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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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Effects of sleep deprivation on cognition

Affiliation.

1 Neuroimaging Center, McLean Hospital, Harvard Medical School, Belmont, MA, USA. [email protected]
PMID: 21075236
DOI: 10.1016/B978-0-444-53702-7.00007-5

Sleep deprivation is commonplace in modern society, but its far-reaching effects on cognitive performance are only beginning to be understood from a scientific perspective. While there is broad consensus that insufficient sleep leads to a general slowing of response speed and increased variability in performance, particularly for simple measures of alertness, attention and vigilance, there is much less agreement about the effects of sleep deprivation on many higher level cognitive capacities, including perception, memory and executive functions. Central to this debate has been the question of whether sleep deprivation affects nearly all cognitive capacities in a global manner through degraded alertness and attention, or whether sleep loss specifically impairs some aspects of cognition more than others. Neuroimaging evidence has implicated the prefrontal cortex as a brain region that may be particularly susceptible to the effects of sleep loss, but perplexingly, executive function tasks that putatively measure prefrontal functioning have yielded inconsistent findings within the context of sleep deprivation. Whereas many convergent and rule-based reasoning, decision making and planning tasks are relatively unaffected by sleep loss, more creative, divergent and innovative aspects of cognition do appear to be degraded by lack of sleep. Emerging evidence suggests that some aspects of higher level cognitive capacities remain degraded by sleep deprivation despite restoration of alertness and vigilance with stimulant countermeasures, suggesting that sleep loss may affect specific cognitive systems above and beyond the effects produced by global cognitive declines or impaired attentional processes. Finally, the role of emotion as a critical facet of cognition has received increasing attention in recent years and mounting evidence suggests that sleep deprivation may particularly affect cognitive systems that rely on emotional data. Thus, the extent to which sleep deprivation affects a particular cognitive process may depend on several factors, including the magnitude of global decline in general alertness and attention, the degree to which the specific cognitive function depends on emotion-processing networks, and the extent to which that cognitive process can draw upon associated cortical regions for compensatory support.

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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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The effects of insufficient sleep and adequate sleep on cognitive function in healthy adults

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ORIGINAL RESEARCH article

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

$\r\nZiyi Peng$

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.

Effects of Sleep Deprivation on Cognitive Functions and Academic Achievement in Students

Rubén González Vallejo University of Salamanca, https://orcid.org/0000-0002-9697-6942
Mary Daohne P. Silvestre PE-Collrge of Education,Bukidnon State University [email protected]

The modern educational environment has observed a concerning trend wherein students increasingly compromise on sleep, which may have significant ramifications on cognitive functions and subsequent academic performance. This study explores the impact of sleep deprivation, bringing to the fore its profound consequences. Cognitively, sleep deprivation manifests in various ways. A marked reduction in attention span, paired with a rise in attentional lapses, severely impedes tasks that require prolonged concentration. The integral role of sleep-in memory consolidation suggests that its deficit can detrimentally affect both the encoding and retrieval facets of memory. Moreover, the executive functions are not immune; sleep-deprived individuals face pronounced challenges in tasks that demand planning, decision-making, and error correction. This review also underscores the impediment of problem-solving abilities and creative thinking following inadequate sleep. Notably, emotional repercussions are evident, with sleep deprivation correlating with emotional disturbances, encompassing irritability and depressive symptomatology. Furthermore, sleep-deprived individuals consistently demonstrate an extended reaction time, detrimental to tasks demanding swift decision-making. From an academic lens, the consequences are similarly concerning. Students experiencing sleep deprivation exhibit a reduced capacity to assimilate novel concepts, leading to hampered comprehension and retention. Quantitatively, studies have discerned a tangible negative correlation between sleep deprivation and grade point averages, pointing to a direct academic decline. Furthermore, there is a palpable reduction in the motivation to partake in academic activities among sleep-deprived students. This, combined with heightened cognitive impairments, escalates the propensity for errors in academic tasks. The health ramifications of prolonged sleep deprivation cannot be sidelined, with an evident association between inadequate sleep and increased absenteeism due to health complications.

Author Biographies

Rubén gonzález vallejo, university of salamanca,.

University of Salamanca,

https://orcid.org/0000-0002-9697-6942

Mary Daohne P. Silvestre, PE-Collrge of Education,Bukidnon State University [email protected]

PE-Collrge of Education,Bukidnon State University

[email protected]

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Published: 17 March 2020

The relationship between subjective sleep quality and cognitive performance in healthy young adults: Evidence from three empirical studies

Zsófia Zavecz ORCID: orcid.org/0000-0003-2532-7491 1 , 2 , 3 ,
Tamás Nagy ORCID: orcid.org/0000-0001-5244-0356 2 ,
Adrienn Galkó 2 ,
Dezso Nemeth ORCID: orcid.org/0000-0002-9629-5856 2 , 3 , 4 na1 &
Karolina Janacsek ORCID: orcid.org/0000-0001-7829-8220 2 , 3 , 5 na1

Scientific Reports volume 10 , Article number: 4855 ( 2020 ) Cite this article

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Human behaviour
Working memory

The role of subjective sleep quality in cognitive performance has gained increasing attention in recent decades. In this paper, our aim was to test the relationship between subjective sleep quality and a wide range of cognitive functions in a healthy young adult sample combined across three studies. Sleep quality was assessed by the Pittsburgh Sleep Quality Index, the Athens Insomnia Scale, and a sleep diary to capture general subjective sleep quality, and the Groningen Sleep Quality Scale to capture prior night’s sleep quality. Within cognitive functions, we tested working memory, executive functions, and several sub-processes of procedural learning. To provide more reliable results, we included robust frequentist as well as Bayesian statistical analyses. Unequivocally across all analyses, we showed that there is no association between subjective sleep quality and cognitive performance in the domains of working memory, executive functions and procedural learning in healthy young adults. Our paper can contribute to a deeper understanding of subjective sleep quality and its measures, and we discuss various factors that may affect whether associations can be observed between subjective sleep quality and cognitive performance.

Optimizing the methodology of human sleep and memory research

Macro and micro sleep architecture and cognitive performance in older adults

Impact of sleep duration on executive function and brain structure

Introduction.

There is a widely accepted belief that experiencing poor sleep quality, including subjective experiences (e.g., reporting difficulties falling asleep, waking up frequently during the night, or feeling tired during the day), indisputably decreases cognitive performance. We can often hear people complaining about weaker memory and/or attentional performance in relation to their experienced sleep insufficiency. This phenomenon can be particularly prevalent amongst university students since the pressure for academic performance in this population is exceptionally high. The possible overestimation of the importance of one’s subjective sleep quality can even lead to placebo or nocebo effects on cognitive performance 1 , 2 . However, scientific evidence on the relationship between experienced subjective sleep quality and cognition is still inconclusive 3 , 4 , 5 , 6 , 7 . Therefore, our aim in the current study was to test whether subjective sleep quality is associated with cognitive performance in healthy young adults.

The role of sleep in cognitive performance has gained increasing attention in neuroscience and sleep research in recent decades 8 , 9 . Numerous experimental methods exist that can be employed for examining the association between sleep and cognitive performance. Sleep parameters can be evaluated based on actigraph or electroencephalograph measurements (i.e., objective measures), which are time-consuming and require expensive equipment. Hence, researchers and clinicians often tend to rely on questionnaires (i.e., subjective measures) to assess sleep parameters (e.g., sleep latency, sleep quality, sleep disturbances, or sleep duration). This inclination has also motivated the current study to explore the relationship between sleep questionnaires and cognitive functions.

