpers UMH, Veltman DJ, van Tol M-J, Kloet RW, Boellaard R, Lammertsma AA, et al. (2015) Neurophysiological Effects of Sleep Deprivation in Healthy Adults, a Pilot Study. PLoS ONE 10(1): e0116906. https://doi.org/10.1371/journal.pone.0116906

Academic Editor: Hengyi Rao, University of Pennsylvania, UNITED STATES

Received: August 17, 2013; Accepted: December 16, 2014; Published: January 21, 2015

Copyright: © 2015 Klumpers et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Funding: This study was supported in part by ZONMW (Dutch Organization for Health Research and Development), The Netherlands, grant no. 016.066.309, to Dr. Ronald Boellaard. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Lack of sleep is a common condition in everyday life, either related to psychosocial demands or related to working shift hours. In healthy individuals, this may induce decreased alertness and vigilance, together with a general decline in mood. Total sleep deprivation (TSD) has been associated with general psychomotor slowing and diminished cognitive performance [ 1 , 2 ]. In affective disorders, only one night of sleep deprivation may improve mood in 40–60% of subjects with major depressive disorder [ 3 ], whereas bipolar patients may even turn into (hypo)mania [ 4 ]. Thus, in humans, sleep deprivation is clearly related to altered emotional and affective functioning.

From an evolutionary perspective, staying awake has served to guard against outside threats, requiring increased alertness. Motivational control over the waking state is necessary and presumed to be modulated by top-down cortical control systems, involving prefrontal executive regions [ 5 ]. Using [ 18 F]-2-fluoro-2-deoxy-D-glucose ([ 18 F]FDG) as a ligand in positron emission tomography (PET) studies, sleep deprivation has been associated with reduced metabolic activity in a network of brain regions, including prefrontal and limbic regions, the thalamo-basal ganglia circuit, and cerebellum [ 6 , 7 ]. Neurophysiologically, dopamine (DA) release is supposed to increase wakefulness, partly through the D2 receptor [ 8 , 9 , 10 ] and partly by acting as a stimulator of corticotropin releasing hormone (CRH) [ 11 ]. Ultimately, CRH releases cortisol from the adrenal cortex via the hypothalamic pituitary adrenal (HPA) axis, a key endocrine response mechanism to a stressful situation. These effects are superimposed upon the circadian rhythm of the HPA axis, and largely controlled by the central body clock, the suprachiasmatic nucleus (SCN). HPA axis functioning can be assessed by the cortisol awakening response (CAR), reflecting the natural HPA response to stress of sleep-wake transitions [ 12 ]. It is unknown, however, how cortical, dopaminergic and HPA axis activities interact to maintain wakefulness. Studying their interaction may also provide insight into the pathophysiology of depressive disorder, with its frequently occurring sleeping problems and HPA-axis hyperactivity [ 13 , 14 ].

The purpose of this pilot study was to assess how the healthy brain responds to TSD and how compensatory and regulatory stress mechanisms may interact as opposed to future clinical studies in mood disorder. It was hypothesized that wakefulness would be associated with an increase in dopamine release and CRH activation, in the presence of altered emotional functioning.

Materials and Methods

Participants.

Twelve healthy adults (6 female, mean age 29.2 ± 10.2 years; 6 male, mean age 28.5 ± 4.8 years) were recruited through newspaper advertisements. Exclusion criteria included a lifetime history of psychiatric disorders, as assessed by Mini international neuropsychiatric interview [ 15 ] and reported contacts with mental health counselors, previous use of psychotropic medication known to interfere with the dopaminergic system, 1 st degree relatives with psychiatric disorder, somatic disorders, pregnancy, use of sleep medication and past or current abuse of psychoactive drugs. All subjects were good sleepers, defined as feeling rested after a night’s sleep, and in good physical health as assessed by medical history, physical examination and routine laboratory tests. On the night preceding TSD, subjects slept 6.6 ± 1.1 hours. Mean body mass index was 21.0 ± 1.4 kg·m −2 , 2 were cigarette smokers (10 per day), and 10 consumed alcohol (1.5 ± 1.1 units day).

Ethics Statement

Written informed consent was obtained from all participants. The study protocol was approved by the Medical Ethics Review Committee of the VU University Medical Center in Amsterdam.

Design and Procedure

Cortisol saliva was collected on both days. At baseline (day 1), after a regular night of sleep at home, all subjects underwent functional magnetic resonance imaging (fMRI) scanning in the morning, followed by a 60min [ 11 C]raclopride PET scan. After this scanning session, participants returned to their daily activities, including study and/or work. They returned to the hospital at 22.00h for effectuation of total sleep deprivation. During the night, subjects were monitored by a trained observer and engaged in reading, conversation, short walks on the ward, and board games in a well-lit room. At arrival, urine toxicology was screened and found negative for a subset of dopaminergic and wake enhancing drugs, including cocaine, tetrahydrocannabinol (THC) and amphetamines. Use of alcohol, caffeinated beverages and smoking was prohibited during the night, as on both days in-between scan experiments. At day 2, a light meal was served at 6.00h. After having been awake for about 25 hours, fMRI scanning was repeated, followed by a second [ 11 C]raclopride PET scan for all participants. After finishing the scan sessions, subjects were asked to stay awake during the remainder of the day, and to postpone sleep until the evening.

Psychometric Data

Depressive symptoms over the prior week were assessed using the Beck Depression Inventory [ 16 ]. At baseline and before scanning, trait and state anxiety were measured using the Spielberger State-Trait Anxiety Inventory (STAI) [ 17 ]. During sleep deprivation, self and observer based visual analogue scales (VAS) were registered every 3 hours, starting at 24.00h and finishing at 12.00h, documenting mood, interest, motor inhibition, speed of thought, self appreciation, energy level and concentration on a scale from 0–100. Psychometric data were analyzed using Statistical Package for the Social Sciences (SPSS) version 15.0 for Windows (SPPS Inc, Chicago, Illinois, USA), using Repeated Measures ANOVA.

Cortisol Measurements

Data acquisition..

At the baseline interview, participants were instructed to collect saliva samples using Salivettes (Starstedt, Germany)[ 18 , 19 ], at 7 time points per day. One hour cortisol awakening response (CAR) measurements included three sampling points, immediately after awakening (T1), at +30min (T2) and at +60min (T3). Additional saliva samples were taken at +90min (T4) after awakening, at 14.00h (T5), 17.00h (T6) and 23.00h (T7). Subjects were instructed to write down the exact sampling time. On the following day, samples were collected at identical time points (T8–T14). Eating, smoking, drinking tea or coffee or brushing their teeth was prohibited within 15min before sampling. No dental work was allowed within 24 hours prior to sampling. Samples were stored in a refrigerator and returned by the participant or by regular mail. Salivettes were centrifuged at 2000g for 10min, aliquoted and stored at −80°C. Free cortisol analysis was performed by competitive electrochemiluminescence immunoassay (Architect, Abbott Laboratories, Illinois, USA) [ 20 ]. The lower limit of quantification was 2.0nmol·L −1 , the intra- and inter-assay variability coefficients were less than 9 and 11%.

Data analysis.

The CAR area under the curve (AUC), with respect to increase (AUC I ) and to ground (AUC G ), was calculated. AUC I is calculated with reference to the baseline measurement at T1, ignoring the distance from zero for all measurements, and emphasizing change over time. AUC G is the total area under the curve of all measurements [ 21 ]. The mean increase in the 1 st hour (MnInc) was calculated by subtracting the baseline value at T1 from the mean of the subsequent values at T2 and T3. Using the real sampling time at T2, T3, T9 and T10, cortisol levels were interpolated using piecewise linear spline to +30 and +60min, in order to derive the individual CAR AUC for identical time points on both days [ 22 ]. For AUC G T1-T7 and T8-T14, mixed model analysis was used to include time points available, with missing values being interpolated [ 23 ].

Task design.

We used a semantic emotional classification task adapted from Murphy [ 24 ] and Elliot [ 25 ], where subjects had to respond as quickly as possible to affective target stimuli and ignore distracter stimuli. The fMRI study consisted of two task sessions (runs), one to be executed at baseline and one after sleep deprivation. Each participant therefore performed two versions of the task, their order randomized across subjects. Each task comprised a blocked design with 16 blocks, programmed in E-prime software (Psychology Software Tools, Inc., Pittsburgh, PA, USA). The first two blocks were practice blocks while being in the magnet, to become acquainted with the task and to reduce anticipation anxiety. Within each session, eight different task conditions were presented twice in a pseudo-randomized order, to generate 16 blocks ( Table 1 ). In each block, 22 trials were presented in a randomized order, half of these being targets, and the other half consisting of distracters. Targets and distracters were defined on the basis of emotional valence, with happy (positive (P)), sad (negative (N)), or neutral (O) words as targets, presented with one of the other categories as distracters (e.g. positive targets with negative distracters). All the words were selected from the Centre for Lexical Information (Celex) Database [ 26 ], and matched for frequency of written use and word length. Affective words were selected on high emotional impact (positive words 6.0 ± 1.6 letters, intensity 2.2 ± 0.5; negative words 5.7 ± 0.4 letters, intensity 5.9 ± 1). A baseline neutral condition was included, where targets and distracters were defined on the basis of physical properties (italic (I) vs. regular (R) font), providing similar visual input. Each of the 16 blocks started with a written instruction for a fixed 5s, followed by a 1s rest, in which subjects were instructed to respond as fast as possible to the appropriate task condition by pressing a button with the preferred index finger. Following a fixation cross for 800ms, a word was shown for 500ms to which subjects were allowed to respond within an additional fixed inter-stimulus interval of 900ms. After pressing, the word was no longer visible. At the end of a block, a 1s rest was included prior to the next block.

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https://doi.org/10.1371/journal.pone.0116906.t001

T1-weighted MRI scans were acquired using a 1.5T Sonata MR system (Siemens Medical Solutions, Erlangen, Germany) to exclude anatomical abnormalities and for PET and fMRI co-registration purposes. A sagittal 3D gradient-echo T1-weighted image was acquired using the following sequence: repetition time (TR) = 2.7ms, echo time (TE) = 3.97ms, matrix 256×160, voxel size 1×1×1.5mm 3 . Echo-planar images (EPI) were obtained using a T2*-weighted gradient echo sequence TR = 2.18s, TE = 45ms, 35 axial slices; voxel size 3×3×3mm 3 , flip angle 90°, matrix 64×64). For the fMRI task, stimuli were projected onto a screen at the end of the scanner table, visible through a mirror mounted above the subject’s head. Two magnetic field compatible response boxes were used to record the subject’s responses.

Data processing.

Functional imaging data were preprocessed and analyzed using Statistical Parametric Mapping (SPM) software (SPM8, Wellcome Trust Neuroimaging Centre, London, UK), implemented in Matlab 7.1.0 (The MathWorks Inc., Natick, MA, USA). Preprocessing included reorientation of the functional images to the anterior commissure, slice time correction, image realignment, co-registration of the T1 scan to the mean image, warping of the co-registered T1 image to Montreal Neurological Institute (MNI) space as defined by SPM’s T1 template, applying the transformations to the slice-timed and realigned images, reslicing to voxels of 3×3×3mm 3 and applying spatial smoothing using an 8mm full width at half maximum (FWHM) Gaussian kernel. Subject movements of more than 3mm in more than one direction resulted in exclusion of data.

In the first level analysis, scanner drifts were modeled using a high pass filter with a cut off of 128s. For each regressor, the onset of the block and the duration of the total block were modeled as a block design, consisting of 22 trial words × [800msec (fixation cross) + 500msec (word presentation) + 900msec (maximum time to press the button)] per word, plus 21 intervals × 32 msec (refresh rate word in scanner), totaling 49.072 ms. Task instructions were modeled separately as a regressor of no interest ( Table 1 ).

The following contrast images were computed:

• 1). [−2 1 0 1 0 0 0 0] positive classification vs. baseline, in which the positive-neutral (P-O) and neutral-positive (O-P) word pairs were grouped and contrasted to the baseline (italic-regular font pairs and vice versa).
• 2). [−2 0 1 0 1 0 0 0] negative classification vs. baseline, in which the negative-neutral (N-O) and neutral-negative (O-N) word pairs were grouped and contrasted to the baseline (italic-regular font pairs and vice versa).
• 3). [−2 0 0 0 0 1 1 0] both emotional valences vs. baseline, in which exclusively emotional valence pairs (P-N and N-P) were grouped and contrasted to the baseline (italic-regular font pairs and vice versa).
• 4). [−6 1 1 1 1 1 1 0] any emotional valence vs. baseline, in which all emotional valences (P-O, N-O, O-P, O-N, P-N and N-P) were grouped and contrasted to the baseline (italic-regular font pairs and vice versa).

These contrasts were defined for both pre-deprivation and post-deprivation sessions.

Next, on a second level, the contrast images for positive vs. baseline for the pre-deprivation session and the post-deprivation session were entered in a two-sample t -test, with session as dependent variable. Additionally, separate models were set up for negative vs. baseline, exclusively emotional valence pairs and any emotional valence vs. baseline. Due to the relative low number of subjects, no additional covariates were entered to these models.

The main effect of time (day 1 vs. day 2) was explored at a threshold of p uncorrected <0.005, with an extent threshold of 10 contiguous voxels. Additionally, correction for multiple comparisons was performed by applying Small Volume Correction (SVC) for regions of interest (ROIs) with known involvement in depression, sleep abnormalities and emotional attention. As described in the introduction, the following regions were selected: dorsolateral prefrontal cortex, subgenual cingulate, hippocampal gyrus/ amgydala and insula, defined using the Automated Anatomical Labeling (AAL) system as implemented in the WFU-pickatlas toolbox [ 27 ]. Effects occurring in these regions were thus followed up using SVC-correction and results are reported at a Family Wise error (FWE) corrected p-value <.05. Psychometric and performance data (correct responses, false alarms, misses and mean response time for events (RT)) for both days were likewise analysed using paired sample t -testing.

[ 11 C]Raclopride PET

[ 11 C]Raclopride scans were performed on an ECAT EXACT HR+ scanner (Siemens/CTI, Knoxville, TN, USA). Participants were studied at rest, in supine position, with a nurse nearby and ice cubes in both hands to prevent them from falling asleep. Head movement was restricted by a head immobilization device and Velcro tape. A venous catheter was placed in the forearm for [ 11 C]raclopride infusion. A 10min 2D transmission scan using three rotating 68 Ge/ 68 Ga sources was acquired for photon attenuation correction. 370MBq [ 11 C]raclopride was dissolved in 5mL saline and administered by an infusion pump (Med-Rad, Beek, The Netherlands), at a rate of 0.8mL·s −1 , followed by a 35mL saline flush at a rate of 2.0mL·s −1 . Meanwhile, a 60min dynamic 3D raclopride scan was acquired, consisting of 20 frames with progressively increasing frame lengths (1×15, 3×5, 3×10, 2×30, 3×60, 2×150, 2×300, 4×600s). All PET sinograms were normalized and corrections were applied for decay, dead time, attenuation scatter and randoms. Emission data were reconstructed using FORE+2D filtered back projection [ 28 , 29 ] applying a 5.0mm Hanning filter with a Y-offset of 4cm and a 2.123 zoom. Frames 12–20 were summed (i.e. 5–60min after injection) to create a single frame emission sinogram with high count statistics. Reconstruction of this emission sinogram was performed using ordered-subset expectation maximization (OSEM) with 4 iterations and 16 subsets. OSEM images underwent a 5mm FWHM Gaussian post smoothing, to obtain a transaxial spatial resolution of 7mm FWHM, equal to that of filtered back projected (FBP) images. Final images consisted of 63 planes of 128×128 voxels, each 2.4×2.4×2.4 mm 3 .

All structural MRI scans were rotated to the axial (horizontal) plane, parallel to the anterior and posterior commissure (AC–PC) line. To correct for possible motion, each frame (1–20) was coregistered to the summed image over frames 12–20. These motion corrected PET images were subsequently coregistered to the realigned MRI scan using Volume Imaging in Neurological Research (VINCI) software [ 30 ].

Kinetic analysis.

Mean non-displaceable binding potential (BP ND ) was used as a measure of dopamine D2/D3 receptor availability. Using the in-house developed software package PPET [ 31 ], parametric BP ND images were generated using receptor parametric imaging (RPM2), a basis function implementation of the simplified reference tissue model (SRTM) [ 32 ]. Cerebellum grey matter was used as reference tissue, for which automated cerebellar volumes of interest (VOIs) were defined using partial volume effect (PVE) lab [ 33 ]. This analysis also provided parametric R 1 images, representing local tracer delivery relative to that to the cerebellar reference region. Basis function settings used were: start exponential = 0.05min −1 , end = 0.5min −1 , number of basis functions 32.

Statistical parametric mapping.

Parametric BP ND images were analyzed using SPM8. After spatial preprocessing, including reorientation and normalization to MNI space, images were analyzed on a voxel by voxel basis, using a basal ganglia mask created with WFU Pickatlas software [ 27 ]. No proportional scaling was applied. SPM RPM2 and R 1 BP ND images were entered in paired sample t -tests. The threshold was set at p uncorrected ≤0.005 with an extent threshold of 10 voxels.

At baseline, depressive symptoms were low to absent (BDI score 1.8 ± 2.0). Using the Spielberger State-Trait Inventory (STAI), containing 20 items to be scored on a four-point Likert scale (range 20–80), mean trait anxiety score was 29.4 ± 4.8 and state anxiety at baseline scanning 30.4 ± 3.9. During the TSD night, VAS energy levels declined significantly (F(1,11) 20.2, p = 0.001), in line with decreased concentration (F(1,11) 10.6, p = 0.01), speed of thought (F(1,11) 12.0, p = 0.007), and increased perceived motor retardation (F(1,11) 12.0, p = 0.007), but not significantly for mood (F(1,11) 2.9, p = 0.122). STAI scores indicated a trendwise increased anxiousness after TSD, 36.3 ± 10.7 ( p = 0.068).

Cortisol Data

After TSD, CAR AUC I and AUC G showed significant blunting ( p = 0.029 and p = 0.022, respectively) ( Table 2 , Fig. 1 ). On day 1, nine subjects showed a rise in cortisol during the first hour after awakening, compared with a much smaller increase in five subjects after TSD, signified by a decreasing MnInc CAR. Similarly, cortisol AUC G T1-7 vs. T8-14 showed a robust decline after TSD. Cortisol levels were normally distributed on both days and showed no significant gender differences. Evening cortisol was not discriminating.

Individual saliva cortisol curves (grey line) and cortisol mean value (nmol/L) per Tx sampling point (solid line). Day 1 shows baseline cortisol sampling at T1-T7, day 2 shows effects of one night of total sleep deprivation on cortisol levels at T8-T14. T1, 2 and 3 comprise the cortisol awakening response (CAR). T8, 9 and 10 are sampled at identical time points the following day. T5 and T12 are sampled at 14.00hr, T6 and T13 at 17.00hr and T7 and T14 at 23.00hr. p values show effects of TSD, # p = 0.016.

https://doi.org/10.1371/journal.pone.0116906.g001

https://doi.org/10.1371/journal.pone.0116906.t002

Twelve data sets were available on day 1, and 11 on day 2 due to scanner logistic problems. After TSD, subjects were significantly slower in reacting during the neutral condition ( p = 0.043), but also to positive targets with a neutral distracter ( p = 0.008). The proportion of correct versus false answers decreased trendwise for neutral words ( p = 0.082) and for positive targets with a negative distracter ( p = 0.079) ( Table 3 ). Post hoc , results were additionally analyzed using general linear model statistics (GLM). When performing multivariate testing, the effect of sleep deprivation on reaction time for emotional words was significant at F(1,20) = 34.14, p <0.001; the effect of time for sleep deprivation was significant at F(1,20) = 5.78, p = 0.037, indicating that participants were slower at day 2, due to sleep deprivation. The interaction effect of sleep deprivation on emotion* time was not significant (F(1,20) = 0.81, p = 0.475), indicating that the general slowing following deprivation was common for all emotions presented.

https://doi.org/10.1371/journal.pone.0116906.t003

After 25 hours of wakefulness, the neutral condition showed no significant activation differences at a group level ( Table 4 ). Evaluation and processing of positive words was associated with increased bilateral prefrontal activation in addition to increased activation of left medial prefrontal working memory areas ( Fig. 2A ). Left DLPFC activation remained significant after Small Volume Correction (SVC; AAL p FWE 0.02). Processing of negative words was associated with increased activity in left insular area, but this effect did not survive SVC. During conditions containing emotional words only, viz. positive targets and negative distracters (P-N), or vice versa (N-P), left insular, limbic and parahippocampal lobes were activated, as well as right parietal lobe ( Fig. 2B ), showing SVC subthreshold increased activation in the hippocampal/parahippocampal region. All emotional conditions (i.e. target and/or distracter) resulted in increased activation in the anterior part of the left insula (AAL p FWE 0.043), mainly driven by the response to words with a negative valence, in addition to activation of the parietal lobe.

p <0.005, extent threshold 10 voxels. A and B are task related fMRI results, showing increased prefrontal and limbic activation respectively, in the conditions (A) positive valence versus baseline and (B) both emotional valences. C is a [ 11 C]raclopride PET image, showing decreased voxel-by-voxel RPM2 binding potential (BP ND ) in nucleus caudatus in n = 8. At the bottom right is the Z-score scale depicted.

https://doi.org/10.1371/journal.pone.0116906.g002

https://doi.org/10.1371/journal.pone.0116906.t004

[ 11 C]Raclopride

A subset of 8 paired data sets was available due to a failed synthesis (1 TSD scan) and technical problems with 1 baseline and 2 TSD scans. For n = 8, injected masses of raclopride were 2.36 ± 1.08 and 1.45 ± 0.55μg, on days 1 and 2 respectively ( p = 0.06) and injected doses of [ 11 C]raclopride were 378 ± 12 and 390 ± 19MBq on days 1 and 2, respectively ( p = 0.230). TSD resulted in a significantly decreased voxel-by-voxel based BP ND in left caudate nucleus, as shown in Table 4 and Fig. 2C . In addition, there was a TSD induced decrease in R 1 in right caudate nucleus.

Clinical Interactions

Post hoc we tested for correlations for TSD related changes in cortisol AUC, regions of interest (ROI) based BOLD response to emotional words, and altered [ 11 C]raclopride binding, using anatomical automatic labeling (AAL) defined striatal regions, according to WFU Pick atlas [ 27 ]. No statistically significant correlations were observed.

In the present study the effects of total sleep deprivation on stress regulating brain systems in healthy subjects were investigated as preliminary work for a TSD study in mood disorder. During a sleep deprived night, VAS scores on energy, concentration and speed of thought, but not mood, declined significantly. Although at baseline participants were not clinically depressed or overly anxious, as witnessed by BDI and STAI-scores, validated instruments like the Positive and Negative Affect Schedule (PANAS)[ 34 , 35 ] and the Profile of Mood State (POMS)[ 36 ] could have been used to score a broad range of mood states, both at baseline and during the night of sleep deprivation and the subsequent day.

After 24 hours of prolonged wakefulness, significant blunting of the cortisol awakening response (CAR) and secretion over the day (AUC G ) were found. Normally, under the influence of the SCN, HPA activity increases during the night, resulting in a cortisol rise two to three hours after sleep onset, which continues to rise into the early waking hours [ 12 , 14 ]. The present results indicate robust attenuation of the HPA-mediated stress response after TSD, congruous with decreasing VAS scores and lowered arousal, which may be due to the absence of the initial physiological awakening response [ 12 , 37 ]. These findings are in line with Vzontgas and colleagues, finding lowered, albeit not significantly, 24 hour plasma cortisol levels in blood in a laboratory setting in a group of 10 men [ 38 ].

In the present study, no significantly altered cortisol levels were found after 14.00h (T5-T12). Evening cortisol, indicating return to baseline levels, was slightly lower than those reported by Vreeburg [ 19 ] in healthy subjects.

In order to investigate effects of TSD on processing of both positive and negative stimuli, as well as on cognitive inhibition, we chose to adapt the Murphy and Elliott fMRI paradigm [ 24 , 25 , 39 , 40 ], as this task was originally developed to investigate emotional bias in mood disorders in the context of cognitive processing. After TSD, in healthy adults, task performance during fMRI was slower, indicating that TSD overruled any learning or practice effects. Slowing of task performance after TSD is in line with previous reports and likely due to loss of sustained attention and vigilance [ 41 ]. Slowing was particularly evident for positive targets with a neutral distracter. Accuracy was trendwise decreased for the neutral (italic vs. regular font) condition and for positive targets with negative distracters, suggesting decreased sensitivity to detect positive valence. On processing emotionally salient versus neutral words, TSD was associated with increased left dorsolateral prefrontal activity, suggesting increased mental effort to perform semantic judgements and to maintain control, in a setting of less efficient functional circuitry. Although we do not intend to overstate the relevance of these findings in this emotionally healthy group, cognitive biases in depressive disorder are thought to reflect maladaptive bottom-up processes, which are generally perpetuated by weakened cognitive control [ 42 ]. Processing of solely affective stimuli (target and distracter) showed subthreshold increased activation in the left parahippocampal /hippocampal region. Activation of the subgenual gyrus and amygdala was remarkably absent, though for amygdala this is line with findings by Elliott et al., [ 25 ], fostering [presumably reflecting] a lower affective salience for words compared to pictures. Processing of any affective stimulus (target and/or distracter) showed increased activation of the anterior part of the insula in a context of performance anxiety as indicated by trendwise increased STAI scores in these healthy, but weary adults [ 43 ]. Activation of the insula was mainly driven by the response to words with a negative valence, suggestive of an increased effort to handle negative affect [ 1 ] and in line with the insular function of emotional interference resolution in working memory [ 44 ]. With due caution, we propose that these neural responses reflect modulation of cognitive performance by emotional tone. Therefore, these regions likely represent an interface between cognition and emotion processing [ 25 ].

After TSD, voxel-by-voxel based BP ND of [ 11 C]raclopride was significantly decreased in left caudate, which is partly in accordance with a report by Volkow [ 9 ]. This was not explained by regional altered delivery (R 1 ) of the tracer, although metabolic activity in the cerebellar reference tissue may be altered after sleep deprivation [ 6 , 7 ]. A reduction in [ 11 C]raclopride specific binding is consistent with either an increase in dopamine release, or a decreased affinity of the synaptic D2/D3 receptor in these regions [ 45 ], which may be due to internalization of receptors [ 46 ]. This could not be determined on the basis of our design, and may have resulted from a combination of these factors.

Using both [ 11 C]raclopride and a dopamine transporter blocking radioligand, [ 11 C]methylphenidate, Volkow [ 10 ] argued TSD induced decreased [ 11 C]raclopride binding not to be due to increased dopamine availability, but to decreased affinity of the D2/D3 receptor, resulting in dopamine receptor downregulation in the synaptic cleft. As dopamine D2 receptors are thought to be involved in wakefulness, and partially responsible for maintaining arousal and alertness [ 8 , 47 ], the present reduced VAS on energy and concentration and efficiency in fMRI task performance, are in line with D2 down-regulation. This would further be exemplified by the blunted cortisol response, since dopaminergic stimulation of the HPA axis is mediated through D1 and D2 receptors [ 11 ]. Decreased affinity in the head of the left caudate could be in line with increased difficulty in controlling word interference from task unrelated processing [ 48 ], explaining both the general slowing and increased prefrontal activity. However, we were not able to corroborate this explanation in a correlational analysis, which may be primarily due to insufficient power, but may also indicate that regional brain activation as measured with fMRI is not tightly coupled to either striatal dopaminergic transmission or HPA axis activity. Excluding two smokers did not change results significantly, although smoking may influence dopamine release and therefore raclopride binding [ 49 ].

Clinical Relevance

Individual vulnerability to sleep deprivation is known to be variable [ 3 ]. From the present study, it cannot be ruled out that decreased D2 receptor affinity is the brain’s response to initially increased dopamine levels, induced by TSD. Blunting of the HPA axis response may reflect the absence of awakening stress and possibly explain some of the beneficial effects of sleep deprivation in depressive mood disorder.

Limitations

This pilot study in healthy adults contains several potential limitations. In view of our modest sample size and fixed-order design, the current results are in clear need of replication.

Regarding baseline characteristics, the participants’ number of hours of sleep was adequate at the start of the experiment, but we did not control objectively for sleep quality and duration. Baseline CAR may have been affected by waking up earlier, or by the excitement of taking part in a research study, which may have released additional ACTH [ 50 ]. A higher CAR has been associated with shorter sleep duration [ 51 ]. However, excluding three subjects who slept 6 hours or less, did not have a major effect on the CAR ( p = 0.022). During the night, participants were kept in a well-lit room. Melatonin suppression may have dampened the SCN-mediated CRH response.

For our fMRI runs, we have chosen to adapt the original Murphy and Elliott task [ 24 , 25 ], who described their paradigm to investigate emotional bias in depressive disorder as a go/no-go task. However, go/no-go paradigms do not typically feature an even split of valid and invalid targets, and therefore we have renamed the task as a semantic affective classification task. The task was modeled as a block design, and because the inter-stimulus interval (ISI) was fixed, could not be analyzed as an event-related design. Evidently, a block design is preferable when sample sizes are modest, as it is generally more robust [ 52 ], although it lacks the flexibility of event-related designs. Therefore, for assessing individual cognitive and emotional responses in e.g. a patient population, an event-related design would be more appropriate [ 53 ]. Finally, for our voxel-based analyses we set an a priori threshold of p = 0.005 and 10 voxels to obtain a reasonable balance between Type I and Type II error [ 54 ], again highlighting the need for a replication in a larger sample.

