research suggests that memory consolidation happens predominantly during

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Memory processes during sleep: beyond the standard consolidation theory

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1 Department of Epileptology, University of Bonn, Sigmund-Freud-Strasse 25, Bonn, Germany. [email protected]
PMID: 19322518
PMCID: PMC11115869
DOI: 10.1007/s00018-009-0019-1

Two-step theories of memory formation suggest that an initial encoding stage, during which transient neural assemblies are formed in the hippocampus, is followed by a second step called consolidation, which involves re-processing of activity patterns and is associated with an increasing involvement of the neocortex. Several studies in human subjects as well as in animals suggest that memory consolidation occurs predominantly during sleep (standard consolidation model). Alternatively, it has been suggested that consolidation may occur during waking state as well and that the role of sleep is rather to restore encoding capabilities of synaptic connections (synaptic downscaling theory). Here, we review the experimental evidence favoring and challenging these two views and suggest an integrative model of memory consolidation.

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21 Memory Consolidation

John Wixted is Distinguished Professor of Psychology at the University of California San Diego.

Denise J. Cai, University of California, San Diego

Published: 16 December 2013
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Memory consolidation is a multifaceted concept. At a minimum, it refers to both cellular consolidation and systems consolidation. Cellular consolidation takes place in the hours after learning, stabilizing the memory trace—a process that may involve structural changes in hippocampal neurons. Systems consolidation refers to a more protracted process by which memories become independent of the hippocampus as they are established in cortical neurons—a process that may involve neural replay. Both forms of consolidation may preferentially unfold whenever the hippocampus is not encoding new information, although some theories hold that consolidation occurs exclusively during sleep. In recent years, the notion of reconsolidation has been added to the mix. According to this idea, previously consolidated memories, when later retrieved, undergo consolidation all over again. With new findings coming to light seemingly every day, the concept of consolidation will likely evolve in interesting and unpredictable ways in the years to come.

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Physiol Rev

About Sleep's Role in Memory

Over more than a century of research has established the fact that sleep benefits the retention of memory. In this review we aim to comprehensively cover the field of “sleep and memory” research by providing a historical perspective on concepts and a discussion of more recent key findings. Whereas initial theories posed a passive role for sleep enhancing memories by protecting them from interfering stimuli, current theories highlight an active role for sleep in which memories undergo a process of system consolidation during sleep. Whereas older research concentrated on the role of rapid-eye-movement (REM) sleep, recent work has revealed the importance of slow-wave sleep (SWS) for memory consolidation and also enlightened some of the underlying electrophysiological, neurochemical, and genetic mechanisms, as well as developmental aspects in these processes. Specifically, newer findings characterize sleep as a brain state optimizing memory consolidation, in opposition to the waking brain being optimized for encoding of memories. Consolidation originates from reactivation of recently encoded neuronal memory representations, which occur during SWS and transform respective representations for integration into long-term memory. Ensuing REM sleep may stabilize transformed memories. While elaborated with respect to hippocampus-dependent memories, the concept of an active redistribution of memory representations from networks serving as temporary store into long-term stores might hold also for non-hippocampus-dependent memory, and even for nonneuronal, i.e., immunological memories, giving rise to the idea that the offline consolidation of memory during sleep represents a principle of long-term memory formation established in quite different physiological systems.

I. INTRODUCTION

The capability to form memory is critical to the strategic adaptation of an organism to changing environmental demands. Observations indicating that sleep benefits memory date back to the beginning of experimental memory research, and since then have been fitted with quite different concepts. This review targets this field of “sleep and memory” research, which has experienced a unique renaissance during the last three decades. Although we have aimed at comprehensively covering the field, we might have missed out or overlooked some aspects, owing to the vast progress achieved in the last years. Before we begin, we will briefly introduce the core concepts of sleep and memory, respectively.

Sleep is defined as a natural and reversible state of reduced responsiveness to external stimuli and relative inactivity, accompanied by a loss of consciousness. Sleep occurs in regular intervals and is homeostatically regulated, i.e., a loss or delay of sleep results in subsequently prolonged sleep ( 113 ). Sleep deprivation and sleep disruptions cause severe cognitive and emotional problems ( 142 , 634 , 1243 ), and animals deprived of sleep for several weeks show temperature and weight dysregulation and ultimately die of infections and tissue lesions ( 973 ). Sleep probably occurs in all vertebrates, including birds, fishes, and reptiles, and sleeplike states are similarly observed in invertebrates like flies, bees, and cockroaches ( 209 ).

