research paper about sleep deprivation

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• v.3(5); 2007 Oct

Sleep deprivation: Impact on cognitive performance

Paula alhola.

1 Department of Psychology

Päivi Polo-Kantola

2 Sleep Research Unit (Department of Physiology), University of Turku, Turku, Finland

Today, prolonged wakefulness is a widespread phenomenon. Nevertheless, in the field of sleep and wakefulness, several unanswered questions remain. Prolonged wakefulness can be due to acute total sleep deprivation (SD) or to chronic partial sleep restriction. Although the latter is more common in everyday life, the effects of total SD have been examined more thoroughly. Both total and partial SD induce adverse changes in cognitive performance. First and foremost, total SD impairs attention and working memory, but it also affects other functions, such as long-term memory and decision-making. Partial SD is found to influence attention, especially vigilance. Studies on its effects on more demanding cognitive functions are lacking. Coping with SD depends on several factors, especially aging and gender. Also interindividual differences in responses are substantial. In addition to coping with SD, recovering from it also deserves attention. Cognitive recovery processes, although insufficiently studied, seem to be more demanding in partial sleep restriction than in total SD.

Introduction

A person’s quality of life can be disrupted due to many different reasons. One important yet underestimated cause for that is sleep loss ( National Sleep Foundation 2007 ). Working hours are constantly increasing along with an emphasis on active leisure. In certain jobs, people face sleep restriction. Some professions such as health care, security and transportation require working at night. In such fields, the effect of acute total sleep deprivation (SD) on performance is crucial. Furthermore, people tend to stretch their capacity and compromise their nightly sleep, thus becoming chronically sleep deprived.

When considering the effects of sleep loss, the distinction between total and partial SD is important. Although both conditions induce several negative effects including impairments in cognitive performance, the underlying mechanisms seem to be somewhat different. Particularly, results on the recovery from SD have suggested different physiological processes. In this review, we separately consider the effects of acute total and chronic partial SD and describe the effects on cognitive performance. The emphasis on acute total SD reflects the quantity of studies carried out compared with partial SD. The effects of aging and gender, as well as interindividual differences are discussed. We concentrate on the studies that have been published since 1990.

Sleep and sleep loss

The need for sleep varies considerably between individuals ( Shneerson 2000 ). The average sleep length is between 7 and 8.5 h per day ( Kripke et al 2002 ; Carskadon and Dement 2005 ; Kronholm et al 2006 ). Sleep is regulated by two processes: a homeostatic process S and circadian process C (eg, Achermann 2004 ). The homeostatic process S depends on sleep and wakefulness; the need for sleep increases as wakefulness continues. The theory for circadian process C suggests a control of an endogenous circadian pacemaker, which affects thresholds for the onset and offset of a sleep episode. The interaction of these two processes determines the sleep/wake cycle and can be used to describe fluctuations in alertness and vigilance. Although revised “three-process models” (eg, Akerstedt and Folkard 1995 ; Van Dongen et al 2003b ; Achermann 2004 ) have been suggested, this classical model is the principal one used for study designs in SD research.

There are many unanswered questions regarding both the functions of sleep and the effects of sleep loss. Sleep is considered to be important to body restitution, like energy conservation, thermoregulation, and tissue recovery ( Maquet 2001 ). In addition, sleep is essential for cognitive performance, especially memory consolidation ( Maquet 2001 ; Stickgold 2005 ). Sleep loss, instead, seems to activate the sympathetic nervous system, which can lead to a rise of blood pressure ( Ogawa et al 2003 ) and an increase in cortisol secretion ( Spiegel et al 1999 ; Lac and Chamoux 2003 ). Immune response may be impaired and metabolic changes such as insulin resistance may occur (for review, see Spiegel et al 2005 ). People who are exposed to sleep loss usually experience a decline in cognitive performance and changes in mood (for meta-analyses, see Pilcher and Huffcutt 1996 ; Philibert 2005 ).

Sleep deprivation is a study design to assess the effects of sleep loss. In acute total SD protocols, the subjects are kept awake continuously, generally for 24–72 hours. In chronic partial SD, subjects are allowed restricted sleep time during several consecutive nights. Although chronic sleep restriction is more common in the normal population and thus offers a more accurate depiction of real life conditions, total SD has been more thoroughly explored.

Cognitive performances measured in SD studies have included several domains. The most thoroughly evaluated performances include different attentional functions, working memory, and long-term memory. Visuomotor and verbal functions as well as decision-making have also been assessed. Sleep deprivation effects on cognitive performance depend on the type of task or the modality it occupies (eg, verbal, visual, or auditory). In addition, task demands and time on task may play a role. The task characteristics are discussed in more detail in following sections where the existing literature on the cognitive effects of SD is reviewed.

Mechanisms behind sleep loss effects

Some hypotheses are proposed to explain why cognitive performance is vulnerable to prolonged wakefulness. The theories can be divided roughly in two main approaches, in which SD is assumed to have (1) general effects on alertness and attention, or (2) selective effects on certain brain structures and functions. In addition, individual differences in the effects have been reported.

The general explanation relies on the two-process model of sleep regulation. Cognitive impairments would be mediated through decreased alertness and attention through lapses, slowed responses, and wake-state instability. Attentional lapses, brief moments of inattentiveness, have been considered the main reason for the decrease in cognitive performance during sleep deprivation (on lapse hypothesis, eg, Williams et al 1959, see Dorrian et al 2005 ; Kjellberg 1977 ). The lapses are caused by microsleeps characterized by very short periods of sleep-like electro-encephalography (EEG) activity ( Priest et al 2001 ). Originally, it was thought that in between the lapses, cognitive performance almost remained intact, but the slowing of cognitive processing has also been observed independent of lapsing ( Kjellberg 1977 ; Dorrian et al 2005 ). According to these hypotheses, performance during SD would most likely deteriorate in long, simple, and monotonous tasks requiring reaction speed or vigilance. In addition to the lapses and response slowing, considerable fluctuations in alertness and effort have been observed during SD. According to the wake-state instability hypothesis, those fluctuations lead to variation in performance ( Doran et al 2001 ).

According to explanations on selective impact, SD interferes with the functioning of certain brain areas and thus impairs cognitive performance. This approach is also referred to as the ‘sleep-based neuropsychological perspective’ ( Babkoff et al 2005 ). Perhaps the most famous theory in this category is the prefrontal vulnerability hypothesis, first proposed by Horne (1993) . It suggests that SD especially impairs cognitive performances that depend on the prefrontal cortex. These include higher functions, such as language, executive functions, divergent thinking, and creativity. In order to show the SD effect, the tests should be complex, new, and interesting. A good performance would require cognitive flexibility and spontaneity. This theory also assumes that the deterioration of subjects’ performance in simple and long tasks is merely due to boredom ( Harrison and Horne 1998 ; Harrison and Horne 1999 ; Harrison and Horne 2000 ). The specific brain areas that are vulnerable to sleep loss have been explored using functional magnetic resonance imaging (fMRI) and positron emission tomography (PET). Those studies, however, have mainly measured working memory or other attentional functions with the type of tasks that are not traditionally emphasized in the prefrontal vulnerability hypothesis (for summary, see Chee et al 2006 ).

