research on sleep and memory

How Memory and Sleep Are Connected

Danielle Pacheco

Staff Writer

Danielle is originally from Vancouver, BC, where she has spent many hours staring at her ceiling trying to fall asleep. Danielle studied the science of sleep with a degree in psychology at the University of British Columbia

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Dr. Anis Rehman

Internal Medicine Physician

Dr. Rehman, M.D., is a board-certified physician in Internal Medicine as well as Endocrinology, Diabetes, and Metabolism.

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Table of Contents

Sleep’s Role in Memory Consolidation

How does sleep deprivation affect brain function and memory, sleep apnea and memory loss.

Scientists and researchers have studied the relationship between memory and sleep for more than 100 years. The general consensus today is that memory consolidation – the process of preserving key memories and discarding excessive information – takes place during both the non-rapid eye movement (NREM) and rapid eye movement (REM) stages of your sleep cycle .

Recent studies also suggest that insufficient and excessive sleep Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source can affect memory processing and other cognitive processes. A good night’s rest not only promotes good physical health but also enables our brains to function properly, so getting the recommended amount of sleep each night is key to consolidating memories.

Sleep and memory Trusted Source National Institutes of Health (NIH) The NIH, a part of the U.S. Department of Health and Human Services, is the nation’s medical research agency — making important discoveries that improve health and save lives. View Source share a complex relationship. Getting enough rest helps you process new information Trusted Source NIH News in Health The NIH, a part of the U.S. Department of Health and Human Services, is the nation’s medical research agency — making important discoveries that improve health and save lives. View Source once you wake up, and sleeping after learning can consolidate this information into memories, allowing you to store them in your brain.

A healthy adult’s sleep cycle consists of four distinct stages. The first two stages are considered light NREM sleep, and the third is deep (or “slow-wave”) NREM sleep. These three stages prepare your brain to learn new information the following day. Not sleeping or getting enough sleep can lower your learning abilities by as much as 40%.

During these NREM stages, the brain also sorts through your various memories from the previous day, filtering out important memories and eliminating other information. These selected memories will become more concrete as deep NREM sleep begins, and this process will continue during REM sleep. Emotional memories are also processed in the REM stage, which can help you cope with difficult experiences.

Most dreaming occurs in REM sleep. The thalamus of the brain transmits cues from your five senses to the cerebral cortex, a thin layer of the cerebrum that interprets and processes information from your memories. The thalamus is largely inactive during NREM stages, but when REM sleep begins, it will relay images, sounds, and other sensations to the cerebral cortex that are then integrated into your dreams.

People who don’t get enough sleep may experience the effects of sleep deprivation . Difficulty remembering things Trusted Source National Heart, Lung, and Blood Institute (NHLBI) The NHLBI is the nation's leader in the prevention and treatment of heart, lung, blood and sleep disorders. View Source is one common symptom. Since the brain does not have sufficient time to create new pathways for the information you’ve recently learned, sleep deprivation often affects how memories are consolidated. Other potential cognitive impacts include trouble learning and focusing, reduced decision-making skills, and poor emotional and behavioral control.

How much sleep you should get each night largely depends on your age. In addition to adults, studies have concluded children experience stronger memory consolidation Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source after a good night’s sleep. That said, excessive sleep can also lead to cognitive impairments. Every person should strive for the optimal amount of nightly sleep, as too little or too much can have negative repercussions .

Our recommendations for nightly sleep based on age are as follows:

Some studies have found sleep quality decreases with age Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source . This is tied to slow-wave sleep. Slow waves are produced in an area of the brain known as the medial prefrontal cortex. The medial prefrontal cortex will deteriorate over time, and as a result, older people typically experience less slow-wave sleep during a normal sleep cycle and have a harder time processing memories.

Since sleep is so crucial to the formation and consolidation of memories, some sleep disorders Trusted Source American Academy of Sleep Medicine (AASM) AASM sets standards and promotes excellence in sleep medicine health care, education, and research. View Source are associated with memory problems. Insomnia , defined as persistent difficulty initiating or maintaining sleep, is known to cause daytime cognitive impairments including reduced memory functioning. Sleep disorders that lead to excessive daytime sleepiness such as narcolepsy can cause memory lapses.

One disorder, sleep apnea , may actually promote memory loss. Sleep apnea is characterized by the temporary cessation of the airway during sleep that can cause people to choke or gasp for air. Heavy snoring and excessive daytime sleepiness are other common symptoms of sleep apnea.

More than 900 million people Trusted Source ScienceDaily ScienceDaily features breaking news about the latest discoveries in science, health, the environment, technology, and more -- from leading universities, scientific journals, and research organizations. View Source across the globe live with obstructive sleep apnea (OSA) , a subtype of the disorder that occurs when a physical blockage impedes the airway. OSA has long been linked to chronic depression. People with depression often have a difficult time processing memories, specifically autobiographical memories that pertain to their own experiences. People with OSA have also demonstrated difficulty with memory consolidation.

One study sought to explore the relationship between OSA and depression in terms of memory processing. The findings show subjects with OSA struggled more to form semantic memories, or individual facts from their personal history, than the control group. This is not surprising since healthy sleep is needed to properly consolidate semantic memories, and OSA causes sleep fragmentation that interferes with the sleep cycle. Interestingly, OSA did not affect the consolidation of episodic memories – or those related to events and experiences – to the same extent.

These results suggest sleep apnea can interfere with the memory consolidation process , causing people to have a hard time recalling certain memories of their own life. However, more research is needed to explore whether OSA leads to both depression and memory problems, or if OSA and depression independently affect memory consolidation.

About Our Editorial Team

Danielle Pacheco, Staff Writer

Medically Reviewed by

Dr. Anis Rehman, Internal Medicine Physician MD

References 9 sources.

Ma, Y., Liang, L., Zheng, F., Shi, L., Zhong, B., & Xie, W. (2020). Association Between Sleep Duration and Cognitive Decline. JAMA network open, 3(9), e2013573.

National Institutes of Health. (2019, September 19). The brain may actively forget during dream sleep [Press release].

NIH News in Health. Sleep On It. How Snoozing Strengthens Memories. (April 2013).

National Heart, Lung, and Blood Institute. (n.d.). Sleep deprivation and deficiency.

Peiffer, A., Brichet, M., De Tiège, X. et al. The power of children’s sleep – Improved declarative memory consolidation in children compared with adults. Sci Rep 10, 9979 (2020).

Mander, B., Rao, V., Lu, B. et al. Prefrontal atrophy, disrupted NREM slow waves and impaired hippocampal-dependent memory in aging. Nat Neurosci 16, 357–364 (2013).

American Academy of Sleep Medicine. (2014). The International Classification of Sleep Disorders – Third Edition (ICSD-3). Darien, IL.

Science Daily. (2019, January 31). Sleep apnea creates gaps in life memories [Press release].

Delhikar, N., Sommers, L., Rayner, G., Schembri, R., Robinson, S., Wilson, S., & Jackson, M. (2019). Autobiographical Memory From Different Life Stages in Individuals With Obstructive Sleep Apnea. Journal of the International Neuropsychological Society, 25(3), 266-274.

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Sleep and Memory: How They Work Together

New research indicates a poor night’s sleep negatively impacts brain function..

Posted August 19, 2019 | Reviewed by Jessica Schrader

Medieval philosopher Thomas Aquinas once wrote that “sorrow can be alleviated by good sleep.” Now, scientists are learning he was not far from the truth.

A study published in July 2019 in the journal Current Biology indicates a poor night’s sleep—specifically, restless rapid-eye-movement ( REM ) sleep—negatively impacts brain function, including the work of amygdalae. These are almond-sized clusters of nuclei located deep within the brain’s temporal lobes and responsible for the consolidation of memories for long-term learning, as well as the processing and storage of memories associated with events that elicit strong emotions like sorrow, embarrassment , fear , and anxiety . Upon awakening, study volunteers who experienced disrupted REM sleep remained reactive to emotional events from the previous day while well-rested individuals labeled prior-day events as being of lesser emotional significance than they originally thought, according to the researchers.

We have long been aware that a good night's sleep benefits mood, alertness, concentration , and judgment. Science also has established that sleep plays a vital role in memory retention. What we have not known clearly, at least until now, how these sleep and memory processes, are potentially linked and how a negative impact on how one affects the other.

Study authors writing in Proceedings of the National Academy of Sciences in 2018 suggest even a single night of sleep deprivation can cause beta-amyloid, a metabolic waste product, to accumulate in brain structures, including the amygdalae, which regulate mood, emotion , memory, and ability to learn and are implicated in development of Alzheimer’s disease. The amygdalae maintain neural pathways to the hypothalamus, which regulates important biological systems, such as sleep, the menstrual cycle, and circadian rhythm , and they interact with the hippocampus, a key component in memory processing. In fact, neural circuits connecting the hippocampus to other regions of the brain are considered repositories for the storage of episodic memories, specifically events, their location, and the emotions associated with them.

For memory to function properly, three vital processes must occur:

• Acquisition: learning or experiencing something new
• Consolidation: integrating the new information in the brain, making it stick
• Recall: accessing the information after it is stored

Acquisition and recall occur when one is awake; consolidation, while asleep. When awake, the brain reacts to external stimuli and encodes new memories that are, at that point, unstable and subject to forgetting. The sleeping brain, with greatly reduced exposure to external stimuli, provides optimal conditions for memory consolidation, which strengthens and integrates new memory into existing knowledge networks.

At one time, experts thought sleep simply protected memory from interference by external stimuli. Now we know that both REM and slow-wave sleep (SWS) take more active roles in memory consolidation, with different kinds of memories being processed during different stages of sleep. A study in a 2018 issue of the Journal of Sleep Research, for example, indicates that one night of sleep loss can impair working memory, which is important for reasoning and planning.

However, of more concern, study participants most affected by sleep deprivation, women, were unaware of the decline in their performance, increasing their risk for accidents and mistakes. The relationship between car crashes and sleep deprivation is a prime example of such a risk. Other studies suggest that declarative memory, which is fact-based, benefits primarily from sleep periods dominated by SWS, and procedural memory, remembering how to do something, is related to REM sleep.

Although, as physicians and scientists, we still have much to learn about sleep and memory, we can say with certainty that a good night’s sleep improves concentration for learning and remembering what we learned. Here are a few tips for improving the quantity and quality of sleep:

• Exercise earlier in the day—not several hours before bedtime.
• Reduce or avoid stimulants such as caffeine later in the day and alcohol in the evening.
• Limit naps to 30 minutes; don't nap after midday.
• Stick to a sleep schedule; go to bed and wake up about the same time each day, including weekends and holidays.
• Relax and clear the mind before bedtime; read a book, listen to quiet music.
• Keep the bedroom cooler at night. Use “white noise” like that from a fan motor to mask distracting sounds. Install room-darkening shades.
• Make sure your mattress is comfortable and try sleeping on one pillow—not two or three.
• Don't eat a heavy meal or drink an excessive amount of liquid just prior to bedtime.
• Avoid using a computer, tablet, or smartphone right before going to bed. The light from the screen stimulates the brain, making it difficult to fall asleep.

Sleep architecture or quality is as important as quantity. Proper bed and wake times allow us to go through the phases of slow-wave and REM sleep. Alcohol, sedatives, and many drugs can also diminish slow wave as well as REM sleep. And a cool bedroom, or hot bath before bed, enhances deep, slow-wave sleep.

Current Biology/July 2019; https://www.cell.com/current-biology/fulltext/S0960-9822(19)30761-4?_re…

Consumer-Related Overview of Study of Current Biology Study; https://www.the-scientist.com/news-opinion/karaoke-sleep-study-links-di…

Proceedings of the National Academy of Sciences (2018); https://www.pnas.org/content/pnas/115/17/4483.full.pdf

Journal of Sleep Research (2018); https://onlinelibrary.wiley.com/doi/full/10.1111/jsr.12651

Alex Dimitriu M.D . applies expertise in sleep medicine and in psychiatry to help people obtain balance and improve performance.

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Annual Review of Psychology

Volume 72, 2021, review article, memory and sleep: how sleep cognition can change the waking mind for the better.

• Ken A. Paller 1 , Jessica D. Creery 1 , and Eitan Schechtman 1
• View Affiliations Hide Affiliations Affiliations: Department of Psychology and Cognitive Neuroscience Program, Northwestern University, Evanston, Illinois 60208, USA; email: [email protected] [email protected] [email protected]
• Vol. 72:123-150 (Volume publication date January 2021) https://doi.org/10.1146/annurev-psych-010419-050815
• First published as a Review in Advance on September 18, 2020
• Copyright © 2021 by Annual Reviews. All rights reserved

The memories that we retain can serve many functions. They guide our future actions, form a scaffold for constructing the self, and continue to shape both the self and the way we perceive the world. Although most memories we acquire each day are forgotten, those integrated within the structure of multiple prior memories tend to endure. A rapidly growing body of research is steadily elucidating how the consolidation of memories depends on their reactivation during sleep. Processing memories during sleep not only helps counteract their weakening but also supports problem solving, creativity, and emotional regulation. Yet, sleep-based processing might become maladaptive, such as when worries are excessively revisited. Advances in research on memory and sleep can thus shed light on how this processing influences our waking life, which can further inspire the development of novel strategies for decreasing detrimental rumination-like activity during sleep and for promoting beneficial sleep cognition.

