sleep research study hypothesis

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• Published: 17 June 2021

Relationship between sleep habits and academic performance in university Nursing students

• Juana Inés Gallego-Gómez 1 ,
• María Teresa Rodríguez González-Moro 1 ,
• José Miguel Rodríguez González-Moro 2 ,
• Tomás Vera-Catalán 1 ,
• Serafín Balanza 1 ,
• Agustín Javier Simonelli-Muñoz 3 &
• José Miguel Rivera-Caravaca 4  

BMC Nursing volume  20 , Article number:  100 ( 2021 ) Cite this article

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Sleep disorders are composed of a group of diseases of increasing prevalence and with social-health implications to be considered a public health problem. Sleep habits and specific sleep behaviors have an influence on the academic success of students. However, the characteristics of sleep and sleep habits of university students as predictors of poor academic performance have been scarcely analyzed. In the present study, we aimed to investigate sleep habits and their influence on academic performance in a cohort of Nursing Degree students.

This was a cross-sectional and observational study. An anonymous and self-administered questionnaire was used, including different scales such as the ‘Morningness and Eveningness scale’, an author-generated sleep habit questionnaire, and certain variables aimed at studying the socio-familial and academic aspects of the Nursing students. The association of sleep habits and other variables with poor academic performance was investigated by logistic regression. The internal consistency and homogeneity of the ‘sleep habits questionnaire’ was assessed with the Cronbach’s alpha test.

Overall, 401 students (mean age of 22.1 ± 4.9 years, 74.8 % females) from the Nursing Degree were included. The homogeneity of the ‘sleep habits questionnaire’ was appropriate (Cronbach’s alpha = 0.710). Nursing students were characterized by an evening chronotype (20.2 %) and a short sleep pattern. 30.4 % of the Nursing students had bad sleep habits. Regarding the academic performance, 47.9 % of the students showed a poor one. On multivariate logistic regression analysis, a short sleep pattern (adjusted OR = 1.53, 95 % CI 1.01–2.34), bad sleep habits (aOR = 1.76, 95 % CI 1.11–2.79), and age < 25 years (aOR = 2.27, 95 % CI 1.30–3.98) were independently associated with a higher probability of poor academic performance.

Conclusions

Almost 1/3 of the Nursing students were identified as having bad sleep habits, and these students were characterized by an evening chronotype and a short sleep pattern. A short sleep pattern, bad sleep habits, and age < 25 years, were independently associated with a higher risk of poor academic performance. This requires multifactorial approaches and the involvement of all the associated actors: teachers, academic institutions, health institutions, and the people in charge in university residences, among others.

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Introduction

Sleep is a complex phenomenon resulting from the interaction between the neuroendocrine system, biological clock and biochemical processes, with environmental, social and cultural aspects that are very relevant in the life stages of adolescence and youth [ 1 ]. Indeed, the chronic lack of sleep is a recent worry among adolescents and young university students and it is associated with worse health and clinical outcomes [ 2 , 3 ].

Among biological factors determining sleep, there are “chronotypes” and sleep patterns. The first term refers to the personal preferences of scheduling the sleep-wake cycle, emphasizing three basic chronotypes: morning (early-risers), and evening (night-owls) and those who are intermediate, defined as those who do not have clear preferences towards any of the extreme schedules for the fulfilling of their activities [ 4 ]. The sleep pattern refers to the personal schedule of bedtime and wake-up time. In this sense, a circadian rhythm is a natural, internal process, driven by a circadian clock that repeats roughly every 24 h and regulates the sleep-wake cycle [ 5 ].

On the other hand, the sleep habits are in the intersection between biological and cultural values. Endogenous, exogenous or environmental factors are included here, as well as those activities that are developed by the population to induce or maintain sleep, with its study and care becoming a challenge for Nursing [ 6 ]. Currently, spontaneous abusive behaviors regarding sleep habits are becoming frequent, leading to a state of chronic sleep deprivation, which translates to fatigue and somnolence during the day [ 7 ]. Hence, there is a high prevalence of sleep disorders in university students, especially those that affect the wake-sleep rhythm [ 2 ]. For this reason,the interest in establishing relationships between sleep and cognitive processes such as memory, learning ability and motivation, has gained attention during the last years. However, studies that relate sleep with academic problems are scarce, despite previous authors have shown that the reduction of sleep time in teenagers and university students was associated with poor academic performance, accidents and obesity [ 8 , 9 ]. Since good-quality sleep does not only imply sleeping well at night but also an adequate level of attention during the day for performing different tasks, appropriate sleep has an influence in efficient learning processes in university students [ 10 , 11 , 12 ].

Although some scientific evidence has shown a relationship between sleep and low academic performance [ 13 , 14 ], so far, there are no questionnaires to specifically evaluate sleep habits in Nursing students. Considering that this population has special characteristics, they are mostly young, combine hospital training at the same time they attend classes at the university, they present lifestyles that can negatively influence the academic performance. To study the sleep habits using a specific tool, in addition to analyze the sleep pattern and chronotype, could help to identify students with inappropriate sleep habits for developing interventions to modify these habits. This might have a positive impact on their academic performance and avoid potentially serious negative consequences for their physical and mental health. In the present research, we aimed (a) to design a ‘sleep habits questionnaire’, (b) to analyze the sleep habits, sleep pattern and chronotype, and (c) to investigate sleep habits and their influence on academic performance, in a cohort of Nursing Degree students.

Design and study population

This was an observational, prospective and cross-sectional study involving Nursing students, all of them distributed among the 4 years of the Nursing Degree. There were no inclusion criteria, i.e. all Nursing students were suitable for the study, unless those who did not attend class on the day of data collection, or those who did not wish to participate (from 420 students, 19 refused to participate in the study). The study was fully carried out during the first semester of the 2019–2020 academic year.

Study Variables

Circadian rhythm: the reduced “horne & östberg morningness-eveningness questionnaire”.

Preferences of schedule for the sleep-wake cycle and its influence on academic performance were assessed using the reduced version of the Horne & Östberg Morningness-Eveningness Questionnaire (rMEQ) proposed by Adan & Almirall [ 15 ], translated to Spanish, that is composed of 5 items. The score determines the following five types of schedule: clearly morning type (22–25 points), moderately morning type (18–21 points), no preference (12–17 points), moderately evening type (8–11 points), and clearly evening type (4–7 points). The internal consistency of the circadian rhythm scale assessed using the rMEQ by Adan & Almirall is good, as the scores from all the items are correlated among themselves [ 15 , 16 ].

Sleep habits questionnaire

For the initial design of the sleep habits questionnaire, a panel of 10 voluntary experts was included. This panel was composed of 5 registered nurses and 5 physicians, with a minimum of 5 years of experience in sleep. All of them were interviewed and informed individually about the study. Items composing of the questionnaire were obtained according to the scientific literature and the main factors influencing sleep habits as the discretion of the expert panel [ 14 , 17 , 18 ]. Eleven questions were finally included in a self-reported questionnaire, each ranging from 1 to 4 (never (1), sometimes (2), usually (3), always (4)) ( Supplementary file ). Sleep habits, including sleep routines, study schedule preference, and napping were also evaluated. The overall score of the questionnaire ranges from 11 to 44 points, with the highest scores indicating the worst sleep habits. As there is no specific cut-off point for this questionnaire, students over the fourth quartile (4Q, i.e. ≥25 points) were categorized as having inappropriate habits. Therefore, these Nursing students were included in the “bad sleeping habits” group.

• Academic performance

The academic performance was measured by the ratio “failed exams/performed exams” and checked in the student’s academic records. A good academic performance was considered if the final grade of every exam completed during the Nursing Degree was ≥ 5 (in a 0–10 range, where an exam is considered passed if the score is ≥ 5).

Other variables

Other variables such as gender, age and hours of sleep (sleep pattern), were analyzed. To describe the sleep pattern of the Nursing students, we used the classification described by Miró et al. (2002) [ 19 ]. This classification was composed of three categories as a function of the hours slept, so that we found subjects that had a short sleep pattern (< 6 h per day), subjects with a long sleep pattern (≥ 9 h per day), and subjects with an intermediate sleep pattern (6–9 h per day).

Ethical considerations

The study protocol was approved by an accredited Ethics Committee (Reference: CE-6191) and was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki. All students were informed and gave consent to participation in the study. The anonymity and confidentiality were guaranteed.

Statistical analysis

The sample size was calculated by a non-probabilistic sampling technique using Ene 2.0 (GlaxoSmithKline) with a precision ± 5 % and α error = 0.05. This calculation was based on the estimation that the prevalence of bad sleep habits in Nursing students of our university was 30.4 %, which resulted in a minimum sample of 229 subjects.

Categorical variables were expressed as frequencies and percentages. Continuous variables were presented as mean ± standard deviation (SD) or median and interquartile range (IQR), as appropriate.

The Pearson Chi-squared test was used to compare proportions whereas comparison of continuous variables was performed using the Student t test. Correlations between different scales were performed using the Pearson’s correlation test.

In order to investigate if sleep habits and other variables were independently associated with poor academic performance, a logistic regression model (with odds ratios [OR] and two-sided 95 % confidence intervals [CI]) was performed. To measure the internal consistency and homogeneity of the sleep habits questionnaire, the Cronbach’s alpha test was performed.

A p -value < 0.05 was accepted as statistically significant. Statistical analyses were performed using SPSS v. 21.0 (SPSS, Inc., Chicago, IL, USA).

We included 401 Nursing students (100 students from 1st year, 105 from 2nd year, 101 from 3rd year, and 95 from 4th year) in the study. The students were characterized for being predominantly females (300, 74.8 %), with a mean age of 22.1 ± 4.9 years, and the majority of them (88.5 %) were singles.

Sleep habits of the Nursing students were examined using our previously designed (as described in the Methods section) self-reported ‘sleep habits questionnaire’. The homogeneity of the questionnaire was appropriate, with a Cronbach’s alpha value of 0.710. The mean score in the questionnaire was 22.3 ± 3.9, and 30.4 % of the Nursing students had bad sleep habits (i.e. score > 4Q), which were characterized by a clear preference of studying at night, easily lose a night of sleep for work-related or academic tasks that imply staying up late, and showing difficulties in maintaining sleep routines.

Table  1 shows the summarized results for each question of the sleep habits questionnaire.

The Nursing students in our sample were characterized by an evening chronotype (20.2 %, 81) and a short sleep pattern (i.e. <6 h of sleep daily), with 51.1 % (205) of the students sleeping less than 6 h/day, 42.1 % (169) sleeping 6–9 h/day, and 6.7 % (27) sleeping more than 9 h/day. The mean duration of sleep found in the Nursing students was 6.52 ± 1.4 h.

Of note, most of the Nursing students that had an evening chronotype were < 25 years old (22.2 %, p  = 0.011). In addition, age showed a positive association with circadian rhythm and as age increased, the students tended to have a predominantly morning chronotype ( R  = 0.223, p  < 0.001). Nursing students < 25 years of age had also worse sleep habits according to the sleep habits questionnaire than those ≥ 25 years (22.61 ± 3.79 vs. 21.19 ± 4.37, p  = 0.005). A negative correlation was found between the overall sleep habits questionnaire score and age as a continuous variable ( R = -0.105, p  = 0.03).

In addition, 29.5 % of patients that had bad sleep habits ( p  = 0.001), and 23.9 % that had poor academic performance ( p  = 0.020), had also an evening chronotype (Table  2 ). A significant negative correlation was found between the sleep pattern and sleep habits ( R = -0.293, p  < 0.001), and between circadian rhythm and sleep habits, hence Nursing students with good sleep habits have predominantly a morning circadian rhythm ( R = -0.201, p  < 0.001).

Regarding the academic performance, 93 % (373) of the Nursing students attended all the exams planned, and 47.9 % (192) of the students showed poor academic performance. When we investigated specifically if the sleep habits, as assessed by the ‘sleep habits questionnaire’, influenced the academic performance, we found that 32 % (140) of the Nursing students that had bad sleep habits obtained poor academic results ( p  < 0.001). Those that had the worst academic results were the ones that did not have a regular hour for waking up and going to sleep (2.66 ± 1.03, p  = 0.031), presented difficulties to maintain the sleep during the night (1.73 ± 0.77, p  = 0.003), and preferred to study for an exam at night (1.33 ± 0.48, p  = 0.030), as well as going to bed late to obtain better results (1.46 ± 0.51, p  = 0.041). Also, those students with poorer academic results where those listening to music before going to bed (1.84 ± 1.10, p  = 0.007), and going out at night even if they had to get-up early the next day (1.58 ± 0.72, p  = 0.012). Overall, those Nursing students whose work or academic activities entailed going to bed late to attain their objectives, had the lowest academic performance (2.25 ± 1.01, p  = 0.001). Lastly, we can confirm that the Nursing students that had better academic performance were the ones who had the best sleep habits. Indeed, the overall ‘sleep habits questionnaire’ score was significantly lower compared to those Nursing students who had poor academic performance (21.91 ± 3.90 vs. 24.18 ± 3.55, p  < 0.001) (Table  3 ).

Finally, the profile of Nursing students with more failed courses was characterized by an evening circadian rhythm ( R = -0.134, p  = 0.007), bad sleep habits ( R  = 0.216, p  < 0.001), and less hours of sleep daily ( R = -0.211, p  < 0.001).

To confirm these observations, a multivariate logistic regression analysis was performed. Therefore, a short sleep pattern (adjusted OR = 1.53, 95 % CI 1.01–2.34), bad sleep habits (adjusted OR = 1.76, 95 % CI 1.11–2.79), and age < 25 years (adjusted OR = 2.27, 95 % CI 1.30–3.98) were independently associated with a higher probability of poor academic performance (Table  4 ).

Sleep is an excellent indicator of the health status and an element that favors good quality of life [ 20 ], but entering university is a change that highly impacts the student in every dimension, including sleep habits [ 21 , 22 ]. A potential barrier for maximizing performance during the university stage is the irregular sleep schedule, which lead to sleep deficit and high prevalence of somnolence during the day [ 23 ]. A review by Shochat et al. (2014) [ 24 ] examined the consequences of lack of sleep among Nursing students, and confirmed the relationship between sleep disorders and changes in sleep patterns with a reduced academic performance. Other studies have established that sleep has an integral role in learning and memory consolidation [ 25 , 26 ]. Therefore, despite some scientific evidence has shown a relationship between sleep and low academic performance [ 13 , 14 ], the originality of our study was to examine the influence that sleep characteristics exert (chronotypes and sleep patterns), as well as sleep habits of the university population on academic performance.

Overall, the academic performance of our Nursing students was suboptimal. When analyzing how sleep pattern, sleep habits, and circadian rhythms influenced this academic performance, we observed that all of them may be determine factors for learning, as other studies have done [ 27 ].

Concerning the sleep pattern, it should be noted that most of the students enrolled in the Nursing Degree slept less than 6 h per day. Of note, our results seem to establish a relationship between the hours slept and the academic performance during the first semester, as gathered from the academic records. This finding is in accordance to observations by other authors in university students from Medicine [ 9 ], Pharmacy [ 2 ] or Nursing [ 28 ], which also showed evidence between the hours slept and the academic achievement. In a previous study, we already observed that university students from the Faculty of Nursing attributed the hours slept with academic performance [ 29 ]. Indeed, it should be highlighted that chronic lack of sleep is not only associated with alterations of attention and academic performance, but also to a series of adverse consequences for health such as risky behaviors, depression, anxiety, alterations in social relations, and obesity, among others [ 30 ].

In addition, our study has evidenced how the sleep habits directly influenced the academic performance of these Nursing students, and approximately 1/3 of the students with bad sleep habits obtained poor academic results. Certainly, the sleep pattern and inadequate sleep habits could be related. Good sleep hygiene includes aspects such as a regular sleep-wake schedule, adequate environment, avoiding stimulating activities before going to bed, and limiting the use of technology in bed or immediately before going to bed. In the present study, 30.4 % of the students had bad sleep habits, characterized by having a clear preference for studying at night, often losing a night of sleep for work or academic activities that imply go to bed late, and show difficulties in maintaining sleep routines. An important proportion of our Nursing degree students declared that they watched television, listened to music, worked or read academic documents during the last hour before going to bed. In this sense, LeBourgeois et al. (2017) [ 31 ] have described the university population as great consumers of technology, and have associated the frequent use of technology before going to bed with problems to sleep and daytime somnolence.

Finally, age was another factor that should be considered in the analysis of sleep habits. According to our results, the Nursing students that were < 25 years of age had the worst sleep habits and used to have more difficulties in maintaining sleep routines, modifying them on the weekends and holidays, preferring to stay up late to obtain better study results, and going out at night without considering that they had to get up early. As other studies [ 21 ], we observed that social activities were a priority in the life of the university adolescents and the substituting of hours of sleep for enjoying and sharing activities with friends and classmates did not constitute a problem for them. These behaviors were added to the physiological delay of the start of sleep that is typical in this stage of life and might unleash deprivation or a chronic deficit of sleep, maintained throughout the entire week. The students then tried to compensate for this lack of sleep by increasing their hours of sleep during the weekend. We agree with previous studies that this circumstance, far from minimizing or compensating the effects of sleep deprivation, aggravates them, worsening the pattern and the quality of sleep of the students [ 22 ].