With respect to mood enhancers, other drugs of abuse were not tested for. At baseline, we did not control for caffeine use at home before the start of the experiments. Caffeine evokes its stimulating effects through blockade of the adenosine receptor [ 55 ], which in turn is involved in the control of dopamine release [ 56 ]. As raclopride is a dopamine receptor antagonist, in theory, TSD induced changes in raclopride binding may therefore have been underestimated.

Although changes in [ 11 C]raclopride BP ND clearly show a dose dependent relationship with extracellular DA levels, the nature of this relationship is complex [ 57 ]. [ 11 C]Raclopride BP ND does not differentiate between binding to receptors in high or low affinity states, whereas endogenous dopamine is mainly conveyed by high affinity state receptors [ 58 ], acting on pre- and postsynaptic (extra)-striatal dopaminergic D1 receptors to bring about its effect [ 59 ]. Therefore, dopaminergic effects due to TSD may have been underestimated and future research should resolve this issue, for example by comparing [ 11 C]raclopride to the purported high-affinity ligand [ 11 C]PHNO [ 60 ]. As [ 11 C]raclopride scans were performed in the second half of the morning, and the time sequence of dopamine release is not known, effects may have been either over- or underestimated. A variable response to TSD is in line with observations in depressed patients, where the therapeutic response to TSD may vanish within hours to a day [ 3 ].

Sleep deprivation in healthy adults induces widespread neurophysiological and endocrine changes, characterized by impaired cognitive functioning, despite increased regional brain activity. Our pilot findings indicate that activation of the dopaminergic system occurs together with a blunted cortisol response, suggesting augmented motivational top down control and requiring increased involvement of prefrontal and limbic cortical areas. Sustained wakefulness requires the involvement of compensatory brain systems, and may help to understand the therapeutic effects of sleep deprivation in affective disorders.

Acknowledgments

The authors thank Ms Marieke Mink for accompanying the participants to the PET scanning sessions, Dr. Marjan Nielen for help in designing the fMRI task, Dr. Sophie Vreeburg for help in interpreting cortisol data, Dr. Adriaan Hoogendoorn for statistical support, Neuroradiology staff for interpretation of MRI scans, staff of the department of Nuclear Medicine & PET Research for tracer production, technical assistance and data acquisition and staff of the VU Medical Center Neuroendocrine lab for cortisol saliva analysis.

Author Contributions

Conceived and designed the experiments: UK DV MJT RK RB AAL WH. Performed the experiments: UK DV MJT RK. Analyzed the data: UK DV MJT RK RB AAL WH. Contributed reagents/materials/analysis tools: UK DV MJT RK RB AAL WH. Wrote the paper: UK DV MJT RK RB AAL WH.

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• 36. McNair DM, Lorr M, Droppleman LF (1971) Manual for the Profile of Mood States.
• 53. Huettel SA, Song AW, McCarthy G (2009) Functional Magnetic Resonance Imaging. Sunderland MA: Sinauer Associates.

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Sleep Duration and Executive Function in Adults

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• Published: 14 November 2023
• Volume 23 , pages 801–813, ( 2023 )

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• Aayushi Sen 1 , 2 &
• Xin You Tai 1 , 2  

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Purpose of Review

To review the literature examining the relationship between sleep and cognition, specifically examining the sub-domain of executive function. We explore the impact of sleep deprivation and the important question of how much sleep is required for optimal cognitive performance. We consider how other sleep metrics, such as sleep quality, may be a more meaningful measure of sleep. We then discuss the putative mechanisms between sleep and cognition followed by their contribution to developing dementia.

Recent Findings

Sleep duration and executive function display a quadratic relationship. This suggests an optimal amount of sleep is required for daily cognitive processes. Poor sleep efficiency and sleep fragmentation are linked with poorer executive function and increased risk of dementia during follow-up. Sleep quality may therefore be more important than absolute duration. Biological mechanisms which may underpin the relationship between sleep and cognition include brain structural and functional changes as well as disruption of the glymphatic system.

Sleep is an important modifiable lifestyle factor to improve daily cognition and, possibly, reduce the risk of developing dementia. The impact of optimal sleep duration and sleep quality may have important implications for every ageing individual.

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Introduction 

Sleep is an integral part of human life and is linked to optimal performance across a broad range of physiological and psychological functions [ 1 , 2 , 3 , 4 ]. The relationship between sleep and executive function is an area of intense interest as optimising sleep may be one avenue to improve cognition as we grow older. Executive function, a critical cognitive domain for day-to-day living, has been closely linked to sleep patterns as we grow older. For instance, sleep deprivation is associated with increased frequency of mistakes by shift workers [ 5 ] and increased reliance on habits rather than goal-directed decisions that require executive control [ 6 ]. Despite a growing body of literature, the exact nature of the relationship between sleep and cognition remains unclear. Importantly, what is the optimal amount of sleep required for cognitive functioning? Does this change as we age? These are not straightforward questions, as studies have highlighted different sleep lengths as detrimental or beneficial for cognitive performance. Additionally, how strong is the causal relationship between sleep and cognition? This is crucial if sleep is to be considered a key modifiable lifestyle factor to optimise cognition and mitigate risk of certain brain disorders, especially dementia.

This article offers an overview of the current literature examining sleep duration and executive function in mid-to-late life and will explore key issues including potential underlying mechanisms, the link with brain structural health, and potential contribution to developing dementia.

The Importance of Executive Function

Executive function is the orchestration of goal-oriented processes that include attention, problem solving, planning, and working memory. This includes the ability to hold information in your short-term memory, manipulate that information, and decide which part of the information is important for the task at hand. Executive functioning is particularly developed in humans compared to other animals, and is important for performing everyday tasks ranging from getting dressed, following a recipe, driving a car to more complex problems [ 7 ]. During adulthood, executive function declines with age along with several other cognitive domains [ 8 , 9 , 10 ]. Furthermore, executive function is commonly affected across a wide range of neurological and psychological disorders such as dementia (particularly fronto-temporal dementia), stroke, and head trauma [ 11 , 12 , 13 , 14 ]. It is therefore critical to understand modifiable factors, such as sleep, that could potentially optimise executive function.

How is Executive Function Tested?

Executive function is tested in several ways. Common measures include goal-oriented tasks like the trail-making test [ 15 ], digit-symbol substitution test (DSST) [ 16 ], Wisconsin card sorting test [ 17 ], or the Stroop test [ 18 , 19 ]. Such tasks require a combination of attention, online processing of information, and cognitive effort. Some tasks will engage cognitive control, whereby participants have to decide when to act (‘GO’) but also when not to act (‘NO-GO’), while others may require a participant to place themselves in the mind of another person (theory of mind). More general questionnaires of cognition, such as the Mini Mental State Examination (MMSE) [ 20 ] and Montreal Cognitive Assessment (MoCA) [ 21 ], may have subcomponents of executive function. These can be useful as scalable tests of cognitive ability for large-scale studies but do not assess executive function with the detail of dedicated tasks. By contrast, there are specific cognitive tasks of executive function created to answer specific questions [ 22 , 23 ], but these may be hard to incorporate into larger studies. Box 1 demonstrates 3 tests you can try yourself.

Sleep deprivation is common, with 11.8% of respondents reporting less than 5 h sleep on average in a large US survey [ 25 ]. Deficits in motor performance due to sleep deprivation are equivalent to blood alcohol content of 0.05–0.1%, which is comparable to the legal driving limit of 0.08% [ 26 ] in England and the USA. A single night of sleep deprivation has been shown to affect several components of executive function such as sustained attention, reaction time, and working memory, as well as other cognitive domains of consolidation of episodic and procedural memory [ 27 ].

Sleep deprivation experiments can involve keeping participants awake for an extended period of time (usually over 24 h), or restricting sleep to only a few hours over multiple days. Cognition is tested before, during, and after sleep deprivation periods. Tasks requiring sustained attention show worse performance over 28 h of sleep deprivation [ 28 ]; with more pronounced effects, the more mundane the task is [ 29 ]. Creative thought processes are affected more than rule-based processes [ 30 ], and people revert to habitual actions rather than goal-directed actions for the task at hand [ 6 ].

The real-life impact of sleep deprivation is exemplified in studies of risk aversion, with several prominent studies examining occupations such as the military. Sleep-deprived individuals have impaired risk perception, where they performed worse in a simulated balloon overinflation experiment after 36 h of staying awake. Doing well on this task requires participants to pay attention to the balloon; contextualise it with previous inflation attempts; assess, in real time, the odds of the balloon popping; and inhibit the urge to score higher with a bigger balloon. Interestingly, poor performance in sleep-deprived participants corresponded to altered brain measures of network connectivity, compared to when the same individual was not sleep deprived [ 31 ]. This intra-subject analysis suggests that sleep deprivation may alter the way information is communicated through the brain. A meta-analysis, which pooled large amounts of data from multiple studies, involving 1341 sleep restricted military participants identified a significant negative effect on reaction times, processing speed, accuracy, and moral decision making [ 32 ].

In addition to executive function, sleep has been shown to be important for memory consolidation. Even short naps (as little as 6 min) can improve memory retention, with longer durations being particularly useful for procedural memory. Behaviourally relevant memories are favoured in sleep-dependent consolidation [ 33 ]. Therefore, unsurprisingly, sleep deprivation can negatively affect the consolidation of new memories, especially episodic [ 33 , 34 ] and procedural memory [ 35 , 36 , 37 ]. Importantly, sleep recovery (being able to sleep a ‘normal’ amount) over the course of a week can lead to improved performance in previously sleep-deprived individuals, back to the level of controls [ 36 ].

Sleep extension (sleeping longer than normal) in the short term can reduce the effect of sleep deprivation on sustained attention tasks [ 38 ] and memory [ 36 ]. There are also interesting studies investigating factors that affect resilience against sleep deprivation. Older adults showed worse performance, compared to younger adults, following sleep deprivation in multiple cognitive tasks, including those testing vigilance and reaction times [ 39 ]. By contrast, older adults did not get a benefit from interval sleep after a motor-sequence learning task, unlike their younger counterparts [ 40 ], indicating unequal reliance on sleep for different ages and types of memory consolidation. This effect has not been observed in related non-motor learning paradigms [ 41 ].

Therefore, there is robust experimental and real-life evidence that acute periods of sleep deprivation can detrimentally affect cognition. A different question remains—what is the optimal daily duration of sleep to maximise our cognitive functioning? This is relevant to the daily lifestyle habits of all ageing individuals, and may provide insight into those with the worst cognitive functioning, such as in dementia.

Long-Term Effect of Sleep: Both Short and Long Sleep Durations are Associated with Poorer Executive Function

Numerous studies examine the relationship between average sleep duration and executive function. A common way to probe this question has been to ask participants to self-report the average hours of sleep they had recently, and combine it with cognitive tests of executive function administered at a time point within the study. Using this approach, important findings have emerged.

Short and Long Sleep Durations are Related to Worse Executive Function in Cross-sectional Studies

Earlier cross-sectional studies have associated worse executive function with either short or long extremes of sleep duration [ 42 , 43 , 44 , 45 , 46 , 47 , 48 ], while more recent studies tend to link both short and long sleep durations with poor executive function [ 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 ]. There may be several reasons for these mixed findings.

Firstly, studies tend to use a variety of methods to test cognition. One study of 3212 individuals aged over 60 demonstrated a worse MMSE score for every hour over 7 h of sleep per day in a linear analysis, but no difference in short sleepers [ 42 ]. Similar findings were also identified in a study which accounted for sleep-disordered breathing, which is often cited as a missed confounder in long sleep duration and cognitive impairment [ 50 ]. A linearly worsening trend at longer sleep durations was also seen in a study of executive function (testing DSST). This trend persisted after adjusting for sex, age, education, and BMI, but unfortunately, hypertension and hypnotic medications were not accounted for [ 44 ]. A smaller study of 189 individuals interestingly showed significantly lower MoCA scores with long sleep duration, but not MMSE scores [ 45 ]. In contrast with these studies however, worse MMSE scores [ 59 , 60 ] and immediate and delayed recall [ 60 ] have also been associated with shorter sleep. One study showed a linear relationship of worsening global cognition over 2 years with every hour of sleep less than 7 h, when adjusted for sex, age, education, and BMI [ 61 ]. Therefore, the broad nature of these cognitive tests may have contributed to different findings.

Secondly, different definitions and thresholds of sleep durations have been applied across studies as ‘long’ sleep duration can range from greater than 7 h to greater than 11 h [ 42 , 43 , 44 , 45 , 47 , 48 ]. Conversely, a ‘short’ sleep duration can range from less than 8 h to less than 4 h depending on the specific study [ 56 , 59 , 60 , 61 , 62 , 63 ], which may also contribute to the heterogeneity of results.

Sleep thresholds are now less important, as recent studies have been able to investigate how every hour of sleep reported relates to executive function. This has been made possible by large study cohorts which have consistently identified a quadratic, or inverted ‘U’-shaped, relationship between self-reported sleep duration and executive function with increasingly worse performance with both less and more sleep around a baseline of 7–8 h [ 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 64 , 65 ]. A study of around 480,000 individuals, aged 38–73 years, showed that 7 h of sleep per day was associated with the highest executive function performance, using a measure derived from specific computer-based tasks of attention and working memory. Furthermore, there was a parametric decline in executive function associated with every hour of sleep below and above 7 h suggesting an optimal sleep duration (Fig.  1 ). This finding was consistent for individuals who were below and above the age of 60 years, suggesting that an optimal sleep duration exists as individuals age. This study also showed a similar quadratic relationship between sleep duration and brain volume across 46 different cortical regions which highlights how sleep may be important for brain health [ 54 •].

Association between sleep duration and standardised executive function score from a study of 474,417 individuals in the UK Biobank. Seven hours of self-reported sleep duration was associated with the highest executive function score. A negative relationship was present with sleeping less than 6 h and more sleep from 8 h (Tai et al. 2022, reproduced with permission from the author)

The quadratic relationship between executive function and sleep [ 54 , 64 ] is observed in other cognitive domains including memory [ 51 , 53 , 57 , 64 ], visuospatial abilities [ 51 , 65 ], verbal fluency [ 49 , 57 ], and global cognitive tests like the MMSE [ 50 , 55 , 56 ]. This quadratic association is also observed in a large study of around 513,000 participants, aged 15–89 years old, in a non-supervised, online ‘game-based’ test of processing speed, working memory, arithmetic, and visuospatial memory [ 66 ]. These findings are additionally confirmed in a more recent meta-analysis that self-reported short and long sleep increased the odds of cognitive impairment by 1.40 and 1.58 respectively [ 58 ]. Therefore, cross-sectional studies indicate both long and shorter sleep durations may be detrimental to executive functioning and, importantly, not just the extremes of sleep deprivation and over-sleeping.

Longitudinal Measurements of Sleep Duration Identify Similar Patterns with Executive Function

Cross-sectional studies compare executive function and sleep duration at a single time point. A limitation of this approach is the inability to infer causality. Longitudinal studies, which are more costly to run, offer more information in this regard although cannot strictly determine causality either. Several longitudinal studies have examined sleep duration and executive function. A study where both cognition and self-reported sleep were measured at 3 time points over 10 years showed that long sleep duration was associated with worse global cognition, but not specific cognitive domains. Unfortunately, this study failed to adjust for confounders such as depression, hypnotic use, and sleep apnoea [ 67 ]. Another longitudinal study used a combination of EEG measurement and self-reported sleep following 100 participants over 4 years, and showed worsening cognition over time was associated with both short and long sleep durations, similar to cross-sectional studies [ 68 •]. A change in sleep duration out of the optimal range, over a 5-year follow-up, was also related to worse performance in MMSE, fluency, and reasoning tasks, but had no effect on memory [ 69 ]. Longitudinal studies therefore indicate that average sleep duration, in a similar pattern to cross-sectional studies, can affect executive function and cognitive ability in the future.

Sub-optimal Sleep Duration May Predict Dementia Onset

An important consideration is whether worsening executive function over time may represent the development of dementia. One study of 2457 elderly participants from the Framingham cohort showed double the risk in those who reported long sleep to be diagnosed with dementia, even when adjusted for a genetic predisposition for Alzheimer’s. Transitioning to long sleep was also associated with a higher risk, than those who previously slept for long durations [ 70 ]. Two 2019 meta-analyses support the findings of long sleep being associated with incident dementia [ 71 , 72 ]. However, a well-controlled longitudinal study of 7959 participants of the Whitehall II study over 25 years indicated that self-reported short sleep duration in mid-life was associated with incident dementia when elderly. They further confirmed this association with objective sleep measures in a subpopulation of 3888 participants. There was no link with long sleep, which they report is due to the fact they are looking at sleep durations from mid-life, whereas other studies focus on the elderly—when any impending dementia may already be affecting sleep patterns [ 73 ••]. Therefore, there is evidence that both short and long reported sleep duration may be associated with developing dementia.

Objective Measurements of Sleep Duration and Executive Function

Why are both long and short durations associated with worse executive function? There may be biological reasons, which will be discussed below, as well as practical reasons. Self-reported sleep habits from large cross-sectional studies may not represent true sleep characteristics, as individuals may either over- or under-estimate how long they sleep. Generally, people tend to report ‘time in bed’ rather than actual time asleep [ 74 ]. There are also a tendency for those with insomnia to under-report sleep duration and a tendency of those with fragmented sleep (e.g. those with obstructive sleep apnoea (OSA) or depression) to over-report sleep duration [ 75 , 76 ].

More accurate data comes from electroencephalography (EEG) or actigraphy studies, which objectively measure when an individual is sleeping and delineate sleep stages. While EEG studies are more difficult to carry out and often have smaller sample sizes, actigraphy is increasingly used in large samples to investigate sleep duration [ 46 , 77 , 78 ]. Results from some EEG and actigraphy studies conflict with self-reported sleep data, with total sleep time showing little association with executive function. Blackwell et al. found that total sleep time (TST) measured by actigraphy was related to MMSE score, but not to the trail-making test [ 46 ], while Suemoto et al. demonstrated no impairments related to actigraphy-measured TST (10-word list, verbal fluency, and trail-making tests) [ 79 ]. Similarly, a meta-analysis of actigraphy and EEG studies showed no associations with TST. Importantly though, early studies were limited by the use of linear analysis models, which may have missed the quadratic relationship recently described between sleep duration and performance. However, a longitudinal study that used both EEG measurement and self-reported sleep had findings consistent with cross-sectional studies with worsening cognition over time in short and long sleep durations [ 68 •]. These objective sleep studies hint at a relationship between sleep and cognition that may go beyond just length of sleep.

In summary, recent literature has emphasised the quadratic relationship between sleep duration and executive function and suggests that there may be an optimal duration of sleep to maximise our cognitive performance. This has both personal and public health implications. However, an interesting and important question that has emerged from objective sleep monitoring studies is whether total sleep duration alone is the best measure of sleep. In the next section, we consider how sleep characteristics other than sleep duration may be relevant to the relationship with executive function.

Sleep Quality May Be More Relevant for Long-Term Cognitive Outcomes than Absolute Duration

Sleep quality may be more important than sleep duration alone when considering the impact on executive function. Evidence from studies with objective sleep recording rather than self-reported sleep duration indicates that time spent in different sleep stages and sleep fragmentation may correlate better to cognitive function than total sleep time alone [ 78 , 79 , 80 , 81 , 82 , 83 ]. Subjective sleep quality, such as asking whether the participants felt well rested, also correlates better to cognitive function over absolute duration [ 84 ].

There are three main reasons for why this effect of sleep ‘quality over quantity’ in relation to cognition may be relevant. Firstly, as discussed previously, there may be several biases with self-reported sleep duration [ 74 , 75 , 76 ], which may mean the extremes of self-reported sleep durations are acting as a surrogate for poor sleep quality.

Secondly, conditions associated with poor sleep quality are also often related to poor general cognition. This could confound the relationship between sleep and executive function. For example, individuals with OSA can wake numerous times overnight with brief apnoeic spells resulting in poor sleep. This condition is associated with obesity and resistant hypertension [ 85 , 86 ], which can lead to a decline in cognition via small vessel disease in the brain. Thirdly, sleep architecture evolves with age. Sleep efficiency (SE) (time spent asleep between first falling asleep and waking in the morning) decreases from 89 to 79% from middle age to 70 years old, and the change accelerates over the age of 70 [ 87 ]. Given there is concurrent cognitive decline naturally in this time [ 8 , 9 , 10 ], parsing out the effect of sleep and other factors is difficult but important.

Several studies have explored the link between sleep quality and executive function using various methods. Actigraphy uses a wearable device that measures movements when going to bed to assess parameters like total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO), and general restlessness to gauge quality of sleep. EEG studies tend to use time in slow wave sleep (SWS), rapid eye movement sleep (REM), and non-REM sleep (nREM), as well as the presence and density of sleep spindles [ 88 , 89 ]. Other studies rely on participants reporting whether or not they had a restful and restorative sleep [ 83 , 84 ]. Together, these may be used to give an indication of ‘sleep quality’, or of ‘sleep fragmentation’, rather than self-reported sleep duration.

Feeling rested after sleep or not, regardless of actual time reported asleep, was reported to be more indicative of speed and flexibility of processing [ 67 ]. Similarly, Teräs et al. reported better executive function in those who reported restorative sleep, in a cross-sectional study of healthy mid-old age participants [ 83 ]. Those with more restful nights did better in memory tasks, and those with decreased SOL did better in executive function tasks in a meta-analysis of actigraphy-measured sleep and cognition [ 81 ••]. More time in REM was associated with better executive function in adults aged 20–84 (tested with a goal neglect task), and more SWS and sleep spindles were associated with faster response times, an indication of attention and reaction time [ 80 ]. Sleep spindle density was also recently reported to be associated with better executive function (using DSST, card sorting, and Stroop) and MoCA scores in a cross-sectional study of sedentary 63 middle-older aged participants [ 90 ].

A recent prospective study investigating incident cognitive impairment 4 years after baseline polysomnography found a small, but statistically significant, association of shorter average sleep cycle length and average REM duration [ 82 ]. A similar study published in 2023 additionally reported no association on executive function or global cognitive performance with actigraphy-measured TST or SOL. They did however have small associations between lower SE and poorer visuospatial ability. A limitation of the study was only having follow-up data on 70% of the original participants. They also commented on different associations seen between the (self-identified) White and Black participants; with poor sleep having a greater effect on Black participants [ 91 ].

In summary, executive function appears to be reliably related to sleep quality as measured by sleep onset latency, wake after sleep onset, and whether participants seem rested or not. EEG studies indicate that time in REM and SWS may be important to the mechanism by which sleep affects cognition. Sleep quality should be considered and investigated specifically in any future studies investigating the link between sleep and executive function.

Biological Mechanisms Underlying the Link Between Sleep and Executive Function

Why is sleep so important to executive function? Studies have explored several biological mechanisms that may underlie the link between sleep and executive function. These include potential changes in brain volume, alterations in brain connectivity, accumulation of neurodegenerative proteins, and disrupted glymphatic drainage (summarised in Fig.  2 ).

A summary diagram illustrating the relationship between self-reported sleep duration and executive function and the potential mechanisms by which this may occur. SWS slow wave sleep, REM rapid eye movement, OSA obstructive sleep apnoea

Poor sleep may lead to reduced brain volume which affects cognition. A large imaging study has demonstrated a quadratic relationship between sleep duration and executive function and multiple areas of reduced cortical volume [ 54 •]. Cortical thinning was seen with reduced REM sleep [ 92 ] and in patients with severe OSA and sleep fragmentation; this was, importantly, shown to be partially reversible after 18 months of CPAP therapy [ 93 ].

Changes in brain connectivity may also underpin the effects of sleep deprivation. Diffusion tensor imaging, used to visualise white matter tracts in the brain, has demonstrated changes in structural brain connectivity after just one night of sleep deprivation [ 27 , 94 ] and with prolonged sleep restriction [ 95 , 96 ]. A study of young healthy volunteers indicated that goal-directed learning mainly recruited the ventro-medial prefrontal cortex (vmPFC) on fMRI; after sleep deprivation, this activation was less pronounced reflecting worse functional brain connectivity [ 6 ].

Beyond changes in brain structure, studies using positron emission tomography (PET) imaging [ 97 ] suggest accumulation of neurodegenerative proteins is associated with sleep deprivation [ 98 ]. Beta-amyloid is one of two main pathological proteins described in Alzheimer’s disease, the most common form of dementia. Increased amyloid plaques on PET scan, along with reduced cerebrospinal fluid (CSF) amyloid (indicating increased amyloid deposition) over 2 years, were described in 208 cognitively healthy elderly people with OSA [ 99 ]. Beta-amyloid plaques are also increased in cognitively intact adults who have shorter and poorer quality reported sleep, as well as those with poor objective sleep quality [ 100 ]. While not the remit of this review, there is a growing animal model literature which corroborates findings of accelerated amyloid plaque and tau tangle formation in sleep-deprived states [ 101 ].

More recently, the role of glymphatic drainage, representing the waste clearance system of the brain, has been proposed as a mechanism by which amyloid and other toxic metabolites are removed from the brain. One line of evidence suggests that amyloid production in Alzheimer’s may be the same as in healthy people, but that clearance is significantly slowed [ 100 ]. The glymphatic system is primarily active during sleep and affected by several factors including sleep architecture (more active during SWS) and the general physiological milieu including hormones like cortisol and noradrenaline [ 100 , 102 , 103 ]. Amyloid uptake shows an inverse relationship with nREM slow wave activity [ 104 ]. Amyloid levels in the interstitia are higher in wakefulness in mice, and a small human study found similar results [ 105 ], indicating that sleep deprivation may lead to higher amyloid plaque levels via reduced clearance from the brain. This offers a tangible mechanism linking poor sleep to worse cognitive functions and, possibly, increased risk of dementia.

In summary, there are several mechanisms in which poor sleep may contribute to impaired executive function with reduced quality of sleep. The underlying process is likely to be multifactorial involving a complex relationship between these biological processes (Fig.  2 ). Future studies must consider this complexity to better understand the causal nature between sleep and cognition.

Conclusion and Future Directions

The prospect that sleep may be a modifiable lifestyle factor that can improve our executive function and reduce risk of dementia is both tantalising and real. This is important, especially considering that the worldwide prevalence of dementia is expected to increase by 117% from 2019 to 2050 [ 106 ]. There is consistent evidence for an optimal duration of sleep for cognitive function which is relevant to the personal health of every ageing individual. It is important, however, to remember that these findings reflect a group effect and the exact optimal duration may differ between individuals. Furthermore, other sleep factors may be equally important as the duration of sleep.

Future studies should consider both objective sleep duration and quality by incorporating detailed sleep measurements using actigraphy or EEG where possible. The potential benefits justify the costs of such studies at large scale, while the advent of machine learning and artificial intelligence will allow better data processing and interpretation. Development of short, pragmatic cognitive batteries [ 107 ] which can be performed remotely and are not culturally specific would improve the feasibility of large-scale, standardised multicentre studies. These technologies would allow longitudinal tracking of cognition and sleep in which, as described, very little research has been conducted to date. The causal direction between sleep and executive function should be further explored through interventional trials which may have an active arm of individuals with targeted sleep advice and support compared to match controls. Furthermore, using alternate approaches such as Mendelian randomisation to leverage genetic information [ 108 ] should be performed in larger, diverse populations.

From a scientific perspective, we must better understand the underlying mechanisms linking sleep to cognition and, especially, to the risk of dementia. Moving forward, sleep studies in humans would benefit from the arrival of plasma biomarkers of neurodegeneration such as beta-amyloid, phosphorylated tau, and neurofilament light chain [ 109 ]. Such in vivo and minimally invasive measurements of pathological processes have revolutionised the current landscape of dementia research and clinical trials. It is a clear next step for the study of sleep, cognition, and dementia.

In this review, we have tried to identify what is currently understood around sleep and executive function. We have highlighted the expansive literature around sleep duration and executive function and the growing importance of examining sleep quality. We have considered important questions around causality and underlying mechanisms while showing broadly what is currently understood. Finally, we have discussed areas of future research that may expand our understanding around sleep as a modifiable lifestyle factor for cognition, specifically executive function, and the global problem of dementia.

Data Availability

Not applicable.

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

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Sen, A., Tai, X.Y. Sleep Duration and Executive Function in Adults. Curr Neurol Neurosci Rep 23 , 801–813 (2023). https://doi.org/10.1007/s11910-023-01309-8

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Effect of Inadequate Sleep on Frequent Mental Distress

ORIGINAL RESEARCH — Volume 18 — June 17, 2021

Amanda Blackwelder, MPH 1 ; Mikhail Hoskins, MPH 1 ; Larissa Huber, PhD 1 ( View author affiliations )

Suggested citation for this article: Blackwelder A, Hoskins M, Huber L. Effect of Inadequate Sleep on Frequent Mental Distress. Prev Chronic Dis 2021;18:200573. DOI: http://dx.doi.org/10.5888/pcd18.200573 external icon .

PEER REVIEWED

Introduction

Acknowledgments, author information.

What is already known on this topic?

One-third of US adults report that they sleep less than the recommended amount, and approximately 20% have received a diagnosis of a mental illness. The link between inadequate sleep and mental distress has been viewed historically as a symptom–disease association with sleep inadequacies deriving from preexisting mental distress.

What is added by this report?