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Literature Cited

• Aly M , Ranganath C. 2018 . New perspectives on the hippocampus and memory. Neurosci. Lett. 680 : 1– 3 [Google Scholar]
• Andrillon T , Kouider S. 2019 . The vigilant sleeper: neural mechanisms of sensory (de)coupling during sleep. Curr. Opin. Physiol. 15 : 47– 59 [Google Scholar]
• Andrillon T , Pressnitzer D , Léger D , Kouider S 2017 . Formation and suppression of acoustic memories during human sleep. Nat. Commun. 8 : 1 179 [Google Scholar]
• Antony JW , Gobel EW , O'Hare JK , Reber PJ , Paller KA 2012 . Cued memory reactivation during sleep influences skill learning. Nat. Neurosci. 15 : 8 1114– 16 [Google Scholar]
• Antony JW , Paller KA. 2017 . Using oscillating sounds to manipulate sleep spindles. Sleep 40 : 3 zsw068 [Google Scholar]
• Antony JW , Piloto L , Wang M , Pacheco P , Norman KA , Paller KA 2018 . Sleep spindle refractoriness segregates periods of memory reactivation. Curr. Biol. 28 : 11 1736– 43.e4 [Google Scholar]
• Arzi A , Holtzman Y , Samnon P , Eshel N , Harel E , Sobel N 2014 . Olfactory aversive conditioning during sleep reduces cigarette-smoking behavior. J. Neurosci. 34 : 46 15382– 93 [Google Scholar]
• Arzi A , Shedlesky L , Ben-Shaul M , Nasser K , Oksenberg A et al. 2012 . Humans can learn new information during sleep. Nat. Neurosci. 15 : 10 1460– 65 [Google Scholar]
• Axmacher N , Elger CE , Fell J 2008 . Ripples in the medial temporal lobe are relevant for human memory consolidation. Brain 131 : 7 1806– 17 [Google Scholar]
• Backhaus J , Born J , Hoeckesfeld R , Fokuhl S , Hohagen F , Junghanns K 2007 . Midlife decline in declarative memory consolidation is correlated with a decline in slow wave sleep. Learn. Mem. 14 : 5 336– 41 [Google Scholar]
• Bao Y-P , Han Y , Ma J , Wang R-J , Shi L et al. 2017 . Cooccurrence and bidirectional prediction of sleep disturbances and depression in older adults: meta-analysis and systematic review. Neurosci. Biobehav. Rev. 75 : 257– 73 [Google Scholar]
• Belal S , Cousins J , El-Deredy W , Parkes L , Schneider J et al. 2018 . Identification of memory reactivation during sleep by EEG classification. NeuroImage 176 : 203– 14 [Google Scholar]
• Bendor D , Wilson MA. 2012 . Biasing the content of hippocampal replay during sleep. Nat. Neurosci. 15 : 10 1439– 44 [Google Scholar]
• Bergmann TO , Mölle M , Diedrichs J , Born J , Siebner HR 2012 . Sleep spindle-related reactivation of category-specific cortical regions after learning face-scene associations. NeuroImage 59 : 3 2733– 42 [Google Scholar]
• Berkers RMWJ , Ekman M , van Dongen EV , Takashima A , Paller KA , Fernández G 2018 . Cued reactivation during slow-wave sleep induces connectivity changes related to memory stabilization. Sci. Rep. 8 : 16958 [Google Scholar]
• Bolinger E , Cunningham TJ , Payne JD , Bowman MA , Bulca E et al. 2019 . Sleep's benefits to emotional processing emerge in the long term. Cortex 120 : 457– 70 [Google Scholar]
• Borbély AA , Tobler I , Loepfe M , Kupfer DJ , Ulrich RF et al. 1984 . All-night spectral analysis of the sleep EEG in untreated depressives and normal controls. Psychiatry Res 12 : 1 27– 33 [Google Scholar]
• Born J , Rasch B , Gais S 2006 . Sleep to remember. Neuroscientist 12 : 5 410– 24 [Google Scholar]
• Born J , Wilhelm I. 2012 . System consolidation of memory during sleep. Psychol. Res. 76 : 2 192– 203 [Google Scholar]
• Brodt S , Pöhlchen D , Täumer E , Gais S , Schönauer M 2018 . Incubation, not sleep, aids problem-solving. Sleep 41 : 10 155 [Google Scholar]
• Buysse DJ , Angst J , Gamma A , Ajdacic V , Eich D , Rössler W 2008 . Prevalence, course, and comorbidity of insomnia and depression in young adults. Sleep 31 : 4 473– 80 [Google Scholar]
• Buzsáki G. 1998 . Memory consolidation during sleep: a neurophysiological perspective. J. Sleep Res. 7 : S1 17– 23 [Google Scholar]
• Cai DJ , Mednick SA , Harrison EM , Kanady JC , Mednick SC 2009 . REM, not incubation, improves creativity by priming associative networks. PNAS 106 : 25 10130– 34 [Google Scholar]
• Cairney SA , Durrant SJ , Hulleman J , Lewis PA 2014 . Targeted memory reactivation during slow wave sleep facilitates emotional memory consolidation. Sleep 37 : 4 701– 7 [Google Scholar]
• Cairney SA , Guttesen AÁV , El Marj N , Staresina BP 2018 . Memory consolidation is linked to spindle-mediated information processing during sleep. Curr. Biol. 28 : 6 948– 954.e4 [Google Scholar]
• Cairney SA , Lindsay S , Sobczak JM , Paller KA , Gaskell MG 2016 . The benefits of targeted memory reactivation for consolidation in sleep are contingent on memory accuracy and direct cue-memory associations. Sleep 39 : 5 1139– 50 [Google Scholar]
• Cartwright R. 1990 . A network model of dreams. Sleep and Cognition RR Bootzin, JF Kihlstrom, DL Schacter 179– 89 Washington, DC: Am. Psychol. Assoc. [Google Scholar]
• Cartwright RD. 2010 . The Twenty-Four Hour Mind: The Role of Sleep and Dreaming in Our Emotional Lives Oxford, UK: Oxford Univ. Press [Google Scholar]
• Clemens Z , Fabó D , Halász P 2006 . Twenty-four hours retention of visuospatial memory correlates with the number of parietal sleep spindles. Neurosci. Lett. 403 : 1 52– 56 [Google Scholar]
• Cordi MJ , Rossier L , Rasch B 2020 . Hypnotic suggestions given before nighttime sleep extend slow-wave sleep as compared to a control text in highly hypnotizable subjects. Int. J. Clin. Exp. Hypn. 68 : 1 105– 29 [Google Scholar]
• Cousins JN , El-Deredy W , Parkes LM , Hennies N , Lewis PA 2016 . Cued reactivation of motor learning during sleep leads to overnight changes in functional brain activity and connectivity. PLOS Biol 14 : 5 e1002451 [Google Scholar]
• Cox R , Hofman WF , Talamini LM 2012 . Involvement of spindles in memory consolidation is slow wave sleep-specific. Learn. Mem. 19 : 7 264– 67 [Google Scholar]
• Cox R , Rüber T , Staresina BP , Fell J 2019 . Heterogeneous profiles of coupled sleep oscillations in human hippocampus. NeuroImage 202 : 116178 [Google Scholar]
• Cox R , van Driel J , de Boer M , Talamini LM 2014 . Slow oscillations during sleep coordinate interregional communication in cortical networks. J. Neurosci. 34 : 50 16890– 901 [Google Scholar]
• Creery JD , Oudiette D , Antony JW , Paller KA 2015 . Targeted memory reactivation during sleep depends on prior learning. Sleep 38 : 5 755– 63 [Google Scholar]
• Crick F , Mitchison G. 1983 . The function of dream sleep. Nature 304 : 111– 14 [Google Scholar]
• Diekelmann S , Born J. 2010 . The memory function of sleep. Nat. Rev. Neurosci. 11 : 2 114– 26 [Google Scholar]
• Dudai Y. 2012 . The restless engram: Consolidations never end. Annu. Rev. Neurosci. 35 : 227– 47 [Google Scholar]
• Duncan CP. 1949 . The retroactive effect of electroshock on learning. J. Comp. Physiol. Psychol. 42 : 1 32– 44 [Google Scholar]
• Everaert J , Koster EHW , Derakshan N 2012 . The combined cognitive bias hypothesis in depression. Clin. Psychol. Rev. 32 : 5 413– 24 [Google Scholar]
• Fattahi Asl A , Ghanizadeh A , Mollazade J , Aflakseir A 2015 . Differences of biased recall memory for emotional information among children and adolescents of mothers with MDD, children and adolescents with MDD, and normal controls. Psychiatry Res 228 : 2 223– 27 [Google Scholar]
• Fernandez LMJ , Luthi A. 2020 . Sleep spindles: mechanisms and functions. Physiol. Rev. 100 : 805– 68 [Google Scholar]
• Fernández-Ruiz A , Oliva A , de Oliveira EF , Rocha-Almeida F , Tingley D , Buzsáki G 2019 . Long-duration hippocampal sharp wave ripples improve memory. Science 364 : 6445 1082– 86 [Google Scholar]
• Foster DJ. 2017 . Replay comes of age. Annu. Rev. Neurosci. 40 : 581 – 602 [Google Scholar]
• Franzen PL , Buysse DJ. 2017 . Sleep in psychiatric disorders. Sleep Disorders Medicine: Basic Science, Technical Considerations and Clinical Aspects S Chokroverty 977– 96 New York: Springer [Google Scholar]
• Fuentemilla L , Miró J , Ripollés P , Vilà-Balló A , Juncadella M et al. 2013 . Hippocampus-dependent strengthening of targeted memories via reactivation during sleep in humans. Curr. Biol. 23 : 18 1769– 75 [Google Scholar]
• Gais S , Mölle M , Helms K , Born J 2002 . Learning-dependent increases in sleep spindle density. J. Neurosci. 22 : 15 6830– 34 [Google Scholar]
• Germain A , Buysse DJ , Nofzinger E 2008 . Sleep-specific mechanisms underlying posttraumatic stress disorder: integrative review and neurobiological hypotheses. Sleep Med. Rev. 12 : 3 185– 95 [Google Scholar]
• Geva-Sagiv M , Nir Y. 2019 . Local sleep oscillations: implications for memory consolidation. Front. Neurosci. 13 : 813 [Google Scholar]
• Girardeau G , Benchenane K , Wiener SI , Buzsáki G , Zugaro MB 2009 . Selective suppression of hippocampal ripples impairs spatial memory. Nat. Neurosci. 12 : 10 1222– 23 [Google Scholar]
• Glazier BL , Alden LE. 2019 . Social anxiety disorder and memory for positive feedback. J. Abnorm. Psychol. 128 : 3 228– 33 [Google Scholar]
• Groch S , Preiss A , McMakin DL , Rasch B , Walitza S et al. 2017 . Targeted reactivation during sleep differentially affects negative memories in socially anxious and healthy children and adolescents. J. Neurosci. 37 : 9 2425– 34 [Google Scholar]
• Harrington MO , Johnson JM , Croom HE , Pennington K , Durrant SJ 2018 . The influence of REM sleep and SWS on emotional memory consolidation in participants reporting depressive symptoms. Cortex 99 : 281– 95 [Google Scholar]
• Hebscher M , Wing E , Ryan J , Gilboa A 2019 . Rapid cortical plasticity supports long-term memory formation. Trends Cogn. Sci. 23 : 12 989– 1002 [Google Scholar]
• Helfrich RF , Mander BA , Jagust WJ , Knight RT , Walker MP 2018 . Old brains come uncoupled in sleep: slow wave-spindle synchrony, brain atrophy, and forgetting. Neuron 97 : 1 221– 30.e4 [Google Scholar]
• Henin S , Borges H , Shankar A , Sarac C , Melloni L et al. 2019 . Closed-loop acoustic stimulation enhances sleep oscillations but not memory performance. eNeuro 6 : 6 0306– 19.2019 [Google Scholar]
• Hu X , Antony JW , Creery JD , Vargas IM , Bodenhausen GV , Paller KA 2015 . Unlearning implicit social biases during sleep. Science 348 : 6238 1013– 15 [Google Scholar]
• Hu X , Cheng L , Chiu MH , Paller KA 2020 . Promoting memory consolidation during sleep: a meta-analysis of targeted memory reactivation. Psychol. Bull. 146 : 218– 44 [Google Scholar]
• Huber R , Felice Ghilardi M , Massimini M , Tononi G 2004 . Local sleep and learning. Nature 430 : 6995 78– 81 [Google Scholar]
• Humiston GB , Wamsley EJ. 2019 . Unlearning implicit social biases during sleep: a failure to replicate. PLOS ONE 14 : 1 e0211416 [Google Scholar]
• Ji D , Wilson MA. 2007 . Coordinated memory replay in the visual cortex and hippocampus during sleep. Nat. Neurosci. 10 : 1 100– 7 [Google Scholar]
• Jindal RD , Friedman ES , Berman SR , Fasiczka AL , Howland RH , Thase ME 2003 . Effects of sertraline on sleep architecture in patients with depression. J. Clin. Psychopharmacol. 23 : 6 540– 48 [Google Scholar]
• Karni A , Tanne D , Rubenstein BS , Askenasy JJ , Sagi D 1994 . Dependence on REM sleep of overnight improvement of a perceptual skill. Science 265 : 5172 679– 82 [Google Scholar]
• Khodagholy D , Gelinas JN , Buzsáki G 2017 . Learning-enhanced coupling between ripple oscillations in association cortices and hippocampus. Science 358 : 6361 369– 72 [Google Scholar]
• Kleim B , Wysokowsky J , Schmid N , Seifritz E , Rasch B 2016 . Effects of sleep after experimental trauma on intrusive emotional memories. Sleep 39 : 12 2125– 32 [Google Scholar]
• Klinzing JG , Niethard N , Born J 2019 . Mechanisms of systems memory consolidation during sleep. Nat. Neurosci. 22 : 10 1598– 610 [Google Scholar]
• Konkoly KR , Appel K , Chabani E , Mangiaruga A , Gott J et al. 2020 . Real-time dialogue between experimenters and dreamers during REM sleep Work. Pap., Northwestern Univ Evanston, IL: [Google Scholar]
• Kupfer DJ , Reynolds CF , Ulrich RF , Grochocinski VJ 1986 . Comparison of automated REM and slow-wave sleep analysis in young and middle-aged depressed subjects. Biol. Psychiatry 21 : 2 189– 200 [Google Scholar]
• Latchoumane C-FV , Ngo H-VV , Born J , Shin H-S 2017 . Thalamic spindles promote memory formation during sleep through triple phase-locking of cortical, thalamic, and hippocampal rhythms. Neuron 95 : 2 424– 35.e6 [Google Scholar]
• Laventure S , Benchenane K. 2020 . Validating the theoretical bases of sleep reactivation during sharp-wave ripples and their association with emotional valence. Hippocampus 30 : 1 19– 27 [Google Scholar]
• Lechner HA , Squire LR , Byrne JH 1999 . 100 years of consolidation—remembering Müller and Pilzecker. Learn. Mem 6 : 2 77 – 87 [Google Scholar]
• Lehmann M , Schreiner T , Seifritz E , Rasch B 2016 . Emotional arousal modulates oscillatory correlates of targeted memory reactivation during NREM, but not REM sleep. Sci. Rep. 6 : 1 39229 [Google Scholar]
• LePort AKR , Mattfeld AT , Dickinson-Anson H , Fallon JH , Stark CEL et al. 2012 . Behavioral and neuroanatomical investigation of Highly Superior Autobiographical Memory (HSAM). Neurobiol. Learn. Mem. 98 : 1 78– 92 [Google Scholar]
• Lewis PA , Bendor D. 2019 . How targeted memory reactivation promotes the selective strengthening of memories in sleep. Curr. Biol. 29 : 18 R906– 12 [Google Scholar]
• Lewis PA , Durrant SJ. 2011 . Overlapping memory replay during sleep builds cognitive schemata. Trends Cogn. Sci. 15 : 8 343– 51 [Google Scholar]
• Lustenberger C , Boyle MR , Alagapan S , Mellin JM , Vaughn BV , Fröhlich F 2016 . Feedback-controlled transcranial alternating current stimulation reveals a functional role of sleep spindles in motor memory consolidation. Curr. Biol. 26 : 16 2127– 36 [Google Scholar]
• Lutz ND , Diekelmann S , Hinse-Stern P , Born J , Rauss K 2017 . Sleep supports the slow abstraction of gist from visual perceptual memories. Sci. Rep. 7 : 42950 [Google Scholar]
• Manber R , Edinger JD , Gress JL , Pedro-Salcedo MGS , Kuo TF , Kalista T 2008 . Cognitive behavioral therapy for insomnia enhances depression outcome in patients with comorbid major depressive disorder and insomnia. Sleep 31 : 4 489– 95 [Google Scholar]
• Maquet P , Péters J-M , Aerts J , Delfiore G , Degueldre C et al. 1996 . Functional neuroanatomy of human rapid-eye-movement sleep and dreaming. Nature 383 : 6596 163– 66 [Google Scholar]
• Marr D. 1971 . Simple memory: a theory for archicortex. Philos. Trans. R. Soc. B 262 : 841 23– 81 [Google Scholar]
• Marshall L , Helgadóttir H , Mölle M , Born J 2006 . Boosting slow oscillations during sleep potentiates memory. Nature 444 : 7119 610– 13 [Google Scholar]
• Massimini M , Huber R , Ferrarelli F , Hill S , Tononi G 2004 . The sleep slow oscillation as a traveling wave. J. Neurosci. 24 : 31 6862– 70 [Google Scholar]
• McClelland JL , McNaughton BL , O'Reilly RC 1995 . Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychol. Rev. 102 : 3 419– 57 [Google Scholar]
• Mednick SC , McDevitt EA , Walsh JK , Wamsley E , Paulus M et al. 2013 . The critical role of sleep spindles in hippocampal-dependent memory: a pharmacology study. J. Neurosci. 33 : 10 4494– 504 [Google Scholar]
• Miller TD , Chong TT-J , Aimola Davies AM , Johnson MR , Irani SR et al. 2020 . Human hippocampal CA3 damage disrupts both recent and remote episodic memories. eLife 9 : e41836 [Google Scholar]
• Milner CE , Fogel SM , Cote KA 2006 . Habitual napping moderates motor performance improvements following a short daytime nap. Biol. Psychol. 73 : 2 141– 56 [Google Scholar]
• Mölle M , Bergmann TO , Marshall L , Born J 2011 . Fast and slow spindles during the sleep slow oscillation: disparate coalescence and engagement in memory processing. Sleep 34 : 10 1411– 21 [Google Scholar]
• Mölle M , Marshall L , Gais S , Born J 2002 . Grouping of spindle activity during slow oscillations in human non-rapid eye movement sleep. J. Neurosci. 22 : 24 10941– 47 [Google Scholar]
• Müller G , Pilzecker A. 1900 . Experimentelle Beiträge zur Lehre vom Gedächtnis [Experimental contributions to the theory of memory] Leipzig, Ger: Verlag von Johann Ambrosius Barth [Google Scholar]
• Murray EA , Bussey TJ , Saksida LM 2007 . Visual perception and memory: a new view of medial temporal lobe function in primates and rodents. Annu. Rev. Neurosci. 30 : 99– 122 [Google Scholar]
• Nádasdy Z , Hirase H , Czurkó A , Csicsvari J , Buzsáki G 1999 . Replay and time compression of recurring spike sequences in the hippocampus. J. Neurosci. 19 : 21 9497– 507 [Google Scholar]
• Nadel L , Moscovitch M. 1997 . Memory consolidation, retrograde amnesia and the hippocampal complex. Curr. Opin. Neurobiol. 7 : 2 217– 27 [Google Scholar]
• Ngo H-VV , Fell J , Staresina BP 2020 . Sleep spindles mediate hippocampal-neocortical coupling during sharp-wave ripples. eLife 9 : e57011 [Google Scholar]
• Ngo H-VV , Martinetz T , Born J , Mölle M 2013 . Auditory closed-loop stimulation of the sleep slow oscillation enhances memory. Neuron 78 : 3 545– 53 [Google Scholar]
• Nir Y , Staba RJ , Andrillon T , Vyazovskiy VV , Cirelli C et al. 2011 . Regional slow waves and spindles in human sleep. Neuron 70 : 1 153– 69 [Google Scholar]
• Nishida M , Walker MP. 2007 . Daytime naps, motor memory consolidation and regionally specific sleep spindles. PLOS ONE 2 : 4 e341 [Google Scholar]
• Norman KA , Newman EL , Perotte AJ 2005 . Methods for reducing interference in the Complementary Learning Systems model: oscillating inhibition and autonomous memory rehearsal. Neural Netw 18 : 9 1212– 28 [Google Scholar]
• Norman Y , Yeagle EM , Khuvis S , Harel M , Mehta AD , Malach R 2019 . Hippocampal sharp-wave ripples linked to visual episodic recollection in humans. Science 365 : 6454 eaax1030 [Google Scholar]
• Ohayon MM , Roth T. 2003 . Place of chronic insomnia in the course of depressive and anxiety disorders. J. Psychiatr. Res. 37 : 1 9– 15 [Google Scholar]
• Oswald FL , Mitchell G , Blanton H , Jaccard J , Tetlock PE 2013 . Predicting ethnic and racial discrimination: a meta-analysis of IAT criterion studies. J. Pers. Soc. Psychol. 105 : 2 171– 92 [Google Scholar]
• Oudiette D , Paller KA. 2013 . Upgrading the sleeping brain with targeted memory reactivation. Trends Cogn. Sci. 17 : 3 142– 49 [Google Scholar]
• Pail G , Huf W , Pjrek E , Winkler D , Willeit M et al. 2011 . Bright-light therapy in the treatment of mood disorders. Neuropsychobiology 64 : 3 152– 62 [Google Scholar]
• Paller KA. 2002 . Cross-cortical consolidation as the core defect in amnesia: prospects for hypothesis testing with neuropsychology and neuroimaging. The Neuropsychology of Memory LR Squire, DL Schacter 73– 87 New York: Guilford. , 3rd. ed. [Google Scholar]
• Paller KA. 2009 . Memory consolidation: systems. Encyclopedia of Neuroscience LR Squire 741– 49 Oxford, UK: Academic [Google Scholar]
• Paller KA , Voss JL. 2004 . Memory reactivation and consolidation during sleep. Learn. Mem. 11 : 6 664– 70 [Google Scholar]
• Papalambros NA , Santostasi G , Malkani RG , Braun R , Weintraub S et al. 2017 . Acoustic enhancement of sleep slow oscillations and concomitant memory improvement in older adults. Front. Hum. Neurosci. 11 : 109 [Google Scholar]
• Papalambros NA , Weintraub S , Chen T , Grimaldi D , Santostasi G et al. 2019 . Acoustic enhancement of sleep slow oscillations in mild cognitive impairment. Ann. Clin. Transl. Neurol. 6 : 7 1191– 201 [Google Scholar]
• Pavlides C , Winson J. 1989 . Influences of hippocampal place cell firing in the awake state on the activity of these cells during subsequent sleep episodes. J. Neurosci. 9 : 8 2907– 18 [Google Scholar]
• Payne JD , Kensinger EA. 2018 . Stress, sleep, and the selective consolidation of emotional memories. Curr. Opin. Behav. Sci. 19 : 36– 43 [Google Scholar]
• Pitman RK , Shalev AY , Orr SP 2000 . Posttraumatic stress disorder: emotion, conditioning, and memory. The New Cognitive Neurosciences MS Gazzaniga 1133– 47 Cambridge, MA: MIT Press. , 2nd. ed. [Google Scholar]
• Plihal W , Born J. 1997 . Effects of early and late nocturnal sleep on declarative and procedural memory. J. Cogn. Neurosci. 9 : 4 534– 47 [Google Scholar]
• Purcell SM , Manoach DS , Demanuele C , Cade BE , Mariani S et al. 2017 . Characterizing sleep spindles in 11,630 individuals from the National Sleep Research Resource. Nat. Commun. 8 : 15930 [Google Scholar]
• Rasch B , Born J. 2013 . About sleep's role in memory. Physiol. Rev. 93 : 2 681– 766 [Google Scholar]
• Rasch B , Büchel C , Gais S , Born J 2007 . Odor cues during slow-wave sleep prompt declarative memory consolidation. Science 315 : 5817 1426– 29 [Google Scholar]
• Riemann D , Spiegelhalder K , Nissen C , Hirscher V , Baglioni C , Feige B 2012 . REM sleep instability—a new pathway for insomnia. Pharmacopsychiatry 45 : 5 167– 76 [Google Scholar]
• Ritter SM , Strick M , Bos MW , Baaren RBV , Dijksterhuis A 2012 . Good morning creativity: task reactivation during sleep enhances beneficial effect of sleep on creative performance. J. Sleep Res. 21 : 6 643– 47 [Google Scholar]
• Roediger HL , Karpicke JD. 2006 . Test-enhanced learning: taking memory tests improves long-term retention. Psychol. Sci. 17 : 3 249– 55 [Google Scholar]
• Roffwarg HP , Herman JH , Bowe-Anders C , Tauber ES 1978 . The effects of sustained alterations of waking visual input on dream content. The Mind in Sleep: Psychology and Psychophysiology AM Arkin, JS Antrobus, SJ Ellman 295– 349 Hillsdale, NJ: Erlbaum [Google Scholar]
• Rothschild G. 2019 . The transformation of multi-sensory experiences into memories during sleep. Neurobiol. Learn. Mem. 160 : 58– 66 [Google Scholar]
• Rothschild G , Eban E , Frank LM 2017 . A cortical-hippocampal-cortical loop of information processing during memory consolidation. Nat. Neurosci. 20 : 2 251– 59 [Google Scholar]
• Roumis DK , Frank LM. 2015 . Hippocampal sharp-wave ripples in waking and sleeping states. Curr. Opin. Neurobiol. 35 : 6– 12 [Google Scholar]
• Rudoy JD , Voss JL , Westerberg CE , Paller KA 2009 . Strengthening individual memories by reactivating them during sleep. Science 326 : 5956 1079 [Google Scholar]
• Sanders KEG , Osburn S , Paller KA , Beeman M 2019 . Targeted memory reactivation during sleep improves next-day problem solving. Psychol. Sci 30 : 11 1616– 24 Corrigendum. 2020. Psychol. Sci . 31(8):1048 [Google Scholar]
• Schapiro AC , McDevitt EA , Chen L , Norman KA , Mednick SC , Rogers TT 2017 . Sleep benefits memory for semantic category structure while preserving exemplar-specific information. Sci. Rep. 7 : 1 14869 [Google Scholar]
• Schönauer M , Alizadeh S , Jamalabadi H , Abraham A , Pawlizki A , Gais S 2017 . Decoding material-specific memory reprocessing during sleep in humans. Nat. Commun. 8 : 15404 [Google Scholar]
• Schönauer M , Brodt S , Pöhlchen D , Breßmer A , Danek AH , Gais S 2018 . Sleep does not promote solving classical insight problems and magic tricks. Front. Hum. Neurosci. 12 : 72 [Google Scholar]
• Schouten DI , Pereira SIR , Tops M , Louzada FM 2017 . State of the art on targeted memory reactivation: Sleep your way to enhanced cognition. Sleep Med. Rev. 32 : 123– 31 [Google Scholar]
• Schreiner T , Doeller CF , Jensen O , Rasch B , Staudigl T 2018 . Theta phase-coordinated memory reactivation reoccurs in a slow-oscillatory rhythm during NREM sleep. Cell Rep 25 : 2 296– 301 [Google Scholar]
• Schreiner T , Rasch B. 2015 . Boosting vocabulary learning by verbal cueing during sleep. Cereb. Cortex 25 : 11 4169– 79 [Google Scholar]
• Scoville WB , Milner B. 1957 . Loss of recent memory after bilateral hippocampal lesions. J. Neurol. Neurosurg. Psychiatry 20 : 1 11– 21 [Google Scholar]
• Shanahan LK , Gjorgieva E , Paller KA , Kahnt T , Gottfried JA 2018 . Odor-evoked category reactivation in human ventromedial prefrontal cortex during sleep promotes memory consolidation. eLife 7 : e39681 [Google Scholar]
• Simon KCNS , Gómez RL , Nadel L 2018 . Losing memories during sleep after targeted memory reactivation. Neurobiol. Learn. Mem. 151 : 10– 17 [Google Scholar]
• Sirota A , Csicsvari J , Buhl D , Buzsáki G 2003 . Communication between neocortex and hippocampus during sleep in rodents. PNAS 100 : 4 2065– 69 [Google Scholar]
• Skaggs WE , McNaughton BL. 1996 . Replay of neuronal firing sequences in rat hippocampus during sleep following spatial experience. Science 271 : 5257 1870– 73 [Google Scholar]
• Smith C. 1995 . Sleep states and memory processes. Behav. Brain Res. 69 : 1–2 137– 45 [Google Scholar]
• Squire LR. 1992 . Declarative and nondeclarative memory: multiple brain systems supporting learning and memory. J. Cogn. Neurosci. 4 : 3 232– 43 [Google Scholar]
• Squire LR , Wixted JT. 2011 . The cognitive neuroscience of human memory since H.M. Annu. Rev. Neurosci. 34 : 259 – 88 [Google Scholar]
• Staresina BP , Bergmann TO , Bonnefond M , van der Meij R , Jensen O et al. 2015 . Hierarchical nesting of slow oscillations, spindles and ripples in the human hippocampus during sleep. Nat. Neurosci. 18 : 11 1679– 86 [Google Scholar]
• Steiger A , Pawlowski M. 2019 . Depression and sleep. Int. J. Mol. Sci. 20 : 3 607 [Google Scholar]
• Steriade M , Nunez A , Amzica F 1993 . A novel slow (<1 Hz) oscillation of neocortical neurons in vivo: depolarizing and hyperpolarizing components. J. Neurosci. 13 : 8 3252– 65 [Google Scholar]
• Stickgold R , Scott L , Rittenhouse C , Hobson JA 1999 . Sleep-induced changes in associative memory. J. Cogn. Neurosci. 11 : 2 182– 93 [Google Scholar]
• Tambini A , Davachi L. 2019 . Awake reactivation of prior experiences consolidates memories and biases cognition. Trends Cogn. Sci. 23 : 10 876– 90 [Google Scholar]
• Tamminen J , Lambon Ralph MA , Lewis PA 2017 . Targeted memory reactivation of newly learned words during sleep triggers REM-mediated integration of new memories and existing knowledge. Neurobiol. Learn. Mem. 137 : 77– 82 [Google Scholar]
• Tempesta D , Socci V , De Gennaro L , Ferrara M 2018 . Sleep and emotional processing. Sleep Med. Rev. 40 : 183– 95 [Google Scholar]
• Tilley AJ. 1979 . Sleep learning during stage 2 and REM sleep. Biol. Psychol. 9 : 3 155– 61 [Google Scholar]
• Timofeev I , Chauvette S. 2017 . Sleep slow oscillation and plasticity. Curr. Opin. Neurobiol. 44 : 116– 26 [Google Scholar]
• Tononi G , Cirelli C. 2014 . Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration. Neuron 81 : 1 12– 34 [Google Scholar]
• Tucker MA , Hirota Y , Wamsley EJ , Lau H , Chaklader A , Fishbein W 2006 . A daytime nap containing solely non-REM sleep enhances declarative but not procedural memory. Neurobiol. Learn. Mem. 86 : 2 241– 47 [Google Scholar]
• van Dongen EV , Takashima A , Barth M , Zapp J , Schad LR et al. 2012 . Memory stabilization with targeted reactivation during human slow-wave sleep. PNAS 109 : 26 10575– 80 [Google Scholar]
• Vanderheyden WM , George SA , Urpa L , Kehoe M , Liberzon I , Poe GR 2015 . Sleep alterations following exposure to stress predict fear-associated memory impairments in a rodent model of PTSD. Exp. Brain Res. 233 : 8 2335– 46 [Google Scholar]
• Varela F , Lachaux J-P , Rodriguez E , Martinerie J 2001 . The brainweb: phase synchronization and large-scale integration. Nat. Rev. Neurosci. 2 : 4 229– 39 [Google Scholar]
• Vaz AP , Inati SK , Brunel N , Zaghloul KA 2019 . Coupled ripple oscillations between the medial temporal lobe and neocortex retrieve human memory. Science 363 : 6430 975– 78 [Google Scholar]
• Verleger R , Rose M , Wagner U , Yordanova J , Kolev V 2013 . Insights into sleep's role for insight: studies with the number reduction task. Adv. Cogn. Psychol. 9 : 4 160– 72 [Google Scholar]
• Vertes RP , Eastman KE. 2000 . The case against memory consolidation in REM sleep. Behav. Brain Sci. 23 : 6 867– 76 [Google Scholar]
• Volgushev M , Chauvette S , Mukovski M , Timofeev I 2006 . Precise long-range synchronization of activity and silence in neocortical neurons during slow-wave sleep. J. Neurosci. 26 : 21 5665– 72 [Google Scholar]
• Wagner U , Gais S , Haider H , Verleger R , Born J 2004 . Sleep inspires insight. Nature 427 : 6972 352– 55 [Google Scholar]
• Walker MP. 2017 . Why We Sleep: Unlocking the Power of Sleep and Dreams New York: Simon & Schuster [Google Scholar]
• Walker MP , Brakefield T , Morgan A , Hobson JA , Stickgold R 2002 . Practice with sleep makes perfect: sleep-dependent motor skill learning. Neuron 35 : 1 205– 11 [Google Scholar]
• Walker MP , van der Helm E 2009 . Overnight therapy? The role of sleep in emotional brain processing. Psychol. Bull. 135 : 5 731– 48 [Google Scholar]
• Wang D , Clouter A , Chen Q , Shapiro KL , Hanslmayr S 2018 . Single-trial phase entrainment of theta oscillations in sensory regions predicts human associative memory performance. J. Neurosci. 38 : 28 6299– 309 [Google Scholar]
• Wassing R , Lakbila-Kamal O , Ramautar JR , Stoffers D , Schalkwijk F , Van Someren EJW 2019 . Restless REM sleep impedes overnight amygdala adaptation. Curr. Biol. 29 : 14 2351– 58.e4 [Google Scholar]
• Weigenand A , Mölle M , Werner F , Martinetz T , Marshall L 2016 . Timing matters: Open-loop stimulation does not improve overnight consolidation of word pairs in humans. Eur. J. Neurosci. 44 : 6 2357– 68 [Google Scholar]
• Westerberg CE , Florczak SM , Weintraub S , Mesulam M-M , Marshall L et al. 2015 . Memory improvement via slow-oscillatory stimulation during sleep in older adults. Neurobiol. Aging 36 : 9 2577– 86 [Google Scholar]
• Westerberg CE , Mander BA , Florczak SM , Weintraub S , Mesulam M-M et al. 2012 . Concurrent impairments in sleep and memory in amnestic mild cognitive impairment. J. Int. Neuropsychol. Soc. 18 : 3 490– 500 [Google Scholar]
• Westerberg CE , Paller KA , McGaugh JL , Zee PC , Warby SC et al. 2020 . Highly superior autobiographical memory is associated with superior sleep physiology Paper presented at the 61st Annual Meeting of the Psychonomic Society, online Nov. 19–21 [Google Scholar]
• Williams JMG , Barnhofer T , Crane C , Herman D , Raes F et al. 2007 . Autobiographical memory specificity and emotional disorder. Psychol. Bull. 133 : 1 122– 48 [Google Scholar]
• Wilson MA , McNaughton BL. 1994 . Reactivation of hippocampal ensemble memories during sleep. Science 265 : 5172 676– 79 [Google Scholar]
• Winson J. 1985 . Brain and Psyche: The Biology of the Unconscious New York: Doubleday [Google Scholar]
• Winson J. 2004 . To sleep, perchance to dream. Learn. Mem. 11 : 6 659 [Google Scholar]
• Yonelinas AP , Ranganath C , Ekstrom AD , Wiltgen BJ 2019 . A contextual binding theory of episodic memory: systems consolidation reconsidered. Nat. Rev. Neurosci. 20 : 6 364– 75 [Google Scholar]