Previous studies have shown that subjective and objective sleep parameters, such as sleep latency, sleep duration, or sleep efficiency could differ 10 , 11 , 12 ; the strength of correlation between the subjective and objective measures of the same parameters varied between 0.21 and 0.62 for sleep latency and duration, while it was close to 0 for sleep efficiency. Subjective sleep quality can vary from objective sleep quality as it is typically estimated from a combination of parameters, such as sleep initiation, sleep continuity (number of awakenings), and/or depth of sleep. For instance, extreme deviations can occur between subjective and objective measures in sleep disorders, such as insomnia or sleep-state misperception. According to Zhang and Zhao 13 , the subjective and objective measures together should determine the type of treatment and medication in sleep disorders. Stepanski et al . 7 showed that, within insomniac patients, the decisive factor of whether a patient seeks medication is their subjective evaluation of their sleep quality and daytime functioning. Furthermore, Gavriloff et al . 1 found that providing sham feedback about their sleep to patients with insomnia influenced their daytime symptoms and performance in attention and vigilance tasks. Similarly, in a placebo sleep study, young adults were randomly told they had below or above average sleep quality based on their brainwaves and other psychophysiological measures 2 . This constructed belief about their sleep quality affected their performance in attentional and executive function tasks. Thus, beyond therapeutic importance, it appears that subjective sleep quality can have further explanatory value for cognitive performance compared to objective measures.

One of the most widely-used sleep questionnaires is the Pittsburgh Sleep Quality Index (PSQI) 14 , a self-administered questionnaire, in which participants rate their subjective sleep quality based on several questions. These questions deal with various aspects of sleep that range from the average amount of sleep during the night, the difficulty experienced in falling asleep, and other sleep disturbances. Nevertheless, there are other popular measurements, such as the Athens Insomnia Scale (AIS) 15 , which measures difficulties in falling asleep or maintaining sleep, as well as sleep diaries, which capture the sleeping habits of the participants from day to day, spanning a few days or weeks. Sleep questionnaires and sleep diaries are two different types of self-reported measures: while sleep questionnaires are administered at a single point in time, and ask about various aspects of sleep experience in a longer time period retrospectively, sleep diaries are ongoing, daily self-monitoring tools. Libman et al . 16 showed that the two measurement types are tapping the same domains but lead to somewhat different results due to methodological differences: questionnaires can be susceptible to memory distortion while sleep diaries may be distorted by atypical sleep experiences during the monitored period.

Previous research on subjective sleep quality and cognitive performance has led to mixed findings. While some studies focusing on healthy participants have shown that poorer sleep quality as measured by the PSQI score was associated with weaker working memory 4 , executive functions 5 , and decision-making performance 17 , others have failed to find an association between subjective sleep quality and cognitive performance 6 , 7 . Bastien et al . 3 showed different associations between subjective sleep quality as measured by a sleep diary and cognitive performance in patients with insomnia who received or did not receive treatment and in elderly participants who reported good sleep quality. Interestingly, in good sleepers, greater subjective depth, quality, and efficiency of sleep were associated with better performance on attention and concentration tasks but poorer memory performance. These findings suggest that further studies are needed to clarify the complex relationship between subjective sleep quality and aspects of cognitive functioning.

Notably, these previous studies focused on diverse populations, including adolescents, elderly and clinical groups, and relied on sample sizes ranging from around 20 to 100, with smaller sample sizes potentially limiting the robustness of the observed results. In these studies, subjective sleep quality was assessed by a combination of self-reported measures, such as difficulty in sleep initiation, sleep continuity, and/or depth of sleep. In contrast to subjective sleep quality captured by a combination of such measures, self-reported sleep duration has been studied more thoroughly. In a large study with more than 100,000 participants, Sternberg et al . 18 reported a quadratic relationship between self-reported sleep duration and performance in cognitive tasks assessing working memory and arithmetics. Furthermore, a recent powerful meta-analysis focusing on elderly participants also showed that both short and long sleep increased the odds of poor cognitive performance 19 . A similar association was shown in another study investigating insomnia symptoms and cognitive performance in a large sample of participants 20 : self-reported sleep duration extremes were associated with impaired performance. Systematic investigations on the relationship between subjective sleep quality as captured by a combination of parameters (such as sleep latency, subjective sleep quality, sleep disturbances) and cognitive performance using larger sample sizes are, however, still lacking.

Moreover, in previous investigations focusing on the association between subjective sleep quality and various aspects of cognitive performance, the potential relationship with procedural learning/memory has largely been neglected. The procedural memory system underlies the learning, storage, and use of cognitive and perceptual-motor skills and habits 21 . Evidence suggests that the system is multifaceted in that it supports numerous functions that are performed automatically, including sequences, probabilistic categorization, and grammar, and perhaps aspects of social skills 22 , 23 , 24 , 25 , 26 . Considering the importance of this memory system, the clarification of its relationship with subjective sleep quality would be indispensable.

Here we aimed to fill the gaps identified in previous research by providing an extensive investigation on the relationship between subjective sleep quality and cognitive performance in healthy young adults. Within cognitive functions, we focused on working memory, executive functions, and procedural learning. We chose these domains because 1) the relationship between working memory, executive functions and subjective sleep quality has remained inconclusive, and 2) the relationship between procedural learning/memory and subjective sleep quality has largely been neglected in previous studies. Therefore, in the latter case, we explored several measures of procedural learning in order to obtain a more detailed picture of the potential associations with subjective sleep quality. To increase the robustness of our analyses, we created a database of 235 participants’ data by pooling three separate datasets from our lab. We assessed subjective sleep quality by PSQI and AIS (Study 1–3), Groningen Sleep Quality Scale (GSQS, Study 2), and a sleep diary (Study 2). These separate measures capture somewhat different aspects of self-reported sleep quality and thus provide a detailed picture. We tested working memory, executive functions and several sub-processes of procedural learning in all three studies. To control for possible confounding effects, we included age, gender and chronotype as covariates in our analyses. To test the amount of evidence either for associations or no associations between subjective sleep quality and cognitive performance, we calculated Bayes Factors that offer a way of evaluating the evidence against or in favor of the null hypothesis, respectively.

Participants

Participants were selected from a large pool of undergraduate students from Eötvös Loránd University. The selection procedure was based on the completion of an online questionnaire assessing mental and physical health status. Respondents reporting current or prior chronic somatic, psychiatric or neurological disorders, or the regular consumption of drugs other than contraceptives were excluded. In addition, individuals reporting the occurrence of any kind of extreme life event (e.g., accident) during the last three months that might have had an impact on their mood or daily rhythms were also excluded from the study.

The data was obtained from three different studies, each with a slightly different focus. Importantly, the analyses presented in the current paper are completely novel, none of the separate studies focused on the relationship between subjective sleep quality and cognitive performance. Forty-seven participants took part in Study 1 27 , 103 participants took part in Study 2 28 , and 85 participants took part in Study 3 29 . The descriptive characteristics of participants in the three studies are listed in Table 1 . All participants were white/Caucasian. All participants provided written informed consent and received course credits for taking part. The studies were approved by the Research Ethics Committee of Eötvös Loránd University (201410, 2016/209). The study was conducted in accordance with the Declaration of Helsinki.

We conducted three separate studies on the association of subjective sleep quality and procedural learning, working memory, and executive functions in healthy young adults. The sleep questionnaires included in the studies and the timing of the procedural learning task slightly differed. While we assessed subjective sleep quality by PSQI and AIS in all three studies, in Study 2, we included further measures of subjective sleep quality as well: (1) a sleep diary to assess day-to-day general sleep quality and (2) Groningen Sleep Quality Scale (GSQS) to assess prior night’s sleep quality. To control for the potential confounding effect of chronotype, we also administered the Morningness-Eveningness Questionnaire (MEQ) 30 , 31 , henceforth referred to as morningness score because a larger score on this questionnaire indicates greater morningness.