Further, we found an association between age and circadian type. We observed that most of the university students with evening chronotypes were aged < 25, had bad sleep habits, and a poor academic performance. Physiologically, adolescents and adults tend to have delayed circadian preferences and are “lovers of the night” [ 23 ]. In our study, 20.2 % of students had an evening chronotype, which is lower than that reported in other studies, where 59 % of the students between 18 and 29 years of age described themselves as night owls [ 32 ]. Our results also showed a clear normalization of the evening behaviors of the students. These data are in agreement with other authors who highlighted the influence exerted by the aforementioned normalization of evening habits among the youth on the quality of sleep, leading to a medium to long-term sleep deficit [ 20 ]. As Crowley et al. (2018) [ 33 ], we think that evening behavior leads to asynchrony between the biological rhythm and the social life of the student, having negative consequences on the academic performance. However, how this really affects academic results requires extending researches, since the circadian rhythm was not significantly associated with academic performance.

The results of this study evidence the need to seriously take into consideration the sleep deficits that are associated with inadequate sleep habits, with the aim of developing preventative and educational initiatives to improve the sleep habits of the university population. The challenge ahead starts with the social awareness of the importance of having good-quality sleep since many times, adequate knowledge about sleep does not translate into a change of sleep habits [ 23 ].

Limitations

Some limitations should be noted. Due to the cross-sectional design of the study, we could not establish an exact causal relationship between sleep pattern and academic performance. In addition, it should be note that the ‘sleep habits questionnaire’ is a subjective questionnaire, and therefore the result could be biased if the student did not answer honestly. Another limitation is the difficulty in conceptualizing academic performance, due to its complex and multi-causal character, where many factors intervene. The factors include attitudes, habits, the character of the staff, methodologies, family environment, organization of the educational system, socio-economic condition, as well as other social, economic, and psychological aspects [ 34 ]. Finally, the study was conducted only in Nursing students, so our results must be prospectively validated in University students from a larger variety of academic sectors. Similarly, this study was conducted in a single University, so more studies involving other Universities are also necessary. Despite these circumstances, we believe that our hypothesis that the duration of sleep could lead to better academic performance is based on current scientific data.

Using the 11-item ‘sleep habits questionnaire’, 30.4 % of the Nursing students were identified as having bad sleep habits. In addition, Nursing students included in this research were characterized by an evening chronotype and a short sleep pattern. Regarding academic performance, half of the Nursing students showed a poor one. A short sleep pattern, bad sleep habits, and younger age, were independently associated with a higher risk of poor academic performance. This requires multifactorial approaches and the involvement of all the associated actors: teachers, academic institutions, health institutions, and the people in charge in university residences, among others.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Faculty of Health Sciences, Catholic University of Murcia, 30107, Murcia, Spain

Juana Inés Gallego-Gómez, María Teresa Rodríguez González-Moro, Tomás Vera-Catalán & Serafín Balanza

Department of Pneumology, Alcalá de Henares, Hospital Universitario Príncipe de Asturias, 28805, Madrid, Spain

José Miguel Rodríguez González-Moro

Department of Nursing, Physiotherapy and Medicine, Faculty of Health Sciences,, University of Almería, Ctra. Sacramento, s/n 04120 La Cañada de San Urbano, 04007, Almería, Spain

Agustín Javier Simonelli-Muñoz

Department of Cardiology, Hospital Clínico Universitario Virgen de la Arrixaca, Universidad de Murcia, Instituto Murciano de Investigación Biosanitaria (IMIB-Arrixaca), CIBERCV, 30120, Murcia, Spain

José Miguel Rivera-Caravaca

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JIGG, AJSM, MTRGM, TVC, and JMRGM conceptualized and designed the current study, and were major contributors in the data collection, and reviewing of the manuscript. JIGG and AJSM performed data curation, formal analysis, data interpretation, and writing of the original draft manuscript. JMRC and SB were major contributors in the writing and statistical analysis. All authors read and approved the final manuscript.

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Correspondence to Agustín Javier Simonelli-Muñoz .

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Gallego-Gómez, J.I., González-Moro, M.T.R., González-Moro, J.M.R. et al. Relationship between sleep habits and academic performance in university Nursing students. BMC Nurs 20 , 100 (2021). https://doi.org/10.1186/s12912-021-00635-x

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August 1, 2013

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New Hypothesis Explains Why We Sleep

During sleep, the brain weakens the connections among nerve cells, apparently conserving energy and, paradoxically, aiding memory

By Giulio Tononi & Chiara Cirelli

Every night, while we lie asleep, blind, dumb and almost paralyzed, our brains are hard at work. Neurons in the sleeping brain fire nearly as often as they do in a waking state, and they consume almost as much energy. What is the point of this unceasing activity at a time when we are supposedly resting? Why does the conscious mind disconnect so completely from the external environment while the brain keeps nattering on?

The brain's activity during rest likely serves some essential function. The evidence for this importance starts with sleep's ubiquity. All animals apparently sleep even though being unconscious and unresponsive greatly raises the risk of becoming another creature's lunch. Birds do it, bees do it, iguanas and cockroaches do it, even fruit flies do it, as we and others demonstrated more than a decade ago.

Furthermore, evolution has devised a few extraordinary adaptations to accommodate sleep: dolphins and some other marine mammals that must surface often to breathe, for example, sleep by alternately switching off one hemisphere of their brain while the other remains in a waking state.

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Like many scientists and nonscientists, the two of us have long wondered what benefit sleep provides that makes it so crucial to living creatures. More than 20 years ago, when we worked together at the Sant'Anna School of Advanced Studies in Pisa, Italy, we began to suspect that the brain's activity during slumber may somehow restore to a baseline state the billions of neural connections that get modified every day by the events of waking life. Sleep, in this telling, would preserve the ability of the brain's circuitry to form new memories continually over the course of an individual's lifetime without becoming oversaturated or obliterating older memories.

We also have an idea of why awareness of the external environment must be shut off during sleep. It seems to us that conscious experience of the here and now has to be interrupted for the brain to gain the chance to integrate new and old memories; sleep provides that respite.

Our hypothesis is somewhat controversial among our fellow neuroscientists who study sleep's role in learning and memory because we suggest that the return to baseline results from a weakening of the links among the neurons that fire during sleep. Conventional wisdom holds, instead, that brain activity during sleep strengthens the neural connections involved in storing newly formed memories. Yet years of research with organisms ranging from flies to people lend support to our notions.

School of Nod

Scientists first proposed the idea that sleep is important to memory nearly a century ago, and plenty of experiments since then have shown that after a night of sleep, and sometimes just a nap, newly formed memories “stick” better than they would if one had spent the same amount of time awake. This pattern holds for declarative memories, such as lists of words and associations between pictures and places, as well as for procedural memories, which underlie perceptual and motor skills, such as playing a musical instrument.

The evidence that sleep benefits memory led scientists to look for signs that the brain rehashes newly learned material at night. They found them: studies performed over the past 20 years, first in rodents and then in humans, show that patterns of neural activity during sleep sometimes do resemble those recorded while subjects are awake. For example, when a rat learns to navigate a maze, certain neurons in a part of the brain called the hippocampus fire in specific sequences. During subsequent sleep, rats “replay” these sequences more often than predicted by chance.

Because of such findings, many researchers came to assume that sleep “replay” consolidates memories by further reinforcing synapses—the contact points between neurons—that have been strengthened when an individual is awake. The idea is that, as linked neurons fire repeatedly, the synapses connecting them more readily convey signals from one neuron to another, helping neuronal circuits to encode memories in the brain. This process of selective strengthening is known as synaptic potentiation, and it is the favored mechanism by which the brain is thought to accomplish learning and remembering.

Yet while replay and potentiation are known to occur during waking activities, scientists have so far found no direct evidence that the synapses in replayed circuits get strengthened during sleep. This lack of evidence hardly surprises us. It is consistent with our suspicion that while the sleeper lies unaware, all that brain activity—the “replay” as well as other, seemingly random firings—might actually be weakening neural connections, not strengthening them.

The Price of Plasticity

There are many good reasons to propose that synapses must become weakened as well as strengthened for the brain to function properly. For one thing, strong synapses consume more energy than weak ones, and the brain does not have infinite stores of energy. In humans the brain accounts for almost 20 percent of the body's energy budget—more than any other organ by weight—and at least two thirds of that portion goes to supporting synaptic activity. Building and bolstering synapses is also a major source of cellular stress, requiring cells to synthesize and deliver components ranging from mitochondria (the cell's power plants), to synaptic vesicles (which ferry signaling molecules), to various proteins and lipids that are needed for communication across synapses.

It seems clear to us that this strain on resources is unsustainable. The brain cannot go on strengthening and maintaining revved-up synapses both day and night for the whole of an individual's lifetime. We do not doubt that learning occurs mainly through synaptic potentiation. We simply doubt that strengthening continues to happen during sleep.

In contrast, synaptic weakening during sleep would restore brain circuitry to a baseline level of strength, thereby avoiding excessive energy consumption and cellular stress. We refer to this baseline-restoring function of sleep as preserving synaptic homeostasis, and we call our overall hypothesis about the role of sleep the synaptic homeostasis hypothesis, or SHY. In principle, SHY explains the essential, universal purpose of sleep for all organisms that do it: sleep restores the brain to a state where it can learn and adapt when we are awake. The risk we take by becoming disconnected from the environment for hours at a time is the price we pay for this neural recalibration. Most generally, sleep is the price we pay for the brain's plasticity—its ability to modify its wiring in response to experience.

But how does SHY explain sleep's salutary effects on learning and memory? How can weakened synapses improve the overall retention of skills and facts? Consider that, over the course of a typical day, almost everything you experience leaves a neural trace in the brain and that the significant events, like meeting a new person or learning a piece of music on the guitar, make up just a trifling portion of that neural encoding. To improve memory, the sleeping brain must somehow distinguish the “noise” of irrelevant information from the “signal” of significant happenings.

We suggest that in sleep, the spontaneous firing of neurons in the brain activates many different circuits in many different combinations, encompassing both new memory traces and old networks of learned associations. (You get a glimpse of this neural free-for-all in dreams.) The spontaneous activity lets the brain try out which new memories fit better with stored memories of proved significance and weakens those synapses that do not fit well in the grand scheme of memory. We and other investigators are exploring possible mechanisms by which brain activity could selectively weaken synapses that encode the “noise” while preserving those that correspond to the “signal.”

While the brain tries out these imaginary scenarios and enacts weakening where appropriate, we had best be unaware of the surrounding environment and be incapable of acting in it; that is, we had best be asleep. Likewise, restoring synaptic homeostasis should not take place while we are awake because the events of the day would dominate the process, giving salience to them rather than to all the knowledge the brain has accumulated over a lifetime. The profound disconnection of sleep frees our brain from the tyranny of the present, creating an ideal circumstance for integrating and consolidating memories.

A Weak Connection

Our proposal that the brain uses neuronal firing during sleep to weaken rather than strengthen synapses is supported in part by close analyses of data from a standard workhorse of sleep research: the electroencephalogram, or EEG. EEGs record patterns of electrical activity in the cerebral cortex via electrodes attached to the scalp. Decades ago EEG recordings of the sleeping brain revealed two main categories of sleep, called rapid eye movement (REM) and non-REM (NREM), that alternate throughout the night. Each has distinctive brain-wave patterns. In addition to the jittering of eyeballs underneath closed lids that gives REM sleep its name, that stage is dominated by relatively fast oscillations—quick ups and downs in the curves of the EEG readout, resembling EEG recordings of the waking state. In contrast, slow oscillations—with frequencies of about one cycle per second—are the most prominent feature of NREM sleep.

A decade ago the late Mircea Steriade of Laval University in Quebec discovered that the slow oscillations of NREM sleep arise when groups of neurons fire together for a little while (so-called on periods), then fall silent for about a fraction of a second (off periods) and then resume their synchronized firing. This was one of the fundamental discoveries in sleep research. Since then, scientists have also discovered that in birds and mammals, the slow waves are large if preceded by a long period of wakefulness and become smaller as sleep goes on.

We reasoned that if synapses are strong, neurons will synchronize their firing more, producing larger slow waves. If synapses are weak, neurons will be less synchronized and the resulting slow waves will be smaller. Results of computer simulations and experiments in humans and animals led us to conclude that the big, steep slow waves early in the night indicate that synapses have been strengthened by prior wakefulness, whereas the small, shallow slow waves early in the morning indicate that synapses have become weaker during sleep.

Direct support for the idea that synapses become weaker during sleep, and may even be pruned away, comes from studies in animals. In fruit flies, for instance, we find that sleep reverses a progressive increase in the number and size of synapses that occurs during the day, especially when the flies are exposed to stimulating environments. Synaptic spines are specialized protrusions on a neuron's signal-detecting arm. When fruit flies spend the day interacting with other flies, neurons throughout their brain sprout more synaptic spines by evening than were present in the morning.

Just as remarkably, the number of spines goes back to the baseline level by the following morning if—and only if—the flies are allowed to sleep. We saw a similar phenomenon in the cerebral cortex of adolescent mice: the number of synaptic spines tended to rise when the animals were awake and to fall when they slept. In adult rodents, the upshot is the same, although it is not the number of synaptic spines that changes with wakefulness and sleep but rather the abundance of certain spine molecules, known as AMPA receptors, that determine the strength of a synapse. When we monitored these AMPA receptors, we found that their number per synapse increases after wakefulness and decreases after sleep. More receptors make for stronger synapses; fewer mean the synapses have weakened.

Synaptic strength can be gauged directly by using an electrical probe to stimulate neural fibers in the cortex. The neurons respond with an induced electrical discharge that is larger when synapses are strong and smaller when the connections are weak. We showed that in rats, stimulated neurons fire more strongly after a few hours of wakefulness and less strongly after sleep. Marcello Massimini of the University of Milan in Italy and Reto Huber, now at the University of Zurich, performed a similar experiment in humans. Instead of an electrical probe, they turned to transcranial magnetic stimulation—a short magnetic pulse applied to the scalp—to stimulate the underlying neurons. They then recorded the strength of the cortical responses with high-density EEG. The results were clear: the longer a subject was awake, the larger the EEG responses. It took a night of sleep for cortical responses to return to the baseline.

Less Is More

The common conclusion of these experiments, which we performed over two decades, is that spontaneous cortical activity in sleep does indeed weaken the synaptic connections in neural circuits, whether by damping their ability to send electrical impulses or by erasing them outright.

This process, which we call down selection, would ensure the survival of the circuits that are “fittest,” either because they were activated strongly and consistently during wakefulness (say, by playing the right notes on a guitar while trying to master a new piece) or because they were better integrated with previous, older memories (as would be the case for a new word encountered in a known language). Meanwhile synapses in circuits that were only mildly enhanced during wakefulness (such as fumbled notes on the guitar) or that fit less with old memories (such as a new word presented in an unknown language) would be depressed.

Down selection would ensure that insignificant events would leave no lasting trace in our neural circuitry, whereas memories of note would be preserved. As an additional bonus, down selection would also make room for another cycle of synaptic strengthening during wakefulness. Indeed, some findings imply that among its many other benefits for learning and memory, sleep aids the subsequent acquisition of new memories (material encountered before the next bout of sleep). Quite a few studies have shown that after a night of sleep, you can learn new material much better than you can after having been awake all day. (Students, take note.)

Although we have no direct evidence for a mechanism that would produce selective weakening of activated synapses as yet, we have a notion of how synaptic weakening could occur. We suspect the slow waves of mammalian NREM sleep somehow play a role. In lab studies of rat brain tissue, nerve cells became less effective at passing signals to one another when stimulated in ways that mimic the synchronized on/off cycles of slow-wave sleep.

The chemistry of the brain also changes in NREM sleep in a way that could lead to synaptic weakening. In the awake individual, a concentrated soup of signaling chemicals, or neuromodulators—including acetylcholine, norepinephrine, dopamine, serotonin, histamine and hypocretin—bathe the brain and bias synapses toward strengthening when signals pass through them. During sleep—especially NREM sleep—the soup becomes much less concentrated. This diluted milieu of neuromodulators may bias the neural circuitry so that synapses become weakened, rather than strengthened, when signals flow across them. The process might also involve a substance called brain-derived neurotrophic factor (BDNF), which is known to promote synaptic strengthening and to be involved in memory acquisition. BDNF levels are high in neurons during wakefulness and minimal during sleep.

Local Sleep

Regardless of specific mechanisms and selective processes, the evidence is strong in several species that overall synaptic strength goes up during wakefulness and down during sleep: the core prediction of SHY. We can test SHY further by examining some of its intriguing corollaries.