We examined the association between inadequate sleep and frequent mental distress in a diverse, population-based sample of adults aged 18 to 65.

What are the implications for public health practice?

By identifying the correlation between inadequate sleep and frequent mental distress we can better understand this relationship as a risk factor instead of a symptom–disease relationship.

One-third of US adults report sleeping less than the recommended amount, and approximately 20% live with a mental illness. The objective of our study was to examine the association between inadequate sleep and frequent mental distress in a population-based sample of US adults.

We conducted a cross-sectional study by using 2018 Behavioral Risk Factor Surveillance System (BRFSS) data that included 273,695 US adults aged 18 to 64. Inadequate sleep was defined as 6 hours or less in a given night, and frequent mental distress was defined as self-reporting 14 days of mental health status as “not good” within the last month. We used weighted logistic regression to calculate odds ratios (ORs) and 95% CIs.

Thirteen percent of study participants experienced inadequate sleep, and 14.1% experienced frequent mental distress. Participants who averaged 6 hours or less of sleep per night were about 2.5 times more likely to have frequent mental distress when controlling for confounders (OR, 2.52; 95% CI, 2.32–2.73) than those who slept more than 6 hours.

Inadequate sleep was associated with significantly increased odds of frequent mental distress. Our findings suggest that further research is necessary to evaluate the temporal relationship between inadequate sleep and frequent mental distress.

Poor mental health is common in the US. Nearly 1 in 5 US adults live with mental illness (1). Furthermore, an estimated 50% of all Americans will be diagnosed with a mental illness or disorder at some point in their life (1,2). Mental health illness includes many different conditions and symptoms, such as anxiety, depression, stress, and other psychological illnesses. Moderate and severe mental disorders that need psychological treatment require regular visits to a health care provider, thus lowering workplace productivity (3). Furthermore, depression, schizophrenia, and bipolar disorder are risk factors for coronary heart disease, hypertension, diabetes, dyslipidemia, metabolic syndrome, obesity, stroke, and substance abuse disorders (3,4). Depression and anxiety alone cost over $1 trillion annually for medications, outpatient and primary care visits, and inpatient care (3,4).

The Centers for Disease Control and Prevention (CDC) and the American Academy of Sleep Medicine emphasize the importance of an adequate night’s sleep, which is defined as 7 or more hours per night with no upper limit (5,6). Anything less than this amount may lead to the development of various chronic diseases. More than one-third of the US population does not get adequate sleep (5). The people that most often get inadequate sleep are Native Hawaiian/Pacific Islander people, non-Hispanic Black people, and multiracial people (6). Those who most often get adequate sleep are married people and people with a college degree or more.

Studies have demonstrated an association between inadequate sleep and frequent mental distress (7,8), and sleep deprivation causes substantial negative health outcomes (4). The link between inadequate sleep and frequent mental distress has been viewed historically as a symptom–disease association with sleep inadequacies deriving from preexisting mental distress (9). However, at least 1 study researched the opposite hypothesis, evaluating frequent mental distress leading to a lack of sleep (10). These studies found that in certain populations, risk for inadequate sleep is increased if a person is experiencing depression or anxiety. Most current research on the potential association between inadequate sleep and mental distress focuses on a specifically defined group, such as college students, nurses, or people with diagnosed sleep disorders (9,11,12). Furthermore, current research focuses primarily on diagnosed mental health disorders (4,8). The purpose of our study was to examine the association between inadequate sleep and frequent mental distress in a diverse, population-based sample of adults aged 18 to 64.

We used 2018 Behavioral Risk Factor Surveillance System (BRFSS) data to analyze the association between sleep and self-reported mental distress. BRFSS is a cross-sectional survey that uses a standardized questionnaire to collect prevalence data regarding risk behaviors and preventive behavioral health practices among adult US residents (13). Participants self-report information during telephone interviews conducted by trained personnel. Interviewers make calls for interviews 7 days a week during the day and evening (14). BRFSS raw data, which are collected during the survey, are submitted to CDC each year for processing and are made available to researchers the following calendar year through annual reports available on the CDC website (https://www.cdc.gov/brfss/index.html). BRFSS is conducted in all 50 states, the District of Columbia, and 3 US territories. Noninstitutionalized adults aged 18 or older are eligible to complete the BRFSS survey (15). Over 400,000 adults are interviewed each year. The land line response rate for BRFSS is 53.3%, and the cellular telephone response rate is 43.3% (16). A total of 437,436 people completed the BRFSS survey in 2018. After excluding those participants who were not aged 18 to 64 (n = 160,115) and those who did not have information on frequent mental distress (n = 3,626), 273,695 survey participants remained for analysis.

The survey question used to identify the exposure variable of interest reads, “On average, how many hours of sleep do you get in a 24-hour period?” Participants were asked to provide a value from 1 to 24 hours. Sleep values were recorded as whole numbers, and values greater than 30 minutes were rounded up per BRFSS coding. Inadequate sleep was defined as 6 hours or less of sleep in a given 24-hour period, which is 1 hour less than the minimum recommended number of hours of sleep for adults (5,17,18). We chose this definition of inadequate sleep because the rounding done by BRFSS personnel could have created situations where people who actually had inadequate sleep were classified as having the minimum recommended hours. Furthermore, previous studies also defined inadequate sleep as 6 hours or less per night (5,17). Thus, using this same definition allows for better comparison across studies.

The survey question selected to identify the outcome of interest, frequent mental distress, was “Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?” (13). The responses were recorded as the number of days (ie, 1–30 days). Frequent mental distress was considered present if 14 or more days were reported as mental health “not good” in the previous month. This definition was based on recommendations from previous studies on frequent mental distress (19) and guidance from the American Psychiatric Association on the necessary duration of symptoms to diagnose depression (20).

Potential confounders were selected on the basis of prior research and included age, race/ethnicity, sex, current smoking status, binge drinking, marital status, education, income, and loss of insurance (21–28). Study participants were asked the following question regarding alcohol consumption: “One drink is equivalent to a 12-ounce beer, a 5-ounce glass of wine, or a drink with one shot of liquor. During the past 30 days, on the days when you drank, about how many drinks did you drink on the average?” (13). We used this information to create a dichotomous binge drinking variable; in the past 30 days on the days when they drank, men who had 5 or more drinks and women who had 4 or more drinks were classified as binge drinkers (29). To assess current smoking, participants were asked, “Do you now smoke cigarettes every day, some days, or not at all?” Ultimately, this variable was dichotomized into people who smoked every day or some days and people who did not smoke at all.

A primary univariate analysis was performed to obtain frequencies and weighted percentages of the exposure, outcome, and potential confounders at the P < .20 level. We used logistic regression to assess the association between self-reported sleep and frequent mental distress and to identify other risk factors for frequent mental distress. Multivariate logistic regression was used to obtain the odds ratio for the association between inadequate sleep and frequent mental distress while adjusting for potential confounders. A backward elimination approach was used to retain confounders at the P < .05 level. Ultimately, age, marital status, income, smoking status, and education level were identified as confounders. Because of the complex sampling design used by BRFSS, weighted analyses were performed using Stata version 15.1 (StataCorp LLC).

Most study participants were non-Hispanic White (59.1%), female (50.2%), married (49.3%), and had at least a high school diploma (87.4%) ( Table 1 ). Most participants reported that they had adequate nightly sleep (87.0%), and 14.1% experienced frequent mental distress (≥14 d/mo). Mean hours of sleep per 24-hour period were similar across age groups (18–34: 6.9 h; 35–49: 6.8 h; 50–64: 6.9 h).

People with inadequate sleep had nearly a threefold increased odds of frequent mental distress compared with those who had adequate sleep, and this finding was significant (OR, 2.67; 95% CI, 2.51–2.84) ( Table 2 ). Participants who were divorced/separated/widowed had twice the odds of frequent mental distress compared with study participants who were married (OR, 2.14; 95% CI, 2.01–2.29). There was a dose–response association between education level and frequent mental distress. As education levels decreased, the odds of frequent mental distress increased (high school diploma, GED, associate degree, or no university degree: OR, 2.06; 95% CI, 1.95–2.18; no high school diploma: OR, 3.35; 95% CI, 3.06–3.67).

After adjustment for age, marital status, income, smoking status, and education level, the inadequate sleep–frequent mental distress association was attenuated but remained significant. Participants with inadequate sleep had nearly 2.5 times increased odds of frequent mental distress compared with those with adequate sleep (OR, 2.52; 95% CI, 2.32–2.73; P < .001).

In our population-based study of US adults, inadequate sleep was associated with significantly increased odds of mental distress after controlling for confounding variables. Our findings align with previous research with the caveat that prior research has often looked at sleep as the outcome (8). Because our study used a large sample of adults and excluded only those who did not respond to qualifying questions, our results further confirm a potential association between inadequate sleep and mental health in a broader population.

Our study findings suggest an association between inadequate sleep and frequent mental distress. Because BRFSS is a cross-sectional study design, determining the true temporal sequence is not possible. Previous research has not closely examined the association between inadequate sleep as a risk factor for frequent mental distress. However, inadequate sleep has been linked to poor biological measures, including hypertension, anemia, and dyslipidemia (7). Low amounts of sleep and the attributed chronic conditions could possibly have a negative impact on depressive symptoms (7).

Limitations to this study include the potential for nondifferential misclassification of both the exposure and outcome variables; failure to recall information or misunderstanding questions asked possibly resulted in inaccurate responses. Also, self-reporting of mental distress is subjective. People may differ in their self-reporting and interpretation of what is “not good” for mental health. Furthermore, the use of a telephone interview could possibly influence self-reporting of mental distress. However, research demonstrates that the reporting of mental health information does not differ between face-to-face interviews and telephone interviews (31,32). In some instances, telephone interviews reduced embarrassment to participants when discussing mental health. We used a cut point of 6 hours to determine inadequate sleep rather than 7 hours, which is the minimum recommended hours of sleep for adults. We reran our model using 7 hours as the cut point for inadequate sleep, and our findings were of similar magnitude and remained significant. Given our aforementioned concerns that the rounding done by BRFSS personnel as it relates to the sleep duration variable could have incorrectly classified participants, we ultimately decided to retain our 6-hour cut point. In addition, because this definition of inadequate sleep has been used by others, it allows for better comparison across studies (5,17). Regardless, any nondifferential misclassification in our study would likely bias the results toward the null. Because we used a secondary data source, we were limited to the questions asked in the BRFSS survey. Thus, confounding by variables not measured in the BRFSS was possible. Selection bias is possible given that the response rate for BRFSS was 53.3% for landline responses and 43.3% for cellular telephone responses (15). The extent to which participation in BRFSS would be related to inadequate sleep and frequent mental distress is unknown; however, BRFSS is widely considered to be a valid and reliable measure of mental health and health behaviors (33).

Our study had numerous strengths. Information bias is unlikely because of the use of trained interviewers and standardization of interview methods. The exposure question may capture sleep data with more precision because it asks how many hours the participant slept in a 24-hour period. Thus, naps are included in the reporting. In addition, the wording enables the sleeping habits of people who do not work traditional day-time jobs to be more accurately reported. Establishing hours slept in a 24-hour period is consistent with prior research, giving a continuity of comparison across studies (4,7,8). Finally, our study included all participants aged 18 to 64 and did not focus only on those with preexisting conditions or on populations at risk for inadequate sleep (4,8). Thus, given the large sample size and the complex sampling design used by BRFSS, our findings are likely generalizable to adults living in the US.

Because one-third of the US population is not attaining adequate sleep, our findings warrant further research to expand on the true association between inadequate sleep and frequent mental distress (5). Thorough clinical assessment of sleep by age and length and quality of sleep could strengthen the measurement of the exposure. More thorough follow-up questions related to mental distress, including clinical diagnoses, may allow for a clearer evaluation of the temporal sequence between inadequate sleep and mental distress.

No copyrighted material was used in this article.

Corresponding Author: Amanda Blackwelder, MPH, UNC Charlotte, Department of Public Health Sciences, Charlotte, NC 28223. Telephone: 704-687-8719. Email: [email protected] .

Author Affiliations: 1 University of North Carolina at Charlotte, Charlotte, North Carolina.

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a Some totals may not equal the total number of participants because of missing data. b Inadequate average sleep was defined as 6 hours or less in a 24-hour period. c In the past 30 days on the days when they drank, men who had 5 or more drinks and women who had 4 or more drinks.

a Inadequate average sleep was defined as 6 hours or less in a 24-hour period. b Significant at P < .20. c In the past 30 days on the days when they drank, men who had 5 or more drinks and women who had 4 or more drinks.

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• < Back To Sleep Deprivation and Deficiency
• How Sleep Affects Your Health
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MORE INFORMATION

Sleep Deprivation and Deficiency How Sleep Affects Your Health

Language switcher.

Getting enough quality sleep at the right times can help protect your mental health, physical health, quality of life, and safety.

How do I know if I’m not getting enough sleep?

Sleep deficiency can cause you to feel very tired during the day. You may not feel refreshed and alert when you wake up. Sleep deficiency also can interfere with work, school, driving, and social functioning.

How sleepy you feel during the day can help you figure out whether you're having symptoms of problem sleepiness.

You might be sleep deficient if you often feel like you could doze off while:

• Sitting and reading or watching TV
• Sitting still in a public place, such as a movie theater, meeting, or classroom
• Riding in a car for an hour without stopping
• Sitting and talking to someone
• Sitting quietly after lunch
• Sitting in traffic for a few minutes

Sleep deficiency can cause problems with learning, focusing, and reacting. You may have trouble making decisions, solving problems, remembering things, managing your emotions and behavior, and coping with change. You may take longer to finish tasks, have a slower reaction time, and make more mistakes.

Symptoms in children

The symptoms of sleep deficiency may differ between children and adults. Children who are sleep deficient might be overly active and have problems paying attention. They also might misbehave, and their school performance can suffer.

Sleep-deficient children may feel angry and impulsive, have mood swings, feel sad or depressed, or lack motivation.

Sleep and your health

The way you feel while you're awake depends in part on what happens while you're sleeping. During sleep, your body is working to support healthy brain function and support your physical health. In children and teens, sleep also helps support growth and development.

The damage from sleep deficiency can happen in an instant (such as a car crash), or it can harm you over time. For example, ongoing sleep deficiency can raise your risk of some chronic health problems. It also can affect how well you think, react, work, learn, and get along with others.

Mental health benefits

Sleep helps your brain work properly. While you're sleeping, your brain is getting ready for the next day. It's forming new pathways to help you learn and remember information.

Studies show that a good night's sleep improves learning and problem-solving skills. Sleep also helps you pay attention, make decisions, and be creative.

Studies also show that sleep deficiency changes activity in some parts of the brain. If you're sleep deficient, you may have trouble making decisions, solving problems, controlling your emotions and behavior, and coping with change. Sleep deficiency has also been linked to depression, suicide, and risk-taking behavior.

Children and teens who are sleep deficient may have problems getting along with others. They may feel angry and impulsive, have mood swings, feel sad or depressed, or lack motivation. They also may have problems paying attention, and they may get lower grades and feel stressed.

Physical health benefits

Sleep plays an important role in your physical health.

Good-quality sleep:

• Heals and repairs your heart and blood vessels.
• Helps support a healthy balance of the hormones that make you feel hungry (ghrelin) or full (leptin): When you don't get enough sleep, your level of ghrelin goes up and your level of leptin goes down. This makes you feel hungrier than when you're well-rested.
• Affects how your body reacts to insulin: Insulin is the hormone that controls your blood glucose (sugar) level. Sleep deficiency results in a higher-than-normal blood sugar level, which may raise your risk of diabetes.
• Supports healthy growth and development: Deep sleep triggers the body to release the hormone that promotes normal growth in children and teens. This hormone also boosts muscle mass and helps repair cells and tissues in children, teens, and adults. Sleep also plays a role in puberty and fertility.
• Affects your body’s ability to fight germs and sickness: Ongoing sleep deficiency can change the way your body’s natural defense against germs and sickness responds. For example, if you're sleep deficient, you may have trouble fighting common infections.
• Decreases   your risk of health problems, including heart disease, high blood pressure, obesity, and stroke.

Research for Your Health

NHLBI-funded research found that adults who regularly get 7-8 hours of sleep a night have a lower risk of obesity and high blood pressure. Other NHLBI-funded research found that untreated sleep disorders rase the risk for heart problems and problems during pregnancy, including high blood pressure and diabetes.

Daytime performance and safety

Getting enough quality sleep at the right times helps you function well throughout the day. People who are sleep deficient are less productive at work and school. They take longer to finish tasks, have a slower reaction time, and make more mistakes.

After several nights of losing sleep — even a loss of just 1 to 2 hours per night — your ability to function suffers as if you haven't slept at all for a day or two.

Lack of sleep also may lead to microsleep. Microsleep refers to brief moments of sleep that happen when you're normally awake.

You can't control microsleep, and you might not be aware of it. For example, have you ever driven somewhere and then not remembered part of the trip? If so, you may have experienced microsleep.

Even if you're not driving, microsleep can affect how you function. If you're listening to a lecture, for example, you might miss some of the information or feel like you don't understand the point. You may have slept through part of the lecture and not realized it.

Some people aren't aware of the risks of sleep deficiency. In fact, they may not even realize that they're sleep deficient. Even with limited or poor-quality sleep, they may still think they can function well.

For example, sleepy drivers may feel able to drive. Yet studies show that sleep deficiency harms your driving ability as much or more than being drunk. It's estimated that driver sleepiness is a factor in about 100,000 car accidents each year, resulting in about 1,500 deaths.

Drivers aren't the only ones affected by sleep deficiency. It can affect people in all lines of work, including healthcare workers, pilots, students, lawyers, mechanics, and assembly line workers.

Lung Health Basics: Sleep

People with lung disease often have  trouble sleeping. Sleep is critical to overall health, so take the first step to sleeping better: learn these sleep terms, and find out about treatments that can help with sleep apnea.

ORIGINAL RESEARCH article

Effect of sleep deprivation on the working memory-related n2-p3 components of the event-related potential waveform.

• 1 School of Psychology, Beijing Sport University, Beijing, China
• 2 Institute of Psychology, Chinese Academy of Sciences, Beijing, China
• 3 Naval Special Forces Recuperation Center, Qingdao, China

Working memory is very sensitive to acute sleep deprivation, and many studies focus on the brain areas or network activities of working memory after sleep deprivation. However, little is known about event-related potential (ERP)-related changes in working memory after sleep loss. The purpose of this research was to explore the effects of 36 h of total sleep deprivation (TSD) on working memory through ERPs. Sixteen healthy college students performed working memory tasks while rested and after 36 h of TSD, and electroencephalography (EEG) data were simultaneously recorded while the subjects completed working memory tasks that included different types of stimulus materials. ERP data were statistically analyzed using repeated measurements analysis of variance to observe the changes in the working memory-related N2-P3 components. Compared with baseline before TSD, the amplitude of N2-P3 components related to working memory decreased, and the latency was prolonged after TSD. However, the increased amplitude of the P2 wave and the prolonged latency were found after 36 h of TSD. Thus, TSD can impair working memory capacity, which is characterized by lower amplitude and prolonged latency.

Introduction

With the progress of society and changes in work rhythm, an increasing number of people are suffering from sleep deprivation. Sleep deprivation not only damages the physical and mental health of the individual but also seriously affects work performance, causing work errors and even accidents. Therefore, understanding the mechanism of sleep deprivation that affects cognitive function is of great significance for effectively preventing the effects of sleep deprivation.

Previous studies have revealed that sleep deprivation can cause a series of changes in an individual’s mood, cognitive ability, work performance, and immune function ( Choo et al., 2005 ). The lack of sleep disrupts body circulation and affects the cognitive and emotional abilities of individuals ( Raymond, 1988 ). Several studies have revealed that sleep deprivation impairs response inhibition ( Harrison and Horne, 1998 ; Muzur et al., 2002 ; Jennings et al., 2003 ). For example, after 36 h of sleep deprivation, the individual’s ability to suppress negative stimuli decreased ( Chuah et al., 2006 ). Neuroimaging studies have suggested that sleep deprivation reduces an individual’s low-level of visual processing ability ( Anderson and Platten, 2011 ; Ning et al., 2014 ). In addition, sleep deprivation impairs the hippocampus and could affect memory by destroying synaptic plasticity ( Cote et al., 2014 ). Thomas (2003) has indicated that lack of sleep reduced cerebral blood flow and metabolic rate in the thalamus, prefrontal cortex, and parietal cortex ( Géraldine et al., 2005 ). Jarraya and colleagues found that partial sleep deprivation significantly affected neuropsychological functions such as verbal instant memory, attention, and alertness ( Thomas, 2003 ). Furthermore, some studies have revealed that the cumulative effects of partial sleep deprivation could severely impair cognitive function and behavior ( Van Dongen, 2004 ; Scott et al., 2006 ; Jarraya et al., 2013 ).

Working memory is a system that used to store and process information and which is a cognitive function with limited capacity ( Bartel et al., 2004 ). Moreover, the information stored in the working memory system can be changed from short-term memory to long-term memory through retelling and other memory methods. Working memory is the transition between short-term and long-term memory systems, which is very pivotal in human message processing ( Miyake and Shah, 1999 ). It provides a temporary storage space and the resources needed to process information, such as voice understanding, reasoning, and learning. Sleep deprivation has been shown to affect working memory first.

Previous studies have used the n-back working memory paradigm in participants who underwent sleep deprivation and found that lack of sleep induces a decrease in metabolic activity in the brain’s regional network, which is mainly effected information processing and reaction inhibition ( Baddeley, 2000 ; Zhang et al., 2019 ). Impaired working memory after sleep deprivation is related to the activation of the default network in tasks ( Chee and Chuah, 2008 ), which may be related to the important role of the thalamus in cortical alertness. For instance, sleep deprivation increased the connection between the hippocampus, thalamus, and default network, which was often accompanied by higher subjective drowsiness and worse performance of working memory ( Lei et al., 2015 ; Li et al., 2016 ). Studies on sleep deprivation identi?ed that increased latency and reduced amplitude of the P3 component were associated with prolonged sobriety ( Morris et al., 1992 ; Jones and Harrison, 2001 ; Panjwani et al., 2010 ). The decrease in the P3 wave might reflect a decrease in participants’ attention and a reduction in the discernment of target stimuli ( Koslowsky and Babkoff, 1992 ).

However, few studies have provided electrophysiological evidence for impaired working memory after sleep deprivation. The n-back task is considered a common method to assess working memory ( Owen et al., 2005 ; Jaeggi et al., 2010 ). Zhang et al. designed a two-back pronunciation working memory task to explore the decreased message alternate of working memory during sleep deprivation, but few studies have used different types of working memory tasks in a single experiment. In the present study, we designed different types of working memory tasks (pronunciation working memory, spatial working memory, and object working memory) to explore the impairment of cognitive function by TSD and recorded participant EEG data at 2 time points (baseline and 36 h-TSD). All of the tasks adopted a 2-back paradigm. This study evaluated the changes in the N2-P3 wave related to working memory during TSD and analyzed the temporal characteristics of the effects of sleep deprivation on working memory. Our findings provide experimental evidence for the effects of sleep deprivation on cognitive function.

Materials and Methods

Participants.

Sixteen young, healthy, right-handed male students participated in this study. We recruited participants by advertising on the campus. The participants all had good sleep habits (PSQI<5). All participants were aged between 21 and 28 years with an average age of 23 years, and none of the participants had any mental or physical illness. All participants had normal vision or corrected vision above 1.0 and intelligence scores >110 on the Raven Test. Before the experiment, the experimenter explained the procedure and points for attention to the participants to make sure they were familiar with the method and procedure. In the 2 weeks before the experiment, the participants slept regularly for 7–9 h per day, without smoking, drinking coffee, drinking alcohol, or consuming any medication for 2 days before the experiment. Before the experiment, all the participants provided written informed consent. The experimental scheme was approved by the Ethics Committee of the Fourth Military Medical University and Beihang University.

Experimental Design

Three types of working memory tasks were presented to all participants. They were two-back pronunciation working memory task (see Figure 1 ), two-back spatial working memory task (see Figure 2 ), and two-back object working memory task (see Figure 3 ). The stimulate materials of the tasks were 15 case-insensitive English letters that excluding the ones with similar letters, such as L/l, M/m; small black squares; and 12 geometric figures, respectively. All of the materials were shown in black color on a white background, with an approximate visual angle of 1.5° × 1.5° (width: 2.0 cm, height: 2.0 cm) subtending. 122 trails were comprised in each task and, in each trail, the target stimulus was presented for 400 ms two trails after the presentation of objective stimulus, with the 1,600 ms stimulus onset asynchrony time (SOA) that was marked by a white “+.”The participants were asked to click the left mouse button when the target and objective stimulus were the same (“matching”), while click the right mouse button when they were not (“mismatching”). The matching or not condition were presented in a pseudorandom order with a 1:1 ratio.

Figure 1. Schematic diagram of the pronunciation working memory task.

Figure 2. Schematic diagram of the spatial working memory task.

Figure 3. Schematic diagram of the object working memory task.

Experimental Procedures

Before the experiment, the participants were instructed of the experimental task. They were informed to practice the three types of working memory tasks until an accuracy rate of 90% was achieved. Participants visited the laboratory the day before the experiment and slept in the laboratory that night. The two partner participants performed the experiments at the same time. Three types of working memory tasks were performed at 7:30 am to 8:30 am the next morning with simultaneous electroencephalogram (EEG) recording (baseline). The second EEG recording (36 h-TSD) was conducted after a 36-h period during which the participants were not allowed to sleep. During the entire experiment time, central inhibition and stimulant drugs were forbidden. The participants were accompanied, observed and reminded by nursing staff in order to keep them awake throughout the TSD session.

EEG Recordings

A continuous scalp EEG was recorded using electrode caps placed in 64 locations using the 10–20 system with a SynAmps2 amplifier. The bilateral mastoids (A1 and A2) were used for reference, and the forehead was used as a ground. EEGs were recorded at 1,000 Hz, and the impedance of all channels was maintained below 5 kΩ. Four additional electrodes were placed above and below the right and left eyes to record a bipolar vertical and horizontal electrooculogram.

Data Analysis of Behavioral Experiments

Due to technical errors, two cases were deleted while other 14 cases were included in the following statistical analysis. Behavioral data included the mean reaction time, correct rate and the correct number per unit time. Behavioral data in baseline and 36 h-TSD states were recorded for analyzing. The analyses were run by IBM SPSS (V22.2), where the repeated measures analysis of variance (ANOVA) method with Greenhouse-Geisser was Bonferroni post-hoc analysis were launched. The statistical results were presented as the mean and standard deviation (SD).

EEG Data Analysis

Scan 4.3 program was used to analyze the EEG data, where the EEG artifacts of the eye movement were corrected by ocular artifact reduction method. Epochs ranging from -100 to 800 ms of the continuous EEG data were extracted and filtered by a bandpass filter from 0.5 to 30 Hz with the frequency slope of 24 dB/oct. The trials in which the voltage exceeded ± 100 μV were rejected and the baseline was corrected to a mean amplitude of 100 ms. The EEG components were averaged and calculated with only the corrected responses. The ERP components P2 (100–250 ms), N2 (150–350 ms), and P3 (250–450 ms) of the stimulus trials were identified and quantified. The grand-average peak amplitudes and latencies of the N2 and P3 components were calculated separately at F3, Fz, F4, C3, Cz, C4, P3, Pz, and P4, and the P2 component was calculated at F3, Fz, F4, C3, Cz, and C4 ( Casement et al., 2006 ; Verweij et al., 2014 ).

Repeated measures ANOVA was employed for all ERP results. The main effects and the interactions between sleep states (baseline and 36 h-TSD), tasks (pronunciation working memory, spatial working memory, and object working memory), regions (frontal, central, and parietal; the P2 component was analyzed only on the frontal and central regions), and sites (left, middle, and right) were statistically analyzed employing repeated measures ANOVA, which included Greenhouse-Geisser corrections for non-sphericity and Bonferroni post-hoc tests.

Behavioral Performance

The results of the behavioral experiments are shown in Table 1 . The mean reaction time was longer in the 36 h-TSD state than at baseline with a trend to increase [ F (1, 13) = 2.563, P = 0.133] but without significant differences. ANOVA revealed that the correct rate of the task was significantly different between the baseline and 36 h-TSD [ F (1, 13) = 10.153, P = 0.007]. The correct number per unit time showed a significant main effect of time during 36 h-TSD [ F (1, 13) = 7.010, P = 0.020].

Table 1. Performance data (mean ± SD) on the 2-back task at baseline and after 36 h-TSD.

Compared to the baseline, a significant decrease was observed in the amplitude of P3 [ F ( 1, 13 ) = 12.692, P = 0.003], and a significant increase was observed in the amplitude of P2 [ F ( 1, 13 ) = 69.357, P = 0.000] after TSD. Although the N2 amplitude decreased after 36 h of TSD, the difference did not reach statistical significance ( Table 2 ).

Table 2. Grand-average peak amplitude of the P2, N2, and P3 components in the correct response condition across multiple electrode sites at baseline and after 36 h-TSD.

