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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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Scientists believe it plays a role in how we learn and form long-term memories

Why do we sleep? Scientists have debated this question for millennia, but a new study adds fresh clues for solving this mystery.

The findings, published in the  Journal of Neuroscience, may help explain how humans form memories and learn, and could eventually aid the development of assistive tools for people affected by neurologic disease or injury. The study was conducted by Massachusetts General Hospital in collaboration with colleagues at Brown University, the Department of Veterans Affairs, and several other institutions.

Scientists studying laboratory animals long ago discovered a phenomenon known as “replay” that occurs during sleep, explains neurologist Daniel Rubin of the MGH Center for Neurotechnology and Neurorecovery, the lead author of the study. Replay is theorized to be a strategy the brain uses to remember new information. If a mouse is trained to find its way through a maze, monitoring devices can show that a specific pattern of brain cells, or neurons, will light up as it traverses the correct route. “Then, later on while the animal is sleeping, you can see that those neurons will fire again in that same order,” says Rubin.

Scientists believe that this replay of neuronal firing during sleep is how the brain practices newly learned information, which allows a memory to be consolidated — that is, converted from a short-term memory to a long-term one.

However, replay has only been convincingly shown in lab animals. “There’s been an open question in the neuroscience community: To what extent is this model for how we learn things true in humans? And is it true for different kinds of learning?” asks neurologist Sydney S. Cash, co-director of the Center for Neurotechnology and Neurorecovery at MGH and co-senior author of the study. Importantly, says Cash, understanding whether replay occurs with the learning of motor skills could help guide the development of new therapies and tools for people with neurologic diseases and injuries.

To study whether replay occurs in the human motor cortex — the brain region that governs movement — Rubin, Cash, and their colleagues enlisted a 36-year-old man with tetraplegia (also called quadriplegia), meaning he is unable to move his upper and lower limbs, in his case due to a spinal cord injury. The man, identified in the study as T11, is a participant in a clinical trial of a brain-computer interface device that allows him to use a computer cursor and keyboard on a screen. The investigational device is being developed by the BrainGate consortium, a collaborative effort involving clinicians, neuroscientists, and engineers at several institutions with the goal of creating technologies to restore communication, mobility, and independence for people with neurologic disease, injury, or limb loss. The consortium is directed by Leigh R. Hochberg of MGH, Brown University, and the Department of Veterans Affairs.

In the study, T11 was asked to perform a memory task similar to the electronic game Simon, in which a player observes a pattern of flashing colored lights, then has to recall and reproduce that sequence. He controlled the cursor on the computer screen simply by thinking about the movement of his own hand. Sensors implanted in T11’s motor cortex measured patterns of neuronal firing, which reflected his intended hand movement, allowing him to move the cursor around on the screen and click it at his desired locations. These brain signals were recorded and wirelessly transmitted to a computer.

That night, while T11 slept at home, activity in his motor cortex was recorded and wirelessly transmitted to a computer. “What we found was pretty incredible,” says Rubin. “He was basically playing the game overnight in his sleep.” On several occasions, says Rubin, T11’s patterns of neuronal firing during sleep exactly matched patterns that occurred while he performed the memory-matching game earlier that day.

“This is the most direct evidence of replay from motor cortex that’s ever been seen during sleep in humans,” says Rubin. Most of the replay detected in the study occurred during slow-wave sleep, a phase of deep slumber. Interestingly, replay was much less likely to be detected while T11 was in REM sleep, the phase most commonly associated with dreaming. Rubin and Cash see this work as a foundation for learning more about replay and its role in learning and memory in humans.

“Our hope is that we can leverage this information to help build better brain-computer interfaces and come up with paradigms that help people learn more quickly and efficiently in order to regain control after an injury,” says Cash, noting the significance of moving this line of inquiry from animals to human subjects. “This kind of research benefits enormously from the close interaction we have with our participants,” he adds, with gratitude to T11 and other participants in the BrainGate clinical trial.

Hochberg concurs. “Our incredible BrainGate participants provide not only helpful feedback toward the creation of a system to restore communication and mobility, but they also give us the rare opportunity to advance fundamental human neuroscience — to understand how the human brain works at the level of circuits of individual neurons,” he says, “and to use that information to build next-generation restorative neurotechnologies.”

Rubin is also an instructor in neurology at Harvard Medical School. Cash is an associate professor of neurology at HMS. Hochberg is a senior lecturer on neurology at HMS and professor of engineering at Brown University.

This work was supported by the Department of Veterans Affairs, the National Institute of Neurologic Disease and Stroke, the National Institute of Mental Health, Conquer Paralysis Now, the MGH-Deane Institute, the American Academy of Neurology, and the Howard Hughes Medical Institute at Stanford University.  

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Harvard Health Blog

Sleep helps learning, memory


Picture the peaceful sleeper nestled under the covers: body at rest, breathing and pulse slow and steady. But beneath that serene surface, the brain is hard at work, processing the events of the day. It sorts and files, makes connections, and even solves problems. As I write in the February 2012 Harvard Men’s Health Watch , even a brief nap may boost learning, memory, and creative problem solving.

Several recent studies strengthen the connection between sleep and learning.

Reactivate and reorganize. A 2010 Harvard study suggested that dreaming may reactivate and reorganize recently learned material, which would help improve memory and boost performance. In the study, volunteers learned to navigate a complex maze. During a break, some were allowed to nap for 90 minutes, others weren’t. When the volunteers tackled the maze again, only the few who dreamed about it during their naps did better.

Shorter naps. In another Harvard study, college student volunteers memorized pairs of unrelated words, worked on a maze puzzle, and copied an intricate figure. All were tested on their work, and half were allowed to nap for 45 minutes. During a retest, napping boosted the performance of volunteers who initially did well on the test, but didn’t help those who scored poorly the first time around.

Micro naps. For many people, it’s difficult, if not impossible, to find 45 minutes to nap. In a German study, a six-minute snooze helped volunteers recall a list of 30 words they had memorized earlier.

Sleep and creativity. Naps are generally too short to let a person drop into the deep phase of sleep known as rapid eye movement (REM) sleep. This is the phase during which most dreams happen. California researchers gave volunteers a series of creative problems in the morning and asked them to spend the day mulling solutions before being tested late in the afternoon. Half the volunteers were asked to stay awake during the day, the others were encouraged to nap. Those whose naps were long enough to enter REM sleep for a while did 40% better on the test than nappers who didn’t get any REM sleep and non-nappers. Rather than simply boosting alertness and attention, REM sleep allowed the brain to work creatively on the problems that had been posed before sleep.

Napping won’t make you smart or assure success, but it can help improve your memory and solve problems. Sleeping well at night, and long enough, is associated with good health. The combination is a two-step approach that should give everyone something to sleep on.

You can read the complete article on the Harvard Men’s Health Watch home page.

About the Author

Harvey B. Simon, MD , Editor, Harvard Health


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Memory and Sleep: How Sleep Cognition Can Change the Waking Mind for the Better

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.


People acquire and maintain an immense amount of information during their lifetimes, but how that happens is hard to fathom. Whereas memory research has focused heavily on acquisition and retrieval as the two most essential steps for memories to be of use, in this article we emphasize the additional processing that memories undergo during offline periods.

New knowledge is not assimilated instantaneously; rather, memory storage changes over time. Forgetting may be the usual fate of newly acquired information, unless memory consolidation can counter forgetting, producing enduring memories that are less susceptible to decay or interference ( Paller 2009 ). Although consolidation may include many mechanisms operative over long periods of time ( Dudai 2012 ), a crucial part of the consolidation process may occur over periods of sleep. In this article, we elaborate on how memory reactivation during sleep contributes to memory stability and memory use. We also consider how sleep-based processing can contribute to creative insights and psychological well-being.

As a nightly ritual, some people like to document each day’s highlights by writing a diary entry. Indeed, before going to sleep at night, each of us (diary writer or not) is capable of remembering a great deal about the day’s happenings, including activities, thoughts, emotions, and interactions with others. These are fully viable memories; however, most do not remain so easy to remember. On subsequent days, forgetting and interference take their toll. It would be much more challenging to retrospectively produce a complete diary entry after a few days have passed, and extremely difficult to remember the experiences of a random and not especially eventful day from the past. 1

How is it that some memories can be recalled in detail years later? Forgetting and interference can apparently be avoided when memory storage is modified through consolidation mechanisms, considered in detail below. Still, memory storage falls short of providing us with a verbatim readout or a complete diary entry of the various events, thoughts, and emotions we experience each day. The information that we do maintain serves an arguably more important role than mere documentation: These memories shape our personalities, our perspectives, and our decision making. The human brain controls a remarkable capacity to adaptively learn from our experiences, thereby improving the way we cope with life’s challenges.

As a fanciful analogy, imagine a handyman’s magic toolbox, replete with tools that gradually adjust their functionality in accordance with the jobs they are given. The tools thus evolve to become optimized for jobs they are likely to encounter in the future. Likewise, the brain’s plasticity enables it to change its functionality in accordance with how it is used. Thanks to continual changes in memory storage, we not only have new knowledge that we can use, but we also have the ability to use our brains in different ways.

We postulate here that consolidation largely transpires without us knowing about it—because we are asleep at the time. Moreover, understanding the neurophysiology of memory processing during sleep may be the key to understanding how the memories formed while we are awake are preserved and transformed, and how they transform us. Ultimately, changes in memory storage during sleep may shape not only what we can remember but also who we are.


Declarative memories encompass many things that “we know that we know.” This knowledge is used in the decisions we make and forms the basis of a life story that we can tell ourselves. We also acquire nondeclarative memories, and therefore we have knowledge that “we don’t know that we know.” Nondeclarative knowledge is often used without concomitant remembering of where and when it was acquired. Memories in both categories, declarative and nondeclarative, do not necessarily function in isolation; they can interact with each other and with working memory (which functions to maintain and manipulate information online). Importantly, multiple types of memory commonly affect the way we think and the decisions we make.

A guiding principle in contemporary memory research is that we cannot rely on a single mechanism to explain all human memory phenomena. The challenge of understanding a given type of memory thus includes detailing how a specified brain system operates to support memory function and how that operation is or is not different from that of other brain systems. Despite this guiding principle, some concepts may apply in basically the same way for many types of memory. For example, sleep could be universally relevant—for perceptual learning ( Karni et al. 1994 ), skill learning ( Walker et al. 2002 ), paired-associate learning ( Plihal & Born 1997 ), and maybe all types of learning. The jury is still out on this idea, but it seems clear at this point that many types of memory are subject to change in one way or another during sleep ( Rasch & Born 2013 ).

Declarative memories depend on specific parts of the neocortex, each specializing in certain types of information processing. Memories for facts and events invariably depend on links among these cortically based pieces of a memory, such as different sensory qualities or conceptual attributes. Accordingly, declarative memories are characterized by their reliance on novel cross-cortical connections ( Paller 2002 ). Plasticity in connections across brain regions, including multiple cortical regions and other brain structures, can be key to consolidation—or, more specifically, to systems consolidation, in contrast to synaptic consolidation, which concerns cellular and molecular facets of consolidation. Here we focus on the former type of consolidation.

The neocortex, on its own, is not well-equipped for transforming entire experiences into lasting memory traces. The apparatus of the medial temporal lobe, centrally including the hippocampus, provides the neocortex with the capacity for storing declarative memories in an enduring way. In keeping with this idea, damage to the medial temporal lobe can lead to a profound impairment in learning, or anterograde amnesia. The hippocampus presumably helps fulfill the need to rapidly acquire declarative memories ( Marr 1971 , McClelland et al. 1995 , Norman et al. 2005 ), which is not the forte of the cortex working by itself (perhaps with some exceptions; see Hebscher et al. 2019 ). Declarative memories can be gradually stabilized via a slower process whereby cortical networks are altered under hippocampal guidance. In this way, some declarative memories can become independent of the hippocampus; memories may become schematic, may lose details including various contextual features concerning the circumstances of their acquisition, and may be transformed to only hold the bare facts, general outlines, or a portion of the information acquired. In addition, the hippocampus may continue to be involved in retrieval for some memories with regard to specific episodic details ( Nadel & Moscovitch 1997 , Miller et al. 2020 ). Although this scenario provides a sketch of the gradual memory changes of consolidation, the contributions of hippocampal–neocortical interactions to memory change are not fully understood.

The idea that memory stabilization is a gradual process that takes place after an initial encoding stage has its roots in the work of Müller & Pilzecker (1900) . On the basis of their studies of learning nonsense syllables, they inferred that there are “certain physiological processes, which serve to strengthen the associations … [that] continue with increasing intensity for a period of time” (cited in Lechner et al. 1999 , p. 81). Decades later, retrograde amnesia was observed in rodents given an electroconvulsive shock 15 minutes after learning ( Duncan 1949 ). In contrast, memory remained intact when the shocks were delivered 1 hour after learning. This pattern of experimentally induced retrograde amnesia substantiates the notion of post-learning consolidation. For different types of memory and different animal species, the amount of time required for a memory to become immune to an amnestic insult may differ. The proposal that human declarative memories undergo consolidation for many months after learning, initially regarded by researchers as surprising, is now widely accepted ( Squire 1992 , Squire & Wixted 2011 ). There is certainly still room for alternative views and for debate on many aspects of consolidation (e.g., Yonelinas et al. 2019 ), including how to characterize the hippocampal contribution (e.g., Murray et al. 2007 , Aly & Ranganath 2018 ). The path to ascribing a major role to the hippocampus in memory consolidation followed from reports of severe memory impairments in patient H.M., who received a medial temporal lobectomy as treatment for intractable epilepsy ( Scoville & Milner 1957 ). Damage to this brain region generally produces not only anterograde amnesia, but also temporally graded retrograde amnesia, which spares remote but not recent declarative memories. Evidence from retrograde memory deficits thus fits with the notion that there can be a protracted time period following initial acquisition, as consolidation progresses, when declarative memories depend on a hippocampal–neocortical dialogue. What does this dialogue entail?

A putative mechanism for declarative memory consolidation involves the offline engagement of the same neural circuits involved in learning, thereby reinstating the learning-related activity in the service of memory reactivation. This reactivation may support consolidation whether or not there is a concurrent experience of conscious retrieval. A relevant sort of physiological reactivation was first observed in the activity of rodent hippocampal place neurons, which fire selectively when the animal occupies a specific patch of space. Rodent hippocampal place cells with fields used in recent exploration fired more frequently during sleep compared to cells with fields not encountered during pre-sleep exploration ( Pavlides & Winson 1989 ). In a seminal study on memory consolidation, Wilson & McNaughton (1994) found that coordinated place cell activity in the hippocampus was likely to reoccur again during sleep. This finding was later extended to show that the temporal patterns of action potentials exhibited during wake are repeated during sleep, constituting memory replay ( Skaggs & McNaughton 1996 ). These results brought forward the hypothesis that memory is strengthened via replay within the hippocampus. Additional studies in rodents showing coordinated replay between the hippocampus and the neocortex during sleep suggest that replay is an essential aspect of the hippocampal–neocortical dialogue that can stabilize cortical representations ( Foster 2017 ). Many findings now support the idea that memory reactivation during both waking rest periods and sleep is relevant for the modification, integration, and stabilization of memories.

Memory processing may be particularly effective during sleep due to its unique physiological and neurochemical properties (see the sidebar titled Sleep Physiology). Of course, memories can readily be modified or updated during wake in light of new experiences (for a review of wake reactivation and consolidation, see Tambini & Davachi 2019 ). During sleep, as a consequence of increased sensory gating and environmental factors—when sleeping in a secluded space that is quiet and dark—there is usually far less incoming sensory information (nevertheless, sensory processing continues; see below). Consolidation may indeed be more effective, and less subject to interference or contamination, when memories are reactivated during sleep.


Behavioral studies of memory consolidation during sleep have produced ample evidence of superior retrieval of various types of information after a period of sleep compared to a period of wake (e.g., Karni et al. 1994 , Plihal & Born 1997 , Walker et al. 2002 , Tucker et al. 2006 ). In rodents, consistent evidence has suggested a causal role for rapid eye movement (REM) sleep in memory consolidation ( Smith 1995 , Rasch & Born 2013 , Klinzing et al. 2019 ). Historically, there have been many attempts to tie human REM sleep to memory (e.g., Crick & Mitchison 1983 , Winson 1985 , Paller & Voss 2004 ), and diverse ideas about its role in consolidation are still debated. Here we emphasize the better understood role of non-REM (NREM) sleep.

A prominent account of sleep-based consolidation, sometimes termed the active systems consolidation hypothesis, suggests that memory reactivation in the hippocampus during NREM sleep dictates changes in cortical networks ( Buzsáki 1998 , Born et al. 2006 ). This proposed mechanism embraces selectivity, as some memories are reactivated and others not. An alternative account, the synaptic homeostasis hypothesis, instead attributes the memory benefits of sleep to widespread downscaling of synaptic strength, thereby increasing the signal-to-noise ratio for memory retrieval ( Tononi & Cirelli 2014 ). Importantly, synaptic downscaling and active consolidation are not mutually exclusive. Recent accounts emphasize the critical contributions of both reactivation and synaptic downscaling for retaining old memories and for encoding new ones ( Klinzing et al. 2019 ).

The implementation of sleep-related consolidation through reactivation appears to be intimately related to certain physiological features of sleep characterized as field-potential oscillations, particularly during slow-wave sleep (SWS). These oscillations appear to be orchestrated together in the form of nested oscillations (e.g., Staresina et al. 2015 ), as described below and depicted in Figure 1 . Sharp-wave/ripple complexes (SWRs), found locally in the hippocampus, are nested in the troughs of thalamo-cortical sleep spindles, which, in turn, ride on the peaks (or up-states) of cortical slow oscillations (SOs). Accordingly, consolidation is enabled by a synchronized temporal frame for communication among brain areas ( Diekelmann & Born 2010 ).