In all three studies, PSQI and AIS sleep quality questionnaires and the MEQ were administered online, while the GSQS in Study 2 and the tasks assessing cognitive performance in all studies were administered in a single session in the lab. Due to technical problems, the data of six participants on executive functions are missing. To ensure that participants do the tests in their preferred time of the day, the timing of the session was chosen by the participants themselves (between 7 am and 7 pm). The timing of the sessions was normally distributed in all three studies, with most participants performing the tasks during the daytime between 11 am and 3 pm. The sleep diary in Study 2 was filled by the participants for at least one week, and to a maximum of two weeks, prior to the cognitive assessment that was scheduled based on the participants’ availability.

Questionnaires and tasks

All cognitive performance tasks and subjective sleep questionnaires are well-known and widely used in the field of psychology and neuroscience (for details about each task and questionnaire, see Supplementary methods).

Subjective sleep quality questionnaires

To capture the general sleep quality of the last month, we administered the Pittsburgh Sleep Quality Index (PSQI) 14 , 32 and the Athens Insomnia Scale (AIS) 15 , 33 . Additionally, in Study 2, we administered a Sleep diary 34 to assess the sleep quality of the last one-two weeks, and the Groningen Sleep Quality Scale (GSQS) 35 , 36 to capture the sleep quality of the night prior testing.

Cognitive performance tasks

Working memory was measured by the Counting Span task 37 , 38 , 39 , 40 . Executive functions were assessed by the Wisconsin Card Sorting Test (WCST) 41 , 42 , 43 . The outcome measure of the WCST task was the number of perseverative errors, which shows the inability/difficulty to change the behavior despite feedback. Procedural learning was measured by the explicit version of the Alternating Serial Reaction Time (ASRT) task (Figure S1 , see also 44 ). There are several learning indices that can be acquired from this task. Higher-order sequence learning refers to the acquisition of the sequence order of the stimuli. Statistical learning refers to the acquisition of frequency information embedded in the task. However, previous ASRT studies often assessed Triplet learning, which is a mixed measure of acquiring frequency and sequential information (for details, see Supplementary methods). In addition to these learning indices, we measured the average reaction times (RTs) and accuracy (ACC), which reflect the average general performance of the participants across the task, and the changes in RT and ACC from the beginning to the end of the task, which indicate general skill learning that occurs due to more efficient visuomotor and motor-motor coordination as the task progresses 45 .

Data analysis

Statistical analyses were conducted in R 3.6.1 46 using the lme4 package 47 . Bootstrapped confidence intervals and p-values were calculated using the boot package 48 , 49 . The data and analysis code can be found on the following link: https://github.com/nthun/performance_sleep_quality/

Analysis of the relationship between subjective sleep quality and cognitive performance

Subjective sleep quality scales (PSQI and AIS) were combined into a single metric, using principal component analysis. Then separate linear mixed-effect models were created for each outcome measure (i.e., performance metric), where the aggregated sleep quality metric (hereinafter referred to as sleep disturbance) was used as a predictor, and ‘Study’ (1, 2 or 3) was added as a random intercept. This way we could estimate an aggregated effect while accounting for the potential differences across studies. To control for possible confounding effects, we included age, gender and morningness score as covariates in our analyses. Thus, the estimates reported in the Results section are controlled for these factors.

As the residuals did not show normal distribution, we used bootstrapped estimates and confidence intervals, using 1000 bootstrap samples, from which we calculated the p-values 48 , 49 . Bayes Factors (BF 01 ) were calculated by using the exponential of the Bayesian Information Criterion (BIC) of the fitted models minus the BIC of the null models – that contained the confounders only, and a random intercept by study 50 . The BF is a statistical technique that helps conclude whether the collected data favors the null-hypothesis ( H 0) or the alternative hypothesis ( H 1); thus, the BF could be considered as a weight of evidence provided by the data 51 . It is an effective mathematical approach to show if there is no association between two measures. In Bayesian correlation analyses, H 0 is the lack of associations between the two measures, and H 1 states that association exists between the two measures. Here we report BF 01 values. According to Wagenmakers et al . 51 , BF 01 values between 1 and 3 indicate anecdotal evidence for H 0, while values between 3 and 10 indicate substantial evidence for H 0. Conversely, while values between 1/3 and 1 indicate anecdotal evidence for H 1, values between 1/10 and 1/3 indicate substantial evidence for H 1. If the BF is below 1/10, 1/30, or 1/100, it indicates strong, very strong, or extreme evidence for H 1, respectively. Values around 1 do not support either H 0 or H 1. Thus, Bayes Factor is a valuable tool to provide evidence for no associations between constructs as opposed to frequentists analyses, where no such evidence can be obtained based on non-significant results.

To test the association between the additional subjective sleep quality measures and cognitive performance in Study 2, we used robust linear regression, this time without random effects. We included the same potential confounders (age, gender, morningness score), and Bayes factors were calculated in the previously described way.

Analysis of the ASRT data

Performance in the ASRT task was analyzed by repeated-measures analyses of variance (ANOVA) in each study (for details of these analyses, see Supplementary methods). Based on these ANOVAs, Triplet learning, Higher-order sequence learning, and Statistical learning occurred in all three studies, both in ACC and RT (all p s < 0.001; for details, see Supplementary results and Figure S2 ).

Cognitive performance in the three studies

The working memory capacity (measured by the counting span) and executive functions (measured by the number of perseverative errors in the WCST task) of the participants were in the standard range for their age 52 , 53 . The mean counting span for the entire sample was 3.59 ( SD = 0.85) in the three studies. This average score represents a mid-range cognitive performance, as obtainable scores range from 1 to 6. The mean number of perseverative errors was 14.76 ( SD = 5.27) in the three studies (no maximum score can be defined in this case). For procedural learning, mean scores were 26.48 ( SD = 26.37) for RT Triplet learning, 16.63 ( SD = 40.34) for RT Higher-order sequence learning, 16.74 ( SD = 9.94) for RT Statistical learning, 359.88 ( SD = 40.94) for average RT, and 31.13 ( SD = 30.15) for RT general skill learning. Accuracy scores were as follows: 0.04 ( SD = 0.03) for ACC Triplet learning, 0.02 ( SD = 0.03) for ACC Higher-order sequence learning, 0.03 (SD = 0.03) for ACC Statistical learning, 0.90 ( SD = 0.10) for average ACC, −0.02 ( SD = 0.09) for ACC general skill learning, in all three studies. Note that for accuracy, these values represent proportions (e.g., the average ACC was 90%, hence 0.90), and the learning scores are difference scores (e.g., the ACC Triplet learning score shows that participants were on average 4% more accurate on high-frequency triplets compared to the low-frequency ones). All presented RT and ACC scores represent typical values in ASRT studies with healthy young adults.

We also provide descriptive data for Study 2 separately, as additional analyses were run on cognitive performance from this dataset and GSQS and sleep diary scores. In Study 2, the mean counting span was 3.65 ( SD = 1.01), and the mean number of perseverative errors was 14.46 ( SD = 6.37). For procedural learning in Study 2, mean scores were 33.04 ( SD = 27.96) for RT Triplet learning, 28.53 ( SD = 51.44) for RT Higher-order sequence learning, 18.77 ( SD = 9.78) for RT Statistical learning, 348.29 ( SD = 42.26) for average RT, and 39.30 ( SD = 34.74) for RT general skill learning. Accuracy scores were as follows: 0.03 ( SD = 0.02) for ACC Triplet learning, 0.01 ( SD = 0.02) for ACC Higher-order sequence learning, 0.02 ( SD = 0.02) for ACC Statistical learning, 0.94 ( SD = 0.03) for average ACC, 0.02 ( SD = 0.03) for ACC general skill learning.