Individuals differ in terms of the length, timing, and structure of sleep. Therefore, it is logical to hypothesize that interindividual differences are also important in reaction to SD. Studies have consistently found that some people are more vulnerable to sleep loss than others (for review, see Van Dongen et al 2005 ). In reference to trait differential vulnerability to SD, Van Dongen et al (2005) have proposed the concept of the “trototype”, as compared to the terms “chronotype” and “somnotype”, which define interindividual differences in the timing of circadian rhythmicity and sleep duration. Since a comprehensive review of the interindividual differences in sleep and performance has been published recently ( Van Dongen et al 2005 ), we will focus here on the studies with group comparisons and just briefly address the trait-like vulnerability.

Acute total sleep deprivation

Attention and working memory.

The two most widely studied cognitive domains in SD research are attention and working memory, which in fact are interrelated. Working memory can be divided into four subsystems: phonological loop, visuospatial sketchpad, episodic buffer and central executive ( Baddeley and Hitch 1974 ; Baddeley 2000 ). The phonological loop is assumed to temporarily store verbal and acoustic information (echo memory); the sketchpad, to hold visuospatial information (iconic memory), and the episodic buffer to integrate information from several different sources. The central executive controls them all. Executive processes of working memory play a role in certain attentional functions, such as sustained attention ( Baddeley et al 1999 ), which is referred to here as vigilance. Both attention and working memory are linked to the functioning of frontal lobes (for a review, see Naghavi and Nyberg 2005 ). Since the frontal brain areas are vulnerable to SD ( Harrison et al 2000 ; Thomas et al 2000 ), it can be hypothesized that both attention and working memory are impaired during prolonged wakefulness.

The decrease in attention and working memory due to SD is well established. Vigilance is especially impaired, but a decline is also observed in several other attentional tasks ( Table 1 ). These include measures of auditory and visuo-spatial attention, serial addition and subtraction tasks, and different reaction time tasks ( Table 1 ). The most frequently used task is the psychomotor vigilance test (PVT, lasts usually 10 min) ( Dinges and Powell 1985 ), which is sensitive to sleep loss effects and provides information about both reaction speed and lapses. In working memory, the tests have varied from n-back style tasks with different demand levels to choice-reaction time tasks with a working memory component ( Table 1 ). However, some studies have also failed to find any effect. After one night of SD, no difference was observed between deprived and non-deprived subjects in simple reaction time, vigilance, or selective attention tasks in one study ( Forest and Godbout 2000 ). Performance on the Wisconsin Card Sorting Test, a measure of frontal lobe function, also remained even ( Binks et al 1999 ; Forest and Godbout 2000 ). These results may be partly biased because of small sample sizes, inadequate control of the subjects’ sleep history or the use of stimulants before the study.

Cognitive tests in which deterioration of performance has been reported during acute total sleep deprivation

Abbreviations: SD, sleep deprivation; WAIS, Wechsler Adult Intelligence Scale; WAIS-R, Wechsler Adult Intelligence Scale-Revised.

Outcomes are inconsistent in various dual tasks used for measuring divided attention. Sleep deprivation of 24 h impaired performance in one study ( Wright and Badia 1999 ), whereas in two others, performance was maintained after 25–35 h of SD ( Drummond et al 2001 ; Alhola et al 2005 ). The divergent findings in these studies may be explained by the uneven loads between different subtests as well as by uncontrolled practice effect. Although dividing attention between different tasks puts high demands on cognitive capacity, subjects often attempt to reduce the load by automating some easier procedures of a dual or multitask. In the study by Wright and Badia (1999) , the test was not described; in the study by Alhola et al (2005) , subjects had to count backwards and carry out a visual search task simultaneously, and in the study by Drummond et al (2001) subjects had to memorize words and complete a serial subtraction task sequentially. In addition, differences in essential study elements, like the age and gender of participants, as well as the duration of SD, further complicate comparison of the results.

In the tasks measuring attention or working memory, two aspects of performance are important: speed and accuracy. In practice, people can switch their emphasis between the two with attentional focusing ( Rinkenauer et al 2004 ). Oftentimes, concentrating on improving one aspect leads to the deterioration of the other. This is called the speed/accuracy trade-off phenomenon. Some SD studies have found impairment only in performance speed, whereas accuracy has remained intact ( De Gennaro et al 2001 ; Chee and Choo 2004 ). In others, the results are the opposite ( Kim et al 2001 ; Gosselin et al 2005 ). De Gennaro et al (2001) proposed that in self-paced tasks, there is likely to be a stronger negative impact on speed, while accuracy remains intact. In experimenter-paced tasks, the effect would be the opposite. However, many studies show detrimental effect on both speed and accuracy (eg, Smith et al 2002 ; Jennings et al 2003 ; Chee and Choo 2004 ; Habeck et al 2004 ; Choo et al 2005 ). The speed/accuracy trade-off phenomenon is moderately affected by gender, age, and individual differences in response style ( Blatter et al 2006 ; Karakorpi et al 2006 ), which could be a reason for inconsistencies in the SD results. It has been argued that low signal rates increase fatigue during performance in SD studies and that subjects may even fall asleep during the test ( Dorrian et al 2005 ). Therefore, tasks with different signal loads may produce different results in terms of performance speed and accuracy.

Long-term memory

Long-term memory can be divided between declarative and non-declarative (procedural) memory. Declarative memory is explicit and limited, whereas non-declarative memory is implicit and has a practically unlimited capacity. Declarative memory includes semantic memory, which consists of knowledge about the world, and episodic memory, which holds autobiographical information. The contents of declarative memory can be stored in visual or verbal forms and they can be voluntarily recalled. Non-declarative or procedural memory includes the information needed in everyday functioning and behavior, eg, motor and perceptual skills, conditioned functions and priming. In previous studies, long-term memory has been measured with a variety of tasks, and the results are somewhat inconsistent.

In verbal episodic memory, SD of 35 h impaired free recall, but not recognition ( Drummond et al 2000 ). The opposite results were obtained with one night of SD ( Forest and Godbout 2000 ). The groups in both studies were quite small (in Drummond’s study, N = 13; in Forest and Godbout’s study, experimental group = 9, control group = 9), which offers a possible explanation for the variation in results. In addition, Drummond et al (2000) used a within-subject design, whereas Forest and Godbout (2000) had a between-subject design. In visual memory, recognition was similar in the experimental and control groups when the measurement was taken once after 36 h SD ( Harrison and Horne 2000 ), whereas the practice effect in visual recall was postponed by SD in a study with three measurements (baseline, 25 h SD, recovery; Alhola et al 2005 ). Performance was impaired in probed forced memory recall ( Wright and Badia 1999 ), and memory search ( McCarthy and Waters 1997 ), but no effect was found in episodic memory ( Nilsson et al 2005 ), implicit memory, prose recall, crystallized semantic memory, procedural memory, or face memory ( Quigley et al 2000 ). In the studies failing to find an effect, however, the subjects spent only the SD night under controlled conditions ( Quigley et al 2000 ; Nilsson et al 2005 ).