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Object name is nihms-1680049-f0001.jpg

The consolidation of declarative memories during slow-wave sleep is thought to involve multiple brain regions and neuronal interactions, reflected in the set of brain oscillations pictured here. The slowest of these are neocortical slow oscillations ( red ). The so-called up-states of these oscillations coincide with high levels of neuronal activity across many regions of the cortex, which makes the up-states conducive to cross-cortical interactions. The synchronization of thalamo-cortical spindles ( blue ) with slow-oscillation up-states facilitates memory processing. Sharp-wave/ripple complexes ( green , shown at a larger scale) can be generated in the hippocampus and are also synchronized with spindles. Ripples coincide with hippocampal replay and are causally associated with memory processing. Figure adapted from Born & Wilhelm (2012) .

Sharp-Wave/Ripple Complexes

SWRs are commonly observed in recordings from area CA1 of the rodent hippocampus. Somewhat similar observations have been made in human intracranial recordings. The high-frequency ripples (70–110 Hz in humans, 150–200 Hz in rodents) coincide with a characteristic high-amplitude wave (i.e., a sharp wave). Hippocampal-place-cell replay patterns are predominantly observed contemporaneously with SWRs, during both sleep ( Wilson & McNaughton 1994 ) and wake ( Nádasdy et al. 1999 ). Furthermore, disruption of SWRs during sleep negatively affects memory ( Girardeau et al. 2009 ), whereas their artificial extension benefits memory during wake ( Fernández-Ruiz et al. 2019 ). In fact, the causal role of SWRs in memory consolidation has received more support than that of hippocampal replay ( Laventure & Benchenane 2020 ).

SWRs likely contribute to memory consolidation in conjunction with other physiological events. Rodent SWRs take place predominantly within the time frame of thalamo-cortical spindles, and their propagation from the hippocampus may add to the excitation of spindles ( Sirota et al. 2003 ). Human findings are also consistent with the idea that SWRs are nested within SOs and spindles ( Helfrich et al. 2018 , Staresina et al. 2015 ). Indeed, SWRs have been considered to coincide with hippocampal output that can eventually reach various cortical zones. In support of this idea, SWRs in human hippocampal recordings precede activity in adjacent cortex ( Axmacher et al. 2008 , Nir et al. 2011 ). Notably, coupling between SWRs in the hippocampus and ripple-like activity in the neocortex was strengthened by sleep that followed learning ( Khodagholy et al. 2017 ). Additional evidence linking wake SWRs to memory includes their timing, anticipating recollection in both free-recall tests ( Norman et al. 2019 ) and cued-recall tests ( Vaz et al. 2019 ).

SWRs and simultaneous neocortical ripple-like activity could reflect the aforementioned hippocampal–neocortical dialogue. Sleep spindles could play a synchronizing role for this mechanism ( Ngo et al. 2020 ). Complicating this story, however, Axmacher et al. (2008) reported that memory performance correlated with ripples in the human rhinal cortex but not the hippocampus, and that more ripples occurred during quiet wake than sleep in both the hippocampus and the rhinal cortex. How exactly is memory processing different during wake and during sleep? Differences between SWRs may provide clues. In rodents, wake SWRs coincide with high-fidelity replay and remain local, whereas sleep SWRs coincide with noisier replay and, as described below, are coordinated with widespread cortical SOs ( Roumis & Frank 2015 ). Accordingly, high-fidelity reactivation may support memory-guided planning during wake, whereas noisier, distributed reactivations may support generalization during sleep.

Hippocampal SWRs seem to be triggered by SOs and spindles from the neocortex ( Sirota et al. 2003 , Nir et al. 2011 ). A cortical-to-hippocampal trajectory is also consonant with replay in the visual cortex preceding replay in the hippocampus ( Ji & Wilson 2007 ). Moreover, replay in these two brain regions tends to occur during distinct temporal frames, such that both replay and SWRs may be synchronized by SO timing.

Slow Oscillations

SOs (≤1 Hz), observed in the scalp electroencephalogram (EEG) or in local field potentials, reflect coordinated neural activity of large areas of the cortex and typically serve to coordinate higher-frequency local activity ( Varela et al. 2001 ). During the SO trough (or down-state), pyramidal neurons and interneurons are silenced, whereas during the SO peak (or up-state), neuronal burst-like activity predominates ( Volgushev et al. 2006 ). SOs during sleep do not occur simultaneously throughout cortex; they typically appear frontally first and travel posteriorly ( Massimini et al. 2004 ). Intracranial recordings of SO propagation documented various paths, such as from neocortex to parahippocampal gyrus to entorhinal cortex to hippocampus ( Nir et al. 2011 ). Whereas many SOs follow such cortical trajectories, many other SOs occur locally. Different types of information processing may thus correspond to different types of SO patterns, particularly in relation to cortical origin and trajectory. Indeed, SO activity over a specific cortical area can reflect previous learning dependent on that area ( Huber et al. 2004 ).

SOs are the hallmark of SWS. Both low-frequency EEG power and SWS duration have repeatedly been found to be correlated with memory improvement over sleep, primarily for declarative memory (e.g., Backhaus et al. 2007 , Westerberg et al. 2012 ). More directly, SOs have been linked with consolidation in studies using transcranial direct current stimulation or auditory stimulation to entrain SOs. Many such studies have shown enhanced SOs and a concurrent improvement in retention over sleep ( Marshall et al. 2006 , Ngo et al. 2013 , Westerberg et al. 2015 , Papalambros et al. 2017 ). However, SO enhancement has also occurred in the absence of observed memory improvements ( Cox et al. 2014 , Weigenand et al. 2016 , Henin et al. 2019 , Papalambros et al. 2019 ). One possible explanation for the mixed memory results in these studies is that the word-pair recall measures used often had low test-retest reliability, which would not be conducive to producing consistent results in repeated-measure designs comparing stimulation and sham stimulation. Studies with improved memory measures or more powerful experimental designs are needed to clarify this discrepancy in the literature. Nevertheless, the existing results offer considerable support for the notion that SOs function as a driving mechanism for strengthening memories during sleep.

Sleep Spindles

A sleep spindle is defined as a waxing-and-waning oscillation at 11–16 Hz with a duration between 0.5 s and 3 s. Spindles originate in the thalamus and propagate to different neocortical sites ( Fernandez & Luthi 2020 ). Although models explaining spindles’ contribution to memory focus on their interaction with SOs, they are most frequent in N2, when SOs are less common than during SWS ( Purcell et al. 2017 ). However, given findings that memory changes after a nap correlated with SWS spindle activity but not with N2 spindle activity ( Antony et al. 2012 , Cox et al. 2012 ), spindles during SWS may be particularly important for consolidation.

The importance of the link between SOs and spindles is underscored by the finding that their coupling predicts the degree of memory strengthening over sleep (e.g., Latchoumane et al. 2017 ). Furthermore, manipulating SOs during sleep also increases sleep spindles (e.g., Marshall et al. 2006 ). Phase-amplitude coupling between SOs and spindles is often observed, as spindles are prevalent during the SO up-state (e.g., Steriade et al. 1993 , Mölle et al. 2002 ). Yet, spindles and SOs in human intracranial recordings often do not coincide ( Nir et al. 2011 ). Analyses of spindle-SO coupling patterns must take into account ( a ) that spindles are highly variable across individuals ( Cox et al. 2019 ) and ( b ) that there may be multiple spindle subtypes. Spindles have been distinguished based on their spatial distribution in the brain, their frequency, and their behavioral relevance. For example, slow spindles (≤13 Hz) tend to be nested in the down-state of the SO, whereas fast spindles (>13 Hz) are nested in the up-state ( Mölle et al. 2011 , Cox et al. 2014 ).

Increasing empirical support has convincingly linked spindles with memory consolidation ( Antony et al. 2018 , Cairney et al. 2018 ), though the literature on spindles historically emphasized many other functions, such as sensory processing and intelligence ( Fernandez & Luthi 2020 ). Higher spindle density has been associated with better memory in word-pair learning ( Gais et al. 2002 ), visuospatial learning ( Clemens et al. 2006 ), episodic learning ( Cox et al. 2012 ), and procedural learning ( Milner et al. 2006 , Nishida & Walker 2007 ). In a pharmacological study, the GABA A agonist zolpidem (Ambien®) was found to increase sleep spindles and to improve word-pair recall but not motor-sequence learning ( Mednick et al. 2013 ).

Further insights about spindles and their role in memory consolidation may be obtained using methods to manipulate spindles. For example, auditory entrainment produced an increase in spindles with characteristics resembling those of endogenous spindles ( Antony & Paller 2017 ). Similarly, electrical stimulation with transcranial alternating current was used to boost spindle activity, also benefiting motor memory consolidation ( Lustenberger et al. 2016 ). Future studies should investigate the functional relevance of cross-frequency coupling via conjoint manipulations, such as manipulations of spindles and SOs, combined with extensive memory testing.

Nested Oscillations and Hippocampal-Cortical Communication

Given that systems consolidation of a declarative memory entails coordinated memory processing across cortex and hippocampus, cross-frequency coupling across regions may be a critical ingredient. Because thalamo-cortical SOs can propagate throughout large parts of the cortex, a plurality of cortical regions participating in a declarative memory can be primed to interact. Hippocampal projections can then precisely engage the relevant cortical circuits, as coordination between spindles and ripples could allow SWRs to dictate replay in those specific circuits ( Geva-Sagiv & Nir 2019 ).

A rapid flow of information engaging a cortical-hippocampal-cortical loop can thus be described ( Rothschild et al. 2017 , Lewis & Bendor 2019 , Rothschild 2019 ). The initial cortical activity, corresponding to an incomplete memory trace, determines which memory will be reactivated. For example, the first step could be that one or more pieces of a recent experience might come to be reactivated cortically. Second, this information would be projected to the hippocampal networks that were also activated during the original experience, via the same anatomical pathways from cortex to hippocampus. Then, wider cortical involvement would unfold in the third step, based on hippocampal replay accompanied by SWRs, instantiating pattern completion to recruit other pieces of that recent experience. Consequently, the multimodal and multidimensional components initially encoded across the cortex could be fully reactivated.

In this way, an interaction between hippocampal networks and cortical networks could enhance cortico-cortical connections to solidify a complete declarative memory trace. While this cortical-hippocampal-cortical loop is steadily being characterized in more anatomical detail, specific functional consequences can also be investigated by manipulating the content of the memory reactivation, as described below.

Toward a Causal Link Between Reactivation and Consolidation

Neural evidence that could causally link reactivation with consolidation faces several interpretive challenges. When a person reactivates a memory in the normal course of remembering, it can be difficult to disentangle neural signatures of reactivation from new learning. Hippocampal activation observed using neuroimaging during recall, for example, could signify that the hippocampus is enabling reactivation or, alternatively, that the hippocampus is contributing to learning the novel experience of the recall episode itself (which could then be remembered later). Both types of processing are likely to occur concurrently. Similarly, we cannot easily dissociate acquisition from retrieval: If a certain oscillation is observed in the hippocampus during acquisition, it could be because old semantic information must be retrieved to make sense of the new information in context.

During sleep, some of these same interpretive issues may be in play but to a lesser extent, as new episodic memories are not readily formed as they are during wake. Importantly, plasticity can still be operative in neural circuits, though various views on the cellular mechanisms of plasticity during sleep have been controversial. Although this literature is beyond the scope of this review, we note that some views emphasize a facilitation of cortical plasticity during SWS (e.g., Timofeev & Chauvette 2017 ).

How can mechanisms of reactivation, and the function of reactivation in the service of consolidation, be studied in humans? Here we advocate for using sensory stimulation methods. Although the sleeping brain suppresses sensory input during sleep, some sensory processing nevertheless persists, perhaps in order to monitor the surroundings ( Andrillon & Kouider 2019 ). We can leverage this standby mode of stimulus processing in two different ways. First, as described above, we can use stimuli to manipulate brain physiology, as in entraining SOs and spindles. Second, as described below, we can use learning-related stimulation to systematically manipulate memory processing.


Targeted memory reactivation (TMR) is a technique for probing memory reactivation during sleep ( Oudiette & Paller 2013 , Schouten et al. 2017 ). TMR can also be used during wake, although the term has most often been used in the context of sleep studies. First, stimuli such as sounds or scents are associated with specific newly learned information ( Figure 2a ). Then, the same sensory stimuli are unobtrusively presented during sleep. To avoid awakenings, sounds are often presented at a low intensity. The chief finding from recent TMR studies was confirmed by a meta-analysis that included results from over 90 experiments ( Hu et al. 2020 ): When TMR was applied during SWS or N2, memory was selectively improved for the associated information compared to comparable information for which TMR was not applied.

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( a ) Targeted memory reactivation experiments generally include three phases. In the first phase (pre-sleep), participants acquire some new information. This information is coupled with stimuli that are included either as background context or as part of the learning (e.g., a meow sound paired with the spatial location of a cat image). In the second phase (sleep), these stimuli are unobtrusively presented during sleep. In the third phase (post-sleep), memory is tested after sleep. ( b ) In the experiment of Rasch et al. (2007) , spatial learning of 15 objects, each shown in two locations on a 5 × 6 grid, was accomplished while a rose odor was present. Next, during an overnight sleep session with polysomnographic monitoring, the odor or an odorless vehicle was presented during sleep. Finally, spatial memory was assessed the next morning. The results showed relatively better recall when the odor had been presented during slow-wave sleep compared to when it had not. This memory effect was not observed when odors were presented during rapid eye movement (REM) sleep or when the memory test was one of motor learning instead. ( c ) In the experiment of Rudoy et al. (2009) , spatial learning of 50 objects shown in random locations on a grid was accomplished while sounds were presented. For each object, a matching sound was used. Next, during an afternoon nap with polysomnographic monitoring, half of the sounds were presented at a low intensity. Finally, spatial memory was assessed when participants attempted to place each object at precisely the correct screen location. The results showed relatively better memory performance for objects if corresponding sounds had been presented during slow-wave sleep than if not. Error bars represent standard error of the mean.

Many older studies paved the way for the recent proliferation of TMR experiments. The earliest studies seldom used polysomnographic methods [i.e., continuous EEG, electrooculogram (EOG), and electromyogram (EMG) recordings] to ensure that individuals were asleep at the time of stimulation and to determine sleep stage. TMR experiments lacking polysomnography leave room for doubt about what happened during sleep. Also, some studies were ignored because they did not fit with the Zeitgeist on memory and sleep. For example, a study by Tilley (1979) did not have a large impact because sounds played during REM did not enhance memory performance, and at the time REM was the central focus of questions about consolidation during sleep. However, Tilley also reported a TMR effect for sounds presented during N2.

This research approach began to gain widespread interest following a study by Rasch et al. (2007) , wherein a spatial-learning improvement was produced using a rose odor during learning and again during SWS ( Figure 2b ). In a subsequent TMR study by Rudoy et al. (2009) , spatial learning was similarly improved but using sounds ( Figure 2c ): The results showed less forgetting for object locations that were cued by a sound during sleep compared to those that were not cued. In this and many subsequent experiments, half of the sounds were selected for presentation during sleep using a form of stratified sampling (i.e., matching the two sets on pre-sleep recall accuracy). Within-subject comparisons then allowed nonspecific effects of sleep, such as alertness and interference, to be ruled out. The relative benefit in recall accuracy for cued compared to uncued conditions, as now observed in many TMR studies ( Hu et al. 2020 ), demonstrates that stimuli presented during sleep can function to reactivate specific memories.

Memory changes observed in studies comparing post-sleep to pre-sleep performance are commonly used to support the inference that memories are indeed reactivated by TMR during sleep. This estimate of memory change is often subject to the proviso that the act itself of testing memory before sleep changes storage, as predicted by the testing effect ( Roediger & Karpicke 2006 ). Yet, sound experimental designs in typical TMR studies still allow for valid comparisons between two conditions, one with and one without TMR cues presented during sleep, using either between-subjects or within-subjects comparisons. A further advantage of this type of design is that participants commonly exhibit no knowledge of whether cues were presented or which cues were presented, so no demand characteristics or strategic confounds are operative during post-sleep memory testing. Although factors such as testing effects, forgetting, interference, and consolidation may all affect retrieval after a delay, and potentially confound memory comparisons between a retention period with sleep and one without sleep, differential memory changes due to a TMR manipulation can be firmly attributed to differential consolidation for cued relative to uncued memories.

These methods for manipulating reactivation of specific memories complement other methods of studying sleep-related memory consolidation and provide a valuable tool for deciphering the neural mechanisms whereby reactivation engages memory consolidation. Sound delivery, in particular, provides a marker for specifying a time frame during which reactivation may be more likely.

To establish TMR as an effective tool for investigating consolidation, and to better understand its boundary conditions, it is essential to determine what types of learned material can be reactivated, what methods produce reactivation, and which brain mechanisms are engaged. TMR can improve many other types of learning in addition to spatial learning, including many examples of both declarative and nondeclarative memory [ Hu et al. (2020) provide a comprehensive list as of mid-2019]. For example, TMR studies have shown a benefit for skill learning ( Antony et al. 2012 ), vocabulary learning ( Schreiner & Rasch 2015 ), and word recall ( Fuentemilla et al. 2013 ). TMR studies have used multiple types of auditory stimulation during sleep, including pure tones, frequency-modulated tones, segments of popular music, environmental sounds, and spoken words.

In rodents, TMR was used to directly link memory reactivation with hippocampal replay: When tones associated with spatial learning on a two-arm maze were played during NREM sleep, corresponding hippocampal place cells were preferentially reactivated over the next few seconds ( Bendor & Wilson 2012 ). Two tones were used in this experiment, one associated with a leftward response and the other with a rightward response, and both were presented during sleep. Therefore, unlike in most human TMR studies, there was no opportunity to determine whether memory changed due to the TMR procedure. In a subsequent study in rodents, Rothschild et al. (2017) found that a sound cue associated with pre-sleep learning biased auditory cortex activity, which predicted hippocampal SWR activity. These results were thus used to support the cortical-hippocampal-cortical loop theory of reactivation-dependent memory consolidation outlined above.