Overall, these values represent a mid-range cognitive performance with a sufficient level of variability in the sample to conduct the planned analyses.

Subjective sleep questionnaire scores in the three studies

The obtainable scores, means, standard deviations, and proportions of good, moderate and poor sleepers for each questionnaire are presented in Table 2 . The mean scores of PSQI in the current sample were higher than the score of 1.91 for the same components in Buysse et al . 14 , and in the range or even higher than the global PSQI score (which aggregates seven components; M = 2.67) for the control participants, whose age was between 24 and 83 years. In the same study 14 , the participants with sleep disorders had a mean score of 4.78 for the three components of PSQI, suggesting that ~18% of the current sample had a score higher than the average score of sleep-disordered patients. The mean scores of AIS were somewhat higher than the mean score of 3 reported for a representative Hungarian adult sample in Novak et al . 33 . According to the cut-off score of 10 suggested in that paper, ~5% of our sample would fall into the diagnostic category of insomnia. However, according to a stricter cut-off score of 6 suggested by Soldatos, Dikeos & Paparrigopoulos 54 , up to 23% of the participants would have complaints comparable to those of insomniac patients. The mean of the GSQS score was lower than the mean score reported for a Hungarian sample of young adults ( M = 4.70, SD = 1.78) in Simor et al . 35 . The mean of the Sleep diary score in Study 2 was comparable to the mean PSQI score of 1.3 for the same components for the control participants and lower than the score of 6.36 for the participants with sleep disorders in Buysse et al . 14 .

Although with some differences across questionnaires, these sleep scores suggest a moderate to poor sleep quality of the current sample, with about 15% of participants experiencing very poor sleep quality, comparable to those of patients with sleep disorders. Overall, all sleep measures used in the current study appear to have a sufficient level of variability to conduct the planned analyses.

Combining sleep quality metrics

Principal component analysis was used to combine PSQI and AIS into a single ‘sleep disturbance’ metric. The Bartlett’s test of sphericity indicated that the correlation between the scales was adequately large for a PCA, χ 2 (235) = 84.88, p < 0.0001. One principal factor with an eigenvalue of 1.55 was extracted to represent sleep disturbance. The component explained 83.7% of the variance, and it was named ‘sleep disturbance’ as higher values of this metric show more disturbed sleep. The aggregated sleep disturbance index across the three studies ranged from -1.9 to 3.86.

Associations between subjective sleep quality and cognitive performance

As described above, to study the associations between subjective sleep quality and cognitive performance, separate linear mixed-effect models were created for each outcome measure (i.e., cognitive performance metric), where sleep disturbance was used as a fixed predictor, and ‘Study’ was added as a random intercept. Sleep disturbance did not show an association with any of the cognitive performance metrics (see Table 3 and Fig. 1 ). Bayes Factors ranged from 5.01 to 14.35, indicating substantial evidence for no association between subjective sleep quality and the measured cognitive processes 51 .

Association between sleep disturbance and cognitive performance metrics by study. Horizontal axes represent the sleep disturbance index, while vertical axes represent the outcome variables, with their names shown in the panel titles. The scatterplots and the linear regression trendlines show no association between subjective sleep quality and procedural learning indices in terms of reaction time (RT, A ), or accuracy (ACC, B ), general skill indices in terms of RT or ACC ( C ), and working memory and executive function indices ( D ).

To test whether AIS or PSQI scores separately are associated with cognitive performance, we performed similar analyses as for the sleep disturbance metric. Additionally, we also tested whether cognitive performance differed between “good” and “poor” sleepers as defined by the extremes in the overall PSQI score. For this analysis, we considered those with a score of 0 or 1 as good sleepers (N = 36), while those with a score of 5 to 8 as poor sleepers (N = 43), corresponding to approximately the upper and lower 15% of the data (see Table 2 ). These additional analyses (reported in the Supplementary results) are consistent with the above findings for the sleep disturbance metric, suggesting no relationship between subjective sleep quality and cognitive performance using these measures.

In Study 2, to investigate the associations between further subjective sleep quality questionnaires and cognitive performance, we created a separate linear mixed-effect model for each outcome measure (i.e., cognitive performance metric), and each additional sleep questionnaire (i.e., sleep diary and GSQS). Sleep diary scores did not show association with any of the cognitive performance metrics (all p s > 0.05, see Table 4 and Fig. 2 ). Bayes Factors ranged from 2.51 to 12.58, indicating, in all but one cases, substantial evidence for no association between subjective sleep quality and measures of cognitive performance 51 . The lowest value of 2.51 for ACC general skill learning also pointed to the same direction, indicating slightly weaker evidence for no association with subjective sleep quality.

Association between sleep diary and GSQS scores and cognitive performance metrics. Horizontal axes represent the sleep disturbance index, while vertical axes represent the outcome variables, with their names shown in the panel titles. The scatterplots and the linear regression trendlines show no association between subjective sleep quality (measured with a sleep diary (blue) or the GSQS (red)) and procedural learning indices in terms of reaction time (RT, A ), or accuracy (ACC, B ), general skill indices in terms of RT or ACC ( C ), and working memory and executive function indices ( D ).

Similarly, GSQS scores did not show association with any of the cognitive performance metrics (all p s > 0.11, see Table 5 and Fig. 2 ). Bayes Factors ranged from 3.46 to 16.46, indicating substantial evidence for no association between subjective sleep quality and the measured cognitive processes 51 .

Our aim was to investigate the relationship between subjective sleep quality and cognitive performance in healthy young adults. Cognitive performance was tested in the domains of working memory, executive functions, and procedural learning. To provide more reliable results, we pooled data from three different studies, controlled for possible confounders, such as age, gender, and chronotype, and performed robust frequentists as well as Bayesian statistical analyses. We did not find associations between subjective sleep quality and cognitive performance measures using the robust frequentist statistical analyses. Moreover, the Bayes factors provided substantial evidence for no association between subjective sleep quality and measures of working memory, executive functions, and procedural learning. This pattern held when subjective sleep quality was reported retrospectively for a longer period (i.e., a month; with PSQI and AIS), as well as when monitored daily (for one to two weeks; with the sleep diary) or reported for the night prior to testing (with GSQS). These results suggest that neither moderately persistent nor transient subjective sleep quality is associated with cognitive performance in healthy young adults.

There are several factors to consider why subjective sleep quality showed no associations with cognitive performance in our sample of healthy young adults. First, it is possible that methodological issues contributed to the null effects. For example, having a lower range of obtainable scores on the selected subjective sleep quality and cognitive performance measures can limit the possibility of finding a relationship between these measures. Importantly, all measures that we used in the current study have been well-established in previous research and have a reasonable range of obtainable values. Although the sample choice of healthy young adults has naturally limited the range of scores on the used measures, our analyses showed a sufficient level of variability in all measures. Therefore, the obtained null results seem unlikely to be explained by such methodological issues.

Second, as we studied healthy university students, there may be a ceiling effect in subjective sleep quality. Sleep disturbance can be more prevalent in elderly populations and clinical disorders 14 , 33 . Consequently, variance and extremities in subjective sleep quality could be greater in these populations, while it can remain relatively low in healthy young adults. Nevertheless, previous research has found that university students are also prone to sleep disturbances, and in particular to chronic sleep deprivation 55 . Although with some variation across sleep questionnaires, most participants’ subjective sleep quality ranged from moderate to poor in our sample, with about 15% of participants experiencing very poor sleep quality similar to those of patients with sleep disorders. Thus, it seems unlikely that the obtained results are due to a ceiling effect in subjective sleep quality.