For example, if the hypothesis is correct, then the more plasticity a part of the brain undergoes during wakefulness, the more that part should need to sleep. “Sleep need” can, in turn, be indicated by an increase in the size and duration of NREM slow waves. To explore this prediction, we asked human subjects to learn a novel task: how to reach a target on a computer screen while the cursor (controlled by a mouse) is systematically rotated. The part of the brain that engages in this kind of learning is the right parietal cortex. Sure enough, when our subjects slept, the slow waves over their right parietal cortex were larger, relative to waves from the same area on the night before learning occurred. These large waves did flatten out in the course of the night, as such oscillations do. But those large, localized waves at the start of the night tell us that particular part of the brain had been exhausted by the task we assigned.

Many other experiments by the two of us and others have since confirmed that learning, and more generally the activation of synapses in circuits, produces a local increase in sleep need. Recently we have even found that prolonged or intense use of certain circuits can make local groups of neurons “fall asleep” even though the rest of the brain (and the organism itself) remains awake. Thus, if a rat stays awake longer than usual, some cortical neurons show brief periods of silence that are basically indistinguishable from the off periods observed during slow-wave sleep. Meanwhile the rat is running around, its eyes open, tending to its business, as any awake rat would do.

This phenomenon is called local sleep, and it is attracting scrutiny from other investigators. Our latest studies indicate that localized off periods also occur in the brains of sleep-deprived humans and that those periods become more frequent after intense learning. It seems that when we have been awake for too long or have overexerted certain circuits, small chunks of the brain may take quick naps without giving notice. One wonders how many errors of judgment, silly mistakes, irritable responses and foul moods result from local sleep in the brains of exhausted people who believe they are fully awake and in complete control.

SHY also predicts that sleep is especially important in childhood and adolescence, times of concentrated learning and of intense synaptic remodeling, as many studies have shown. In youth, synapses are formed, strengthened and pruned at an explosive rate never approached in adulthood. It makes sense that down selection during sleep would be crucial to minimize the energy costs of this frenzied synaptic remodeling and to favor the survival of adaptive neural circuits in these stages of life. One can only wonder what happens when sleep is disrupted or insufficient during critical periods in development. Might the deficit corrupt the proper refinement of neural circuits? In that case, the effect of sleep loss would not merely be occasional forgetfulness or misjudgment but a lasting change in the way the brain is wired.

We look forward to testing SHY's predictions and exploring its implications further. For example, we hope to discover whether sleep deprivation during neural development leads to changes in the organization of brain circuitry. We would also like to learn more about the effect of sleep on deep-brain areas, such as the thalamus, cerebellum, hypothalamus and brain stem, and about the role of REM sleep in synaptic homeostasis. Perhaps we would then learn if sleep is indeed the price of waking plasticity, a price that every brain and every neuron must pay.

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Sleep to Remember

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Scientific investigation into the possible role of sleep in memory consolidation began with the early studies of Jenkins and Dallenbach (1924 ). Despite nearly a century of investigation with a waxing and waning of interest, the role of sleep in memory processing remains controversial and elusive. This review provides the historical background for current views and considers the relative contribution of two sleep states, rapid eye movement sleep and slow-wave sleep, to offline memory processing. The sequential hypothesis, until now largely ignored, is discussed, and recent literature supporting this view is reviewed.

• locus coeruleus
• noradrenaline
• slow-wave sleep

Introduction

Hypnos, the god of sleep, lived on the Island of Lemos, in a cave through which flowed the river Lethe, the river of forgetfulness. Hypnos had a twin brother, Thanatos, the god of death. So it is quite evident from their mythology that the ancient Greeks regarded sleep as a state closely related to death and a state in which memories were likely to be forgotten. According to Aristotle, the sleep state was “a border-land between living and not-living: a person who is asleep would appear to be neither completely nonexistent nor completely existent” ( Sprague, 1977 ). Clearly, ancient Greek thought did not attribute any important functional role to the sleep state.

Scientific investigation into the possible role of sleep in memory consolidation began with the early studies of Jenkins and Dallenbach (1924 ) who reported that subjects who slept immediately after learning had better retention than those who did not sleep. They interpreted the results in terms of sleep protecting the memory from interference likely to occur during awake states. Subsequent years saw a waxing and waning of interest in the role of sleep in memory processing, but accumulated evidence from both human and animal experimentation lends unequivocal support that sleep is somehow important for memory (for a comprehensive review with 1358 references, see Rasch and Born, 2013 ). Nevertheless, many questions remain concerning the mechanisms by which underlying neural activity particular to specific sleep stages impacts memory performance. This review will provide a historical perspective for understanding current questions and review some recent developments that further our understanding of sleep-dependent memory processing.

The discovery of REM sleep

Jenkins and Dallenbach (1924 ) and other early researchers did not know that behavioral sleep comprises two distinct brain states: one characterized by high voltage, slow oscillations, known as slow-wave sleep (SWS) and the other by low voltage, mainly desynchronized activity, resembling wakefulness, called rapid eye movement (REM) sleep. Present day interest in sleep as an active state in which memories may be relived and reinforced, dates from the discovery of REM sleep associated with dreams ( Aserinsky and Kleitman, 1953 ; Jouvet and Michel, 1960 ). This state of sleep was first named paradoxical sleep (PS) because associated activity in the cerebral cortex resembled wakefulness while the subject appeared to be in a deep sleep with total muscle atonia. Most early research focused on dreams related to this sleep stage (for review, see Miller, 1975 ).

Early rodent studies

Seminal work by Vincent Bloch and collaborators tested the hypothesis that offline memory consolidation occurs during the period of low voltage, high-frequency cortical activity that is the hallmark of PS. They showed that selective deprivation of PS caused learning impairment and memory deficits in rats ( Leconte and Bloch, 1970a ; Leconte and Bloch, 1970b ), lending concrete support to the intuitive notion that memory consolidation took place during this phase of sleep, associated with dreams and a high level of cortical activity. This same group later showed that learning-dependent increases in PS were correlated with the rate of acquisition of a complex maze task ( Hennevin and Leconte, 1971 ). These studies were complemented by many other studies in rodents ( Pearlman, 1969 ; Fishbein, 1971 , Smith et al., 1974 ; Fishbein and Gutwein, 1977 ), mostly using some version of a selective REM sleep deprivation protocol (for review, see Smith, 1985 ). This involved constraining the animal on an inverted flower pot surrounded by a pool of water. Muscle atonia accompanying REM sleep caused the animal to fall off the flower pot into the water, to a rude awakening. Such studies yielded conflicting results and were highly criticized for not controlling for the effect of stress induced by the sleep deprivation methods on subsequent behavioral evaluation of “memory” (for critical reviews with different viewpoints, see Vogel, 1975 ; Crick and Mitchison, 1983 ; Horne and McGrath, 1984 ; Vertes and Eastman, 2000 ; Siegel, 2001 ).

Cuing for directed memory consolidation

Using another research strategy to provide support to the hypothesis that REM sleep is a substrate for memory consolidation, Hars and Hennevin (1987 ) performed a series of experiments in rats in which a cue associated with the learning was presented during subsequent paradoxical sleep bouts. Rats that received a mild ear shock below awakening threshold, which had served as the conditioned stimulus during learning, remembered the avoidance task better when the cue was presented during REM sleep than when it was presented during the awake state ( Hars et al., 1985 ). On the other hand, when the ear shock was presented during SWS sleep, it impaired acquisition of the task ( Hars and Hennevin, 1987 ). These studies were based on the idea that a specific memory was activated by the cue, so as to become labile. The high-frequency cortical activity associated with REM sleep would then promote consolidation of the active network ( Sara, 2010 ). It should be noted that the Hars and Hennevin (1987 ) experiments used a highly emotional multisession active avoidance task. The results thus support current notions that non-REM (NREM) and REM sleep stages may be involved in consolidation of different types of memories or different aspects of the same memory ( Smith, 2001 ). This new insight has promoted a renewed interest in REM sleep-related memory processes ( Llewellyn and Hobson, 2015 ; Fogel et al., 2015 ).

REM sleep for consolidation of procedural and emotional memories

There is some evidence that the REM stage of sleep is involved in the consolidation of procedural memory. Procedural memory can best be described as “knowing how” as opposed to declarative memory or “knowing that.” Motor skills, such as walking, talking, playing a musical instrument, and skiing, are considered to be based upon procedural memory. These memories involve brain regions other than the hippocampus, are characterized by a very slow acquisition period (sometimes over months or years), and are very persistent, once acquired. Evidence that REM sleep plays a privileged role in motor skill acquisition comes mainly from studies in which the subject is awakened either early in the sleep cycle, sleep that is mostly NREM, or late in the cycle containing “REM-rich” sleep. In this way, it can be shown that memory for procedural tasks is more likely to be disrupted with loss of REM-rich sleep, whereas memory for declarative tasks is dependent on earlier SWS ( Plihal and Born, 1997 ). The weakness of this study lies in the fact that it uses the “split-night” design where there is no EEG control for the time of waking, so the relative amount of REM and NREM sleep deprivation can only be loosely estimated. This study is still highly cited as evidence for a dissociation of the roles of REM and NREM sleep in procedural and declarative memory, respectively. Subsequent supporting evidence is sparse, with Genzel et al. (2009 ) and Saxvig et al. (2008 ) failing to find any evidence for a clear dissociation using a carefully controlled protocol with EEG monitoring the stages of sleep. Moreover, there is other direct evidence that SWS and associated sleep spindles play an important role in consolidation of motor skills ( Walker et al., 2002 ; Clemens et al., 2006 ; Wamsley et al., 2012 ). Thus, consolidation of procedural memory does not appear to be a specific function of REM sleep.

Evidence for a role of REM sleep in consolidation of the emotional components of memories is stronger, with several recently published studies in humans (for review, see Gilson et al., 2015 ; Groch et al., 2016 ; Wiesner et al., 2015 ; Hutchison and Rathore, 2015 ). This growing body of literature converges to support the view that “emotional arousal during encoding is beneficial to recall only if REM sleep occurs after learning” ( Wiesner et al., 2015 ).

Mechanisms of memory consolidation in REM sleep

The precise mechanisms by which REM sleep promotes consolidation of emotional memories remains unresolved, although there are some studies suggesting that LTP processes are activated in REM sleep. Ravassard et al. (2015 ) have shown impairment of both contextual fear conditioning and LTP in the dentate gyrus of the hippocampus after short periods of REM sleep deprivation. In these same studies, an increase in REM sleep is correlated with better retention of the task and stronger LTP.

Hippocampal theta rhythm.

There is a large body of literature suggesting that theta oscillations, characteristic of REM sleep, play a role in memory consolidation, particulary in fear conditioning and emotional memories (for review, see Hutchison and Rathore, 2015 ). Causal evidence has recently been provided by an elegant experiment in which hippocampal theta was suppressed by optogenetically silencing GABAergic neurons in the medial septum, that drive hippocampal theta rhythm ( Boyce et al., 2016 ). Genetically modified mice were injected into the medial septum with a virus delivering the photoinhibitory gene Archaerhodopsin. Mice were subsequently trained in a spatial-object recognition task or a contextual fear conditioning, both dependent on the hippocampus. The septal GABAergic neurons driving hippocampal theta were silenced selectively during each REM sleep episode after the learning session. Compared with various control groups, mice with suppression of REM sleep theta expressed significantly less memory for either task. Importantly, there was no memory deficit in a cued memory recall test that is not dependent on the hippocampus. Thus, theta oscillation in the hippocampus during REM sleep is a necessary condition for consolidation of hippocampal-dependent memories.

Plasticity genes and protein synthesis.

The first report of experience dependent upregulation of plasticity related immediate early genes was from Ribiero et al. (1999) . Rats were exposed for 3 h to an enriched novel environment, after which they were allowed to sleep and brains states were monitored electrophysiologically. Rats were killed 30 min after a bout of REM sleep, SWS, or wakefulness, and brains were processed by in situ hybridization. Rats killed after the REM bout had significantly higher levels of expression of the plasticity- related immediate early gene zif-268 in cortex and hippocampus. Using a similar strategy, this group recently showed upregulation of multiple immediate early genes during REM sleep in the hippocampus, after object recognition learning ( Calais et al., 2015 ).

A study using LTP in the hippocampal dentate gyrus as a model of plasticity revealed waves of upregulation of extrahippocampal zif-268 gene expression during subsequent episodes of REM sleep. The signal was found in more proximal structures, such as amygdala and entorhinal cortex, after the first bout of REM, then propagated to more distal regions in subsequent REM bouts. These results are taken as evidence for an REM sleep-dependent mechanism by which a memory trace originating in the hippocampus propagates to other brain regions. This takes place over many hours through successive bouts of REM sleep. Importantly, inactivation of the hippocampus immediately after the onset of the REM sleep episode prevents the expression of the zif-268 signal downstream ( Ribeiro et al., 2002 ), suggesting the necessity of the hippocampal signal in initiating the systems-level consolidation process.

SWS and memory

The two stage hypothesis.

In his seminal paper, Buzsáki (1989 ) put forth the hypothesis of a “two stage” memory formation. Information is acquired during wakefulness and exploratory activity when the hippocampus activity oscillates in a theta/gamma mode, during which cortical information is transmitted and weakly potentiated onto CA3 neurons. During offline sleep (or rest), fast oscillations, called ripples, with synchronous population bursts 5–8 times higher than during wakefulness, predominate in CA3 resulting in LTP of the CA1 target synapses and potentiation of hippocampal output to cortex ( Buzsáki, 1998 ). This model was highly influential in refocusing interest in the role of sleep in memory formation, but this time on NREM or SWS.

The model predicts that sequences of neuronal discharges occurring during behavioral exploration should be replayed in a “time-compressed manner” ( Buzsáki, 1998 ). Many publications appeared confirming this prediction ( Wilson and McNaughton, 1994 ; Skaggs and McNaughton, 1996 ), some showing that the replay of sequences occurs during the sharp wave ripple (SWR) high-frequency oscillation, when a select population of neurons fires at a very high frequency. ( Kudrimoti et al., 1999 ; Atherton et al., 2015 ).

Cuing in SWS

Replay does not appear to be random. Indeed, presenting specific cues associated with the original learning during subsequent SWS can facilitate memory performance upon awakening, for the auditory modality in rodents ( Bendor and Wilson, 2012 ) and humans ( Creery et al., 2015 ). In the olfactory modality, odors that were part of the learning context, when presented during SWS, memory retention of paired associate learning in humans ( Rasch et al., 2007 ). In a more explicit test of replay during SWS, a recent study in rats used “olfactomimetic” stimulation comprised of electrical pulses to the olfactory bulb (conditioned stimulus), paired with footshock (unconditioned stimulus). When the conditioned stimulus was presented in absence of the unconditioned stimulus during the awake state, extinction occurred; when presented during SWS, the conditioned fear memory was stronger and more accurately tuned to the conditioned stimulus ( Barnes and Wilson, 2014 ). These results together provide strong evidence for a cue-induced bias determining which neuronal ensembles will be replayed during sleep. The phenomenon is in contrast to earlier studies discussed above, where a reminder during sleep impaired subsequent memory performance when administered during SWS, but enhanced it when delivered during the REM state ( Hars et al., 1985 ; Hars and Hennevin, 1987 ).

Reward, replay, and the noradrenergic system

Reward associated with spatial learning increases the probability of offline replay of ensembles of cells active during spatial exploration ( Singer and Frank, 2009 ). Indeed, rewarded trials induced a fourfold increase in SWR rate associated with the intertrial interval. During epochs where new reward contingencies were learned, the increase in these fast oscillations between trials was even greater. How can the presence of reward on a learning trial influence the probability of SWRs being generated offline? We know that these SWRs are generated by high-frequency synchronous firing of a population of neurons in CA3 region of the hippocampus. Although this occurs most often during SWS, SWRs are also observed during quiet rest periods between learning trials. It is well established that neuromodulatory neurons of the VTA and locus ceruleus (LC), releasing dopamine and noradrenaline (NE), respectively, emit phasic bursts of action potentials in response to primary reward, or to stimuli that predict reward delivery ( Sara and Segal, 1991 ; Schultz, 2001 ; Bouret and Sara, 2004 ). So it is reasonable to suppose that, on rewarded trials during spatial learning, dopaminergic and noradrenergic neurons are firing in tandem with place cell ensembles engaged in the learning. This “firing together” during acquisition could recruit the LC and/or VTA neurons into the broad network underlying the new learning, through a Hebbian-like mechanism. Offline firing of these neuromodulatory neurons in time with replay of the “memory ensemble” during sleep would increase the probability and rate of firing of individual pyramidal cells by decreasing after hyperpolarization, thereby increasing excitability ( McCormick and Prince, 1988 ). It is indeed well established that NE promotes LTP ( Neuman and Harley, 1983 ), so if LC cells are firing in close temporal proximity to the replay ensemble, then the connections within that ensemble will be reinforced.