Significant main effects of regions and sites on the P2 amplitude were found [ F ( 1, 13 ) = 15.889, P = 0.002; F (2, 26 ) = 26.190, P = 0.000, respectively] under the TSD condition. During TSD, the maximum amplitude of P2 appeared in the frontal region ( Figure 4 ). In addition, the differences in P2 amplitudes in different regions (frontal vs. central) were more significant [ F ( 2 , 26 ) = 8.996, P = 0.001] in the bilateral electrodes (left: P = 0.001; right: P = 0.000) than in the middle electrodes ( Figure 4 ). A significant main effect of the region [ F (2, 26) = 4.137, P = 0.050] and site [ F (2, 26) = 7.46, P = 0.003] on N2 revealed that the N2 amplitude was more negative in the frontal than in the central region ( P = 0.008, Figure 4 ) and was smaller on the right than on the left side ( P = 0.011, Figures 5A2,B2 ). A main effect of site on the P3 amplitude was observed [ F (2, 26) = 5.363, P = 0.023]. The amplitude of P3 was more positive in the middle than on the left side ( P = 0.009, Figure 4 ). A significant interaction effect between time and region was observed for the P3 amplitude [ F (2, 26) = 7.375, P = 0.012]. During TSD, the reduction in P3 amplitude was more significant in the frontal and central regions than in the parietal region ( P = 0.005; P = 0.003) ( Figure 5C3 ). No other main effects or interaction effects reached statistical significance.

Figure 4. ERP amplitude at baseline and 36 h-TSD for the correct response condition for the working memory task. The channels are ordered from left to right and top to bottom as follows: F3, Fz, F4, C3, Cz, C4, P3, Pz, and P4. Compared to the baseline, the latencies of the N2-P3 components were prolonged, and the amplitudes of N2-P3 were decreased after 36 h-TSD.

Figure 5. Topographic map of the correct response in the working memory task in different sleep conditions (A1–C3) . (A1) P2, 100–250 ms, at baseline. (A2) N2, 200–300 ms, at baseline. (A3) P3, 300–400 ms, at baseline. (B1) P2, 100–250 ms, at 36 h-TSD. (B2) N2, 200–300 ms, for 36 h-TSD. (B3) P3, 300–400 ms, for 36 h-TSD. (C1) P2, 100–250 ms, 36 h-TSD with baseline subtracted. (C2) N2, 200–300 ms, 36 h-TSD with baseline subtracted. (C3) P3, 300–400 ms, 36 h-TSD with baseline subtracted.

The latencies of N2 [ F (1, 13 ) = 6.673, P = 0.023] and P2 [ F ( 1, 13 ) = 8.439, P = 0.012] were significantly prolonged after TSD. Although the P3 latency was prolonged after 36 h of TSD, the difference did not reach statistical significance ( Table 3 ).

Table 3. Grand-average peak latency of the P2, N2, and P3 components in the correct response condition across multiple electrode sites at baseline and after 36 h-TSD.

The significant main effect of region on N2 [ F (2, 26 ) = 13.789, P = 0.001] and P3 [ F (2, 26 ) = 45.226, P = 0.000] revealed that the latency of the N2-P3 components was shorter in the parietal region than in the frontal region ( P = 0.002; P = 0.000) and central region ( P = 0.000; P = 0.000) ( Figure 4 ). The latency of the P3 wave was significantly longer on the left side than on the right side [ F (2, 26) = 8.812, P = 0.001] ( Figure 4 ).

No other main effects or interaction effects reached statistical significance.

The N2, P2, and P3 amplitudes and latencies that were elicited at the nine electrode sites are presented in Figure 4 . The topographic map of the correct response in the working memory task in different sleep conditions (baseline, 36 h-TSD and the difference between the two conditions) is presented in Figure 5 .

In this study, we reported the influences of 36 h sleep deprivation on working memory, combining behavioral data in two sleep states (baseline and 36 h-TSD) with contemporaneous EEG recordings. The analysis of the results indicated that the changes in the behavioral data in accordance with impaired working memory after 36 h TSD: an increase in the mean reaction time of the cognitive tasks and a decrease in accuracy.

Sleep deprivation impaired the individual’s control of attentional resources. Although individuals tried to maintain wakefulness and work performance, including the reaction time and correct rate, during sleep deprivation, the information processing capacity of their working memory was still affected because of the decrease in the speed of processing information ( Casement et al., 2006 ; Wiggins et al., 2018 ). In this study, the N2 and P3 waves related to working memory were measured to show an increase in latency and a decrease in amplitude after sleep deprivation compared with the baseline readings. Studies have demonstrated that sleep deprivation leads to a continuous decline in attention, and the phenomenon of decreased P3 amplitude indicates that individuals’ top-down control of cognition gradually collapses. Sleep deprivation has a more adverse effect on cognitive functions, especially those that depend on mental or cognitions ( Kusztor et al., 2019 ).

The P3 component reflects the deployment of attention resources, and the latency of P3 is widely seen as the time window for stimulus categorization and evaluation. The decrease in the P3 wave amplitude also confirmed that the decision-making in the matching response after TSD had been damaged to a certain extent ( Gosselin et al., 2005 ). Studies have suggested that sleep deprivation can affect the information processing stage of working memory. In this study, the performance indicators also supported the conclusion that the response time to the target stimulus was increased and that the latency of the P3 wave was prolonged ( Cote et al., 2008 ). It was speculated that the effect of sleep deprivation on P3 components might also take place because of the failure to respond to information alter, which is consistent with previous conclusions that the P3 components are related to the updating of working memory content ( Donchin and Fabiani, 1991 ).

Previous studies have considered the N2 component as an electrophysiological index reflecting the ability of the individual to suppress the response ( Kreusch et al., 2014 ). After sleep deprivation, the prolonged latency of the NoGo-N2 component indicates that the individual’s ability to suppress the response is impaired ( Jin et al., 2015 ). The decreased amplitude and prolonged latency of the N2 component related to pronunciation working memory after sleep deprivation reveals that sleep deprivation impairs the information processing of pronunciation working memory ( Zhang et al., 2019 ). The N2 component is generally thought to reflect the brain’s selective attention and processing of emotional stimuli or signals ( Schacht et al., 2008 ) and is an endogenous component related to an individual’s mental state, attention, and degree of attention. In this study, we found that the latency of the N2 component was significantly prolonged, but the amplitude showed only a downward trend. According to previous studies, prolonged N2 latency reflected an increase in response time after sleep restriction ( Zhang et al., 2014 ). However, the finding that N2 amplitude was not significantly altered may have been due to cerebral compensatory responses ( Drummond and Brown, 2001 ). In the case of limited cognitive resources, there was a compensation mechanism to restore impaired cognitive function ( Jin et al., 2015 ).

According to the scalp topography, the changes in the N2-P3 components related to sleep deprivation are more obvious in the frontal area. Frontoparietal control (FPC) plays an important role in cognitive control. Studies have shown that FPC can bypass top-down cognitive control, enabling individuals to focus on information related to the target while suppressing information that is not related to the target ( Smallwood et al., 2011 ; Wen et al., 2013 ). FPC is important for information retention and information processing in working memory, and the degree of activation of FPC after sleep deprivation was reduced compared to a normal sleep group ( Ma et al., 2014 ). Although the EEG results did not reflect the changes in specific brain regions in detail, it intuitively reflected the effect of TSD on the retention and processing of working memory information.

Although the exact cognitive process that the P2 component underlies is still widely debated, as a broad definition, the P2 component reflects the process of attention and visual processing and is generally considered to be related to selective attention and working memory, reflecting the early judgment of the perceptual process ( Saito et al., 2001 ). In this study, we found a significant increase in the P2 wave amplitude after sleep deprivation. Studies have reported that P2 waves which might be a part of the early cognitive matching system for message processing and may compare sensory inputs to stored memory ( Freunberger et al., 2007 ) are sensitive to alterations in mission attention and working memory demands ( Smith et al., 2002 ). Functional compensation is one of the unique functions of the human brain and an important factor for maintaining cognitive function. Excessive activation of the dorsolateral prefrontal cortex (DLPFC) after sleep deprivation indicates that, as brain resources decrease, the DLPFC appears to have a compensatory function ( Drummond et al., 2004 ; Choo et al., 2005 ). Therefore, we speculate that the significant increase in P2 amplitude observed in this study may be due to functional compensation in which individuals appear to maintain normal cognitive function after sleep deprivation. Although a large number of studies have used ERP technology to explore the effect of sleep deprivation on cognitive functions, early components such as N1 and P2 have not been systematically studied, and the results are inconsistent ( Evans and Federmeier, 2007 ; Wiggins et al., 2018 ; Zhang et al., 2019 ). There are few researches explore the change of P2 component during sleep deprivation ( Mograss et al., 2009 ). Therefore, the effects of sleep deprivation on early components of ERP, such as P2, still need to be further studied and explored.

In this experiment, we used the 2-back model to design pronunciation, spatial, and object working memory tasks and examined the impairment of working memory after 36 h of TSD. Compared with previous studies that focused only on the effects of sleep deprivation on a specific type of information, such as pronunciation working memory, or specific cognitive function, such as response inhibition, we have considered the contents of the working memory model and comprehensively analyzed the effects of sleep deprivation on working memory.

However, the study has some limitations. First, we only used the 2-back task and failed to compare the performance of the participants in working memory tasks of different difficulties. Therefore, there are limitations in explaining and inferring changes in workload. Second, only male volunteers were used in the study, and the conclusions need to be assessed when extending them to female volunteers. Due to the limited number of participants, we found only that the amplitude of the N2 wave had a downward trend and that the P3 wave latency had a prolonged trend. Stable results might be obtained after increasing the number of participants. Third, combining our procedure with fMRI for working memory may facilitate further interpretation of the results. Previous studies have shown that circadian biorhythms affect behavioral performance, and there are individual differences ( Montplaisir, 1981 ; Lavie, 2001 ). We did not record the EEG data at the same time point in this experimental, so the influence of circadian biorhythms on the test results cannot be completely ruled out.

This research showed that working memory ability was impaired after TSD and that this damage was not associated with the stimulus content of working memory. The lack of sleep reduced the quality of the information stored in memory, which might occur with the degenerative process of attention ( Ratcliff and Van Dongen, 2018 ). This study provides electrophysiology evidence for understanding the mechanism under the impaired working memory after sleep deprivation. It is necessary to pay attention to the adverse effects of working memory impairment caused by sleep deprivation and to explore effective interventions for such damage.

Data Availability Statement

The datasets generated for this study are available on request to the corresponding author.

Ethics Statement

The studies involving human participants were reviewed and approved by The Fourth Military Medical University Beihang University. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

YS designed the experiments. ZP produced the results and wrote the manuscript. CD and LZ analyzed and interpreted the data. JT and YS performed the experiments, acquainted the data, and the guarantors of this study. YB, LZ, and JT contributed to participating in data collection and reviewing the literature. All authors listed have read and approved the final manuscript.

This research was supported by the National Science Foundation of Winter Olympics Technology Plan of China under Grant Nos. 2019YFF0301600 and HJ20191A020135.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords : sleep deprivation, working memory, event related potentials, electroencephalography, n-back

Citation: Peng Z, Dai C, Ba Y, Zhang L, Shao Y and Tian J (2020) Effect of Sleep Deprivation on the Working Memory-Related N2-P3 Components of the Event-Related Potential Waveform. Front. Neurosci. 14:469. doi: 10.3389/fnins.2020.00469

Received: 12 February 2020; Accepted: 15 April 2020; Published: 19 May 2020.

Reviewed by:

Copyright © 2020 Peng, Dai, Ba, Zhang, Shao and Tian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yongcong Shao, [email protected] ; Jianquan Tian, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Sleep quality, circadian rhythm and metabolism differ in women and men – new review reveals this could affect disease risk

Associate Professor, Cognitive and Affective Neuroscience, University of Southampton

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Sarah Chellappa does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

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Sleep is critical for our health and wellbeing. But with poor sleep becoming a growing problem around the world, it’s more important now than ever to understand what factors affect sleep quality.

Surprisingly, one factor that affects how well a person sleeps at night is their sex. Research shows sleep problems appear to be more common in women. Other studies have also shown women may be more affected by circadian rhythm disruptions (the almost 24-hour cycle that controls many of our body’s processes) compared to men.

But there’s still a lot we don’t know about how men and women might differ when it comes to sleep and the circadian rhythm – and what effect such differences may have on health.

This is what a new review conducted by myself and my colleagues sought to uncover. We revealed key differences in sleep quality and circadian rhythm function in men and women. We also found that these factors may affect metabolism, which could have long-term effects on a person’s health and risk of certain diseases.

Body clock differences

To conduct our review, we assessed around 150 articles, most of which were published in the last decade, that explored different aspects of sleep, circadian rhythms and metabolism, as well as a few studies on potential sex differences in relation to these aspects.

We revealed some key differences in how women and men sleep – finding variations in their circadian rhythms as well as the way their metabolism functions as a result.

We showed that women tend to report lower quality sleep compared to men. We also found that their quality of sleep tended to fluctuate more than men’s did.

Moreover, our review revealed women are up to 50% more likely than men to develop certain sleep disorders, such as restless leg syndrome . On the other hand, men are up to three times more likely to be diagnosed with sleep apnoea than women are. However, this may be due to differences in how the condition presents itself in women compared to men.

Our review also showed that differences between men and women don’t only exist for sleep and sleep problems.

Melatonin, a hormone that helps with the timing of circadian rhythms and sleep, is secreted a bit earlier in women compared to men. Internal body temperature, which is at its highest before sleep and its lowest a few hours before waking up, follows a similar pattern – with the body temperature peak shown to happen earlier in women than men. This might help explain why women tend to prefer earlier sleep times compared to men.

But men tend to prefer going to sleep and waking up later , which may clash with social demands, such as work.

Overall, women reported worse sleep quality on average and were at higher risk of insomnia. Men, however, were at greater risk of developing sleep apnoea.

Metabolism changes

Sleep quality and the circadian rhythm both have strong effects on metabolism , with previous research showing a link between circadian rhythm disruption and higher risk of metabolic diseases, such as obesity and type 2 diabetes. So, our review also investigated the link between these two factors and metabolism – and whether this also differed in men and women.

We found that women’s and men’s brains respond differently to pictures of food when they’re sleep deprived. We revealed that brain areas associated with emotion are twice as active in sleep-deprived women than in sleep-deprived men. But men who were sleep deprived reported feeling hungrier than women. These responses might suggest it could affect a person’s eating choices the next day – such as what foods they choose to eat and how much they eat. But it will be important for future studies to test this idea.

Our review also identified links between circadian rhythm disruption and metabolic disease.

We found that people who worked nightshifts were more likely to be diagnosed with type 2 diabetes compared to those who worked during the day. But a man’s risk of developing type 2 diabetes was twice as high when working night shifts than a woman’s.

But female night shift workers were shown to be around one-and-a-half times more likely to be overweight or obese compared to women who worked day shifts.

These findings all show just how important sleep and circadian rhythms are when it comes to our metabolism and risk of certain diseases, including diabetes and those related to body weight.

Overall, these findings reinforce what other studies before us have shown, which is that biological sex can affect many aspects of sleep – including the quality of sleep a person gets each night, what sleep problems they may be at greater risk of developing and how their body responds to sleep deprivation.

Our findings also highlight the need to tailor treatment for sleep and circadian rhythm disorders depending on a person’s sex – with our research highlighting some of the possible reasons why women and men may respond differently to existing treatments .

But though the evidence is beginning to show how a person’s sex can affect their circadian rhythm and quality of sleep they get, there’s a lot we still don’t know. This is largely due to the under-representation of women in sleep and circadian rhythm research. It’s also currently unknown what specific mechanisms explain why sleep and circadian rhythm problems are linked to greater risk of certain health conditions, and why sleep and circadian rhythm differ between women and men.

We also need to consider the menstrual cycle and contraceptive use when designing studies, as they affect sleep and circadian rhythms.

By investigating these issues, we may be better able to understand why these differences exist between men and women when it comes to sleep and health – and may be better equipped to provide more effective treatments for women and men.

• Circadian rhythms
• Metabolic diseases
• Sleep quality

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Study finds single high dose of creatine boosts cognitive performance during sleep deprivation

03-Apr-2024 - Last updated on 05-Apr-2024 at 10:34 GMT

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Published in the journal  Nature ​, the study was conducted by a team of researchers from the Institute of Neuroscience and Medicine and Aachen University Hospital in Germany who hypothesized that a combination of increased creatine availability and intracellular energy consumption would temporarily increase central creatine uptake.

"Our study showed the effect of a high dose of creatine against sleep deprivation-induced deterioration in cognitive performance, lasting up to 9 h and showing its maximum cognitive effect at 4 h after oral administration," they reported.

Creatine and sleep deprivation ​

The study noted that the modern lifestyle is often propped up by psychoactive substances like caffeine and accompanied by sleep deprivation, a state which lends itself to negative outcomes ranging from reduced performance to chronic disease.

While creatine supplementation has been extensively studied for its ergogenic benefits in sports nutrition (with the muscle loaded over time), research has also revealed its potential cognitive benefits, as well as changes in creatine-related metabolites linked to sleep disorders and states of sleep deprivation.

"The inverse effects of creatine supplementation and sleep deprivation on high energy phosphates, neural creatine and cognitive performances suggest that creatine is a suitable candidate for reducing the negative effects of sleep deprivation," the researchers wrote.

Commenting independently on the current study, Dr. Ralf Jäger, FISSN, CISSN, MBA, managing member of global consulting firm Increnovo, explained that creatine seems to be most effective during times of stress—either as a result of sleep deprivation, hypoxia, more demanding cognitive tasks or in people with low creatine levels like vegetarians. 

"This new study showed that even an acute supplementation with creatine can be effective to enhance mental performance, which is an exciting new finding, and in contrast to what is needed for physical performance," he said.

Study details ​

For this double-blind, randomized, prospective crossover trial, 15 participants between the ages of 20 and 28 (8 female) performed cognitive tests during sleep deprivation after consuming either a high single dose (0.35 g/kg) of creatine monohydrate (Alzchem) or a corn starch placebo. After a minimum interval of five days, they switched test groups.

Evaluations included two consecutive  31 ​P-magnetic resonance spectroscopy (MRS) scans and  1 ​H-magnetic resonance spectroscopy (MRS) used to quantify biochemical compounds or metabolites in the brain tissue, along with a collection of cognitive tests performed at evening baseline and at 3 hours, 5.5 hours, and 7.5 hours after supplementation. 

Findings indicated that sleep deprivation led to a profound cognitive and metabolic response and that acute creatine was bio-available to the brain as suggested by increased total creatine (tCR)/total N-acetylaspartate (tNAA) and reduced subjective fatigue compared to placebo. 

"Creatine alleviated changes in phosphates, pH levels and fading of cognitive performance evoked by sleep deprivation," the researchers reported. "Creatine induced increases in PCr/Pi, declines in ATP and improvements in cognitive performance and processing speed exceeding wake baseline."

They suggested that the crucial factor to overcoming marginal intracellular creatine uptake was, as hypothesized, the increased energy demand of the neuronal cells in combination with an increased extracellular creatine availability.

"It can be concluded that creatine has the potential to be used in prolonged cognitive activity during sleep deprivation," the study noted, calling for future research to investigate appropriate dosage and to specify the time point at which creatine reaches peak effect.

Source:  Nature ​ doi:  10.1038/s41598-024-54249-9 ​ “Single dose creatine improves cognitive performance and induces changes in cerebral high energy phosphates during sleep deprivation” Authors: Ali Gordji‐Nejad   ​et al.

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• Review Article
• Open access
• Published: 18 November 2021

Role of sleep deprivation in immune-related disease risk and outcomes

• Sergio Garbarino   ORCID: orcid.org/0000-0002-8508-552X 1 ,
• Paola Lanteri 2 ,
• Nicola Luigi Bragazzi 3 ,
• Nicola Magnavita 4 , 5 &
• Egeria Scoditti 6  

Communications Biology volume  4 , Article number:  1304 ( 2021 ) Cite this article

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• Sleep deprivation
• Sleep disorders

Modern societies are experiencing an increasing trend of reduced sleep duration, with nocturnal sleeping time below the recommended ranges for health. Epidemiological and laboratory studies have demonstrated detrimental effects of sleep deprivation on health. Sleep exerts an immune-supportive function, promoting host defense against infection and inflammatory insults. Sleep deprivation has been associated with alterations of innate and adaptive immune parameters, leading to a chronic inflammatory state and an increased risk for infectious/inflammatory pathologies, including cardiometabolic, neoplastic, autoimmune and neurodegenerative diseases. Here, we review recent advancements on the immune responses to sleep deprivation as evidenced by experimental and epidemiological studies, the pathophysiology, and the role for the sleep deprivation-induced immune changes in increasing the risk for chronic diseases. Gaps in knowledge and methodological pitfalls still remain. Further understanding of the causal relationship between sleep deprivation and immune deregulation would help to identify individuals at risk for disease and to prevent adverse health outcomes.

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Introduction

Sleep is an active physiological process necessary for life and normally occupying one-third of our lives, playing a fundamental role for physical, mental, and emotional health 1 . Sleep patterns and need are influenced by a complex interplay between chronological age, maturation stage, genetic, behavioral, environmental, and social factors 2 , 3 , 4 , 5 , 6 . Adults should sleep a minimum of 7 h per night to promote optimal health 7 , 8 .

Besides medical problems including obstructive sleep apnea and insomnia, factors associated mostly with the modern 24/7 society, such as work and social demands, smartphone addiction, and poor diet 9 , 10 , 11 , contribute to cause the current phenomenon of chronic sleep deprivation, i.e., sleeping less than the recommended amount or, better to say, the intrinsic sleep need 12 .

Sleep deprivation may be categorized as acute or chronic. Acute sleep deprivation refers to no sleep or reduction in the usual total sleep time, usually lasting 1–2 days, with waking time extending beyond the typical 16–18 h. Chronic sleep deprivation is defined by the Third Edition of the International Classification of Sleep Disorders as a disorder characterized by excessive daytime sleepiness caused by routine sleeping less than the amount required for optimal functioning and health maintenance, almost every day for at least 3 months 13 .

Population studies reported a stably increasing prevalence of adults sleeping less than 6 h per night over a long period 12 , 14 , 15 , also affecting children and adolescents 16 , 17 . Sleep duration decline is present not only in high-income and developed countries 18 but also in low-income or racial/ethnic minorities 19 , thus representing a worldwide problem.

In addition to fatigue, excessive daytime sleepiness, and impaired cognitive and safety-related performance, sleep deprivation is associated with an increased risk of adverse health outcomes and all-cause mortality 20 , 21 , 22 , 23 , 24 . Indeed, epidemiological and experimental data support the association of sleep deprivation with the risk of cardiovascular (CV) (hypertension and coronary artery disease) and metabolic (obesity, type 2 diabetes (T2DM)) diseases 24 , 25 , 26 , 27 . In the United States, sleep deprivation has been linked to 5 of the top 15 leading causes of death including cardio- and cerebrovascular diseases, accidents, T2DM, and hypertension 28 . Data also point to a role for sleep deprivation in the risk of stroke, cancer, and neurodegenerative diseases (NDDs) 26 , 29 , 30 . Sleep deprivation is also associated with psychopathological and psychiatric disorders, including negative mood and mood regulation, psychosis, anxiety, suicidal behavior, and the risk for depression 31 , 32 , 33 , 34 , 35 , 36 .

Both too short or too long sleep durations have been found to be associated with adverse health outcomes and all-cause mortality with an U-shaped relationship 37 , 38 , 39 . Although the relation of long sleep duration to adverse health outcomes may be confounded by poor health conditions occurring in older adults 37 , the causal association of sleep deprivation with negative health effects is substantiated by experimental evidence providing biological plausibility 24 , 40 , 41 .

Sleep profoundly affects endocrine, metabolic, and immune pathways, whose dysfunctions play a determinant role in the development and progression of chronic diseases 42 , 43 , 44 . Specifically, in many chronic diseases, a deregulated/exacerbated immune response shifts from repair/regulation towards unresolved inflammatory responses 45 .

Regular sleep is crucial for maintaining immune function integrity and favoring a homeostatic immune defense to microbial or inflammatory insults 46 , 47 . Sleep deprivation may result in deregulated immune responses with increased pro-inflammatory signaling, thus contributing to increase the risk for the onset and/or worsening of infection, as well as inflammation-related chronic diseases.

Here we reviewed the evidence regarding the impact of sleep deprivation on immune-related diseases by discussing the major points as follows: (1) the immune–sleep relationship; (2) the association of sleep deprivation with the development and/or progression of immune-related chronic diseases; and (3) the immune consequences of sleep deprivation and their implications for diseases. Finally, possible measures to reverse sleep deprivation-associated immune changes were discussed.

Basic immune mechanisms of sleep regulation

The discovery of muramyl peptide, a bacterial cell wall component that is able to activate the immune system and induce the release of sleep-regulatory cytokines, primary regulators of the inflammatory system, provided the first molecular link between the immune system and sleep 48 . Thereafter, other microbial-derived factors such as the endotoxin lipopolysaccharide (LPS) 49 , as well as mediators of inflammation, such as the cytokines interleukin (IL)-1 and tumor necrosis factor (TNF)-α, prostaglandins (PGs), growth hormone-releasing hormone (GHRH), and growth factors, were recognized as sleep-regulating factors 50 .

Along this line, most animal studies have consistently shown a role in particular for IL-1, TNF-α, and PGD 2 in the physiologic, homeostatic non-rapid eye movement (NREM) sleep regulation, so that the inhibition of their biological action resulted in decreased spontaneous NREM sleep, whereas their administration enhanced NREM sleep amount and intensity, and suppressed rapid eye movement (REM) sleep 51 , 52 , 53 . Moreover, the circulating levels of IL-1, IL-6, TNF-α, and PGD 2 are highest during sleep 54 . Their effects are dose- and time-of-day-dependent so that, for instance, low doses of IL-1 enhance NREMS, whereas high doses inhibit sleep 55 . Reciprocal effects may be involved in sleep regulation: for instance, the effects of systemic bacterial products such as LPS may also involve TNF-α 49 . Links exist between IL-1β and GHRH/growth hormone (GH) in promoting sleep so that IL-1 induced GH release via GHRH 56 , and hypothalamic γ-aminobutyric acid (GABA)ergic neurons (promoting sleep) are responsive to both GHRH and IL-1β 57 . Instead, anti-inflammatory cytokines, including IL-4, IL-10, and IL-13, inhibited NREM sleep in animal models 58 .

Through these substances, the immune system may signal to the brain and interact with other factors involved in sleep regulation such as neurotransmitters (acetylcholine, dopamine, serotonin, norepinephrine, and histamine), neuropeptides (orexin), nucleosides (adenosine), the hormone melatonin, and the hypothalamus-pituitary axis (HPA) axis. Signaling mechanisms to the brain also involve vagal afferents: for instance, vagotomy attenuates intraperitoneal TNF-α-enhanced NREMS responses 59 .

Cytokines are produced by a vast array of immune cells, including those resident in the central nervous system (CNS), and non-immune cells, e.g., neurons, astrocytes and microglia, and peripheral tissue cells 60 , 61 . Cytokines interact with the brain through humoral, neural, and cellular pathways, and form a brain cytokine network (Fig.  1 ) able to produce cytokines, their receptors, and amplify cytokine signals 50 . Peripheral cytokines reach the brain through different non-exclusive mechanisms, including blood–brain barrier (BBB) disruption 62 , penetration of peripheral immune cells, and via afferent nerve fibers, such as the vagus nerve, a bundle of parasympathetic sensory fibers that conveys information from peripheral organs to the CNS 63 .

CCL-2: C-C motif chemokine ligand-2; CXCL-10: C-X-C motif chemokine ligand 10; IL-1: interleukin-1; mNTS: medial nucleus tractus solitarius; PGE 2 : prostaglandin E2; TNF-α: tumor necrosis factor-α.

In the CNS, cytokines mediate a multiplicity of immunological and nonimmunologic biological functions 64 , such as synaptic scaling, synapse formation and elimination, de novo neurogenesis, neuronal apoptosis, brain development, cortical neuron migration 65 , circuit homeostasis and plasticity 66 , and cortical neuron migration 65 , and complex behaviors, sleep, appetite, aging, learning and memory 65 , and mental health status 67 , 68 .

A common experimental finding is that after damage to any brain area, if the animal or human survives, sleep always ensues 69 . Recent evidence indicates that sleep is a self-organizing emergent neuronal/glial network property of any viable network regardless of size or location, whether in vivo or in vitro 53 , 70 , 71 , 72 , 73 . Several sleep-regulatory substances, e.g., TNF, IL-1, nitric oxide, PGs, and adenosine are all produced within local cell circuits in response to cell use 74 , 75 .

From this point of view, TNF-α and IL-1 are closely interconnected and play a similar role in the regulation of sleep 76 , 77 , 78 , 79 , 80 , 81 . IL-1β and TNF-α self-amplify and increase each other’s mRNA expression in the brain 82 . In rats, IL-1 83 and TNF-α 84 mRNAs show diurnal variations in different brain areas, with the highest concentrations recorded during increased sleep propensity and peaks occurring at time of sleep period onset in rats and mice 85 .

Sleep-like states in mixed cultures of neurons and glia are dependent in part on the IL-1 receptor accessory protein (AcP) 69 , 86 . In the brain, there is an AcP isoform, neuron-specific (AcPb) 87 , whose mRNA levels increase with sleep loss 88 , 89 . AcPb is anti-inflammatory, whereas AcP is pro-inflammatory 87 , 88 .

TNF signaling promotes sleep, whereas reverse TNF-α signaling (the soluble TNF receptor) promotes waking 90 . The brain production of TNF-α is neuron activity-dependent 91 . Afferent activity into the somatosensory cortex enhances TNF expression 92 , and in vitro optogenetic stimulation enhances neuronal expression of TNF immunoreactivity 93 .