Neuroimaging has provided additional anatomical perspectives. For example, Cousins et al. (2016) used functional magnetic resonance imaging (fMRI) to reveal post-sleep changes due to TMR in brain activity associated with motor learning. Time in SWS correlated with greater hippocampal and caudate activity for cued versus uncued sequences in subsequent wake. Also, time in REM correlated with an increase in cerebellar and cortical motor activity for cued versus uncued sequences, suggesting that the effects of TMR during NREM sleep may depend on subsequent REM sleep as well. In an fMRI study of spatial learning using an auditory TMR procedure, the degree of memory benefit was found to be correlated with activity in the medial temporal lobe and the cerebellum, as well as with the degree of parahippocampal-precuneus connectivity, thus identifying several candidate measures of brain activity associated with sound-cued memory reactivation ( van Dongen et al. 2012 ; see also Berkers et al. 2018 ). An fMRI study using an olfactory TMR procedure found that scents associated with prior learning elicited learning-related patterns of brain activity during sleep, and this activity was correlated with post-sleep spatial memory improvement ( Shanahan et al. 2018 ). Complementing these neuroimaging findings, results from a study in epileptic patients suggested that one requirement for TMR is a relatively intact medial temporal region ( Fuentemilla et al. 2013 ). Word recall benefits from TMR were found for individuals in a healthy control group and for patients who had brain damage due to hippocampal sclerosis, but not if there was bilateral damage. Furthermore, across all patients the degree of benefit correlated with the volume of spared hippocampus.

The efficacy of TMR cues undoubtedly depends on many factors. Four such factors are as follows: ( a ) The cue must gain sufficient sensory processing but not produce awakening; ( b ) the association between the cue and the learned information must be sufficiently strong and specific, so that the cue preferentially reactivates the intended information; ( c ) memory measures must be highly sensitive and reliable; and ( d ) at the time of TMR, memories must be sufficiently strong for veridical memory reactivation to occur, but not so strong that there is no possibility for improvement ( Creery et al. 2015 , Cairney et al. 2016 ). Also, the improvement must be sufficient to produce superior performance compared to the uncued condition, which can be difficult to evaluate with a binary memory measure (correct/incorrect) that may be relatively insensitive to gradations of memory strength. The testing procedures, including the delay, must be selected with these requirements in mind, and in consideration of possible floor or ceiling effects.

Whereas TMR can selectively improve memory, we do not know whether the reactivation it produces is the same as spontaneous reactivation. To answer this question, neural signals could be compared for TMR-induced and spontaneous reactivation during sleep. Encouragingly, recent TMR studies have made progress in monitoring reactivation. Reinstatement of memory-specific neural signatures following cues has been observed using both EEG and fMRI ( Belal et al. 2018 , Cairney et al. 2018 , Schreiner et al. 2018 , Shanahan et al. 2018 , Wang et al. 2018 ). Two of these studies showed that this reactivation coincided with spindle activity ( Cairney et al. 2018 , Wang et al. 2018 ), converging with the aforementioned evidence implicating spindles. These results from TMR studies also converge with findings from studies of spontaneous reactivation that have also implicated spindles as a crucial component in decoding reactivated content ( Bergmann et al. 2012 , Schönauer et al. 2017 ).

Antony et al. (2018) used TMR to directly investigate the relevance of spindles. Their results showed that memory reactivation was most effective when a spindle occurred shortly after a TMR cue. Spindles are unlikely to occur in close succession and are typically separated by 3–6 s. Accordingly, an optimal time for cue presentation is at the end of this refractory period. Timing cue presentations accordingly was indeed found to favor memory benefits. Speculatively, the gap between sleep spindles may be helpful for segregating reactivation events during sleep ( Antony et al. 2018 ).

Going beyond research strategies that focus on solidifying prior learning during sleep, a different approach considers the potential to learn new things during sleep. Such efforts have achieved limited success in the circumscribed territory of associative learning ( Arzi et al. 2012 ) and perceptual learning ( Andrillon et al. 2017 ). Also, Arzi et al. (2014) showed that sleep conditioning changed waking behavior in participants who wished to quit smoking. Conditioned pairing of a noxious odor with the odor of smoking during stage N2 and REM reduced the number of cigarettes smoked compared to various control conditions. New learning during sleep may be limited to simple types of memory. On the other hand, new procedures derived from TMR studies may open up other opportunities. For example, new learning may be possible during lucid dreaming ( Konkoly et al. 2020 ). In another study, TMR was accomplished using a sound associated with learning to suppress a memory ( Simon et al. 2018 ). The results showed that new associations were formed during sleep when this suppression-related sound was presented along with sounds related to other memories. Because this TMR procedure apparently weakened those memories, we can infer that those memories became associated with the suppression action. Accordingly, a wide range of strategies should be pursued to map out the potential for TMR to change memories.

In summary, TMR methods provide a powerful tool for probing sleep-related memory consolidation and its consequences. Future studies using novel variations of these methods hold promise for providing additional insights into the relevant neurocognitive mechanisms. Furthermore, applying TMR methods outside the laboratory has potential for shaping sleep consolidation to improve memory and various other aspects of life.


Processing memories during sleep can lead not only to the strengthening of particular memories but also to other sorts of memory modification. For example, two distinct memories may become associated, such as when a connection emerges between a recent event and a remote event. Such connections can also be the basis for creativity and problem solving. The time-honored belief that a difficult decision can be dealt with more effectively after sleeping on it calls attention to the role of sleep in creativity and problem solving. Historically, creative insight has routinely been linked with dreaming, as in the anecdotes of August Kekulé’s discovery of the structure of benzene, Otto Loewi’s discovery of chemical transmission, Dmitry Mendeleev’s discovery of the periodic table of elements, and Paul McCartney’s creation of the melody for Yesterday .

Problem Solving and Creativity

A TMR study by Sanders et al. (2019) strongly corroborates the relevance of sleep for problem solving. In this study, participants were stumped by a set of puzzles, each of which was accompanied by a repeating musical theme ( Figure 3 ). Overnight, half of the themes were presented during SWS, which functioned to selectively increase the likelihood of producing solutions for those puzzles compared to the remaining puzzles.

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Experimental design and results from Sanders et al. (2019) . ( a ) The participants attempted to solve a set of challenging puzzles (including matchstick, spatial, verbal, and rebus puzzles). Each puzzle was presented together with an arbitrary musical theme or sound, and participants were required to master these associations. The pre-sleep learning phase of the experiment ended when there were six puzzles that could not be solved in the 2 minutes allotted for working on each one. Participants slept in their own homes using a wireless sleep-monitoring device linked to a computer, which covertly presented sounds when slow-wave sleep (SWS) was detected. In this way, three of the six unsolved puzzles were reactivated using the corresponding sounds. The matchstick puzzle shown as an example came with instructions to move three matchsticks to create four equilateral triangles. ( b ) The results obtained the next day confirmed that memory reactivation had the predicted effect: When participants attempted to solve the same six puzzles, solving rates were higher for cued puzzles than for uncued puzzles. In the left panel, each orange circle represents a single participant’s success rate for one type of puzzle. The blue circles represent the average for each condition. Error bars represent 95% confidence intervals. The right panel shows the difference between the two conditions similarly. Figure adapted from Sanders et al. (2019) .

Even though puzzle memories were presumably reactivated during SWS in this experiment, the investigators could not exclude the possibility that REM was also important for the observed effect on problem solving. Other evidence supports the idea that REM sleep may be an optimal time for broad semantic associations that are perhaps indicative of creativity ( Stickgold et al. 1999 ). REM has also been empirically associated with solving word problems using solution hints from an ostensibly unrelated task ( Cai et al. 2009 ). Yet, some studies failed to find sleep benefits for problem solving ( Brodt et al. 2018 , Schönauer et al. 2018 ). Another example of problem solving was examined using a tedious numerical task that could instead be completed on the basis of a hidden shortcut. Following an 8-hour break including either sleep or wake, only 35% of the participants made this discovery. Participants who slept, compared to those who did not, were more likely to discover the shortcut, presumably by restructuring their memories of the task ( Wagner et al. 2004 ). A follow-up study showed that EEG activity during SWS in the form of power in the beta band (17–25 Hz) and perhaps part of the spindle band (10–11 Hz) predicted which participants would discover the shortcut ( Verleger et al. 2013 ).

Although creativity is inherently connected with problem solving, very few studies have directly addressed the relevance of sleep for creativity. Only one study adopted the TMR methodology in this context. This study used a creativity task that required participants to generate ways to motivate other people to do volunteer work ( Ritter et al. 2012 ). Instructions were given at night and the test was taken the next morning. Impartial raters scored the degree of creativity in the responses. Higher creativity was found when task-related odors were presented overnight compared to different-odor and no-odor control conditions in other participants. Additionally, participants in the task-related odor condition performed better than controls in selecting their most creative idea. However, the design did not allow the investigators to connect any specific sleep stage to this benefit. Odors presumably reactivated the task instructions and prompted ideas for creative solutions, just as musical sounds helped participants in the study of Sanders et al. (2019) reach solutions.

The available evidence is broadly consistent with the supposition that memory processing during sleep can be useful for various cognitive challenges that arguably share some properties with creativity. For example, reprocessing of recent episodic memories during sleep can support gist abstraction and generalization, which are useful modes of memory transformation ( Lewis & Durrant 2011 , Lutz et al. 2017 , Schapiro et al. 2017 , Tamminen et al. 2017 ). Ideally, sleep could support creative ways to relate recent experiences to current goals ( Paller & Voss 2004 ). Indeed, Winson’s (1985 , 2004 ) prescient proposal emphasized that offline memory processing engaged in the sleeping brain would help in dealing with ongoing issues encountered during the waking state. Cartwright (2010) conducted many studies of dreaming that led her to also emphasize this view. Earlier dreams in the night are generally based on recent memory fragments, whereas later dreams incorporate memory fragments from an increasingly farther past ( Roffwarg et al. 1978 ). Cartwright (1990) found that dreams during a single night sometimes all relate to a common theme, drawing on relevant knowledge from progressively further back in time. She identified systematic relationships between the dreams of recently divorced individuals and their postdivorce coping strategies, and critically, she found better emotional adjustment the more dreaming was used in this fashion.

A reasonable speculation, then, is that in dreams, and perhaps in sleep more generally, emotional issues can be worked through and behavioral strategies can be adjusted with reference to very recent experiences, older experiences, and their relationships. Strikingly, this use of memory processing during sleep is in keeping with the conception of consolidation described above, whereby recent memories are integrated with older ones to facilitate storage. Two benefits of sleep can thus be described as ( a ) reactivating and reorganizing memories and ( b ) creatively fine-tuning strategies in the service of solving current problems. These two benefits correspond roughly to modifying declarative memories and nondeclarative memories, respectively.

Emotional Memories and Emotional Regulation

How might sleep zero in on the specific memory processing that would be optimal for guiding problem solving? Ideally, the specific memories reactivated should be those relevant in some way for important upcoming challenges. It is thus sensible that emotional factors should enter into this computation.

Various findings support the claim that emotional memories are preferentially consolidated during sleep. Emotional memories likely benefit from arousal-related tagging during encoding ( Payne & Kensinger 2018 ). TMR was used in one study that compared results for negative emotional pictures and neutral pictures ( Cairney et al. 2014 ). The participants first learned picture-location associations as in many other TMR experiments, in this case with just six locations. Half of the pictures were cued during SWS using semantically related sounds. After sleep, an interesting pattern of results was revealed: Total time spent in SWS predicted faster spatial recall responses for cued pictures, but only in the negative emotion condition. Cuing did not influence spatial recall accuracy. The number of spindles during SWS also predicted the speed of recall responses, suggesting that sleep spindles mediate a selective enhancement of reactivated emotional memories.

REM may be particularly important for processing emotional memories, but the evidence is mixed. Although amygdala activity is increased during REM sleep ( Maquet et al. 1996 ), TMR cues improved emotional memories not when presented during REM but rather when presented during NREM ( Lehmann et al. 2016 ). Of course, both NREM and REM may be relevant. The evidence on whether memory processing during sleep increases or decreases arousal regulation for emotional memories in also unclear ( Tempesta et al. 2018 ). In one study, women exposed to a traumatic movie followed by a period of sleep experienced fewer traumatic memories when compared with women exposed to the traumatic movie followed by a neutral movie ( Kleim et al. 2016 ). In another study, sleep preserved the autonomic response to emotional stimuli after sleep, but it reduced the autonomic response as well as valence ratings to emotional stimuli one week later ( Bolinger et al. 2019 ). That is, sleep may be helpful for arousal regulation not on the next day but after some number of days. Although more research is certainly needed in this area, multiple investigators have championed the view that sleep aids the overnight resolution of emotional distress (e.g., Walker & van der Helm 2009 ). Notably, emotional benefits from sleep may be secondary to memory reorganization ( Vanderheyden et al. 2015 ).

Lack of sleep can certainly contribute, perhaps indirectly, to emotional dysregulation (as may be particularly evident, anecdotally, in cranky children in need of sleep). In an fMRI study, reduced top-down cognitive control was evident in sleep-deprived compared to control participants ( Yoo et al. 2007 ). Decreased connectivity between the medial prefrontal cortex and the amygdala was presumed to have caused greater amygdala activation and thus to have increased emotional reactivity.

Going beyond the commonsense idea that an insufficient quantity of sleep can make someone grumpy, we should also consider both the quality of sleep and the quality of the memory processing that transpires during sleep. How memories are processed during sleep could determine whether there are negative or positive consequences for psychological well-being. In the next section we explore the negative consequences more broadly.


Bringing together the critical role of sleep in memory consolidation with the fact that memory is used for many cognitive functions leads to the following further inference: Sleep disturbances may have far-reaching cognitive consequences. Certainly drowsiness can affect cognition in many ways, but there’s more to it than simply the quantity of sleep. Memory processing during sleep might not be working properly. Even if there is sufficient sleep and plenty of time for overnight memory processing, that processing may become dysfunctional. A flaw could develop in the way memories are selected for reactivation or in the way they are processed, with the consequence that the normal progress of nightly consolidation could go awry. If so, the outcome may extend to pervasive problems for one’s well-being and mental health. Speculatively, some affective disorders may have at their core a dysfunction in memory processing during sleep.

Major depressive disorder (MDD), for example, is characterized by impaired sleep continuity, lower-than-normal density of NREM sleep, and higher-than-normal density of REM sleep ( Steiger & Pawlowski 2019 ). Additionally, slow-wave activity (conventionally defined as EEG power at 0.5–4 Hz), which includes the SOs described above, is abnormal in two ways. First, patients suffering from MDD exhibit reduced slow-wave activity relative to healthy controls ( Borbély et al. 1984 ). Second, the dynamics of this activity is altered. A decline in slow-wave activity over the course of a night of sleep is the typical pattern, thought to be a sign of normal sleep physiology. This decline is disrupted in depression ( Kupfer et al. 1986 ) and restored upon effective treatment ( Jindal et al. 2003 ). Moreover, insomnia is common in patients with depression ( Buysse et al. 2008 , Manber et al. 2008 ). Relatedly, bright-light therapy can be helpful in depression, in conjunction with its effects on circadian rhythms and sleep ( Pail et al. 2011 ). Memory symptoms noted in depression, particularly overgeneral autobiographical retrieval ( Williams et al. 2007 ), may also fit with the dependence of memory function on sleep and the alteration of sleep in depression.

A persistent controversy in depression research has concerned whether sleep abnormalities are a result of depressive illness or a contributing factor to it. The latter direction of causality has received considerable support, including evidence that sleep abnormalities often precede depressive episodes ( Ohayon & Roth 2003 ). Importantly, however, these do not need to be mutually exclusive alternatives. A bidirectional relationship is likely, as sleep and affective symptoms may reinforce each other throughout the progression of MDD ( Bao et al. 2017 ).

To further understand the mechanisms whereby sleep abnormalities may be operative in producing or exacerbating mood disorders such as MDD, research is needed to relate measures of sleep physiology in patients to cognition and mood. In particular, we concern ourselves here with the memory functions of sleep and the contribution of memory to MDD progression. Some of the most debilitating symptoms of MDD are tied to memory. By one account, patients suffering from MDD hold and empower negative representations about the self that serve to bias memory processing toward negative experiences ( Everaert et al. 2012 ). Consonant with this idea, depressed patients tend to recall negative memories better than healthy individuals do ( Fattahi Asl et al. 2015 ). It is therefore tempting to hypothesize that sleep disturbance and MDD are mediated by biasing effects of memory consolidation. Multiple investigators thinking along these lines have emphasized the role of REM sleep, suggesting that its prominence in MDD serves to overemphasize the consolidation of negative memories ( Walker & van der Helm 2009 , Harrington et al. 2018 ). Correspondingly, the three major classes of antidepressant drugs all profoundly suppress REM sleep ( Vertes & Eastman 2000 ). SWS has garnered far less attention in research on depression and related disorders, but given the wealth of evidence supporting the role of SWS in memory consolidation, it may be more relevant in this context than commonly assumed.

Insights into other psychiatric disorders beyond depression may also emerge through investigations of the relevance of memory consolidation during SWS. For example, other mood disorders such as social anxiety disorder can involve a negative memory bias as in depression ( Glazier & Alden 2019 ), and the bias may be operative during sleep. Supporting sleep’s suggested role in overemphasizing the consolidation of negative memories, one TMR study found that cueing negative memories resulted in more negative ratings one week later in socially anxious adolescents but not in healthy ones ( Groch et al. 2017 ).

Sleep disturbances are reported in 50–80% of people with psychiatric disorders ( Franzen & Buysse 2017 ). In posttraumatic stress disorder (PTSD), memory issues are particularly prominent, as are sleep-related symptoms, and some PTSD symptoms have been attributed to an excessive consolidation of negative memories ( Pitman et al. 2000 ). Sleep disturbances immediately following traumatic experiences may serve as a protective mechanism to prevent the consolidation of traumatic memories, though consolidation of emotional memories may span many nights ( Bolinger et al. 2019 ).