LC activity during SWS after learning

Several years ago, a series of pharmacological studies in the rat implicated the noradrenergic system in a late offline stage of memory consolidation ∼1–2 h after learning ( Tronel et al., 2004 ). Complementary studies showed an increase in release of NE in the same time window, after learning. These results encouraged us to look at activity of LC neurons during the hours after learning, when the rats spent most of the time sleeping. Recording unit activity from this tiny pontine nucleus (∼1500 neurons in the rat), we found an increase in LC activity during SWS, 2 h after learning of the same odor discrimination task. This increase was seen only in rats that had learned the task. Interestingly, it was also seen after retrieval from remote memory, suggesting an LC involvement of reconsolidation of a reactivated memory trace ( Eschenko and Sara, 2008 ).

SWS is characterized by high-amplitude, low-frequency oscillations (∼1 Hz) called slow waves (hence SWS). During these oscillations, cortical neurons are hyperpolarized in the “down” state and fire action potentials in the “up” state. Examination of the temporal relation between LC action potentials and cortical oscillations reveals that LC neurons are phase-locked to the slow wave. LC neurons tend to fire on the rising phase during the transition from down to up state. Thus, unit firing of LC neurons precedes the synchronous firing of cortical neurons with almost no overlap ( Eschenko et al., 2012 ). This observation corroborated an earlier study reporting that LC neurons and neurons of the frontal cortex fire in phasic opposition, when both regions are in an oscillating mode ( Sara and Hervé-Minvielle, 1995 ). Release of NE in the forebrain by LC neurons firing immediately before the synchronous firing of cortical neurons would be the ideal situation for promoting synaptic plasticity within a reactivated network ( Sara, 2015 ).

Slow waves, spindles, and ripples in memory consolidation

Slow waves play a crucial role in offline memory consolidation during sleep. Driving the sleeping brain with transcranial direct current stimulation to function in a slow oscillating mode during SWS (or “boosting the slow oscillations”) facilitates memory in human subjects ( Marshall et al., 2006 , 2011 ). It has recently been discovered that mild acoustic stimulation is effective in increasing the magnitude of slow waves during sleep and that this enhancement leads to the same memory enhancement as the transcranial DC stimulation ( Bellesi et al., 2014 ).

The depolarizing phase of the slow wave generates spindle oscillations at 12–15 Hz within the “up” state, via corticothalamic feedback loops ( Steriade et al., 1993 ). In this way, spindles are nested within the slow oscillations ( Mölle et al., 2002 ; Cox et al., 2014 ). A recent study confirmed the coupling of spindles with the slow oscillation and further showed that the timing of the spindles in relation to the slow oscillation was important for memory consolidation. Spindle peaks occurring during the transition from down to up state were predictive of subsequent improvement of performance of a word paired associate task ( Niknazar et al., 2015 ). Earlier studies in humans showed an increase in spindle activity during sleep following learning ( Gais et al., 2002 ; Mölle et al., 2004 ). We found a strikingly similar increase in spindles in rats after learning of an odor discrimination task, and this increase was positively correlated with subsequent improvement in memory performance ( Eschenko et al., 2006 ).

Learning of semantically new words is followed by an increase in spindles in subsequent sleep, compared with spindle density during sleep following the learning of words that had some semantic relation to existing knowledge ( Tamminen et al., 2013 ). Increase not only in density, but in spindle power, has been associated with improved memory performance ( Rihm et al., 2014 ). Reexposure during SWS to an olfactory cue present during learning a visuospatial task increased power in the fast spindle range and improved retention the next day. The authors suggest that the learning-associated cue induced a high-frequency replay of ensemble activity in the hippocampus, which in turn drove the cortical spindle oscillations. Spontaneously occurring persistent offline environmental cues could be a mechanism by which a bias concerning which ensembles are replayed during sleep is introduced.

Finally, local spindles can be detected with intracerebral electrodes, and they are reported to be quite common, suggesting that sleep spindles can promote synaptic plasticity and memory consolidation in a “circuit-specific” manner ( Nir et al., 2011 ).

The high-frequency ripple oscillation in the hippocampus is an intricate part of the two stage model. High-frequency firing of ensembles of neurons associated with the previous learning experience should promote the LTP underlying lasting memories. Indirect support for this essential role of high-frequency oscillation in the hippocampus comes from a few key experiments in rats. A learning-dependent increase in ripples during SWS was initially observed after a single session acquisition of an odor discrimination task. Rats that did not learn the task did not show this increase in ripples ( Eschenko et al., 2008 ). A subsequent study, using a multisession spatial discrimination task, showed an increase in ripples during the sleep session immediately before behavioral expression of task mastery ( Ramadan et al., 2009 ). Interruption of ripples by ripple-triggered electrical stimulation of the hippocampus prevented learning of that same spatial discrimination task ( Ego-Stengel and Wilson, 2010 ; Girardeau et al., 2009 ). Because ensemble replay happens during the ripple, it is assumed that the increase of ripples after learning will include more replay. Interruption of the ripples will prevent the replay, thus accounting for the behavioral deficit.

Recent studies have confirmed in humans what was reported earlier in rats ( Siapas and Wilson, 1998 ; Sirota et al., 2003 Mölle et al., 2006 , 2009 ), that both spindles and hippocampal ripples are nested in slow oscillations. Staresina et al. (2015 ) used intracranial electrodes implanted in epileptic patients to study the temporal relation among slow oscillations, spindles, and ripples. Spindles were confined to the up state of the slow oscillation, and ripples tended to be clustered in the troughs of the spindles. An earlier fMRI study revealed increased hippocampal/neocortical connectivity during spindles ( Andrade et al., 2011 ). These studies, together, provide evidence that temporally fine-tuned NREM sleep oscillatory activity provides ideal conditions for effective transfer of information from hippocampus to neocortex, an essential step in systems-level memory consolidation.

Clinical considerations

Disturbance of normal sleep–wake cycles and sleep stages is likely to contribute to cognitive deficits in several psychiatric disorders ( Goerke et al., 2015 ). There is a large literature documenting sleep disturbances and their relation to memory function in schizophrenic patients. These disturbances include a reduction in the number of spindles ( Göder et al., 2015 ; Manoach et al., 2016 ) and reduced coherence among spindles recorded from different cortical regions ( Ferrarelli et al., 2007 ). Wamsley et al. (2012 ) found that schizophrenic patients did not show spontaneous improvement in performance of a motor sequence task after sleeping, as is seen in healthy controls. Moreover, there was a direct correlation in these patients, between the reduction in number and density of sleep spindles and the deficit in motor learning. This was taken by the authors as strong support for an essential role of sleep spindles in memory consolidation and opens new avenues of approach to treat cognitive deficits in schizophrenia.

A sequential hypothesis

Giuditta (1985 ) proposed early on a “sequential hypothesis” for SWS and REM sleep in memory processing, with each stage of sleep playing a distinct and complementary role. This was several years before there was any interest or experimental evidence for memory processing during SWS, and all eyes were focused on REM. The hypothesis was based on data from rats learning a very stressful multitrial avoidance task. Very large differences were observed in the structure of subsequent sleep, both SWS and REM, depending on whether the rats had learned or not. This led these investigators to propose, for the first time, that both stages of sleep participated in offline memory consolidation in a sequential manner. During SWS, “irrelevant” information is preprocessed and downgraded; during REM, “relevant” information is integrated into preexisting memories. There was no attempt to explain how information is “tagged” as relevant or not, and this question remains outstanding today. Giuditta's (1985 ) sequential hypothesis received little attention at the time, even though it was remarkably predictive of current views on the function of sleep. For example, “sleep processing is likely to result in the elimination of trivial or unusable information and the integration of useful information …with preexisting traces. The latter operation might involve the relocation of memory traces into different cerebral sites” ( Ambrosini et al., 1988 ; for review, see Giuditta, 2014 ). Current theories of memory consolidation have emphasized this integration of new information with existing “schema” as an essential part of the process ( Sara, 2000 , 2010 ; Tse et al., 2007 ; Dudai et al., 2015 ). The notion of relocation of the memory trace to “different cerebral sites” is now widely accepted, with strong supporting evidence ( Dudai et al., 2015 for review).

Recent support was added for the sequential view from a study in humans using a combined polygraphic, fMRI technique in an emotional learning task. SWS was found to be involved in the initial selection of information to be processed and REM sleep in the subsequent consolidation of the selected information ( Cairney et al., 2015 ), much as Giuditta's (1985 ) model suggests.

Summary and perspectives

Contrary to the beliefs of the ancient Greeks, modern science has produced an impressive body of evidence documenting the essential role played by sleep states in offline memory consolidation. From the early, rather crude attempts to deprive rats of REM sleep by confining them on inverted flower pots in a pool of water, great progress has been made. Advances in computational capacities have facilitated analysis of multiple channels of EEG to provide new insights into the complex interaction of neocortical and hippocampal oscillatory activity, supporting the hypothesis of the essential role of dialogue between these two regions in offline memory consolidation. fMRI studies will continue to further our understanding of the dynamics of interplay among brain regions for systems-level consolidation. Multisite high-volume electrophysiological recordings permitted the discovery of ensemble replay during SWRs, especially during sleep.

Many other questions concerning the neurophysiological mechanisms underlying memory consolidation and the role of sleep, in particular, remain. For example, how are specific ensembles “tagged” for offline replay? Are these same memory ensembles actually reactivated again during retrieval of the memory ( Dupret et al., 2010 )? The specific roles of REM and NREM sleep remain elusive, although the evidence points to reactivation of neuronal activity during SWS and transcriptional storage during REM ( Ribeiro et al., 1999 ). Advances in modern biotechnology are beginning to bring answers to these and other questions concerning the neurobiology of memory consolidation (e.g., Boyce et al., 2016 ). Meanwhile, unequivocal evidence points to the essential role of sleep states in these processes.

The authors declare no competing financial interests.

• Correspondence should be addressed to Dr. Susan J. Sara, Center for Interdisciplinary Research in Biology, College de France, 12 pl Marcelin Bertelot, 75005 Paris, France. Susan.sara{at}college-de-france.fr
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Response from Dual Perspective Companion Author–Gina Poe

Susan J. Sara provides a thorough review of research demonstrating that sleep is for memory consolidation and reconsolidation. The question remains: How? Sara's article has particular strength over many other reviews in that she pays particular attention to mechanism, highlighting rhythms, timing of reactivation, and neurochemical milieu. In the accompanying perspective, I parallel many of these lines, arguing with a different emphasis: that consolidation and reconsolidation of memories during sleep are the only time the normal brain can strategically erase components of memories to form a coherent schema. When new pieces of information are learned, synaptic weakening is an important part of reconsolidation. I argue that sleep, particularly sleep when the LC is silent (during spindle onset in the NREM transition to REM [TR] sleep phase and REM sleep), is the only time when synapses associated with inaccurate bits of information can be weakened, allowing that information to be forgotten. It is perhaps a stretch to say “forgetting” when referring to the targeted elimination of now incorrect bits of information, but I also discuss forgetting in terms of unwanted forgetting that would occur under circumstances when the LC is compromised during slow-wave sleep.

Sara's review clarifies a puzzle in sleep-for-memory research (is REM sleep for consolidation of procedural and emotional memories), showing how slow-wave sleep, rather than REM sleep, seems to be important for consolidating procedural memory. Her own research shows a key mechanism by which sleep serves to strengthen new learning while preserving old memories. But as argued in my accompanying article, additional waking practice could continue to strengthen synapses underlying learning, without need of sleep. In contrast, more waking practice cannot integrate new memories into old schema or erase irrelevant information during new learning. Studies in birds learning their song ( Deregnaucourt et al., 2005 ) and imprinting ( Jackson et al., 2008 ) and in humans learning to ride a reversed handlebar bicycle ( Hoedlmoser et al., 2015 ), support the idea that targeted, careful pruning during REM sleep and NREM sleep spindles is necessary to remove noise that would otherwise crowd and obscure the memories we want to maintain.

A compelling question is how synapses encoding incorrect pieces of information, or those overpotentiated during prolonged activity of a sensory or motor circuit during wakefulness, as in Blake and Merzenich (2002 ) and Hanlon et al. (2009 ), are tagged for forgetting during sleep, while others are marked for strengthening. The finding that the density of sleep spindles correlated with worsened performance on the reverse handlebar bicycle ( Hoedlmoser et al., 2015 ) is fascinating. Under what conditions would sleep serve to strengthen rather than erase that new oddball motor memory? If subjects knew they would have to ride with this reverse handlebar configuration for the rest of their lives, could they somehow tag that new circuit for retention and strengthening rather than elimination during sleep? In a question also asked at the end of Sara's article: how does the brain work to tag a new memory for retention while leaving others to be erased during sleep? Indeed, the opposite mechanism could serve: useless memories or interfering memory bits may be tagged for depotentiation, whereas those slated to be strengthened by sleep processes follow the usual consolidation course outlined in Sara's prospective. It is still early days in the collection of experimental evidence that sleep is for forgetting. Although indirect evidence abounds, direct evidence of the importance of depotentiation for memory and the exclusivity of depotentiation to LC-off periods is still needed.

• Merzenich MM
• Deregnaucourt S ,
• Tchernichovski O
• Hanlon EC ,
• Faraguna U ,
• Hoedlmoser K ,
• Birklbauer J ,
• Schabus M ,
• Eibenberger P ,
• Jackson C ,
• McCabe BJ ,

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The Impact of Sleep on Learning and Memory

By Kelly Cappello, B.A.

For many students, staying awake all night to study is common practice. According to Medical News Today , around 20 percent of students pull all-nighters at least once a month, and about 35 percent stay up past three in the morning once or more weekly.

That being said, staying up all night to study is one of the worst things students can do for their grades. In October of 2019, two MIT professors found a correlation between sleep and test scores : The less students slept during the semester, the worse their scores.

So, why is it that sleep is so important for test scores? While the answer seems simple, that students simply perform better when they’re not mentally or physically tired, the truth may be far more complicated and interesting.

In the last 20 years, scientists have found that sleep impacts more than just students’ ability to perform well; it improves their ability to learn, memorize, retain, recall, and use their new knowledge to solve problems creatively. All of which contribute to better test scores.

Let’s take a look at some of the most interesting research regarding the impact of sleep on learning and memory.

How does sleep improve the ability to learn?

When learning facts and information, most of what we learn is temporarily stored in a region of the brain called the hippocampus. Some scientists hypothesize that , like most storage centers, the hippocampus has limited storage capacity. This means, if the hippocampus is full, and we try to learn more information, we won’t be able to.

Fortunately, many scientists also hypothesize that sleep, particularly Stages 2 and 3 sleep, plays a role in replenishing our ability to learn. In one study, a group of 44 participants underwent two rigorous sessions of learning, once at noon and again at 6:00 PM. Half of the group was allowed to nap between sessions, while the other half took part in standard activities. The researchers found that the group that napped between learning sessions learned just as easily at 6:00 PM as they did at noon. The group that didn’t nap, however, experienced a significant decrease in learning ability [1].

How does sleep improve the ability to recall information?

Humans have known about the benefits of sleep for memory recall for thousands of years. In fact, the first record of this revelation is from the first century AD. Rhetorician Quintilian stated, “It is a curious fact, of which the reason is not obvious, that the interval of a single night will greatly increase the strength of the memory.”

In the last century, scientists have tested this theory many times, often finding that sleep improves memory retention and recall by between 20 and 40 percent. Recent research has led scientists to hypothesize that Stage 3 (deep non-Rapid Eye Movement sleep, or Slow Wave Sleep) may be especially important for the improvement of memory retention and recall [2].

How does sleep improve long-term memory? 

Scientists hypothesize that sleep also plays a major role in forming long-term memories. According to Matthew Walker, professor of neuroscience and psychology at UC Berkeley, MRI scans indicate that the slow brain waves of stage 3 sleep (deep NREM sleep) “serve as a courier service,” transporting memories from the hippocampus to other more permanent storage sites [3].

How does sleep improve the ability to solve problems creatively?

Many tests are designed to assess critical thinking and creative problem-solving skills. Recent research has led scientists to hypothesize that sleep, particularly REM sleep, plays a role in strengthening these skills. In one study, scientists tested the effect of REM sleep on the ability to solve anagram puzzles (word scrambles like “EOUSM” for “MOUSE”), an ability that requires strong creative thinking and problem-solving skills.

In the study, participants solved a couple of anagram puzzles before going to sleep in a sleep laboratory with electrodes placed on their heads. The subjects were woken up four times during the night to solve anagram puzzles, twice during NREM sleep and twice during REM sleep.

The researchers found that when participants were woken up during REM sleep, they could solve 15 to 35 percent more puzzles than they could when woken up from NREM sleep. They also performed 15 to 35 percent better than they did in the middle of the day [4]. It seems that REM sleep may play a major role in improving the ability to solve complex problems.

So, what’s the point?

Sleep research from the last 20 years indicates that sleep does more than simply give students the energy they need to study and perform well on tests. Sleep actually helps students learn, memorize, retain, recall, and use their new knowledge to come up with creative and innovative solutions.