Peripheral immune activation following acute or chronic infection or inflammatory diseases is marketed by altered cytokine concentrations and profiles, and is transmitted to the CNS initiating specific adaptive responses. Among these, a sleep response is induced and has been hypothesized to favor recovery from infection and inflammation, supposedly via the timely functional investment of energy into the energy-consuming immune processes 54 , 94 . Accordingly, acute mild immune activation enhances NREM sleep and suppresses REM sleep, whereas severe immune response with an upsurge of cytokine levels causes sleep disturbance with the suppression of both NREM and REM sleep 49 , 95 , 96 , 97 , 98 . This sleep change correlates to the course of the host immune response as observed in bacterial and Trypanosoma infections 97 , 99 . Supportively, the increase in NREM sleep was a favorable prognostic factor for rabbits during infectious diseases 96 .

Immune regulators also mediate the complex interrelation between sleep and the circadian systems 74 . Circadian rhythms in behavior and physiology are generated by a molecular clockwork located in the suprachiasmatic nucleus, i.e., the master circadian pacemaker, and peripheral tissues, and involving the so-called clock genes ( Clock , Bmals , Npas2 , Crys , Pers , Rors , and Rev-erbs ) 100 . Cytokines, including TNF-α, IL-1β 101 , 102 , and LPS 103 , 104 , 105 , suppress the peripheral and hypothalamic expression of core clock genes and clock-controlled genes, resulting in reduced locomotor activity accompanied by prolonged rest time 101 .

Sleep deprivation and immune-related disease outcomes

In the following section, the association between sleep deprivation and risk or outcomes of immune-related disorders, as observed in human studies (mostly observational) and animal experimentations, will be examined. In this context, considering the sleep–immunity relationship, research has also begun to explore whether and how immune deregulation and inflammation may link sleep deprivation with adverse health outcomes.

A breakdown of host defense against microorganisms has been found in sleep-deprived animals, as shown by the increased mortality after septic insult in sleep-deprived mice compared with control mice 106 , or by systemic invasion by opportunistic microorganisms leading to increased morbidity and lethal septicemia in sleep-deprived rats 107 . There is growing evidence associating longer periods of sleep with a substantial reduction in parasitism levels 108 and reduced sleep quality with increased risk of infection and poor infection outcome 109 , 110 . Accordingly, patients with sleep disorders exhibited a 1.23-fold greater risk of herpes zoster than did the comparison cohort 111 . Furthermore, sleep-deprived humans, as those with habitual short sleep (≤5 h) compared with 7–8 h sleep, are more vulnerable to respiratory infections in cross-sectional and prospective studies 112 , 113 , and after an experimental viral challenge 109 , 114 . Similarly, compared with long sleep duration (around 7 h), short sleep duration (around 6 h) is associated with an increased risk of common illnesses, including cold, flu, gastroenteritis, and other common infectious diseases, in adolescents 115 .

Compared with non-sleep-deprived mice, REM-sleep-deprived mice failed to control Plasmodium yoelii infection and, consequently, presented a lower survival rate 110 . This was correlated to an impaired T-cell effector activity, characterized by a reduced differentiation of T-helper cells (Th) into Th1 phenotype and following production of pro-inflammatory cytokines, such as interferon (IFN)-γ and TNF-α, and compromised differentiation into T-follicular helper cells (Tfh), essential to B-cell maturation, which therefore resulted to be reduced 110 . Accordingly, both Maf, a Tfh differentiation factor, and T-bet, a pro-Th1 transcription factor, were reduced in the REM-sleep-deprived group 110 . The combination of REM-sleep deprivation and P. yoelii infection resulted in an additive effect on glucocorticoid synthesis, and chemical inhibition of this exacerbated glucocorticoid synthesis reduced parasitemia, death rate, and restored CD4 T-cell, Tfh, and plasma B-cell differentiation in infected sleep-deprived mice 110 , suggesting a role of HPA axis hyperactivation in impairing host immune response under sleep deprivation.

Seep deprivation may exert detrimental effects on sepsis-induced multi-organ damage. Sleep deprivation (3 days) after LPS administration increased the levels of pro-inflammatory cytokines (IL-6 and TNF-α) in the plasma and organs (lung, liver, and kidney), which could be abrogated by subdiaphragmatic vagotomy or splenectomy 14 days prior to LPS administration 116 . Gut microbiota-vagus nerve axis and gut microbiota-spleen axis may play essential roles in post-septic sleep deprivation-induced aggravation of systemic inflammation and multi-organ injuries 116 .

Considering the association between sleep deprivation and immune response to infections, vaccination studies allow to assess the impact of sleep and sleep loss on ongoing immune response and the clinical outcome. Studies in which sleep deprivation (one or few nights) was applied to healthy humans during (mostly after) the immunological challenge of vaccination demonstrate that sleep deprivation reduced both the memory and effector phases of the immune response, as indexed by suppressed antigen-specific antibody and Th cell response compared with undisturbed sleep 117 .

Congruently, habitual (and hence chronic) short sleep duration (<6 h) compared with longer sleep duration was associated with reduced long-term clinical protection after vaccination against hepatitis B 118 . Sleep deprivation did not exert any impairing effect on mice already immunized 119 . From these studies, it seems that sleep supports—and sleep deprivation impedes—the formation of the immunological memory. Potential mechanisms involved in the beneficial effect of normal sleep on the vaccination response include: (i) the sleep-induced reduction in circulating immune cells that most likely accumulate into lymphatic tissues, increasing the probability to encounter antigens and trigger the immune response; (ii) the sleep-associated profile of inflammatory activation towards Th1 cytokines (increased IL-2, IFN-γ, etc.), which may favor macrophage activation, antigen presentation, and T-cell and B-cell activation; (iii) the effect of sleep stage on the formation of immunological memory through specific immune-active hormones: indeed, during slow wave sleep-rich early sleep, the profile of immune-active hormones, characterized by minimum concentrations of cortisol, endowed with anti-inflammatory activity, and high levels of GH, prolactin, and aldosterone, which support Th1 cell-mediated immunity, may facilitate the mounting of an effective adaptive immune response to a microbial challenge 54 .

Sleep deprivation has increasingly been recognized as a risk factor for impaired anti-tumor response. Epidemiological studies suggest, albeit not consistently 120 , a significant association between short sleep duration and the risk for several cancers, including breast, colorectal, and prostate cancer 29 , 121 , 122 , 123 . Potential mechanisms underlying this association include a shorter duration of nocturnal secretion of melatonin (putatively due to increased light exposure at night) 124 , which exerts anti-cancer properties through antimitotic, antioxidant, apoptotic, anti-estrogenic, and anti-angiogenic mechanisms 125 . Melatonin also plays immunomodulatory and anti-inflammatory effects with relevance for its anti-cancer activity, being able to inhibit the pro-inflammatory nuclear factor-κB (NF-κB)/NLRP3 inflammasome pathways, and to support T/B-cell activation and macrophage function 126 . However, besides melatonin, impaired anti-tumor immune response has been invoked in the sleep deprivation-associated risk for cancer development. A reduced cytotoxic activity of natural killer (NK) cells, which are immune cells with anti-tumor effect, has been reported in 72 h sleep-deprived mice compared with control mice, accompanied by reduced numbers of the cytotoxic cells such as CD8 T cells and NK cells in the tumor microenvironment after chronic sleep deprivation (for 18 h/day during 21 days) in an animal model of experimental pulmonary metastasis 127 , 128 . In this model, the reduced anti-tumor immunity of sleep-deprived animals was also indexed by the reduced number of antigen-presenting cells (dendritic cells) in the lymph nodes, as well as by the decreased effector CD4 T-cell numbers and corresponding cytokine profile (decreased IFN-γ), resulting in lowered Th1 response of Th cells, i.e., the most effective immune response against tumors. Therefore, an immunosuppressive environment develops with sleep deprivation, which could translate into an early onset and increased growth rate of cancer 128 or increased mortality 129 .

An integrated meta-analysis of transcriptomic data showed that circadian rhythm-related genes are downregulated and upregulated in the cortex and hypothalamus samples of mice with sleep deprivation, respectively, with downregulated genes associated with the immune system and upregulated genes associated with oxidative phosphorylation, cancer, and T2DM 130 . Several circadian rhythm-related genes were common to both T2DM and cancer, and seem to associate with malignant transformation and patient outcomes 130 .

Hence, although these sleep deprivation-induced immune-mediated mechanisms in cancer warrant further confirmation in humans, the importance of the immune function in the anti-tumor host defense is well recognized 131 , thus suggesting that the impaired immune response after sleep deprivation may represent a plausible mediator of the associated increased risk for cancer as described in animal models and in humans.

Neurodegenerative diseases

NDDs are aging-related diseases that selectively target different neuron populations in the CNS, and include Alzheimer’s disease, multiple sclerosis, Parkinson’s disease, Huntington’s disease, and amyotrophic lateral sclerosis. One prevailing hypothesis is that altered sleep habits and specifically sleep deprivation may be a consequence and frequently a marker of the disease 132 , 133 , 134 . However, human and animal studies have also suggested a causative or contributing role for sleep deprivation in the development and/or worsening of neurodegenerative processes 132 , 133 , 134 .

Potential pathophysiological mechanisms involve, among others, neuro-immune dysregulation. Indeed, a common feature –and a potential therapeutic target- of NDDs is the chronic activation of the immune system, where aspects of peripheral immunity and systemic inflammation integrate with the brain’s immune compartment, leading to neuroinflammation and neuronal damage 135 . Neuroinflammation following sleep deprivation has been studied as a pathogenic mechanism potentially mediating the association between sleep deprivation and neurodegenerative processes. Low-grade neuroinflammation as indexed by heightened levels of pro-inflammatory mediators (e.g., TNF-α, IL-1β, and COX-2) and activation of astrocytes and microglia, main immune cells in the brain, was observed in the hippocampus and piriform cortex regions of the brain of chronic sleep-deprived rats along with neurobehavioral alterations (anxiety, learning, and memory impairments) 136 . The sleep deprivation pro-inflammatory milieu was accompanied by oxidative stress in the brain 137 and BBB disruption with consequent increased permeability to blood components 138 . After acute sleep deprivation, there was a significant increased recruitment of B cells in the mouse brain, which could be important given evidence of B cells involvement in NDDs 139 .

Progressive and chronic aggregations of unique proteins in the brain and spinal cord are hallmarks of NDDs 140 and trigger inflammatory responses, gradual loss of physiological functions of the nerve cells, and cell death 141 . Impaired autophagy in humans, a catabolic process of cytoplasmic components, contributes to the aggregation and accumulation of β-amyloid (Aβ), cytoskeleton-related protein τ , and synuclein in neuronal cells and tissues 140 . Sleep plays an important role in the clearance of metabolic waste products accumulated during wakefulness and neural activity. Indeed, the Aβ protein is predominantly cleared from the brain during sleep, possibly through the glymphatic pathway. Congruently, acute and chronic experimental sleep deprivation in animals 142 , 143 and humans 144 resulted in brain Aβ accumulation and plaque formation, a typical pathological change in Alzheimer’s disease process, the most common type of dementia. Imaging studies have revealed that healthy humans with self-reported short sleep were more prone to have cerebral Aβ plaque pathology 145 and disruption of deep sleep (slow wave sleep) increases Aβ in human cerebrospinal fluid (CSF) 146 . Likewise, patients with insomnia present higher CSF levels of Aβ 147 .

This pathological Aβ accumulation might reflect disrupted balance of Aβ production and clearance after sleep deprivation. On the one hand, sleep deprivation results in reduced clearance as suggested by clinical studies showing that Aβ levels in CSF are the highest before sleep and the lowest after wakening, whereas Aβ clearance from CSF was impaired by sleep deprivation 148 . Impaired clearance might also derive from disrupted peripheral Aβ transport, as suggested by the sleep deprivation-induced downregulation of low-density lipoprotein receptor-related protein-1 (LRP-1), which promotes Aβ efflux from the brain to the peripheral circulation across the BBB, and elevations of receptor of advanced glycation end products (RAGE), which promotes on the contrary the influx of peripheral Aβ into the brain, thus preventing Aβ clearance 149 . On the other hand, apart from impairing Aβ and τ interstitial fluid clearance, sleep deprivation may also have a role in increasing Aβ and τ exocytosis, thereby increasing CSF Aβ and τ levels 150 . In animals, sleep deprivation also leads to upregulation of β-secretase 1 (BACE-1), the most important enzyme regulating Aβ generation in the brain 142 , 143 , 149 , thus opening the hypothesis of increased Aβ production by sleep deprivation. Sleep deprivation-induced neuroinflammatory mediators correlate and could lead to disturbed Aβ clearance and stimulated amyloidogenic pathway 143 , being pro-inflammatory cytokines able to suppress the expression of LRP-1 and to increase RAGE 151 and BACE-1 levels 152 . Likewise, oxidative stress induced by sleep deprivation may also contribute to the neuroinflammatory burden and the increased expression of BACE-1 153 . Furthermore, patients with insomnia, compared with healthy controls, showed decreased serum levels of neurotrophins, including brain-derived neurotrophic factor (BDNF), proteins especially relevant in neuroplasticity, memory and sleep, and this reduction was significantly related to the insomnia severity 154 .

Sleep deprivation is associated with a rapid decline in circulatory melatonin levels, which may be linked to rapid consumption of melatonin as a first-line defense against the sleep deprivation-associated rise in oxidative stress 155 . Melatonin is a potent antioxidant, interacts with BDNF 156 , and promotes neurogenesis and inhibits apoptosis 157 . The neuroprotective potential of melatonin can target events leading to Alzheimer’s disease development including Aβ pathology, τ hyperphosphorylation, oxidative stress, glutamate excitotoxicity, and calcium dyshomeostasis 150 , 158 . Accordingly, melatonin treatment could restore the autophagy flux, thereby preventing tauopathy and cognitive decline in Alzheimer’s disease mice 159 .

Patients with Alzheimer’s disease have an increased incidence of sleep-disordered breathing 160 . In addition, sleep-disordered breathing is associated with an increased risk of mild cognitive impairment or dementia and with earlier onset of Alzheimer’s disease 161 . Sleep-disordered breathing is also associated with altered levels of Alzheimer’s disease biomarkers in CSF, including decreased levels of Aβ and elevated levels of phosphorylated τ 162 . Sleep-disordered breathing possibly via hypoxia, inflammation, and sleep disruption/deprivation could contribute to Alzheimer’s disease processes, e.g., increase of Aβ production and aggregation, suppression of glymphatic clearance of Alzheimer’s disease pathogenic proteins ( τ , Aβ) and oxidative stress, inflammation, and synaptic damage 134 , 163 .

To summarize, the sleep deprivation-associated risk for Alzheimer’s disease could be linked to the induction of inflammation in the brain and disorders of systemic innate and adaptive immunity 164 . However, the relationship of sleep deprivation to inflammation in Alzheimer’s disease is mostly speculative and needs to be confirmed.

Similar to Aβ in Alzheimer’s disease, abnormal levels of α-synuclein are common to Parkinson’s disease, the second most common NDDs 165 . Sleep disturbances are not only a common comorbidity in Parkinson’s disease, but often precede the onset of classic motor symptoms 166 . The main pathological features of Parkinson’s disease are the reduction of dopaminergic neurons in the extrapyramidal nigrostriatal body and the formation of Lewy bodies formed by the aggregation of α-synuclein and its oligomers surrounded by neurofilaments. Due to the degeneration of the dopaminergic neurons, affected people show muscle stiffness, resting tremors, and posture instability; other pathways involved in sleep, cognition, mental abnormalities, and other non-motor symptoms are also affected 167 . Epidemiological studies also suggest that disturbed sleep may increase the risk of Parkinson’s disease 168 , 169 . Such disease-modifying mechanisms may include activation of inflammatory and immune pathways, abnormal proteostasis, changes in glymphatic clearance, and altered modulation of specific sleep neural circuits that may prime further propagation of α-synucleinopathy in the brain 169 . Melatonin could reduce neurotoxin-induced α-synuclein aggregation in mice. Furthermore, melatonin pretreatment reduced neurotoxin-induced loss of axon and dendritic length in dopaminergic neurons through suppression of autophagy activated by CDK5 and α-synuclein aggregation, thereby reducing dyskinesia symptoms in Parkinson’s disease animal models 170 . A few reports have shown that melatonin exerts protective effects in several experimental models of Parkinson’s disease 171 .

However, although animal experimentations suggest a link between sleep deprivation and immune dysfunction in neurodegenerative processes, no human investigations have yet confirmed the mediating role of immune dysregulation in the association between sleep deprivation and risk or outcomes of NDDs.

Autoimmune diseases

Sleep disturbances are frequently reported in autoimmune diseases, and immunotherapy in patients with autoimmune pathologies results in sleep improvement 172 . However, knowledge of the immunopathology of autoimmune diseases have disclosed new concepts on the impact of sleep deprivation on autoimmune disease process, showing that sleep deprivation can promote a breakdown of immunologic self-tolerance. Human cohort studies found that non-apnea sleep disorders, including insomnia, were associated with a higher risk of developing autoimmune diseases such as rheumatoid arthritis, ankylosing spondylitis, systemic lupus erythematosus, and systemic sclerosis (adjusted hazard ratio: 1.47, 95% confidence interval (CI) 1.41–1.53) 173 Similarly, in relatives of systemic lupus erythematosus patients, and hence at increased risk for systemic lupus erythematosus, self-reported short sleep duration (<7 h/night) was associated with transitioning to systemic lupus erythematosus (adjusted odds ratio: 2.0, 95% CI 1.1–4.2), independent of early preclinical features that may influence sleep duration such as prednisone use, depression, chronic fatigue, and vitamin D deficiency 174 . This role of sleep deprivation as a risk factor for autoimmune diseases is corroborated by animal studies. In mice genetically predisposed to develop systemic lupus erythematosus 175 , chronic sleep deprivation, applied at an age when animals were yet clinically healthy, caused an early onset of the disease, as indexed by the increased number of antinuclear antibodies, without affecting disease course or severity, according to data on proteinuria, a surrogate marker of autoimmune nephritis, and longevity. Several mechanisms have been postulated to explain the link between sleep deprivation and autoimmune disease risk. Sleep deprivation can accelerate disease development through mechanisms including sleep deprivation-induced increased production of several pro-inflammatory cytokines 44 , 54 , as better discussed below. Indeed, cytokines are synergistically involved in the pathogenesis of autoimmunity, such as IL-6, whose abnormal production results in polyclonal B-cell activation and the occurrence of autoimmune features 176 , and IL-17 and the related Th17-cell response 177 , which require IL-6 for activation 178 and can cause greater amounts of autoantibody production and immune complex formation, or can intensify chronic inflammation by promoting angiogenesis and recruiting of inflammatory cells at inflammation sites as well as cartilage and bone erosion 179 . Furthermore, experimentally sleep-deprived healthy humans showed impaired suppressive activity of CD4 regulatory T cells (Treg), which normally is highest during the night and lowest in the morning 180 . The suppressive function of Treg towards excessive immune response is an important homeostatic mechanism, whose impairment is implicated in autoimmune disease pathogenesis 181 . Hence, sleep deprivation may not be merely an early symptom or a consequence of an autoimmune disease, but may contribute directly to the pathogenesis increasing the susceptibility to develop an autoimmune disease. More studies are warranted in this field.

Metabolic and vascular diseases

Prospective epidemiological evidence associate sleep deprivation (commonly <7 h/night, often <5 h/night) with the incidence of fatal and non-fatal CV outcomes, with a 48% higher risk of coronary heart disease 25 , a 15% higher risk of stroke 182 , and a 12% increased risk of all-cause mortality 37 , which is mainly due to CV causes, according to some authors 183 . In a recent prospective cohort, a low-stable sleep pattern (<5 h sleep/night) during the 4-year follow-up had the highest risk of death and CV events 184 . Short sleep has also been associated with increased subclinical atherosclerotic burden, the dominant underlying cause of CV diseases 185 .

In addition, sleep deprivation increases the risk for obesity (about 55% higher risk) 39 , 186 , insulin resistance, T2DM (28% higher risk) 38 , and hypertension (21% higher risk) 187 , which are powerful and preventable risk factors for CV diseases. Notably, the risk for diabetes attributable to sleep deprivation is comparable to that of other established traditional cardiometabolic risk factors 188 , thus underscoring the clinical significance of targeting sleep deprivation in the prevention of cardiometabolic diseases. In contrast with normal nocturnal sleep and in particular NREM sleep characterized by a marked decrease in sympathetic activity, catecholamine plasma levels, and blood pressure, experimental sleep deprivation (acute or chronic) is accompanied by increased sympathetic outflow, with consequent higher blood pressure and heart rate, thus providing a pathogenic link between sleep deprivation and hypertension risk 189 , 190 , 191 , 192 .

Regarding the influence of sleep deprivation on metabolic pathways, studies support a plausible causal link between sleep deprivation and the risk of overweight and obesity, possibly mediated by the effect of sleep deprivation on circulating levels of hormones (leptin, ghrelin) controlling hunger, satiety and energy balance, besides other factors intervening during sleep deprivation, including physical inactivity and overfeeding 193 . Furthermore, human experimental evidence with chronic sleep deprivation protocol demonstrate that sleep deprivation may alter glucose metabolism 194 and insulin sensitivity 195 , thus increasing the risk for obesity and T2DM. The reduction in total body insulin sensitivity observed after sleep deprivation (4.5 h per night for 4 days) in healthy subjects was paralleled by impaired peripheral insulin sensitivity, as demonstrated in subcutaneous fat playing a pivotal role in energy metabolism 195 . Considering a more chronic sleep deprivation, reduced insulin sensitivity was reported in overweight adults after 14 days of experimental sleep deprivation (5.5 h per night) compared with 8.5 h per night of sleep 196 , and after habitual curtailment in sleep duration of 1.5 h (<6 h of sleep per night) in healthy young adults with a family history of T2DM 197 .

Although the mechanisms that underlie most associations between short sleep duration and adverse cardiometabolic outcomes are not fully understood, potential causative mechanisms involving immune-inflammatory activation have been postulated. It is indeed well established that the subclinical inflammatory status induced by sleep deprivation has pathogenic implications for metabolic and CV risk factors (glucose metabolism, diabetes, hypertension, atherogenic lipid profile, endothelial dysfunction, and coronary calcification) and outcomes (stroke and coronary heart disease) 24 . Accordingly, most of the markers of systemic and cellular inflammation (leukocyte counts and activation state, cytokines, acute-phase proteins, and adipose tissue-derived adipokines) found to be altered after sleep deprivation have been epidemiologically and pathogenically associated with insulin resistance, T2DM, and vascular complications 198 . In fact, inflammation is an early pathogenic process during the development of obesity and insulin resistance 199 . Many adipose tissue-released inflammatory factors with pro-atherogenic and pro-thrombotic actions have also been regarded as a molecular link between obesity and atherosclerotic CV diseases 200 . Furthermore, chronic inflammatory processes are firmly established as central to the development and clinical complications of CV diseases, form the initiation, promotion and progression of atherosclerotic lesions to plaque instability, and the precipitation of thrombosis, the main underlying cause of myocardial infarction or stroke. Most CV risk factors (adiposity, insulin resistance, T2DM, hypertension, and dyslipidemia) act by inducing or intensifying such underlying inflammatory processes that ultimately promote endothelial dysfunction, altered vascular reactivity, innate and adaptive immune system activation, leukocyte infiltration into the vessel wall, and thus atherogenesis 201 . Experimental sleep deprivation leads to endothelial dysfunction, an early marker of atherosclerosis, as indexed by impaired endothelial-dependent vasodilation or increased levels of endothelial adhesion molecules 191 .

Among the inflammatory markers, besides being a biomarker of future risk for CV diseases and a predictor of clinical response to statin therapy 202 , C-reactiove protein (CRP) has been shown to be involved in the immunologic process that triggers vascular remodeling and atherosclerotic plaque deposition 202 . CRP levels lack diurnal rhythm and its liver production is stimulated by cytokines including IL-6 and IL-17, which are upregulated by sleep deprivation 203 . As such, although limited evidence have found an elevation of circulating CRP following sleep deprivation 204 , CRP is a prototypical inflammatory factor with the potential to mark and—to some extent mediate—CV risk following sleep deprivation. Congruently, elevated and sustained plasma levels of CRP have been observed in healthy humans after prolonged sleep deprivation (5 or 10 nights), in concomitance with increased heart rate 190 , 203 , lymphocyte pro-inflammatory activation, and production of cytokines (e.g., IL-1, IL-6, and IL-17) 203 . Similarly, the increase in blood pressure and heart rate observed after acute total sleep deprivation (40 h) was accompanied and even preceded by impaired vasodilation and by increased levels of IL-6 and markers of endothelial dysfunction and activation, such as cellular adhesion molecules (E-selectin, ICAM-1, etc.) 191 . The sleep deprivation pro-atherogenic effect in animal model of sleep fragmentation is mediated, at least in part, by reduced hypothalamic release of hypocretin (i.e., orexin), a wake-inducing neuropeptide, which limits the production of leukocytes (monocytes and neutrophils) and atherosclerosis development, and has been inversely associated with the risk of myocardial infarction, heart failure, and obesity 205 . The activation of the sympathetic nervous system (SNS) may be another mechanism for the inflammatory link between sleep loss and atherosclerotic CV disease, because such activation increases the bone marrow release of progenitor cells, the production of innate immune cells (monocytes), and the levels of inflammatory cytokines, and triggers endothelial dysfunction, thereby leading to systemic and vascular inflammation and atherosclerosis 206 , 207 . Playing a key role in instigating inflammatory responses and promoting atherosclerosis 208 , the sleep deprivation-associated oxidative stress may also contribute to CV risk. It has also been hypothesized a role for melatonin suppression following sleep deprivation in the vascular impairment associated with sleep deprivation, given that melatonin inhibits oxidative stress and cytokine production by immune and vascular cells, and represses atherosclerotic lesion formation in vivo 209 .

Therefore, a significant and consistent association exists between sleep deprivation and cardiometabolic risk and clinical outcomes, with several plausible immune-mediated causative mechanisms explaining this association.

Immune mechanisms linking sleep deprivation and diseases

As shown above, sleep deprivation has been found to alter inflammatory immune processes via multiple pathways, which could lead to increased susceptibility to chronic inflammatory diseases (Fig.  2 ). Most of the current knowledge on immune effects of sleep deprivation come from studies using controlled experimental sleep deprivation protocols, among which chronic partial sleep deprivation, lasting 2–15 days, is that mostly resembling the human condition of chronic insufficient sleep.

Sleep deprivation, as induced experimentally or in the context of habitual short sleep, has been found to be associated with alterations in the circulating numbers and/or activity of total leukocytes and specific cell subsets, elevation of systemic and tissue (e.g., brain) pro-inflammatory markers including cytokines (e.g., interleukins [IL], tumor necrosis factor [TNF]-α), chemokines and acute phase proteins (such as C reactive Protein [CRP]), altered antigen presentation (reduced dendritic cells, altered pattern of activating cytokines, etc.), lowered Th1 response, higher Th2 response, and reduced antibody production. Furthermore, altered monocytes responsiveness to immunological challenges such as lipopolysaccharide (LPS) may contribute to sleep deprivation-associated immune modulation. Hypothesized links between immune dysregulation by sleep deprivation and the risk for immune-related diseases, such as infectious, cardiovascular, metabolic, and neurodegenerative and neoplastic diseases, are shown. The illustrations were modified from Servier Medical Art ( http://smart.servier.com/ ), licensed under a Creative Common Attribution 3.0 Generic License. APC: antigen-presenting cells.

Some studies have observed that sleep deprivation, compared with regular nocturnal sleep, leads to increased circulating numbers of total leukocytes and specific cell subsets mainly neutrophils, monocytes, B cells, CD4 T cells, and decreased circulating numbers and cytotoxic activity of NK cells 203 , 210 , 211 , 212 , 213 . Other studies, however, found contrasting results, including a decrease in CD4 T cells after sleep deprivation 213 , 214 , probably due to differences in sleep deprivation protocol, sampling methodologies, and other factors. Sleep deprivation has also shown to alter circadian rhythm of circulating leukocytes 215 , with higher levels during the night and at awakening and a flattened rhythm 210 , 212 . Additional findings are suggestive of immune deregulation by sleep deprivation, including a decreased neutrophils phagocytic activity 213 , altered lymphocytes adhesion molecule expression 216 , and reduced stimulated production of IL-2 and IL-12, which are important for adaptive immunity 211 , 217 .

Experimental sleep deprivation has been reported to affect systemic markers of inflammation, with studies showing increased circulating pro-inflammatory molecules (IL-1, IL-6, CRP, TNF-α, and MCP-1); this associated in some studies with a subsequent homeostatic increase in endogenous inhibitors, including IL-1 receptor antagonist and TNF receptors 203 , 218 , 219 , 220 . In agreement with experimental sleep deprivation, population studies found a direct independent association between habitual short sleep duration (generally < 5 or 6 h) and elevated circulating pro-inflammatory markers, e.g., acute phase proteins (CRP and IL-6), cytokines (TNF-α, IFN-γ, IL-1, etc.), adhesion molecules, and leukocyte counts 183 , 221 , 222 , 223 , 224 , 225 . Furthermore, a reduced NK cell activity 226 and a decline in naive T cells 227 , compatible with reduced immune competence, was reported in association with habitual short sleep. Shortening of leukocyte telomere length, a cellular senescence marker linked with inflammation, was also associated with shorter sleep duration 228 , 229 .