Sleep in mammals consists of two core sleep stages: slow-wave sleep (SWS) and rapid-eye-movement (REM) sleep, which alternate in a cyclic manner ( FIGURE 1 A ). In human nocturnal sleep, SWS is predominant during the early part and decreases in intensity and duration across the sleep period, whereas REM sleep becomes more intense and extensive towards the end of the sleep period. SWS is hallmarked by slow high-amplitude EEG oscillations (slow wave activity, SWA), whereas REM sleep (also termed paradoxical sleep) is characterized by wakelike fast and low-amplitude oscillatory brain activity. In addition, REM sleep is characterized by phasic REMs and by muscle atonia. Almost 50% of sleep in adult humans is marked by a lighter form of non-REM sleep (stage “N2”) that is characterized by the occurrence of distinct (waxing and waning) sleep spindles ( FIGURE 1 B ) and K-complexes in the EEG, but minor SWA. Sleep stage N2 is not discriminated from SWS in rodents.

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Typical human sleep profile and sleep-related signals. A : sleep is characterized by the cyclic occurrence of rapid-eye-movement (REM) sleep and non-REM sleep. Non-REM sleep includes slow-wave sleep (SWS) corresponding to N3, and lighter sleep stages N1 and N2 ( 591 ). According to an earlier classification system by Rechtschaffen and Kales ( 974 ), SWS was divided into stage 3 and stage 4 sleep. The first part of the night (early sleep) is dominated by SWS, whereas REM sleep prevails during the second half (late sleep). B : the most prominent electrical field potential oscillations during SWS are the neocortical slow oscillations (∼0.8 Hz), thalamocortical spindles (waxing and waning activity between 10–15 Hz), and the hippocampal sharp wave-ripples (SW-R), i.e., fast depolarizing waves that are generated in CA3 and are superimposed by high-frequency (100–300 Hz) ripple oscillation. REM sleep, in animals, is characterized by ponto-geniculo-occipital (PGO) waves, which are associated with intense bursts of synchronized activity propagating from the pontine brain stem mainly to the lateral geniculate nucleus and visual cortex, and by hippocampal theta (4–8 Hz) activity. In humans, PGO and theta activity are less readily identified. C : sleep is accompanied by a dramatic change in activity levels of different neurotransmitters and neuromodulators. Compared with waking, cholinergic activity reaches a minimum during SWS, whereas levels during REM sleep are similar or even higher than those during waking. A similar pattern is observed for the stress hormone cortisol. Aminergic activity is high during waking, intermediate during SWS, and minimal during REM sleep. [Modified from Diekelmann and Born ( 293 ).]

Is sleep essential? From an evolutionary perspective, reduced responsiveness to potentially threatening stimuli during sleep represents a significant danger to survival. The fact that almost all animals sleep strongly argues in favor of an adaptive role of sleep in increasing the overall fitness of an organism, although its exact functions are still a matter of debate ( 407 , 1077 ). Sleep has been proposed as serving an energy-saving function ( 82 , 1311 ), the restoration of energy resources and the repairing of cell tissue ( 875 ), thermoregulation ( 973 ), metabolic regulation ( 651 , 1229 ), and adaptive immune functions ( 695 ). However, these functions could be likewise achieved in a state of quiet wakefulness and would not explain the loss of consciousness and responsiveness to external threats during sleep. These prominent features of sleep strongly speak for the notion that sleep is mainly “for the brain” ( 553 , 625 ). Here, different functions have been proposed, ranging from detoxication of the brain from free radicals ( 594 , 978 ), glycogen replacement ( 1041 ) to an involvement of sleep in memory and synaptic plasticity ( 293 , 1204 ). In this review we discuss this latter function, i.e., the critical role sleep serves in the formation of memory.