Third, it is possible that because young adults typically show a peak cognitive performance, poor subjective sleep quality may not have a substantial impact on it. In line with this explanation, the studies that reported associations between subjective sleep quality and cognitive performance 4 , 5 , 17 focused primarily on adolescents, older adults, or clinical populations, where cognitive performance has not yet peaked or have declined. Further supporting this explanation, Saksvik et al . 56 found in their meta-analysis that young adults are not as prone to the negative consequences of shift work as the elderly. Moreover, Gao et al . 57 in a recent study showed that above-average cognitive abilities buffer against insufficient sleep durations. However, not all cognitive functions peak in adulthood: while previous studies have reported the best performance in working memory and executive functions in young adulthood 58 , 59 , 60 , 61 , some aspects of procedural learning (as measured by the ASRT task) has been shown to peak in childhood and to decline already around adolescents 44 , 62 , 63 . Consequently, a cognitive peak may explain finding no relationship between subjective sleep quality and aspects of working memory and executive functions, while this explanation for the measures of procedural learning seems unlikely.

Fourth, the conditions under which the data collection took place could have also contributed to the null results. We conducted our experiments during the term-time when the workload in the university is typically moderate. Moreover, students could choose the time of day for cognitive testing, and they may have chosen a time when they typically felt well-rested. There is evidence that performing in a preferred circadian time period can attenuate the effect of sleep disturbances 64 . Consistently, previous studies showed that participants exhibit better performance on working memory and executive functions tasks in their preferred time of day 65 , 66 . However, a recent study found that participants, in fact, exhibit weaker performance in procedural learning in their preferred time of day, and better performance in their non-preferred time of day, suggesting variability in the relationship between circadian effects and cognitive functions 67 . Additionally, independent of the time of day, participants may have perceived the session with the cognitive tasks as a testing situation and may have been motivated to show their best performance, compensating for any possible effect of poor subjective sleep quality. Indeed, there is evidence that highly motivated participants are less prone to the effect of sleep deprivation 68 . Thus, the time of testing and participants’ motivation may have contributed to our findings by potentially compensating for any negative effects of poor subjective sleep quality on cognitive performance.

Fifth, the relationship between sleep and cognitive performance can vary depending on what parameters of sleep are assessed. Associations between objective sleep quality (measured by actigraphy or electroencephalography) and various aspects of working memory, executive functions, and procedural learning have been frequently reported in previous studies (for a review, see 8 , 9 ). Here we showed that subjective sleep quality is not associated with these cognitive functions, at least under the circumstances described above. As outlined in the Introduction, this dissociation suggests that objective and subjective sleep quality, although measure the same domains, do not necessarily capture the same aspects of sleep quality and sleep disturbances 11 . Subjective sleep quality may be estimated based on a combination of objective sleep parameters. Moreover, some objective parameters of sleep that contribute to cognitive performance may not be captured with self-reported instruments. For example, it is often reported that spindle activity or time spent in slow-wave sleep (SWS) or in rapid eye movement (REM) sleep is essential for memory consolidation 69 , 70 , 71 . Also, in laboratory sleep examinations, sleep quality is usually carefully controlled for several days prior to the examination. Potentially, the objective sleep parameters showing associations with cognitive performance may only be measured in these carefully controlled conditions (i.e., when sleep quality on the night of testing as well as in the preceding days are good). Hence, it is possible that while results with objective sleep quality may show how healthy sleep is related to cognitive functioning, results with subjective sleep quality may reflect how aspects of sleep disturbances are related to cognitive functioning.

Sixth, and relatedly, there could be differences in the association with cognitive performance within self-reported measures of sleep as well. In our study, we captured the perceived disturbances in initiating and maintaining sleep rather than the self-reported duration of sleep. While we found no associations between these measures of subjective sleep quality and cognitive performance, there is solid evidence that self-reported extreme sleep durations (both long and short sleep times) are associated with worse cognitive performance 18 , 19 , 20 . These findings suggest a dissociation between sleep quality as measured by extreme self-reported sleep durations and other types of sleep quality disturbances.

Seventh, it is possible that while interindividual differences in subjective sleep quality do not contribute to at least some aspects of cognitive performance, intraindividual fluctuations do. The possible importance of intraindividual rather than interindividual differences was also suggested by Ackerman et al . 72 in a large study, in which contrary to previous studies they showed no associations between declarative memory consolidation and objective sleep parameters. Further studies are warranted to test whether day-to-day variations in subjective sleep quality predict day-to-day changes in cognitive performance.

Finally, our paper has some limitations. As mentioned above, it is possible that investigating populations more susceptible to sleep disturbances or cognitive performance problems could yield different results and the lack of associations could be specific to healthy young adults. Furthermore, it would be interesting to test whether individual differences in other factors (for example, interoceptive ability, i.e., how accurately one perceives their own body sensations) influence the relationship between subjective sleep quality and cognitive performance.

Conclusions

In conclusion, we showed that self-reported, subjective sleep quality is not associated with working memory, executive functions, and various aspects of procedural learning in a relatively large sample of healthy young adults. These findings were supported not only by frequentist statistical analyses but also by Bayes factors that provided substantial evidence for no associations between these functions. Importantly, however, our findings do not imply that sleep per se has no relationship with these cognitive functions; instead, it emphasizes the dissociation between subjective and objective sleep quality. We believe that our approach of systematically testing the relationship between self-reported sleep questionnaires and a relatively wide range of cognitive functions can inspire future systematic studies on the relationship between subjective/objective sleep parameters and cognition. Within healthy young adults, future studies are warranted to probe the relationship between subjective sleep quality and cognitive performance assessed in the non-preferred time of day, include other aspects of cognitive functions, and test intraindividual, day-to-day variations in the relationship between sleep and cognitive performance.

Data availability

The dataset and analysis code of the current study are available in the Open Science Framework repository, https://osf.io/hcnsx/ .

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Acknowledgements

This research was supported by the Research and Technology Innovation Fund, Hungarian Brain Research Program (National Brain Research Program, project 2017-1.2.1-NKP-2017-00002); IDEXLYON Fellowship of the University of Lyon as part of the Programme Investissements d’Avenir (ANR-16-IDEX-0005); Hungarian Scientific Research Fund (NKFIH-OTKA PD 124148, PI: KJ; NKFIH-OTKA K 128016, to DN); and Janos Bolyai Research Fellowship of the Hungarian Academy of Sciences (to KJ). The authors are thankful to Csenge Török, Kata Horváth, Eszter Tóth-Fáber, Orsolya Pesthy, Noémi Éltető, Andrea Kóbor, and Ádám Takács for their help in data collection, to Kate Schipper for proofreading the manuscript, and to the reviewers for their helpful comments and suggestions to improve the paper.

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These authors contributed equally: Dezso Nemeth and Karolina Janacsek

Authors and Affiliations

Doctoral School of Psychology, ELTE Eötvös Loránd University, Budapest, Hungary

Zsófia Zavecz

Institute of Psychology, ELTE Eötvös Loránd University, Budapest, Hungary

Zsófia Zavecz, Tamás Nagy, Adrienn Galkó, Dezso Nemeth & Karolina Janacsek

Institute of Cognitive Neuroscience and Psychology, Hungarian Academy of Sciences, Budapest, Hungary

Zsófia Zavecz, Dezso Nemeth & Karolina Janacsek

Lyon Neuroscience Research Center (CRNL), INSERM, CNRS, Université Claude Bernard Lyon 1, Lyon, France

Dezso Nemeth

School of Human Sciences, Faculty of Education, Health and Human Sciences, University of Greenwich, London, United Kingdom

Karolina Janacsek

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Contributions

Z.Z., K.J. and D.N. designed the present study and wrote the manuscript. A.G. and Z.Z. collected the data. A.G., Z.Z., K.J. and T.N. analyzed the data. Z.Z., K.J., T.N. and D.N. contributed to the interpretation of the results and critically revised the previous versions of the manuscript. All authors read and approved the final version of the manuscript.