An excess of arousals during REM sleep may be one sign of maladaptive sleep in relation to emotional distress. This type of unstable or disrupted REM sleep has been observed in PTSD ( Germain et al. 2008 ) and in insomnia ( Riemann et al. 2012 ). To examine the association between this sign of low-quality sleep and emotional coping, Wassing and colleagues (2019) subjected healthy individuals to an episode of self-focused distress, and they found that sleep with abundant spindles followed by uninterrupted REM sleep predicted a healthier orientation the next day, as reflected by an adaptation in amygdala fMRI activity ( Figure 4 ). If we extend ideas from this experiment to patients, it could be that individuals who suffer the most persistent distress following trauma or emotional insult are those plagued by maladaptive memory processing during sleep; REM sleep punctuated by signs of arousal, presumably mediated by ascending noradrenergic systems, could be a sign of this specific type of maladaptive sleep-thinking. A fruitful direction for future research would be to flesh out the connections among REM stability, spindles prior to REM, and memory processing during sleep in relation to distressing memories.

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Wassing et al. (2019) used a variant of the targeted memory reactivation (TMR) procedure to examine how participants responded to a distressing episode. Before sleep, participants listened to a recording of their own off-key singing, which elicited a strong amygdala response. During sleep, odors linked with the stressful episode (and a control odor) were presented over multiple stages of sleep. Regardless of the TMR manipulation, two patterns of sleep were observed: sleep with undisturbed rapid eye movement (REM) periods ( top ) or REM interrupted by brief arousals ( bottom ). Amygdala responses the next day were reduced to the extent that REM was uninterrupted and also to the extent that spindles were frequent in the non-REM (NREM) period prior to REM. TMR with the stress-associated odor was found to enhance these effects. These findings demonstrate a specific connection between sleep after a stressful experience and future emotional responses. Figure adapted with permission from Wassing et al. (2019) .

Overall, this conceptualization of the interactions among maladaptive consolidation, sleep problems, and memory-related emotional problems may prove to be fruitful for understanding mental disorders. The memory work accomplished during sleep ideally supports adaptive memory function, but it can become maladaptive instead. In depression and anxiety, a patient’s day may be filled with excessive rumination and worry. Negative thoughts may be recirculated to the detriment of the person’s well-being. Harmful thinking may pervade sleep as well, producing further negative consequences. In this sense, optimal memory processing during sleep may elude psychiatric patients—and by extension, perhaps the same applies to those who experience less extreme psychological difficulties. Further investigation of sleep-related memory consolidation mechanisms could thus lead to new insights into sleep that apply whether an individual has clinical symptoms or not.


The cumulative research using TMR shows that sleep-based consolidation is modifiable. Although much remains to be understood about this memory processing—including its neural substrates, its consequences, and the factors that dictate which memories are reactivated during sleep—at this point we can suggest some possible strategies for promoting beneficial sleep cognition. Efforts to optimize memory processing during sleep could have ramifications not only for memory but also for improving quality of life.

The importance of one’s quality of sleep is widely recognized. Standard ideas about sleep hygiene are now promoted widely (e.g., Walker 2017 ). Good sleep habits include endeavoring to obtain a sufficient quantity of sleep every night; arranging a dark, quiet, and cool sleep environment; avoiding alcohol, caffeine, and late-night screen use; and adopting a consistent bedtime and wake-up time seven days a week.

But what constitutes good-quality sleep? Our view is that the definition of sleep quality should be widened. It should not be limited to standard sleep physiology metrics such as the total time asleep, the continuity of sleep, or the density of slow waves and spindles. Such measures might be insensitive to subtle memory transactions. The importance of how memories are processed during sleep, and of the specific sorts of memories that are reactivated, must also be considered. Ultimately, functional measures should be developed to assess sleep’s contribution to memory, which means taking into account the degree of beneficial or detrimental memory reactivation.

A major factor that may influence sleep quality is the reemergence of negative memories. Much as waking rumination can be harmful, a proliferation of negative memories may also occur during sleep and may likewise have negative psychological consequences. Sleep-based rumination might be quite prevalent, regardless of clinical diagnosis. If rumination while awake can take a toll on well-being and reinforce a preoccupation with negative concerns, then the same may be true for sleep—harmful thoughts may pervade one’s sleep cognition, with harmful consequences. One’s remembered dream content might occasionally reflect these negative thoughts. Indeed, recurring nightmares or recurring stressful dreams could be a symptom of more pervasive suboptimal sleep. With reference to the literature reviewed above, memory processing during SWS could be very important, whether or not negative dreams are recalled after awakening.

Broadening this idea, rumination may be just one variant of a larger category of maladaptive memory processing during sleep. To take an extreme example, if the mind is excessively agitated during sleep, incessantly revisiting negative thoughts and memories, one consequence could be objectively poor sleep. This poor sleep could include a difficulty staying asleep or maintaining certain sleep stages, as in the arousal-filled REM periods noted above. Alternatively, sleep could look fairly normal electrophysiologically, but the nature of the memory processing could still have unwanted consequences for the individual after sleep. Intuitively, many individuals experience waking up “on the wrong side of the bed” after a full night of sleep, feeling unrested and ill-tempered. In both cases—maladaptive sleep with or without obvious signs of sleep disruption—there could be harmful ramifications for the waking mind.

A key empirical question is whether cultivating calm sleep can produce benefits for the waking mind. A worthy future goal for TMR research would be to test various methods to calm the mind during sleep. Exploring new strategies in this direction requires departing from the orthodox assumption that nothing can be done about the paths our minds take while we are asleep.

Such exploration must proceed together with research aimed at advancing our understanding of sleep, given that sleep may help with adaptive processing of traumatic memories. It could be that overnight emotional turmoil is the price we pay for subsequent waking benefits. If so, this sort of sleep-work should be perpetuated, not eliminated. This possibility deserves to be investigated further, but here we present an opposing line of thought: We suggest that sleep with maladaptive memory processing can be detrimental, in which case it could be helpful to change it, for instance through the counter-reactivation or inception of positive memories, feelings, or concepts.

Given that memory processing during sleep is modifiable, sleep-based consolidation affords the opportunity not just for improving memory function but also for restructuring the self. It may be possible to adjust the memories that we emphasize and maintain at the forefront of our psyche in an intentional and strategic way, in order to reach one’s goals of self-improvement. For example, if one values a reduction in self-centeredness and an increase in compassion for others, this intention can lead one to prioritize certain memories for reactivation and subsequent consolidation. Such an approach does not require something as unrealistic as drastically altering one’s set of autobiographical memories or starting all over by forgetting them, as in psychogenic amnesia. Rather, one might seek to gradually adjust both one’s declarative knowledge and one’s habits in a prosocial direction. In this sense, changing for the better concerns both declarative and nondeclarative memories. Change is possible based on what information we emphasize and recapitulate while awake, which is subject to further memory processing during sleep.

The natural, daily course of intentional wake memory processing followed by sleep memory processing may be sufficient to put into play this scenario of change for the better. A TMR protocol could also be adapted for use in conjunction with wake training to encourage positive changes. One example of such an application is in the context of the social biases that can implicitly affect our decisions and behavior toward others. Counter-stereotype training is one method to attempt to adjust such biases. In one study, TMR during sleep following such training was found to enhance bias changes, as indexed by performance measures from the Implicit Association Test ( Hu et al. 2015 ). These measures, however, are not high in test-retest reliability and are poor predictors of discriminatory behavior ( Oswald et al. 2013 ). Also, one attempt to closely replicate these TMR results failed to do so ( Humiston & Wamsley 2019 ). Ingrained attitudes may be difficult to change and also difficult to measure. Yet, various sorts of efforts along related lines might be worth pursuing in the interest of developing stronger cognitive control—for example, to increase prosocial tendencies.

Aside from TMR, there may be other ways to change the specific processing engaged by the sleeping brain. One tactic would be to control pre-sleep mental activity, in that positive waking thoughts could produce positive sleeping thoughts. Mental content during the last few minutes before falling asleep might be particularly influential. Indeed, a pre-sleep hypnosis procedure has been shown in several studies to increase sleep physiology measures of SWS in highly hypnotizable young subjects (e.g., Cordi et al. 2020 ). Adding TMR might extend these benefits further. More research should examine the consequences of tactics such as holding the pre-sleep intention to fill one’s sleep with positive thoughts. Dream control provides a further demonstration that we are not helplessly at the mercy of whatever happens during sleep. In the context of a lucid dream, for example, an individual can engage the intention to change what happens next. Changing a dream can be accomplished by using pre-sleep intentions and by using a variant of the TMR procedure to prompt particular dream content ( Konkoly et al. 2020 ).

The TMR research summarized above could be extended in many ways to attempt to instill particular thoughts during sleep. TMR procedures begin with pre-sleep learning, which in this case could involve a positive cognitive-affective orientation. Various tasks can be used to increase calm and peaceful states. Another option would be to decrease negative thinking, engaging strategies to dampen or reframe anxious, stressful, or otherwise maladaptive thoughts. Innovative TMR strategies could take aim at both of these goals to determine whether such changes are possible. Whereas Wassing and colleagues (2019) used TMR to reinstate the context of a particularly stressful and negative affect-laden experience, a variation on their design could instead reinstate positive experiences or invoke strategies that effectively reduce the unfortunate impact of negative experiences.

Finally, we acknowledge that the link between sleep and waking cognition is bidirectional. The quality of sleep reflects cognitive activity recently engaged while awake; the quality of wakefulness, in turn, reflects consolidation and other cognitive activity engaged while asleep; the quality of life reflects both. We may tend to ignore the portion of our lives occupied by sleep because it is predominantly out of view (like the dark side of the moon), but we do so at our peril. By understanding the mutual relationships between sleep and wake, and by embracing innovative ways to improve sleep, we can change the waking mind for the better.


Sleep is classically divided into stages characterized by distinct neural and bodily functions. Although future advances in sleep research and neural decoding may lead to refinements in our thinking about sleep physiology, contemporary schemes distinguish rapid eye movement (REM) sleep from three stages of non-REM (NREM) sleep. Each stage has characteristic electrophysiological features evident in the electroencephalogram, electrooculogram, and electromyogram. NREM includes stage 1 (N1), stage 2 (N2), and stage 3 (N3). Sleep progresses from light sleep to deeper sleep across these three stages as the ease of arousability decreases. N3 (formerly divided into N3 and N4) is also termed slow-wave sleep due to the high-amplitude slow waves (0.5–4 Hz) observed in the EEG. Nocturnal sleep consists of multiple cycles, typically 90 minutes in duration, including light sleep, slow-wave sleep, REM, and then a return to NREM. The majority of sleep is usually spent in N2 and slow-wave sleep, the stages most strongly implicated in memory consolidation to date.


We thank our colleagues for many fruitful discussions, including Jessica Payne, Marcia Grabowecky, Bjorn Rasch, Ken Norman, Phyllis Zee, John Wixted, and members of Ken Paller’s lab group. We are also grateful for funding from the Human Frontier Science Program (to E.S.) and from the Mind Science Foundation, the McKnight Foundation, DARPA, NIH (R01-NS112942, T32-NS047987, and T32-HL007909), and NSF (BCS-1921678 and BCS-1829414).


The authors are not aware of any affiliations, memberships, funding, or financial holdings that might be perceived as affecting the objectivity of this review.

1 A remarkable exception to this rule has been documented in people with so-called highly superior autobiographical memory. Fewer than 100 individuals have been reported to have this capability. Their superior abilities have yet to be explained, but they are limited to autobiographical events and certain facts that are personally meaningful ( LePort et al. 2012 ). Instead of forgetting most days, they seem to remember nearly every day. They can recall events from long ago with the same ease that most of us recall what happened yesterday. Sleep may contribute to this capability. Our preliminary finding of a high density of sleep spindles in several of these individuals ( Westerberg et al. 2020 ) suggests that their sleep may be particularly beneficial, but additional evidence is needed to confirm this possibility.


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Sleep improves ability to recall complex events

Sleep helps consolidate our memory of complex associations, thus supporting the ability to complete memories of whole events.

Researchers had known for some time that sleep consolidates our memories of facts and episodic events. However, the research to date has concentrated mainly on simple associations -- that is to say, connections between elements, such as we make when learning new vocabulary. "But in real life, events are generally made up of numerous components -- for example, a place, people, and objects -- which are linked together in the brain," explains Dr. Nicolas Lutz from LMU's Institute of Medical Psychology. These associations can vary in strength and some elements might be connected with each other only indirectly. "Thanks to the neural connections that underlie these associations, a single cue word is often all it takes for somebody to recall not only individual aspects of an event but multiple aspects at once." This process, which is known as pattern completion, is a fundamental feature of episodic memory. Lutz is lead author of a study recently published in the journal Proceedings of the National Academy of Sciences (PNAS) , which investigated the effect of sleep on memory of such complex events.

After the study participants had learned events with complex associations, in one condition they spent the night in a sleep laboratory, where they were allowed to sleep as usual, while in another condition, they had to stay up all night. In both conditions, the participants were allowed to spend the following night at home to recover. Then they were tested on how well they could recall different associations between elements of the learned events. "We were able to demonstrate that sleep specifically consolidates weak associations and strengthens new associations between elements that were not directly connected with each other during learning. Moreover, the ability to remember multiple elements of an event together, after having been presented with just a single cue, was improved after sleep compared to the condition in which the participants had stayed awake," summarizes Nicolas Lutz. This demonstrates the importance of sleep for completing partial information and processing complex events in the brain.

By monitoring the brain activity of the study participants during sleep, the authors of the study were also able to show that the improvement in memory performance is connected with so-called sleep spindles -- bursts of neural oscillatory activity during sleep, which are associated with the active consolidation of memory contents. This occurs through reactivation of the underlying neural structures while sleeping. "This finding suggests that sleep spindles play an important role in the consolidation of complex associations, which underlie the completion of memories of whole events," says Professor Luciana Besedovsky, lead researcher of the study.

According to Lutz and Besedovsky, the identified effects of sleep on memory can be seen as an important adaptation of the human brain, because they help people draw a more coherent picture of their environment, which in turn enables them to make more comprehensive predictions of future events. "And so our results reveal a new function by which sleep can offer an evolutionary advantage," reckons Luciana Besedovsky. "Furthermore, they open up new perspectives on how we store and access information about complex multielement events."

  • Sleep Disorders
  • Obstructive Sleep Apnea
  • Disorders and Syndromes
  • Intelligence
  • Brain-Computer Interfaces
  • Child Development
  • Memory-prediction framework
  • Limbic system
  • Memory bias
  • Delayed sleep phase syndrome
  • Circadian rhythm sleep disorder
  • Mental confusion

Story Source:

Materials provided by Ludwig-Maximilians-Universität München . Note: Content may be edited for style and length.

Journal Reference :

  • Nicolas D. Lutz, Estefanía Martínez-Albert, Hannah Friedrich, Jan Born, Luciana Besedovsky. Sleep shapes the associative structure underlying pattern completion in multielement event memory . Proceedings of the National Academy of Sciences , 2024; 121 (9) DOI: 10.1073/pnas.2314423121

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February 21, 2024

16 min read

How Sleep Engineering Could Help Heal the Brain

Stimulating the sleeping brain may ease suffering from memory loss, stroke or mental health problems

By Ingrid Wickelgren

Illustration of a person's facial profile, inside the head is the same person sitting on a chair and looking into a telescope.

Tim O'Brien

I t was late, and Sonia was alone in an unfamiliar town, trying to find her way home. The map showed a route through a dark forest lit by an occasional lantern. She viewed it with foreboding but, seeing other people also using this passage, took it. Walking fast, she neared a couple ahead of her—a man and a woman—who suddenly stopped, turned and grabbed her. The man covered her face with a cloth. She found herself on a stage with a ceiling spanned by a mirror. A crowd of men armed with guns and knives encircled her; she was about to be tortured and killed. Sonia picked up a stone and threw it at the ceiling, which shattered. Pieces of glass rained down, piercing her shoulder and foot. She fled into the forest, pursued by the couple, who could read each other's minds. The woman saw where Sonia was running and informed the man—Sonia knew she would be hunted down.

This nightmare and similar ones disturbed Sonia's sleep about twice a week for months. (Her real name has been withheld for privacy.) Those awful nights left her sleepy, irritable and emotionally spent—symptoms of nightmare disorder. The condition can occur by itself or alongside deeper issues such as post-traumatic stress or anxiety disorders. Sleep specialists at the Geneva University Hospitals prescribed “imagery rehearsal” therapy. Sonia was to create a positive ending for a bad dream and practice it daily. A fresh take on a dream tends to carry over into sleep, reducing the frequency of nightmares.

But the trick doesn't always work, so Sonia joined a study to test an embellished version of it. The trial leveraged sleep's power to fortify memories —in this instance, the new dream narrative. For five minutes each evening over two weeks, Sonia relaxed in a quiet space at home and imagined that the route through the forest led to a door that opened onto a bright, colorful field that felt safe. While she and 17 other people with nightmare disorder rehearsed their new storylines, they listened through headphones to a piano chord that was played every 10 seconds, eventually associating the sound with the narrative.

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And throughout that fortnight, they wore a sleep-engineering headband when they went to bed. The device detected when the participants entered rapid eye movement (REM) sleep (so named because the eyes dart from side to side during this phase), when people experience their most vivid dreams. While they dreamed, the headband transmitted, through the bones of their skull, the same piano chord they had heard while awake.

During sleep the brain replays select memories from the day to emblazon them into its neurons. Experts call this process memory consolidation. In the nightmare study, the chord reminded the participants of their happier dreams. “We want to enhance this specific memory,” says psychiatrist Lampros Perogamvros of the University of Geneva, who led the research.

The association led people to experience fewer nightmares and more positive dreams overall. “Even if you work with only one scenario when you're awake, nightmares about any kind of theme [such as being chased] go down,” Perogamvros says. The effect was significantly stronger for those who heard the chord while rehearsing their revised dream than for those who had not, the researchers reported in 2022. Sonia, for one, stopped having nightmares altogether, and her mood improved.