1. Memory processes

To form and retrieve memories is a fundamental ability of any living organism, enabling it to adapt its behavior to the demands of an ever-changing environment, and allowing it to appropriately select and improve the behaviors of a given repertoire. Memory functions comprise three major subprocesses, i.e., encoding, consolidation, and retrieval. During encoding, the perception of a stimulus results in the formation of a new memory trace, which is initially highly susceptible to disturbing influences and decay, i.e., forgetting. During consolidation, the labile memory trace is gradually stabilized possibly involving multiple waves of short and long-term consolidation processes ( 803 ), which serve to strengthen and integrate the memory into preexisting knowledge networks. During retrieval, the stored memory is accessed and recalled. This review discusses sleep's critical role in the consolidation of memory. We assume that whereas the waking brain is optimized for the acute processing of external stimuli that involves the encoding of new information and memory retrieval, the sleeping brain provides optimal conditions for consolidation processes that integrate newly encoded memory into a long-term store. Encoding and consolidation might be mutually exclusive processes inasmuch they draw on overlapping neuronal resources. Thus sleep as a state of greatly reduced external information processing represents an optimal time window for consolidating memories.

The so-called consolidation account of memory processing was first proposed by Müller and Pilzecker ( 840 ) who, based on studies of retroactive interference between learning lists of syllables, concluded: “After all this, there is no alternative but to assume that after reading a list of syllables certain physiological processes, which serve to strengthen the associations induced during reading of that list, continue with decreasing intensity for a period of time.” (p. 196 in Ref. 840 , cited based on Ref. 709 ). The consolidation hypothesis is now widely accepted based on numerous studies showing that psychological, pharmacological, and electrophysiological manipulations, such as interference learning, the administration of norepinephrine and protein synthesis inhibitors or electroconvulsive shocks, can effectively impair or enhance memory, when administered after encoding (e.g., Refs. 803 , 1332 ). Importantly, these manipulations are time dependent and have strongest effects when applied immediately after learning (for reviews, see Refs. 194 , 805 ). The consolidation possibly involves multiple waves of stabilizing processes, which exhibit different time courses and depend on different underlying processes of neuronal plasticity. Recent evidence suggests that memory traces are not consolidated once but, upon their reactivation by a reminder or active retrieval, undergo a period of reconsolidation to persist for the long term ( 844 ).

At the neuronal level, memory formation is thought to be based on the change in the strength of synaptic connections in the network representing the memory. Encoding induces synaptic long-term potentiation (LTP) or long-term depression (LTD) as major forms of learning-induced synaptic plasticity ( 222 , 527 , 616 , 629 , 1209 ). Activity reverberating in the neuronal representation following encoding is thought to promote two kinds of consolidation processes, termed “synaptic consolidation” and “systems consolidation” ( 330 ). Synaptic consolidation leads to the remodeling of the synapses and spines of the neurons contributing to a memory representation, eventually producing enduring changes in the efficacy of the participating synapses (e.g., Refs. 555 , 616 , 977 ). System consolidation builds on synaptic consolidation and refers to processes in which reverberating activity in newly encoded representations stimulate a redistribution of the neuronal representations to other neuronal circuitries for long-term storage ( 418 ).

2. Memory systems

In neuropsychology, declarative and nondeclarative memory systems are distinguished depending on the critical involvement of medial temporal lobe regions, particularly of the hippocampus, in the acquisition of memory ( 1134 ). Declarative memory encompasses 1 ) episodic memories for events that are embedded in a spatiotemporal context (including autobiographical memories) and 2 ) semantic memories for facts that are stored independently of contextual knowledge ( 1218 ). Declarative memories can be encoded intentionally or unintentionally, but are typically explicitly (i.e., with awareness) accessible by active recall attempts. Episodic memories are learned very quickly, i.e., in one trial, but are also subject to fast forgetting ( 1332 ). Semantic memories can be regarded as a result of the repeated encoding or activation of overlapping episodic memories ( 1327 ). Integrity of hippocampal circuitry is a prerequisite for retaining an episode as well as spatial and temporal context information in memory for more than 15 min ( 231 , 418 ).

In contrast to declarative memories, nondeclarative memories can be acquired without involvement of medial temporal lobe structures ( 1134 ). Nondeclarative memory encompasses quite different memory systems that rely on different areas of the brain. It includes procedural memories for motor skills (motor areas, striatum, cerebellum) and perceptual skills (sensory cortices), certain forms of conditioning and implicit learning (priming), etc. Nondeclarative memories can be implicitly (i.e., without awareness) acquired and recalled, and learning is slow, usually requiring multiple training trials. It is of note that experimentally disentangling nondeclarative from declarative memory processing is often complicated by the fact that these memory systems interact during acquisition of new knowledge in the healthy brain. Thus acquisition of skills like language learning and finger sequence tapping, especially at the initial stages, incorporates declarative in addition to procedural components ( 910 ).