Free recall and recognition are both episodic memory functions which seem to be affected differently by SD. Temporal memory for faces (recall) deteriorated during 36 h of SD, although in the same study, face recognition remained intact ( Harrison and Horne 2000 ). In verbal memory, the same pattern was observed ( Drummond et al 2000 ). One explanation may be different neural bases, which supports the prefrontal vulnerability hypothesis. Episodic memory is strongly associated with the functioning of the medial temporal lobes ( Scoville and Milner 2000 ), but during free recall in a rested state, even stronger brain activation is found in the prefrontal cortex ( Hwang and Golby 2006 ). It is unclear whether this prefrontal activation reflects episodic memory function, the organization of information in working memory, or the executive control of attention and memory. Recognition, instead, presumably relies on the thalamus in addition to medial temporal lobes ( Hwang and Golby 2006 ). Since SD especially disturbs the functioning of frontal brain areas ( Drummond et al 1999 ; Thomas et al 2000 ), it is not surprising that free recall is more affected than recognition.

Although the prefrontal cortex vulnerability hypothesis has received wide support in the field of SD research, other brain areas are also involved. For instance, the exact role of the thalamus remains unknown. Some studies measuring attention or working memory have noted an increase in thalamic activation during SD (eg, Portas et al 1998 ; Chee and Choo 2004 ; Habeck et al 2004 ; Choo et al 2005 ). This may reflect an increase in phasic arousal or an attempt to compensate attentional performance during a demanding condition of low arousal caused by SD ( Coull et al 2004 ). In other cognitive tasks such as verbal memory ( Drummond and Brown 2001 ) or logical reasoning ( Drummond et al 2004 ), no increase in thalamic activation was found despite the fact that behavioral deterioration occurred. This implies that thalamic activation during SD is mainly related to some attentional function or compensation, providing further support for the hypothesis that “prefrontal dependent” recall is more affected by SD than “thalamus dependent” recognition. However, it is possible that the brain activation patterns during SD reflect something more than merely different cognitive domains. Harrison and Horne (2000) stated that their results may also reflect the difficulty of the task assigned to subjects.

Other cognitive functions

Sleep deprivation impairs visuomotor performance, which is measured with tasks of digit symbol substitution, letter cancellation, trail-making or maze tracing ( Table 1 ). It is believed that visual tasks would be especially vulnerable to sleep loss because iconic memory has short duration and limited capacity ( Raidy and Scharff 2005 ). Another suggestion is that SD impedes engagement of spatial attention, which can be observed as impairments in saccadic eye movements ( Bocca and Denise 2006 ). Decreased oculomotor functioning is associated with impaired visual performance ( De Gennaro et al 2001 ) and sleepiness (eg, De Gennaro et al 2001 ; Zils et al 2005 ). However, further research is needed to confirm this explanation, since not all studies have found oculomotor impairment with cognitive performance decrements ( Quigley et al 2000 ).

Reasoning ability during SD has for the most part been measured with Baddeley’s logical reasoning task or its modified versions. Again the results are inconsistent (deteriorated performance was reported by Blagrove et al 1995 ; McCarthy and Waters 1997 ; Monk and Carrier 1997 , and Harrison and Horne 1999 ; no effects were noted by Linde and Bergstrom 1992 ; Quigley et al 2000 , or Drummond et al 2004 ). The studies reporting no effect have mainly used SD of ca. 24 h ( Linde and Bergström 1992 ; Quigley et al 2000 ), whereas in the studies showing an adverse effect, the SD period has been longer (36 h). Thus reasoning ability seems to be maintained during short-term SD. However, choosing divergent study designs may result in different outcomes. Monk and Carrier (1997) repeated the cognitive test every 2 h and found deterioration after as little as 16 h of SD. In the studies with zero-results, cognitive tests were carried out in the morning ( Linde and Bergström 1992 ; Quigley et al 2000 ) or the practice effect was not adequately controlled ( Drummond et al 2004 ). In the studies with longer SD, the tests have been conducted either in the late afternoon ( McCarthy and Waters 1997 ; Harrison and Horne 1999 ) or have been repeated several times ( Blagrove et al 1995 ; Monk and Carrier 1997 ). Therefore, the different results may reflect the effect of circadian rhythm on alertness and cognitive performance. In the morning or before noon, the circadian process reaches its peak, inducing greater alertness, whereas the timing of the circadian nadir coincides with the late afternoon testing (see Achermann 2004 ).

In addition to the cognitive domains already introduced, total SD affects several other cognitive processes as well. It increases rigid thinking, perseveration errors, and difficulties in utilizing new information in complex tasks requiring innovative decision-making ( Harrison and Horne 1999 ). Deterioration in decision-making also appears as more variable performance and applied strategies ( Linde et al 1999 ), as well as more risky behavior ( Killgore et al 2006 ). Several other tasks have been used in the sleep deprivation studies ( Table 1 ). For example, motor function, rhythm, receptive and expressive speech, and memory measured with the Luria-Nebraska Neuropsychological Battery deteriorated after one night of SD, whereas tactile function, reading, writing, arithmetic and intellectual processes remain intact ( Kim et al 2001 ).

The adverse effects of total SD shown in experimental designs have also been confirmed in real-life settings, mainly among health care workers, professional drivers and military personnel ( Samkoff and Jacques 1991 ; Otmani et al 2005 ; Philibert 2005 ; Russo et al 2005 ). Performance of residents in routine practice and repetitive tasks requiring vigilance becomes more error-prone when wakefulness is prolonged (for a review, see Samkoff and Jacques 1991 ). However, in new situations or emergencies, the residents seem to be able to mobilize additional energy sources to compensate for the effects of tiredness. More recent meta-analysis shows that SD of less than 30 h causes a significant decrease in both the clinical and overall performance of both residents and non-physicians ( Philibert 2005 ).

What role does motivation play in cognitive performance? Can high motivation reverse the adverse effect of SD? Does poor motivation further deteriorate performance? According to a commonly held opinion, high motivation compensates for a decrease in performance, but only a few attempts have been made to confirm this theory. Estimating the compensatory effect of motivation in performance during SD is generally difficult, because persons participating in research protocols, especially in SD studies, usually have high initial motivation. The concept of motivation is closely linked to the “attentional effort” that is considered a cognitive incentive (for a review, see Sarter et al 2006 ). According to Sarter et al (2006) , “increases in attentional effort do not represent primarily a function of task demands but of subjects’ motivation to perform.” Furthermore, attentional effort is a function of explicit and implicit motivational forces and may be increased especially when the subjects are motivated or when they detect signals of performance decrements ( Sarter et al 2006 ).