The reported elevation of systemic inflammation is clinically relevant, because it is suggested to specifically mediate the increased risk of mortality associated with short sleep 23 , 230 , 231 and, as observed, the risk for chronic disease development.

Regarding cellular markers of inflammation, some studies found that the ex-vivo LPS-stimulated production of TNF-α 232 , 233 , IL-1β, and IL-6 203 , 232 , 233 , 234 by human monocytes increased during sleep deprivation but decreased during regular nocturnal sleep 54 , 203 , 232 , 233 , 234 . However, other studies reported a decrease of TNF-α production by activated monocytes after sleep deprivation compared with regular nocturnal sleep 203 , 235 . These contrasting results need further investigations and may depend on differences in the cytokine sensitivity to different sleep deprivation protocols or sampling methods and time. For instance, it seems that partial acute sleep deprivation increased stimulated monocytic TNF-α production 232 , 233 , whereas more sustained sleep deprivation decreased it 203 , 235 .

Undisturbed sleep is predominantly characterized by a Th1 polarization of Th cells (expressing IFN-γ, IL-2, and TNF-α), and experimental sleep deprivation in humans leads to a shift from a Th1 pattern towards a Th2 pattern (expressing IL-4, IL-5, IL-10, and IL-13) 217 , 236 . Accordingly, conditions featured by disturbed sleep with specific deficit in slow wave sleep, as observed in elderly people 237 , alcoholic 238 , and insomnia 239 patients, show a cytokine shift towards Th2. The balance of Th1/Th2 immunity and its shift during sleep deprivation may have crucial implications in anti-microbial and anti-tumor immune responses. Th2 over-activity is known to be involved in some forms of allergic responses, and to increase the susceptibility to infection 240 . Likewise, regarding the anti-tumor immune action, Th1 response supports cytotoxic lymphocytes and tumor cells destruction with the potential of elimination or control of tumor cell growth, so that a type 1 adaptive immune response (increased antigen presentation, IFN-γ signaling, and T-cell receptor signaling) may be associated with an improved survival or prognosis 241 , 242 . In contrast, Th2 over-response is thought to contribute to tumor development and progression, by limiting cytotoxic T lymphocytes proliferation and by the modulation of other inflammatory cell types 241 .

Several cellular and molecular signaling pathways may be involved in mediating the influence of sleep deprivation on immune and inflammatory functions (Fig.  3 ). Increased oxidative stress markers and/or decreased antioxidant defense have been found after sleep deprivation 243 , 244 , 245 . Sleep shows an antioxidant function, responsible for eliminating reactive oxygen species produced during wakefulness, and contrarily sleep deprivation may cause oxidative stress, which leads to cell senescence, unbalanced local/systemic inflammation, dysmetabolism, and immune derangements 246 , 247 .

A schematic model of potential mechanistic pathways linking sleep deprivation and inflammatory immune activation is depicted. Sleep deprivation is associated with activation of the sympathetic nervous system and release of norepinephrine and epinephrine into the systemic circulation, as well as to some extent with impaired hypothalamus-pituitary axis stimulation. These neuromediators may act along with other potential stimuli accumulated following sleep deprivation including reactive oxygen species (ROS), adenosine, metabolic waste products (e.g., β-amyloid) not cleared during normal sleep, gut microbiota dysbiosis leading to altered local and systemic pattern of metabolic products, as well as with changes in the profile of neuro-endocrine hormones, such as prolactin, growth hormone, and altered circadian rhythm of melatonin secretion. In immune cells located in the brain and the peripheral tissues, these stimuli may in concert trigger inflammatory activation, with release of cytokines, chemokines, acute phase protein, etc. via the recruitment of transcriptional regulators of pro-inflammatory gene expression, mainly nuclear factor (NF)-κB, and disturbing the circadian rhythmicity of gene expression of both clock genes and metabolic, immune and stress response genes (see text for further detail). E: epinephrine; NE: norepinephrine; TLR: Toll-like receptor. Arrows indicate stimulation; lines indicate inhibition. The illustrations were modified from Servier Medical Art ( http://smart.servier.com/ ), licensed under a Creative Common Attribution 3.0 Generic License.

Effects of sleep deprivation on the immune response may derive from the activation of the SNS with the corresponding increase in systemic catecholamines 22 , 248 . Catecholamines signal to immune cells via adrenergic receptors, which are primarily α- and β-adrenergic in myeloid cells and β-adrenergic in lymphocytes 249 . The immune outcome of the sympathetic signaling is complex, and includes both stimulatory and inhibitory effects depending on cell and receptor types, cell development/activation states, and local microenvironment 249 , 250 . Some evidence suggest that β-adrenergic signaling inhibits and α-adrenergic signaling promotes excessive inflammation under endotoxemia 250 . Activation of α-adrenergic signaling in peripheral tissues induces the upregulation of pro-inflammatory cytokines 250 , 251 . Sympathetic activation also suppresses the transcription of type I IFNs ( IFN-α and IFN-β ) genes and interferon response genes, which play a key role in anti-viral immunity 252 , and inhibits via β-adrenergic signaling the anti-tumor cytotoxicity of T lymphocytes 253 . In vitro β-adrenergic stimulation repressed Th1 response and stimulated Th2 response, with varying effects found in vivo 249 , 254 . Although the specific role of SNS activation in the immune phenotype associated with sleep deprivation is not clearly established, data suggest a pro-inflammatory effect of SNS under sleep deprivation. Indeed, chemical sympathectomy has been recently shown to alleviate the inflammatory response following chronic sleep deprivation in mice 255 , and both α- and, to a lesser extent, β-adrenergic receptors seem to contribute to the sympathetic regulation of inflammatory responses to sleep deprivation 256 .

At the molecular levels, sleep deprivation led to significant gene expression changes in animal tissues 257 , 258 , 259 and human blood monocytes 203 , 233 , 260 , 261 , 262 , with affected genes mostly related to immune and inflammatory processes (leukocyte function, Th1/Th2 balance, cytokine regulation, and TLR signaling), oxidative stress, stress response, apoptosis, and circadian system, collectively indicating immune activation and hyperinflammation.

Sleep loss and mistimed sleep also led in the blood transcriptome to alteration and reduction in the circadian rhythmicity of gene expression 261 , 263 , which is an integral part of basic biological processes and homeostasis 264 , 265 , 266 .

The activation of the pro-inflammatory NF-κB/Rel family of transcription factors by sleep deprivation, first demonstrated in the late 1990s in mice 267 , and subsequently widely confirmed 233 , 260 , 261 , 268 , 269 , 270 , 271 , 272 , is one of the most consistent findings regarding upstream transcriptional regulation. NF-κB induces the expression of genes (e.g., cytokines/chemokines, growth factors, receptors/transporters, enzymes, adhesion molecules) involved in inflammation, immunity, proliferation, and apoptosis 273 , circadian clock activity 274 , and sleep propensity 275 . Potential signals for NF-κB activation under sleep deprivation include increased adenosine levels, oxidative stress, altered metabolism (adiposity and decreased insulin sensitivity), brain proteins/metabolites (e.g., Aβ), melatonin suppression 276 , circadian clock proteins 277 , and catecholamine surge due to increased sympathetic activity 278 . Given the role of NF-κB in the pathophysiology of inflammatory diseases 273 , its activation under sleep deprivation may be a common pathway for the risk of morbidity and mortality.

The intestinal microbiota is also affected by sleep loss 279 , 280 , 281 , showing indices of dysbiosis (increased Firmicutes:Bacteroidetes ratio; decreased diversity and richness), which may affect the immune system 282 , and are similar to those associated with cardiometabolic diseases 45 .

Countermeasures for sleep deprivation: effect on immune parameters

Although the impact of strategies to improve sleep duration on neurobehavioral performance and alertness after sleep deprivation have been assessed 283 , 284 , 285 , sleep deprivation countermeasures to improve immune and inflammatory parameters, and, correspondingly, disease risk and outcomes have been studied to a lesser extent.

Although extension of habitual short sleep did not show to significantly counterbalance the immune consequence of sleep deprivation 286 , 287 , 288 , mixed results derive from nighttime recovery sleep following sleep deprivation (Table  1 ), with limited evidence of effectiveness for specific immune parameters 210 , 214 , and mostly after multiple consecutive nights of 8 h sleep recovery or with an extended nocturnal sleep duration 212 , 289 .

Although daytime napping (<20 min) restores alertness, and mental and physical performance without provoking sleep inertia associated with longer nap 290 , 291 , 292 , the effects of a short nap on immune/inflammatory parameters after sleep deprivation have yet to be firmly established. Differently form population studies 293 , laboratory studies found immune benefit from nap 218 , 289 , 294 , 295 . Regarding immune-related clinical outcomes, controversy exists, with studies finding no association 296 , inverse associations 297 , 298 or positive association 296 , and a J-shaped relationship 299 , 300 , 301 between napping and CV and metabolic diseases or cancer events and mortality. Whether changes in immune parameters could contribute to the associations between napping and immune-related diseases remains unclear.

Among the strategies to recover sleep deprivation-induced immune changes, cognitive behavior therapy improves sleep outcomes in insomnia and lowers cellular and systemic inflammatory markers 302 , 303 , and the risk score composed of CV and metabolic risk factors 304 . This highlights the potential role of targeting sleep in reducing the inflammatory risk and the associated chronic diseases.

Summary and concluding remarks

Sleep exerts immune-supportive functions and impairments of the immune-inflammatory system are a plausible mechanism mediating the negative health effects of sleep deprivation, and in particular, its role in the risk and outcomes of chronic diseases such as infections, CV, metabolic and autoimmune diseases, NDDs, and cancer. Caution should be exercised in interpreting cellular and molecular outcomes of sleep deprivation in experimental studies conducted till now as a result of an independent effect of sleep deprivation, because other factors may play a role, including extended wakefulness-associated processes, other features of sleep-wakefulness, their temporal and functional segregation or methodologies of sleep manipulation.

Randomized controlled trials assessing the effect of treatment of sleep deprivation on inflammatory immune dysfunction and/or health outcomes are needed. Knowledge of inflammatory and immunological signatures in response to sleep curtailment may inform not only on the underlying molecular links, but also contribute to refine risk profiles to be used for developing biomarkers of sleep deprivation and sleep disturbance-related health outcomes, which may also represent potential targets of interventions. Recent metabolomic 305 and transcriptomic 306 studies hold promise in biomarker discovery 306 .

These efforts may converge towards a new ground fostering interactions between the sleep research and the medical community to translate scientific knowledge into the clinic, prioritize health issues, and develop strategies and policies for subject risk stratification, to include evidence-based sleep recommendations in guidelines for optimal health and to address sleep hygiene at the individual and the population levels, as a means to prevent the negative health consequences of sleep deprivation. These actions might also foster health literacy and empowerment of individuals to actively better manage their own health and well-being throughout their life course by means of lifestyle, nutritional, and behavioral habits including sleep hygiene 307 .

Conclusively, in the perspective of staying healthy in this rapidly changing society, the sleep–immunity relationship raises relevant clinical implications for promoting sleep health and, as evidenced here, for improving or therapeutically controlling inflammatory response by targeting sleep. This may ultimately translate, in the era of preventive medicine, into addressing sleep as a lifestyle approach along with diet and physical activity to benefit overall public health.

Reporting summary

Further information on research design is available in the  Nature Research Reporting Summary linked to this article.

Data availability

All data generated or analysed during this study are included in this published article.

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Garbarino, S., Lanteri, P., Bragazzi, N.L. et al. Role of sleep deprivation in immune-related disease risk and outcomes. Commun Biol 4 , 1304 (2021). https://doi.org/10.1038/s42003-021-02825-4

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Research uncovers differences between the sexes in sleep, circadian rhythms and metabolism

by University of Southampton

A new review of research evidence has explored the key differences in how women and men sleep, variations in their body clocks, and how this affects their metabolism.

Published in Sleep Medicine Reviews , the paper highlights the crucial role sex plays in understanding these factors and suggests a person's biological sex should be considered when treating sleep, circadian rhythm and metabolic disorders .

Differences in sleep

The review found women rate their sleep quality lower than men's and report more fluctuations in their quality of sleep, corresponding to changes throughout the menstrual cycle.

"Lower sleep quality is associated with anxiety and depressive disorders, which are twice as common in women as in men," says Dr. Sarah L. Chellappa from the University of Southampton and senior author of the paper.

"Women are also more likely than men to be diagnosed with insomnia, although the reasons are not entirely clear. Recognizing and comprehending sex differences in sleep and circadian rhythms is essential for tailoring approaches and treatment strategies for sleep disorders and associated mental health conditions."

The paper's authors also found women have a 25% to 50% higher likelihood of developing restless legs syndrome and are up to four times as likely to develop sleep-related eating disorder, where people eat repeatedly during the night.

Meanwhile, men are three times more likely to be diagnosed with obstructive sleep apnea (OSA). OSA manifests differently in women and men, which might explain this disparity. OSA is associated with a heightened risk of heart failure in women, but not men.

Sleep lab studies found women sleep more than men, spending around 8 minutes longer in non-REM (Rapid Eye Movement) sleep, where brain activity slows down. While the time we spend in NREM declines with age, this decline is more substantial in older men. Women also entered REM sleep, characterized by high levels of brain activity and vivid dreaming, earlier than men.

Variations in body clocks

The team of all women researchers from the University of Southampton in the UK, and Stanford University and Harvard University in the United States, found differences between the sexes are also present in our circadian rhythms.

They found melatonin, a hormone that helps with the timing of circadian rhythms and sleep, is secreted earlier in women than men. Core body temperature, which is at its highest before sleep and its lowest a few hours before waking, follows a similar pattern, reaching its peak earlier in women than in men.

Corresponding to these findings, other studies suggest women's intrinsic circadian periods are shorter than men's by around six minutes.

Dr. Renske Lok from Stanford University, who led the review, says, "While this difference may be small, it is significant. The misalignment between the central body clock and the sleep/wake cycle is approximately five times larger in women than in men. Imagine if someone's watch was consistently running six minutes faster or slower. Over the course of days, weeks, and months, this difference can lead to a noticeable misalignment between the internal clock and external cues, such as light and darkness.

"Disruptions in circadian rhythms have been linked to various health problems, including sleep disorders , mood disorders and impaired cognitive function. Even minor differences in circadian periods can have significant implications for overall health and well-being."

Men tend to be later chronotypes, preferring to go to bed and wake up later than women. This may lead to social jet lag, where their circadian rhythm doesn't align with social demands, like work. They also have less consistent rest-activity schedules than women on a day-to-day basis.

Impact on metabolism

The research team also investigated if the global increase in obesity might be partially related to people not getting enough sleep—with 30 percent of 30- to 64-year-olds sleeping less than 6 hours a night in the United States, with similar numbers in Europe.

There were big differences between how women's and men's brains responded to pictures of food after sleep deprivation. Brain networks associated with cognitive (decision-making) and affective (emotional) processes were twice as active in women than in men. Another study found women had a 1.5 times higher activation in the limbic region (involved in emotion processing, memory formation, and behavioral regulation) in response to images of sweet food compared to men.

Despite this difference in brain activity , men tend to overeat more than women in response to sleep loss. Another study found more fragmented sleep, taking longer to get to sleep, and spending more time in bed trying to get to sleep were only associated with more hunger in men.

Both women and men nightshift workers are more likely to develop type 2 diabetes, but this risk is higher in men. Of women nightshift workers, 66% experienced emotional eating and another study suggests they are around 1.5 times more likely to be overweight or obese compared to women working day shifts.

The researchers also found emerging evidence on how women and men respond differently to treatments for sleep and circadian disorders. For example, weight loss was more successful in treating women with OSA than men, while women prescribed zolpidem (an insomnia medication) may require a lower dosage than men to avoid lingering sleepiness the next morning.

Dr. Chellappa added, "Most of sleep and circadian interventions are a newly emerging field with limited research on sex differences. As we understand more about how women and men sleep, differences in their circadian rhythms and how these affect their metabolism, we can move towards more precise and personalized health care which enhances the likelihood of positive outcomes."

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Sleep Deprivation and Memory: Meta-Analytic Reviews of Studies on Sleep Deprivation Before and After Learning

Chloe r. newbury.

1 Department of Psychology, Royal Holloway, University of London

Rebecca Crowley

Kathleen rastle, jakke tamminen.

This work was funded by Economic and Social Research Council grant ES/P001874/1 awarded to Kathleen Rastle and Jakke Tamminen.

Chloe R. Newbury and Rebecca Crowley contributed equally to this work.

The data and analysis scripts are available at osf.io/5gjvs/ .

Associated Data

Research suggests that sleep deprivation both before and after encoding has a detrimental effect on memory for newly learned material. However, there is as yet no quantitative analyses of the size of these effects. We conducted two meta-analyses of studies published between 1970 and 2020 that investigated effects of total, acute sleep deprivation on memory (i.e., at least one full night of sleep deprivation): one for deprivation occurring before learning and one for deprivation occurring after learning. The impact of sleep deprivation after learning on memory was associated with Hedges’ g = 0.277, 95% CI [0.177, 0.377]. Whether testing took place immediately after deprivation or after recovery sleep moderated the effect, with significantly larger effects observed in immediate tests. Procedural memory tasks also showed significantly larger effects than declarative memory tasks. The impact of sleep deprivation before learning was associated with Hedges’ g = 0.621, 95% CI [0.473, 0.769]. Egger’s tests for funnel plot asymmetry suggested significant publication bias in both meta-analyses. Statistical power was very low in most of the analyzed studies. Highly powered, preregistered replications are needed to estimate the underlying effect sizes more precisely.

Public Significance Statement

The health risks associated with lack of sleep are well known, but the consequences of missing one or more nights of sleep for learning and memory are less well appreciated. In two meta-analyses pooling studies across 5 decades of research, we found that total sleep deprivation before learning as well as after learning had a detrimental impact on memory for the newly learned materials. These data suggest sleep supports learning and memory in two ways: It prepares the brain for learning over the next day, and it helps strengthen new memories learned during the previous day.

There is a growing body of evidence suggesting a critical role of sleep in learning and memory ( Diekelmann & Born, 2010 ). On the one hand, offline memory consolidation during sleep benefits both declarative and procedural memories acquired during preceding wake ( Klinzing et al., 2019 ). On the other hand, memory encoding capacity has been argued to saturate gradually during wake, with sleep restoring this capacity ( Cirelli & Tononi, in press ; Tononi & Cirelli, 2012 ). These theoretical advances have been accompanied by practical societal concerns regarding the prevalence of poor sleep, especially for students ( Twenge et al., 2017 ) and shift workers ( Vidya et al., 2019 ). The proposed importance of sleep for memory processes has been supported by many studies showing detrimental effects of total sleep deprivation on the learning and retrieval of new information. Yet, the effect sizes associated with the total sleep deprivation impairment are variable, and some studies have failed to find significant effects altogether (e.g., Diekelmann et al., 2008 ). Therefore, we used a meta-analytic approach to estimate the effect size associated with the impact of sleep deprivation, both when the deprivation occurs before learning and when it occurs after learning.

Impact of Total Sleep Deprivation After Learning and Potential Moderators of the Effect

The active systems consolidation theory suggests that sleep after learning strengthens new memories (e.g., Klinzing et al., 2019 ; Kumaran et al., 2016 ; McClelland et al., 1995 ). Information learned during wakefulness is initially encoded rapidly in the hippocampus, where memories are stored separately from existing memory stores. Repeated reactivation of these new memories, primarily during slow-wave sleep (SWS), supports the strengthening of memory representations and leads to the integration of these memories in the neocortex. Such neocortical representations are less liable to disruption and form interrelated semantic networks with existing memories, yielding memory representations that allow abstraction, generalization, and discovery of statistical patterns across discrete memories ( Lewis & Durrant, 2011 ; Lewis et al., 2018 ; Stickgold & Walker, 2013 ). Notably, since the mechanisms outlined in this theory relate only to hippocampal-dependent memory consolidation ( Inostroza & Born, 2013 ), these mechanisms may primarily support the consolidation of declarative (or explicit) memory.

Effects of sleep on nondeclarative memory have also been observed; for example, sleep enhances motor skills such as finger-tapping sequence learning ( Korman et al., 2007 ; Walker et al., 2002 ; see King et al., 2017 for a review). However, this beneficial effect of sleep on procedural memories may be evident only when learning is intentional (explicit memory), rather than unintentional (implicit memory). Robertson et al. (2004) found that awareness of learning a finger-tapping task led to a sleep benefit, whereas improvements in implicit learning performance, when participants had little awareness of the task, were similar regardless of whether the retention interval contained sleep or wakefulness. Thus, there are suggestions that the mechanisms involved in the consolidation of hippocampal-dependent declarative memories may also facilitate the consolidation of some procedural tasks that rely on explicit learning and thus show some hippocampal-dependency ( Schönauer et al., 2014 ; Walker et al., 2005 ).

However, this latter theory does not take into account observations that some procedural tasks that do not rely on explicit learning or an intact hippocampus still show superior performance after sleep. Stickgold et al. (2000) found that a period of sleep after learning a visual discrimination task benefited later performance, and Schönauer et al. (2015) found that improvements in performance on a mirror-tracing task were only observed after a period of offline consolidation. Recent studies in both animals ( Sawangjit et al., 2018 ) and humans ( Schapiro et al., 2019 ) have suggested that the hippocampus may be involved in the sleep-dependent consolidation of memories that do not rely on the hippocampus during encoding. For example, Schapiro et al. (2019) trained amnesic patients with hippocampal damage on the motor sequence task, a classic procedural memory task typically considered to be non-hippocampus-dependent. The patients were able to learn the task equally well compared to matched controls, suggesting that the hippocampus is not required for learning of the task. However, while the controls showed the expected overnight consolidation benefit, the patients did not, leading the authors to conclude that the hippocampus may be involved in the consolidation of procedural tasks that do not require it for initial learning.

As reviewed above, theories of memory consolidation predict that depriving participants of sleep after learning should impair memory for the information encoded before sleep deprivation, relative to control conditions where participants are allowed to sleep normally after learning. In our first meta-analysis, we analyze the current research into both declarative and procedural memories to estimate the size of this sleep deprivation after learning effect. We focus on the literature using manipulations of total sleep deprivation, as this is the strongest and most direct manipulation to test theories of sleep-associated memory consolidation. In doing so, we exclude from our analyses studies of sleep restriction. In standard sleep restriction studies, participants are allowed to sleep for a shorter duration than they would do otherwise, and the manipulation typically continues for multiple nights. This chronic sleep restriction can have a detrimental impact on learning and memory (e.g., Cousins et al., 2018 ) although not always (e.g., Voderholzer et al., 2011 ). In selective sleep restriction studies, participants are only deprived of the first half of the night, rich in SWS, or the second half of the night, rich in rapid eye movement (REM) sleep (e.g., Plihal & Born, 1997 ). These studies can reveal important information about the precise brain mechanisms underlying benefits of sleep on memory. There are two theoretically motivated reasons for excluding both types of sleep restriction from our meta-analyses. Standard sleep restriction studies still allow participants to sleep for several hours each night, and it may be sufficient for sleep-associated memory consolidation to occur. Thus, these manipulations do not provide a strong test of the relevant theories, and their inclusion might lead us to underestimate the effect size associated with sleep deprivation. Selective sleep restriction studies on the other hand are designed to address the more fine-grained question of which specific sleep stages are the most beneficial for memory. Different theories make different predictions in this regard: For example, the active systems consolidation theory emphasizes the role of SWS ( Klinzing et al., 2019 ); the theory of Lewis et al. (2018) emphasizes REM (at least for learning involving creative thought); and the sequential theory of Giuditta (2014) proposes that an interaction between SWS and REM is key. However, all current theories make the common prediction that sleep should benefit memory more than wake. We do not attempt to adjudicate between the competing theories and therefore restrict our analysis to studies using total sleep deprivation where a clear prediction is made by all theories.

Where possible we use moderators to establish whether the effect size is modulated by variables that have been hypothesized to be important. For example, it is not clear whether sleep deprivation after learning impacts declarative and procedural memories similarly. If the neural and cognitive mechanisms associated with the consolidation of declarative and procedural memories differ, one may observe different effect sizes in studies targeting the two memory types. To test this hypothesis, we entered declarative versus procedural memory type as a moderator in our meta-analysis.

Some studies have tested the effects of sleep deprivation after one or more nights of recovery sleep while others have tested immediately after a night of sleep deprivation with no intervening recovery sleep. The primary reason for allowing recovery sleep before testing is that sleep deprivation has well-established impacts on attention (e.g., Lim & Dinges, 2008 ; Vargas et al., 2017 ) that may compromise performance in an immediate memory test and make it difficult to distinguish between effects of sleep deprivation due to fatigue and effects due to disruption to memory consolidation processes. Using recovery sleep to rule out potential effects of fatigue at test assumes that the first night of sleep following learning is of critical importance in memory consolidation and that consolidation may not occur in subsequent sleep periods or has a weaker impact after the first missed sleep opportunity. Although we are not aware of any studies that have explicitly tested this assumption, for example, by systematically manipulating the number of nights of sleep following learning, some support has been derived from studies such as that of Gais et al. (2007) who observed effects of one night of sleep deprivation after two recovery nights and even 6 months later.

Recent studies have cast doubt on the privileged status of the first night of sleep, however. Schönauer et al. (2015) found only a short-term cost of sleep deprivation after learning for hippocampal-dependent memories, with sleep deprivation after learning impairing retrieval of word pairs after one night of deprivation, but no difference in retrieval between the sleep and sleep deprivation conditions after two nights of recovery sleep. They suggested that for such hippocampal-dependent memories, the hippocampus may act as a temporary buffer, storing memories until the first sleep opportunity, even if that opportunity is delayed. Thus, sleep deprivation after learning would only have a detrimental effect on memory performance if there is no sleep opportunity before testing. In contrast, they found that procedural memories that do not rely on the hippocampus suffered a more long-term effect of sleep deprivation, supporting previous evidence that the first night of sleep is crucial for improving performance on procedural tasks ( Stickgold et al., 2000 ). Therefore, for procedural memories, the loss of the first night of sleep may be critical. Given the mixed evidence in the literature on the impact of recovery sleep, we entered the presence or absence of recovery sleep as a moderator in the meta-analysis. If the first night of sleep after learning is of critical importance, on the one hand, we should see a sleep deprivation impairment both in studies that test immediately after deprivation and in studies that test after recovery sleep. If, on the other hand, consolidation processes can be delayed, we should see a sleep deprivation impairment only in studies that test immediately (i.e., they provide no opportunity for delayed consolidation). The latter scenario is also consistent with the possibility that the sleep deprivation impairment is due entirely to fatigue at test (although see Schönauer et al., 2015 , for data suggesting fatigue does not impair memory recall).

Some studies of recognition memory have failed to find a beneficial effect of sleep on memory performance leading to a debate about whether sleep has no or only limited impact on recognition memory (e.g., Drosopoulos et al., 2005 ; Hu et al., 2006 ; Morgan et al., 2019 ; Rauchs et al., 2004 ; Stepan et al., 2017 ). The difference between recall and recognition tasks is particularly stark in the literature using the Deese–Roediger–McDermott (DRM) paradigm of false memory formation. Here, studies using recognition tasks have reported that sleep may reduce false memories ( Fenn et al., 2009 ; Lo et al., 2014 ), whereas studies using recall tasks have reported that sleep may increase false memories ( Diekelmann et al., 2010 ; McKeon et al., 2012 ; Pardilla-Delgado & Payne, 2017 ; Payne et al., 2009 ). Consequently, a recent meta-analysis of sleep studies using the DRM paradigm concluded that the impact of sleep on false memory is restricted to recall tasks ( Newbury & Monaghan, 2019 ). This meta-analysis also found that the sleep benefit for studied words (i.e., veridical memory) was dramatically larger in recall tasks than in recognition tasks ( g = 0.407 vs. g = 0.005). The discrepancy between recall and recognition tasks might be explained by dual-process accounts, which suggest that recognition memory has both an explicit recollection element as well as an implicit familiarity element (e.g., Jacoby, 1991 ; Yonelinas, 2002 ), and these two elements may rely on different neural structures, with only recollection depending on the hippocampus. Support for this account has come from findings that sleep only facilitates recognition memory based on recollection, not familiarity (e.g., Drosopoulos et al., 2005 ). On the other hand, studies that have directly compared memory for emotionally negative and neutral stimuli appear to suggest that sleep benefits recognition memory for emotional stimuli but not for neutral stimuli ( Alger et al., 2018 ; Hu et al., 2006 ; Payne et al., 2008 , 2015 ; see Kim & Payne, 2020 for a review) suggesting that sleep’s impact on recognition memory may be modulated by emotionality. Given the inconsistency in the existing literature on the extent to which sleep after learning benefits recognition memory tasks, it is important to quantify and compare the size of the sleep benefit across recognition and recall tasks. Thus, we entered memory type as a moderator in our meta-analysis.