3. The standard two-stage memory system

Why does the consolidation of memory have to take place during sleep? The hypotheses that sleep serves memory consolidation is conceptually rooted in the standard two-stage memory system which is currently the most influential model of human memory, and has been developed as a solution to several key problems arising from simple associative network models of memory ( 175 , 780 , 800 ). The foremost of these problems is that although simple association networks are in fact able to store information very rapidly, as is the case in the declarative memory system, the uptake of new conflicting information has a strong tendency to erase the older memories, thus inducing so-called “catastrophic interference” ( 1005 ). The critical question is how the neuronal network can learn new patterns without simultaneously forgetting older memories, an issue that has also been referred to as the “stability-plasticity dilemma” (e.g., see Ref. 3 ). In addition, unstructured recurrent networks have been demonstrated to face essential capacity constraints ( 723 ). The two-stage memory formation mechanism first proposed by Marr ( 780 ) offers a solution to these problems. It assumes that memories are initially encoded into a fast learning store (i.e., the hippocampus in the declarative memory system) and then gradually transferred to a slow learning store for long-term storage (i.e., the neocortex). The fast learning store ensures quick and efficient encoding of memories, even in one attempt (one-trial learning). Yet, these representations are unstable and vulnerable to (retroactive) interference by newly encoded information. Over time, the information is gradually integrated in the slowly learning long-term store without overwriting older, more remote memories. It is assumed that by the repeated reactivation of the new memories during off-line periods like sleep, the slowly learning long-term store is trained and the new memories are gradually strengthened and adapted to preexisting long-term memories. The transformation new memory representations undergo in this system consolidation process comprises also the extraction of invariants and the development of prototypes and schemas, as the core of the newly learned information is reactivated more frequently than divergent details ( 734 , 800 , 1239 ). For the declarative memory system, the two-stage model has received strong support from lesion studies, indicating that lesions of the hippocampus abolish the ability to acquire new declarative memory and simultaneously produce a temporally graded retrograde amnesia where older memories remain intact ( 231 , 418 ). The time interval for a memory to reach a state of hippocampus-independent retrieval can vary from one day to several months or years, depending on the acquired information and the schemas preexisting in long-term memory ( 1210 , 1308 ). The standard two-stage model of memory has been also successfully applied to nondeclarative kinds of memory, like procedural memory ( 668 ), suggesting that the offline reactivation of recent memories and their redistribution from a fast encoding temporary to a slowly learning permanent store could be a general feature of long-term memory formation.

II. OVERVIEW OF APPROACHES AND CONCEPTS

In this section, we review evidence from behavioral studies in support of the notion that sleep benefits memory consolidation. Key experiments for the different theoretical accounts and concepts will be described in more or less chronological order, thus complementing previous reviews of studies on sleep and memory ( 23 , 49 , 99 , 116 , 121 , 125 , 199 , 200 , 226 , 246 , 251 , 293 , 297 , 319 , 348 , 377 , 379 , 411 , 475 , 524 , 537 , 538 , 734 , 765 , 782 , 807 , 883 , 894 , 910 , 915 , 928 , 957 , 958 , 967 , 984 , 988 , 1001 , 1025 , 1092 , 1094 , 1096 , 1098 , 1149 , 1151 , 1152 , 1156 , 1202 , 1242 , 1279 – 1281 , 1283 , 1289 – 1291 , 1305 , 1321 ).

A. Sleep Acts by Passively Protecting Memory From Retroactive Interference

In 1885, Ebbinghaus, the father of experimental memory research, published a series of studies, on himself, about the forgetting of lists of nonsense-word pairs that established the well-known “forgetting curve” indicating that forgetting occurs rapidly in the first hours after learning and levels out after several days ( 339 ). He noticed already in this work that forgetting is reduced when sleep occurred in the retention interval, a phenomenon similarly observed in follow-up studies examining the forgetting curve (reviewed in Ref. 1242 ). Others reported that depriving a participant of a night of sleep impaired his ability to remember ( 890 ). Rosa Heine ( 528 , 1046 ) was the first to show in a more systematic study (in 6 subjects) that learning in the evening before sleep resulted in less forgetting 24 h later than learning before a daytime retention interval of wakefulness. This work provided the first clues as to the importance of sleep for memory.