Manipulating sleep might be a new route out of the proverbial forest—whether the affliction is nightmares or a problem with mood, memory or even motor skills. “Sleep is an unguarded time. It's a time when our executive control, our rational thinking, our logical decision-making, our impulse control are turned off. So stimuli that manage to get in are processed differently and possibly more effectively,” says Robert Stickgold, a cognitive neuroscientist at Harvard Medical School.

The techniques investigators use to “get in” while someone is asleep range from electrically stimulating the patient's brain to exposing them to sounds or smells that remind them of specific facts or experiences. Many of these techniques were devised to decode sleep's role in memory and cognition. But they also offer ways to speed recovery from stroke or to restore memories lost with age. They might even be able to tamp down negative emotions attached to specific memories, which could help ease post-traumatic stress, anxiety, depression, or other mental health conditions.

“One of my latest hopes is that we can have new methods to help people wake up on the right side of the bed,” says Ken Paller, a memory and sleep researcher at Northwestern University. To make such methods practical and widespread, researchers are developing a range of sleep-engineering devices people can use at home. Experts say clinical use of some of the devices is years away, and they also warn of potential risks.

Messing with memory could have unforeseen consequences, such as creating imbalances that impede learning, says neuroscientist Gina Poe of the University of California, Los Angeles. “It's kind of a scary time,” Poe says. “We don't know enough. It's kind of like [being] a toddler. We can walk but don't know where we are going or how to avoid danger.”

Scholars have suspected that sleep shores up memories for millennia. In the first century C.E., Roman writer and teacher Marcus Fabius Quintillian wrote “that the interval of a single night will greatly increase the strength of the memory.” The details of this process remained obscure until the 20th century, when the invention of the electroencephalogram, a recording of brain activity made by an array of probes placed on the scalp, spawned studies showing that the sleeping brain whirs to its own electrical rhythms.

People sleep in cycles that repeat roughly every 90 minutes and usually go through a total of four to six cycles over a full night's sleep. The first cycle starts with a period of light sleep, which has two distinct stages. During the second stage, neurons produce clusters of electrical signals called sleep spindles—evidently because when drawn as a graph of voltage changing with time, they reminded scientists of wool wound on a stick. Light sleep descends into deep “slow wave” sleep, in which the spindles continue while slow, rhythmic pulses of electrical excitation sweep across the brain, overlaid with bursts of high-frequency “ripples.” In REM sleep, the fourth stage, brain neurons fire as actively and randomly as they do during the day, and people experience emotionally charged and bizarre dreams.

Some of this sleep-time brain activity, researchers surmised, might serve memory. In the 1970s David Marr, a computational neuroscientist then at Trinity College Cambridge, floated a theory of how the brain integrates new information with existing knowledge. In this model, the hippocampus, a seahorse-shaped structure located in both hemispheres of the brain, stores information during the day. But the memory traces remain fragile until sleep, when they are reinforced and relayed to the brain's cerebral cortex, or outer layer, for long-term storage and integration with other memories.

In a landmark 1994 study, investigators took brain recordings that showed the hippocampus fortifying memories during slow-wave sleep by retracing them. As a rat navigated a maze during its waking hours, patterns of activity among neurons in its hippocampus specified the rat's whereabouts on its trek. While the rat slept, researchers recorded its brain activity again and found the same neural patterns—as if the brain were rehearsing the path through the maze to commit it to memory. A decade later scientists obtained evidence of replay in people by using positron-emission tomography, which detects blood flow as a proxy of neuronal activity. Areas of the brain that became active when people learned routes in a virtual town were reactivated during deep sleep—and the amount of activity correlated with a person's ability to remember the routes.

As Marr had predicted, the replay of memories in the hippocampus is key to consolidation. It seems to flag certain memories for safekeeping, allowing the rest of daily life to fall by the wayside. “You went grocery shopping, and they were out of the little tomatoes ... you don't want to keep that memory for the rest of your life,” Stickgold says. “So almost everything gets forgotten. The game of sleep is to figure out what you don't want to forget.”

By the early 2000s scientists knew that most of the high-voltage waves of deep sleep originate in the brain's decision-making center, the prefrontal cortex, and move as smoothly and regularly as waves in a calm sea from the front to the back of the brain. And studies in animals and in people with epilepsy (in particular, individuals who had electrodes implanted in their brains to detect seizures) had implicated other sleep-time rhythms in memory processes. These include the ripples of electrical activity from the hippocampus that probably reflect replay—and which coincide with the troughs of sleep spindles originating in the thalamus. When a person is awake, this relay station sends selected information from the senses to the cerebral cortex for interpretation, but when someone is asleep, it shuts most signals out so the person remains generally unaware of their surroundings. Intriguingly, the number of sleep spindles per minute correlates with the person's ability to learn, according to Poe.

In a further, striking coincidence—or more likely not a coincidence at all but something integral to a process of nightly information transfer perfected by evolution—both the ripples and the spindles rise and fall with the slow waves. “There's this three-part symphony,” Stickgold says. “The hippocampus and the thalamus and the cortex all work in unison to strengthen specific memories.”

Still, the evidence that the sleeping brain analyzes and integrates memory remained circumstantial until experimenters found ways to influence the process. “Can we manipulate the waves?” wondered Jan Born, a behavioral neuroscientist now at the University of Tübingen. He and his team at the University of Lübeck applied oscillating current through the scalp of sleeping subjects to increase the amplitude of slow waves. The manipulation enhanced memory, they reported in a 2006 publication. But the electrical field seemed to vary unpredictably across the brain's anatomical folds. So the team switched to sound, which would be processed more reliably, Born felt, through a biological channel: the ear.

The researchers played soft clicks to sleepers timed to the up phase of their slow waves . The stimulation, given for a single night, greatly enhanced the size and duration of the slow waves and the spindles. Critically, compared with their performance after sleep alone, the intervention improved participants' memory of 120 word pairs, the team reported in 2013. The work directly tied the oscillations of slow-wave sleep to memory—and pointed to a way of using slow-wave sleep to improve memory.

“That's a sleep-engineering idea: Can we make that physiology run its course more effectively? Or, if it's not quite working well, can we adjust it so that it works better?” Paller asks. Slow waves weaken with age, which might explain age-related memory problems. Would supplementing slow waves mitigate memory decline? Northwestern neurologists Roneil Malkani and Phyllis Zee, in collaboration with Paller, among others, successfully used sound to enhance the ability to recall word pairs in five of nine people with mild cognitive impairment.

These interventions lasted just one night, however. In practice, staving off memory decline most likely requires longer-term treatment. Stimulating the brain during sleep through surgically implanted electrodes could theoretically shore up memory on a consistent basis. Neurosurgeon Itzhak Fried of U.C.L.A. Health and his colleagues recently showed that they could use such deep-brain stimulation to enhance memory. Fried had implanted the electrodes to detect seizures in people with severe epilepsy. But when these patients were asleep and seizure-free, he used the electrodes to sense and alter their deep-sleep oscillations.

As a slow wave was on the upswing, one of the electrodes sent a pulse of electricity to boost “the triple coincidence of ripples, spindles and slow waves,” Fried says. All six individuals who received this stimulation in the prefrontal cortex showed better recall of pairs of pictures after the night the electrode was live compared with their memory after undisturbed sleep, the scientists reported in 2023. The degree of memory improvement correlated with the shift in the brain's electrical patterns.

“We are changing the architecture of sleep,” Fried says. “Our goal is to really try to see whether we could have a memory aid or a memory neuroprosthetic device”—akin to a cochlear implant for people with impaired hearing.

Graphic shows the stages of sleep. During stage 3, the brain consolidates select memories from the day. Slow electrical waves travel across the cerebral cortex; sleep spindles emanate from the thalamus; and high-frequency ripples arise from the hippocampus.

I n addition to improving general memory by enhancing electrical waves in the sleeping brain, scientists have found diverse ways to enhance specific memories but not others. The first attempt at this strategy involved odors . Born's team asked people to sniff a rose scent while they learned the location of objects in a grid. They then exposed some of the participants to the fragrance while they slept. When delivered during slow-wave sleep, the scent spurred the sleepers' brains to revisit what they had learned—and significantly improved their recall of the locations (compared with that by people who were not exposed to the odor during sleep or were exposed to it only during REM sleep), the researchers reported in 2007. Brain imaging revealed that the scent strongly activated the hippocampus, further indicating that the stimulus enhanced replay.

Two years later Paller and his colleagues showed that they could do something similar with sound. The researchers played unique sounds while people memorized the locations of 50 objects on a computer screen. When seeing a picture of a cat, for example, the participants heard a meow; when seeing a kettle, they heard a whistle. The scientists then played 25 of the sounds during a nap, after which people remembered the locations of the associated objects better than they remembered the others—if they heard a whistle and not a meow, they would be more accurate in recalling the kettle's location on the screen than the cat's.

Paller's method, which he termed targeted memory reactivation , or TMR, gained traction as a way to bolster specific memories. In 2022 his then graduate student Nathan Whitmore showed that TMR could improve memory for faces and names , with the strongest effects in those who had the longest and most uninterrupted slow-wave sleep. This method might help older people with memory problems remember facts important to them, such as their grandchildren's names, Paller says.

TMR can also improve procedural memory, which underlies skills ranging from playing a piano piece to perfecting a jump shot. People execute learned sequences of finger movements faster after sleeping. Performance improves further if the memory for the sequence is reactivated during slow-wave sleep—by, say, a playback of tones the person listened to while learning each finger movement.

A similar method could speed recovery from strokes that leave people unable to perform basic movements. Rehabilitation involves practicing those skills daily. “If you want to use your toothbrush or pick up the salt, you have to control some muscles selectively and not other muscles,” Paller says. To teach these kinds of skills, Northwestern neurologist Mark Slutsky developed a simple 1980s-style video game in which users must activate one or two muscles to move a cursor from the center of a screen to one of eight targets—red squares that turn green when the cursor reaches them—on the perimeter.

In a 2021 study, Paller, Slutsky and their colleagues showed that TMR can improve people's performance in this game . While aiming for each target, 20 healthy young adults heard a unique sound such as a meow, drumroll or bell. After few hours of practice, they took a 90-minute nap. When they entered slow-wave sleep, they heard some of the sounds at five-second intervals. After they awoke, they showed improved performance—in speed, efficiency and muscle selection—in navigating to the red-square targets that were linked to the sounds played during their nap. Paller, Slutsky and their colleagues are testing a similar procedure in stroke patients who have difficulty moving their arms.

Cutting-edge versions of TMR synchronize the sound cues with the slow waves. “It matters exactly when we apply these triggers,” says neuroscientist Penelope Lewis of Cardiff University in Wales. She and her colleagues find that the technique can improve the learning of relations among objects —in this case a hidden ranking in groups of six photographs—but only if the sounds denoting that relation are played back during the peak, and not the trough, of the slow wave. In a related finding, cognitive neuroscientist Bernhard Staresina of the University of Oxford and Hong-Viet V. Ngo, now at the University of Essex in England, reported improved memory for verb-picture associations when they synchronized specific sound cues to the slow wave's rise. Moreover, cueing during this phase prolonged the wave and increased the power of associated spindles.

Intervening in slow-wave sleep can also alter emotions attached to specific memories—which can potentially boost mental health. Cognitive neuroscientist Xiaoqing Hu of the University of Hong Kong and his colleagues used TMR to put a positive spin on aversive memories by building associations with upbeat words. They taught people to associate nonsense words with disturbing photographs and then, during slow-wave sleep, replayed the nonsense cues along with positive words . Afterward people were less repulsed by the cued pictures than they had been before, the researchers reported in 2023. Again, the effect was strongest when the positive words coincided with the up phase of slow oscillations.

T he role of slow-wave sleep in memory consolidation is now well established, but the function of REM sleep is less clear. The dreams in this stage often seem illogical because parts of the brain's prefrontal cortex, which controls rational thought, are offline while brain regions controlling vision, movement and emotions remain active. Yet one emerging theory is that the fantastical dreams experienced during REM sleep tame emotions attached to memories and help people gain a broader understanding of what happens to them.

“REM-sleep dreaming offers a form of overnight therapy,” writes neuroscientist Matthew Walker in Why We Sleep: Unlocking the Power of Sleep and Dreams (Scribner, 2017). “[It] takes the painful sting out of difficult, even traumatic emotional episodes.” During REM sleep, levels of norepinephrine—a neurotransmitter that drives fear responses such as sweating, rapid heart rate and pupil dilation—get tamped down. As a result, memories that surface during REM sleep are divorced from those responses, Walker and others say, decoupling them from their emotional charge. (In patients with post-traumatic stress disorder, however, levels of norepinephrine remain high, and nightmares recur.)

If the theory is correct, inducing people to relive difficult experiences during REM sleep might help defuse the disturbing emotions associated with them. In a 2021 study, people rated upsetting pictures as less bothersome after associating the pictures with specific sounds and being exposed to those sounds during REM sleep. In contrast, there was no effect when the sounds were played during slow-wave sleep. If something similar works on people's real-life memories, it might be an avenue for treating depression or PTSD, according to Lewis.

REM sleep dreams might also help defuse strong emotions attached to an event through subconscious learning. Instead of dreaming about the upsetting event itself, people often dream about a more benign, related memory, leading them to subliminally connect the two experiences. Stickgold offered an example: if he were distraught after having a near-miss car accident during the day, he might dream about playing bumper cars with his son. The dream would help Stickgold realize that the car crash, if it had actually happened, “might have just meant my fender got bashed in. [But] I was reacting to it as if I had just barely stayed alive,” Stickgold speculates. “And that might have become clear to me because I had this linked memory of bumper cars where nothing bad happens.”

Illustration of a little girl sleeping with a faded image of the moon and flying birds over her.

In this way, REM sleep dreams can provide perspective. “You have to let the brain build this dream narrative to evaluate the emotional response to it,” Stickgold says. TMR could be used to shape that narrative, and the nightmare-disorder study in Geneva highlighted the possibility of such interventions. It could also make traditional forms of psychotherapy more effective. “Any psychotherapeutic approach aims at a change in behavior, habits, thoughts. Psychotherapy is therefore a form of learning,” says neuroscientist Sophie Schwartz of the University of Geneva, first author of the nightmare-disorder study. “Using TMR, we can boost such learning.”

Most sleep-engineering studies require patients or volunteers to come into a laboratory or other institutional setting, which limits the scope and efficacy of the intervention. People don't want to sleep in a lab for more than a night or two. But “if the technology were wearable and portable, it could plausibly be embedded in somebody's life,” says Heidi Johansen-Berg, a cognitive neuroscientist at Oxford. “So even if the benefit of any single day is quite small, you could imagine those incremental benefits building up significantly over time.”

Commercial devices that can be used at home are likely to be an important gateway to enhanced healing during sleep. One such invention, currently being tested for its ability to burnish verbal memory and to speed stroke recovery, involves a smartwatch that collects movement and data on heart rate, as well as a smartphone that plays sounds. A machine-learning model identifies periods of deep sleep and triggers TMR sounds within these periods. In research published in 2022, Whitmore and others found that using this technology at home for three nights improved people's memory for object locations —as long as the sounds were played softly enough that they did not disturb the sleeper.

For debilitating nightmares, doctors can already prescribe a phone app that uses artificial intelligence to analyze biometric data from Apple Watch sensors. When the sensors detect the rising heart rate and restlessness associated with a nightmare, the watch delivers intermittent gentle vibrations to disrupt the dream without waking the sleeper. Data published in 2023 from a trial of 65 veterans with trauma-induced nightmares suggest the device, when worn at least half the time, significantly enhanced sleep quality, as reported by the veterans.

A glovelike sleep detector developed by Adam Haar Horowitz, then at the Massachusetts Institute of Technology, and his colleagues might also reduce nightmares. The device monitors biological signs of sleep onset through contacts on the wrist and hand. It also connects to an app that gives voice prompts such as “tree” that, in a recent study, made nappers dream about trees and enhanced their creativity on tasks related to trees .

Despite the promise of sleep engineering, experts warn of risks inherent in tampering with memories. “You are biasing which ones are preferentially strengthened in the brain,” Lewis says. If you start doing it every night, who knows what kinds of imbalances that might cause?” It is also possible that these interventions could disrupt sleep. In another of Whitmore and Paller's experiments, for example, when the sounds were played too loudly, memory actually worsened. “There are lots of things still to understand about this before we would be ready to recommend it to the general population,” Lewis says.

Meanwhile the experiments have deepened scientists' understanding of sleep's role in memory and emotion—and how it shapes people's outlook on the world and themselves. “That is what the night is for,” Stickgold says. “It's to take all the information that came during the day and integrate it with all the information we already have in a way that helps you build that story of how the world works and what your life means.”

For Sonia, at least, the targeted memory reactivation has ended her nighttime siege in the forest. Instead one night she dreamed of being invited to a party in a chalet. “There was a terrace which gave a view of the mountains,” she wrote in her dream diary. “We all went out to watch the sunset. The sky was dark pink, the weather was very beautiful. All of a sudden, I feel a hand on my waist ... This person took my hand and took me to the center of the terrace, we started dancing without music. It was like in the movies, the world around began to spin quickly, I felt butterflies in my stomach for the first time in my life.”

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Your First Step Toward a Better Mood

Poor sleep can make anxiety, depression and other mental health issues worse. Here’s what to do about it.

An illustration of a person lying on their back in a bed with eyes open. The bedroom walls and floor tiles are deteriorating, breaking off and floating away.

By Christina Caron

It started with mild anxiety.

Emily, who asked to be identified only by her first name because she was discussing her mental health, had just moved to New York City after graduate school, to start a marketing job at a big law firm.

She knew it was normal to feel a little on edge. But she wasn’t prepared for what came next: chronic insomnia.