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Sleep Improves Memory: The Effect of Sleep on Long Term Memory in Early Adolescence

* E-mail: [email protected] (WEB); [email protected] (KTP)

Affiliation Department of Human Biology, Brown University, Providence, Rhode Island, United States of America

Affiliation Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California, United States of America

• Katya Trudeau Potkin, 
• William E. Bunney Jr

• Published: August 7, 2012
• https://doi.org/10.1371/journal.pone.0042191
• Reader Comments

Sleep plays an important role in the consolidation of memory. This has been most clearly shown in adults for procedural memory (i.e. skills and procedures) and declarative memory (e.g. recall of facts). The effects of sleep and memory are relatively unstudied in adolescents. Declarative memory is important in school performance and consequent social functioning in adolescents. This is the first study to specifically examine the effects of normal sleep on auditory declarative memory in an early adolescent sample. Given that the majority of adolescents do not obtain the recommended amount of sleep, it is critical to study the cognitive effects of normal sleep. Forty male and female normal, healthy adolescents between the ages of ten and fourteen years old were randomly assigned to sleep and no sleep conditions. Subjects were trained on a paired-associate declarative memory task and a control working memory task at 9am, and tested at night (12 hours later) without sleep. The same number of subjects was trained at 9pm and tested 9am following sleep. An increase of 20.6% in declarative memory, as measured by the number correct in a paired-associate test, following sleep was observed compared to the group which was tested at the same time interval without sleep (p<0.03). The performance on the control working memory task that involved encoding and memoranda manipulation was not affected by time of day or relationship to sleep. Declarative memory is significantly improved by sleep in a sample of normal adolescents.

Citation: Potkin KT, Bunney WE Jr (2012) Sleep Improves Memory: The Effect of Sleep on Long Term Memory in Early Adolescence. PLoS ONE 7(8): e42191. https://doi.org/10.1371/journal.pone.0042191

Editor: Antonio Verdejo García, University of Granada, Spain

Received: February 10, 2012; Accepted: July 4, 2012; Published: August 7, 2012

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

Funding: The authors have no support or funding to report.

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

Introduction

Several studies primarily in adults have shown that sleep improves procedural memory, i.e. skills and procedures [1] , [2] as well as declarative memory [3] . REM and slow-wave sleep (SWS) have been implicated in memory consolidation [3] – [5] . Lack of REM sleep is associated with poor recall of visual location [6] . Decline in declarative memory consolidation is correlated with a decline in slow-wave sleep [7] . Spencer et al. observed similar initial procedural learning in older and younger adults; however, the older adults’ performance did not improve following sleep, suggesting that sleep dependent memory consolidation decreases with age [8] . This may reflect the disturbed sleep and disrupted SWS in the elderly [3] , [8] , [9] . Slow wave sleep increases until shortly before puberty and then shows a prominent drop across adolescence, decreasing by more than 60% between ages 10 and 20 years [10] . It is critical to understand the cognitive effects of normal sleep in order to understand the consequences of disrupted sleep. This is important since the majority of adolescents do not obtain the recommended amount of sleep and that disrupted sleep is a key symptom in most adolescent psychiatric and developmental disorders [11] .

Backhaus et al. studied twenty-seven children with an average age of 10.1 years (range of nine to twelve), on a learned word pairs list, employing a within subject design and two post-learning assessments. They found that declarative memory was significantly increased immediately after an interval of sleep, as well as with delayed post-learning sleep [12] . As the authors had noted, no control task was administered to determine if circadian confounds were responsible for this increase in recall post sleep. Our study addressed this limitation by administering a control task and evaluating the effect of sleep on auditory declarative memory consolidation in early adolescence. Visual declarative memory has been reported to be enhanced following sleep in children; however, auditory declarative memory has not been previously studied [13] .

Participants

Twenty female and twenty male adolescents, between the ages of 10 and 14, were recruited in a public middle school. The study was considered exempt by the institutional review board because it involved the use of educational tests without personal subject identifiers. In accord with the principals of the Declaration of Helsinki, subjects were asked to participate in a school class project and only told that they would be tested two times for about 15 minutes each time. Subjects with academic failure or accelerated academic performance or sleep problems were not included. The subjects agreeing to participate were grouped by sex and assigned to sleep or no sleep conditions with a separate randomization table for each group, to ensure a balanced design.

Subjects were tested in their homes in a quiet room without distractions for the duration of the learning and testing. The testing sessions were conducted during weekends or during school break. All subjects were given the paired-associate test, one of the standard tests of declarative memory [14] , which consisted of repeating semantically related and unrelated pairs of words (e.g. tree/leaf; lamp/shoe), in a standardized manner. After each word pair was presented out loud, the subject repeated the pair out loud to ensure registration of the paired associate. The list of the same 10 pairs was administered three times in immediate succession. Subjects assigned to the sleep condition learned the paired associates at 9∶00pm (±30 minutes), and were tested for cued recall twelve hours later, after a night of sleep. The no-sleep group received the same paired-associate presentation at 9∶00am (±30 minutes) and was tested for recall twelve hours later, with no intervening sleep or naps. The control working memory task, letter-number, was given just prior to learning the paired-associate words and again just prior to being tested on the paired-associate words. The letter-number test was administered to control for possible circadian confounds and to control for attention and encoding. The letter-number control task (LN, immediate recall and reordering of letters and numbers) is a subtest of the WAIS-III (Wechsler Adult Intelligence Scale) and WMS-III (Wechsler memory Scale), the most widely used intelligence and memory scales. An increasing long series of mixed letters and numbers is read to the subject and the subject then orders the numbers and letters in ascending order, e.g. b3a1 is read and subject correctly responds with 13ab. The letters and numbers must be encoded and then manipulated to get the correct answer. Two versions of the letter-number task were used in random order. The number correct was scored for the paired-associate and the letter-number tests. The memory scores were transformed into Z scores to determine if outliers were present; an exclusionary Z score of ±2.57 was applied (1% of the normal distribution). Between group comparisons were calculated by students t-test (2 tailed) after testing for equal variances by Levene’s test, and ANCOVA as necessary. Within subject comparisons were calculated by paired t-test.

Subjects were instructed to eat their usual meals approximately one hour before learning the paired-associates and one hour before being tested on the paired-associates. Subjects were instructed to get a good night’s sleep. All the subjects included reported having had typical night of sleep and rated the quality of the sleep as good to very good prior to the testing.

The sleep group’s mean age was 12.9 compared to 12.4 for the non-sleep group (t = (1.52), df (1,38), p = 0.14). (See Table 1 for demographic characteristics and performance scores). There was no statistically significant sex difference in performance for either task.

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https://doi.org/10.1371/journal.pone.0042191.t001

Three outliers were identified and removed; one high scoring subject assigned to the sleep and two lower scoring subjects assigned to no sleep. After removing outliers, 19 sleep subjects and 18 no- sleep subjects remained. The Levene’s Test showed equality of variances for all comparisons. The number correct on the letter-number control task at initial testing was 6.58 for the sleep group and 6.06 for the no-sleep group, (t = (1.54), df (1,35), p = 0.13). The letter-number correct score on the second administration was 6.26 and 6.33, respectively, (t = (-.16), df (1,35), p = 0.88), ( Figure 1 ). There was also no statistically significant difference in performance for either group on letter-number task between the first and second administration (paired t test, p = 0.32 for sleep group and 0.45 for no-sleep group).

A histogram of mean number correct (± SD) for the Paired-Associate Test (PA) and Letter Number Test (Letter #), with (n = 19) (outliers removed) and without sleep (n = 18).

https://doi.org/10.1371/journal.pone.0042191.g001

An increase of 20.6% in long-term memory ( Figure 1 ) was found as measured by the number correct in the paired-associate test following sleep, compared to the group which was tested at the same time interval, but without sleep (p<0.029). When the three outliers are included, the number correct for recall of the paired-associates was statistically greater for the sleep group (7.5) compared to the no sleep group (5.9, t = (2.76), df (1,37), p<0.009), a 32.7% increase.

The paired-associate test is one of the standard tests of declarative memory and has been previously used to study declarative memory and the effects of sleep on declarative memory in adults and children [3] . All subjects were evaluated at the same two times of day, approximately 9 AM and 9 PM, using standardized conditions. Performance on the paired-associate test was significantly affected by sleep in our adolescent sample. In contrast, working memory performance as measured by the letter-number test, a standard subtest of the WAIS-III and WMS-III was not affected by time of day or in relation to sleep. Correct performance on the letter-number working memory task (LN) requires that the letters and numbers presented to the subject must be encoded and then correctly manipulated. We had 80% power to detect a standardized difference of.76 correct (∼11% change) or greater, following sleep. A small difference in working memory performance (<11%), however, may exist that could not be detected with our sample size.

The equal performance at both sessions and between groups on the LN supports the view that equal registration and encoding of the memoranda was comparable at both time points and between groups. Performance on the working memory control task did not change with the second session for either group, suggesting that the time of day had no effect on performance on the working memory control task. Consequently, the observed difference in paired-associate performance, i.e. consolidation of working memory, is most likely related to sleep itself and not any differences in encoding. Memory consolidation has been reported to be affected by sleep [1] , [2] , [8] , [9] . Both REM and slow-wave sleep have been associated with improved memory [3] – [5] . Slow wave sleep particularly enhances declarative memory. 7 .