Memory research in the first half of the 20th century was preoccupied with the cause of forgetting. Two concepts were proposed, i.e., the “decay” account, assuming that memory traces decay over time resulting in time-dependent forgetting ( 1188 ), and the “interference” account, assuming that forgetting results from learning of new information which (retroactively) interferes and overwrites the old memory traces ( 806 ). In a classic study, Jenkins and Dallenbach ( 603 ) compared (in two participants, which were repeatedly examined every day and night over a period of almost 2 mo) the retention of nonsense syllables across 1-, 2-, 4-, and 8-h retention periods that were filled either with sleep or wakefulness ( FIGURE 2 ) . Sleep after learning reduced the amount of forgetting. Because the time retention interval was identical for the sleep and awake conditions, the authors concluded that “… results of our study as a whole indicate that forgetting is not so much a matter of the decay of old impressions and associations as it is a matter of interference, inhibition, or obliteration of the old by the new” (p. 612 in Ref. 603 ). Because sleep represents a time in which new encoding of external and, perhaps, also internal information is strongly reduced, the reduction of interference by sleep appears to be crucial. However, the findings by Jenkins and Dallenbach also pose a challenge to the interference theory, because learning of highly similar material did not occur during the waking periods in these studies. Interference is considered to depend on the similarity between learning and interference materials with stronger interference for highly similar tasks (see Ref. 632 for a review). Regardless of this issue, the findings were interpreted as evidence that any waking mental activity increases forgetting by a kind of nonspecific interference ( 1332 ).

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Effects of sleep and wake intervals of different length after learning on memory for senseless syllables. Sleep after learning leads to superior recall of syllables after the 1-, 2-, 4-, and 8-h retention interval, compared with wake intervals of the same length. Two subjects (H. and Mc.) participated in this classic study by Jenkins and Dallenbach ( 603 ). For each data point, each participant completed 6–8 trials, with the different retention intervals performed in random order. The study took ∼2 mo during which the participants lived in the laboratory and were tested almost every day and night. Data are based on Table 3 in Reference 603 , as the original figure contains an erroneous exchange of data points at the 4-h wake retention interval. Values are means ± SE. ** P ≤ 0.01; *** P ≤ 0.001.

Many studies subsequently confirmed the positive effect of sleep on memory ( 63 , 79 , 80 , 248 , 341 – 343 , 350 , 408 , 478 , 479 , 592 , 745 , 853 , 855 , 1068 , 1131 , 1192 , 1241 ), examining also longer retention intervals of from 24 h up to 6 days ( 79 , 80 , 474 , 592 , 990 ). The underlying concept was that sleep acts as a “temporary shelter” that simply postpones the effect of interference and, thereby, passively maintains the memory traces (p. 717 in Ref. 348 ). However, the pure hypothesis that simply the amount of interference between learning and recall determines the degree of forgetting is critically challenged by the fact that effects of retroactive interference are time dependent and much stronger when occurring immediately after learning than at a later time, speaking in favor of a time-dependent process of consolidation after encoding that strengthens the original memory trace, rendering it less susceptible to interference with time ( 840 ).

A time dependency of the effects of sleep on memory formation is indicated by studies showing stronger effects for sleep occurring shortly after learning than for sleep at a later time ( 80 , 343 , 431 , 899 , 1172 ). For example, sleep occurring within 3 h after learning vocabulary was more beneficial than sleep delayed by more than 10 h ( 431 ). Furthermore, recall of word pairs after 24 h was better when sleep occurred immediately after learning than after a day of wakefulness ( 899 ). Importantly, because the time between learning and retrieval as well as the time spent sleeping was identical for the immediate versus delayed sleep conditions of these studies, the findings cannot be explained by interference reduction per se, but stress the importance of the timing of reduced interference with reference to the learning period. That sleep after learning actually benefits the consolidation of memories and strengthens memory traces against future interference was compellingly demonstrated by Ellenbogen and co-workers ( 346 , 347 ). In two studies, they revealed that the enhancing effect on word recall of sleep compared with wakefulness was strongly enhanced when the subjects had learned an interference list shortly before final recall testing. Further studies confirmed that a 90-min sleep period as well as 60-min naps, both containing mainly SWS, likewise protect memory against future interference ( 18 , 290 , 1069 ).