It’s no surprise that the MIT study previously mentioned revealed no improvement in scores for those who only prioritized their sleep the night before a big test. In fact, the MIT researchers concluded that if students want to see an improvement in their test scores, they have to prioritize their sleep during the entire learning process. Staying up late to study just doesn’t pay off.

Interested in learning more about the impact of sleep on learning and memory? Check out this Student Sleep Guide .

Author Biography

Kelly Cappello graduated from East Stroudsburg University of Pennsylvania with a B.A. in Interdisciplinary Studies in 2015. She is now a writer, specialized in researching complex topics and writing about them in simple English. She currently writes for Recharge.Energy , a company dedicated to helping the public improve their sleep and improve their lives.

• Mander, Bryce A., et al. “Wake Deterioration and Sleep Restoration of Human Learning.” Current Biology, vol. 21, no. 5, 2011, doi:10.1016/j.cub.2011.01.019.
• Walker M. P. (2009). The role of slow wave sleep in memory processing. Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 5(2 Suppl), S20–S26.
• Walker, Matthew. Why We Sleep. Scribner, 2017.
• Walker, Matthew P, et al. “Cognitive Flexibility across the Sleep–Wake Cycle: REM-Sleep Enhancement of Anagram Problem Solving.” Cognitive Brain Research, vol. 14, no. 3, 2002, pp. 317–324., doi:10.1016/s0926-6410(02)00134-9.

Posted on Dec 21, 2020 | Tagged: learning and memory

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Sleep resets neurons for new memories the next day, study finds

by Cornell University

While everyone knows that a good night's sleep restores energy, a new Cornell University study finds it resets another vital function: memory.

Learning or experiencing new things activates neurons in the hippocampus, a region of the brain vital for memory. Later, while we sleep, those same neurons repeat the same pattern of activity, which is how the brain consolidates those memories that are then stored in a large area called the cortex. But how is it that we can keep learning new things for a lifetime without using up all of our neurons?

A study, "A Hippocampal Circuit Mechanism to Balance Memory Reactivation During Sleep," published in Science , finds at certain times during deep sleep , certain parts of the hippocampus go silent, allowing those neurons to reset.

"This mechanism could allow the brain to reuse the same resources, the same neurons, for new learning the next day," said Azahara Oliva, assistant professor of neurobiology and behavior and the paper's corresponding author.

The hippocampus is divided into three regions: CA1, CA2 and CA3. CA1 and CA3 are involved in encoding memories related to time and space and are well-studied; less is known about CA2, which the current study found generates this silencing and resetting of the hippocampus during sleep.

The researchers implanted electrodes in the hippocampi of mice, which allowed them to record neuronal activity during learning and sleep. In this way, they could observe that, during sleep, the neurons in the CA1 and CA3 areas reproduce the same neuronal patterns that developed during learning in the day.

But the researchers wanted to know how the brain continues learning each day without overloading or running out of neurons.

"We realized there are other hippocampal states that happen during sleep where everything is silenced," Oliva said. "The CA1 and CA3 regions that had been very active were suddenly quiet. It's a reset of memory, and this state is generated by the middle region, CA2."

Cells called pyramidal neurons are thought to be the active neurons that matter for functional purposes, such as learning. Another type of cell, called interneurons, has different subtypes. The researchers discovered that the brain has parallel circuits regulated by these two types of interneurons—one that regulates memory, the other that allows for resetting of memories.

The researchers believe they now have the tools to boost memory, by tinkering with the mechanisms of memory consolidation, which could be applied when memory function falters, such as in Alzheimer's disease. Importantly, they also have evidence for exploring ways to erase negative or traumatic memories, which may then help treat conditions such as post-traumatic stress disorder .

The result helps explain why all animals require sleep, not only to fix memories, but also to reset the brain and keep it working during waking hours. "We show that memory is a dynamic process," Oliva said.

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Sleep on it: how the brain processes many experiences — even when ‘offline’.

(AI-generated image, created and edited by Michael S. Helfenbein)

Humans and other animals encounter and remember countless experiences each day; when we sleep, groups of cells in the brain known as neuronal ensembles replay these experiences to consolidate them into memories and “preplay” futures ones, which enables faster encoding of new experiences into memories later on.

Better understanding how these neuronal ensembles represent, or depict, these experiences would offer important insights into how memory and the mind function. But most studies to date have focused solely on animals undergoing just one or a small handful of sequential experiences — and it remains unclear how exactly the brain is able to process numerous experiences simultaneously during sleep.

In a new study, Yale scientists revealed a generative coding capacity in the brain’s hippocampus — an area responsible for memory and learning — which enables the brain to bundle together the representations of some 15 unrelated experiences that occurred across one full day within single sub-second events, known as frames, during sleep.

This computer-like capacity for parallel processing of different chains of information helps explain why humans and other animals are able to process a cascade of experiences and either keep separate or combine their meanings.

The findings were published in the journal Nature Neuroscience .

“ The brain mechanisms we uncovered are relevant for how we form and express internally generated representations about the world, like memories, imagining, and insight,” said George Dragoi , an associate professor of psychiatry and of neuroscience at Yale School of Medicine and corresponding author of the study.

An increasing amount of neuroscience research is exploring how neuronal ensembles represent experiences in the brain, a complex process that has implications for learning and memory, cognitive mapping, and spatial navigation. The fact that most studies involve animals undergoing a single experience, however, has prevented researchers from assembling a more complete picture.

“ In real life we are continuously experiencing new contexts and events that all need to be encoded distinctly and remembered later without a major interference between them,” Dragoi said. “The way the brain solves this problem is not known and experiments attempting to address this topic are essentially missing.

“ Computational models have proposed that exposure to multiple experiences would lead to a ‘catastrophic interference’ between the brain representations of new and older experiences, causing the individual to forget the latter,” Dragoi said. “That, of course, is not how daily life works.”

For the new study, the researchers recorded the neuronal activity of hippocampal neurons in rats that were allowed to move freely through 15 different spatial contexts over 19 ½ hours, a time span that included periods of extended sleep, during a single day. (The periods of sleep were used to investigate “offline” activity in the animals’ brains, in terms of preplay and replay of experiences.)

Using innovative data analysis, they identified several coding schemes in the hippocampus that boost its network capacity and efficiency during sleep and allow the brain to process representations of several experiences without interference.

Specifically, they found that the brain is able to “flicker” between time-compressed representations from two or more distinct experiences within the same sleep preplay and replay events, a feature that greatly increases network capacity for parallel information processing without interference during sleep. In addition, independent experiences can be bound together into longer preplay/replay episodes representing sequential aspects of day-long experiences in the order in which they occurred compressed into replay episodes less than a second in duration.

The researchers also identified a kind of serial position effect in this process, whereby the first and most recent experiences had the strongest representations during the animals’ sleep, a phenomenon similar to the effect observed in human memory in which people tend to recall the first and last events in a series of events or items.

Other authors of the paper are Kefei Liu and Jeremie Sibille, a former associate research scientist and postdoctoral associate, respectively, in Dragoi’s lab at Yale School of Medicine.

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Sleep, Brain, and Cognition

The functions of sleep: a cognitive neuroscience perspective, katharine c. simon.

a Department of Psychology, University of California, Irvine, CA 92697;

b Psychology and Cognitive Science, University of Arizona, Tucson, AZ 85721;

Jessica D. Payne

c Department of Psychology, University of Notre Dame, Notre Dame, IN 46556

Author contributions: K.C.S., L.N., and J.D.P. wrote the paper.

This Special Feature explores the various purposes served by sleep, describing current attempts to understand how the many functions of sleep are instantiated in neural circuits and cognitive structures. Our feature reflects current experts' opinions about, and insights into, the dynamic processes of sleep. In the last few decades, technological advances have supported the updated view that sleep plays an active role in both cognition and health. However, these roles are far from understood. This collection of articles evaluates the dynamic nature of sleep, how it evolves across the lifespan, becomes a competitive arena for memory systems through the influence of the autonomic system, supports the consolidation and integration of new memories, and how lucid dreams might originate. This set of papers highlights new approaches and insights that will lay the groundwork to eventually understand the full range of functions supported by sleep.

Humans spend roughly one-third of their lives sleeping, and other animals sleep even more ( 1 , 2 ). Despite how much time is spent in this offline state, why we sleep remains a mystery. There are various candidate answers related to the immune system, hormonal systems, thermoregulatory systems, and basic metabolic processes, as sleep is essential for all of these bodily functions. Although the entire body benefits from sleep ( 3 ), the most immediate, detrimental, and unavoidable consequences of sleep loss impact the brain and the various cognitive functions it supports ( 2 , 4 ). This insight has led some researchers to conclude that “sleep is of the brain, by the brain, and for the brain” ( 5 ). This PNAS Special Feature addresses the topic of sleep from a cognitive neuroscience perspective, one that should be of broad interest given the necessity of sleep in our lives.

One link between sleep and the brain concerns the processes by which newly acquired information is stored. The notion that sleep benefits memory dates back to Ebbinghaus ( 6 ), who pioneered the experimental study of memory by demonstrating the effect of time on forgetting. He observed that forgetting occurs rapidly in the first hours after learning but progresses more slowly over the days that follow. Ebbinghaus ( 6 ) observed that forgetting seemed to slow or perhaps stop altogether between 8.8 hours postlearning and a day later. While only 2.1% of information was forgotten across this 15.2-hour period, three times that amount was forgotten over the next 24 hours. He noted that much of this period was occupied by sleep, but 40 years passed before Jenkins and Dallenbach ( 7 ) experimentally confirmed that memory retention following sleep was superior to retention following an equivalent interval of wake. Jenkins and Dallenbach ( 7 ) explained this finding by suggesting that sleep passively protects newly forming memories from the impact of interfering information. That sleep passively shelters memories against interference seems clear, but as the papers in this Special Feature demonstrate, sleep does much more for memory. It also appears to actively strengthen and shape memories as they undergo consolidation—a time-dependent process that helps to stabilize memories in brain circuits. Since the seminal research by Ebbinghaus ( 6 ) and then, Jenkins and Dallenbach ( 7 ), dozens of studies have reported that sleep benefits consolidation of memories about our daily experiences, termed episodic memory, known to be dependent on the hippocampus (e.g., ref. 7 )—especially when compared with a period of wake (refs. 7 – 9 have reviews). The putative mechanisms by which sleep influences memory consolidation are actively being explored (e.g., ref. 11 ), and the articles in this collection offer new insights into the essential nature of the sleep–memory connection.

Sleep across the Life Span

One pressing question about the sleep–memory link concerns how it manifests over one’s lifetime. Spencer and Riggins ( 10 ) examined this link at the younger end of the age spectrum. They review evidence that naps in early childhood are essential for memory consolidation, presenting a fascinating new hypothesis connecting the psychological, physiological, and neurobiological changes that accompany “nap transitions” in early childhood. Transitioning from multiple bouts of sleep each day (i.e., naps) to a single bout of overnight sleep is universal in human development, but why and exactly when this transition occurs remain unknown. Spencer and Riggins ( 10 ) argue that as the hippocampal-dependent episodic memory network matures, more efficient memory storage becomes possible. This, in turn, reduces the buildup of the pressure to sleep in the brain, known as sleep homeostasis ( 12 ), eventually enabling a young child to abandon naps in favor of consolidated overnight sleep. This hypothesis has interesting implications for how sleep supports cognitive development during the early years of life.

Denis et al. ( 13 ) extend this life span analysis to include individuals from young adulthood through middle age (18 to 59 years) using a large sample of participants spanning various backgrounds. Denis et al. ( 13 ) confirmed the importance of the sleep–memory connection across the life span, demonstrating that sleep selectively benefits memory for negative emotional information at the expense of memory for neutral information ( 14 ). This “emotional memory trade-off effect” was observed in both young and middle-aged adults, with sleep’s benefit selective to negative (but not positive) emotional memories. This work begins to address a gap in our knowledge about sleep and cognition in middle-aged adults.

Sleep within the Brain and How It Is Influenced by the Body

Guthrie et al. ( 15 ) provide a bold new insight into human sleep—replicating animal research—concerning the various stages of sleep. Basically, sleep throughout the night can be subdivided into epochs of “rapid eye movement” (REM) sleep and “nonrapid eye movement” (NREM) sleep, defined as the terms suggest by the presence or absence of eye movements, respectively. These epochs alternate over the typical night’s sleep, with NREM sleep being most prominent early in the night and REM sleep being most prominent later on. Guthrie et al. ( 15 ) confirm that two regions critical for memory—the hippocampus and cortex—can simultaneously be in different stages of sleep. In evaluating the sleep data from eight patients with both intracranial and scalp electrodes, the authors found that overall, the cortex and hippocampus spent more time in divergent than congruent states of sleep. Interestingly, different patterns emerge in the divergent, contrasting stages of sleep. The cortex appears to spend more time in wake and REM compared with the hippocampus, yet the amount of time spent in various phases of NREM appeared equivalent across the two brain regions. The findings of Guthrie et al. ( 15 ) have important functional implications for sleep-dependent cognition mechanisms during congruent and divergent states and are sure to invigorate the field of sleep research.

Chen, Zhang et al. ( 16 ) provide novel insights into the overlooked influence of the autonomic nervous system (ANS) on sleep-dependent memory mechanisms. The ANS shows clear physiologic shifts across wake and sleep stages and has recently been implicated in sleep-dependent cognition ( 17 ). In their review of the literature, these authors provide robust evidence for two distinct ANS–central nervous system networks, in which electrophysiologic features and changes in heart rate beats, known as heart rate variability, are interconnected and facilitate either the consolidation of episodic memory or performance gains in working memory. Their slow oscillation switch model highlights the competitive trade-off between these two functional networks for the limited resources available during NREM sleep and how either episodic memory or working memory domains could gain or lose performance benefits. Given the ANS life span–associated changes, their model provides new directions and testable hypotheses for sleep-dependent cognition during early and later life.

Mechanisms Underlying the Sleep–Memory Connection

Sleep, as we have seen, contributes to the storage and consolidation of memories, and developmental changes in memory storage needs may, in turn, have an impact on sleep. The “active systems consolidation” hypothesis ( 18 ) offers an integrative account of the role of sleep in memory, arguing that memory representations are repeatedly reactivated and reorganized across large-scale neuronal networks during sleep. The hippocampus is thought to orchestrate this process, which stabilizes some memories and transforms others. According to this view, the neocortex serves to integrate related and overlapping memory traces, yielding abstract representations that can be flexibly and efficiently used for the purposes of generalization and adaptive future forecasting ( 11 , 19 ). This view of sleep dovetails with modern views of memory, which hold that medial temporal lobe (MTL) regions, including the hippocampus, are at least as important for predicting the future as they are for recollecting the past ( 20 , 21 ); these regions comprise part of the default mode network (DMN), a set of brain regions active when individuals are not engaged in specific experimenter-defined tasks ( 22 ) but rather, in mental activities, such as reminiscing, future thinking, and generally constructing scenarios that help make sense of the world—all of which involve memory ( 23 ).

There remains some debate about how long the hippocampus is needed to support a fully consolidated episodic memory ( 24 , 25 ), with the active systems consolidation hypothesis favoring the view that a literal “transfer” of memory from the hippocampus to the neocortex occurs during consolidation (e.g., refs. 11 and 18 ). According to this view, episodic memories lose their dependence on the hippocampus over time and with the intervention of sleep.

Vanasse et al. ( 26 ) tested this idea using the publicly available Natural Scenes Dataset to examine memory recognition in eight human subjects on a weekly basis over the course of a year. This study deployed high-resolution (7-tesla) functional MRI to take an unprecedented look at memory consolidation as it unfolds over time. The authors examined whether memory recognition continues to engage the hippocampus and other MTL structures over the long term or whether the memory comes instead to rely entirely on the neocortex in the course of sleep-assisted consolidation. They found that recognition memory was associated with increased MTL activity at both early and late time points, with the surviving memory traces becoming more robust in and around the hippocampus in the weeks after encoding and persisting for more than 200 days. This finding is inconsistent with aspects of the active systems consolidation framework, favoring the idea that the hippocampus remains involved in those memories that retain detailed episodic information, as suggested by multiple trace theory ( 27 ).

The systems consolidation account for the role of sleep in memory consolidation is not the only viable notion of how sleep affects the brain. According to the synaptic homeostasis hypothesis, there is a net, and unsustainable, increase in synaptic strength in brain circuits that accumulates during exposure to the events of the day ( 12 ). Sleep promotes a general downscaling of synaptic weights as an antidote. Synaptic downscaling, in this view, avoids saturation of synaptic connections and keeps the high energy costs of synaptic activity under control. Because sleep-dependent downscaling is thought to be selective and to afford relative protection to synapses recently engaged in new learning, sleep-dependent downscaling could also promote memory consolidation by increasing signal to noise in key brain regions. To test this hypothesis, Vanasse et al. ( 26 ) examined whether the relative enhancement of some memory traces is linked to the concomitant forgetting of others. They found that maximal forgetting of learned material (images) overall was positively correlated with stronger brain activation generated by surviving memory traces, which they argue might reflect a reduction of noise in the system. Given that only postsleep recognition memory displayed this noise removal effect, the authors suggest that sleep may have renormalized synaptic weights, which in turn, produced the increased activation of surviving memory traces. While an increased memory signal alone would support the active systems model of consolidation discussed above, the observed correlation with forgetting supports the synaptic homeostasis hypothesis, whereby synaptic downscaling results in the selective preservation of some (previously active and/or especially relevant) memories but not others.