Harrison and Horne (1998 , 1999) suggest that the deterioration of cognitive performance during SD could be due to boredom and lack of motivation caused by repeated tasks, especially if the tests are simple and monotonous. They used short, novel, and interesting tasks to abolish this motivational gap, yet still noted that SD impaired performance. In contrast, other researchers suggest that sleep-deprived subjects could maintain performance in short tasks by being able to temporarily increase their attentional effort. When a task is longer, performance deteriorates as a function of time. A meta-analysis by Pilcher and Huffcutt (1996) provides support for that: total SD of less than 45 h deteriorated performance more severely in complex tasks with a long duration than in simple and short tasks. Based on this, it is probably necessary to make a distinction between mere attentional effort and more general motivation. Although attentional effort reflects motivational aspects in performance, motivation in a broader sense can be considered a long-term process such as achieving a previously set goal, eg, completing a study protocol. If one has already invested a great deal of time and effort in the participation, motivation to follow through may be increased.

Different aspects of motivation were investigated in a study with 72 h SD, where the subjects evaluated both motivation to perform the tasks and motivation to carry out leisure activities ( Mikulincer et al 1989 ). Cognitive tasks were repeated every two hours. Performance motivation decreased only during the second night of SD, whereas leisure motivation decreased from the second day until the end of the study on the third day. The authors concluded that the subjects were more motivated to complete experimental testing than to enjoy leisure activities because by performing the tasks, they could advance the completion of the study. The researchers suggested that the increased motivation towards the tasks on the third day reflected the “end spurt effect” caused by the anticipation of sleep.

Providing the subjects with feedback on their performance or rewarding them for effort or good performance is shown to help maintain performance both in normal, non-deprived conditions ( Tomporowski and Tinsley 1996 ) and during SD ( Horne and Pettitt 1985 ; Steyvers 1987 ; Steyvers and Gaillard 1993 ). In a large study with 61 subjects (experimental group = 29), with SD of 34–36 h, and with a comprehensive test battery, the subjects were continuously encouraged and provided with 2–3 minute breaks between the tests ( Binks et al 1999 ). Furthermore, they were told they would receive a monetary award for completing all tests with “honest effort”. As result, no deteriorating effect on cognitive performance was found. Unfortunately, a non-motivated control group was not included and thus the effect of motivation remained uncertain. In general, since this issue has not been addressed sufficiently, it is difficult to specify the role of motivation in performance. It seems that motivation affects performance, but it also appears that SD can lead to a loss of motivation.

Self-evaluation of cognitive performance

It has been suggested that the self-evaluation of cognitive performance is impaired by SD. During 36 h SD, the subjects became more confident that their answers were correct as the wakefulness continued ( Harrison and Horne 2000 ). Confidence was even stronger when the answer was actually wrong. In another study, performance was similar between sleep-deprived and control groups in several attentional assessments, but the deprived subjects evaluated their performance as moderately impaired ( Binks et al 1999 ). The controls considered that their performance was high.

The ability to evaluate one’s own cognitive performance depends on age and on the study design. Young people seem to underestimate the effect of SD, whereas older people seem to overestimate it. In a simple reaction time task, both young (aged 20–25 years) and aging (aged 52–63 years) subjects considered that their performance had deteriorated after 24 h SD, although performance was actually impaired only in young subjects ( Philip et al 2004 ). When it comes to the study design and methodology, the way in which the self-evaluation is done may affect the outcome. The answers possibly reflect presuppositions of the subjects or their desire to please the researcher. The repetition of tasks is also essential. Evaluation ability is poor in studies with one measurement only ( Binks et al 1999 ; Harrison and Horne 2000 ; Philip et al 2004 ), whereas in repeated measures, the subjects are shown to be able to assess their performance quite reliably during 60–64 h SD and recovery ( Baranski et al 1994 ; Baranski and Pigeau 1997 ). Thus, self-evaluation is likely to be more accurate when subjects can compare their performance with baseline.

Chronic partial sleep restriction

Although chronic partial sleep restriction is common in everyday life and even more prevalent than total SD, surprisingly few studies have evaluated its effects on cognitive performance. Even fewer studies have compared the effects of acute total sleep deprivation and chronic partial sleep restriction. Belenky and co-workers (2003) evaluated the effect of partial sleep restriction in a laboratory setting in groups which were allowed to spend 3, 5 or 7 h in bed daily for seven consecutive days. The control group spent 9 h in bed. In the 3 h group, both speed and accuracy in the PVT deteriorated almost linearly as the sleep restriction continued. In this group, performance was clearly the worst. In the 5- and 7 h groups, performance speed deteriorated after the first two restriction nights, but then remained stable (though impaired) during the rest of the sleep restriction from the third night onwards. Impairment was greater in the 5- than 7 h group. Accuracy followed the same pattern in the 7 h group, but further declined in the 5 h group as the study went on. The control group’s performance did not change during the study. Intriguingly, a highly similar pattern was observed in another study with the same task when sleep was restricted by 33% of the subject’s habitual nightly sleep, which resulted in 5 h of sleep per night on average ( Dinges et al 1997 ). Both speed and accuracy were impaired at the beginning of the sleep restriction period followed by a plateau and finally, another drop after the seventh night of deprivation. However, no change was found in probed recall memory or serial addition tests, probably because of the practice effect and short duration of the tests (serial addition test: 1 min).

It is difficult to compare the effects of total and partial SD based on existing literature due to large variation in methodologies, including the length of SD or the type of cognitive measures. The only study that has compared total and partial SD found that after controlling learning effects, cognitive performance declined almost linearly in the course of the study in all four experimental groups ( Van Dongen et al 2003a ): one group was exposed to 3 nights total SD, and in other experimental groups, time in bed was restricted to 4 or 6 h for 14 consecutive days. The control group was allowed 8 h in bed for 14 days. Impairment in psychomotor vigilance test and digit symbol substitution task for the 4 h group after 14 days was equal to that of the total SD group after 2 nights. Deterioration in the serial addition/subtraction task for the 4 h group was similar to that of the total SD group after 1 night. The effect of 6 h restricted sleep corresponded to 1 night of total SD in psychomotor vigilance and digit symbol. Performance remained unaffected in the control group.