Sleep has previously been found to improve emotional episodic memories more than neutral memories, with some studies suggesting a specific role of REM sleep in the consolidation of emotional memories (e.g., Groch et al., 2015 ; Payne & Kensinger, 2010 ; Wagner et al., 2001 ; see Kim & Payne, 2020 for a review). Two recent meta-analyses investigated the preferential role of sleep in emotional memory consolidation. Schäfer et al. (2020) found that sleep improved both emotional and neutral memory equally, with no evidence for preferential impact on emotional memory; in fact, the difference between emotional and neutral memory was larger after wake than sleep. However, Schäfer et al. (2020) did find that when their analysis was restricted to experiments that contrasted SWS and REM sleep, sleep that consisted primarily of REM did show a preferential consolidation effect for emotional memory, although the number of studies included in this analysis was small. Lipinska et al. (2019) also found no meta-analytic evidence in favor of sleep’s preferential impact on emotional memory. However, they did find that the preferential effect was larger in recall tasks compared to recognition tasks and in studies that controlled for initial learning. In the current meta-analysis, we shed new light on these issues by focussing on sleep deprivation manipulations. To investigate whether the effect of sleep deprivation on memory performance is modulated by emotionality of the to-be-remembered stimuli, we entered emotionality as a moderator in our meta-analysis.

Impact of Total Sleep Deprivation Before Learning and Potential Moderators of the Effect

Recent research has also proposed a role for sleep before learning. According to the synaptic homeostasis hypothesis ( Cirelli & Tononi, in press ; Tononi & Cirelli, 2003 , 2012 ), learning occurs during wake when a neuron detects a statistical regularity in its input and begins to fire in response to this regular input. In other words, successful learning requires neurons to be able to fire selectively in response to statistically regular patterns observed in the environment. To do so, strength of the synapses carrying these inputs must be increased. However, the neuron now faces the plasticity-selectivity dilemma. As an increasing number of input lines become strengthened, a larger range of input patterns can make the neuron fire, reducing the neuron’s ability to fire selectively. This loss of selectivity corresponds to reduced ability to encode new information. During sleep, the brain spontaneously activates both new information encoded during previous wake and information encoded in the past. Over the course of this activation, those synapses that are activated most strongly and consistently during wake survive, while at the same time, those synapses that were less activated are weakened. This weakening occurs primarily during the transitions between intracellular up and down states experienced during SWS. This competitive down-selection of weaker synapses restores memory encoding ability.

The restorative function of sleep is supported by evidence showing decreased episodic learning ability across a 6-hr retention interval in which participants remained awake, whereas encoding capacity was restored after a daytime nap ( Mander et al., 2011 ). Further, neuroimaging evidence suggests sleep deprivation prior to learning is associated with disrupted encoding-related functional activity in the bilateral posterior hippocampus ( Yoo et al., 2007 ; for similar findings, see Alberca-Reina et al., 2015 ; Drummond & Brown, 2001 ; Van Der Werf et al., 2009 ). Thus, sleep deprivation before learning may be detrimental specifically for the encoding of hippocampal-dependent declarative memories. Our second meta-analysis seeks to estimate the effect size associated with this impairment. As only two studies have looked at the impact of sleep deprivation on procedural learning when it occurs after deprivation, we were not able to assess the potential moderating effect of declarative versus procedural memory. We were also not able to use emotionality as a moderator here due to the low number of relevant studies. Yet, there are several studies that have used recall tasks and recognition memory tasks, so we were able to evaluate the moderating effects of recall versus recognition, as in the first meta-analysis.

The Present Meta-Analyses

Despite the breadth of evidence for an effect of total sleep deprivation both before and after learning on memory performance, there is no comprehensive review and analysis of the strength of the effect of sleep deprivation on long-term memory. Previous reviews and meta-analyses investigating a role of sleep deprivation have focused on tasks that are likely more susceptible to fatigue. Pilcher and Huffcutt (1996) conducted a meta-analytic review of the effect of sleep deprivation on cognitive and motor task performance in 19 primary studies and found that sleep deprivation had a significant impact on performance. Still, this meta-analysis does not address long-term memory performance. Similarly, Lim and Dinges (2010) found an effect of sleep deprivation on a range of cognitive tasks including attention, working memory, and short-term memory, though the size of the effect varied depending on the task (e.g., a nonsignificant effect on reasoning accuracy, but a large effect on attention). Finally, Harrison and Horne (2000b) in a review found that sleep deprivation impacted decision-making ability. The tasks studied in these reviews are often repetitive and monotonous (e.g., the Psychomotor Vigilance Task; Dinges & Powell, 1985 , the go/no go paradigm, and tests of serial addition), and they tend to probe lower-level functions that are particularly susceptible to fatigue, such as reaction times and processing speed. Therefore, the conditions of these studies are arguably better suited to finding adverse effects of sleep deprivation on performance than studies looking at higher-level learning and long-term memory. Thus, a review of the effects of sleep deprivation on such high-level, long-term memory is required.

Taking a meta-analytic approach will permit not only a quantitative assessment of the size of the main effect of sleep deprivation and its moderators but also an investigation of methodological quality within this literature including the statistical power of studies proposing to find a sleep deprivation effect. Variety in sample selection and methodological designs used within this literature raises the possibility of variations in methodological quality. Such variations could lead to biases in the meta-analysis by overestimating or underestimating the effect size ( Higgins et al., 2011 ). Thus, we developed a checklist to assess multiple aspects of methodological quality, including questions specifically relevant to the assessment of sleep effects (e.g., excluding participants with sleep disorders), questions on study design (e.g., within-subject vs. between-subjects design, and random allocation to conditions), and questions on data analysis (e.g., preregistration and a priori power analyses). We used similar questions to other meta-analyses in the sleep literature ( Lim & Dinges, 2010 ; Lo, Groeger, et al., 2016 ; Schäfer et al., 2020 ) and examined methodological quality as a continuous moderator in the analysis. The full methodological quality checklist is provided in Supplemental Appendix A .

It has been suggested that psychological science more broadly is currently suffering from a replication crisis due to low power, publication bias, selection biases, and analysis errors ( Nosek et al., 2015 ). Low power limits potential to detect genuine effects but also results in Type I errors and exaggerated effect sizes ( Ioannidis, 2005 ; Pollard & Richardson, 1987 ). Szucs and Ioannidis (2017) conducted an analysis of almost 4,000 cognitive neuroscience and psychology papers and found that the overall mean power to detect small, medium, and large effects was 17%, 49%, and 71%, respectively, with even lower power in the subfield of cognitive neuroscience. Given the convention that power to detect an effect size should be at least 80% ( Di Stefano, 2003 ), it is clear that a large number of studies within psychology are underpowered (see Button et al., 2013 ; for further evidence of low statistical power within neuroscience). In the sleep literature, sample sizes tend to be low, potentially due to the resource intensity of conducting these experiments. Thus, we investigated whether the low power seen more broadly in psychological science and neuroscience is also evident in the sleep deprivation literature. For each individual effect size entered into the meta-analysis, we calculated the study’s power (defined as power to detect our meta-analytic effect size) and investigated whether there is an association between a study’s power and the effect size observed in the study.

Search Strategy

For study selection, we generated the Boolean search term “Sleep AND (deprivation OR restriction OR loss) AND (learning OR memory OR conditioning)” and conducted a search in the electronic databases EBSCOhost (included PsycARTICLES, PsycEXTRA, PsycINFO, and PsycTESTS) and PubMED on July 29th, 2020. This search yielded 2,213 empirical articles published between January 01, 1970 and July 29, 2020 in peer-reviewed journals in English using human participants.

In line with best practice guidelines ( Rothstein et al., 2005 ; Siddaway et al., 2019 ), we ran several searches on July 13th, 2020, using the same search terms as above, to identify gray literature in an attempt to mitigate against publication bias. These searches yielded a total of 553 items. Specifically, we widened our search criteria in EBSCOhost and PubMED to include unpublished dissertations and theses, conference materials, and preprints; we searched the bioRxiv and PsyArXiv repositories for preprints; and we searched the ProQuest and OpenGrey (a European database in which national libraries submit unpublished studies) databases for unpublished dissertations and theses, conference materials, and for research grants and fellowship awards. Additionally, we contacted all authors who had published data included in our initial screening results to ask for unpublished data that fit our inclusion criteria, and this yielded one preprint article. We also had one in-press article during the search period that fits our inclusion criteria ( Tamminen et al., 2020 ) and was therefore included in our search results. In sum, we identified both published and unpublished data with search strategies spanning (a) peer-reviewed published articles, (b) in-press articles, (c) preprints uploaded to repositories, (d) unpublished dissertations and theses, (e) conference materials, and (f) research grants and fellowship awards.

We scanned the abstracts and full texts of all articles according to our inclusion and exclusion criteria, and separated them into articles that investigated the effect of sleep deprivation after learning, and those that investigated sleep deprivation before learning. Figure 1 displays a screening process flowchart showing that after exclusions were removed, 130 effect sizes (extracted from 45 reports) were included in the sleep deprivation after learning meta-analysis and 55 effect sizes (extracted from 31 reports) were included in the sleep deprivation before learning meta-analysis. The number of effect sizes included in each meta-analysis is greater than the number of full-text articles that fit our inclusion criteria. The reason for this is that several studies measured performance differences between a sleep deprivation and sleep control group using multiple tasks, multiple conditions (e.g., stimulus valence or procedural instructions), and across multiple time points. Effect sizes were calculated for each of these data sets within an article because each variation represents a different, yet correlated, measurement of the impact of sleep deprivation on memory. However, there were some studies that used multiple outcome measures to assess performance in a single task (e.g., accuracy and reaction time). Given that multiple outcome measures within the same task are different ways of assessing the same manipulation, we chose only one outcome measure for calculating an effect size in these instances, according to the following hierarchy from most to least preferred outcome measure: accuracy as measured by retention performance (i.e., performance change from training to test), accuracy at test only, reaction time measured by retention performance (i.e., performance change from training to test), and reaction time at test only. Further, in recognition tasks, if both signal detection analyses and analyses based on proportion correct were reported, we chose to include the signal detection measure only. For example, if a study reported both d -prime and reaction times in a recognition memory task (e.g., Tamminen et al., 2020 ), we only used the d -prime data. ​ data.

A list of studies included in the two meta-analyses and their key properties can be found in Supplemental Appendix B (studies investigating sleep deprivation after learning) and Supplemental Appendix C (studies investigating sleep deprivation before learning), as well as at osf.io/5gjvs/ .

Inclusion/Exclusion Criteria

To select relevant studies, we applied the following inclusion/exclusion criteria.

• a Participants had to be healthy adults aged 18 years and older.
• b Studies must have included, as a primary independent variable, a manipulation of sleep deprivation that was a minimum of one night of total sleep deprivation with an appropriate sleep control condition consisting of one normal night of sleep. Residency studies (studies conducted in a medical setting) were excluded due to the lack of control over whether total sleep deprivation occurred (sleep deprivation is often reported despite naps having occurred on shift; e.g., Bartel et al., 2004 ; Guyette et al., 2013 ). Additionally, studies using sleep restriction protocols, which involve multiple nights of limited sleep duration rather than one or more nights of no sleep, were excluded because the neural and cognitive effects of sleep restriction may differ from those caused by total sleep deprivation ( Banks & Dinges, 2007 ; Lowe et al., 2017 ).
• c Studies must have included, as a primary dependent variable, at least one measure of learning or long-term memory where the task was described in sufficient detail to ascertain which cognitive function it assessed.
• d For the meta-analysis investigating sleep deprivation after learning, the cognitive task must have had a single encoding phase and a retrieval phase(s) that were temporally separated by either a period of sleep deprivation or an equivalent period of sleep. For the meta-analysis investigating sleep deprivation before learning, the single encoding phase and the retrieval phase(s) must have been temporally separated by a retention interval that had a minimum duration of at least 1 min, rather than being part of the same task session. The reason for this criterion is that our meta-analyses aimed to investigate effects of sleep deprivation on learning and long-term memory. The inclusion of studies with temporally indistinct encoding and retrieval phases would have included short-term and working memory tasks that form a separate body of literature ( Lim & Dinges, 2010 ), the analysis of which was beyond the scope of these meta-analyses.
• e In cases in which studies assessed the effects of other interventions (e.g., caffeine; Kilpeläinen et al., 2010 ) in ameliorating sleep deprivation effects, studies were included only if data could be obtained from the control sleep deprivation and control sleep groups. This criterion was included because the goal of the current meta-analyses was to assess effects of sleep deprivation in the absence of alertness-promoting strategies.
• f Studies must have reported sufficient statistical detail to calculate effect sizes (means, SD , F , and t ). When statistical details were not reported in the text, we either contacted corresponding authors to request relevant data or we extracted the data needed from published figures in the article using WebPlotDigitizer ( Rohatgi, 2019 ).

Methodological Quality

Through our survey of the literature, it became clear that sleep deprivation studies differ considerably in various aspects of methodological rigor (e.g., lack of control over adherence to sleep manipulations in the sleep deprivation and sleep control groups; Fischer et al., 2002 vs. complete control; Chatburn et al., 2017 ). For this reason, we assessed the methodological quality of each study entered into our meta-analyses and included this in our moderator analyses.

To assess methodological quality, we developed a 22-item checklist based on criteria for standard sleep deprivation experiments (e.g., preexperimental sleep monitored using actigraphy and exclusion of sleep disorders) and more general experimental psychology experiments (e.g., a priori power analysis and study design). For each item on the checklist, studies were scored with either a zero or a one according to whether they satisfied the criterion. To transform the total methodological quality score for each study into a risk of bias that reflects a rank of all the studies on a common scale, we normalized the total scores by dividing each study’s total methodological quality score by the maximum total methodological quality score that was achieved among all studies ( Stone et al., 2020 ). Lower values imply lower ranked studies (minimum score of 0) and higher values imply higher ranked studies (maximum score of 1) relative to the best study. The full methodological quality checklist can be found in Supplemental Appendix A .

Given that the checklist items form a multidimensional scale, the items were clustered according to the Downs and Black’s (1998) instrument for assessing methodological quality, which assesses five types of bias: “reporting,” “internal validity—bias,” “internal validity—confounding,” “power,” and “external validity.” The “reporting” cluster determines whether sufficient information is provided to make an unbiased assessment of study findings. In our methodological quality checklist, the items in this cluster referred to the reporting of exclusion criteria for participant characteristics (e.g., “Did the study exclude participants with a history of sleep disorders?”). The “internal validity—bias” cluster assesses whether biases were present in the intervention or outcome measure that would favor one experimental group [e.g., “Was interference for the sleep deprivation group low (nondemanding activities given and monitored in the lab)?”]. The “internal validity—confounding” cluster assesses whether biases were present in the selection and allocation of participants (e.g., “For within-group studies, was the order of deprivation and control conditions counterbalanced?”). The “power” cluster assesses whether a study used a priori power analyses to avoid Type II errors (e.g., “Did the study report an a priori power analysis with power set at 80% or higher and an α at .05 or lower?”). The Downs and Black’s (1998) “external validity” cluster determines the extent to which findings can be generalized to the population from which a sample was taken (e.g., “Were the staff, places, and facilities where the patients were treated, representative of the treatment the majority of patients receive?”; Downs & Black, 1998 , p. 383). Since the items in this cluster were designed for clinical intervention studies with nontypical populations, we dropped the external validity cluster from our checklist. In line with Cochrane Collaboration recommendations ( Higgins et al., 2011 ), the four clusters in our methodological quality checklist (hereon referred to as reporting, bias, confounding, and power) were then included in moderator analyses. The percentage of studies that passed on each item of the quality checklist for both Meta-Analysis 1 and Meta-Analysis 2 can be found in Supplemental Appendix D . Total methodological quality scores for each study, as well as the item-level ratings, can be found at osf.io/5gjvs/ .

Effect Size Calculation

Information on study means, standard deviation, and effect sizes for each item, as well as formulas used to calculate effect sizes, can be found at osf.io/5gjvs/ . We report the standardized mean difference in task performance between a sleep deprivation and sleep control group, with positive values indicating that sleep deprivation influenced learning and memory such that performance was significantly worsened compared to a sleep control group. For studies with independent samples (between-subjects designs), we computed Cohen’s d s based on the means and variance reported in each study for the sleep and sleep deprivation group. For within-subject designs, in which participants took part in both the sleep deprivation and sleep control conditions, we calculated Cohen’s d av , as recommended by Lakens (2013) .

Data Analysis

Overall meta-analytic effect size.

All analysis code can be found at osf.io/5gjvs/ . To analyze whether there was an overall meta-analytic effect of sleep deprivation versus overnight sleep on memory performance, we fitted a multilevel random-effects model using the R package metafor ( Viechtbauer, 2010 ). A random-effects model allows for inconsistencies between effect sizes from varying study designs, assuming systematic variability between effect sizes in addition to random sampling error. A random-effects model therefore provides more conservative effect size estimates than a fixed-effect model ( Borenstein et al., 2010 ). A multilevel model allows for the inclusion of both within-study effect sizes and between-study effect sizes ( Assink & Wibbelink, 2016 ). Many experiments included in the meta-analysis report multiple dependent effect sizes, such as results from multiple test sessions, multiple within-group experimental conditions (e.g., performance on emotional vs. neutral stimuli), or multiple outcomes (e.g., a procedural and declarative memory task). Including multiple dependent effect sizes from the same experiment violates the assumption of data independence assumed in a typical random-effects model. A multilevel meta-analysis accounts for such dependencies by modeling both within-study and between-study effects. Thus, we were able to model variance accounted for by (a) random error, (b) within-study differences among effect sizes within the same experiment, and (c) between-study differences across different experiments.

Heterogeneity

To investigate whether moderating variables may influence the size of the effect of sleep deprivation, we examined heterogeneity within the data set using the Q test ( Cochran, 1954 ). The Q test indicates whether there is heterogeneity within the data set and is calculated by the weighted sum of the squared deviations of individual study effect estimates and the overall effect across studies. Significant heterogeneity suggests that some of the variance within the data set may not be due to random sampling error, and thus moderating variables may influence the effect. Since we were interested in both the within-study and between-study variance, we ran two separate one-sided log-likelihood-ratio tests. As such, the fit of the overall multilevel model was compared to the fit of a model with only within-study variance and to a model with only between-study variance. This allowed us to determine whether it was necessary to account for both within- and between-study variances within our model.

Assink and Wibbelink (2016) suggest that such log-likelihood ratio tests may be subject to the issues of statistical power when the data set comprises a small number of effect sizes. Low statistical power may lead to nonsignificant effects of heterogeneity when in fact there is variance within or between studies. To account for this, it is recommended to also calculate the I 2 statistic, which indicates the percentage of variation across studies that is due to heterogeneity and that which is due to random sampling error ( Higgins & Thompson, 2002 ). Hunter and Schmidt (2004) suggest the 75% rule, such that if less than 75% of overall variance is attributed to sampling error, then moderating variables on the overall effect size should still be examined. Using the formula of Cheung (2014) , we calculated the percentage of variance that can be attributed to each level of our model.

However, although I 2 reports the proportion of variation in observed effect sizes, it does not provide us with absolute values that tell us the variance in true effects ( Borenstein et al., 2017 ). Thus, as recommended by Borenstein et al. (2011) , we report the τ 2 , which provide an estimate of the true effect size, and we report prediction intervals, which indicate that 95% of the time, effect sizes will fall within the range of those prediction intervals.

Publication Bias

To assess publication bias, we first examined a contour enhanced funnel plot. Funnel plots show each effect size plotted against its standard error, with contour lines corresponding to different levels of statistical significance. If studies are missing almost exclusively from the white area of nonsignificance, then there may be publication bias. If studies are missing from areas of statistical significance, the bias is likely due to other causes such as poor methodological quality, true heterogeneity, chance, or the bias may be artifactual ( Johnson, 2021 ; Sterne et al., 2011 ). We also conducted a variation of Egger’s regression test for funnel plot asymmetry ( Egger et al., 1997 ) that can be conducted with multilevel meta-analyses.

Meta-Analysis 1: Sleep Deprivation After Learning

This meta-analysis summarizes research from 45 reports investigating effects of sleep deprivation after learning (130 effect sizes) published in English between 1994 and 2020 across a total of 1,616 participants. All reports used healthy adult populations and deprived participants of one night of sleep postlearning. Notably, two reports from this meta-analysis also report data that are relevant to the sleep deprivation before learning meta-analysis ( Fischer et al., 2002 ; Tamminen et al., 2020 ). See Table 1 for central tendencies and frequency data for moderator and descriptive variables of studies included. The table shows that this literature is heavily biased toward young adults, severely limiting conclusions that can be drawn about older age groups. The literature predominantly uses between-groups designs rather than the statistically more powerful within-group designs, partly explaining and exacerbating the low power highlighted later in our analysis. Recognition memory and recall memory tasks are the most often employed measures of memory, and most of the literature probes declarative memory rather than procedural memory. We return to these memory type distinctions in our moderator analyses. Most studies used human observation to ensure participants in the sleep deprivation condition did not sleep during the night, but few ensured that they did not sleep during the day to the same standard. Low compliance during the day could possibly dilute the effect size. Finally, most but not all studies allowed recovery sleep after deprivation. We return to this in the moderator analyses. ​ analyses.

The overall effect size for the mean difference in memory performance between the sleep deprivation and sleep control group, measured by Hedges’ g , was 0.277 ( SE = 0.050), indicating a small-to-medium effect according to Cohen’s categorization, and a significant difference from zero, 95% CI [0.177, 0.377], p < .001. Figure 2 displays a forest plot of the effect sizes. See Supplemental Appendix B for a summary of all studies included in the meta-analysis. ​ meta-analysis.

Note . Effect sizes to the right indicate an effect of sleep deprivation after learning such that memory was significantly worse than in a sleep control group.

Some of the variance within the data set could not be explained by random error, highlighted by overall significant heterogeneity, Q (129) = 244.891, p < .001. An analysis of heterogeneity of between-study variance (Level 2) revealed a significant difference between a full and a reduced model ( p < .001), suggesting significant variability between studies. An analysis of heterogeneity of within-study variance (Level 3) also revealed a significant difference between a full model and a reduced model ( p < .001), suggesting significant variability between within-study effect sizes. We further calculated the I 2 statistic, which indicates the percentage of variance that could be attributed to each level of the model. Using the formula from Cheung (2014) , approximately 52% of variance can be attributed to sampling error, 14% to within-study variance, and 34% to between-study variance. Next, we calculated τ 2 , which provides a measure of the variance of the true effects; τ 2 = .026 for within-study variance, and τ 2 = .061 for between-study variance. Prediction intervals indicated that 95% of effect sizes would fall in the range of −0.316 and 0.870.

Based on the significant heterogeneity between studies, the large prediction intervals, as well as the 75% rule, such that moderators should be explored if less than 75% of the variance can be attributed to random sampling error ( Hunter & Schmidt, 2004 ), we therefore explored the effect of potential moderating variables on the direction of the effect.

Figure 3 shows a funnel plot of the effect sizes. Visual inspection of the funnel plot indicates that effect sizes are not evenly distributed across the funnel plot, raising the potential for publication bias in which studies reporting a positive effect are more likely to be published. Egger’s regression test reveals significant funnel plot asymmetry ( z = 2.297, p = .022), supporting this assessment of the funnel plot. Further visual inspection of the funnel plot reveals two potential outlier effect sizes in the area of high statistical significance; these potential outliers are characterized by large effect sizes and large standard error (therefore smaller sample sizes). These large effect sizes on the right-hand side of the plot suggest that there may be a bias in this literature toward publishing significant effects, regardless of the precision with which the study effect size can be estimated. However, the presence of multiple studies in the area of nonsignificance suggests other biases may also contribute to the asymmetry. ​ asymmetry.

Because Egger’s test indicates the presence of publication bias, we sought to quantify the impact of this bias on the estimated effect size. We conducted a trim-and-fill analysis ( Duval & Tweedie, 2000 ), which calculates potential missing effect sizes to create a symmetric funnel plot and then provides an adjusted overall meta-analytic effect size based on this funnel plot symmetry. Although this is a well-used method to assess publication bias, it assumes that effect sizes are independent of each other. The current meta-analysis uses a multilevel approach, with dependencies between some effect sizes. The adjusted effect size should therefore be considered a preliminary estimate. The trim-and-fill method estimated 12 missing studies from the left-hand side of the funnel plot. With these effect sizes included, the adjusted overall meta-analytic effect size was smaller than the original effect size of g = 0.277, although it was still significantly greater than zero, Hedges’ g = 0.166, SE = 0.041, 95% CI [0.086, 0.247], p < .001.

Outlier Analysis

We explored whether outliers and influential cases may have significantly influenced the meta-analytic effect size. To identify the presence of outliers, we identified any effect sizes with studentized residuals greater than or smaller than three, which identified one effect size as an outlier ( Albouy, Sterpenich, et al., 2013 ). However, an outlier may not necessarily influence the size of the overall effect ( Viechtbauer & Cheung, 2010 ). Therefore, based on suggestions by Viechtbauer and Cheung, we conducted influential case analyses, to identify any effect sizes that exerted a significant influence on the size of the overall meta-analytic effect. We measured Cook’s distance to examine the influence of deleting each study on the overall size of the effect, and DFBETAs to examine the effect of deleting each study on individual parameter estimates. Cook’s analysis identified a further one effect size that was found to be an influential case ( Darsaud et al., 2011 , Know judgements). Removal of the outlier and influential case reduced the overall meta-analytic effect size to 0.271 (from 0.277). Since moderator analyses examine smaller subsets of effect sizes, we removed these two specific effect sizes from all moderator analyses conducted.

Moderator Analysis

We introduced four categorical moderating variables and analyzed the effect of each moderator separately on the size of the effect of sleep deprivation on learning and memory: (a) whether it was a declarative ( k = 108) or procedural ( k = 20) memory task, (b) for declarative tasks, whether task type was recall ( k = 42) or recognition ( k = 59), (c) whether participants received one or more recovery nights of sleep ( k = 83) or no recovery sleep ( k = 45), and (d) for those studies that investigated emotionality, whether the stimuli were emotional ( k = 31) or neutral ( k = 20). Supplemental Appendix B shows the classification of each study on these dimensions.

Whether participants received a recovery night of sleep before the test session as a moderator had a significant effect, Q (1) = 10.496, p < .001. Thus, we ran separate effect size analyses for those studies where participants had a night of recovery sleep and those that did not. For those studies that had one or more nights of recovery sleep, there was a small effect of sleep deprivation on learning and memory, Hedges’ g = 0.176 ( SE = 0.058), which is significantly different from zero, 95% CI [0.060, 0.292], p = .003; Q (82) = 133.766, p < .001. For those studies that did not have a night of recovery sleep, the effect size was larger, Hedges’ g = 0.410, SE = 0.044, 95% CI [0.320, 0.499], p < .001; Q (44) = 50.042, p = .246.

Whether the task probed declarative or procedural memory also had a significant moderating effect, Q (1) = 5.301, p = .021. We therefore ran separate effect size analyses for those studies that implemented a declarative memory task and those that implemented a procedural memory task. For those studies with a declarative memory task, there was a small effect of sleep deprivation on learning and memory, Hedges’ g = 0.218 ( SE = 0.055), which is significantly different from zero, 95% CI [0.109, 0.327], p < .001; Q (107) = 174.802, p < .001. For those studies with a procedural memory task, the effect size was larger, Hedges’ g = 0.449, SE = 0.083, 95% CI [0.276, 0.623], p < .001; Q (19) = 24.794, p = .167.

Since we found a significant moderating effect of both recovery sleep and task type (declarative or procedural), we ran a further analysis to investigate whether there was an interaction between the two significant moderators. The analysis revealed no significant interaction between recovery sleep and memory type, Q (1) = 0.804, p = .370, suggesting that whether participants received recovery sleep or not affected declarative and procedural memory task performance in a similar way. Whether studies used a recall or recognition task did not have a significant effect on the size of the effect of sleep deprivation on learning and memory. Studies using a recall task had a mean effect size of Hedges’ g = 0.209, SE = 0.082, 95% CI [0.045, 0.374], p = .014; Q (41) = 76.317, p < .001, and studies with a recognition task had an overall effect size of Hedges’ g = 0.175, SE = 0.077, 95% CI [0.021, 0.330], p = .027; Q (58) = 91.950, p = .003. Effect sizes associated with emotional and neutral stimuli were also not significantly different, with an overall effect size of Hedges’ g = 0.251, SE = 0.080, 95% CI [0.084, 0.417], p = .005; Q (19) = 25.789, p = .136, for studies using neutral stimuli, and an overall effect size of Hedges’ g = 0.207, SE = 0.085, 95% CI [0.033, 0.380], p = .021; Q (30) = 41.888, p = .073, for emotional stimuli.

We then introduced four continuous moderating variables and analyzed the effect of each moderator separately on the size of the effect of sleep deprivation on learning and memory: (a) methodological quality reporting cluster, (b) methodological quality bias cluster, (c) methodological quality confounding cluster, and (d) statistical power to find the mean effect size established in the meta-analysis ( g = 0.277). Since methodological quality was divided into clusters in our methodological quality checklist, we introduced these clusters (reporting, bias, and confounding) as moderators. We did not include the power cluster as a moderator, since the majority of studies did not calculate power and thus scored zero on this cluster, with only one study (contributing eight effect sizes) providing a power analysis. For each of the methodological quality clusters, we created a meta-analytic scatter plot using the metafor package ( Viechtbauer, 2010 ), showing the Hedges’ g of each individual study plotted against each moderator (see Figure 4 ). The figure shows that an effect size of zero falls within the 95% confidence interval in the reporting cluster for studies scoring 0.8 or higher, suggesting that these studies show no effect of sleep deprivation while the lower scoring studies do. Consistent with this observation, the reporting cluster was a significant moderator, Q (1) = 9.214, p = .002. Visual inspection of the plot for the confounding cluster suggests that studies with scores of 0.3 or lower on this cluster may not show an effect of sleep deprivation. However, this cluster did not show a statistically significant moderating effect, perhaps because there were no studies that scored below 0.3 on this cluster. Bias did not show a significant moderating effect either. ​ either.