This idea may be related to the selective memory effects observed by Denis et al. ( 13 ) noted above, namely that the emotional elements of one’s experience are selectively retained postsleep, even while neutral elements deteriorate. Of course, such emotional selectivity in memory could reflect active consolidation of important memories only, which underscores the idea that active systems and synaptic downscaling accounts may complement each other.

Using a rodent model, Pedrosa et al. ( 28 ) also focus on hippocampal–neocortical interactions during sleep-based memory consolidation. In the rodent literature, numerous studies have demonstrated an association between “replay” of memories in the hippocampus and several cortical areas, including the prefrontal, visual, retrosplenial, and entorhinal cortices (e.g., 29 , 30 , and 31 ). Exactly how hippocampal activity relates to this broader, cortex-wide activity is not well understood. Pedrosa et al. ( 28 ) used voltage imaging, electrocorticography, and laminarly resolved hippocampal potentials in mice to provide a wide-scale picture of spontaneous cortical activity during sleep and to examine how this activity organizes itself into functional networks. Their data-driven procedure revealed spontaneous neocortical activation signals spanning various spatial scales, which were organized in a small number of functional networks (retrosplenial cortex and medial cortical bank of the cortex, somatosensory cortex, and lateral cortex). The authors then analyzed the hippocampal CA1 layer–resolved local field potential correlates of spontaneous waves involving these three cortical networks. They found that a particular form of brain oscillatory activity, “slow gamma” (20 to 50 hertz), was strongly correlated with the retrosplenial network in particular. This, they claim, argues for a role for slow gamma in memory processing, such that spontaneous activity in the cortex acts as a “cue” for such processing, indicating that interactions between the neocortex and hippocampus are bidirectional. Importantly, the retrosplenial network most involved in this spontaneous dialogue strongly overlaps with the DMN, pointing to a potentially dynamic interchange that may help us understand the involvement of the DMN in memory ( 22 ).

Aleman-Zapata et al. ( 32 ) explored the connection between hippocampal ripples and high-frequency oscillations in the cortex during sleep-dependent consolidation after one-trial spatial learning in rats. With this hippocampal ripple–dependent task, they report cortical oscillations of two high frequencies, with each high frequency involving a distinct neural network—a prefrontal–parietal network for faster oscillations and a hippocampal–parietal network for slower oscillations. Disrupting hippocampal ripples reduced learning and diminished parietal high-frequency oscillations, suggesting that when learning is interrupted, there is less of a need for information in cortex to be consolidated.

The issue of spontaneous hippocampal–neocortical dialogue is further explored in a computational model by Singh et al. ( 33 ), who raise the question of how these brain systems are able to interact and accomplish useful learning and representational sculpting during periods with virtually no environmental input. Their proof-of-concept neural network model shows that when new information is acquired, the hippocampus can facilitate stabilization and integration during sleep by replaying the newly learned neural representations, providing the opportunity for the cortex to integrate and dissect common features of the material being learned. Their model is able to account for the fact that sleep is particularly supportive of new learning about aspects of experience that share features in categorically meaningful ways. Their model also provides insight into the dual roles of consecutive, alternating NREM and REM epochs, wherein NREM facilitates the stabilization of the newly learned representations and REM sleep reduces potential interference between old and new neocortical representations that share overlapping features. Their contribution provides insight into how consolidation mechanisms are initiated and maintained without external influence. However, as already noted, Guthrie et al. ( 15 ) demonstrated that sleep stages in the neocortex and hippocampus can diverge, so it will be important to see if and how future versions of this model could account for divergent sleep stages influencing hippocampal–cortical communication during consolidation.

Although most prior studies have linked the mechanisms of sleep and memory processing indirectly, more recent work has used specific facets of sleep to experimentally and directly alter the fate of memories. One means of doing so involves the use of a procedure called targeted memory reactivation (TMR). A seminal study in humans examined the effect of presenting olfactory cues during sleep that had been part of memory associations learned during the day. In this study, these olfactory cues, which did not wake the participants, improved memory performance ( 34 , 35 ). A growing body of literature has established that presenting olfactory or auditory reminder cues during sleep, especially during slow-wave sleep, can meaningfully boost memory consolidation ( 36 ). Other effects of TMR have also been demonstrated, including studies that have tried to weaken, instead of strengthen, certain memory traces ( 37 , 38 ).

Ngo and Staresina ( 39 ) take the novel step of pairing TMR with experimental augmentation of slow-wave sleep, another method of experimentally enhancing memory consolidation ( 40 ). By combining the two approaches, these authors show that delivering TMR cues during the depolarizing up states of slow oscillations both triggered strong reactivation of memory representations and led to enhanced memory consolidation as measured by improvement in performance. Ngo and Staresina ( 39 ) speculate that the relatively late increase in reinstatement they observe in the scalp electroencephalography (EEG; at about one second) might reflect a slower hippocampal contribution to memory reactivation. However, they note that intracranial EEG recordings would be necessary to test this conjecture.

Creery et al. ( 41 ) do just that by testing five patients with depth electrodes implanted in or near the hippocampus to determine the feasibility of surgery to relieve their epilepsy. While patients slept in the hospital, EEG responses to sounds (half of which had previously been associated with spatial memories) were recorded. These sounds elicited oscillatory intracranial EEG activity increases in the theta, sigma, and gamma EEG bands, with gamma responses in particular predicting the degree of improvement in memory after sleep. Similar to the results presented by Pedrosa et al. ( 28 ), this study provides additional evidence that gamma oscillations might be especially important for sleep-based memory-processing effects.

Sleep and Dreaming

One of the most fascinating aspects of sleep is the occurrence of dreams. Simor et al. ( 42 ) provide a novel multicomponent neurocognitive framework for the onset and maintenance of what is known as lucid dreaming. They extend and build upon prior theories of dreaming suggesting that lucid dreaming is induced when top-down models of the self clash with bottom-up interoceptive pathways, generating prediction errors. They argue that when prediction error is high between current dream content and interoceptive input signaling, the body is in a state of muscle atonia, and lucid dreaming can arise. In nonlucid dreamers, the same mismatch and resultant prediction error might result in sensory input being incorporated into the dream or in an arousal from sleep. Further, top-down attentional control governs prediction error, attenuating the contrast between dream content (i.e., flying) and the true state of the body and motor system, determining whether arousal, dream inclusion, or a lucid dream state results.

This Special Feature provides a current, although necessarily selective, snapshot of the broad and exciting landscape of the field of sleep research and a sense of where the field is moving. The papers included here highlight the dynamic nature of sleep, tying in the role of the autonomic system and shedding light on the mechanisms influencing the sleep–memory connection and how it changes across the life span. They also address aspects of one of the most fascinating aspects of sleep—the nature of dreaming and in particular, the phenomenon known as lucid dreaming. While a full understanding of the range of functions of sleep remains elusive, this set of papers highlights aspects of sleep and sleep research that will eventually provide the detailed mechanistic picture such understanding requires.

The authors declare no competing interest.

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Why a Full Moon Might Mess With Your Sleep, According to Science

An admission: I’m one of those people who will entertain most things on the spiritual front. You think Mercury retrograde is ruining your life? I can relate. You had a gut feeling that turned out to be true? Tell me more. I’m obviously down for all sorts of full-moon lore too. And yet, the effect of a full moon on sleep isn’t just supernatural. Research shows that it may interfere with your ability to doze off or get quality shut-eye—whether you’re a sucker for superstition or a show-me-the-science skeptic.

One of the first studies to draw the full moon–sleep connection was a retrospective analysis published in 2013. Meaning, the scientists didn’t set out to study the moon, but decided to comb through existing sleep data and look for relationships with the lunar phases. The results were a surprise, according to lead study author Christian Cajochen, PhD , head of the Centre for Chronobiology at the University of Basel in Switzerland: Around the full moon, people took five minutes longer to fall asleep, had 30% less deep sleep, and slept 20 minutes less overall. Another small retrospective study published in 2014 mirrored those findings: Participants got, on average, 25 minutes less sleep when the moon was full.

You might think that a full moon could interfere with your rest because it’s the brightest phase—and it’s true that exposure to light and sleep don’t mix. But we all have access to plenty of artificial light before bed these days. What’s more, the people in the studies above were in sleep labs, without much access to the outside world (“Kind of a bunker or basement,” Dr. Cajochen tells SELF of his study). In a 2021 study , to better understand the sleep-moon connection in real-life settings, researchers analyzed sleep markers in people who had varying levels of access to electricity. Unexpectedly, they found a similar pattern across all the groups: Remote communities in Argentina and students in Seattle all went to bed later and got less sleep around full moons (in this case, mostly in the days just prior).

Of course, not all research on the topic confirms that a full moon equals worse sleep; certain studies haven’t found significant evidence of a connection. But the potential role of the moon in the scenarios above still begs the question of how this celestial body might steal your snooze. Here’s why experts think a full moon could impact your sleep and what to do if you find yourself restless during this lunar phase (beyond cursing the cosmos).

Why might a full moon mess with your sleep?

While it’s unlikely that the light of a full moon alone would keep you awake or reduce your sleep quality today, it might have led our ancestors (who lacked artificial light) to stay up later—and perhaps that tendency became ingrained in us, lead author of the 2021 study Horacio de la Iglesia, PhD , a professor in the department of biology at the University of Washington, tells SELF. As noted above, this study found that people usually had the shortest amount of sleep on the nights just before a full moon—which is also when moonlight is most visible in the early evening.

“What we think is that when our ancestors were hunting and gathering, it was highly adaptive to extend the end of the day,” Dr. de la Iglesia says. The extra light of a nearly full moon at dusk would grant them more time to find food before darkness fell—so, as Dr. de la Iglesia posits, why wouldn’t they evolve to stay up later on those nights? Even though we may not be as reliant on natural light now, he notes that our bodies may still be primed, evolutionarily speaking, to delay sleep before a full moon.

That likely applies even if you can’t see the moon’s light (whether it’s shrouded by clouds or light pollution or just a great blackout curtain ). One hypothesis is that our bodies can sense the full moon’s greater gravitational pull, kinda like the oceans —after all, we’re mostly made up of water. But there are a couple limitations to that theory. For starters, “the tidal effect on humans is estimated to be extraordinarily small,” Brandon Peters, MD , a board-certified neurologist, sleep-medicine specialist, and author of The Sleep Apnea Hypothesis , tells SELF. And the gravitational pull of the moon is about the same during a full or new moon, so you’d expect to see similar effects in both phases, Dr. Peters adds.

Perhaps a more likely explanation involves other changes that happen alongside the shift in gravity of a full moon, Dr. de la Iglesia says, like variations in the Earth’s magnetic field, an energetic shield protecting us from solar winds and other space weather. Though experts don’t quite know how, research suggests that certain geomagnetic shifts might affect your cardiovascular system or even alter your melatonin levels , futzing with your sleep.

It’s also possible that, over time, a bunch of biological and evolutionary ties to the moon have led us to develop a circa lunar rhythm—similar to a circadian rhythm, but oriented around the roughly month-long moon cycle, versus a single day. So just like you get sleepy at night and feel more awake as the morning progresses thanks to your internal 24-hour clock, experts theorize that your body might also become more active at night (or less naturally prone to sleep) around a full moon as compared to other phases throughout the month, Dr. Cajochen says.

What to do if you struggle to sleep well around a full moon

The moon’s going to keep moon-ing, but that doesn’t mean you’re doomed to restless nights whenever it’s approaching full status. If you find yourself getting in bed only to toss and turn for a while on those days, Dr. Peters actually suggests delaying your bedtime temporarily by, say, 30 or 45 minutes (while keeping your wakeup time consistent ). “By reducing your overall time in bed, you’ll build a stronger sleep drive, which will help you fall asleep more easily,” he says. Also, aim to get 15 to 30 minutes of sunlight after waking up each day, he adds, to keep your circadian rhythm bopping along as usual.

Otherwise, simply knowing that your sleep might be affected as a full moon arrives can help you be proactive, Dr. de la Iglesia says. That means following all the common advice for good sleep to a T: Be sure to dim your lights as it gets dark out, ditch the screens about two hours before you cozy up in bed, and practice whatever nighttime routine might help you settle down (whether that includes meditating, reading a book, listening to a soothing podcast, or something else). The idea? The more variables you can actively control, the less likely it is that your sleep will be thrown off by the cosmic powers that be.

Keep in mind, too, that any effects of a full moon on your sleep will let up once the lunar phase passes; and it’s possible that getting, say, 20 or 30 minutes less sleep for a few nights around that time of the month won’t impact you too much. Perhaps you just avoid planning important meetings or calls for those days, if this seems to affect you, Dr. Cajochen says.

For other folks, however, even a slight variation in sleep could be significant, particularly those who have a neurological or mental health condition that is heavily intertwined with sleep disruptions, Dr. de la Iglesia says, such as depression or bipolar disorder (the latter of which has also been shown to be influenced by the lunar cycle). So if you notice that you’re dealing with a surge in symptoms alongside sleep struggles around each full moon, that’s a sign to check in with your doctor, who may be able to tweak your treatment or offer a new solution to help regulate your sleep—moon phase notwithstanding.

• How to Fall Asleep Fast When Your Mind Is Racing
• For Years Doctors Told Me My Constant Exhaustion and Night Terrors Were Normal. They Weren’t
• I’m a Sleep Specialist. Here’s the Bedtime Habit I Swear By for Better Rest

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• Published: 03 July 2024

Sleep as a driver of pre- and postnatal brain development

• Eline R. de Groot 1 ,
• Jeroen Dudink 1 , 2 &
• Topun Austin   ORCID: orcid.org/0000-0002-8428-8624 3  

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In 1966, Howard Roffwarg proposed the ontogenic sleep hypothesis, relating neural plasticity and development to rapid eye movement (REM) sleep, a hypothesis that current fetal and neonatal sleep research is still exploring. Recently, technological advances have enabled researchers to automatically quantify neonatal sleep architecture, which has caused a resurgence of research in this field as attempts are made to further elucidate the important role of sleep in pre- and postnatal brain development. This article will review our current understanding of the role of sleep as a driver of brain development and identify possible areas for future research.

The evidence to date suggests that Roffwarg’s ontogenesis hypothesis of sleep and brain development is correct.

A better understanding of the relationship between sleep and the development of functional connectivity is needed.

Reliable, non-invasive tools to assess sleep in the NICU and at home need to be tested in a real-world environment and the best way to promote healthy sleep needs to be understood before clinical trials promoting and optimizing sleep quality in neonates could be undertaken.

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Introduction.

Humans sleep about one third of their lives and for infants this is even longer. By the time an infant turns one year old, it has spent more than half of its life asleep. The increased prevalence of sleep in infants, and especially neonates, suggests that sleep plays an important role in early development.

Recently, technological advances have enabled researchers to automatically quantify neonatal sleep architecture, which has caused a resurgence of research in this field, attempting to explore the potential role of sleep in pre- and postnatal brain development. This article will review the history of newborn sleep research, our current understanding of the important role of sleep as a driver of brain development and identify possible areas for future research.

A brief history of infant sleep research

In January 2024, Kristina Denisova published an English translation of the 1926 article “Periodic phenomena in the sleep of children” 1 . This research, by Maria Denisova and Nicholai Figurin, is one of the first to report cyclical periods of increased respiration and eye and body motility during sleep in infants, a precursor to what is now known as rapid eye movement (REM) sleep and non-REM sleep (in newborn infants this is referred to as active and quiet sleep respectively). They also found that exogenous stimuli had different effects depending on the depth of sleep. In 1937, Isabelle Wagner published a similar study in which she described seven sleep stages, based on her observations of the reactivity of 197 infants to a total of 5342 stimuli 2 . Her classification also explicitly mentions movements, including ‘eyelid movements’.

These two studies precede the work from Kleitman & Aserinsky in the 1950s in which they studied adult eye movements 3 and infant motility cycles 4 during sleep. In the latter study, Aserinsky distinguished two major stages: (1) “No Eye Movement periods”, which lasted 20–25 min for all infants and (2) an “active portion of the motility cycle”. The length of this “active period” showed high inter- and intraindividual variability. Aserinsky mentioned that the underlying mechanism behind this motility cycle is unclear but might be due to “an inherent rhythm within the CNS”.