Our results are consistent with Gais et al.’s study of young males (mean age 17.4) showing that enhanced declarative memory was related to periods of sleep, and not to time of day effects [15] . Naps improve declarative memory regardless of time of nap [16] and closely resembled memory improvement after an eight-hour night of sleep [17] . In reviewing the timing of sleep and circadian rhythms, Diekelmann et al. conclude that sleep promotes memory consolidation independently of the time of day in which it occurs [3] . Voderholzer et al. studying 14–16 year old adolescents showed that several nights of sleep restriction did not impact memory consolidation nor performance in a working memory task, when two recovery night of sleep were provided, an effect they ascribed to a compensatory enhancement of SWS [18] . The paired-associate test begins as a working memory task and after a period of time with consolidation becomes a declarative memory task. Correct performance on the letter-number test and the paired-associate tests are dependent upon encoding the memoranda.

A limitation of this study is that we did not test for encoding strength by immediate recall after the administration of the paired-associate test. The letter-number test requires attention and encoding. An element of immediate recall is to prove that the subject was attending. This was assured by having the subjects read the words (similar to other learning tests like the CERAD and ADAS-COG) and supported by the finding of the performance on the letter-number test. It is likely that if immediate recall following each presentation was obtained, higher accuracy rates would have been observed. Recent studies have demonstrated that salience increases declarative memory performance [13] , [19] . Nevertheless, our data demonstrate that sleep improves memory consolidation even in conditions where encoding has not been reinforced.

Neither time of day or sleep affected the performance on the letter-number test suggesting that the material was being learned and encoded. There is no evidence that memory consolidation depends on time of day independent of sleep. The lack of interference during sleep has been considered as a possible cause of the beneficial effects of sleep on declarative memory, i.e. there are no daytime demands to interfere with memory consolidation. Our design tested subjects on non-school days, thus mitigating the effects of interference of memory consolidation during the day by learning competition and other demands of a normal school day. Gais et al. controlled for waking associated interference and found no effect of interference on memory [15] . In a review of controversy regarding whether absence of interference accounts for memory improvement during sleep, Ellenbogen at al. point out “although sleep might passively protect declarative memories from interference, consolidation must also occur during sleep for the memories to become resistant to interference the following day”. Based on their review of related animal and human studies, they point out that “hippocampus-dependent memories are reactivated during sleep, and that this reactivation leads to strengthened memory traces”, finally concluding “that specific, sleep-dependent, neurobiological processes directly lead to the consolidation of declarative memories” [1] . Diekelmann et al. hypothesized that both encoding and sleep-dependent consolidation during sleep involve prefrontal-hippocampal circuitry [3] .

Children have high amounts of slow wave sleep and sleep in general. Sleep has been shown to improve declarative and procedural memory in children and older age groups. Subjects were asked about their sleep and confirmed that they had a typical night sleep, consisting of 8–10 hours of sleep, average for adolescents [20] . We did not, however, specifically measure sleep. Lack of sleep can result in poor cognitive performance, which was not observed in our sample, and is consistent with the subjects’ report of a good night sleep and that poor sleepers were excluded from the sample.

A cross-over design would have provided additional confirmation at the individual subject level in contrast to our parallel group design. Our study was limited as the sample was opportune, from a California middle school, and was not epidemiologically based. No subjects approached declined to participate. No accelerated or failing students were included, although this was not a strict exclusion criterion. There were 3% African-American, 5% Asian, and 92% Caucasian. The sample population reflected the general school population in this geographic area, although Asians were underrepresented (12.8%).

Our sample size was relatively small and limited to early adolescence, ages 10–14, although twice the sample of Prehn-Kristensen et al. who found 10 to 13 year olds improved visual memory following sleep, especially to emotional pictures [13] . The 10–14 age group was deliberately chosen because of the importance of declarative memory on adolescent school performance and related social functioning [21] . Marked changes in sleep and sleep architecture are a defining feature of adolescence [22] . Disorders of adolescence frequently disrupt sleep. Twenty-five to forty percent of adolescents have sleep disorders that can have an important effect on daytime school and consequent social functioning [23] . Sleep disorders are even more prevalent in adolescents with psychiatric disorders and developmental disabilities [24] . It is important to have data on the effects of normal sleep on declarative memory in normal adolescents to better understand the consequences of lack of sleep and abnormal sleep patterns.

Given the importance of adolescent memory on school performance and consequent social functioning, a fuller understanding of the effects of sleep on memory consolidation is needed. Other studies are needed to investigate the specific effects of sleep on other types of memory, such as visual, procedural, and emotional. Understanding the role of normal sleep on memory consolidation in adolescence is critical in identifying the consequences of disrupted sleep in adolescent disorders and their treatment.

Author Contributions

Conceived and designed the experiments: KTP WEB. Performed the experiments: KTP. Analyzed the data: KTP WEB. Contributed reagents/materials/analysis tools: KTP WEB. Wrote the paper: KTP WEB.

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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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The complex relationship between sleep and cognitive reserve: a narrative review based on human studies.

1. Introduction

2. literature search strategy.

• The first section discusses findings related to sleep as a factor that enhances CR;
• The second section discusses findings regarding CR as a moderator between sleep and cognitive functions.

3.1. Sleep as Factor Involved in Cognitive Reserve

3.1.1. healthy population, 3.1.2. population with alzheimer’s disease.

3.2. Cognitive Reserve as a Moderator in the Relationship between Sleep and Cognitive Functions

3.2.1. healthy population, 3.2.2. population with sleep disorders, 3.2.3. population with parkinson’s disease and sleep disorders.

4. Discussion

Click here to enlarge figure

5. Conclusions

Author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• Mandolesi, L.; Gelfo, F.; Serra, L.; Montuori, S.; Polverino, A.; Curcio, G.; Sorrentino, G. Environmental Factors Promoting Neural Plasticity: Insights from Animal and Human Studies. Neural Plast. 2017 , 2017 , 7219461. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Sale, A.; Berardi, N.; Maffei, L. Environment and Brain Plasticity: Towards an Endogenous Pharmacotherapy. Physiol. Rev. 2014 , 94 , 189–234. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Berlucchi, G.; Buchtel, H.A. Neuronal Plasticity: Historical Roots and Evolution of Meaning. Exp. Brain Res. 2009 , 192 , 307–319. [ Google Scholar ] [ CrossRef ]
• Stern, Y. What Is Cognitive Reserve? Theory and Research Application of the Reserve Concept. J. Int. Neuropsychol. Soc. 2002 , 8 , 448–460. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Stern, Y.; Albert, S.; Tang, M.-X.; Tsai, W.-Y. Rate of Memory Decline in AD Is Related to Education and Occupation. Neurology 1999 , 53 , 1942. [ Google Scholar ] [ CrossRef ]
• Gelfo, F.; Mandolesi, L.; Serra, L.; Sorrentino, G.; Caltagirone, C. The Neuroprotective Effects of Experience on Cognitive Functions: Evidence from Animal Studies on the Neurobiological Bases of Brain Reserve. Neuroscience 2018 , 370 , 218–235. [ Google Scholar ] [ CrossRef ]
• Stern, Y.; Barnes, C.A.; Grady, C.; Jones, R.N.; Raz, N. Brain Reserve, Cognitive Reserve, Compensation, and Maintenance: Operationalization, Validity, and Mechanisms of Cognitive Resilience. Neurobiol. Aging 2019 , 83 , 124–129. [ Google Scholar ] [ CrossRef ]
• Stern, Y. Cognitive Reserve in Ageing and Alzheimer’s Disease. Lancet Neurol. 2012 , 11 , 1006–1012. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Cutuli, D.; Landolfo, E.; Petrosini, L.; Gelfo, F. Environmental Enrichment Effects on the Brain-Derived Neurotrophic Factor Expression in Healthy Condition, Alzheimer’s Disease, and Other Neurodegenerative Disorders. J. Alzheimers Dis. 2022 , 85 , 975–992. [ Google Scholar ] [ CrossRef ]
• Landolfo, E.; Cutuli, D.; Decandia, D.; Balsamo, F.; Petrosini, L.; Gelfo, F. Environmental Enrichment Protects against Neurotoxic Effects of Lipopolysaccharide: A Comprehensive Overview. IJMS 2023 , 24 , 5404. [ Google Scholar ] [ CrossRef ]
• Steffener, J.; Stern, Y. Exploring the Neural Basis of Cognitive Reserve in Aging. Biochim. Biophys. Acta 2012 , 1822 , 467–473. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Stern, Y.; Arenaza-Urquijo, E.M.; Bartrés-Faz, D.; Belleville, S.; Cantilon, M.; Chetelat, G.; Ewers, M.; Franzmeier, N.; Kempermann, G.; Kremen, W.S.; et al. Whitepaper: Defining and Investigating Cognitive Reserve, Brain Reserve, and Brain Maintenance. Alzheimers Dement. 2020 , 16 , 1305–1311. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Serra, L.; Gelfo, F. What Good Is the Reserve? A Translational Perspective for the Managing of Cognitive Decline. Neural Regen. Res. 2019 , 14 , 1219–1220. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Serra, L.; Gelfo, F.; Petrosini, L.; Di Domenico, C.; Bozzali, M.; Caltagirone, C. Rethinking the Reserve with a Translational Approach: Novel Ideas on the Construct and the Interventions. J. Alzheimers Dis. 2018 , 65 , 1065–1078. [ Google Scholar ] [ CrossRef ]
• Song, S.; Stern, Y.; Gu, Y. Modifiable Lifestyle Factors and Cognitive Reserve: A Systematic Review of Current Evidence. Ageing Res. Rev. 2022 , 74 , 101551. [ Google Scholar ] [ CrossRef ]
• Decandia, D.; Gelfo, F.; Landolfo, E.; Balsamo, F.; Petrosini, L.; Cutuli, D. Dietary Protection against Cognitive Impairment, Neuroinflammation and Oxidative Stress in Alzheimer’s Disease Animal Models of Lipopolysaccharide-Induced Inflammation. Int. J. Mol. Sci. 2023 , 24 , 5921. [ Google Scholar ] [ CrossRef ]
• Gelfo, F.; Petrosini, L.; Mandolesi, L.; Landolfo, E.; Caruso, G.; Balsamo, F.; Bonarota, S.; Bozzali, M.; Caltagirone, C.; Serra, L. Land/Water Aerobic Activities: Two Sides of the Same Coin. A Comparative Analysis on the Effects in Cognition of Alzheimer’s Disease. J. Alzheimers Dis. 2024 , 98 , 1181–1197. [ Google Scholar ] [ CrossRef ]
• Petrosini, L.; De Bartolo, P.; Foti, F.; Gelfo, F.; Cutuli, D.; Leggio, M.G.; Mandolesi, L. On Whether the Environmental Enrichment May Provide Cognitive and Brain Reserves. Brain Res. Rev. 2009 , 61 , 221–239. [ Google Scholar ] [ CrossRef ]
• Walker, M.P. The Role of Slow Wave Sleep in Memory Processing. J. Clin. Sleep Med. 2009 , 5 , S20–S26. [ Google Scholar ] [ CrossRef ]
• Raven, F.; Van der Zee, E.A.; Meerlo, P.; Havekes, R. The Role of Sleep in Regulating Structural Plasticity and Synaptic Strength: Implications for Memory and Cognitive Function. Sleep Med. Rev. 2018 , 39 , 3–11. [ Google Scholar ] [ CrossRef ]
• Killgore, W.D.S. Effects of Sleep Deprivation on Cognition. Prog. Brain Res. 2010 , 185 , 105–129. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zijlmans, J.L.; Riemens, M.S.; Vernooij, M.W.; Ikram, M.A.; Luik, A.I. Sleep, 24-Hour Activity Rhythms, and Cognitive Reserve: A Population-Based Study. J. Alzheimers Dis. 2023 , 91 , 663–672. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Keene, A.C.; Duboue, E.R. The Origins and Evolution of Sleep. J. Exp. Biol. 2018 , 221 , jeb159533. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Acerbi, A.; McNamara, P.; Nunn, C.L. To Sleep or Not to Sleep: The Ecology of Sleep in Artificial Organisms. BMC Ecol. 2008 , 8 , 10. [ Google Scholar ] [ CrossRef ]
• Frank, M.G. The Ontogenesis of Mammalian Sleep: Form and Function. Curr. Sleep Med. Rep. 2020 , 6 , 267–279. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zielinski, M.R.; McKenna, J.T.; McCarley, R.W. Functions and Mechanisms of Sleep. AIMS Neurosci. 2016 , 3 , 67–104. [ Google Scholar ] [ CrossRef ]
• Borbély, A.A. A Two Process Model of Sleep Regulation. Hum. Neurobiol. 1982 , 1 , 195–204. [ Google Scholar ]
• Borbély, A.A.; Achermann, P. Sleep Homeostasis and Models of Sleep Regulation. J. Biol. Rhythms. 1999 , 14 , 557–568. [ Google Scholar ] [ CrossRef ]
• Moore, R.Y.; Eichler, V.B. Loss of a Circadian Adrenal Corticosterone Rhythm Following Suprachiasmatic Lesions in the Rat. Brain Res. 1972 , 42 , 201–206. [ Google Scholar ] [ CrossRef ]
• Borbély, A.A.; Daan, S.; Wirz-Justice, A.; Deboer, T. The Two-Process Model of Sleep Regulation: A Reappraisal. J. Sleep Res. 2016 , 25 , 131–143. [ Google Scholar ] [ CrossRef ]
• Eydipour, Z.; Nasehi, M.; Vaseghi, S.; Jamaldini, S.H.; Zarrindast, M.-R. The Role of 5-HT4 Serotonin Receptors in the CA1 Hippocampal Region on Memory Acquisition Impairment Induced by Total (TSD) and REM Sleep Deprivation (RSD). Physiol. Behav. 2020 , 215 , 112788. [ Google Scholar ] [ CrossRef ]
• Mukai, Y.; Yamanaka, A. Functional Roles of REM Sleep. Neurosci. Res. 2023 , 189 , 44–53. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Xia, Z.; Storm, D. Role of Circadian Rhythm and REM Sleep for Memory Consolidation. Neurosci. Res. 2017 , 118 , 13–20. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Villafuerte, G.; Miguel-Puga, A.; Rodríguez, E.M.; Machado, S.; Manjarrez, E.; Arias-Carrión, O. Sleep Deprivation and Oxidative Stress in Animal Models: A Systematic Review. Oxid. Med. Cell. Longev. 2015 , 2015 , 234952. [ Google Scholar ] [ CrossRef ]
• Antonioni, A.; Raho, E.M.; Sensi, M.; Di Lorenzo, F.; Fadiga, L.; Koch, G. A New Perspective on Positive Symptoms: Expression of Damage or Self-Defence Mechanism of the Brain? Neurol. Sci. 2024 , 45 , 2347–2351. [ Google Scholar ] [ CrossRef ]
• Fox, S. Accessing Active Inference Theory through Its Implicit and Deliberative Practice in Human Organizations. Entropy 2021 , 23 , 1521. [ Google Scholar ] [ CrossRef ]
• Priorelli, M.; Stoianov, I.P. Flexible Intentions: An Active Inference Theory. Front. Comput. Neurosci. 2023 , 17 , 1128694. [ Google Scholar ] [ CrossRef ]
• Diagnostic and Statistical Manual of Mental Disorders. Available online: https://dsm.psychiatryonline.org/doi/book/10.1176/appi.books.9780890425596 (accessed on 31 May 2024).
• Sateia, M.J. International Classification of Sleep Disorders-Third Edition. Chest 2014 , 146 , 1387–1394. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Baglioni, C.; Battagliese, G.; Feige, B.; Spiegelhalder, K.; Nissen, C.; Voderholzer, U.; Lombardo, C.; Riemann, D. Insomnia as a Predictor of Depression: A Meta-Analytic Evaluation of Longitudinal Epidemiological Studies. J. Affect. Disord. 2011 , 135 , 10–19. [ Google Scholar ] [ CrossRef ]
• Benz, F.; Meneo, D.; Baglioni, C.; Hertenstein, E. Insomnia Symptoms as Risk Factor for Somatic Disorders: An Umbrella Review of Systematic Reviews and Meta-Analyses. J. Sleep Res. 2023 , 32 , e13984. [ Google Scholar ] [ CrossRef ]
• Hertenstein, E.; Benz, F.; Schneider, C.L.; Baglioni, C. Insomnia-A Risk Factor for Mental Disorders. J. Sleep Res. 2023 , 32 , e13930. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Daley, M.; Morin, C.M.; LeBlanc, M.; Grégoire, J.-P.; Savard, J. The Economic Burden of Insomnia: Direct and Indirect Costs for Individuals with Insomnia Syndrome, Insomnia Symptoms, and Good Sleepers. Sleep 2009 , 32 , 55–64. [ Google Scholar ] [ PubMed ]
• Leger, M.; Quiedeville, A.; Paizanis, E.; Natkunarajah, S.; Freret, T.; Boulouard, M.; Schumann-Bard, P. Environmental Enrichment Enhances Episodic-Like Memory in Association with a Modified Neuronal Activation Profile in Adult Mice. PLoS ONE 2012 , 7 , e48043. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• White, D.P. Pathogenesis of Obstructive and Central Sleep Apnea. Am. J. Respir. Crit. Care Med. 2005 , 172 , 1363–1370. [ Google Scholar ] [ CrossRef ]
• Howell, M.J. Rapid Eye Movement Sleep Behavior Disorder and Other Rapid Eye Movement Parasomnias. Continuum 2020 , 26 , 929–945. [ Google Scholar ] [ CrossRef ]
• Miglis, M.G.; Adler, C.H.; Antelmi, E.; Amaldi, D.; Baldelli, L.; Boeve, B.F.; Cesari, M.; Antonia, I.D.; Diederich, N.J.; Doppler, K.; et al. Biomarkers of Conversion to α-Synucleinopathy in Isolated Rapid-Eye-Movement Sleep Behaviour Disorder. Lancet Neurol. 2021 , 20 , 671–684. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Iranzo, A.; Ramos, L.A.; Novo, S. The Isolated Form of Rapid Eye Movement Sleep Behavior Disorder: The Upcoming Challenges. Sleep Med. Clin. 2021 , 16 , 335–348. [ Google Scholar ] [ CrossRef ]
• Gnarra, O.; Wulf, M.-A.; Schäfer, C.; Nef, T.; Bassetti, C.L.A. Rapid Eye Movement Sleep Behavior Disorder: A Narrative Review from a Technological Perspective. Sleep 2023 , 46 , zsad030. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Buysse, D.J. Sleep Health: Can We Define It? Does It Matter? Sleep 2014 , 37 , 9–17. [ Google Scholar ] [ CrossRef ]
• Meltzer, L.J.; Williamson, A.A.; Mindell, J.A. Pediatric Sleep Health: It Matters, and so Does How We Define It. Sleep Med. Rev. 2021 , 57 , 101425. [ Google Scholar ] [ CrossRef ]
• Cappuccio, F.P.; Taggart, F.M.; Kandala, N.-B.; Currie, A.; Peile, E.; Stranges, S.; Miller, M.A. Meta-Analysis of Short Sleep Duration and Obesity in Children and Adults. Sleep 2008 , 31 , 619–626. [ Google Scholar ] [ CrossRef ]
• Gallicchio, L.; Kalesan, B. Sleep Duration and Mortality: A Systematic Review and Meta-Analysis. J. Sleep Res. 2009 , 18 , 148–158. [ Google Scholar ] [ CrossRef ]
• Bock, J.; Covassin, N.; Somers, V. Excessive Daytime Sleepiness: An Emerging Marker of Cardiovascular Risk. Heart 2022 , 108 , 1761–1766. [ Google Scholar ] [ CrossRef ]
• Yan, B.; Yang, J.; Zhao, B.; Fan, Y.; Wang, W.; Ma, X. Objective Sleep Efficiency Predicts Cardiovascular Disease in a Community Population: The Sleep Heart Health Study. J. Am. Heart Assoc. 2021 , 10 , e016201. [ Google Scholar ] [ CrossRef ]
• Xi, B.; He, D.; Zhang, M.; Xue, J.; Zhou, D. Short Sleep Duration Predicts Risk of Metabolic Syndrome: A Systematic Review and Meta-Analysis. Sleep Med. Rev. 2014 , 18 , 293–297. [ Google Scholar ] [ CrossRef ]
• Becker, S.P.; Sidol, C.A.; Van Dyk, T.R.; Epstein, J.N.; Beebe, D.W. Intraindividual Variability of Sleep/Wake Patterns in Relation to Child and Adolescent Functioning: A Systematic Review. Sleep Med. Rev. 2017 , 34 , 94–121. [ Google Scholar ] [ CrossRef ]
• Yan, B.; Zhao, B.; Jin, X.; Xi, W.; Yang, J.; Yang, L.; Ma, X. Sleep Efficiency May Predict Depression in a Large Population-Based Study. Front. Psychiatry 2022 , 13 , 838907. [ Google Scholar ] [ CrossRef ]
• Sharma, S.; Kavuru, M. Sleep and Metabolism: An Overview. Int. J. Endocrinol. 2010 , 2010 , 270832. [ Google Scholar ] [ CrossRef ]
• Tai, X.Y.; Chen, C.; Manohar, S.; Husain, M. Impact of Sleep Duration on Executive Function and Brain Structure. Commun. Biol. 2022 , 5 , 201. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yoo, S.-S.; Gujar, N.; Hu, P.; Jolesz, F.A.; Walker, M.P. The Human Emotional Brain without Sleep--a Prefrontal Amygdala Disconnect. Curr. Biol. 2007 , 17 , R877–R878. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Meneo, D.; Samea, F.; Tahmasian, M.; Baglioni, C. The Emotional Component of Insomnia Disorder: A Focus on Emotion Regulation and Affect Dynamics in Relation to Sleep Quality and Insomnia. J. Sleep Res. 2023 , 32 , e13983. [ Google Scholar ] [ CrossRef ]
• Kerner, N.; Goldberg, T.E.; Cohen, H.R.; Phillips, J.G.; Cohen, D.E.; Andrews, H.; Pelton, G.; Devanand, D.P. Sleep-Wake Behavior, Perceived Fatigability, and Cognitive Reserve in Older Adults. Alzheimers Dement. 2024 , 20 , 4020–4031. [ Google Scholar ] [ CrossRef ]
• Buzsáki, G. Hippocampal Sharp Wave-ripple: A Cognitive Biomarker for Episodic Memory and Planning. Hippocampus 2015 , 25 , 1073–1188. [ Google Scholar ] [ CrossRef ]
• Khan, M.A.; Al-Jahdali, H. The Consequences of Sleep Deprivation on Cognitive Performance. Neurosciences 2023 , 28 , 91–99. [ Google Scholar ] [ CrossRef ]
• Diekelmann, S.; Born, J. The Memory Function of Sleep. Nat. Rev. Neurosci. 2010 , 11 , 114–126. [ Google Scholar ] [ CrossRef ]
• Papalambros, N.A.; Santostasi, G.; Malkani, R.G.; Braun, R.; Weintraub, S.; Paller, K.A.; Zee, P.C. Acoustic Enhancement of Sleep Slow Oscillations and Concomitant Memory Improvement in Older Adults. Front. Hum. Neurosci. 2017 , 11 , 109. [ Google Scholar ] [ CrossRef ]
• Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. BMJ 2021 , 372 , n71. [ Google Scholar ] [ CrossRef ]
• Zimmerman, M.E.; Bigal, M.E.; Katz, M.J.; Brickman, A.M.; Lipton, R.B. Sleep Onset/Maintenance Difficulties and Cognitive Function in Nondemented Older Adults: The Role of Cognitive Reserve. J. Int. Neuropsychol. Soc. 2012 , 18 , 461–470. [ Google Scholar ] [ CrossRef ]
• Parker, D.; Bucks, R.S.; Rainey-Smith, S.R.; Hodgson, E.; Fine, L.; Sohrabi, H.R.; Martins, R.N.; Weinborn, M. Sleep Mediates Age-Related Executive Function for Older Adults with Limited Cognitive Reserve. J. Int. Neuropsychol. Soc. 2021 , 27 , 711–721. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yeh, A.-Y.; Pressler, S.J.; Algase, D.; Struble, L.M.; Pozehl, B.J.; Berger, A.M.; Giordani, B.J. Sleep-Wake Disturbances and Episodic Memory in Older Adults. Biol. Res. Nurs. 2021 , 23 , 141–150. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ourry, V.; Rehel, S.; André, C.; Mary, A.; Paly, L.; Delarue, M.; Requier, F.; Hendy, A.; Collette, F.; Marchant, N.L.; et al. Effect of Cognitive Reserve on the Association between Slow Wave Sleep and Cognition in Community-Dwelling Older Adults. Aging 2023 , 15 , 9275–9292. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Alchanatis, M.; Zias, N.; Deligiorgis, N.; Amfilochiou, A.; Dionellis, G.; Orphanidou, D. Sleep Apnea-Related Cognitive Deficits and Intelligence: An Implication of Cognitive Reserve Theory. J. Sleep Res. 2005 , 14 , 69–75. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Olaithe, M.; Pushpanathan, M.; Hillman, D.; Eastwood, P.R.; Hunter, M.; Skinner, T.; James, A.; Wesnes, K.A.; Bucks, R.S. Cognitive Profiles in Obstructive Sleep Apnea: A Cluster Analysis in Sleep Clinic and Community Samples. J. Clin. Sleep Med. 2020 , 16 , 1493–1505. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hlaing, E.E.; Dollinger, S.M.C.; Brown, T.M. The Role of Education in Cognitive Functions among Middle-Age and Older Patients with Untreated Obstructive Sleep Apnea. Sleep Sci. 2021 , 14 , 319–329. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• D’Este, G.; Berra, F.; Carli, G.; Leitner, C.; Marelli, S.; Zucconi, M.; Casoni, F.; Ferini-Strambi, L.; Galbiati, A. Cognitive Reserve in Isolated Rapid Eye-Movement Sleep Behavior Disorder. Brain Sci. 2023 , 13 , 176. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Prete, M.; Cellini, N.; Ronconi, L.; Di Rosa, E. Cognitive Reserve Moderates the Relationship between Sleep Difficulties and Executive Functions in Patients with Parkinson’s Disease. Sleep Med. 2023 , 111 , 82–85. [ Google Scholar ] [ CrossRef ]
• Zavecz, Z.; Shah, V.D.; Murillo, O.G.; Vallat, R.; Mander, B.A.; Winer, J.R.; Jagust, W.J.; Walker, M.P. NREM Sleep as a Novel Protective Cognitive Reserve Factor in the Face of Alzheimer’s Disease Pathology. BMC Med. 2023 , 21 , 156. [ Google Scholar ] [ CrossRef ]
• Diekelmann, S. Sleep for Cognitive Enhancement. Front. Syst. Neurosci. 2014 , 8 , 46. [ Google Scholar ] [ CrossRef ]
• Born, J.; Rasch, B.; Gais, S. Sleep to Remember. Neuroscientist 2006 , 12 , 410–424. [ Google Scholar ] [ CrossRef ]
• Wick, A.; Rasch, B. Targeted Memory Reactivation during Slow-Wave Sleep vs. Sleep Stage N2: No Significant Differences in a Vocabulary Task. Learn. Mem. 2023 , 30 , 192–200. [ Google Scholar ] [ CrossRef ]
• Manly, J.J.; Touradji, P.; Tang, M.-X.; Stern, Y. Literacy and Memory Decline among Ethnically Diverse Elders. J. Clin. Exp. Neuropsychol. 2003 , 25 , 680–690. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wilckens, K.A.; Ferrarelli, F.; Walker, M.P.; Buysse, D.J. Slow-Wave Activity Enhancement to Improve Cognition. Trends Neurosci. 2018 , 41 , 470–482. [ Google Scholar ] [ CrossRef ]
• Himali, J.J.; Baril, A.-A.; Cavuoto, M.G.; Yiallourou, S.; Wiedner, C.D.; Himali, D.; DeCarli, C.; Redline, S.; Beiser, A.S.; Seshadri, S.; et al. Association Between Slow-Wave Sleep Loss and Incident Dementia. JAMA Neurol. 2023 , 80 , 1326–1333. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Stern, Y. Cognitive Reserve. Neuropsychologia 2009 , 47 , 2015–2028. [ Google Scholar ] [ CrossRef ]
• Pettigrew, C.; Soldan, A. Defining Cognitive Reserve and Implications for Cognitive Aging. Curr. Neurol. Neurosci. Rep. 2019 , 19 , 1. [ Google Scholar ] [ CrossRef ]
• Ramos, H.; Alacreu, M.; Guerrero, M.D.; Sánchez, R.; Moreno, L. Lifestyle Variables Such as Daily Internet Use, as Promising Protective Factors against Cognitive Impairment in Patients with Subjective Memory Complaints. Preliminary Results. J. Pers. Med. 2021 , 11 , 1366. [ Google Scholar ] [ CrossRef ]
• Kaur, A.; Sonal, A.; Ghosh, T.; Ahamed, F. Cognitive Reserve and Other Determinants of Cognitive Function in Older Adults: Insights from a Community-Based Cross-Sectional Study. J. Fam. Med. Prim. Care 2023 , 12 , 1957–1964. [ Google Scholar ] [ CrossRef ]
• Savarimuthu, A.; Ponniah, R.J. Cognition and Cognitive Reserve. Integr. Psychol. Behav. Sci. 2024 , 58 , 483–501. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Bubu, O.M.; Brannick, M.; Mortimer, J.; Umasabor-Bubu, O.; Sebastião, Y.V.; Wen, Y.; Schwartz, S.; Borenstein, A.R.; Wu, Y.; Morgan, D.; et al. Sleep, Cognitive Impairment, and Alzheimer’s Disease: A Systematic Review and Meta-Analysis. Sleep 2017 , 40 . [ Google Scholar ] [ CrossRef ]
• Vance, D.E.; Roberson, A.J.; McGuinness, T.M.; Fazeli, P.L. How Neuroplasticity and Cognitive Reserve Protect Cognitive Functioning. J. Psychosoc. Nurs. Ment. Health Serv. 2010 , 48 , 23–30. [ Google Scholar ] [ CrossRef ]
• Fortier-Brochu, E.; Beaulieu-Bonneau, S.; Ivers, H.; Morin, C.M. Insomnia and Daytime Cognitive Performance: A Meta-Analysis. Sleep Med. Rev. 2012 , 16 , 83–94. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Uddin, M.S.; Tewari, D.; Mamun, A.A.; Kabir, M.T.; Niaz, K.; Wahed, M.I.I.; Barreto, G.E.; Ashraf, G.M. Circadian and Sleep Dysfunction in Alzheimer’s Disease. Ageing Res. Rev. 2020 , 60 , 101046. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Stern, Y.; Barulli, D. Cognitive Reserve. Handb. Clin. Neurol. 2019 , 167 , 181–190. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Cadar, D.; Robitaille, A.; Clouston, S.; Hofer, S.M.; Piccinin, A.M.; Muniz-Terrera, G. An International Evaluation of Cognitive Reserve and Memory Changes in Early Old Age in 10 European Countries. Neuroepidemiology 2017 , 48 , 9–20. [ Google Scholar ] [ CrossRef ]