Note . The size of each point is proportional to the weight the study received in the analysis, with larger size indicating larger weight. The solid regression lines represent the effect size predicted by the meta-regression model as a function of each cluster score, with corresponding 95% confidence intervals.

To assess the achieved statistical power of each individual experiment to detect the mean meta-analytic effect size, we conducted a post hoc power analysis in G*Power ( Faul et al., 2007 ). For each study, we calculated the power to detect the mean meta-analytic effect size ( g = 0.277), as well as the power to detect the 95% upper and lower confidence intervals of the effect size. The distribution of the mean power and lower and upper confidence interval bounds of the power estimate are plotted in Figure 5 . We found the mean power to find the average meta-analytic effect to be 13.98% ( SD = 4.60%, range = 7.2%–30.2%). The moderator analysis revealed no significant effect of power on the size of the meta-analytic effect. All moderator analyses are reported in Table 2 . ​ . ​

Note . See the online article for the color version of this figure.

Meta-Analysis 2: Sleep Deprivation Before Learning

This meta-analysis summarizes research from 31 reports investigating effects of sleep deprivation before learning (55 effect sizes) published in English between 1989 and 2020 across a total of 927 participants. All reports used healthy adult populations and deprived participants of one night of sleep prior to learning. Notably, two reports from this meta-analysis also include data that are relevant to the sleep deprivation after learning meta-analysis ( Fischer et al., 2002 ; Tamminen et al., 2020 ). See Table 3 for central tendencies and frequency data for moderator and descriptive variables of reports included. Again, the literature mostly involves young adults, leaving a gap in our understanding of how the effects of interest change with age. The discrepancy between use of between- and within-group designs is lower here than in the first meta-analysis, and recognition memory and recall memory tasks are roughly equally represented. However, nearly all studies look at declarative memory, suggesting that more work on procedural memory is needed. While most studies ensured compliance with the sleep deprivation manipulation with direct observation at night, few did so during the preceding day, potentially diluting the impact of sleep deprivation. ​ deprivation.

Overall Sleep Deprivation Effect Size

The overall effect size for the mean difference in memory performance between the sleep deprivation and sleep control group, measured by Hedges’ g , was 0.621 ( SE = 0.074), indicating medium to large effect according to Cohen’s categorization, and a significant difference from zero, 95% CI [0.473, 0.769], p < .001. Figure 6 provides a forest plot of the effect sizes. See Supplemental Appendix C for a summary of all studies included in the meta-analysis. ​ meta-analysis.

Note . Effect sizes to the right indicate an effect of sleep deprivation before learning such that memory was significantly worsened compared to a sleep control group.

Some of the variance within the data set could not be explained by random error, highlighted by overall significant heterogeneity, Q (54) = 118.166, p < .001. An analysis of heterogeneity of between-study variance (Level 2) also revealed a significant difference between a full and a reduced model ( p < .001), suggesting significant variability between studies. An analysis of heterogeneity of within-study variance (Level 3) revealed a significant difference between a full model and a reduced model ( p < .001), suggesting significant variability between within-study effect sizes. The I 2 statistic indicates that approximately 41% of variance can be attributed to sampling error, 9% to within-study variance, and 50% to between-study variance. τ 2 = .096 for between-study variance, and τ 2 = .017 for within-study variance, and prediction intervals indicated that 95% of effect sizes would fall in the range of −0.069 and 1.312. Based on this evidence for heterogeneity, we explored the effect of potential moderating variables on the direction of the effect.

Figure 7 shows a contour enhanced funnel plot of effect sizes. Similar to Meta-Analysis 1, visual inspection of the funnel plot indicates that effect sizes are not evenly distributed across the funnel plot, raising the potential for publication bias. Egger’s regression test supports this conclusion, indicating significant funnel plot asymmetry ( z = 3.363, p < .001). Further inspection of the funnel plot indicates that the majority of effect sizes are clustered toward the right side of the funnel plot. While many of these studies fall in the area of nonsignificance, there appear to be studies missing from the left-hand side of the plot. It is possible these missing studies are due to researchers being unable to publish findings that contradict their hypotheses, and that the bias indicated by Egger’s test may therefore be due to publication bias rather than other types of bias. ​ bias.

Because Egger’s test indicates the presence of publication bias, we conducted a trim-and-fill analysis ( Duval & Tweedie, 2000 ), in the same way as in the first meta-analysis. The trim-and-fill method estimated seven missing studies from the left-hand side of the funnel plot. With these effect sizes included, the adjusted overall meta-analytic effect size was smaller than the original effect size of g = 0.621, although it was still significantly greater than zero, Hedges’ g = 0.463, SE = 0.070, 95% CI [0.326, 0.601], p < .001.

In the same way as the first meta-analysis, we explored whether any outliers and influential cases significantly influenced the size of the effect. We identified any effect sizes with studentized residuals greater than or smaller than three ( k = 1; Tempesta et al., 2016 , Recognition Task). Following recommendations by Viechtbauer and Cheung (2010) , influential case analysis (Cook’s distance and DFBETAs) identified two further effect sizes that may have had a significant influence on the results ( Poh & Chee, 2017 ; Yoo et al., 2007 ). Removal of the one outlier and two influential cases reduced the overall meta-analytic effect size to 0.525 (from 0.621). We removed these three specific outliers and influential cases from all moderator analyses.

We introduced the categorical moderating variable task type, recall ( k = 26) versus recognition ( k = 20). We excluded studies that used a different task type, including a recency discrimination task ( k = 3), a prototype learning task ( k = 2), and a finger-tapping task ( k = 1). Analysis of the moderator recall versus recognition revealed that the type of task used did not moderate the size of the effect, Q (1) = 0.028, p = .868. There were only two studies of procedural memory ( Fischer et al., 2002 ; McWhirter et al., 2015 ) and only five entries where the participants were given a night of recovery sleep (two further entries did not report whether recovery sleep was given). Likewise, only two studies investigated the effects of sleep deprivation on emotional memory ( Kaida et al., 2015 ; Tempesta et al., 2016 ). Thus, there was insufficient variability within the data set to assess whether one or more nights of recovery sleep, the type of memory (declarative, procedural), and emotionality moderated the size of the sleep deprivation effect.

We tested the influence of the three continuous moderating variables assessing methodological quality (reporting, bias, and confounding), as well as statistical power to detect the meta-analytic effect size, on the size of the sleep deprivation effect. No methodological quality cluster had a significant moderating effect. For each of the methodological quality clusters, we created a meta-analytic scatter plot (metafor package; Viechtbauer, 2010 ; see Figure 8 ). We did not include the power cluster as a moderator, since the majority of studies did not calculate power and thus scored zero on this cluster, with only two studies (contributing a total of 10 effect sizes) providing a power analysis. ​ analysis.

We then investigated whether statistical power to find the meta-analytic effect moderated the size of the effect of sleep deprivation on memory. In the same way as in the first meta-analysis, for each study, we calculated the power to find the mean meta-analytic effect size, as well as the power to detect the upper and lower confidence interval bounds around the mean. The distribution of power to detect the three estimates is plotted in Figure 9 . We found the mean power to find the meta-analytic effect to be larger than in the first meta-analysis ( M = 54.77%, SD = 20.81%, range = 21.22%–98.06%). The moderator analysis revealed that power to find the mean meta-analytic effect size did not moderate the effect of sleep deprivation on memory, Q (1) = 3.179, p = .075. All moderator analyses are reported in Table 4 . ​ . ​

The two meta-analyses presented here aimed to quantify the size of the effect of sleep deprivation after learning and before learning on memory performance. Based on previous evidence for an effect of sleep on both declarative and procedural memories ( Klinzing et al., 2019 ), we predicted that sleep deprivation would have a detrimental effect on learning and memory. We found that sleep deprivation after learning was associated with a mean effect size of g = 0.277. The effect size is positive, indicating that sleep deprivation has a detrimental rather than facilitatory impact on memory, as predicted by theory. Furthermore, the 95% confidence intervals around the mean do not cross zero, indicating that the effect size is statistically significantly higher than zero. For sleep deprivation before learning, we found an average effect size of g = 0.621. The effect size is positive, indicating that sleep deprivation before learning impairs rather than facilitates memory, as predicted by theory (e.g., Cirelli & Tononi, in press ; Tononi & Cirelli, 2012 ). The 95% confidence intervals around the mean do not cross zero, again indicating that the effect size is statistically significantly higher than zero. Following Cohen’s guidelines for categorizing effect sizes a small (0.20), medium (0.50), and large (0.8), the effect sizes above would correspond to small-to-medium and medium. However, given that these cutoff points are wholly arbitrary and were only ever intended to be used as a last resort, many now argue that effect sizes should be interpreted in the context of typical effect sizes observed in the relevant literature ( Correll et al., 2020 ; Funder & Ozer, 2019 ). According to Brysbaert (2019) , an effect size of d = 0.40 represents an average effect size in experimental psychology and has practical and theoretical relevance. Putting our meta-analytic effect sizes into this context, it appears that sleep deprivation before learning has an effect size somewhat larger than the average effect size in experimental psychology, while sleep deprivation after learning has a somewhat smaller than average effect size, although the latter varies as a function of both recovery sleep and memory type (declarative vs. procedural), as discussed in detail below.

Theory-Based Mediators

Despite the wide range of literature examining an effect of sleep deprivation on memory performance, this is, to our knowledge, the first time that the size of this effect has been formally quantified. A benefit of meta-analyses is that they allow for the investigation of potential moderating factors that may differentially influence the size of the meta-analytic effect. For deprivation after learning, we were able to investigate whether the first night of sleep is essential for the consolidation of newly acquired memories or whether a later sleep opportunity can compensate for the first night of sleep deprivation. We found that studies where memory was tested immediately after one night of sleep deprivation and before recovery sleep showed a significant sleep deprivation associated memory deficit ( g = 0.410). Critically, those studies that had one or more nights of recovery sleep prior to retrieval also showed a small but statistically significant memory deficit ( g = 0.176). Thus, memory impairments caused by sleep deprivation during the first postencoding night were still present but less severe when recovery sleep occurred before testing. On the one hand, this finding suggests that the first night of sleep after learning is important as its disruption is still felt even after recovery sleep. On the other hand, it also suggests that recovery sleep can to some extent mitigate the disruption of the first night of sleep by reducing the effect size by about 50%. While these data are consistent with theories arguing that, for hippocampal-dependent memories at least, the hippocampus may act as a buffer, retaining newly learned information until an offline consolidation opportunity is available ( Schönauer et al., 2015 ), the idea that consolidation processes can be spread over multiple nights of sleep is yet to be explicitly tested.

It is also difficult to establish the extent to which the smaller effect of sleep deprivation after recovery sleep on memory is due to the occurrence of a delayed consolidation opportunity or due to effects of fatigue being diminished. Recent work has suggested that fatigue at time of test might have little or no detrimental impact on tasks assessing long-term memory. Schönauer et al. (2015) found that sleep deprivation before a recall task did not impair memory for previously encoded and consolidated word pairs. Furthermore, despite a difference in the size of the effect of sleep deprivation, it is important to note that we still see a significant detrimental effect of sleep deprivation after learning even when a later sleep opportunity is permitted, albeit a smaller effect. An account based on fatigue alone is insufficient to explain this finding. Another potential alternative explanation for the decrease in effect size after recovery sleep could be based on interference. When tested after one night of sleep or sleep deprivation, participants in the sleep group will have experienced little interference from subsequent cognitive activity after learning. A large difference between the groups at this point could be due to sleep protecting new memories from interference rather than due to active consolidation processes. After one or more nights of recovery sleep, both groups will have experienced some degree of interference, and this could explain the reduction in the effect size. Further research is needed to adjudicate between these different accounts that could both contribute to the effect sizes we have observed.

For deprivation after learning, we also found that whether the task type was declarative or procedural had an effect on the size of the deprivation effect. Although both declarative and procedural tasks elicited a significant effect, a moderator analysis indicated that those studies implementing procedural memory tasks had significantly larger effect sizes on average ( g = .449) than declarative tasks ( g = .218). That both declarative and procedural memory tasks showed detrimental effects of sleep deprivation was unsurprising, given that a benefit of sleep has been observed for both declarative memories (e.g., Gais & Born, 2004 ; Talamini et al., 2008 ; Wagner et al., 2006 ) and procedural memories ( Korman et al., 2007 ; Schönauer et al., 2014 ; Walker et al., 2005 ). Our findings are also consistent with recent studies showing that sleep is beneficial even in tasks that do not require the hippocampus at learning (e.g., Schapiro et al., 2019 ). Although the current meta-analysis indicates a detrimental effect of sleep deprivation after learning on both declarative and procedural memories, the exact mechanisms that drive these effects are still debated, and thus it is unclear why procedural memories may show larger sleep deprivation effects. According to active systems consolidation theory, hippocampal-dependent declarative memories benefit from repeated reactivation of newly learned memories during sleep, supporting the strengthening of memory representations in the neocortex ( Born & Wilhelm, 2012 ; Walker & Stickgold, 2006 ). However, procedural memories that rely on implicit learning are unlikely to be dependent on such hippocampal–neocortical representations. It has been theorized that such implicit memories require more immediate offline consolidation to see a beneficial effect of sleep ( Schönauer et al., 2015 ; Stickgold et al., 2000 ). Thus, it may be that without an immediate sleep opportunity, the detrimental effects of sleep deprivation have a larger impact on procedural memory consolidation, whereas declarative memory consolidation is less impacted by the lack of an immediate sleep opportunity. However, we found no significant interaction between recovery sleep and task type, suggesting that for both declarative and procedural tasks, lack of an immediate sleep opportunity increased the size of the effect of sleep deprivation. Thus, recovery sleep had a similar impact on both procedural and declarative task performance, and procedural tasks elicited larger effect sizes than declarative tasks, regardless of whether recovery sleep occurred.

For deprivation after learning, we found no effect of emotional versus neutral memory on the size of the meta-analytic effect. Although some studies do suggest a preferential effect of sleep for emotional memories (e.g., Payne & Kensinger, 2010 ; Wagner et al., 2001 ), our findings join two recent meta-analyses that report no overall preferential effect of sleep on emotional memory consolidation ( Lipinska et al., 2019 ; Schäfer et al., 2020 ). These existing meta-analyses focussed on emotional memory and were able to uncover mediators that may reveal boundary conditions for the preferential effect; however, the number of studies in this domain is still low and more research is needed to establish the reliability of the effect.

For both deprivation after learning and deprivation before learning, we found no effect of the recall versus recognition moderator on the size of the meta-analytic effect. This is in contrast to some previous studies investigating the beneficial role of sleep on memory that have found a differential effect of recall versus recognition testing, and in contrast to the meta-analysis of Newbury and Monaghan (2019) , which looked at sleep studies using the DRM paradigm. Although performance on recall tasks repeatedly benefits from sleep, performance on recognition tasks has sometimes been found to show little or no offline consolidation benefit ( Ashton et al., 2018 ; Diekelmann et al., 2009 ; Drosopoulos et al., 2005 ; Gais et al., 2006 ; Hu et al., 2006 ). It is posited that, although recall tasks rely on explicit, hippocampal-dependent memory, recognition tasks could include both an explicit recollection and implicit familiarity element ( Jacoby, 1991 ), only the former of which benefits from sleep-associated consolidation. Thus, the mechanisms by which these two types of memories are consolidated may be different. Despite this, the present meta-analyses provide no evidence to suggest that performance on recall and recognition tasks are differentially affected by sleep deprivation either before or after sleep. Whether this finding extends to sleep paradigms other than total sleep deprivation remains to be established.

The null effects from our moderator analyses should be treated with caution, however, as we may not have adequate statistical power to detect smaller moderator effect sizes. Hempel et al. (2013) suggest that power to detect moderator effects is dependent on a combination of the amount of residual heterogeneity within the data set, the number of studies in the data set, the number of participants in the included studies, and the ratio of studies in the conditions compared against each other. Based on power simulations, Hempel et al. (2013) provide estimations of the approximate number of studies and participants required to detect categorical moderator effects of different effect sizes. We used data from these simulations to retrospectively assess the power of our moderator analyses to detect an effect. Since Hempel et al.’s simulations are not based on multilevel meta-analyses, the below estimates should be treated with caution when applied to our analyses and only considered as indicative.

For our deprivation after learning analysis, residual heterogeneity was τ 2 = .026 for within-study variance, and τ 2 = .061 for between-study variance. Therefore, based on a τ 2 of between 0 and 0.1, the simulations suggest that the moderator recall versus recognition was powered to detect an effect of around 0.2–0.3 (based on 100 trials, 20 participants per study, at 80% power). The emotionality moderator was powered to detect only large moderator effects of 0.3–0.4 (based on 50 trials, 20 participants per study, 80% power). For the deprivation before learning analysis, residual heterogeneity was τ 2 = .096 for between-study variance, and τ 2 = .017 for within-study variance. The simulations suggest that the moderator analysis of recall versus recognition was powered to detect only a large effect size of between 0.3 and 0.4 (based on 50 trials, 20 participants per study, at 80% power). Therefore, it appears that our moderator analyses were not sufficiently powered to detect small moderator effects and the null findings in these analyses should be considered preliminary. These analyses need to be repeated as more evidence accumulates over time.

There are other moderators that would be valuable to account for to increase the precision of our meta-analytic effect size, but that we could not include in our analyses due to the small number of studies available. For example, some studies included in the meta-analysis involved manipulations that the authors expected to reverse or eradicate the detrimental effect of sleep deprivation. Kolibius et al. (2021) predicted that large amounts of encoded information (640 word pairs) would increase forgetting in the sleep group compared to a sleep-deprived group. Similarly, Feld et al. (2016) hypothesized that those in a high memory load condition (360 word pairs) should no longer show a sleep benefit compared to a sleep-deprived condition. Vargas et al. (2019) examined memory for emotionally negative and neutral objects and backgrounds, but only predicted an impact of sleep deprivation on neutral objects. It is possible that the inclusion of studies such as these (or conditions within those studies where no sleep deprivation effect is predicted) may have artificially reduced our meta-analytic effect size. A mediator analysis would be the appropriate solution to establish whether this was the case, but the small number of relevant studies prevents this for now.

Quality-Based Moderators

The meta-analyses in this article suggest that there is a detrimental effect of sleep deprivation on learning and memory, and it is observed across a range of methodologies. However, our meta-analyses identified a number of potential limitations of the available data sets in this domain. Methodological quality scores ranged from 4 to 19 out of 22 in the studies investigating deprivation after learning; and they ranged from 7 to 19 in the studies investigating deprivation before learning.

We must be cautious in the way that we interpret the effects of methodological quality on the size of the effect of sleep deprivation. Valentine (2009) argues that methodological scales of this nature frequently lack operational specificity (e.g., that each item deserves equal weight) and include questions that are unclear. In an attempt to increase the validity of our methodological quality checklist, we designed our checklist based on the Downs and Black checklist ( 1998 ), with modified questions relevant to sleep studies. For the first meta-analysis, we found a significant mediating effect of the reporting cluster of our methodological quality checklist on the size of the sleep deprivation effect, Figure 4 shows that studies scoring highest on this cluster show no effect of sleep deprivation while the lower scoring studies do. The items in the reporting cluster are predominantly concerned with the number and nature of exclusion and inclusion criteria used in the study. It therefore appears that the studies showing higher effect sizes may have employed fewer such criteria. However, some of the scores on this cluster may be underestimated due to incomplete reporting. For example, studies stating that they only recruited healthy participants may have used other sleep-related inclusion and exclusion criteria, such as excluding participants who were taking medication that affects sleep or those who had recently traveled between time zones, without reporting these and may have scored higher on this cluster had these criteria been reported. We found no mediating effect of any cluster of methodological quality on the size of the effect of sleep deprivation for the second meta-analysis. Given that only one cluster of the quality score influenced the size of the effect in the first meta-analysis, and no clusters had a significant effect in the second meta-analysis, our effect size estimates are unlikely to be substantially biased by variation in methodological quality.

Taking a broader qualitative view of our quality checklist, we note that only one of the analyzed studies was preregistered, and only three justified their sample size with an a priori power analysis. Given that preregistration has become a mainstream practice only in the past few years ( Nosek & Lindsay, 2018 ), and that an a priori power analysis is part of the preregistration process, the low numbers here are unsurprising and are likely in line with the current broader field of experimental psychology. The key quality measures on study design were met by the clear majority of studies (e.g., equal group sizes, random allocation to groups or counterbalancing of conditions).

Power-Based Moderators

In the current meta-analyses, we calculated statistical power to find the meta-analytic effect size for each experiment and assessed whether statistical power significantly influenced the size of the effect of sleep deprivation. For sleep deprivation after learning, mean statistical power to find the meta-analytic effect size was just 14%; for sleep deprivation before learning, it was higher though still far less than optimal at 55%. Given that power is a function of the effect size, sample size, and the statistical test being employed, the difference in obtained power across the two research questions is understandable: As the effect size decreases, power to detect it decreases if sample size is held constant. Overall, these figures are closely in line with the broader field: For example, Szucs and Ioannidis (2017) found that within psychology and cognitive neuroscience, mean power to detect small, medium, and large effects (in Cohen’s terms) was 17%, 49%, and 71%, respectively.

Given the current convention that statistical power to find an effect is at 80% or higher ( Di Stefano, 2003 ), it is evident that the majority of the studies in these meta-analyses are underpowered. This is problematic as it increases the uncertainty around our meta-analytical effect sizes. To better understand the consequences of the uncertainty introduced by low power in the studies included in our meta-analyses, we investigated whether statistical power to find the mean meta-analytic effect size influenced the size of the sleep deprivation effect by entering obtained power as a moderator. For example, it might be the case that it is only low-powered studies that show an impact of sleep deprivation, while high-powered studies might show no impact. Such a pattern would suggest that our meta-analytic effect size might be overestimated as a consequence of low power. The opposite pattern would suggest that our effect size has been underestimated due to low power. For deprivation after learning, we found no moderating impact of statistical power on the size of the effect. In other words, both low- and high-powered studies yielded similar effect sizes. However, the validity of this analysis is reduced by the fact that there were no studies in this meta-analysis where power exceeded 33%, and therefore we have no way of knowing what effect sizes could be expected when power is higher. For deprivation before learning, we found a broader range of power extending from about 20% to over 90%. However, once again we found no statistically significant moderating impact of power on the size of the effect.

To gain a more precise estimate of the true effect size, future studies should use the current meta-analytic effect size as a guide in determining sample sizes that will yield high power. Studies planning to look at sleep deprivation after learning and running a two-tailed t -test for a between-subjects design with a sleep versus sleep deprivation manipulation would require a sample size of approximately 410 to have 80% power to detect the meta-analytic effect size. For a within-subjects design, the sample size required would be 105. For studies examining deprivation before learning, a two-tailed t -test with a between-subjects design would require a sample size of 82, whereas a within-subjects design would require a much smaller sample size of 23, to have 80% power to detect the meta-analytic effect size. The above numbers are rough indications only, and lower or higher sample sizes may be appropriate depending on the specific design of the experiment and the statistical analysis approach ( Brysbaert, 2019 ; Lakens & Caldwell, 2021 ).

It is clear that there is a significant discrepancy between the high-power sample sizes we have estimated above and the sample sizes found in the majority of the studies included in the current meta-analyses. This discrepancy is important as there are severe limitations to the strength of conclusions that can be drawn from underpowered individual studies (see, e.g., Brysbaert, 2019 , for a detailed discussion). Fraley and Vazire (2014) described three limitations: (a) underpowered studies are less likely than properly powered studies to detect a true effect; (b) underpowered studies are more likely to yield false-positive findings than properly powered studies; and (c) underpowered studies are less likely than properly powered studies to produce replicable findings. Wilson et al. (2020) further demonstrate that underpowered studies are likely to yield inflated effect sizes. Therefore, the results of any single underpowered study should be treated with caution, and a meta-analytic approach such as ours may be the more useful approach for extracting information from these studies. Yet, small-scale studies are not always completely uninformative; we return to this debate in the Conclusions section.

Conducting a meta-analysis allows for an estimation of publication bias within the literature. Publication bias is evident when there are a large number of published studies in the direction of the hypothesis, with few nonsignificant published studies ( Rosenthal, 1979 ). This can lead to overestimation of the size of the effect. We found statistically significant evidence of publication bias in both meta-analyses. Adjusting the deprivation after learning effect size for publication bias using the trim-and-fill method changed the effect size from 0.277 to 0.166, and changed the deprivation before learning effect size from 0.621 to 0.463, although these adjusted effect size should be treated with caution given that the trim-and-fill method was not designed for a multilevel approach. Nonetheless, both estimates remained significantly different from zero after the adjustment. To allow for more accurate effect size estimates in future meta-analyses, we suggest researchers in this field should adopt registered reports as an effective way of ensuring all results find their way into published literature.

Limitations

We focused specifically on the effects of total (overnight) sleep deprivation, and thus future meta-analyses are needed to establish whether the effect size is similar in studies using sleep restriction. We chose to concentrate on studies using total sleep deprivation because depriving a participant of all sleep is a stronger test of the hypothesis that sleep benefits memory than depriving them of a single stage of sleep or restricting their sleep for some hours over a period of time, as discussed in the Introduction section. An alternative approach could have been to include restriction studies and to conduct a moderator analysis to establish whether they lead to similar effect sizes as total deprivation. However, many sleep restriction studies in the literature are field studies that lack the rigorous controls we include in our inclusion criteria (e.g., lack of control over hours slept, Deary & Tait, 1987 ; inappropriate sleep control condition, Piérard et al., 2004 ), and therefore the number of eligible restriction studies would have been smaller than the number of total deprivation studies. As discussed earlier, such imbalance in number of studies can make moderator analyses insensitive ( Hempel et al., 2013 ).

Our search focused solely on English language reports, thus risking a mono-language bias ( Johnson, 2021 ). This restricts our ability to generalize the results of our meta-analyses to non-English language literature. In particular, by using English language sources only, there is the possibility that our search missed much of the gray literature such as PhD theses and conference abstracts written in other languages. The use of solely English language sources limits our understanding of any possible cross-cultural differences in effects of sleep on memory. Indeed, there are many cross-cultural differences in sleep habits (e.g., Cheung et al., 2021 ), and although we are not aware of any studies that have systematically compared sleep-associated memory consolidation effects across cultures, our reliance on English language literature means that we would not have captured such differences if they do exist.

We acknowledge that our inclusion criteria restrict our ability to draw conclusions beyond healthy, typical populations. We excluded studies that included participants under the age of 18 and studies that involved participants suffering from sleep disorders or psychiatric disorders. There is growing interest in understanding how sleep-associated memory consolidation in these groups might differ from healthy adults, however (e.g., Hoedlmoser, 2020 ; Manoach & Stickgold, 2019 ), and future meta-analyses addressing these questions will be valuable both for theoretical development and practical reasons. Finally, we note that there were 17 studies that we were unable to include in the analyses as the required statistical information was not reported (see Figure 1 ); unfortunately, the authors of these papers were unable to provide with the necessary data when contacted. Nonetheless, these studies made up a small proportion of the studies we identified as eligible and would be unlikely to change the conclusions we have drawn.

Conclusions

To conclude, the two meta-analyses presented here provide a comprehensive analysis of the impact of sleep deprivation after learning and before learning. The results of the first meta-analysis suggest that depriving participants of the first night of sleep after encoding new information results in lower performance at test, supporting the theories of sleep-associated memory consolidation (e.g., Diekelmann & Born, 2010 ; McClelland et al., 1995 ). This effect was larger before than after recovery sleep and larger in procedural memory tasks compared to declarative memory tasks. The results of the second meta-analysis suggest that sleep-deprived participants are able to encode less information than rested controls, supporting the theories that propose that sleep restores memory encoding capacity (e.g., Saletin & Walker, 2012 ; Tononi & Cirelli, 2012 ).

We found that levels of statistical power tended to be low, particularly in those studies looking at sleep deprivation after learning in which there was a small estimated effect size. Given that underpowered studies are ubiquitous across disciplines that use human participants ( Dumas-Mallet et al., 2017 ), new ways of interpreting low-powered studies are emerging. One particularly insightful interpretation has recently been offered by Wilson et al. (2020) . In short, Wilson and colleagues draw a distinction between “original science” and “replication science.” Original science is roughly science as practiced today, in that it uses Null Hypothesis Significance Testing combined with study designs whose power falls far short of the gold standard of high-N studies. Original science in this formulation serves an important and inexpensive screening function to identify effects that may be true and would therefore benefit from further, more costly examination of replication science. Replication science consists of high-N, highly powered, and costly direct replications of the key studies from original science, vital for verifying the preliminary results of original science. Applying this framework to the literature targeted in our meta-analyses, we propose that there is now sufficient evidence from original science to warrant a move to replication science in this field. No highly powered, preregistered direct replications looking at the role of sleep deprivation in learning and memory have been conducted thus far. The meta-analytic estimates of the relevant effect sizes provided here will facilitate the design of such urgently needed studies, while also allowing better informed sample size choice for continuing original science efforts.

Supplementary Material

References marked with * 1 indicate studies included in the first meta-analysis (sleep deprivation after learning) and references marked with * 2 indicate studies included in the second meta-analysis (sleep deprivation before learning).

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