Another famous description of infant sleep cycles and their underlying mechanisms came from Peter Wolff in 1959 5 . Wolff described regular and irregular sleep, based primarily on respiration rate. During sleep, Wolff observed a variety of spontaneous startles and other movements, the quality and quantity of which differed between sleep stages. Wolff hypothesized that these spontaneous movements during sleep were caused by “spontaneous activity of the central nervous system” 5 . Howard Roffwarg 6 repeated and further substantiated this hypothesis in 1966, informed by emerging knowledge about the role of sleep and more specifically of the two sleep stages (REM and non-REM sleep) that were distinguished by Kleitman and Aserinsky 3 .

Roffwarg describes the high ratio of REM sleep in newborns, which diminishes rapidly over time 6 . According to Roffwarg “The early large percentages of REM sleep compel us to look to early development for the most important function of REM sleep”. At this time it was already known that during REM sleep “the pontine area sends impulses to motor as well as to sensory areas of the brain. After reaching the thalamus from the pons, the impulses appear to traverse the usual pathways to cortex” 6 . Based on this knowledge Roffwarg molded the ontogenic sleep hypothesis, relating neural plasticity and development to REM sleep, a hypothesis that current fetal and neonatal sleep research is still exploring:

“(REM sleep in newborns) might assist in structural maturation and differentiation of key sensory and motor areas within the central nervous system, partially preparing them to handle the enormous rush of stimulation provided by the postnatal milieu, as well as contributing to their further growth after birth.” 6

Following these early observations and hypotheses, a number of standardized methods to assess infant sleep and behavior have emerged (for an extensive review on this topic, see Bik et al 7 .). Furthermore, recent developments in the field of machine learning have enabled continuous sleep stage assessment using physiological parameters to be made unobtrusively at the cotside 8 , 9 , 10 .

Box 1 Early sleep architecture

During the first few days of its life, a healthy term newborn infant spends most of the time asleep, of which on average just over 50% is spent in active sleep (AS; i.e. the neonatal equivalent of REM sleep) 11 . However, total sleep time rapidly decreases, reaching an average of 12–15 h per day by one month 12 , 13 . At this time, AS still makes up the majority of the sleep cycle (50–80%) 13 . Over the course of the first year, sleep architecture changes, with AS-onset giving way to quiet sleep (QS; i.e. the neonatal equivalent of non-REM sleep) onset and the percentage of AS decreasing to less than 50% 13 .

Prenatally, behaviors which resemble sleep cycles emerge in fetal life from mid gestation and become more apparent from 32 weeks’ gestational age (GA) 14 . Similarly, in preterm infants, basic sleep cycling can be seen from 24–30 weeks’ GA, which again is better defined after 32 weeks GA 15 , 16 . Preterm infants spend around 16–22 h per day asleep, with 40–60% spent in AS 16 , 17 , 18 , 19 . The wide range of reported AS in the preterm infant reflects the amount of sleep investigators classified as indeterminate sleep,

Besides general sleep architecture, changes can be seen in the type of cortical activity measured from an electroencephalogram (EEG). The preterm EEG is visibly immature and defined by alternations between continuous and discontinuous patterns, which are associated with AS and QS respectively 15 . EEG patterns become more distinctly related with sleep stages as preterm infants mature.

From 43-48 weeks post menstrual age (PMA) sporadic spindle-like activity is visible during QS 15 and around 3 months term equivalent age (TEA) definitive sleep spindles appear 20 . Over time, patterns of AS and QS slowly mature and are replaced by REM sleep and non-REM sleep respectively, at around 3–5 months of age which is reflected in the cortical EEG 21 , with sleep spindles occurring during non-REM/QS 20 . 20 By 5–8 months, EEGs of non-REM stages show clear signs of slow waves, with delta bands of 0.5–4.0 Hz and sleep spindles of 7–14 Hz 21 .

Emerging confirmation of Roffwarg’s ontogenesis hypothesis

In both preterm humans and newborn animals, AS is characterized by patterned, endogenously generated, spontaneous activity 22 , 23 . In rodents such spontaneous activity has been shown to be essential for cortical organization and development of thalamocortical connectivity 24 .

In humans, similar activity can be visualized using EEG and has been described as ‘spontaneous activity transients’ (SATs) 22 . SATs first appear in the preterm EEG by 24 weeks PMA and are most easily recognized as a sudden burst of high amplitude activity 22 . SATs are considered a hallmark of the premature EEG and are most frequently observed during AS until 33 weeks PMA 25 . SATs are thought to be involved in the establishment and survival of both thalamocortical sensory pathways and cortico-cortical connections 26 .

SATs can be triggered by endogenous mechanisms in the subplate—a transient layer in the developing brain that serves as the ‘waiting room’ for developing neurons—by spontaneous sensory input (i.e. through endogenously generated motor activity) or by extrinsic sensory input 26 , 27 . Spontaneous sensory input is produced from 10–12 weeks PMA onward and upregulated from 15-16 weeks PMA 28 . Behaviorally, this endogenous activity manifests as twitches. In the last trimester, the number of movements seems to either decline (30–36 weeks PMA) 29 , 30 or remain stable (32–36 weeks PMA) 31 . Movement amplitude increases in this period (30–36 weeks PMA) 29 , 31 . An overview of the parallel development of movements and SATs during sleep is shown in Fig.  1 .

An overview of neurodevelopmental processes and changes in sleep architecture during early development.

Experimental studies

It has been suggested that twitches in utero provide the sensory stimulation necessary to develop cortical body maps in the somatosensory cortex 27 . This mechanism has been further explored in rodent studies, mainly by the group of Mark Blumberg (e.g. refs. 32 , 33 ). It is important to note that rodent brain development before birth is slightly delayed compared to humans. The rat brain reaches the level of maturation of a term newborn at 10 days after birth 34 , 35 , 36 . Until the 8 th postnatal day, SATs in rat pups manifest both localized and large-scale waves. As opposed to the spatially restricted activity that occurs during wakefulness, these large-scale waves are only seen during early life sleep. This distinction suggests that sleep plays a critical role in the development of activity-dependent neural circuits 37 . Moreover, in rat pups, twitches are mainly present during the first two weeks of postnatal life 38 , 39 . Finally, in this period SATs are more easily triggered by AS-related twitches compared to wake-related movements 40 .

To further elucidate the association between SATs and twitches, Blumberg et al. followed the neural pathway of the neonatal rat twitch 33 . The twitches are generated in the red nucleus and project directly onto motor neurons in the spinal cord controlling the fore- and hindlimbs. From the spinal cord, reafferent sensory feedback flows to the external cuneate nucleus and from there to the cerebellum, thalamus and primary somatosensory (S1) and motor (M1) cortex, finally ending in the hippocampus. These findings support the hypothesis that these endogenous signals are specifically attuned to support the development of the somatosensory and motor homunculus. Similar findings in the visual system have shown that spontaneous retinal waves trigger bursts of neural activity in downstream structures, including the visual cortex, primarily during sleep 41 .

The importance of sleep for neurodevelopment is highlighted by findings that show how active wakefulness interferes with the sensory stimulation that is essential for development 42 . For example, in 9- and 12-day old rat pups, cortical activity that was associated with retinal waves was suppressed by both spontaneous and evoked awakenings 41 . In other words, pre- and early postnatal neurodevelopment is mainly supported by cortical activity that occurs during sleep.

Human studies

Research in human infants both confirm and expand on the findings in experimental studies. For example, quality and quantity of endogenously generated movements in human fetuses, preterm infants and term neonates have been associated with behavioral and neurological outcome as children develop 43 . Furthermore, increased numbers of SATs have been linked to larger brain volumes in human preterm infants 44 , 45 , 46 .

There is a growing body of work relating sleep states to distinct functional connectivity network dynamics. Using EEG, Tokariev et al, have shown that there is a reorganization of functional brain networks during the transition from QS to AS and this reorganization is attenuated in preterm infants and predicts visual performance at 2 years 47 . Uchitel et al, using high density diffuse optical tomography (HD-DOT) found stronger interhemispheric connectivity during AS relative to QS, with stronger short-range connections in QS relative to AS in healthy term infants 48 .

Furthermore, more AS between 29- and 32-weeks PMA was associated with increased total brain volume and white matter volume, while ventricular volume was decreased 49 . More specifically, the brain volumes of the left frontal and occipital lobes were increased. Brain volume 46 , 50 and connectivity 51 of the frontal regions is often impaired due to preterm birth. The association between more AS and increased volume in these regions implies a positive influence of AS on the development of neural regions that are most affected.

Changes in sleep architecture over the course of early neurodevelopment

While sleep is important throughout development, the role of sleep changes during different stages of development, adapting to the developmental needs of the body. Consequently, sleep architecture and the characteristics of sleep parameters change, including for example the quantity and pattern of twitching and retinal activity in animals 52 and fetuses 53 .

When looking at the developmental trajectory of spontaneous neural activity, it stands out that before 33–34 weeks PMA in human infants (and before 8 postnatal days in rats; P8), SATs occur mainly during AS. After this period, the number of SATs during QS increases, as well as the relative amount of QS 15 , 19 , 25 . Although the exact role of QS is not fully understood in this developmental stage, the adult equivalent of QS (non-REM sleep) is involved in, among other things, synaptic downscaling in order to facilitate efficient network formation 54 .

The emerging importance of QS—highlighted by the shift to more SATs during this stage seems to indicate a shift in the role of sleep in general. Experimental research supports this notion. For example, in rats before P10, the primary motor cortex (M1) only shows activation as a result of endogenous stimulation during sleep and not during wake movements 33 , 42 . However, after P12, 80% of M1 neurons respond specifically to movements during wakefulness and responses to twitches during sleep are inhibited 33 . When linking these changes in sleep parameters to neurodevelopment, one specific event stands out. A major shift in neurochemistry that occurs around P10 in rats and the last trimester in humans, namely the ‘GABA-shift’.

The GABA-shift

In adult humans and rats, gamma-aminobutyric acid (GABA) is the main inhibitory neurotransmitter in the central nervous system; GABA A receptors are ligand-activated chloride channels, with an inward flux of chloride causing hyperpolarization and therefore increasing the threshold of activity needed to excite a neuron. However, in early development activation of GABA receptors leads to an efflux of chloride from neurons which has a depolarizing effect—i.e. facilitating excitation of neurons. During this period depolarizing GABA plays an essential role in the coordination and timing of key prenatal neurodevelopmental processes, including spontaneous activity 22 , 55 , arborization, synapse formation and (pre-)myelinization.

After the ‘GABA-shift’, when GABA switches to having a hyperpolarizing effect, the inhibitory quality of the neurotransmitter can facilitate the attunement of the postnatal brain to the specific requirements of the outside environment 56 . In other words, the brain seems to be more attuned to endogenous stimulation when GABA has a depolarizing effect, and it is more attuned to exogenous stimulation when GABA has a hyperpolarizing effect. In the context of behavioral states, sleep and endogenously generated activity during sleep might be more beneficial to the developing brain before the GABA-shift, whereas waking activity and exogenous sensory stimulation may be more beneficial after the GABA-shift. Nevertheless, in rat pups, myoclonic twitches and endogenous brain activity still occur during AS after the ‘GABA-shift 32 , 42 . Thus, it is possible that both exogenous stimulation and endogenously generated brain activity have a developmental role after GABA becomes hyperpolarizing.

If the brain has not undergone the GABA-shift yet, which may be the case in preterm infants, an increase of exogenous sensory stimulation—such as during waking activity – might disrupt the ongoing neurodevelopmental processes and consequently the development of brain structures. However, if the brain has already gone through the GABA-shift, a combination of increased sensory stimulation and sufficient sleep might best facilitate neurodevelopment. Knowledge about the exact timing of the GABA-shift is therefore essential to be able to provide an appropriate neuroprotective environment for developing preterm infants.

Timing of the GABA-shift

Unfortunately, the exact timing of GABA shifting from depolarizing to hyperpolarizing in humans remains unclear. Based on both experimental and human research, the shift seems to happen in the third trimester 23 , 56 , 57 , 58 , 59 or just after birth 22 , 23 , 56 , although there appears to be regional heterogeneity in GABA receptor maturation, with, for example the hippocampus maturing earlier than other brain regions 60 . Besides cell type, sex and brain region, Peerboom and Wierenga 56 have identified several factors that may influence the timing of the GABA-shift. The general idea comprises an intrinsic developmental program that might be affected by molecular and environmental factors. Although it is currently unclear to what extent preterm birth influences the GABA-shift 61 , 62 , preterm birth might decrease exposure to molecular factors that are thought to repress the GABA-shift, while increasing the exposure to environmental factors that are thought to induce the GABA-shift. Indeed, recent studies suggest a heightened responsivity of the preterm brain to exogenous stimuli 63 , 64 , 65 —implying that the timing of the GABA-shift might be altered in very preterm infants.

However, neurophysiological studies assessing the developmental trajectory of twitches and associated neural activity in preterm infants, challenge the hypothesis of an early GABA-shift. Between 31- and 42-weeks GA, isolated hand movements during AS induce alpha-beta oscillations with a specific somatotopic distribution 66 . As term equivalent age approaches, the alpha-beta oscillations decline and fully disappear by 41 weeks GA, while increase in the delta oscillations remains. This pattern resembles the pattern found in rodents 33 , 67 and might serve as an indication that preterm birth might not result in a change in the timing of the GABA-shift.

To optimally support the sleep cycle and provide appropriate stimulation for the developing preterm brain, it is necessary to further understand the timing of the GABA-shift in preterm and term-born infants.

The association between sleep and neonatal illness

A growing body of evidence confirms Roffwarg’s ontogenesis hypothesis that sleep is an essential driver of early brain development. Furthermore, the changes in sleep architecture and characteristics may provide insight into the neurodevelopmental trajectory of high-risk infants.

Both sleep and neurodevelopment are affected in the case of early pathology, whether this involves preterm birth, neonatal encephalopathy, congenital disorders or any other illnesses in the neonatal period. It is believed that the alteration in sleep structure following preterm birth is caused by a combination of factors, including exposure of the inherently immature nervous system to the external environment and comorbidities associated with preterm birth, such as discomfort or pain 68 . In the term infants neural insults—such as hypoxic ischemic encephalopathy—influence sleep-wake cycling in the neonatal EEG 69 . Furthermore, less QS and more AS was observed in asphyxiated infants 70 . Finally, brain injury has been associated with a later onset of QS after birth, which is considered a marker of the beginning of sleep cycling 71 .

As development proceeds, cerebral palsy and other forms of acquired brain injury are associated with sleep disturbances 72 and increased asymmetry in sleep spindle spectral power between hemispheres 73 . These asymmetries may be directly related to structural damage sustained, which in turn could result in sleep disturbances.

Other neurodiverse conditions, such as autism spectrum disorder, are also known to be associated with sleep disturbances 74 . Whether sleep disturbances result from the underlying condition or exacerbate the condition remain unclear 75 . Other conditions that are associated with preterm birth, such as bronchopulmonary dysplasia (BPD), are associated with decreased sleep quality due to obstructive sleep apnea at a later age 76 , 77 . Besides this direct association with BPD, a complex interplay between respiratory pathology and neurodevelopmental problems in children born extremely preterm may result in sleep-disordered breathing symptoms and sleep problems in childhood 78 .

In summary, sleep patterns and neurodevelopment are clearly entwined in early development. Preterm sleep patterns may therefore both serve as an indicator of current neurodevelopmental status and present an opportunity for interventions aimed to support preterm neurodevelopment 68 .

Current dilemmas and future research

The final piece of Roffwarg’s puzzle is the question whether impaired sleep causes impaired neurodevelopment or if sleep is just a marker of the current neurodevelopmental state of an infant. To assess if improving sleep quality is a valid and effective neuroprotective strategy, ideally a randomized controlled trial should be conducted, although this would be challenging to carry out.

In order to improve sleep quality in the NICU and at home, several barriers need to be overcome. First, the research community should consider the best way to monitor sleep. Due to technological advances, it has now become feasible to continuously monitor preterm sleep stages unobtrusively 9 , 79 , 80 , 81 , 82 . However, these methods use a variety of modalities with variable quality in terms of reliability and validity in detecting sleep states.

Sleep is defined and classified by a constellation of behavioral, physiological and neurophysiological phenomena, rather than direct measurements of the key processes occurring in the brainstem and thalamus. Most preterm sleep classifications are based upon behavioral assessments alongside EEG and cardiorespiratory monitoring. However, most current algorithms that continuously monitor sleep stages use only one modality, reducing both their validity and reliability. Limiting dimensionality in sleep assessment has the potential of introducing confounders. To circumvent such problems, it may be preferable to develop algorithms that are used for research purposes based on multiple modalities.

Once a consensus has been achieved on monitoring sleep, there comes the question of how to define ‘good quality sleep’. There is a complex interplay between the neurobiological needs of the developing brain and the external environment. A better understanding of the relationship between sleep and functional brain development is needed. While neural activity during AS seems to be the most important driver of early brain development, only protecting AS at the expense of QS may not necessarily be the best course of action. Moreover, the literature about the ‘GABA-shift’ suggests that sensory stimulation may aid further development depending on the developmental stage.