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Balsamo, F.; Berretta, E.; Meneo, D.; Baglioni, C.; Gelfo, F. The Complex Relationship between Sleep and Cognitive Reserve: A Narrative Review Based on Human Studies. Brain Sci. 2024 , 14 , 654. https://doi.org/10.3390/brainsci14070654

Balsamo F, Berretta E, Meneo D, Baglioni C, Gelfo F. The Complex Relationship between Sleep and Cognitive Reserve: A Narrative Review Based on Human Studies. Brain Sciences . 2024; 14(7):654. https://doi.org/10.3390/brainsci14070654

Balsamo, Francesca, Erica Berretta, Debora Meneo, Chiara Baglioni, and Francesca Gelfo. 2024. "The Complex Relationship between Sleep and Cognitive Reserve: A Narrative Review Based on Human Studies" Brain Sciences 14, no. 7: 654. https://doi.org/10.3390/brainsci14070654

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Neuroscientists just uncovered a fascinating link between sleep, memory, and breathing

In a groundbreaking study, researchers have discovered a significant link between breathing patterns during sleep and the brain’s ability to consolidate memories. This finding, published in Nature Communications , sheds light on how the simple act of breathing might play a pivotal role in organizing the brain’s memory-related activities during sleep.

The motivation behind this innovative research stemmed from a desire to understand the complex interplay between various physiological processes and memory consolidation during sleep. Prior research had established the critical role of specific sleep stages, particularly non-rapid eye movement (NREM) sleep, in memory strengthening.

During NREM sleep, the brain goes through distinct oscillations or rhythmic activities, which are believed to be crucial for transferring and solidifying memories. However, the intricacies of how these processes are regulated remained a mystery. With growing evidence suggesting that respiration influences cognitive functions during wakefulness, the researchers were curious to explore whether breathing might have a similar impact during sleep.

To grasp the study’s significance, it’s essential to understand two key concepts: sleep-related oscillations and memory reactivation. Sleep-related oscillations refer to the rhythmic activities in the brain during sleep, notably slow oscillations and sleep spindles. These oscillations are not just random brain activities but are thought to be crucial for memory consolidation – the process of transforming new, potentially fragile memories into stable, long-term ones. Memory reactivation is a phenomenon where memories formed during wakefulness are ‘replayed’ and strengthened during sleep, mainly during these specific oscillations.

“We know quite well that the memory function of sleep relies on the precise interplay of sleep-related oscillations. On the other hand, there is growing evidence that respiration impacts neural activity and cognition during wake. Hence, we were curious to assess whether respiration might take on a similar role during sleep by shaping sleep rhythms and ensuing cognitive processes,” explained study author Thomas Schreiner, leader of the Emmy Noether junior research group at Ludwig-Maximilians-Universität München’s Department of Psychology.

Conducted at a sleep laboratory, the study involved 20 healthy participants who were monitored over two separate sessions, with each session spaced at least a week apart. This design was intentional to avoid any carryover effects from the first session to the second.

Before diving into the main experiment, participants were familiarized with the sleep laboratory environment through an adaptation nap. This step was crucial to ensure that participants were comfortable and to minimize any potential disturbances or anxiety that might arise from sleeping in an unfamiliar setting.

Once the actual experiment commenced, participants engaged in a series of tasks. They started with a psychomotor vigilance task (PVT), a standard test to assess alertness and reaction times. This was followed by a memory task, where participants learned associations between 120 verbs and images of either objects or scenes. This learning phase was crucial, as it set the stage for later testing memory consolidation.

The heart of the experiment was the nap period. Participants were given 120 minutes to sleep, during which their brain activity, muscle activity, heart activity, and breathing were recorded. After the nap, participants’ alertness was reassessed using the PVT, and their memory performance was tested again.

To analyze the data, the researchers focused on specific phases of the respiratory cycle and their synchronization with brain oscillations recorded in the EEG. They looked for patterns and correlations, seeking to understand how these physiological processes might be interlinked.

A key discovery was the significant association between the rhythm of breathing and specific brain activities during sleep, known as slow oscillations and sleep spindles. Slow oscillations are a type of deep, slow brain wave that occur during restorative sleep. Sleep spindles, on the other hand, are sudden bursts of oscillatory brain activity.

The researchers found that these two types of brain activities were intricately synchronized with breathing patterns. Slow oscillations often appeared just before the peak of inhalation, whereas sleep spindles tended to occur right after the inhalation peak. This synchronization appeared to be a fundamental aspect of how the brain organizes its memory consolidation process during sleep.

Delving deeper, Schreiner and his colleagues found that this coupling between breathing patterns and sleep-related brain oscillations had a significant impact on the process of memory reactivation. The strength of the synchronization between breathing and these brain oscillations correlated with the extent to which memories were reactivated during sleep.

Essentially, the way participants breathed while asleep influenced the effectiveness of their memory processing. This finding suggests that the simple act of breathing could play a pivotal role in organizing the brain’s memory-related activities during sleep.

The findings highlight the fact “that sleep is really important both for our physical wellbeing but also for our cognitive functioning,” Schreiner told PsyPost. “Accordingly, it is quite important to maintain/establish a good sleep hygiene and to act accordingly in case of compromised sleep (e.g. due to sleep related breathing disorders).”

However, it’s important to note that the study’s findings were correlational. This means that while a connection between breathing patterns and brain activities during sleep was established, it does not necessarily imply a direct cause-and-effect relationship. Understanding the exact nature of this relationship requires further investigation.

“Our results at hand are correlational in nature. That means we just describe the relationship between respiration during sleep and sleep related rhythms. While this was an important first step, it will be crucial to assess the causality of this relationship (e.g. by directly manipulating breathing during sleep and assess its impact on sleep rhythms).”

Additionally, the sample was predominantly female, with an average age of around 21 years. “A crucial step will be to move on to more diverse populations in terms of age and sleep quality,” Schreiner said.

Future research directions are plentiful and promising. One avenue is exploring interventions that could enhance memory consolidation during sleep by targeting the relationship between breathing and brain rhythms. This could have profound implications, especially for older adults who often experience declines in both respiratory function and memory capabilities.

“I am every time surprised, even though we build upon a rich body of previous work on sleep and memory, how many facets of this relationship we still do not understand,” Schreiner told PsyPost. “Hence, there is still much to do.”

The study, “ Respiration modulates sleep oscillations and memory reactivation in humans “, was authored by Thomas Schreiner, Marit Petzka, Tobias Staudigl, and Bernhard P. Staresina.

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NREM sleep brain networks modulate cognitive recovery from sleep deprivation

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Decrease in cognitive performance after sleep deprivation followed by recovery after sleep suggests its key role, and especially non-rapid eye movement (NREM) sleep, in the maintenance of cognition. It remains unknown whether brain network reorganization in NREM sleep stages N2 and N3 can uniquely be mapped onto individual differences in cognitive performance after a recovery nap following sleep deprivation. Using resting state functional magnetic resonance imaging (fMRI), we quantified the integration and segregation of brain networks during NREM sleep stages N2 and N3 while participants took a 1-hour nap following 24-hour sleep deprivation, compared to well-rested wakefulness. Here, we advance a new analytic framework called the hierarchical segregation index (HSI) to quantify network segregation across spatial scales, from whole-brain to the voxel level, by identifying spatio-temporally overlapping large-scale networks and the corresponding voxel-to-region hierarchy. Our results show that network segregation increased in the default mode, dorsal attention and somatomotor networks during NREM sleep compared to wakefulness. Segregation within the visual, limbic, and executive control networks exhibited N2 versus N3 sleep-specific voxel-level patterns. More segregation during N3 was associated with worse recovery of working memory, executive attention, and psychomotor vigilance after the nap. The level of spatial resolution of network segregation varied among brain regions and was associated with the recovery of performance in distinct cognitive tasks. We demonstrated the sensitivity and reliability of voxel-level HSI to provide key insights into within-region variation, suggesting a mechanistic understanding of how NREM sleep replenishes cognition after sleep deprivation.