According to the well-controlled studies ( Dinges et al 1997 ; Belenky et al 2003 ; Van Dongen et al 2003a ), the less sleep obtained due to sleep restriction, the more cognitive performance is impaired. Otherwise, it is difficult to draw conclusions about the effects of chronic sleep restriction because of methodological problems in the previous studies. Blagrove et al (1995) compared subjects that slept at home either 5 h or 8 h per night for 4 weeks and found no effect in a short task of logical reasoning (duration 5 min). The statistical analyses were compromised by the small sample size (6 subjects in the experimental group and only 4 subjects in the control group). In another protocol, they also carried out auditory vigilance test, two column addition, finding embedded figures, and logical reasoning (10 min) tasks, and again no effect was observed with groups of 6–8 subjects having 4, 5 or 8 h sleep per night for 7, 19 or 40 weeks respectively ( Blagrove et al 1995 ). Casement et al (2006) reported no change in working memory and motor speed in the group whose sleep was restricted to 4 h per night for 9 nights. In the control group, performance improved. The study was carried out in a controlled clinical environment, but only one short test session per day was included, which means that subjects may have been able to temporarily increase their effort and thus maintain their performance. Furthermore, the results were confounded by the practice effect. In other sleep restriction studies, SD cannot be considered chronic, since the length of the restriction has been 1–3 nights ( Stenuit and Kerkhofs 2005 ; Swann et al 2006 ; Versace et al 2006 ).

Since chronic partial SD mimics every day life situations more than acute total SD, additional studies on how it affects cognitive performance are warranted. In addition, the tasks used in previous studies have been quite short and simple, and trials with more demanding cognitive tasks are required. The effects of sleep restriction have also been addressed by drive simulation studies, which are interesting and practical designs. Just one night of restricted sleep (4 h) increased right edge-line crossings in a motorway drive simulation of 90 minutes ( Otmani et al 2005 ). However, neither the drivers’ position in the lane nor the amplitude and frequency of steering wheel movements were affected. One sleep-restricted night did not increase the probability of a crash, but after five nights of partial SD, the quantity of accidents increased ( Thorne et al 1999 ).

Cognitive recovering from sleep deprivation

The recovery processes of cognitive performance after sleep loss are still obscure. In many SD studies, the recovery period has either not been included in the protocol or was not reported. Recovery sleep is distinct from normal sleep. Sleep latency is shorter, sleep efficiency is higher, the amounts of SWS and REM-sleep are increased and percentages of stage 1 sleep and awake are decreased ( Armitage et al 2001 ; Kilduff et al 2005 ). The characteristics of recovery sleep may also depend on circumstances and some differences seem to come with eg, aging ( Kalleinen et al 2006 ). Evidence suggests that one sleep period (at least eight hours) can reverse the adverse effects of total SD on cognition ( Brendel et al 1990 ; Corsi-Cabrera et al 2003 ; Adam et al 2006 ; Drummond et al 2006 ; Kendall et al 2006 ). The tasks have been mainly simple attentional tasks; for example, the PVT used by Adam et al (2006) has been proven to have practically no learning curve and little if any correlation with aptitude ( Durmer and Dinges 2005 ). Thus, it is likely that the improvement was mostly caused by the recovery process and not just the practice effect.

After chronic partial sleep restriction, the recovery process of cognitive functioning seems to take longer than after acute total SD. Performance in the PVT was not restored after one 10 h recovery night, but approached the baseline level after two 10 h nights in a study with seven consecutive sleep restriction nights with 5 h sleep/night ( Dinges et al 1997 ). Using the same test, three 8 h recovery nights were not enough to restore performance after one week of sleep restriction even in the group that spent 7 h time in bed (the study is explained in greater detail in paragraph 1 of “Partial sleep restriction”, Belenky et al 2003 ). The group that spent 3 h in bed showed the greatest decline as well as the greatest recovery, although it did not reach baseline level again. In the 5 h group, a similar deterioration-recovery curve was observed, although it was not as steep. Those authors concluded that during mild and moderate chronic partial SD, the brain adapted to a stressful condition to maintain performance, yet at a reduced level. This adaptation process was obviously so demanding that it postponed the restoration of normal functioning. According to their results, it could be further interpreted that when sleep restriction was severe, no such adaptation occurred, which in turn allowed for greater recovery. However, these results may be biased because of poor statistical sensitivity in multiple comparisons. They have also been criticized by eg, Van Dongen et al (2004) , who pointed out that another confounding factor may have been considerable interindividual differences in recovery rates. Since interindividual differences have been observed in response to SD, it is likely – although not yet adequately verified – that those individual traits also affect the recuperation.

Sleep deprivation in different populations

Sleep structure changes with aging. Slow wave sleep and sleep efficiency decrease, and alterations in the circadian rhythm occur (for reviews, see Dzaja et al 2005 ; Gaudreau et al 2005 ). Sleep complaints also become more frequent ( Leger et al 2000 ). Yet, during prolonged wakefulness, cognitive performance seems to be maintained better in aging people than in younger ones ( Bonnet and Rosa 1987 ; Smulders et al 1997 ; Philip et al 2004 ; Stenuit and Kerkhofs 2005 ). Total SD of 24 h deteriorated vigilance in young subjects (20–25 years), whereas performance in aging subjects (52–63 years) remained unaffected ( Philip et al 2004 ). Similarly, during three consecutive nights of partial SD (4 h in bed) performance in psychomotor vigilance task declined more in young subjects (20–30 years) than in aging ones (55–65 years, Stenuit and Kerkhofs 2005 ). In visual episodic memory, visuomotor performance and divided attention, aging subjects (58–72 years) were able to maintain their performance after 25 h of SD and showed improvement only after a recovery night ( Alhola et al 2005 ). However, no comparison with young subjects was made in that study.

Sleep deprivation deteriorates accuracy of performance, especially in young subjects ( Brendel et al 1990 ; Smulders et al 1997 ; Adam et al 2006 ; Karakorpi et al 2006 ). Regarding performance speed, however, results have been inconsistent and the performance of aging subjects has declined more, less, or equally compared to that of younger people. In simple and two-choice reaction time tasks as well as in a vigilance task, reaction speed was impaired in aging subjects (59–72 years) during 40 h SD, whereas young subjects (20–26 years) kept up their speed ( Karakorpi et al 2006 ). These results followed the speed/accuracy trade-off phenomenon so that aging subjects maintained accuracy at the expense of speed and the younger ones did the opposite. In contrast, two other studies found that young subjects were slower than aging subjects ( Brendel et al 1990 ; Adam et al 2006 ). During 24 h wakefulness, performance speed in a vigilance task was impaired in both 20- and 80-year-olds, but more so in the young subjects ( Brendel et al 1990 ). This was confirmed in another study with 40 h SD ( Adam et al 2006 ). When measuring reaction speed in three different choice-reaction time tasks, performance deteriorated similarly in young (18–24 years) and aging (62–73 years) subjects after 28 h total SD ( Smulders et al 1997 ).

Even though there is some evidence that older subjects tolerate SD better than young subjects, it is difficult to determine the age effect during SD with precision. However, because of age-related changes in many aspects of sleep and wakefulness, it is plausible that aging influences reactions to SD. As suggested previously, the weaker SD effect in aging may be due to attenuation of the circadian amplitude, which is reflected in the performance curve in vigilance tasks ( Blatter et al 2006 ). Also, changes in the homeostatic process may play a role. During wakefulness, the accumulation of sleep pressure seems to be reduced in aging ( Murillo-Rodriguez et al 2004 ), which could leave older subjects more alert. There is also evidence that aging subjects recover faster from SD than young subjects in terms of physiological sleep ( Bonnet and Rosa 1987 ; Brendel et al 1990 ). This faster recovery in sleep state may also mean better restoration of cognitive performance ( Bonnet and Rosa 1987 ; Brendel et al 1990 ). However, more research is necessary to confirm these hypotheses.