Operating on only three or four hours of sleep, it didn’t take long for her anxiety to ramp up: At 25, she was “freaking nervous all the time. A wreck.”

When a lawyer at her firm yelled at her one day, she experienced the first of many panic attacks. At a doctor’s suggestion, she tried taking a sleeping pill, in the hopes that it might “reset” her sleep cycle and improve her mood. It didn’t work.

Americans are chronically sleep deprived: one-third of adults in the United States say they get less than 7 hours a night. Teenagers fare even worse: About 70 percent of high school students don’t get enough sleep on school nights.

And it is having a profound effect on mental health.

An analysis of 19 studies found that while sleep deprivation worsened a person’s ability to think clearly or perform certain tasks, it had a greater negative effect on mood. And when the National Sleep Foundation conducted a survey in 2022, half of those who said they slept less than 7 hours each weekday also reported having depressive symptoms. Some research even indicates that addressing insomnia may help prevent postpartum depression and anxiety .

Clearly, sleep is important. But despite the evidence, there continues to be a shortage of psychiatrists or other doctors trained in sleep medicine, leaving many to educate themselves. So what happens to our mental health if we aren’t getting enough sleep, and what can be done about it?

How does poor sleep affect your mood?

When people have trouble sleeping, it changes how they experience stress and negative emotions, said Aric Prather, a sleep researcher at the University of California, San Francisco, who treats patients with insomnia. “And for some, this can have a feed-forward effect — feeling bad, ruminating, feeling stressed can bleed into our nights,” he said.

Carly Demler, 40, a stay-at-home mother in North Carolina, said she went to bed one night and never fell asleep . From that point onward, she would be up at least once a week until 3 or 4 a.m. It continued for more than a year.

She became irritable, less patient and far more anxious.

Hormone blood work and a sleep study in a university lab offered her no answers. Even after taking Ambien, she stayed up most of the night. “It was like my anxiety was a fire that somehow jumped the fence and somehow ended up expanding into my nights,” she said. “I just felt I had no control.”

In the end, it was cognitive behavioral therapy for insomnia , or C.B.T.-I., that brought Ms. Demler the most relief. Studies have found that C.B.T.-I. is more effective than sleep medications are over the long term: As many as 80 percent of the people who try it see improvements in their sleep.

Ms. Demler learned not to “lay in bed and freak out.” Instead, she gets up and reads so as not to associate her bedroom with anxiety, then returns to bed when she’s tired.

“The feeling of gratitude that I have every morning, when I wake up and feel well rested, I don’t think will ever go away,” she said. “That’s been an unexpected silver lining.”

Adults need between 7 and 9 hours of sleep a night, according to the Centers for Disease Control and Prevention . Teenagers and young children need even more.

It’s not just about quantity. The quality of your sleep is also important. If it takes more than 30 minutes to fall asleep, for example, or if you regularly wake up in the middle of the night, it is harder to feel rested, regardless of the number of hours you spend in bed.

But some people “have a tendency to think they’re functioning well even if they’re sleepy during the day or having a harder time focusing,” said Lynn Bufka, a clinical psychologist and spokeswoman for the American Psychological Association.

Ask yourself how you feel during the day: Do you find that you’re more impatient or quick to anger? Are you having more negative thoughts or do you feel more anxious or depressed? Do you find it harder to cope with stress? Do you find it difficult to do your work efficiently?

If so, it’s time to take action.

How to stop the cycle.

We’ve all heard how important it is to practice good sleep hygiene , employing the daily habits that promote healthy sleep. And it’s important to speak with your doctor, in order to rule out any physical problems that need to be addressed, like a thyroid disorder or restless legs syndrome.

But this is only part of the solution.

Conditions like anxiety, post-traumatic stress disorder and bipolar disorder can make it harder to sleep, which can then exacerbate the symptoms of mental illness, which in turn makes it harder to sleep well.

“It becomes this very difficult to break cycle,” Dr. Bufka said.

Certain medications, including psychiatric drugs like antidepressants, can also cause insomnia. If a medication is to blame, talk to your doctor about switching to a different one, taking it earlier in the day or lowering the dose, said Dr. Ramaswamy Viswanathan, a professor of psychiatry and behavioral sciences at State University of New York Downstate Health Sciences University and the incoming president of the American Psychiatric Association.

The cycle can afflict those without mental health disorders too, when worries worsen sleep and a lack of sleep worsens mood.

Emily, who worked in the big law firm, would become so concerned about her inability to sleep that she didn’t even want to get into bed.

“You really start to believe ‘I’m never going to sleep,’” she said. “The adrenaline is running so high that you can’t possibly do it.”

Eventually she came across “Say Goodnight to Insomnia” by Gregg D. Jacobs. The book, which uses C.B.T.-I. techniques, helped Emily to reframe the way she thought about sleep. She began writing down her negative thoughts in a journal and then changing them to positive ones. For example: “What if I’m never able to fall asleep again?” would become “Your body is made to sleep. If you don’t get enough rest one night, you will eventually.” These exercises helped her stop catastrophizing.

Once she started sleeping again, she felt “way happier.”

Now, at 43, nearly 20 years after she moved to New York, she is still relying on the techniques she learned, and brings the book along whenever she travels. If she doesn’t sleep well away from home, “I catch up on sleep for a few days if necessary,” she said. “I’m way more relaxed about it.”

Christina Caron is a Times reporter covering mental health. More about Christina Caron

Managing Anxiety and Stress

Stay balanced in the face of stress and anxiety with our collection of tools and advice..

These simple and proven strategies will help you manage stress , support your mental health and find meaning in the new year.

First, bring calm and clarity into your life with these 10 tips . Next, identify what you are dealing with: Is it worry, anxiety or stress ?

Persistent depressive disorder is underdiagnosed, and many who suffer from it have never heard of it. Here is what to know .

If you notice drastic shifts in your mood during certain times of the year, you could have seasonal affective disorder. Here are answers to your top questions about the condition .

How much anxiety is too much? Here is how to establish whether you should see a professional about it .

Drawing, music and writing can elevate your mood and benefit your mental health. Here are some easy ways to welcome them into your life .

Stress is unavoidable in modern life, but it doesn’t have to get you down. This guide can help you keep in check .


Emerging trends and hot spots of sleep and genetic research: a bibliometric analysis of publications from 2002 to 2022 in the field.

\r\nYing Tao,&#x;

  • 1 Department of Anesthesiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
  • 2 Key Laboratory of Anesthesiology (Shanghai Jiao Tong University), Ministry of Education, Shanghai, China

Background: Sleep is an important biological process and has been linked to many diseases; however, very little is known about which and how genes control and regulate sleep. Although technology has seen significant development, this issue has still not been adequately resolved. Therefore, we conducted a bibliometric analysis to assess the progress in research on sleep quality and associated genes over the past 2 decades. Through our statistical data and discussions, we aimed to provide researchers with better research directions and ideas, thus promoting the advancement of this field.

Methods: On December 29, 2022, we utilized bibliometric techniques, such as co-cited and cluster analysis and keyword co-occurrence, using tools such as CiteSpace, VOSviewer, and the Online Analysis Platform of Literature Metrology ( ), to conduct a thorough examination of the relevant publications extracted from the Web of Science Core Collection (WoSCC). Our analysis aimed to identify the emerging trends and hot spots in this field while also predicting their potential development in future.

Results: Cluster analysis of the co-cited literature revealed the most popular terms relating to sleep quality and associated genes in the manner of cluster labels; these included genome-wide association studies (GWAS), circadian rhythms, obstructive sleep apnea (OSA), DNA methylation, and depression. Keyword burst detection suggested that obstructive sleep apnea, circadian clock, circadian genes, and polygenic risk score were newly emergent research hot spots.

Conclusion: Based on this bibliometric analysis of the publications in the last 20 years, a comprehensive analysis of the literature clarified the contributions, changes in research hot spots, and evolution of research techniques regarding sleep quality and associated genes. This research can provide medical staff and researchers with revelations into future directions of the study on the pathological mechanisms of sleep-related diseases.

1. Introduction

Sleep is a fundamental biological process that is conserved across a wide range of organisms from invertebrates to vertebrates, but the pathobiology and molecular mechanisms of sleep are still not fully understood. There was a strong interest in the genetics of sleep. In the 1930s, Geyer conducted a twin study in children and first showed that genetic factors were involved in the regulation of sleep. Since then, numerous high-quality studies on gene expression profiling indicate that there is a shared genetic basis for sleep across different species ( 1 , 2 ). In other words, there is a belief that the mechanism responsible for sleep is evolutionarily conserved and that similar genetic pathways are involved in regulating sleep across different organisms. Nowadays, various new programs and techniques have been developed, leading to breakthroughs in the understanding of sleep function, circadian rhythms, and pathological mechanisms of sleep-related diseases ( 3 – 5 ).

However, the molecular and pathobiology mechanisms of sleep and sleep-related diseases are quite complex and least understood phenomena for a long time. Genetic analysis of sleep has only become a significant discipline in the past decade, with the focus expanding to drosophila, zebrafish, and worms. With the help of genetics, many sleep-related problems have been solved, for example, the identification of loci related to various sleep disorders such as obstructive sleep apnea (OSA), major depressive disorder (MDD), and insomnia disorder. As genetic technologies continue to develop, genes will be a powerful tool in solving these problems.

In this study, we utilized bibliometric citation analysis, which is widely employed to map and analyze the performance of specific fields, to gain insights into the temporal trends in the field of sleep quality and associated genes. It provides valuable predictions about the progress and development of specific fields based on factors such as publication volume and citation frequency ( 6 , 7 ). The published studies have played a significant role in advancing specific research fields ( 8 ). To recognize the significance of the sleep and genetics topic and the effectiveness of bibliometric analysis, we conducted an analysis of publications related to this theme. Our objective was to assist researchers to identify the current trends and hot spots in this field, both in the past and future and to provide a comprehensive understanding of the evolution of this field.

2.1. Data source and search strategy

The Web of Science core collection database was searched by retrieval form (TI = (gene OR genetic) OR KP = (gene OR genetic)) AND (TI = (sleep) OR KP=(sleep)) NOT (TI = (guideline OR recommendation OR consensus OR “case report” OR meta OR review)). The retrieval time was limited to January 01, 2003 to December 31, 2022. The inclusion criteria were studies related to the search, excluding reviews, letters, briefings, book reviews, etc., resulting in 3,996 articles. The data were used for visual analysis of authors, institutions, countries, journals, co-cited literature, and keywords. The detailed process of enrollment and screening is shown in Figure 1 .

Figure 1 . Flow diagram of the inclusion process. The detailed process of screening and enrollment.

2.2. Data selection

We used Web of Science ( ) to analyze search results and create a histogram that illustrates publication trends. We then converted the WoSCC data to the UTF-8 format and utilized the “total literature analysis” option to analyze publication trends across different countries and the “partnership analysis” option to analyze intercountry/regional relationships. Additionally, we utilized Web of Science to extract the histogram that displays the publication trend and analyze the research status, hot spots, and trend with the above software.

2.3. Data analysis and visualization

The publications' complete records and cited references were obtained from the WoSCC database and saved as a text file. We used VOSviewer1.6.18 (version 1.6.18, Leiden University, van Eck NJ and Waltman L), which can provide three kinds of visualization maps: network maps, overlay maps, and density maps, to analyze the information of authors, institutions, journals, countries, references, and keywords. We used CiteSpace 6.1.6 to calculate keywords burst and reference co-citation analysis. The graphs were created by the abovementioned software, and the layout was adjusted by Pajek and SCImago Graphica by using the above software to draw a visual map and analyze the research status, hot spots, and trend of sleep quality and associated genes.

3.1. Analysis of publications and time trends

Over the past 20 years, researchers have contributed 4,000 articles to the field of sleep, including editorial materials and reviews. The number of sleep-related publications has shown consistent growth, increasing from 73 in 2003 to 291 in 2022, while three minor dips in publication rates occurred from 2013 to 2014, from 2017 to 2018, and from 2020 to 2021. In 2022, the number of articles on sleep reached a peak at 291, with a significant increase of 37 publications from the previous year ( Figure 2 ).

Figure 2 . Annual and total number of publications on sleep.

3.2. Analysis of countries and institutions

We analyzed the countries and institutions of the authors of these articles. Our findings revealed that 10 countries contributed more than 100 publications related to sleep ( Table 1 ). The United States has the highest number of publications, with a staggering 1,804, which is far ahead compared to other countries. China ranks second with 549 publications. The United States has strong research cooperations with China, Japan, Germany, and the United Kingdom ( Figure 3A ). However, cooperation within Asia, Africa, and South America appears to be weak. The shaped world map ( Figure 3B ) shows the visual analysis of published regions by VOSviewer software. A total of 3,996 articles related to sleep quality and associated genes research have been published in 92 countries. The world map shows the cooperation relationship strength among countries, and some countries including China, Canada, Australia, Brazil, Japan, and South Korea have limited collaborations with other countries except the United States.

Table 1 . Countries with more than 100 publications on sleep.

Figure 3 . Cooperation maps among countries and institutions. (A) Cooperation relationships of countries. The size of points means the number of publications. The colors and the weight of the lines mean the intensity of cooperation. (B) World map of cooperation relationships among countries. The colors show the number of publications. (C) Cooperation relationships among institutions. Colors represent clusters automatically calculated by VOSviewer. (D) Time trend of cooperation among institutions. The colors correspond to the annual time periods of publications.

Regarding institutions, our analysis revealed a substantial amount of collaboration between institutions worldwide. Institutions contributing to sleep research are widely spread out and engage in a broad range of cooperations ( Figure 3C ). Brigham and Women's Hospital is the most willing to work with other institutions. Pennsylvania University, Harvard University, Stanford University, Yale University, and Minnesota University focus on sleep before 2012 with publications. Broad Institute, Harvard Medical School, Peking University, Zhejiang University, and Manchester University began to study sleep more recently ( Figure 3D ).

Fourteen institutions contributed more than fifty studies ( Table 2 ). The University of Pennsylvania institution published the highest number of articles, 143, ranking first. This was followed by Minnesota University and Stanford University with 123 and 85 publications, respectively ( Table 2 ).

Table 2 . Institutions with more than 50 publications on sleep.

3.3. Analysis of authors and co-cited authors

Partnership among authors promotes global cooperation. A total of 20,615 authors contributed 3,996 articles. Tufik Sergio, Pack Allan I., Archer Simon N., Gozal David, and Cirelli Chiara have published at least 15 articles in this field ( Table 3 ). They study on sleep earlier all before 2014. However, Tufik Sergio has a low ratio of total citations (TC) to publications, while Archer Simon N. and Cirelli Chiara have a high number of citation references with a ratio of more than 60 ( Table 3 ). Saxena Richa, Keene Alex C., Li Yan, and Logan Ryan W. start to focus on sleep in recent years ( Figure 4B ).

Table 3 . Authors with more than 15 publications on sleep.

Figure 4 . Cooperation maps among authors publishing articles on sleep. (A) Cooperation relationships among authors. The size of points means the number of publications. Colors represent clusters automatically calculated by VOSviewer. (B) Time trend of cooperation among authors. The colors correspond to the annual time periods of publications.

Most of the studies were conducted by more than one researcher. There seemed to be close cooperation among different authors worldwide. Gozal David and Khalyfa Abdelnaby have the strongest communication. There is no major cluster formed in the map, and the cooperations among authors are various ( Figure 4A ).

3.4. Analysis of journals

In total, 147 journals have published 2,497 articles on sleep. The journals with the top three publications are PLoS ONE (164), Sleep (159), and Scientific Reports (96) ( Table 4 ). There are 32 articles related to sleep on Nature, Science, and Cell, but they have the highest ratio of total citations ( Table 5 ). As shown in Figure 5A , the time trend map shows Molecular Therapy, Brain Research, and Neuroscience published articles on sleep earlier with more than 30 publications ( Figure 5B ). On the contrary, Circadian Clock in Brain Health and Disease, Frontiers in Genetics and iScience start to published research studies on sleep since 2021.

Table 4 . Journals with more than 30 publications on sleep.

Table 5 . Journals with a ratio of TC to publications on sleep >100.

Figure 5 . Maps of journals that published articles on sleep. (A) Cluster map of journals. The size of points means the number of publications on sleep. Colors represent clusters automatically calculated by VOSviewer. (B) Time trend of journals publishing articles on sleep. The colors correspond to the annual time periods of publications.

3.5. Analysis of co-cited references

Subsequently, we utilized clustered network analysis to delve deeper into the co-citations and conduct a thorough investigation of their interconnections. As shown in Figure 6A , the co-cited references were clustered into 12 major cluster labels: shift work, transposon, sleep duration, microarray, GWA, lethargus, per3 , F-box protein, miRNA, non-viral vector, insomnia, and major depressive disorder. A timeline view of the distinct co-citations is shown in Figure 6B to present all the cited literature more clearly. Each ball represents a cited article. The size of the ball is proportional to the number of citations. The connected ball indicates that these articles are cited together. With purple representing relatively old citations and yellow representing more recent citations, we found that in sleep quality and associated genes research, mental illness has been a hot topic since 2005. The study of insomnia and sleep duration is an emerging area of research after 2015 and has received increasing attention in recent years. What is interesting to us is that GWAS has been the focus of recent research, and we look forward to finding more interesting genes related to sleep through this method.

Figure 6 . Co-cited references in sleep publications. (A) Visualization network of co-cited references regarding sleep articles. The size of points means the number of being cited. The colors padded correspond to the annual time periods of publications. (B) Timelines of keywords in co-cited references.

3.6. Analysis of keywords

Keyword analysis plays a crucial role in identifying research hot spots and trends, offering valuable insights into future directions. The VOSviewer software was used to perform co-occurrence cluster analysis on the keywords of the article. The minimum occurrence times of each keyword were set to 10 and 164 keywords were screened from the 6,973 keywords to form a visual map ( Figure 7A ). There were six clusters summarized ( Figure 7B ), including sleep, circadian rhythm, obstructive sleep apnea, genetics, orexin, and transposon.