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Correspondence to Dezso Nemeth or Karolina Janacsek .

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Zavecz, Z., Nagy, T., Galkó, A. et al. The relationship between subjective sleep quality and cognitive performance in healthy young adults: Evidence from three empirical studies. Sci Rep 10 , 4855 (2020). https://doi.org/10.1038/s41598-020-61627-6

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Effects of sleep deprivation on cognitive and physical performance in university students

Yusuf patrick.

1 Imperial College School of Medicine, Imperial College London, South Kensington Campus, Sir Alexander Fleming Building, London, SW7 2DD UK

Oishik Raha

Kavya pillai, shubham gupta, sonika sethi, felicite mukeshimana, lothaire gerard, mohammad u. moghal.

2 Academic Unit of Sleep and Breathing, National Heart and Lung Institute, Imperial College London, London, UK

3 NIHR Respiratory Disease Biomedical Research Unit, Sleep and Ventilation, Royal Brompton and Harefield NHS Foundation Trust, Sydney Street, London, SW3 6NP UK

Sohag N. Saleh

4 Faculty of Medicine, Imperial College London, South Kensington Campus, Sir Alexander Fleming Building, London, SW7 2DD UK

Susan F. Smith

5 Medical Education Research Unit, Faculty of Medicine, Imperial College London, South Kensington Campus, Sir Alexander Fleming Building, London, SW7 2DD UK

Mary J. Morrell

Sleep deprivation is common among university students, and has been associated with poor academic performance and physical dysfunction. However, current literature has a narrow focus in regard to domains tested, this study aimed to investigate the effects of a night of sleep deprivation on cognitive and physical performance in students. A randomized controlled crossover study was carried out with 64 participants [58% male ( n = 37); 22 ± 4 years old (mean ± SD)]. Participants were randomized into two conditions: normal sleep or one night sleep deprivation. Sleep deprivation was monitored using an online time-stamped questionnaire at 45 min intervals, completed in the participants’ homes. The outcomes were cognitive: working memory (Simon game© derivative), executive function (Stroop test); and physical: reaction time (ruler drop testing), lung function (spirometry), rate of perceived exertion, heart rate, and blood pressure during submaximal cardiopulmonary exercise testing. Data were analysed using paired two-tailed T tests and MANOVA. Reaction time and systolic blood pressure post-exercise were significantly increased following sleep deprivation (mean ± SD change: reaction time: 0.15 ± 0.04 s, p = 0.003; systolic BP: 6 ± 17 mmHg, p = 0.012). No significant differences were found in other variables. Reaction time and vascular response to exercise were significantly affected by sleep deprivation in university students, whilst other cognitive and cardiopulmonary measures showed no significant changes. These findings indicate that acute sleep deprivation can have an impact on physical but not cognitive ability in young healthy university students. Further research is needed to identify mechanisms of change and the impact of longer term sleep deprivation in this population.

Introduction

Sleep deprivation is common amongst university students whom live in a culture that promotes reduced sleep, due to the burden of academic work and social pursuits. The reasons for poor sleep hygiene include alcohol and caffeine intake, stimulants, and technology, which prevent students achieving sufficient sleep time and quality [ 1 ]. A cross-sectional survey found that 71% of students did not achieve the recommended 8 h of sleep, with 60% classified as poor sleepers [ 2 ]. An average of 5.7 h sleep has been reported for students studying architecture, and sleepless nights due to academic work throughout the night—defined by the Oxford English Dictionary as an all-nighter—occurred, on average, 2.7 days a month [ 3 ].

While many studies have investigated the effects of acute sleep deprivation, few focus on university students, despite the prevalence and impact of sleep deprivation in this population [ 4 , 5 ]. Such studies often have a narrow focus on disease states, limiting their ability to provide a holistic assessment of physical, emotional and cognitive wellbeing [ 4 – 6 ]. The importance of physical and cognitive function is especially appreciable in the student population, 52% of whom play sport at least once a week. Moreover, students rate sleep problems second only to stress in relation to negative impact on academic performance [ 7 ]. The effect of acute sleep deprivation on physical performance has been well documented with negligible effects on intense periods of exercise, whilst endurance task performance suffers due to decreased motivation [ 8 , 9 ].

The effect of sleep deprivation on cognitive performance has also been documented previously with a correlation between sleep quality and grade point average in first year university students [ 10 ]. Moreover, sleep deprivation has been shown to have a detrimental effect on certain aspects of working memory, such as filtering efficiency, whilst Stroop test scores show degradation; however, this has been evidenced to be due to deficits in reaction time rather than processing skills [ 5 , 11 – 17 ]. Taken together, these data suggest that sleep deprivation may have a limited effect on cognitive ability in university students.

This study aimed to determine whether a night of sleep deprivation, equivalent to an “all-nighter”, would have a negative impact on the motor and cognitive performance of students, specifically focusing on reaction time, executive function, working memory, and cardiopulmonary function.

Materials and methods

Study design, participants, and recruitment.

This was a randomized, controlled crossover study, which took place from June to September 2015. Exclusion criteria were: (1) any medication or medical history that would make participation in the study, in particular the sleep deprivation and exercise test, unsafe, or inappropriate; (2) mental incapacity to provide informed consent, or (3) recent (within 6 months) participation in a research trial. Participants were recruited via direct approach and posters on campus, social media, and a National Heart and Lung Institute newsletter. Participants travel expenses were reimbursed and all participants were offered the opportunity to be entered into a prize draw. Participants were told that the study involved testing parameters following sleep deprivation, but no information was given regarding the anticipated results. All participants gave written informed consent, and the study was approved by Medical Education Ethics Committee (Imperial College London, 23/4/15, MEEC1415-24).

Participants were randomized to either the sleep deprivation or a normal night’s sleep first, using a random number sequence. Twenty-four hours prior to the morning assessment, participants were instructed to refrain from consuming alcohol and caffeinated drinks as well as abstaining from exercise, smoking, and nicotine patches. Those having a normal night’s sleep where asked to report how much they had slept. The sleep deprivation arm were required to fill out a form every 45 min to confirm that they were still awake. This form was checked the following morning. More than two unexplained missed form completions resulted in disqualification from the study. The crossover condition and assessment were undertaken within 3–12 days of one another. Testing occurred between 09:00 to 13:00, with participants being allowed flexible timings; however, all follow-up testing aimed to take place within 1 h of initial session time. The outcome was to measure the change, if any, which occurred between the cognitive and physical performance of participants undergoing sleep deprivation.

Before testing began, height and weight were recorded and participants were asked to fill out a fitness questionnaire. The results of this questionnaire and participant sex were used to estimate the appropriate Monark Ergomedic 828e resistance for each participant, (male: 2.0 kp = unfit, 2.5 kp = fit, 3.0 kp = athlete; female: 1.5 kp = unfit, 2.0 kp = fit, 2.5 kp = athlete). A second questionnaire enquired about recent (within 24 h) intake of food, caffeine, alcohol, and nicotine, and any physical exercise was also completed.

All participants were provided with standardized descriptions of tests and given the opportunity to habituate with procedures.

Cognitive function tests

The working memory mobile application was derived from the SIMON© game, an appropriate test for working memory span [ 18 ]. It involved repeating a random sequence of colors and sounds. As each level progressed, another random color-sound combination was added to the previous sequence. This test was repeated three times.

Standard stroop charts were used: (1) monochrome (reading black text); (2) conflicting color (reading words with a mismatched color); (3) color blocks (articulating the color of colored blocks), and (4) conflicting words (articulating the color of mismatched words) [ 19 ]. Four versions of each test were created, so that no participant used the same chart twice. Time taken to complete each chart and the number of mistakes were recorded providing a measure of selective attention, automatic responses, inhibition, and control of executive functions [ 20 – 22 ].