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Western News

In a step toward combating dementia, Western researchers have received a $1.1 million grant from the Weston Family Foundation to explore the potential of gentle brain-synched sounds to improve sleep and memory performance in older adults. The method, which involves playing those gentle sounds during sleep, is called phase-locked auditory stimulation.

This research, led by psychology professor Laura Batterink, aims to advance brain health and sleep science among older adults with amnestic mild cognitive impairment (AMCI). The condition is characterized by noticeable memory problems that are greater than expected for a person’s age, but not severe enough to interfere significantly with daily life.

“Our goal with this grant is to use phase-locked auditory stimulation to enhance slow-wave sleep in older adults, particularly those at high risk for Alzheimer’s disease,” said Batterink, a member of the Western Institute for Neuroscience (WIN). “Poor sleep can create a vicious cycle of declining brain health, which further worsens sleep. By intervening with this method, we hope to break this cycle and improve overall brain health.”

Understanding phase-locked auditory stimulation

Phase-locked auditory stimulation involves playing gentle sounds at specific times during sleep to sync with the brain’s naturally slowed waves. By using real-time technology to monitor brain activity, researchers can send these sounds at just the right moment to strengthen the brain waves that help with memory consolidation and cognitive functions.

“This method involves reading people’s electroencephalography (EEG) results while they sleep and using real-time algorithms to predict slow oscillations. By emitting a quiet auditory pulse at the right moment, we can increase the amplitude of these slow oscillations, which has been shown to improve memory consolidation and other aspects of cognition,” said Batterink.

Her research focuses on sleep-dependent memory consolidation, which is crucial for retaining information learned during the day. The new project is particularly significant given the world’s aging population and the increasing prevalence of dementia.

“If we can demonstrate this method is effective, it could potentially be used as a widespread biofeedback system to improve sleep and, consequently, brain health,” Batterink said.

Lyle Muller

Lyle Muller, a mathematics professor, is co-principal investigator on the project.

“My lab is developing algorithms to process neural signals in real-time and play sounds at specific points during sleep rhythms. By reducing the time between signal processing and sensory stimulation, we hope to see significant improvements in memory,” he said.

Muller highlighted the potential of those algorithms to detect slow-wave amplitudes – a sign of deep sleep – in older adults, which tend to be smaller and harder to detect.

The collaborative study brings together experts in psychology, neuroscience and computational science. The research team, which also includes Adrian Owen, Stefan Kohler, Stephen Pasternak and Jaspreet Bhangu, will conduct the study in two phases. In the first phase, participants will undergo sleep studies in a controlled laboratory setting.

“They will spend three nights receiving auditory stimulation and three nights with sham stimulation to serve as a control. This allows us to directly compare the effects,” Batterink said. The second phase will involve using portable EEG systems to test the method in participants’ homes, making it more accessible and scalable.

“This research could be directly translatable to real-world applications, potentially helping a large number of aging individuals,” she said.

Combating poor sleep and brain health decline

Recruitment for the study will target individuals aged 60 and older, including those with a clinical diagnosis of AMCI and healthy older adults. The team will assess the impact of the sound stimulation on sleep quality and memory performance.

The team is excited about the potential real-world applications of their research.

“If successful, this method could be a game-changer for brain health in aging populations. It’s exciting to work on something that could have a direct, positive impact on people’s lives.” – Laura Batterink, psychology professor and study lead

Recruitment for the study will begin in September. Participants must be 60 years or older and meet specific health criteria in order to be eligible Those interested in participating in the study can contact Laura Batterink at [email protected] for more information.

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Temporal cluster-based organization of sleep spindles underlies motor memory consolidation

Affiliations.

• 1 CIAMS, Université Paris-Saclay, 91405 Orsay, France.
• 2 CIAMS, Université d'Orléans, 45067 Orléans, France.
• 3 McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada H3A 2B4.
• 4 Functional Neuroimaging Unit, C.R.I.U.G.M, Montréal, QC, Canada H3W 1W5.
• 5 Department of Psychology, Université de Montréal, Montréal, QC, Canada H3T 1J4.
• 6 Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Montréal, QC, Canada H4J 1C5.
• 7 Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada H3A 2B4.
• PMID: 38196349
• PMCID: PMC10777148 (available on 2025-01-10 )
• DOI: 10.1098/rspb.2023.1408

Sleep benefits motor memory consolidation, which is mediated by sleep spindle activity and associated memory reactivations during non-rapid eye movement (NREM) sleep. However, the particular role of NREM2 and NREM3 sleep spindles and the mechanisms triggering this memory consolidation process remain unclear. Here, simultaneous electroencephalographic and functional magnetic resonance imaging (EEG-fMRI) recordings were collected during night-time sleep following the learning of a motor sequence task. Adopting a time-based clustering approach, we provide evidence that spindles iteratively occur within clustered and temporally organized patterns during both NREM2 and NREM3 sleep. However, the clustering of spindles in trains is related to motor memory consolidation during NREM2 sleep only. Altogether, our findings suggest that spindles' clustering and rhythmic occurrence during NREM2 sleep may serve as an intrinsic rhythmic sleep mechanism for the timed reactivation and subsequent consolidation of motor memories, through synchronized oscillatory activity within a subcortical-cortical network involved during learning.

Keywords: EEG-fMRI; hippocampus; memory consolidation; motor sequence learning; sleep spindle; striatum.

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Conflict of interest statement

We declare we have no competing interests.

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• Krakauer JW, Hadjiosif AM, Xu J, Wong AL, Haith AM. 2019. Motor learning. Compr. Physiol. 9, 613-663. (10.1002/cphy.c170043) - DOI - PubMed
• Robertson EM. 2009. From creation to consolidation: a novel framework for memory processing. PLoS Biol. 7, e1000019. (10.1371/journal.pbio.1000019) - DOI - PMC - PubMed
• Doyon J, Gabitov E, Vahdat S, Lungu O, Boutin A. 2018. Current issues related to motor sequence learning in humans. Curr. Opin. Behav. Sci. 20, 89-97. (10.1016/j.cobeha.2017.11.012) - DOI
• Dudai Y, Karni A, Born J. 2015. The consolidation and transformation of memory. Neuron 88, 20-32. (10.1016/j.neuron.2015.09.004) - DOI - PubMed
• Boutin A, Pinsard B, Boré A, Carrier J, Fogel SM, Doyon J. 2018. Transient synchronization of hippocampo-striato-thalamo-cortical networks during sleep spindle oscillations induces motor memory consolidation. Neuroimage 169, 419-430. (10.1016/j.neuroimage.2017.12.066) - DOI - PubMed
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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  

Pediatric Research ( 2024 ) Cite this article

Metrics details

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.

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.

Denisova, K. English translation of the first study reporting cyclical periods of increased respiration and eye and body motility during sleep in infants in 1926, with commentary. Sleep 47 , zsad219 (2024).

Article   PubMed   Google Scholar  

Wagner, I. F. The establishment of a criterion of depth of sleep in the newborn infant. Pedagog Semin J. Genet Psychol. 51 , 17–59 (1937).

Google Scholar  

Aserinsky, E. & Kleitman, N. Regularly occurring periods of eye motility, and concomitant phenomena, during sleep. Science (1979) 118 , 273–274 (1953).

CAS   Google Scholar  

Aserinsky, E. & Kleitman, N. A motility cycle in sleeping infants as manifested by ocular and gross bodily activity. J. Appl Physiol. 8 , 11–18 (1955).

Article   CAS   PubMed   Google Scholar  

Wolff, P. H. Observations on newborn infants. Psychosom. Med 21 , 110–118 (1959).

Roffwarg, H. P., Muzio, J. N. & Dement, W. C. Ontogenetic Development of the Human Sleep-Dream Cycle: The prime role of” dreaming sleep” in early life may be in the development of the central nervous system. Science (1979) 152 , 604–619 (1966).

Bik, A. et al. A scoping review of behavioral sleep stage classification methods for preterm infants. Sleep. Med 90 , 74–82 (2022).

Werth, J. et al. Unobtrusive sleep state measurements in preterm infants–A review. Sleep. Med Rev. 32 , 109–122 (2017).

Sentner, T. et al. The Sleep Well Baby project: an automated real-time sleep–wake state prediction algorithm in preterm infants. Sleep 45 , zsac143 (2022).

Article   PubMed   PubMed Central   Google Scholar  

Ansari, A. H. et al. A convolutional neural network outperforming state-of-the-art sleep staging algorithms for both preterm and term infants. J. Neural Eng. 17 , 016028 (2020).

Korotchikova, I., Stevenson, N. J., Livingstone, V., Ryan, C. A. & Boylan, G. B. Sleep–wake cycle of the healthy term newborn infant in the immediate postnatal period. Clin. Neurophysiol. 127 , 2095–2101 (2016).

Figueiredo, B., Dias, C. C., Pinto, T. M. & Field, T. Infant sleep-wake behaviors at two weeks, three and six months. Infant Behav. Dev. 44 , 169–178 (2016).

Lenehan, S. M., Fogarty, L., O’Connor, C., Mathieson, S. & Boylan, G. B. The architecture of early childhood sleep over the first two years. Matern Child Health J. 27 , 226–250 (2023).

Bennet, L., Walker, D. W. & Horne, R. S. C. Waking up too early–the consequences of preterm birth on sleep development. J. Physiol. 596 , 5687–5708 (2018).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Dereymaeker, A. et al. Review of sleep-EEG in preterm and term neonates. Early Hum. Dev. 113 , 87–103 (2017).

Mirmiran, M., Maas, Y. G. H. & Ariagno, R. L. Development of fetal and neonatal sleep and circadian rhythms. Sleep. Med Rev. 7 , 321–334 (2003).

Giganti, F. et al. Activity patterns assessed throughout 24‐hour recordings in preterm and near term infants. Dev. Psychobiol. 38 , 133–142 (2001).

Kohyama, J. & Iwakawa, Y. Developmental changes in phasic sleep parameters as reflections of the brain-stem maturation: polysomnographical examinations of infants, including premature neonates. Electroencephalogr. Clin. Neurophysiol. 76 , 325–330 (1990).

Bourel-Ponchel, E., Hasaerts, D., Challamel, M.-J. & Lamblin, M.-D. Behavioral-state development and sleep-state differentiation during early ontogenesis. Neurophysiologie Clin. 51 , 89–98 (2021).

Article   Google Scholar  

Sokoloff, G. et al. Twitches emerge postnatally during quiet sleep in human infants and are synchronized with sleep spindles. Curr. Biol. 31 , 3426–3432 (2021).

Mizrahi, E. M. Atlas of Neonatal Electroencephalography . (Lippincott Williams & Wilkins, 2004).

Vanhatalo, S. & Kaila, K. Development of neonatal EEG activity: from phenomenology to physiology. in Seminars in Fetal and Neonatal Medicine vol. 11 471–478 (Elsevier, 2006).

Vanhatalo, S. et al. Slow endogenous activity transients and developmental expression of K+–Cl− cotransporter 2 in the immature human cortex. Eur. J. Neurosci. 22 , 2799–2804 (2005).

Molnár, Z., Luhmann, H. J. & Kanold, P. O. Transient cortical circuits match spontaneous and sensory-driven activity during development. Science 370 , eabb2153 (2020).

Whitehead, K., Pressler, R. & Fabrizi, L. Characteristics and clinical significance of delta brushes in the EEG of premature infants. Clin. Neurophysiol. Pr. 2 , 12–18 (2017).

Vanhatalo, S. & Kaila, K. Emergence of spontaneous and evoked EEG activity in the human brain. in The Newborn Brain: Neuroscience and Clinical Applications 229–244 (Cambridge University Press, 2010).

Milh, M. et al. Rapid cortical oscillations and early motor activity in premature human neonate. Cereb. Cortex 17 , 1582–1594 (2007).

Fagard, J., Esseily, R., Jacquey, L., O’regan, K. & Somogyi, E. Fetal origin of sensorimotor behavior. Front Neurorobot 12 , 23 (2018).

DiPietro, J. A. et al. What does fetal movement predict about behavior during the first two years of life? Dev. Psychobiol. 40 , 358–371 (2002).

Almli, C. R., Ball, R. H. & Wheeler, M. E. Human fetal and neonatal movement patterns: Gender differences and fetal‐to‐neonatal continuity. Dev. Psychobiol. 38 , 252–273 (2001).

DiPietro, J. A., Kivlighan, K. T., Costigan, K. A. & Laudenslager, M. L. Fetal motor activity and maternal cortisol. Dev. Psychobiol. 51 , 505–512 (2009).

Del Rio‐Bermudez, C. & Blumberg, M. S. Active sleep promotes functional connectivity in developing sensorimotor networks. BioEssays 40 , 1700234 (2018).

Blumberg, M. S., Dooley, J. C. & Sokoloff, G. The developing brain revealed during sleep. Curr. Opin. Physiol. 15 , 14–22 (2020).

Harris, M. B. Rat homologues to the human post-neonatal period: models for vulnerability to the sudden infant death syndrome. Pediatr. Pulmonol. 47 , 729 (2012).

Sengupta, P. The laboratory rat: relating its age with human’s. Int J. Prev. Med 4 , 624 (2013).

PubMed   PubMed Central   Google Scholar  

Tucker, A. M., Aquilina, K., Chakkarapani, E., Hobbs, C. E. & Thoresen, M. Development of Amplitude-Integrated Electroencephalography and Interburst Interval in the Rat. Pediatr. Res 65 , 62–66 (2009).

Tabuena, D. R. et al. Large‐scale waves of activity in the neonatal mouse brain in vivo occur almost exclusively during sleep cycles. Dev. Neurobiol. 82 , 596–612 (2022).

Seelke, A. M. H., Dooley, J. C. & Krubitzer, L. A. The emergence of somatotopic maps of the body in S1 in rats: the correspondence between functional and anatomical organization. PLoS One 7 , e32322 (2012).

Altman, J. & Sudarshan, K. Postnatal development of locomotion in the laboratory rat. Anim. Behav. 23 , 896–920 (1975).

Tiriac, A. & Blumberg, M. S. Gating of reafference in the external cuneate nucleus during self-generated movements in wake but not sleep. Elife 5 , e18749 (2016).

Mukherjee, D., Yonk, A. J., Sokoloff, G. & Blumberg, M. S. Wakefulness suppresses retinal wave-related neural activity in visual cortex. J. Neurophysiol. 118 , 1190–1197 (2017).

Blumberg, M. S., Dooley, J. C. & Tiriac, A. Sleep, plasticity, and sensory neurodevelopment. Neuron 110 , 3230–3242 (2022).

Prechtl, H. F. R. State of the art of a new functional assessment of the young nervous system. An early predictor of cerebral palsy. Early Hum. Dev. 50 , 1–11 (1997).

Tataranno, M. L. et al. Changes in brain morphology and microstructure in relation to early brain activity in extremely preterm infants. Pediatr. Res 83 , 834–842 (2018).

Benders, M. J. et al. Early brain activity relates to subsequent brain growth in premature infants. Cereb. Cortex 25 , 3014–3024 (2015).

De Wel, O. et al . Relationship between early functional and structural brain developments and brain injury in preterm infants. The Cerebellum 1–13 (2021).

Tokariev, A. et al. Large-scale brain modes reorganize between infant sleep states and carry prognostic information for preterms. Nat. Commun. 10 , 2619 (2019).

Uchitel, J. et al. Cot-side imaging of functional connectivity in the developing brain during sleep using wearable high-density diffuse optical tomography. Neuroimage 265 , 119784 (2023).

Wang, X. et al . Machine Learning-Derived Active Sleep as an Early Predictor of White Matter Development in Preterm Infants. Journal of Neuroscience 44 , (2024).

Keunen, K. et al. Brain tissue volumes in preterm infants: prematurity, perinatal risk factors and neurodevelopmental outcome: a systematic review. J. Matern.-Fetal Neonatal Med. 25 , 89–100 (2012).

Pittet, M. P., Vasung, L., Huppi, P. S. & Merlini, L. Newborns and preterm infants at term equivalent age: A semi-quantitative assessment of cerebral maturity. Neuroimage Clin. 24 , 102014 (2019).

Lokhandwala, S. & Spencer, R. M. C. Relations between sleep patterns early in life and brain development: a review. Dev. Cogn. Neurosci. 56 , 101130 (2022).

Birnholz, J. C. The development of human fetal eye movement patterns. Science (1979) 213 , 679–681 (1981).

Tononi, G. & Cirelli, C. Sleep function and synaptic homeostasis. Sleep. Med Rev. 10 , 49–62 (2006).

Ben-Ari, Y., Gaiarsa, J.-L., Tyzio, R. & Khazipov, R. GABA: a pioneer transmitter that excites immature neurons and generates primitive oscillations. Physiol. Rev. 87 , 1215–1284 (2007).

Peerboom, C. & Wierenga, C. J. The postnatal GABA shift: a developmental perspective. Neurosci. Biobehav Rev. 124 , 179–192 (2021).

Khazipov, R. et al. Early development of neuronal activity in the primate hippocampusin utero. J. Neurosci. 21 , 9770–9781 (2001).

Shaw, J. C., Palliser, H. K., Walker, D. W. & Hirst, J. J. Preterm birth affects GABAA receptor subunit mRNA levels during the foetal-to-neonatal transition in guinea pigs. J. Dev. Orig. Health Dis. 6 , 250–260 (2015).

Sedmak, G. et al. Developmental expression patterns of KCC2 and functionally associated molecules in the human brain. Cereb. Cortex 26 , 4574–4589 (2016).

Wu, C. & Sun, D. GABA receptors in brain development, function, and injury. Metab. Brain Dis. 30 , 367–379 (2015).

Basu, S. K., Pradhan, S., du Plessis, A. J., Ben-Ari, Y. & Limperopoulos, C. GABA and glutamate in the preterm neonatal brain: in-vivo measurement by magnetic resonance spectroscopy. Neuroimage 238 , 118215 (2021).

Basu, S. K. et al. Age and sex influences gamma-aminobutyric acid concentrations in the developing brain of very premature infants. Sci. Rep. 10 , 10549 (2020).

Mellado, G. S. et al. The impact of premature extrauterine exposure on infants’ stimulus-evoked brain activity across multiple sensory systems. Neuroimage Clin. 33 , 102914 (2022).

Cavalcanti, H. G. et al. Early exposure to environment sounds and the development of cortical auditory evoked potentials of preterm infants during the first 3 months of life. BMC Res. Notes 13 , 1–7 (2020).

De Asis-Cruz, J. et al. Functional brain connectivity in ex utero premature infants compared to in utero fetuses. Neuroimage 219 , 117043 (2020).

Whitehead, K., Meek, J. & Fabrizi, L. Developmental trajectory of movement-related cortical oscillations during active sleep in a cross-sectional cohort of pre-term and full-term human infants. Sci. Rep. 8 , 17516 (2018).

Blumberg, M. S., Gall, A. J. & Todd, W. D. The development of sleep–wake rhythms and the search for elemental circuits in the infant brain. Behav. Neurosci. 128 , 250 (2014).

Gogou, M., Haidopoulou, K. & Pavlou, E. Sleep and prematurity: sleep outcomes in preterm children and influencing factors. World J. Pediatr. 15 , 209–218 (2019).

Osredkar, D. et al. Sleep-Wake Cycling on Amplitude-Integrated Electroencephalography in Term Newborns With Hypoxic-Ischemic Encephalopathy. Pediatrics 115 , 327–332 (2005).

Scher, M. S., Steppe, D. A., Beggarly, M. E., Salerno, D. G. & Banks, D. L. Neonatal EEG-sleep disruption mimicking hypoxic-ischemic encephalopathy after intrapartum asphyxia. Sleep. Med 3 , 411–415 (2002).