The age effect found in previous studies could also be explained by methodological factors, such as inadequate control of the baseline conditions. Younger subjects are usually more chronically sleep deprived ( National Sleep Foundation 2002 ) due to several reasons, such as studying, career building or raising children. Chronic sleep restriction may cause long-term changes in brain functions that are not reversible during short adaptation and baseline periods in sleep laboratory studies. Even though subjects of certain studies were instructed to maintain a regular 8 h sleep schedule for 3–5 days, this may not be enough to erase the previous “sleep debt” ( Brendel et al 1990 ; Philip et al 2004 ; Adam et al 2006 ). Furthermore, in the long run, people tend to get used to experiencing sleepiness ( Van Dongen et al 2003a ) and thus may not even recognize being chronically sleep deprived. Perhaps aging people also have more experience that helps them to cope with the challenges posed by SD. Nevertheless, based on the available studies, it is impossible to distinguish the factors behind the age effect.

There are dissimilarities between genders in sleep structure measured with polysomnography (for a review, see Manber and Armitage 1999 ). Furthermore, women of all ages report more sleeping problems than men ( Leger et al 2000 ). Sex hormones affect sleep through several mechanisms, both genomic and nongenomic, including neurochemical and vascular mechanisms (for a review, see Dzaja et al 2005 ). This ensures instant and short-term effects as well as long-term ones.

It is possible that physiological responses to SD are not equal among men and women. During SD of 38 h, EEG showed more sleep activity in men than in women during waking rest and cognitive performance ( Corsi-Cabrera et al 2003 ). Presumably, therefore, one recovery night of nine hours would be enough to restore waking EEG activity in men, but not in women. Only a few studies have examined gender differences in cognitive performance during SD. In a vigilance task, performance was more impaired in men but returned to the baseline level in both men and women after recovery sleep ( Corsi-Cabrera et al 2003 ). In another study, women performed better than men in verbal and in visuo-constructive tasks during 35 h SD ( Binks et al 1999 ). No gender differences were observed in word fluency, maintenance or suppression of attention, auditory attention or cognitive flexibility. In that study, however, only one point of measurement was included, and so the difference in performance could be caused by SD or initial distinctions between the gender groups.

Few attempts have been made to evaluate the effect of sex hormones on coping with SD. It has been suggested that hormone therapy, which is widely used for women during their menopausal transition to help alleviate climacteric symptoms, attenuates physiological stress response ( Lindheim et al 1992 ). However, after 25 h of total SD, no difference was observed between hormone therapy users and nonusers in visual episodic memory, visuomotor performance, verbal attention and shared attention ( Alhola et al 2005 ). In addition, during 40 h of SD, hormone therapy did not produce any advantage in reaction time or vigilance tasks ( Karakorpi et al 2006 ).

The previous studies suggest that women cope with continuous wakefulness better than men. According to evolution, the demands of child nurturing and rearing in women would support this hypothesis ( Corsi-Cabrera et al 2003 ), but that certainly does not constitute a comprehensive explanation today. Gender differences during SD could be due to either physiological or social factors. There are differences in the brain structure and functioning of men and women ( Ragland et al 2000 ; Cowell et al 2007 ). These can be seen in cognitive performance in normal, non-deprived conditions: men typically have better spatial abilities and mental rotation, and higher visuo-constructive performance, whereas women perform better in visuomotor speed and some verbal functions, especially verbal fluency (for a review, see Kimura 1996 ). Men and women also exhibit behavioral and lifestyle differences, which are mainly due to socialization and gender roles ( Eagly and Wood 1999 ). Current literature, however, provides only minimal evidence of differential effects during SD, and does not resolve the issue of sexual dimorphism in coping with SD.

Interindividual differences

Several studies provide evidence that during total SD, performance becomes more variable as assessed from the within-subject point of view (eg, Smith et al 2002 ; Habeck et al 2004 ; Choo et al 2005 ). This is considered to reflect the wake-state instability caused by prolonged wakefulness. However, Doran et al (2001) were probably the first to also examine between-subjects variability, which they found to increase in PVT as wakefulness was extended to 88 hours. They suggested that some people are more vulnerable to the effects of sleep loss than others, which could probably explain the lack of significant results in some group comparisons. These differences between subjects could have arguably been caused by differences in sleep history, but the sleep patterns for the preceding week were controlled with sleep diaries, actigraph, and calls to the time-stamped voice recorder.

The interindividual variability has been further examined with a thorough protocol where a three night study (baseline, 36 h SD and recovery) was carried out three times ( Van Dongen et al 2004 ). Sleep history was manipulated by instructing subjects to stay in bed for either 6 or 12 h per night for one week before the study. The 12 h procedure was repeated and the order of the conditions was counterbalanced. The cognitive test selection included serial addition/subtraction task, digit symbol, critical tracking, word detection, repeated acquisition of response sequences, and PVT. The authors concluded that interindividual differences were systematic and independent from sleep history. The trait-like differential vulnerability to sleep loss has received support from an fMRI study attempting to reveal the neural basis for the interindividual differences ( Chuah et al 2006 ). They used a go/no-go task to measure response inhibition after 24 h of sleep deprivation. The results indicated that the subjects less vulnerable to SD had lower prefrontal cortex activation at the rested wakefulness than the more vulnerable subjects. During SD, activation increased temporarily in the prefrontal cortex and in some other areas only in the less vulnerable subjects. Since interindividual differences have also been found in other sleep-related variables, such as duration, timing, and quality of sleep, sleepiness, and circadian phase ( Van Dongen 1998 ; Van Dongen et al 2005 ), it is plausible that the tolerance to SD may also vary. Nevertheless, more studies are needed for further support.

Methodological issues and common biases

Although the adverse effects of SD on cognitive performance are quite well established, some studies have failed to detect any deterioration. Inadequate descriptions of study protocols or subject characteristics in some studies make it difficult to interpret the neutral results. However, it is likely that such results are due to methodological shortcomings, such as insensitive cognitive measures, failure to control the practice effect or other confounding factors, like individual sleep history or napping during the study. Also, if the task is carried out only once during the SD period, the results may be influenced by circadian rhythm.

Sleep deprivation studies are laborious and expensive to carry out, which may lead to compromises in the study design: for example, a small sample size can reduce the statistical power of the study, but a larger population may come at the expense of other methodological issues, such as a reduction in the cognitive test selection or in the number of nights spent in the sleep laboratory. Comparison of the results is also complicated because the length of sleep restriction varies and the studies are designed either within- or between-subjects.