Physical function tests

Participants performed two concordant volume-time spirometry traces, in adherence to standard guidelines [ 23 ].

Participants underwent submaximal 8 min cardiopulmonary exercise testing (CPET) using a cycle ergometer target cadence 50 ± 5; this intensity of exercise test was chosen to replicate more closely students’ daily activities (as opposed to maximal exercise testing). Three electrocardiogram (ECG) electrodes were attached, and non-invasive blood pressure (BP) measurements and rating of perceived exertion (RPE) were also recorded throughout the exercise test (Fig. 1 ). Measurement of RPE has repeatedly been shown to have a strong correlation with the intensity of exercise being performed, independently from other factors [ 24 – 26 ].

An external file that holds a picture, illustration, etc.
Object name is 41105_2017_99_Fig1_HTML.jpg

Timeline showing the measurements taken during the CPET. After calibration, a one-minute baseline was conducted followed by 8 min of exercise and 3 min of rest. Non-invasive blood pressure (BP) measurements were taken using a manual sphygmomanometer at three points of the test: pre-exercise, post-exercise, and post-rest period. Rating of perceived exertion (RPE) (53) was recorded at the end of each minute

Reaction time

The ruler drop test was used to assess average reaction speed, a simple and inexpensive test compared to computerized assessments, with comparable reliability [ 27 – 30 ]. Participants carried out three practice runs to eliminate a learning effect [ 27 ].

Statistical analyses

Statistical tests were carried out using IBM SPSS Statistics (V 22.0), Armonk, NY, USA. Due to the novel measures used in this study, a preliminary study was performed to estimate appropriate sample size. The study consisted of eight participants representative of the target population with a target sample size determined of 51 at a power level of 80%; therefore, a target recruitment of 70 was set with an estimated 25% attrition rate. Initially, descriptive statistics were obtained for all variables. Data were inspected for normality using histograms. Parametric data were reported as mean ± standard deviation, and paired two-tailed T tests were carried out to assess the difference between normal night sleep and sleep deprived arms. The tests were carried out with alpha significance level p ≤ 0.05, and 95% confidence intervals were calculated. Levene’s test (homogeneity of variances) was used to assess the differences in variance of samples. Differences in rating of perceived exertion and heart rate during exercise were assessed using multivariate analysis of variance test.

Participant characteristics

The study sample consisted of 64 Imperial College London students, 57 (89%) of which completed the study (Fig. 2 ). The characteristics of the participants are summarised in Table 1 ; upon study entry, participants reported typically sleeping between 5 and 9 h per night, with 94.7% reporting these hours as ‘typical’ and 98.2% reporting sleeping through until morning without waking on most nights. 58% of participants were male and 69% were undergraduates. The participants’ mean age was 22 ± 4 years. Participants in the sleep deprivation arm filled in the online form on average every 49 ± 21 min throughout the night. Participants in the normal night’s sleep arm reported sleeping on average 7.2 ± 1.0 h. The mean difference in time of day that testing occurred (between condition 1 and condition 2) was 32 ± 15 min with no significant difference between testing times ( p = 0.220).

An external file that holds a picture, illustration, etc.
Object name is 41105_2017_99_Fig2_HTML.jpg

Flow chart showing participant numbers during the study. Percentages indicate the percentage of individuals who remained from the previous stage

Participant characteristics ( n = 57)

Characteristic	Normal night sleep ( = 57)	Sleep deprived ( = 57)
Age (years)	22 ± 4
Weight (kg)	67 ± 14
Height (m)	1.7 ± 0
Body mass index (kg/m )	23 ± 4
Normal sleep duration (h)	7.4 ± 1.0
Disrupted normal sleep [ (%)]	1 (1.8%)
Sleep-related characteristics
Quality of sleep (/10)	7.48 ± 1.99	0.67 ± 1.64
Current mood (/10)	7.21 ± 1.69	3.51 ± 2.42
Rating of adequacy of sleep (/10)	7.40 ± 2.02	0.69 ± 1.73

Data are reported as mean ± standard deviation. Sleep-related characteristics were obtained using a questionnaire with a 10-point scale, ranging from 0 (lowest quality sleep, poor mood, and inadequate sleep) to 10 (best quality sleep, good mood, and fully adequate sleep)

Exercise testing

Fifty-four (95%) percent of participants completed both exercise tests. Table 2 shows key variables from the exercise test. There were no significant differences between the normal sleep and sleep deprived arms, except for systolic blood pressure post-exercise (135 ± 12 vs. 140 ± 17 mmHg; p = 0.012). There is no significant difference between the changes in mean heart rate and RPE when compared using a MANOVA test p = 0.723 and p = 0.559, respectively (see Fig. 3 ).

Participant characteristics during CPET

Test	Normal night sleep ( = 54)	Sleep deprived ( = 54)	Difference	value
Heart rate (bpm)
Max heart rate	149 ± 22	146 ± 20	3 ± 14	0.079
Blood pressure (mmHg)
Systolic at rest	116 ± 10	115 ± 12	1 ± 12	0.733
Mean arterial pressure at rest	88 ± 7	86 ± 9	2 ± 7	0.123
Systolic post-exercise	135 ± 12	140 ± 17	−6 ± 17	0.012*
Mean arterial pressure post-exercise	94 ± 7	94 ± 11	0 ± 11	0.812
Systolic post-recovery	120 ± 10	122 ± 15	−1 ± 13	0.429
Mean arterial pressure post-recovery	89 ± 7	89 ± 10	0 ± 9	0.908

Data are reported as mean ± standard deviation. Data analysed using paired two-tailed T test

*Indicates significant result at an alpha of 5% ( p ≤ 0.05)

An external file that holds a picture, illustration, etc.
Object name is 41105_2017_99_Fig3_HTML.jpg

Line graph comparing heart rate during the baseline, exercise, and recovery periods of cardiopulmonary exercise testing between the control and deprived groups ( n = 54). Error bars indicate two standard deviations. MANOVA shows no significant difference in heart rate ( p = 0.723). Average values for blood pressure as measured at rest, post-exercise, and post-recovery are also displayed. Post-exercise systolic BP was found to be significantly different ( p = 0.012)

Cognitive and physical testing

Table 3 compares the results of the cognitive tests and additional physical tests under sleep deprivation and normal sleep test conditions. There was no significant difference observed across all tests except reaction time, with a significantly higher average ( p = 0.03) among individuals who were sleep deprived.

Participant characteristics for cognitive tests

Test	Normal night sleep ( = 57)	Sleep deprived ( = 57)	Difference	value
Memory
Mean sequence length	10 ± 4	10 ± 3	0 ± 3	0.307
Stroop
Monochrome
Time (s)	37 ± 6	38 ± 6	−1 ± 1	0.185
Errors ( )	1 ± 1	2 ± 1	0 ± 0	0.268
Conflicting colors
Time (s)	40 ± 7	41 ± 8	−1 ± 1	0.123
Errors ( )	1 ± 1	1 ± 1	0 ± 0	0.768
Color block
Time (s)	53 ± 10	52 ± 10	0 ± 0	0.437
Errors ( )	1 ± 2	1 ± 1	0 ± 0	0.409
Conflicting words
Time (s)	73 ± 17	72 ± 16	1 ± 1	0.469
Errors ( )	2 ± 3	2 ± 3	0 ± 0	0.866
Reaction time
Mean (s)	0.18 ± 0.04	0.19 ± 0.03	−0.15 ± 0.04	0.030*
Spirometry
FEV (L)	3.63 ± 0.99	3.67 ± 0.95	−0.03 ± 0.25	0.303
FVC (L)	4.33 ± 1.19	4.36 ± 1.22	−0.03 ± 0.19	0.321
FEV /FVC	0.85 ± 0.09	0.85 ± 0.08	−0.01 ± 0.06	0.511
PEFR (L/min)	467 ± 134	468 ± 137	−1 ± 79	0.930

The results of the first visit and second visit were compared irrespective of condition for the cognitive tests and reaction time. There was a significant difference for the monochrome, color blocks, and conflicting words Stroop tests ( p = 0.001, p = 0.000, and p = 0.011, respectively); no significant difference was found in other variables.