Figure 7 . Keywords in sleep publications. (A) Visualization network of keywords with more than 10 occurrences. (B) Density map of keywords with more than 10 occurrences. (C) Cluster map of keywords. The size of points means the occurrences of keywords. The lines mean the relationships between two keywords. Colors represent clusters automatically calculated by VOSviewer. (D) Time trend of keywords related to “genetics”. The colors correspond to the annual time periods of publications. (E) Time trend of keywords related to “sleep”. The colors correspond to the annual time periods of publications. (F) Top 20 keywords with the strongest citation bursts analyzed by CiteSpace. The red areas mean the burst periods of keywords.

In the keyword clustering heat map, the color depth is proportional to the number of studies, and the darker the color, the higher the popularity of the keyword, the more studies. “Sleep” and “circadian rhythm” received the most studies ( Figure 7C ).

By examining the bursts of keywords over time, valuable insights into the research trajectory and emerging areas of focus within the field of study can be gained. Among the identified keywords, “basal forebrain” and “cerebral cortex”, associated with brain regions, emerged as the earliest bursting keywords. Subsequently, keywords such as “diurnal preference,” “shift work,” “mood disorder,” “bipolar disorder,” and “sleep apnea” gained prominence. More recent bursts of interest were observed in keywords such as “Alzheimer's disease,” “DNA methylation,” “circadian clock,” “circadian gene,” “obstructive sleep apnea,” and “polygenic risk score” ( Figures 7D , E ).

In this study, a comprehensive analysis was conducted on 312 keywords that occurred more than 10 times, yielding noteworthy results. The top five frequently occurring keywords were identified as follows: “circadian rhythm” (185 occurrences), “sleep deprivation” (162 occurrences), “circadian rhythms” (148 occurrences), “obstructive sleep apnea” (106 occurrences), and “clock genes” (89 occurrences). The keywords formed five distinct clusters representing key areas of research interest, including sleep, obstructive sleep apnea, circadian rhythm, genetics, and epilepsy ( Figure 7F ).

4. Discussion

In our study, bibliometric mapping was used to visualize the development of sleep quality and associated genes research from 2002 to 2022. Our findings revealed a continuous increase in scientific output in the field of genetics and sleep over the past two decades. The ranking of publication numbers of countries, universities, authors, and journals helped researchers to search for cooperations, to seek positions, or to publish their works conveniently. Analyzing the collaborative relationships among different countries/regions and institutions can also reflect the academic exchange in this field. Recent advancements in sleep research studies have been facilitated by experimental techniques such as genome-wide association studies (GWAS) analysis. Additionally, newly emergent research hot spots, including obstructive sleep apnea, circadian clock, and circadian genes, were indicated by keyword burst detection. However, despite the substantial amount of research in this area, there was a lack of studies focusing on predicting and clarifying current research hot topics and frontiers. To address this gap, we cataloged the attributes of existing studies and analyzed the results of cluster labeling and burst detection techniques to develop a nuanced understanding of the current state of research and identify promising avenues for future exploration in this field.

GWAS, which is the study of related single nucleotide polymorphisms (SNPs) for a given phenotype with large human populations, have been applied in different sleep subtypes since 2015, especially in sleep duration research studies. SNPs near PAX8 gene, responsible for regulating thyroid-specific genes, were identified in multiple GWAS studies on self-reported and objective sleep duration ( 9 – 12 ). Mutations in this gene were found to potentially increase sleep duration by 2–3 min ( 13 , 14 ). Vaccinia-related kinase 2 ( VRK2 ), associated with schizophrenia ( 15 ), was another gene that had been robustly replicated in the previous GWAS ( 10 ). GWAS helped scientists identify lots of new intriguing loci related to sleep such as DRD2, SLC6A3 , or GABRR1 in the neuron system ( 11 ), but more efforts are needed as few novel loci from GWAS are strongly replicated ( 14 ).

Apart from SNPs, epigenetic processes including DNA methylation (DNAm), which means the methyl modification of cytosine by DNA methyltransferase, have been implicated in influencing sleep ( 16 ). The loss of DNAm of differentially methylated positions showed an association with insufficient sleep in a cross-sectional genome-wide analysis ( 17 ). Circadian clock genes were reported to be regulated by DNAm. For example, the expression level of Per1 was increased during perinatal development by demethylating within the SCN ( 18 ), while Cry2 showed significantly increased methylation in older mice ( 19 ). Moreover, researchers have reported that the methylation level of some genes may correlate with sleep disorders. Forkhead Box P3 ( FOXP3 ) and endothelial nitric oxide synthase ( eNOS ) gene DNAm levels have been suggested to play a crucial role in pediatric OSA ( 20 ). Moreover, DNAm modules in MAPT, which is a key regulator of Tau proteins in the brain, were implicated in sleep duration in children ( 21 ). However, there is still an ongoing debate in this area. A meta-analysis in 2022 showed DNA methylation, at birth or in childhood, was not associated with sleep ( 22 ). Consequently, there is an urgent need for epigenome-wide association studies to validate the reliability of specific epigenetic alterations in relation to sleep.

Circadian rhythms, which serve as an endogenous time clock, are present in a wide range of living organisms ( 23 ). The neuronal network and molecular mechanisms of circadian rhythms were well studied. The core regulatory loop of circadian rhythms was controlled by the transcription factors brain and muscle ARNT-like protein 1 ( BMAL1 ) and circadian locomotor output cycles kaput ( CLOCK ), which promoted the expression of period and cryptochrome genes ( Per1, Per2, Cry1 , and Cry2 ). Consequently, the accumulation of heterodimer PER/CRY inhibited their own transcription, forming a self-regulatory feedback loop. This network regulated the expression of countless other genes and was vital for cell physiology and metabolism ( 24 , 25 ). Recently, some research studies showed these circadian clock genes had multiple functions outside the suprachiasmatic nucleus (SCN), a major core brain region that controls timekeeping, to influence sleep ( 26 ). For example, sleep deprivation for 6 h increased the expression of per1 in forebrain lysates ( 26 ) and the expression of per2 in the whole brain, liver, and kidney but not in SCN ( 27 ). Moreover, the expression of Bmal1 in the skeletal muscle was essential and capable of effectively regulating the total amount of sleep ( 28 ). As for Cry1 and Cry2 , double knockout mice showed increased non-rapid eye movement (non-REM) sleep ( 29 ). The molecular mechanism of circadian genes in regulating sleep is still under investigation.

Obstructive sleep apnea (OSA), which is caused by recurrent episodes of upper airway collapse and obstruction during sleep, is a prevalent sleep-disordered breathing ( 30 ). It often manifests through symptoms such as loud snoring, nocturnal awakenings, and excessive daytime sleepiness. The prevalence of moderate OSA in the general population ranged from 9 to 38%, with a higher occurrence among men ( 30 ). There are many risk factors that affect the OSA, including unmodifiable risk factors like sex, age, and race, and modifiable risk factors such as obesity, endocrine disorders, or obstruction ( 31 ). The genetic etiology of OSA was little known. Previous studies using different methods, such as genetic expression analysis and Mendelian randomization analysis, found some genes may be associated with OSA such as PCNA, PSMC6 ( 32 ), LEPR, MMP-9 , and GABBR1 ( 33 ). Recently, one GWAS study of OSA using FinnGen found high genetic correlations between OSA and obesity ( 34 ). Another twin study showed OSA also had a common genetic background with hypertriglyceridemia ( 35 ). Moreover, genetic analyses also showed patients with OSA have higher C-reactive protein and tumor necrosis factor-alpha levels ( 36 ) and were susceptible to diseases such as atrial fibrillation ( 37 ), but the mechanism of these associations was not clear.

Sleep disorders were associated with a higher risk of developing depressed and suicidal thoughts in those with major depressive disorder (MDD). MDD could be influenced by genetic factors, with a heritability of 40–50% based on twin studies. New research studies indicate that depression and sleep disorders share pathogenesis. According to a study conducted by Ma et al., circadian rhythm disturbances might contribute to depression ( 38 ). Park et al. utilized a non-parametric model-free0000000000 multivariate dimensionality reduction (MDR) approach and found that circadian genes, TIMELESS rs4630333 and CSNK1E rs135745, were significantly associated with MDD and bipolar disorder ( 39 ). Moreover, genetic variations in adenosine metabolism were associated with depression, sleep, and attention disturbances. MyD88 , essential for microglial activation, affected microglial homeostasis functions and lowered the serotonergic neuronal output, leading to insomnia and depressive behavior.

Sleep-related disorders are complex phenotypes influenced by both genetic and environmental factors, leading to significant individual variations ( 40 ). In contrast, common genetic-related diseases such as albinism and hemophilia are less affected by environmental factors and are easier to study. Genes involved in the regulation of sleep-related disorders are complex, and genetic research in this area is still in its early stages. Many questions remain unanswered. Many other diseases are often associated with sleep-related disorders. Studying the genes related to sleep-related disorders greatly aids in understanding the mechanisms of other diseases, such as mental illnesses and binge eating disorder (BED) ( 41 ), which are closely linked to sleep disorders. Unlike other diseases, sleep-related disorders do not affect specific organs, making them more challenging to study ( 3 ).

Although many genes related to sleep disorders have been identified through existing technological means, it is difficult to replicate the genes obtained in different laboratories, and many genes cannot be validated. Furthermore, because sleep-related disorders also exhibit environmental susceptibility, it is important to take into account regional and ethnic differences when conducting genetic association analyses ( 40 ).

In conclusion, bibliometric analysis offered an objective and quantitative method for assessing publication performance between countries, researchers, and research institutions. Our results showed considerable interest in the field of sleep quality and associated genes research in recent years, particularly the study of GWAS, circadian rhythms, OSA, and depression. The relationship between genes and sleep disorders has gained increasing attention. Recent literature has gradually revealed the specific mechanisms involved, providing potential avenues for prevention, treatment, and intervention of sleep disorders.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

YT: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Visualization, Writing—original draft. YQ: Data curation, Software, Writing—original draft. SC: Conceptualization, Software, Validation, Writing—original draft. TX: Data curation, Writing—review and editing. JL: Formal analysis, Writing—review and editing. DS: Supervision, Writing—review and editing. WY: Supervision, Writing—review and editing. XC: Funding acquisition, Writing—review and editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by grants from the National Natural Science Foundation of China (Nos. 81874371 and 82201369), Shanghai Engineering Research Center of Perioperative Organ Support and Function Preservation (20DZ2254200), and Shanghai Municipal Key Clinical Specialty (shslczdzk03601 to WY).

Conflict of interest

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

Publisher's note

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

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Keywords: sleep, gene, GWAS, CiteSpace, VOSviewer, bibliometric analysis, co-citation analysis

Citation: Tao Y, Qin Y, Chen S, Xu T, Lin J, Su D, Yu W and Chen X (2023) Emerging trends and hot spots of sleep and genetic research: a bibliometric analysis of publications from 2002 to 2022 in the field. Front. Neurol. 14:1264177. doi: 10.3389/fneur.2023.1264177

Received: 20 July 2023; Accepted: 04 October 2023; Published: 08 November 2023.

Reviewed by:

Copyright © 2023 Tao, Qin, Chen, Xu, Lin, Su, Yu and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Xuemei Chen,

† These authors have contributed equally to this work and share first authorship

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

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Sleep improves ability to recall complex events, shows study

by Ludwig Maximilian University of Munich

Sleep improves ability to recall complex events

Researchers have known for some time that sleep consolidates our memories of facts and episodic events. However, the research to date has concentrated mainly on simple associations—that is to say, connections between elements, such as we make when learning new vocabulary.

"But in real life, events are generally made up of numerous components—for example, a place, people, and objects—which are linked together in the brain," explains Dr. Nicolas Lutz from LMU's Institute of Medical Psychology.

These associations can vary in strength and some elements might be connected with each other only indirectly. "Thanks to the neural connections that underlie these associations, a single cue word is often all it takes for somebody to recall not only individual aspects of an event but multiple aspects at once," says Lutz.

This process, which is known as pattern completion, is a fundamental feature of episodic memory . Lutz is lead author of a study recently published in the Proceedings of the National Academy of Sciences , which investigated the effect of sleep on memory of such complex events.

After the study participants had learned events with complex associations, in one condition they spent the night in a sleep laboratory, where they were allowed to sleep as usual, while in another condition, they had to stay up all night. In both conditions, the participants were allowed to spend the following night at home to recover. Then they were tested on how well they could recall different associations between elements of the learned events.

"We were able to demonstrate that sleep specifically consolidates weak associations and strengthens new associations between elements that were not directly connected with each other during learning. Moreover, the ability to remember multiple elements of an event together, after having been presented with just a single cue, was improved after sleep compared to the condition in which the participants had stayed awake," summarizes Nicolas Lutz. This demonstrates the importance of sleep for completing partial information and processing complex events in the brain.

By monitoring the brain activity of the study participants during sleep, the authors of the study were also able to show that the improvement in memory performance is connected with so-called sleep spindles—bursts of neural oscillatory activity during sleep, which are associated with the active consolidation of memory contents. This occurs through reactivation of the underlying neural structures while sleeping.

"This finding suggests that sleep spindles play an important role in the consolidation of complex associations, which underlie the completion of memories of whole events," says Professor Luciana Besedovsky, lead researcher of the study.

According to Lutz and Besedovsky, the identified effects of sleep on memory can be seen as an important adaptation of the human brain, because they help people draw a more coherent picture of their environment, which in turn enables them to make more comprehensive predictions of future events.

"And so our results reveal a new function by which sleep can offer an evolutionary advantage ," states Besedovsky. "Furthermore, they open up new perspectives on how we store and access information about complex multielement events."

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  • Review Article
  • Published: 04 January 2010

The memory function of sleep

  • Susanne Diekelmann 1 &
  • Jan Born 1  

Nature Reviews Neuroscience volume  11 ,  pages 114–126 ( 2010 ) Cite this article

66k Accesses

2283 Citations

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  • Consolidation
  • Synaptic plasticity

Sleep promotes the consolidation of declarative as well as procedural and emotional memories in a wide variety of tasks. Sleep improves preferentially the consolidation of memories that were encoded explicitly and are behaviourally relevant to the individual.

Consolidation during sleep not only strengthens memory traces quantitatively but can also produce qualitative changes in memory representations. An active process of re-organization enables the formation of new associations and the extraction of generalized features. This can ease novel inferences and insights.

Spatio-temporal patterns of neuronal activity during encoding in the awake state become re-activated during subsequent sleep, specifically during slow-wave sleep (SWS) which is a state of minimum cholinergic activity. Such re-activations might promote the gradual redistribution of hippocampus-dependent memories from the hippocampus to neocortical sites for long-term storage (system consolidation) and might also trigger enduring synaptic changes to stabilize memories (synaptic consolidation).

Neocortical (<1 Hz) slow oscillations, thalamo-cortical spindles and hippocampal sharp-wave ripples are implicated in memory consolidation during SWS. The depolarizing up-states of the slow oscillations synchronously drive the generation of spindles and ripples accompanying hippocampal memory re-activations, thus providing a temporal frame for a fine-tuned hippocampus-to-neocortex transfer of memories.

Neocortical slow oscillations concurrently support a global synaptic downscaling that precludes saturation of synaptic networks and improves the capacity to encode new information.

Rapid eye movement (REM) sleep is characterized by a local upregulation of plasticity-related immediate early genes in the presence of high cholinergic activity and reduced electroencephalographic coherence between brain regions. These conditions might effectively support local synaptic consolidation.

The temporal sequence of SWS and REM sleep in the normal sleep cycle suggests that these sleep stages have complementary roles in memory consolidation: during SWS, system consolidation promotes the re-activation and redistribution of select memory traces for long-term storage, whereas ensuing REM sleep might act to stabilize the transformed memories by enabling undisturbed synaptic consolidation.

Sleep has been identified as a state that optimizes the consolidation of newly acquired information in memory, depending on the specific conditions of learning and the timing of sleep. Consolidation during sleep promotes both quantitative and qualitative changes of memory representations. Through specific patterns of neuromodulatory activity and electric field potential oscillations, slow-wave sleep (SWS) and rapid eye movement (REM) sleep support system consolidation and synaptic consolidation, respectively. During SWS, slow oscillations, spindles and ripples — at minimum cholinergic activity — coordinate the re-activation and redistribution of hippocampus-dependent memories to neocortical sites, whereas during REM sleep, local increases in plasticity-related immediate-early gene activity — at high cholinergic and theta activity — might favour the subsequent synaptic consolidation of memories in the cortex.

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research study on sleep and learning

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We apologize to those whose work was not cited because of space constraints. We thank Drs. B. Rasch, L. Marshall, I. Wilhelm, M. Hallschmid, E. Robertson and S. Ribeiro for helpful discussions and comments on earlier drafts. This work was supported by a grant from the Deutsche Forschungsgemeinschaft (SFB 654 'Plasticity and Sleep').

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Memories that are accessible to conscious recollection including memories for facts and episodes, for example, learning vocabulary or remembering events. Declarative memories rely on the hippocampus and associated medial temporal lobe structures, together with neocortical regions for long-term storage.

Memories for skills that result from repeated practice and are not necessarily available for conscious recollection, for example, riding a bike or playing the piano. Procedural memories rely on the striatum and cerebellum, although recent studies indicate that the hippocampus can also be implicated in procedural learning.

A task in which subjects are required to rapidly respond to different spatial cues by pressing corresponding buttons. This task can be performed implicitly (that is, without knowledge that there is a regularity underlying the sequence of cue positions) or explicitly (by informing the subject about this underlying regularity).

Learning without being aware that something is being learned.

Learning while being aware that something is being learned.

Different types of memory, such as declarative and non-declarative memory, are thought to be mediated by distinct neural systems, the organization of which is still a topic of debate.

Short transitory periods of sleep in rats that, based on EEG criteria, can neither be classified as REM sleep or SWS.