Sleeping in unfamiliar surroundings may impair sleep quality. An adaptation night at the sleep laboratory is used to minimize this first night effect. However, in several studies, this has been neglected and the SD period has been preceded by a “normal” night at home (eg, Harrison and Horne 2000 ; Jennings et al 2003 ; Choo et al 2005 ). Although sleeping at home certainly reflects a subject’s reality more accurately, it does not allow for precise control and information of sleeping conditions. Adding a portable recording, such as an actigraph, provides objective information about eg, bedtime and resting periods. In some studies, the first night in the sleep laboratory has been the baseline (eg, Drummond et al 2000 ; Forest and Godbout 2000 ; De Gennaro et al 2001 ; Drummond et al 2001 ), whereas others have included one adaptation night (eg, Casagrande et al 1997 ; Alhola et al 2005 ). Yet, it may be questionable to use data from the second night as the baseline because sleep quality can be better than normal due to the rebound from the first night. Accordingly, only data from the third night should be accepted, which has been the case in a few studies ( Thomas et al 2000 ; Van Dongen et al 2003a ). This, however, makes the procedure very hard. Furthermore, study protocols can be improved by adding an ambulatory EEG recording to confirm the wakefulness of the subjects during the study.

In sleep studies, a common pitfall is recruitment methods. Enrolment via advertisements or from sleep clinics favors the selection of subjects with sleeping problems or concerns about their cognitive performance. Thus, strict exclusion criteria regarding physical or mental diseases or sleeping problems are essential. Further, sleeping habits should be controlled to make sure that the subjects are not initially sleep deprived. For this, use of a sleep diary for eg, 1–3 weeks before the experiment (eg, De Gennaro et al 2001 ; Habeck et al 2004 ; Alhola et al 2005 ) or an actigraph is applicable ( Harrison and Horne 1999 ; Thomas et al 2000 ).

The use of medication or stimulants, such as caffeine, alcohol or tobacco, is often prohibited before the experiment (eg, Thomas et al 2000 ; Van Dongen et al 2003a ; Habeck et al 2004 ; Alhola et al 2005 ; Choo et al 2005 ). In some studies, the subjects have been required to refrain from these substances only 24 h before the study ( Habeck et al 2004 ; Choo et al 2005 ), which may increase withdrawal symptoms and dropping out of the study. Thus a longer abstinence, eg, 1–2 weeks, is more appropriate ( Van Dongen et al 2003a ; Alhola et al 2005 ).

A variety of cognitive tests, from simple reaction time measures to complex decision-making tasks requiring creativity and reasoning, have been used to evaluate the effect of SD on cognition. The greatest problem in repeated cognitive testing is the practice effect, which easily conceals any adverse effects of SD. Therefore, careful control over learning is essential. Cognitive processes are also intertwined in several ways, which makes it difficult to specify exactly which cognitive functions are utilized in certain performances. Because attention is involved in performing any cognitive task, a decrease in other cognitive domains during SD may be mediated through impaired attention. In complex tasks, however, applying previous knowledge and use of strategies or creativity may be more essential. Some studies have concentrated on neural correlates of cognitive functioning during continuous wakefulness. Both fMRI ( Portas et al 1998 ; Drummond et al 2000 ; Drummond et al 2001 ; Chee and Choo 2004 ; Habeck et al 2004 ; Choo et al 2005 ) and PET have been used ( Thomas et al 2000 ). Although these trials yield interesting information about brain functioning, the use of imaging techniques limits the selection of cognitive tests that could be carried out at the same time.

Dorrian et al (2005) have compiled a list of criteria for neurocognitive tests that would be suitable for investigating sleep deprivation effects. The criteria include psychometric quality, ie, reliability and validity, but the tests should also reflect a fundamental aspect of waking neurocognitive functions and it should be possible to interpret them in a meaningful way. The tasks should be repeatable, independent of aptitude, and they should be short with a high signal load. These criteria are not met in some studies. Dorrian et al (2005) also argued that vigilance is the underlying factor through which the sleep deprivation effects are mediated in all other tasks. However, although attention is needed to perform any task to some extent, the hypothesis that sleep deprivation can have an independent effect on other cognitive functions such as memory cannot be ruled out. Nevertheless, when measuring other cognitive functions, the characteristics of the task should be considered carefully and, eg, for repeated measures of memory, parallel test versions should be used.

The negative effect of both acute total and chronic partial SD on attention and working memory is supported by existing literature. Total SD impairs a range of other cognitive functions as well. In partial SD, a more thorough evaluation of higher cognitive functions is needed. Furthermore, the effects of SD have not been thoroughly compared among some essential subpopulations.

Aging influences a person’s ability to cope with SD. Although in general the cognitive performance of aging people is often poorer than that of younger individuals, during SD performance in older subjects seems to deteriorate less. Based on the scarce evidence, it seems that in terms of cognitive performance, women may endure prolonged wakefulness better than men, whereas physiologically they recover slower. Tolerating SD can also depend on individual traits. However, mechanisms inducing differences between the young and aging and between men and women or different individuals are mostly unclear. Several reasons such as physiological mechanisms as well as social or environmental factors may be involved. In conclusion, there is great variation in SD studies in terms of both subject selections and methods, and this makes it difficult to compare the different studies. In the future, methodological issues should be considered more thoroughly.

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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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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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• 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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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.

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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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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

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

Karolina Janacsek

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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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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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Sleep Duration and Executive Function in Adults

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Purpose of Review

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

Recent Findings

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

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

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Introduction 

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

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

The Importance of Executive Function

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

How is Executive Function Tested?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Longitudinal Measurements of Sleep Duration Identify Similar Patterns with Executive Function

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

Sub-optimal Sleep Duration May Predict Dementia Onset

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

Objective Measurements of Sleep Duration and Executive Function

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

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

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

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

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

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

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

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

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

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

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

Biological Mechanisms Underlying the Link Between Sleep and Executive Function

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

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

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

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

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

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

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

Conclusion and Future Directions

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

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

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

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

Data Availability

Not applicable.

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

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ORIGINAL RESEARCH article

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

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

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

Introduction

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

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

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

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

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

Materials and Methods

Participants.

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

Experimental Design

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

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

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

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

Experimental Procedures

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

EEG Recordings

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

Data Analysis of Behavioral Experiments

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

EEG Data Analysis

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

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

Behavioral Performance

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

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

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

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

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

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

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

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

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

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

No other main effects or interaction effects reached statistical significance.

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

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

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

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

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

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

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

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

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

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

Data Availability Statement

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

Ethics Statement

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

Author Contributions

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

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

Conflict of Interest

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

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

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

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

Reviewed by:

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

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

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

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

by University of Southampton

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

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

Differences in sleep

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

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

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

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

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

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

Variations in body clocks

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

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

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

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

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

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

Impact on metabolism

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

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

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

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

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

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

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Neuroscientists Discover Women and Men Handle Sleep Deprivation Differently

S taying up too late can ruin the next day with fits of yawning and a foggy head, but it turns out that women might be better at handling lack of sleep than men.