Finally the impact of different environmental phenomena on sleep needs to be better understood. A great deal of research has been done investigating the acoustic environment within the NICU. However, it should be noted that the fetus does not develop in a silent environment, and nor should the preterm neonate. However, a better understanding is needed of how different acoustic stimuli—both pleasant (e.g. music therapy) or noxious (e.g. alarms) impact sleep and the sleep-wake cycle.

Developing a more personalized approach to ‘good sleep hygiene’, tailored to the infant’s stage of development, underlying pathologies and family situation both in the NICU and at home is likely to reap the most rewards. However, developing a robust evidence base is one of the biggest challenges currently in this field.

The evidence to date suggests that Roffwarg’s ontogenesis hypothesis of sleep and brain development is correct and that alterations in sleep and sleep-wake cycling are associated with a range of neurodevelopmental and neurodiverse conditions, including prematurity. However, many questions remain unanswered. A better understanding of the relationship between sleep and the development of functional connectivity is needed. Reliable, non-invasive tools to assess sleep in the NICU and at home need to be tested in a real-world environment and the best way to promote healthy sleep needs to be understood before clinical trials promoting and optimizing sleep quality in neonates could be undertaken. Finally, promoting healthy sleep beyond the neonatal period, for both the infants and their caregivers is important to maximize beneficial outcomes.

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Acknowledgements

T.A. is supported by the NIHR Cambridge Biomedical Research Centre (BRC), which is a partnership between Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge, funded by the National Institute for Health Research (NIHR). T.A. is also supported by the NIHR Brain Injury MedTech Co-operative. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

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de Groot, E.R., Dudink, J. & Austin, T. Sleep as a driver of pre- and postnatal brain development. Pediatr Res (2024). https://doi.org/10.1038/s41390-024-03371-5

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How to Boost Metabolism: 12 Evidence-Based Ways

• Factors Affecting Metabolism
• Boosting It Naturally
• Other Ways to Increase It
• Signs of Slow Metabolism

Your need to boost metabolism (how your body converts calories into energy and uses them) can depend on genetics, body fat, health status, thyroid function, and physical activity. “Slow” metabolism—when it takes longer for your body to process the energy it needs—is associated with weight gain and is a risk factor for obesity.

While there are ways to promote metabolic function, products that promise to boost your metabolism may not be effective.

Alexander Spatari / Getty Images

What Factors Play a Role in Metabolism?

In many ways, your metabolism is your body’s engine. It converts calories from food to energy for cell function and reproduction. This process keeps you breathing and walking and keeps your heart pumping.

Your body converts the nutrients in food—sugars, proteins , vitamins, carbohydrates , fats, and amino acids—into a molecular form that cells can digest, known as adenosine triphosphate (ATP). This releases units of energy (calories) that your body uses immediately or stores in body fat for later use.

The thyroid gland regulates your metabolism. Overactivity of this gland ( hyperthyroidism ) increases metabolism to the degree that makes it hard to store body fat, often leading to unintended weight loss. And when there’s too little thyroid activity ( hypothyroidism ), your body stores the excess energy in body fat, often leading to unintentional weight gain.

Various factors affect your metabolism; some of which are in your control, and others are not.

Factors You Can Control

The factors influencing your metabolism you can control are called modifiable risk factors. These changeable factors include:

• Smoking and alcohol use
• Physical activity

Factors You May Not Be Able to Control

Many health factors that affect your metabolism are non-modifiable, meaning you can’t change them, include the following:

Various medical factors can contribute to metabolism function, including:

• Overweight and obesity
• Polycystic ovary syndrome (PCOS)
• Immune system problems
• Medication side effects
• Thyroid disorders

How to Boost Metabolism Naturally

You can naturally boost your metabolism by modifying your diet, sleep, and exercise habits. The following are evidence-based strategies for a natural metabolism boost.

Add More Protein to Your Diet

Diets high in protein , such as meats, eggs, fish, and nuts, increase your metabolism and calorie burn. Some research suggests that the amount of heat calories needed for metabolism (thermic effect) is between 15% and 30% higher for protein than other nutrition sources, like carbohydrates and fat, leading to nearly 300 more calories burned daily.

Drink More Water

The metabolism process requires fluids, which is why water intake is essential. Studies have found that drinking 500 milliliters (mL) (about 19 ounces) of water increases the metabolic rate by 30%. This effect lasts up to an hour due to water-induced thermogenesis, a sympathetic nervous system response that increases your metabolic rate. Drinking water consistently during the day helps your body burn more calories.

Try a High-Intensity Workout

High-intensity workouts, such as high-intensity interval training (HIIT), involve brief, intense periods of heavy aerobic exercise followed by extended periods of lower-intensity work. Your physical activity influences metabolism, and HIIT has a particular effect. In one study, the energy spent at rest (resting energy expenditure) up to an hour following HIIT was higher, even compared to aerobic exercise.

Lift Weights

Strength training, such as resistance bands or lifting weights, can also positively affect your metabolism. In a study of sedentary women, six weeks of moderate resistance training increased their resting metabolic rate. At rest, a person who strength-trains regularly burns 265 more calories daily.

Drink Oolong or Green Tea

Green and oolong tea has traditionally been touted to have numerous health benefits. Among them is that regular consumption can boost your metabolism. Though the evidence is mixed, some research suggests two compounds in this tea—caffeine and catechins —may increase metabolic function.

Eat Spicy Foods

Chili peppers and spicy foods contain capsaicin , which affects metabolism. In a broad-ranging review, researchers noted evidence that diets emphasizing this substance improved the signs of metabolic disease (disorders affecting the metabolism) and helped manage obesity. There’s more research needed, but incorporating spicy foods into your diet may help raise your resting metabolic rate.

Get a Good Night’s Sleep

There are many health benefits to ensuring that you get enough sleep, which for adults should be seven to eight hours a night. Among them is that getting better sleep is linked with improved metabolism, but the growing consensus is that this effect is indirect.

While sleep deprivation may not directly impact metabolism, it raises the risk of weight gain, obesity, and diabetes, which do have an effect on metabolism.

No matter what, you’ll feel better and be healthier if you get adequate rest. This involves:

• Sleeping a minimum of seven to eight hours a night for adults (younger people need even more)
• Going to be and getting up at the same time
• Not using devices or electronics in bed
• Making your bed a quiet, dark space

Drink Caffeine

The caffeine in coffee, tea, or other beverages might be more than just a morning pick-me-up. In a study assessing caffeine’s effect on diabetes, researchers noted that those who drank four cups a day for six months saw an approximately 4% drop in body fat.

People in this study did not make any other lifestyle or dietary changes, further supporting the hypothesis that caffeine contributes to an elevated metabolic rate.

Snack Regularly

Since the body requires energy to digest food, you can improve your metabolism by snacking regularly throughout the day. Healthcare providers sometimes recommend this strategy to help people manage obesity. While medical literature has shown snacking provides some benefits for certain people, like athletes, there is little evidence of a direct metabolic effect.

Healthy Snacks

If you snack, choose nutrient-dense options over foods like cookies, chips, and those with added salts and sugars. The healthiest snacks are nuts, fresh fruit, and vegetables.  

Other Ways to Increase Your Metabolism

Additional ways to boost your metabolism include being active, avoiding crash diets, and reducing stress.

Focus on Standing More

Having a sedentary lifestyle can adversely affect your health. It raises the risk of obesity, affects bone and muscle development, and impacts your metabolism. If you sit for work or are constantly seated at home, take breaks to stand or walk. One study comparing data from Southeast Asia and the United States showed that taking a break to stand or walk every 30 minutes boosted metabolic health in older adults.

Avoid Crash Diets

Countless dietary fads promise to boost your metabolism and manage your weight. Think critically about metabolism-boosting claims since they’re not always based on science. Crash diets focusing on severe food restrictions can prevent your body from getting the nutrients it needs. Studies have found that these diets' results often don’t last. Aim to eat a variety of foods that provide sufficient calories and essential vitamins and minerals.

Reduce Stress

When you’re under stress, your body releases cortisol , the stress hormone. Cortisol activates your body’s fight-or-flight response, physically preparing your body to face a perceived threat. Blood flow and energy expenditure are redirected from digestion and metabolism to the muscles and the brain. This slows your metabolic rate, and if the stress persists, this can cause long-term effects.  

What Are the Signs of a Slow Metabolism?

So how can you tell if your metabolism is slow? There can be many signs, with the most common including:

• Unintended weight gain or difficulty losing weight
• Lower body temperature (hypothermia)
• Sugar or carbohydrate cravings
• Poor wound healing
• Hair loss, brittle fingernails
• Depression and anxiety
• Constipation and bloating

Slower Metabolism and Hypothyroidism

The thyroid is the small, butterfly-shaped gland in your neck that regulates metabolism. Hypothyroidism is when this gland doesn’t produce enough thyroid hormone, which can arise due to disease or other health factors. Low thyroid levels slow your metabolic function and raise the risk of complications, such as high cholesterol or coma.

A big part of managing hypothyroidism and preventing these complications is boosting your metabolic rate. Alongside a prescription of levothyroxine , a synthetic thyroid hormone, lifestyle and dietary changes that promote your metabolism play a key role.    

Metabolism is the process by which your body converts food into energy. If this process is slow, you’re more susceptible to weight gain and associated conditions like diabetes. Modifiable factors influencing your metabolic rate include diet, physical activity, and sleep. Incorporating a high-protein diet, high-intensity exercise, strengthening, and better sleep habits are among the ways to boost your metabolism naturally. You may require medication if an underlying condition is affecting your metabolism.

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Mullur R, Liu YY, Brent GA. Thyroid hormone regulation of metabolism . Physiol Rev . 2014;94(2):355-382. doi:10.1152/physrev.00030.2013

National Heart, Lung, and Blood Institute. Metabolic syndrome: causes and risk-factors .

Clegg DJ, Mauvais-Jarvis F. An integrated view of sex differences in metabolic physiology and disease . Mol Metab . 2018;15:1-2. doi:10.1016/j.molmet.2018.06.011

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Pesta DH, Samuel VT. A high-protein diet for reducing body fat: Mechanisms and possible caveats . Nutr Metab (Lond) . 2014;11(1):53. doi:10.1186/1743-7075-11-53

Vij VA, Joshi AS. Effect of 'water induced thermogenesis' on body weight, body mass index and body composition of overweight subjects . J Clin Diagn Res . 2013;7(9):1894-1896. doi:10.7860/JCDR/2013/5862.3344

Wingfield HL, Smith-Ryan AE, Melvin MN, et al. The acute effect of exercise modality and nutrition manipulations on post-exercise resting energy expenditure and respiratory exchange ratio in women: a randomized trial . Sports Med Open . 2015;2:11. doi:10.1186/s40798-015-0010-3

Stavres JR,  Zeigler MP, Bayles MP. Six weeks of moderate functional resistance training increases basal metabolic rate in sedentary adult women . Int J Exerc Sci. 2018;11(2): 32-41.

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So-ngern A, Chirakalwasan N, Saetung S, Chanprasertyothin S, Thakkinstian A, Reutrakul S. Effects of two-week sleep extension on glucose metabolism in chronically sleep-deprived individuals . J Clin Sleep Medi . 2019;15(05):711-718. doi:10.5664/jcsm.7758

Alperet DJ, Rebello SA, Khoo EYH, et al. The effect of coffee consumption on insulin sensitivity and other biological risk factors for type 2 diabetes: a randomized placebo-controlled trial . American J Clin Nutrition . 2020;111(2):448-458. doi.org/10.1093/ajcn/nqz306

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Yates T, Edwardson CL, Celis-Morales C, et al. Metabolic effects of breaking prolonged sitting with standing or light walking in older South Asians and White Europeans: A randomized acute study . J Gerontol A Biol Sci Med Sci . 2020;75(1):139-146. doi:10.1093/gerona/gly252

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By Mark Gurarie Gurarie is a freelance writer and editor. He is a writing composition adjunct lecturer at George Washington University.  

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How did life begin on earth a lightning strike of an idea..

Yahya Chaudhry

Harvard Correspondent

Researchers mimic early conditions on barren planet to test hypothesis of ancient electrochemistry

About four billion years ago, Earth resembled the set of a summer sci-fi blockbuster. The planet’s surface was a harsh and barren landscape, recovering from hellish asteroid strikes, teeming with volcanic eruptions, and lacking enough nutrients to sustain even the simplest forms of life.

The atmosphere was composed predominantly of inert gases like nitrogen and carbon dioxide, meaning they did not easily engage in chemical reactions necessary to form the complex organic molecules that are the building blocks of life. Scientists have long sought to discover the key factors that enabled the planet’s chemistry to change enough to form and sustain life.

Now, new research zeroes in on how lightning strikes may have served as a vital spark, transforming the atmosphere of early Earth into a hotbed of chemical activity. In the study, published in Proceedings of the National Academy of Sciences , a team of Harvard scientists identified lightning-induced plasma electrochemistry as a potential source of reactive carbon and nitrogen compounds necessary for the emergence and survival of early life.

“The origin of life is one of the great unanswered questions facing chemistry,” said George M. Whitesides, senior author and the Woodford L. and Ann A. Flowers University Research Professor in the Department of Chemistry and Chemical Biology. How the fundamental building blocks of “nucleic acids, proteins, and metabolites emerged spontaneously remains unanswered.”

One of the most popular answers to this question is summarized in the so-called RNA World hypothesis, Whitesides said. That is the idea that available forms of the elements, such as water, soluble electrolytes, and common gases, formed the first biomolecules. In their study, the researchers found that lightning could provide accessible forms of nitrogen and carbon that led to the emergence and survival of biomolecules.

A plasma vessel used to mimic cloud-to-ground lightning and its resulting electrochemical reactions. The setup uses two electrodes, with one in the gas phase and the other submerged in water enriched with inorganic salts.

Credit: Haihui Joy Jiang

Researchers designed a plasma electrochemical setup that allowed them to mimic conditions of the early Earth and study the role lightning strikes might have had on its chemistry. They were able to generate high-energy sparks between gas and liquid phases — akin to the cloud-to-ground lightning strikes that would have been common billions of years ago.

The scientists discovered that their simulated lightning strikes could transform stable gases like carbon dioxide and nitrogen into highly reactive compounds. They found that carbon dioxide could be reduced to carbon monoxide and formic acid, while nitrogen could be converted into nitrate, nitrite, and ammonium ions.

These reactions occurred most efficiently at the interfaces between gas, liquid, and solid phases — regions where lightning strikes would naturally concentrate these products. This suggests that lightning strikes could have locally generated high concentrations of these vital molecules, providing diverse raw materials for the earliest forms of life to develop and thrive.

“Given what we’ve shown about interfacial lightning strikes, we are introducing different subsets of molecules, different concentrations, and different plausible pathways to life in the origin of life community,” said Thomas C. Underwood, co-lead author and Whitesides Lab postdoctoral fellow. “As opposed to saying that there’s one mechanism to create chemically reactive molecules and one key intermediate, we suggest that there is likely more than one reactive molecule that might have contributed to the pathway to life.”

The findings align with previous research suggesting that other energy sources, such as ultraviolet radiation, deep-sea vents, volcanoes, and asteroid impacts, could have also contributed to the formation of biologically relevant molecules. However, the unique advantage of cloud-to-ground lightning is its ability to drive high-voltage electrochemistry across different interfaces, connecting the atmosphere, oceans, and land.

The research adds a significant piece to the puzzle of life’s origins. By demonstrating how lightning could have contributed to the availability of essential nutrients, the study opens new avenues for understanding the chemical pathways that led to the emergence of life on Earth. As the research team continues to explore these reactions, they hope to uncover more about the early conditions that made life possible and to improve modern applications.

“Building on our work, we are now experimentally looking at how plasma electrochemical reactions may influence nitrogen isotopes in products, which has a potential geological relevance,” said co-lead author Haihui Joy Jiang, a former Whitesides lab postdoctoral fellow. “We are also interested in this research from an energy-efficiency and environmentally friendly perspective on chemical production. We are studying plasma as a tool to develop new methods of making chemicals and to drive green chemical processes, such as producing fertilizer used today.”

Harvard co-authors included Professor Dimitar D. Sasselov in the Department of Astronomy and Professor James G. Anderson in the Department of Chemistry and Chemical Biology, Department of Earth and Planetary Sciences, and the Harvard John A. Paulson School of Engineering and Applied Sciences.

The study not only sheds light on the past but also has implications for the search for life on other planets. Processes the researchers described could potentially contribute to the emergence of life beyond Earth.

“Lightning has been observed on Jupiter and Saturn; plasmas and plasma-induced chemistry can exist beyond our solar system,” Jiang said. “Moving forward, our setup is useful for mimicking environmental conditions of different planets, as well as exploring reaction pathways triggered by lightning and its analogs.”

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