Abramsky, R., Stavsky, M., Novack, V. & Shany, E. Appearance of sleep cycling after birth in term neonates: an electro-physiologic study. Pediatr. Res 87 , 711–715 (2020).

Klapp, J. M., Hall, T. A., Riley, A. R. & Williams, C. N. Sleep disturbances in infants and young children following an acquired brain injury. J. Clin. Sleep. Med. 18 , 2387–2395 (2022).

Marchi, V. et al. Asymmetry in sleep spindles and motor outcome in infants with unilateral brain injury. Dev. Med Child Neurol. 64 , 1375–1382 (2022).

Romeo, D. M. et al. Sleep disorders in autism spectrum disorder pre-school children: an evaluation using the sleep disturbance scale for children. Medicina. 57 , 95 (2021).

Mazurek, M. O. & Petroski, G. F. Sleep problems in children with autism spectrum disorder: examining the contributions of sensory over-responsivity and anxiety. Sleep. Med 16 , 270–279 (2015).

Joosten, K., de Goederen, R., Pijpers, A. & Allegaert, K. Sleep related breathing disorders and indications for polysomnography in preterm infants. Early Hum. Dev. 113 , 114–119 (2017).

Trickett, J., Hill, C., Austin, T. & Johnson, S. The impact of preterm birth on sleep through infancy, childhood and adolescence and its implications. Children 9 , 626 (2022).

Griffiths, V. et al. Sleep-disordered breathing symptoms and their association with structural and functional pulmonary changes in children born extremely preterm. Eur. J. Pediatr. 182 , 155–163 (2023).

Wang, X. et al. Feasibility of automated early postnatal sleep staging in extremely and very preterm neonates using dual-channel EEG. Clin. Neurophysiol. 146 , 55–64 (2023).

Cabon, S. et al . Automated quiet sleep detection for premature newborns based on video and ecg analysis. in 2021 Computing in Cardiology (CinC) vol. 48 1–4 (IEEE, 2021).

Werth, J., Serteyn, A., Andriessen, P., Aarts, R. M. & Long, X. Automated preterm infant sleep staging using capacitive electrocardiography. Physiol. Meas. 40 , 055003 (2019).

Arasteh, E. et al. Unobtrusive cot side sleep stage classification in preterm infants using ultra-wideband radar. Front. Sleep. 2 , 1150962 (2023).

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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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Sleep, Brain, and Cognition Special Feature

Contributions of memory and brain development to the bioregulation of naps and nap transitions in early childhood, rebecca m. c. spencer.

a Department of Psychological & Brain Sciences, University of Massachusetts, Amherst, MA 01003;

b Neuroscience & Behavior Program, University of Massachusetts, Amherst, MA 01003;

c Institute for Applied Life Sciences, University of Massachusetts, Amherst, MA 01003;

Tracy Riggins

d Department of Psychology, University of Maryland, College Park, MD 20742

Author contributions: R.M.C.S. and T.R. designed research, performed research, and wrote the paper.

Associated Data

There are no data underlying this work.

The transition from multiple sleep bouts each day to a single overnight sleep bout (i.e., nap transition) is a universal process in human development. Naps are important during infancy and early childhood as they enhance learning through memory consolidation. However, a normal part of development is the transition out of naps. Understanding nap transitions is essential in order to maximize early learning and promote positive long-term cognitive outcomes. Here, we propose a novel hypothesis regarding the cognitive, physiological, and neural changes that accompany nap transitions. Specifically, we posit that maturation of the hippocampal-dependent memory network results in more efficient memory storage, which reduces the buildup of homeostatic sleep pressure across the cortex (as reflected by slow-wave activity), and eventually, contributes to nap transitions. This hypothesis synthesizes evidence of bioregulatory mechanisms underlying nap transitions and sheds new light on an important window of change in development. This framework can be used to evaluate multiple untested predictions from the field of sleep science and ultimately, yield science-based guidelines and policies regarding napping in childcare and early education settings.

Children spend as much as half of their early years asleep ( 1 ). Sleep promotes healthy brain and cognitive development ( 2 ). However, the mechanisms underlying these relations are poorly understood. Understanding children’s sleep needs can be used to promote optimal cognitive function and is deemed a priority in sleep research ( 3 ). Additionally, whether and for how long sleep needs must be met with naps are concerns for parents and early childhood educators.

With growing investments in universal early education in the United States, there is increasing scrutiny and awareness of how time is spent in these settings in order to maximize academic success. Should children be sleeping or learning in the early education setting? Ample data in adults and children support a role of sleep in solidifying memories, and yet, the intuition is that classroom time should be spent learning, not sleeping ( 4 , 5 ). Moreover, some children within the preschool age range seem unable to nap, while others, at the same age, can be hard to keep awake ( 6 ). Because there are currently no formal recommendations regarding nap timing/length or nap transitions (e.g., from the American Academy of Pediatrics or the National Sleep Foundation), some have devalued daytime naps for children over the age of 2 y ( 7 ), even proposing the elimination of naps in publicly funded preschools ( 4 ). Related, sleep advice for parents at this age is provided by a growing number of sleep coaches; however, these individuals lack evidence-based information and guidance on such issues ( 8 ). Therefore, in order to promote optimal learning and development through sleep in infancy and early childhood, a scientific understanding of nap transitions is essential as this will inform guidelines regarding nap needs and transition points.

The need to nap is driven by the bioregulation of sleep and wakefulness ( 9 ). Sleep regulation, which can be thought of as “the drive to sleep when we do,” underlies the need for naps as well as the ability to adjust to schedule changes (e.g., daylight savings, jetlag). Disorders of sleep regulation result in insufficient sleep, which is associated with depression and anxiety, even in childhood ( 10 ). Sleep regulation is also altered in neurodevelopmental disorders (e.g., autism, Down syndrome, Attention-deficit/hyperactivity disorder or ADHD) ( 11 , 12 ). Here, we describe what is known regarding the bioregulation of sleep in early development. We first discuss the relation between nap transitions and sleep regulation, including underlying neural and physiological mechanisms. Next, we consider how changes in the underlying neural substrates are related to changes in early learning and memory. Taken together, these data lead to our novel prediction that nap transitions are driven by changes in memory that are related to brain development.

1. Nap Transitions in Infants and Children

Newborns sleep up to 20 h each day, with sleep distributed across multiple sleep bouts (polyphasic), largely reflecting the need to feed frequently. Overnight sleep becomes distinct from daytime naps between 4 and 6 mo in most infants, but sleep remains polyphasic, with three or more naps in addition to the overnight sleep bout ( 13 ).

Sleep becomes triphasic by 9 mo of age, consisting of two daytime naps and an overnight sleep bout ( 14 ). Between the first and second years of life, the morning nap fades, and sleep becomes biphasic ( 1 , 14 ). The transition to adult-like monophasic sleep most commonly occurs in the early childhood years ( 13 – 15 ). However, there is significant variability in the timing of the transition from triphasic and biphasic sleep (ranging from 6 to 18 mo) and even more variability in the transition out of biphasic sleep (which can be as early as 2 y and as late as 8 y of age) ( 13 , 16 ).

We opt to use the terminology “nap transition” (as opposed to “nap cessation”) to capture the fact that changes in nap patterns are not instantaneous. A longitudinal study of early nap transitions ( 17 ) and meta-analyses of cross-sectional data support that transitions take place over a number of months, with naps gradually reducing in frequency and length over time ( 6 , 13 , 15 ). Central to our research is that these sleep transition periods are interesting windows of change in brain and memory development.

2. Bioregulation of Sleep and Naps

2.1. bioregulation of sleep..

The two-process model of sleep regulation is a prominent conceptual model, which posits that sleep regulation emerges from the interaction of two distinct biological processes ( 18 , 19 ). The first process is the circadian control of sleep, termed Process C, which underlies the 24-h pattern in sleep behaviors. The second process is the homeostatic regulation of sleep through a mechanism termed Process S or the homeostatic sleep drive. This is the process that allows for the self-regulation of sleep, adjusting to changing conditions while preserving sleep.

2.1.1. Circadian regulation of sleep (Process C).

Underlying Process C is a central circadian pacemaker that synchronizes multiple physiological processes to the same 24-h rhythm. Located in the suprachiasmatic nucleus (SCN) of the anterior hypothalamus, the circadian pacemaker is entrained to external cues or zeitgebers. Social cues, such as routine mealtimes and exercise, can serve as zeitgebers ( 20 ). However, the most common zeitgebers are light/dark cues. The SCN signals the pineal gland to synthesize the hormone melatonin on a circadian schedule. Although melatonin does not promote sleep per se, it is released from the pineal gland in darkness and reciprocally inhibits the SCN, dampening its alertness-promoting actions and ultimately, demoting wakefulness ( 21 ).

2.1.2. Homeostatic regulation of sleep (Process S).

If left to the circadian system alone, we would fall asleep at the same time and wake up at the same time regardless of sleep history; overnight sleep timing would be the same following a night of poor sleep and following a luxurious afternoon nap (one not taken to compensate for sleep loss). Rather, the common experience is that it is tempting to fall asleep much earlier following a night of poor sleep and hard to fall asleep at the usual time following an afternoon nap. This reflects the second aspect of sleep regulation, Process S or the homeostatic mechanism that drives sleep. Homeostatic sleep pressure accumulates across periods of wake and dissipates with subsequent sleep ( 19 ). The mechanism underlying homeostatic control is complex, and current evidence suggests that it may be a distributed process across multiple pathways (reviewed in ref. 22 ).

A neurobiological marker of homeostatic sleep pressure is slow-wave activity (SWA), activity in the delta-frequency band (0.5 to 4.5 Hz) in the sleep electroencephalogram (EEG) thought to be cortically generated ( 23 ). SWA reflects the frequency of the high-amplitude slow waves characteristic of slow-wave sleep. SWA is highest early in a sleep bout and dissipates across the sleep bout. Moreover, SWA is higher following sleep deprivation, supporting the use of SWA changes as a measure of the accumulation of homeostatic sleep pressure ( 24 ). Wake-related increases in SWA may result from global increases in synaptic strength related to learning. Slow waves emerge from bistability of cortical neurons in non-rapid eye movement (non-REM) sleep, oscillating between wake-like tonic firing (up state) and neuronal silence (down state) ( 25 ). Following long periods of wake, increased global synaptic strength may contribute to higher neural synchrony or a greater number of neurons contributing to the oscillation. Collectively, there is clear support that SWA is functionally involved in maintaining sleep homeostasis, but little more is understood regarding this mechanism.

2.2. Bioregulation of Naps and Nap Transitions in Early Childhood.

Naps and nap transitions are influenced by multiple factors, including genes (although minimally) ( 17 , 26 ), environment ( 27 , 28 ), and culture ( 29 – 31 ). The two-process model provides a framework to understand how these factors exert their influence on sleep. For example, parents may choose to promote a consistent nap time ( 28 ), reflecting circadian contributions to nap patterns. However, many children will nap even in the absence of nap promotion, and some children are unable to nap even in sleep-promoting conditions ( 6 ), likely reflecting homeostatic processes (i.e., variability in the accumulation of homeostatic sleep pressure).

2.2.1. Circadian contribution to nap patterns.

Consistency of nap times is recommended as it is thought to promote stable and predictable sleep patterns ( 32 ). As such, parents and childcare providers institute many zeitgebers (such as light availability), which may then reinforce a circadian drive to sleep at these times during the day. There is evidence that these changes in environment alter the onset of melatonin and ultimately, timing of sleep ( 33 ). Conversely, parents with negative views toward naps in this age group likely provide no zeitgebers or no nap opportunity, and as such, those children have fewer naps and naps of shorter duration ( 28 ). Cultural variation in naps may likewise reflect the use of light and rule setting, which may promote or demote daytime sleep ( 16 , 33 ).

2.2.2. Homeostatic contributions to nap patterns.

Whereas parent-guided zeitgebers can shift the circadian timing of sleep bouts, homeostatic control of sleep offers an explanation as to why naps can result from late bedtimes. Late bedtimes are often followed by too-early wake times due to parent or childcare schedules. The resulting shortened overnight sleep creates additional sleep pressure or the need to nap the subsequent day. However, while this provides an explanation of how environmental and cultural influences contribute to the presence of napping, they do not provide an obvious explanation of how and when nap transitions may occur. In other words, they do not explain the persistence of napping.

Nap transitions have most commonly been ascribed to Process S ( 34 , 35 ). Specifically, more rapid accumulation of homeostatic sleep pressure in young compared with older children is thought to create the need to more frequently nap to release this sleep pressure. Preliminary support of this comes from a study of a small sample ( n = 8) of 2- to 5-y-old children ( 36 ). Sleep pressure was varied by altering the amount of time that the child was awake before a nap was permitted (4, 7, or 10 h after waking). Consistent with the view that SWA serves as a proxy for sleep pressure, SWA in the nap was greater following longer intervals awake. However, this difference dissipated with age; the difference in SWA across conditions was less in younger compared with older children. This suggests that sleep pressure accumulates more rapidly in young children, who are also more likely to be habitual nappers.

In sum, there is evidence that both circadian (Process C) and homeostatic (Process S) sleep regulation processes contribute to whether a child naps ( Fig. 1 A ). To date, most research regarding these processes and individual differences in nap transitions has focused on external factors, such as the extent to which parents choose to promote napping ( 28 ), a factor that varies across cultures and socioeconomic groups ( 27 , 29 ). Despite these external influences, many children will nap even in the absence of nap promotion, and some children are unable to nap even in sleep-promoting conditions ( 6 ). These differences likely stem from internal processes and reflect variability in the accumulation of homeostatic sleep pressure ( 35 ). However, the biological mechanisms underlying these internal processes remain unclear. Specifically, what underlies the accumulation of homeostatic sleep pressure, and why does it vary developmentally? Additionally, how do these developmental changes contribute to changes in accumulation of homeostatic sleep pressure and ultimately, the transition out of naps? In the following section, we present a novel hypothesis that may provide answers to these questions by linking what we currently know about nap transitions with what we know about the development of the brain and its capacity to learn and remember at an early age.

Understanding nap transitions through the two-process model of sleep regulation. ( A ) Although environmental and cultural factors (yellow; e.g., caregiver schedules and the use of light) may influence the presence of naps, they are unlikely to explain the persistence of naps and nap transitions. Rather, nap transitions are posited to largely be related to homeostatic processes (indicated by the pink arrow), with greater accumulation of sleep pressure in habitually compared with nonhabitually napping children. ( B ) We hypothesize that brain development and memory development underlie this difference in homeostatic sleep pressure. Specifically, as the hippocampal-dependent memory network develops, memories can be held for longer without interference, making more space and/or using less energy resources, which may (directly or indirectly) yield sleep pressure as measured by SWA and result in nap transitions.

3. A Novel Hypothesis of Nap Transitions

A unique problem exists early in human development; there is a massive amount of information that must be learned, yet the neural systems that support learning are still under construction. Schema and semantic networks that support learning in adults ( 37 , 38 ) are nascent at very young ages and must be built from scratch. Moreover, the structural and functional neural circuitry that supports memory is also immature. Specifically, the hippocampus—a brain region associated with learning and memory—undergoes a period of “learning to learn” ( 39 ). Finally, to support early learning, there is overproduction of synapses across the brain, which is most prolonged in prefrontal or higher cortical areas. The increased firing rate that accompanies high synaptic density escalates energy demand ( 40 ).

Seen in this light, naps provide a solution to what is essentially a memory problem; high learning demands on an immature system create an overproduction of synapses that reaches its peak in early childhood. Sleep has been argued to help meet space and energy challenges associated with development ( 40 , 41 ). Thus, we posit that early childhood is a time of competing demands of learning, which loads the brain (the hippocampus in particular), and sleeping, which may unload synapses across the cortex ( 42 ) and free up the hippocampus for ongoing learning ( 43 ).

This leads to our hypothesis that maturation of the brain, particularly the hippocampal-dependent memory network, during early childhood results in more efficient memory storage, which reduces the buildup of homeostatic sleep pressure (as reflected by SWA) and eventually, contributes to nap transitions ( Fig. 1 B ). In the following sections, we provide motivation for this novel hypothesis and review initial support for this view.

3.1. Role of the Hippocampus in Learning and Memory in Early Development.

The ability to learn novel information and recall it later relies on a network of brain regions, including the hippocampus and neocortex. Together, these regions provide “complementary learning systems” that allow for the rapid learning of new information while both preserving existing knowledge and integrating new knowledge into these existing frameworks ( 41 ). The hippocampus is particularly important for the early stages of memory, including formation and consolidation (or stabilization) ( 44 ). In short, this structure provides short-term storage and initially works together with the neocortex to support memory of new information across long delays. Over time, connectivity among distributed cortical regions strengthens, and the role of the hippocampus gradually declines. Although learning initiates memory processes in the hippocampus, these memory traces are vulnerable to interference and forgetting. Once memories are consolidated and stabilized in the cortex, they are more robust against disruption ( 41 ).

The hippocampus is a complex structure composed of multiple subfields (cornu ammonis [CA] areas 1 to 4, dentate gyrus, and subiculum) that are distributed disproportionately along its longitudinal axis (head, body, tail) ( 45 , 46 ). These regions show protracted development as a result of prolonged neurogenesis, synaptic growth, dendritic arborization, pruning, vascularization, and myelination ( 47 – 49 ). Data from nonhuman primates (e.g., ref. 50 ) and human children (e.g., ref. 51 ) suggest that the developmental trajectory of these subfields and their connectivity with each other are related to age-related improvements in memory, which is consistent with theoretical proposals of brain and memory development ( 52 ). Specifically, within the hippocampus, although immature cells continue to accrue within the dentate gyrus throughout the first year of postnatal life [and may be related to the onset of other cognitive abilities, such as spatial navigation ( 50 )], elevated rates of dendritic development and synapse formation persist until at least 5 y of age ( 53 , 54 ). During early childhood (∼3 to 5 y of age), neuronal connections between granule cells of the dentate gyrus and pyramidal neurons of Ammon’s horn form, which alter the functional circuits of the hippocampus ( 54 ) and regions located downstream from the dentate gyrus, particularly CA3 ( 50 ). Because circuitry in the dentate gyrus is critical for adult-like memory formation, its protracted developmental profile suggests that adult-like memory formation in humans may not be expected before 5 y, as morphological development is likely correlated with functional capability ( 54 ).

Prolonged development of memory due to the long maturational time line of neural circuitry is supported by behavioral research. Specifically, as children mature, they are better able to remember individual items as well as associations between items ( 55 , 56 ). In fact, children’s ability to remember such details shows accelerated rates of change between 5 and 7 y of age ( 55 ). These findings fit well with cross-sectional research suggesting that children’s ability to bind information increases between 4 and 6 y of age (e.g., refs. 57 – 59 ). Moreover, these improvements in memory are associated with variations in hippocampal development (as indexed by volume). Across two different studies and two different memory paradigms, brain–behavior relations have been found to change around 6 y of age—following the final nap transition. Specifically, in younger (4- to 6-y) children, better memory performance is associated with larger hippocampal subfield volumes, whereas the opposite is true in older (6- to 8-y) children (i.e., better memory performance is associated with smaller volumes). This difference in brain–behavior relations may be the result of synaptic pruning within the dentate gyrus → CA3 → CA1 circuit, which is critical for binding and ultimately, yields a more efficient system ( 50 , 54 ).

In summary, ample evidence supports the role of the hippocampus (and its associated neocortical network) in learning and memory even from an early age [e.g., infants 3 to 24 mo ( 60 )]. Critical to the present hypothesis, across early childhood, the hippocampus is learning to learn in conjunction with protracted development, a challenge that we posit is met by naps ( Fig. 2 ).