Female rodents appear to be more resilient to sleep deprivation as a result of their hormones, according to two studies being presented at Neuroscience 2023, the annual meeting of the Society for Neuroscience, being held in Washington D.C. from November 11-15.

One of these studies finds that hormonal changes in female mice make them more resilient than males to lack of sleep, while the other study reveals that the effect of estrogen hormones on sleep in rats is moderated by a specific set of cells in the brain called astrocytes.

Female sex hormones have been long thought to play a role in sleep and sleep deprivation, as women are twice as likely as men to experience sleep disruptions, especially around puberty, menarche and menopause.

"It is estimated that 35 percent to 50 percent of perimenopausal/menopausal women will report sleep problems, as compared to approximately 15 percent of the general population," Jessica Mong, co-author of one of the studies and professor of neuropharmacology at the University of Maryland School of Medicine, told Newsweek.

Mong and her colleagues found that astrocyte cells, which are non-neuronal cells in the preoptic area of the brain—an area involved in sleep regulation—may mediate how estrogen hormones affect sleep.

"Astrocytes are a type of glial cells (glial comes from the Latin meaning "glue"). It was thought that these glial cells, like the astrocyte were just holding the brain and the neurons together. In the past three decades, research has shown this is not the case. But a role for astrocytes in mediating sleep is a relativity recent finding. Moreover, a role in estrogen action over sleep is completely novel," Mong said.

The researchers inhibited astrocytes in rats, and found that it prevented the action of estrogen on sleep.

"This suggests that [estrogen] may be stimulating or activating the astrocytes which then signal to the sleep-controlling neurons in the [preoptic area]," Mong said.

"This is a significant finding as it is one of the first demonstrations of how [estrogen] may regulate sleep, [and it] may provide future targets for drugs and sleep-aids that are more effective in women. Understanding the role of estrogenic action in the sleep circuity is a critical first step toward a better understanding of the role of estrogens in sleep pathologies and identification of targets for improved interventions for treating sleep disturbances in menopausal women."

While this study was in rats, Mong thinks that humans may be very similar in how estrogen affects sleep and sleep deprivation.

"This work is translatable to humans as the sleep-wake circuity and pharmacology across mammals is HIGHLY conserved (as one might expect given the overall importance of sleep to health and survival)," Mong said. "Why estrogen affects sleep in humans is also probably an evolutionarily conserved trait related to reproduction. Mammals tend to be more active during times of reproduction. However, although not part of this study, one advantage we have found is that estrogen tends to consolidate vigilance states. For women—especially menopausal women—this may mean less interrupted and better quality sleep."

The second study found that female mice were more resilient to sleep deprivation than male mice. The researchers discovered that gene expression in female mice after being deprived of sleep was much less changed in female mice than in males: 99 genes had changes in expression in the females, with over 1,100 genes were impacted in males.

"Following one week of individual housing and pre-handling for three days, mice were sleep deprived using gentle handling (tapping and cage shakes as needed) for five hours and then brain regions were dissected and frozen. Non-sleep deprived mice were dissected at the same time to avoid any confounds in gene expression due to variations in circadian time," the authors wrote in the paper.

"Using an unbiased RNA sequencing approach, we found that females appeared more resilient to changes in gene expression in the hippocampus after acute sleep than similarly aged male mice. Compared to the more than 1,100 significant changes in hippocampal gene expression seen in male mice following acute sleep deprivation, we found only 99 genes in the hippocampus exhibited significant differential expression from sleep deprived females compared to non-sleep deprived females. Moreover, in the proestrus stage, female mice had no significant differential gene expression in the hippocampus between sleep deprived and non-sleep deprived animals."

This may also be associated with hormonal differences, supporting the other study's findings.

"Although it is still unknown what the impact of this resilience in gene expression means at the cellular or behavioral levels, these results clearly demonstrate that sleep deprivation induces sex-specific differences and that hormonal changes in female mice provide some resilience to acute sleep deprivation," the authors wrote.

Other studies being presented at Neuroscience 2023 delve into how sleep disruption may affect memory, both in mice and in cuttlefish.

"Why we sleep remains one of the major enigmas of neuroscience," Robert Greene, professor of psychiatry and neuroscience at University of Texas Southwestern Medical Center and moderator of a November 14 press conference where the studies will be presented at Neuroscience, said in a statement.

"Recent neuroscience research is beginning to uncover some of these secrets, including understanding the price of sleep loss on brain function. Further studies show the surprisingly gender-specific gateway to sleep in females and female resilience to sleep loss when sleep is curtailed. Finally, pioneering research on dream sleep may take a step forward with cephalopods, like cuttlefish, that wear their dreams on their skin, potentially providing a unique window into their dream content."

Do you have a tip on a science story that Newsweek should be covering? Do you have a question about ? Let us know via [email protected].

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Research uncovers differences between men and women in sleep, circadian rhythms and metabolism

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

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

Differences in sleep

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

"Lower sleep quality is associated with anxiety and depressive disorders, which are twice as common in women as in men," says Dr Sarah L. Chellappa from the University of Southampton and senior author of the paper. "Women are also more likely than men to be diagnosed with insomnia, although the reasons are not entirely clear. Recognising and comprehending sex differences in sleep and circadian rhythms is essential for tailoring approaches and treatment strategies for sleep disorders and associated mental health conditions."

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

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

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

Variations in body clocks

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

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

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

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

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

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

Impact on metabolism

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

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

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

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

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

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

The research was funded by the Alexander Von Humboldt Foundation, the US Department of Defense and the National Institute of Health.

• Sleep Disorder Research
• Insomnia Research
• Gender Difference
• Sleep Disorders
• Obstructive Sleep Apnea
• Circadian rhythm sleep disorder
• Glutamic acid
• Sleep deprivation

Story Source:

Materials provided by University of Southampton . Note: Content may be edited for style and length.

Journal Reference :

• Renske Lok, Jingyi Qian, Sarah L. Chellappa. Sex differences in sleep, circadian rhythms, and metabolism: Implications for precision medicine . Sleep Medicine Reviews , 2024; 75: 101926 DOI: 10.1016/j.smrv.2024.101926

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

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

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

Differences in sleep

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

“Lower sleep quality is associated with anxiety and depressive disorders, which are twice as common in women as in men,” says  Dr Sarah L. Chellappa from the University of Southampton and senior author of the paper. “Women are also more likely than men to be diagnosed with insomnia, although the reasons are not entirely clear. Recognising and comprehending sex differences in sleep and circadian rhythms is essential for tailoring approaches and treatment strategies for sleep disorders and associated mental health conditions.”

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

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

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

Variations in body clocks

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

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

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

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

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

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

Impact on metabolism

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

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

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

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

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

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

Sex differences in sleep, circadian rhythms, and metabolism: Implications for precision medicine  is published in  Sleep Medicine Reviews  and is available online.

The research was funded by the Alexander Von Humboldt Foundation, the US Department of Defense and the National Institute of Health.

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