Genes that encode transcription factors that are induced within minutes of raised neuronal activity without requiring a protein signal. Immediate-early gene activation is, therefore, used as an indirect marker of neuronal activation. The immediate early genes Arc and Egr1 ( zif268 ) are associated with synaptic plasticity.

Refers to the functional changes at synapses that increase the efficacy of synaptic transmission and occurs when the presynaptic neuron repeatedly and persistently stimulates the postsynaptic neuron.

Refers to the functional changes at synapses that alter the efficacy of synaptic transmission depending on the relative timing of pre- and postsynaptic firing ('spiking'). The synaptic connection is strengthened if the presynaptic neuron fires shortly before the postsynaptic neuron, but is weakened if the sequence of firing is reversed.

The slow oscillations that predominate EEG activity during SWS are characterized by alternating states of neuronal silence with an absence of spiking activity and membrane hyperpolarization in all cortical neurons ('down-state') and strongly increased wake-like firing of large neuronal populations and membrane depolarization ('up-state').

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research study on sleep and learning


Just 20 minutes of yoga can boost sleep and memory: study

By Isobel Williams via SWNS

Just 20 minutes of yoga can lead to better sleep and improved memory, according to new research.

Practicing yoga nidra — a kind of mindfulness training — might improve sleep, cognition, learning, and memory - even in beginners, say scientists.

Unlike more active forms of yoga, which focus on physical postures, breathing, and muscle control, yoga nidra guides people into a state of conscious relaxation while they are lying down.

After two weeks of yoga nidra, the researchers from the Armed Forces Medical College in India observed that participants exhibited a significantly increased sleep efficiency and percentage of delta waves in deep sleep.

They also saw faster responses in all cognitive tests with no loss in accuracy and faster and more accurate responses in tasks including tests of working memory, abstraction, fear and anger recognition, and spatial learning and memory tasks.

To get their results, published in PLOS ONE , the team took measurements from a group of novice practitioners before and after two weeks of yoga nidra practice.

This practice was carried out during the daytime using a 20-minute audio recording.

The findings support previous studies that link delta-wave sleep to improved sleep quality as well as better attention and memory.

Professor Karuna Datta said: “Yoga nidra is a low-cost and highly accessible activity from which many people might therefore benefit.

“Yoga nidra practice improves sleep and makes brain processing faster. Accuracy also increased, especially with learning and memory related tasks.”

The post Just 20 minutes of yoga can boost sleep and memory: study appeared first on Talker .

(Photo by Katerina May via Unsplash )

  • Open access
  • Published: 20 February 2024

E-learning in medical education: a perspective of pre-clinical medical students from a lower-middle income country

  • Uzair Abbas 1 ,
  • Memoona Parveen 1 ,
  • Falak Sehar Sahito 1 ,
  • Niaz Hussain 2 &
  • Sundas Munir 1  

BMC Medical Education volume  24 , Article number:  162 ( 2024 ) Cite this article

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Many of the educational institutions in developed countries have shifted to online learning. While transition from traditional to electronic learning (e-learning) has remained a great challenge in low-middle income countries, where limited resources for teaching and learning are important factors. Medical education involves not only lecturing but also deep understanding through laboratories and patient exposure. The debate about the effectiveness of e-learning in medical education is still in contradiction due to its limitations. This cross-sectional survey was conducted to assess pre-clinical undergraduate medical students’ perception of their first online learning in a lower-middle income country.


The survey was conducted among the students who had participated in online learning during COVID-19 for at least a year. A total of 824 preclinical medical students who completed the survey from public and private medical universities in Sindh, Pakistan were included in the study. We used a validated online-based questionnaire, distributed through E-mail and social media platforms to assess the perception of students regarding their first online learning experience.

The response rate of the survey was 87.9%. The mean age of students was 20.7 ± 3.8 years. 392/824 (47%) were males and 57% were females. Our study indicated that 613/824 (75%) of students were experiencing online learning for the very first time while 631/824 (77%) were facing technical issues like internet accessibility and lack of IT-related skills. 381/824 (46%) were not satisfied with the institute’s readiness for online teaching. However, 79% (654/824) of participants were of the idea that traditional learning is more effective in developing their practical skills as compared to e-learning. Of note, 668/824 (81%) showed overall dissatisfaction with e-learning.

Based on our study findings, we concluded that most students have a negative perception of e-learning. Difficulty in connectivity, electricity issues, less interaction with colleagues and teachers, and issues with the structure of online courses were the most frequently reported problems by the students.

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The current medical education curriculum emphasizes the development of a diverse set of professional skills that include a robust theoretical foundation, proficient clinical competencies, and effective interpersonal aptitudes, all of which are predominantly imparted through conventional teaching methodologies [ 1 , 2 ]. Before COVID-19 pandemic, the method of learning used in various medical schools in Low-Middle Income Countries (LMICs) was mostly traditional in which face-to-face lectures were given in a classroom [ 3 ]. The COVID-19 pandemic significantly disrupted teaching in a variety of institutions. In many countries, including Pakistan, typical face-to-face classes had to be suspended to ensure the safety of students and teachers. To minimize the impact, medical schools had to find another approach to teach and, technology-based e-learning was the only option [ 4 ].

E-learning or online learning is a method of acquiring knowledge by using information technology [ 5 ]. The success of e-learning depends on many factors, including accessibility, usage of appropriate method s , course content, and assessment criteria [ 6 ]. E-learning, like any method of teaching, has its advantages and disadvantages for both student s and teachers. The benefits of e-learning that are worth mentioning include increased convenience and access to resources regardless of location and time [ 7 ]. Online classes also have limitations, including problems with internet access, poor internet connection quality, and insufficient digital skills of the participants including students as well as teachers [ 8 ]. Furthermore, the effectiveness of such an educational system is questionable, especially in the field of medicine where group discussions and peer interactions are necessary for knowledge and skill development [ 9 , 10 ]. However, concluding the effectiveness of online and offline education is much more difficult and has failed to conclude [ 11 ]. In high-income countries, many academic institutions are using e-learning but in limited-resourced countries, adapting e-learning requires many adjustments to be made to make sure the e-learning is as effective as possible [ 12 ].

The global educational landscape has witnessed a profound transformation due to the COVID-19 pandemic. Many academic institutions that had previously been hesitant to advance their traditional pedagogical approach, were left with no choice but to completely switch to online teaching and learning [ 13 ]. Many institutes encountered various obstacles and hurdles in implementing e-learning in their institutes. The main difficulties encountered during online learning in LMICs were lack of prior experience, fewer IT resources, insufficient infrastructure, availability of the internet, and a lack of computer availability to all teachers and students [ 14 , 15 ]. Medical institutes across Pakistan also confronted multiple challenges while adopting e-learning [ 16 , 17 ].

Moreover, it is important to consider the student’s perception on a larger scale, regarding their e-learning experience to help finding the gaps in implementing online learning in the future in countries with low or limited resources. This study was conducted to evaluate the pre-clinical medical students’ perception regarding their online learning experience across Karachi Pakistan. This study also highlights the challenges faced by students during their first experience of online learning.

Study design and setting

This cross-sectional study was conducted at Dow University of Health Sciences (DUHS) Karachi Pakistan between the period of July 2022 to July 2023 following ethical approval from the Institutional Review Board (IRB) of the university.

Study participants

The target participants consisted of pre-clinical students of medical sciences (MBBS), Dental sciences (BDS), and allied health sciences (including bachelor programs in nursing, public health, and medical technology), from public and private medical universities across Karachi Pakistan.

Inclusion and exclusion criteria

We included the students in the first and second year of above mentioned undergraduate degree programs who experienced online learning during 2021 and 2022 for at least a year during their academic sessions. The study excluded students who had progressed to their clinical rotation, and individuals who had never participated in online classes.

Exposure to online learning

The students were taught online through a virtual learning environment (VLE) and Zoom application. The subjects taught during their initial year were Anatomy, Physiology, Biochemistry, and Pathology with additional subjects related to their degree programs.

Sample size and data collection

A non-probability purposive sampling technique was employed to select participants [ 18 ]. The sample size of the study was estimated from a reference study [ 19 ]. The questionnaire in Google form format was forwarded to the department of medical education of various universities and the responses were collected. We received 937 responses from the students however only 100% complete responses to all items of the data collection tool were included in the final analysis which were 824 to avoid any potential confounders and bias (Fig.  1 ).

figure 1

Steps of data collection. Figure shows the steps of generation of data collection tool, validation, and process of data collection

Data collection tool

A pretested, self-constructed questionnaire was developed using Google Forms. To safeguard anonymity, the identities of the participants were held in strict confidence. The questionnaire was piloted on 12 participants out of the study participants and modifications were made after suggestions. The final questionnaire was validated by the Department of Medical Education of our university and approved by the Institutional Review Board.

The data collection tool was comprised of a total of 31 items divided into 5 sections including demographics, and perception of experience of online learning, perception of participation in class activity during online sessions. The questionnaire also included perceptions regarding the institute’s preparedness and experience with online exams.

Data analysis

The data obtained from the respondents was analysed using Microsoft Excel. Frequencies and percentages were presented in tabulated form. The comparison of perception between public and private medical institutes was performed on SPSS version 22.0 by applying Pearson’s Chi-square test.

Demographic characteristics

We included 824 participants in our study. The mean age of students was 20.7 ± 3.17 years. 392/824 (47%) were males. There were 622 (75%) of students from public sector institutes while 202 were from private medical colleges. A total of 474/824 (57.5%) students were from MBBS, 212 (26%) from BDS, and 318 (37.5%) from allied health science programs (Table  1 ).

The table shows the mean age and gender of study participants, type of institute and their field of study.

Perception of experience with online learning and participation in class activities

We asked about experiences and problems related to online learning. 613/824 (75%) students were experiencing online learning for the first time. 674 (82%) were facing technical problems during online classes like internet connectivity and electricity supply and only 460 (55.5%) were able to attend classes regularly.

A larger number of students 512 (62.5%) were unable to actively participate during online learning. However, 50.5% experienced proper engagement during classes while 721(87%) were of the idea that they get easily distracted during an online class. Among 824, 680/824 (82%) agreed that having a teacher in the classroom is necessary for their learning. In our survey, we found 536/824 (65.5) of participants were not able to interact with their colleagues and teachers during the online sessions (Table  2 ).

The students were asked about their perceptions regarding their experience with online learning and participation in class activities. The response was collected as YES or NO.

Perception of the Institute’s preparedness and online exams

We then asked about the satisfaction of students regarding their institutions’ preparedness for the sudden shift to online classes. For access to online learning material, 456 (55%) had proper access to that while 512 (64.5%) were not satisfied with the structure of online courses as compared to traditional learning methods. More than 70% of students complained about unresolved queries regarding topics and inability to develop clinical and laboratory skills in online classes as compared to traditional face-to-face learning. Overall, 502 (61%) of students were not satisfied with the institute’s preparedness regarding online teaching (Table  3 ).

About 79% of participants were able to appear in the online exam while 514 (63%) faced connectivity issues while attempting online exams. Moreover, 448/824 (54%) were unable to properly communicate with the examiner during the online viva exam (Table 3 ).

The students were asked about their perceptions regarding the institution’s preparedness and online exam experience. The response was collected as YES or NO.

Important factors highlighted from students’ perception of e-learning

We observed that 674 (82%) of students were facing internet connectivity and electricity issues to connect for online learning while 536 (65.5%) were unable to interact with colleagues and teachers during online learning. Moreover, 512 (64.5%) were not satisfied with the structure of online learning (Fig.  2 ).

figure 2

Important highlights of student’s perception regarding e-learning. N  = 824. The figure shows the percentage of students’ responses

The overall perception of online learning

Of note, only 156 (19%) of students were satisfied with the online learning method however 668 (81%) of the students were not satisfied with the online learning method in our study (Fig.  3 ).

figure 3

Overall perception of medical students regarding online learning experience n  = 824

Comparison of perception of students regarding e-learning from public and private medical institutes

We further evaluated the difference in perception of medical students from public and private sector medical institutes. We had 202 (25%) and 622 (75%) participants from private and public sector medical institutes respectively. Pearson’s Chi-square was applied to observe any difference in perception based on private or public sector medical students. Of all 30 items of our data collection tool including age, gender, and other parameters, we did not find any significant difference in the perception of students regarding e-learning based on their type of institutes (data not shown; p= > 0.05; CI = 95%).

We found that 81% of participants were not satisfied with the e-learning experience, which is the highest reported number to date. The most frequently reported issues were internet connectivity and electricity problems to connect for online learning (82%), no proper interaction with colleagues and teachers during online learning (65.5%), and problems with the structure of online courses (64.5%). Technical issues in connectivity for online learning (Internet inaccessibility and unavailability of electricity) remained a critical concern with a significant portion of the students in our study. As reported by Aristovnik et al., the primary issue in online learning in LMIC is poor internet connectivity and electricity supply issues which are concordant with our study [ 20 ]. We found, 50% of students were not able to take online classes regularly. Bediang et al. reported the same as a major barrier to online learning [ 21 ].

Successful participation and proper interaction during learning activities are essential for ensuring the efficiency and effectiveness of learning programs [ 22 ]. We further evaluated the perception of medical students regarding their participation in class activities during online learning. A noteworthy observation was a considerable number (~ 60%) of students exhibited the least engagement and interaction with both their classmates and teachers during their online sessions which might be because of no proper communication between the teachers and learners which was also reported by Manusov et al. [ 21 ]. In India, 80% of students could not participate actively in their online classes due to lack of facilities and communication gaps [ 23 ]. There are multiple factors including the availability and adequacy of technology resources impacting their ability to effectively engage with online educational content and platforms [ 24 , 25 ]. Studies have highlighted additional difficulties faced by students that lead to the least interaction with colleagues and teachers, including the absence of on-campus interactions, challenges with collaborative group projects, and delays in professors’ response times in online learning [ 26 ]. In contrast to our results, a study has reported that proper usage of technology can lead to increased student engagement and interaction during online learning [ 27 ].

Moving further, students were asked to share their perceptions regarding the accessibility to learning resources and their institution’s preparedness regarding online teaching. Most of the participants were satisfied with the learning material provided to them but 65% were of the idea that online course was not well-structured as compared to traditional learning course used to be. Kheng et al. have reported the important parameters for the preparedness of institutions regarding online teaching which can be attributed to our institutes [ 28 ]. In our study, students reported poor development of their laboratory and clinical skills. Up to 80% of students were not satisfied with their learned skills during e-learning. In contrast, many studies have reported satisfaction of students regarding their skills development and institutional preparedness in e-learning [ 29 , 30 , 31 , 32 ]. A Libyan study has reported the successful development of Lab techniques in medical students during their first experience of online learning [ 33 ]. Furthermore, a meta-analysis reported improvement in skills learned by medical students through online learning [ 34 ].

In our study, we found 75% were satisfied with the structure of online exams which is in accordance with multiple studies. Previous studies have reported that students showed significant satisfaction and better performance with online examinations [ 35 ]. A study reported that students were satisfied with online examinations however their performance did not show any correlation to that [ 36 ]. A study by Milone et al. has also reported the satisfaction of students regarding online exams [ 37 ]. A UK-based study reported more satisfaction and less anxiety experienced in online exams as compared to traditional exam patterns [ 38 ]. A survey of dental students also found higher scores and satisfactory remarks with online exams [ 39 ]. In contrast, one of the studies has reported that students’ performance and scores were better with the traditional method as compared to online and they opted to appear in traditional method of exams in the future [ 40 ].

Our study has reported the least satisfaction of medical students regarding online learning. Eighty-one percent (81%) of our study participants were not satisfied with the online learning experience. A study from India reported more than 50% of students’ dissatisfaction with online learning which is in accordance with our results [ 41 ]. A study from Hamdard University India also reported an unsatisfactory survey report of medical students regarding online learning [ 42 ]. In contrast, multiple studies have reported a positive and satisfactory response from medical students regarding online learning. A study by Sujarwo et al. yielded a more positive response from medical students on online learning [ 43 ]. A study from Saudi Arabia reported a better outcome of e-learning and a satisfactory response from medical students regarding shifting learning towards the online COVID-19 pandemic [ 44 ]. A French study reported more than 60% of medical students satisfaction with online learning during COVID-19 and agreed to continue this after the pandemic era [ 45 ]. A large-scale survey of 30 medical schools in the UK revealed about 70% of students would choose to continue online learning in the future [ 46 ]. Multiple studies have shown a satisfactory response regarding online learning, most of those studies are however from developed countries where there is proper availability of resources, trained staff, and institutes are well prepared for online teaching. In LMICs such as Pakistan, where a significant majority of students face substantial barriers to internet access due to both technical limitations and financial constraints [ 47 ], the feasibility of achieving desired educational outcomes through online learning is greatly hindered.

The survey’s outcomes reveal that students have shown their dissatisfaction towards e-learning which is the highest reported from any LMIC to date. Difficulty in connectivity, electricity issues, less interaction with colleagues and teachers, and issues with the structure of online courses were the most frequently reported problems by the students. These findings will serve as valuable insights for academic institutions striving to design more effective learning environments that enhance the overall educational experience for students.

Availability of data and materials

All data has been included in the study however it is available with the corresponding author and may be provided on request.

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We acknowledge the study participant who participated in the study. We also acknowledge the faculty of department of Physiology, Dow International Medical College, Karachi, Pakistan.


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Uzair Abbas, Memoona Parveen, Falak Sehar Sahito & Sundas Munir

Liaquat University of Medical and Health Sciences, Jamshoro, Pakistan

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Conception/design of the work: UA, MP. Data collection, data analysis and interpretation: UA, NH, FSS. Drafting the article: UA, NH, SM. Critical revision of the article, and final approval: UA, MP, FSS, NH. All authors read and approved the final manuscript.

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Correspondence to Uzair Abbas .

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Abbas, U., Parveen, M., Sahito, F.S. et al. E-learning in medical education: a perspective of pre-clinical medical students from a lower-middle income country. BMC Med Educ 24 , 162 (2024).

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Received : 23 October 2023

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