Growth trajectories across nap transitions (gray shading). Synaptic density [prefrontal cortex ( 113 )], hippocampal volume ( 114 , 115 ), cortical gray matter ( 116 ), and SWA ( 117 ) are illustrated.

3.2. SWA Supports Hippocampal-Dependent Memory.

3.2.1. evidence of swa-related memory processing in adults..

Memories from the day benefit from subsequent sleep. Recall is better following sleep compared with an equivalent interval awake, with memories protected by the sleep interval compared with the decay that typically occurs over wake. For example, in one study, adults retained memory for more word pairs following an interval with sleep compared with an equivalent interval awake ( 61 ). Notably, memory retention was associated with time spent in slow-wave sleep, suggesting an active role of this sleep stage in memory processing as opposed to passive protection of memories during the sleep interval as a whole. Further support for the role of slow-wave sleep is provided by studies that represent auditory or odor cues associated with learned material during subsequent slow-wave sleep, which results in enhanced consolidation of cue-related material ( 62 ). Conversely, when SWA, the primary spectral power of slow-wave sleep, is reduced, performance benefits following sleep are absent ( 63 ). Such results support that consolidation of memories is enhanced by sleep and that SWA in particular supports memory consolidation.

Notably, not all memories benefit from sleep. Rather, engagement of the hippocampus during encoding seems to be a necessary condition for sleep-dependent memory consolidation ( 64 – 66 ). For example, memory for novel objects alone is not reliant on the hippocampus, whereas memory requiring binding of an object with a location or ordering with another object does ( 67 ). When rats performed a task requiring memory for novel objects (“what” memory), object location (“where” memory), or temporal order (“when” memory), performance was better following an interval of sleep compared with wake for the object placement and temporal order tasks. Memory for novel objects did not differ for sleep and wake conditions. This is taken as evidence that sleep specifically supports memory for tasks engaging the hippocampus ( 64 ).

Mechanistically, the synaptic homeostasis hypothesis contends that the association between SWA and memory consolidation over the sleep interval reflects synaptic downscaling over sleep, with superfluous synapses (incidental memories) being downscaled, while intentionally learned memories are benefited by reduced signal to noise ( 38 ). According to the active systems consolidation view, synchronous slow waves aligned with hippocampal activity (i.e., hippocampal sharp-wave ripples) support a hippocampal–neocortical shift of the memory representation. Numerous studies in animals have demonstrated that patterns of hippocampal activation associated with a learning episode are seen again in subsequent sleep and not in the prior sleep bouts ( 68 ). Such “replay events” undergo developmental changes early in life ( 69 ) and have been linked with the emergence of episodic-like memory in rodents ( 70 ). The timing of these developmental changes coincides with the transition to monophasic sleep in humans [i.e., roughly during the early childhood years ( 13 – 15 )]. Replay activity is associated with hippocampal ripples, which co-occur with sleep spindles and slow oscillations ( 71 ). Although hippocampal ripples cannot be measured noninvasively in humans, spindle–slow oscillation coupling predicts performance changes over sleep in adults ( 72 , 73 ). Growing support for both the synaptic homeostasis hypothesis and the active systems consolidation view has brought many to agree that these memory-supporting mechanisms in sleep are not mutually exclusive ( 74 , 75 ). Although both of these theories have influenced our thinking, our proposal intentionally does not ascribe to either one specifically, nor is our work intended to provide direct evidence supporting one or the other. Rather, we focus on the importance of sleep for memory—as it both stabilizes existing memories in cortical regions and optimizes synaptic organization ( 76 ), which ultimately frees up the hippocampus for ongoing learning.

3.2.2. Evidence of SWA-related memory processing in children.

Sleep benefits memory consolidation from a very young age. Benefits of sleep relative to wake have been observed in the performance of children ranging from 6 mo through adolescence, particularly for declarative learning tasks (reviewed in ref. 77 ). Under normal conditions, motor procedural learning does not benefit from sleep in childhood. However, when children receive additional training, performance improves following sleep relative to wake ( 78 ), which may suggest that, like adults, hippocampal engagement with encoding may be essential to sleep-dependent memory consolidation. Moreover, a study in toddlers supports that hippocampal-based memories are reactivated during a nap even in children as young as 2 y ( 79 ), providing evidence that the hippocampus is functional during sleep and directly related to memory at an early age.

Interestingly, SWA in children’s naps predicts the overnap protection of memory for emotional faces ( 80 ) and also, predicts reductions in the emotional attention bias following a nap in young children ( 81 ). We posit that consolidation of emotional memories from the morning decreases emotional load, and as a result, children are less reactive to emotional stimuli thereafter. This provides a potential explanation of the oft observed phenomena that habitually napping children are emotionally dysregulated at the end of the day if they do not nap (the “witching hour”).

Sleep-related declarative memory benefits in children have also been associated with sleep spindles and accompanying slow oscillations in children 3 to 5 y ( 82 , 83 ). Moreover, a longitudinal study of older children (9 y) who were tested subsequently at 16 y of age showed that developmental increases in coupling strength between spindles and slow oscillations provided a strong predictor of developmental changes in sleep-related benefits on a word-pair learning task ( 84 ). This provides additional support for the cortical stabilization of memories, initially encoded in the hippocampus, in the cortex during sleep and the theory that this process is present in children and strengthens with development.

Notably, whether such benefits reflect hippocampal–neocortical transition of memory representations as proposed by the active systems consolidation theory, cortical stabilization via synaptic downscaling as proposed by the synaptic homeostasis hypothesis, or both is unknown. It has been hypothesized that active systems consolidation would be unlikely before 18 to 24 mo due to underdevelopment of the hippocampus ( 85 ). Although few studies in this young age group have included polysomnography ( 77 ), there is some evidence that sleep-dependent memory processing is associated with sleep spindles, even at this young age. For instance, 9- and 16-mo-old infants who learned word–object pairs prior to a nap or wake interval generalized to category exemplars only after sleep. Notable here is that nap-related generalization was associated with the EEG sigma power, the frequency range of sleep spindles (10 to 15 Hz) ( 86 , 87 ). This lends support that key brain areas for active systems consolidation may be sufficiently developed in infancy ( 60 ). An alternative is that synaptic downscaling oversleep accounts for memory benefits at a very young age, while active systems consolidation accounts for sleep-related benefits later on ( 85 ). Importantly, the present hypothesis can accommodate either mechanism.

3.3. Evidence Supporting the Relation between Brain Development and Nap Transitions.

Support for the present hypothesis can be drawn from the coincident development of brain, memory, and sleep described above. Additionally, a growing number of behavioral and neural developmental studies provide more direct support for the relation between brain development and nap transitions, although to date, this work has been focused on the biphasic to monophasic sleep transition.

3.3.1. Behavioral studies.

At a behavioral level, support for relations between brain development and nap transitions can be drawn from studies comparing cognitive performance in habitually and nonhabitually napping children who are the same age. In one such study, children who performed better on a cognitive battery were found to take fewer naps than those with lower performance ( 88 ). In particular, fewer naps corresponded to enhanced memory span for auditory number sequences and larger vocabulary. One interpretation of this result is that those who nap have insufficient overnight sleep (supported by a negative correlation between nighttime sleep and nap length), and this may explain both lower cognitive performance and nap frequency. However, an alternative is that nap habituality is related to brain maturation; children who have more mature brains need to nap less often and also, perform better on cognitive assessments.

Supporting the latter interpretation, our recent study provided experimental evidence of differences in memory performance around the nap transition. In this study, we taught habitually napping and nonhabitually napping children a visuospatial task prior to an afternoon nap and again prior to an equivalent interval awake (within subjects) ( 82 ). Memory for item locations was probed again after the nap or wake interval and once more the following morning. We found that memories were protected by the nap; accuracy following the nap did not differ from immediate recall. However, when children stayed awake during the nap opportunity, recall accuracy was reduced by ∼12% compared with immediate recall. The benefit of the nap remained when performance was assessed again the following morning. We then considered whether the nap benefit varied for children who napped habitually (greater than or equal to five naps per week) compared with those who no longer napped (zero to time per week but were nap promoted for the experiment). Memory consolidation over the nap did not differ for habitually and nonhabitually napping children; naps protected memories regardless of nap habituality. Rather, what differed was how much memories decayed over the waking interval; memory decay over an afternoon awake was greater for children who napped habitually and minimal for those who no longer napped, even when controlling for age. We interpret this as evidence that in habitual nappers, sleep needs to occur more frequently in order to prevent the catastrophic interference between memories that occurs when kept awake. Nonhabitual nappers, on the other hand, may have more developed memory storage and thus, be able to hold memories for longer without interference. Subsequently, other studies have found similar differences between habitually and nonhabitually napping children with a word-learning task ( 89 ) and an emotional face-learning task ( 80 ). Collectively, these studies provide evidence that naps are similarly beneficial regardless of nap habituality but that memory of habitually napping children is much more damaged by a missed nap compared with nonhabitually napping children.

3.3.2. Neural studies.

At a neural level, SWA, the marker of the accumulation of sleep pressure that contributes to naps ( 36 , 90 ), has also been related to brain development (as reviewed in ref. 91 ). Slow-wave amplitude increases during childhood and is highest shortly before puberty. This parallels findings of developmental changes in synaptic density ( Fig. 2 ) ( 92 ). SWA is highly predictive of decreases in gray matter volume, a relation that is strongest in areas undergoing maturation at this age ( 93 ). In addition, the distribution (or topography) of SWA also tracks with the development of underlying cortical areas ( 93 , 94 ). Specifically, SWA, which peaks maximally over occipital regions in younger children, shifts to a peak over frontal regions by adolescence, a trajectory that mirrors that of cortical maturation [cortical thickness ( 93 , 94 )] and is predictive of brain myelin ( 95 ). Taken together, these studies support a link between brain development and global SWA in early childhood.

Finally and more directly, we recently reported a difference in hippocampal volume for habitually and nonhabitually napping children. We compared hippocampal subfield volumes in 4- to 6-y-old children who napped habitually with those who did not nap habitually. Habitually napping children had larger CA1 hippocampal subfield volumes in the hippocampal body compared with nonnapping children ( 96 ). This study provides the first direct evidence for a difference in the hippocampus between habitually napping and nonhabitually napping children that cannot be accounted for by age. Critically, prior reports on this same sample of children have linked volume of the CA1 to children’s memory performance, and this region also shows developmental change across the early childhood period ( 51 , 97 ). Most germane to our hypothesis is the finding that across all children in the study (4 to 8 y, regardless of nap status), smaller CA1 was associated with better memory performance ( 53 ).

3.4. Summary, Caveats, and Future Research Directions.

Collectively, we provide support for a relation between nap transitions and underlying memory and brain development. Together with studies relating nap habituality to SWA and memory to SWA, we provide a parsimonious hypothesis suggesting that maturation of the hippocampal-dependent memory network during early childhood results in more efficient memory storage, which reduces the buildup of homeostatic sleep pressure and in turn, contributes to nap transitions ( Fig. 1 B ).

However, there are some caveats given the limited body and nature of the current literature. First, much of the data supporting our hypothesis lack causality. While we posit that reduced need for memory consolidation is related to changing accumulation of sleep pressure (and related SWA), whether these are related directly (e.g., SWA changes could be related to simultaneous development in cortical regions in the hippocampal–memory network where SWA may be generated) or indirectly (e.g., less adenosine accumulation in the developing hippocampus benefiting memory and less adenosine accumulation in the cortex reducing SWA) is important to consider. We also interpret associations between the development of the hippocampal-dependent memory network and sleep, particularly nap habituality, to indicate that development of the brain drives nap habituality. An alternative developmental time line is that changes in sleep are a necessary precursor to brain development. Indeed, changes in SWA have been shown to precede improvements in some motor skills, which in turn, preceded decreases in gray matter in a cross-sectional sample of those 2 to 26 y of age ( 98 ). It will also be important to consider the development of other brain regions associated with the bioregulation of sleep. For example, hypothalamic development may follow a similar time line ( 99 ). Although this is unlikely to account for the differences in behavior of habitual and nonhabitual nappers described above, it will be important for future work to consider interactions in the development of multiple brain areas, which may support the bioregulation of sleep in early development and their causal role in nap habituality.

Second, while we posit that brain and memory development may underlie the transition from triphasic to biphasic sleep, current data in support of the hypothesis are based on the biphasic to monophasic sleep transition. Moreover, the present hypothesis is based on mostly cross-sectional data supporting individual relationships between sleep–brain, sleep–memory, or brain–memory. It is essential to acquire longitudinal data that capture sleep physiology, structural and functional brain development, and memory changes across the nap transitions, starting at 12 mo to capture both critical transitions, within a single sample that is large enough to overcome individual differences and tease apart general maturation from nap-related change. Such longitudinal data can be probed using latent change score modeling to assess the relations between these factors. Latent change score models evaluate ways in which variables are recursively associated over time in order to isolate temporal components of change within a person or group in order to specify lead vs. lag ( 100 ). For example, latent change score modeling has been used successfully in studies of memory decline to better understand the dynamics among processes, including changes at the neural level (e.g., ref. 101 ).

Third, there is considerable variability in the experimental methods used to examine the impact of sleep on memory early in life. This variation applies to the stimuli used (words vs. pictures), age ranges explored, and durations over which information is retained, as well as other factors. These differences likely contribute to the lack of consensus in results. For example, one study that used a verb-learning task, which required generalization to a novel exemplar, failed to find a difference in performance between habitual and nonhabitual nappers following intervals of wake and sleep (which benefited both groups) ( 102 ). However, it is possible that measurement limitations (as change in performance over the interval cannot be assessed) or other task differences may account for these discrepancies. Moreover, there are clear task nuances that can vary sleep’s function in memory consolidation even in the adult literature ( 103 , 104 ). Better understanding of these tasks and related neural underpinnings is essential for a mechanistic understanding of nap function and nap transitions. Thus, it is important for future work to also consider memory consolidation with various types of tasks as it relates to brain development.

Finally, several factors contribute to the presence of naps. Cultural (including parenting practices) and environmental factors likely play a role ( 105 – 107 ). Although many of these are related to circadian and homeostatic sleep regulation ( section 2 ), many other factors could be considered [e.g., diet, illness ( 108 )] that are likely to contribute to the presence of naps but not the persistence of naps and nap transitions. Importantly, while we posit that brain maturation is a strong factor underlying nap transitions, these factors are not mutually exclusive [e.g., environmental factors can also contribute to brain development ( 109 )]. Moreover, whether other species demonstrate similar nap transitions and factors that may affect these transitions have received little attention and would contribute greatly to a broader understanding of sleep regulation. Identifying factors that moderate or interact with brain development and sleep is an important goal for future research.

4. Conclusions and Implications

Here, we present a first review of the bioregulatory processes that contribute to the napping patterns of young children. Every young child naps and transitions out of naps at some point in early childhood—but the age at which this transition takes places varies dramatically ( 14 , 15 , 110 ).

Better understanding nap transitions would allow educators and caregivers to support these transitions, thereby strengthening children’s health and cognition. Scientific evidence showing that nap transitions are a product of brain development that is quite variable between individual children would help parents and providers appreciate that nap transitions cannot be determined by age and that the opportunity to nap should be protected for those that need it. This would also have substantial policy implications for early education, suggesting that nap opportunities should not only be protected for some children but actively supported and of sufficient length. Moreover, understanding typical sleep development is essential to identifying disorders of sleep as well as the extent of the impact associated with insufficient sleep. Thirty percent of children aged 6 to 11 y have insufficient sleep ( 111 ). Unfortunately, the extent of this deficiency in younger children is unclear due to a dearth of data on sleep in early childhood ( 1 ). This is egregiously problematic as younger children are at greater risk for problems stemming from insufficient sleep due to their immature brain development. Moreover, sleep is particularly reduced in low-income and atypically developing children ( 112 ) of this age and thus, contributes to known health disparities that are widespread and have far-reaching impacts. Given the prevalence of naps, the importance of naps to early cognition, and the policy ramifications, a scientific understanding is essential, as these periods may provide unique windows of opportunity to promote healthy development and optimize cognitive abilities.

Acknowledgments

Support for this work is provided by NSF Grant BCS 1749280 (to R.M.C.S. and T.R.), NIH Grant R21 HD094758 (to R.M.C.S. and T.R.), and NIH Grant R01 HL111695 (to R.M.C.S.). We thank Dr. Marc Weissbluth for dialogue and feedback on this work and Neely Miller, Gina Mason, Bethany Jones, Tamara Allard, and our laboratory members for feedback.

The authors declare no competing interest.

This article is a PNAS Direct Submission. K.S. is a guest editor invited by the Editorial Board.

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Scientists zap sleeping humans' brains with electricity to improve their memory

A little brain stimulation at night appears to help people remember what they learned the previous day.

A study of 18 people with severe epilepsy found that they scored higher on a memory test if they got deep brain stimulation while they slept, a team reports in the journal Nature Neuroscience .

The stimulation was delivered during non-REM sleep, when the brain is thought to strengthen memories it expects to use in the future. It was designed to synchronize the activity in two brain areas involved in memory consolidation : the hippocampus and the prefrontal cortex.

"Some improved by 10% or 20%, some improved by 80%," depending on the level of synchrony, says Dr. Itzhak Fried , an author of the study and a professor of neurosurgery at the University of California, Los Angeles.

The results back a leading theory of how the brain transforms a daily event into a memory that can last for days, weeks, or even years. They also suggest a new approach to helping people with a range of sleep and memory problems.

"We know for instance that in patients with dementia, with Alzheimer, sleep is not working very well at all," Fried says. "The question is whether by changing the architecture of sleep, you can help memory."

Although the results are from a small study of people with a specific disorder (epilepsy), they are "reason to celebrate," says Dr. György Buzsáki , a professor of neuroscience at New York University who was not involved in the research.

Rhythms in the brain

During sleep, brain cells fire in rhythmic patterns. Scientists believe that when two brain areas synchronize their firing patterns, they are able to communicate.

Studies suggest that during non-REM sleep, the hippocampus, found deep in the brain, synchronizes its activity with the prefrontal cortex, which lies just behind the forehead. That process appears to help transform memories from the day into memories that can last a lifetime.

So Fried and his team wanted to know whether increasing synchrony between the two brain areas could improve a person's memory of facts and events.

Their study involved epilepsy patients who already had electrodes in their brains as part of their medical evaluation. This gave the scientists a way to both monitor and alter a person's brain rhythms.

They measured memory using a "celebrity pet" test in which participants were shown a series of images that matched a particular celebrity with a specific animal. The goal was to remember which animal went with which celebrity.

Patients saw the images before going to bed. Then, while they slept, some of them got tiny pulses of electricity through the wires in their brains.

"We were measuring the activity in one area deep in the brain [the hippocampus], and then, based on this, we were stimulating in a different area [the prefrontal cortex]," Fried says.

In patients who got the stimulation, rhythms in the two brain areas became more synchronized. And when those patients woke up they did better on the celebrity pet test.

The results back decades of research on animals showing the importance of rhythm and synchrony in forming long-term memories.

"If you would like to talk to the brain, you have to talk to it in its own language," Buzsáki says.

But altering rhythms in the brain of a healthy person might not improve their memory, he says, because those communication channels are already optimized.

The epilepsy patients may have improved because they started out with sleep and memory problems caused by both the disorder and the drugs used to treat it.

"Maybe what happened here is just making worse memories better," Buzsáki says.

Even so, he says, the approach has the potential to help millions of people with impaired memory. And brain rhythms probably play an important role in many other problems.

"They are not specific to memory. They are doing a lot of other things," Buzsáki says, like regulating mood and emotion.

So tweaking brain rhythms might also help with disorders like depression, he says.