The main findings of this study were that sleep deprivation resulted in a significant increase in reaction time and post-exercise systolic BP in university students after one night of sleep deprivation, compared to a normal night’s sleep.

No significant differences were found in the cognitive tests, suggesting that one night of sleep deprivation has minimal effect on a student’s cognitive capacity.

Working memory and executive function both heavily rely on the prefrontal cortex, anterior cingulate cortex, and salience network. This network has been shown reduced activation post-acute sleep deprivation [ 31 – 35 ]. Interestingly, a comprehensive review has noted that particular tests show no significant difference, such as the digit span test. This test is most similar to the assessment of memory used in our study [ 35 , 36 ]. Another study found that both partial and total sleep deprivation had no effect on visual working memory, but that total sleep deprivation had a significant effect on filtering efficiency [ 37 ]. As the Simon© game tests visual working memory, our results suggest that the effect of sleep deprivation may not be as widespread in university students, with components of memory being preserved [ 31 ].

The stroop test showed no significant increase in time taken to complete, or the number of mistakes made in each set. Acute sleep deprivation has been demonstrated to have no effect on the principal processes of interference or facilitation with performance influenced by increased reaction time [ 15 ]. Previously, the stroop test has been found to show practice effects [ 20 ]. This was evident in the present study with significant improvements made on the second visit, irrespective of participant condition. While it is difficult to interpret the reason for this result, it has previously been demonstrated that with increased age, there is an increase in brain activity in response to acute sleep deprivation [ 38 ]. Therefore, the young student population may be more effective at dealing with acute sleep deprivation.

Working memory and executive function are important with regard to university students, since they are linked to the understanding of complex concepts. Indeed, the previous research has shown a stronger correlation between attainment and working memory than with Intelligence Quotient [ 39 ]. Our study indicates that acute sleep deprivation was not detrimental to students’ cognitive ability; however, a review of other cognitive variables is necessary [ 35 ].

This study found an increase in reaction time after sleep deprivation, which has previously been well described with student subjects [ 40 ]. The underlying physiology of this effect is localised to the anterior cingulate cortex, middle prefrontal gyrus, and inferior parietal lobes which have been shown to be hypoactivated in acute sleep deprivation. This finding is pertinent to students, in particular the large proportion of them (52%) who take part in sport at least once a week [ 7 , 31 , 41 ]. Of greater concern, the previous research has demonstrated that tired students are more likely to drive dangerously, another activity requiring prompt reactions. In one study of 1039 students, 16% reported falling asleep while driving [ 42 ]. Another found that an ‘all-nighter’, had a comparable effect on driving performance to a blood alcohol concentration of 0.1% which is above the UK Drink Driving limit [ 43 ]. Reaction time has also been linked with cognitive processing speed via mental chronometry, indicating that the effect of slowed reaction time may not be wholly in the realm of physical exertion, but also a pseudo measurement of IQ [ 44 ].

RPE is commonly described as involving aspects of both metabolism and CNS activity [ 45 , 46 ]. There was no significant change in RPE in the present study, although mean RPE after sleep deprivation was higher than after a night of sleep. This contradicts much of the pre-existing literature, which suggests that sleep deprivation is associated with a significantly increased RPE but little or no change in physiological parameters [ 35 , 47 ].

This study found a reduction in HR post-exercise after acute sleep deprivation, although not significantly. A previous study has shown following two nights of sleep deprivation, and HR was significantly lower, during, and after an exercise test, compared to two nights of sleep. This was speculated to be due to ACTH concentration, which was lowest on the second day of sleep deprivation [ 48 ]. This is the same period over which exercise testing was carried out in our study, and could explain the effect on HR found.

According to the present study, acute sleep deprivation had no effect on resting blood pressure, but caused an increase in systolic blood pressure post-exercise. Thus, the effect on blood pressure from this study was unclear, a finding which has been reflected in the literature [ 49 , 50 ]. Sleep deprivation has previously been demonstrated to stimulate sympathetic activity and neuroendocrine response to stressor stimuli [ 47 ]. Therefore, continuous periods of sleep deprivation, e.g., before a project deadline, may lead to students developing hypertension or having inappropriate response to intense exercise.

Spirometry was used to assess lung function parameters including FVC, FEV 1 , and the FEV 1 /FVC ratio. Spirometry is commonly used in clinical practice for monitoring baseline lung function. Analyses showed no statistically significant difference when comparing the sleep deprived values against the values for the normal night’s sleep, a finding supported by the literature [ 51 , 52 ].

Limitations

Several limitations need to be considered when interpreting the findings of this study. First, participants carried out their night of sleep deprivation in an environment of their choice rather than a supervised environment. Therefore, the study design was reliant on self-reported sleep deprivation and form completion, which may mean that some students had more sleep than others on the sleep deprivation night. Whilst this reduces the generalisability of our results in more diverse samples, the results are indicative of the effects of acute sleep deprivation on students in higher education. Alternate equipment and a larger test selection would have given a wider holistic prospective on the impact of sleep deprivation in university students; however, the interventions used in the study were chosen to maximise participant familiarity and minimise testing time.

This study found that acute sleep deprivation has a significant effect on postexercise blood pressure and reaction time in students. These changes are likely due to neuroendocrine changes and downregulation in salience and motor areas of the brain. Most notably reduced reaction times will impact competitive sports, and can pose a danger to safety critical actions such as driving. However cognitive and neurophysical impairment in other functions was not as widespread as previously thought. Overall, this study found that an “all-nighter” does not affect a student’s cognitive ability, whilst physical performance is significantly affected.

Acknowledgements

This work was supported by a School of Medicine Education Innovations Award and the NIHR Respiratory Biomedical Research Unit at the Royal Brompton and Harefield NHS Foundation Trust, Imperial College London. Special thanks to Mr Matthew Barrett and Mr Andrew Horn for their technical assistance.

Author contributions

All authors contributed equally to the writing and revision of this manuscript. Data collection and analysis was carried out by LG, SG, AL, FM, YP, KP, OR, SS, and JM.

Compliance with ethical standards

Informed consent.

All participants gave written informed consent, and the study was approved by Medical Education Ethics Committee (Imperial College London, 23/4/15, MEEC1415-24).

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Effects of Sleep Deprivation on Performance: A Meta-Analysis

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

Introduction

Materials and Methods

Ethics Statement

Design and Procedure

Psychometric Data

Cortisol Measurements

Data analysis.

Task design.

Data processing.

[ 11 C]Raclopride PET

Kinetic analysis.

Statistical parametric mapping.

Cortisol Data

[ 11 C]Raclopride

Clinical Interactions

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ORIGINAL RESEARCH article

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Materials and Methods

Experimental Design

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Data Analysis of Behavioral Experiments

EEG Data Analysis

Behavioral Performance

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Effects of Sleep Deprivation on Cognitive Functions and Academic Achievement in Students

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Mary Daohne P. Silvestre, PE-Collrge of Education,Bukidnon State University [email protected]

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The Effects Of Sleep Deprivation Towards The Academic Performance Of Ustp- Oroquieta Students

The relationship between subjective sleep quality and cognitive performance in healthy young adults: Evidence from three empirical studies

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