In fact, recent versions of the interference account on sleep-associated memory consolidation have integrated this issue, assuming that sleep provides a time of reduced interference on consolidation processes, which per se are considered to be time dependent ( 809 , 1332 ). Thus any treatment that reduces interfering influences on consolidation should be more effective the shorter it is applied after learning. Still, these interference accounts assume a passive “opportunistic” role of sleep in memory consolidation occurring regardless of whether the brain is asleep or awake ( 809 ). Yet, with the assumption that sleep generally reduces interference from encoding of external events, this theorizing is challenged by a great body of studies indicating a dependence of consolidation on the composition of sleep, with differential outcomes for sleep rich of REM sleep or SWS ( 63 , 343 , 408 , 930 , 931 , 1048 , 1340 ). Thus explaining the improving effect of sleep on memory retention solely on the basis of reduced interference appears to be untenable unless sleep stages are thought to differ in their degree of interference, e.g., owing to associated dreaming ( 408 ).

B. REM Sleep and Memory Consolidation

The hypothesis has been around for some time that REM sleep contributes to memory consolidation, stimulated in particular by the wakelike EEG activity during this sleep stage together with frequent reports of vivid dreams after awakening from REM sleep. Very consistent evidence for a role of REM sleep for memory was provided by studies in animals (for comprehensive reviews, see, e.g., Refs. 101 , 388 , 807 , 901 , 936 , 943 , 1092 , 1095 , 1098 , 1260 ). With the use of a variety of tasks including classic, aversive, and appetitive conditioning procedures, a large number of studies consistently revealed increases in REM sleep after learning in rats, mice, and cats ( 100 , 284 , 389 , 540 , 708 , 711 – 713 , 747 , 944 , 1102 , 1104 , 1110 , 1111 ). Rats living in enriched compared with impoverished environments likewise exhibited enhanced REM sleep, although increases in non-REM sleep were also observed ( 278 , 493 , 494 , 669 , 823 , 1086 , 1168 ). Increasing REM pharmacologically by administration of carbachol into the pontine reticular formation and of corticotrophin-like intermediate lobe peptide (CLIP) as well as a REM sleep rebound after prior REM deprivation, all improved memory for a Y-maze discrimination task when applied after learning of the task ( 1316 ). Deprivation of REM sleep (mostly without simultaneous sleep recording) appeared to primarily impair memory formation on complex tasks, like two-way shuttle box avoidance and complex mazes, which encompass a change in the animals regular repertoire ( 69 , 100 , 312 , 516 , 525 , 539 , 644 , 710 , 713 , 714 , 787 , 900 , 903 – 906 , 992 , 1021 , 1072 , 1111 , 1113 , 1238 , 1352 , 1353 ). In contrast, long-term memory for simpler tasks, like one-way active avoidance and simple mazes, were less consistently affected ( 15 , 249 , 386 , 390 , 495 , 558 , 611 , 644 , 821 , 872 , 902 , 907 – 909 , 1072 , 1091 , 1334 ).

REM sleep increases were mostly observed in the first hours after learning, partly reflecting the fact that recording was limited to these hours. With prolonged recording sessions, elevated periods of REM sleep occurred up to 4–6 days after learning, sometimes following a cyclic pattern ( 1111 , 1116 ). Here, REM sleep increases were typically most prominent during specific time periods and dependent on the task. In the Morris water maze task, it started more than 2 h after learning and persisted for 22 h ( 1099 ). In several other avoidance tasks, REM sleep increases were less persistent, emerged later (i.e., 9–12 h post-learning), and sometimes reemerged 17–20 h post-learning ( 1094 ). In appetitive learning tasks, REM sleep increases started after 4 h and persisted for 12 h ( 1104 ). The increases in REM sleep during the specific time periods predicted later memory recall and reliably separated between learners and nonlearners ( 1095 , 1110 ). Learning in these studies induced distinct and prolonged waves of REM increases possibly involved in memory formation. Based on these findings, Smith proposed the concept of “paradoxical sleep windows” (PSW) mediating memory formation ( 1092 , 1097 ). Indeed, selective deprivation of REM sleep during, but not outside of, identified PSWs impaired memory ( 719 , 1101 , 1105 , 1106 , 1113 , 1115 ). Inhibition of protein synthesis by anisomycin also impaired memory only when intraperitoneally injected during a PSW 9 h after learning a shuttle avoidance task ( 1108 ). Similar results were obtained when blocking muscarinic cholinergic receptors during a PSW by scopolamine ( 720 , 1108 ), pointing to a crucial involvement of protein synthesis as well as cholinergic activation in PSW-associated memory processes. Interestingly, blocking of NMDA receptors was most effective in impairing memory consolidation when administered after a PSW, suggesting that PSW-associated memory processing induces subsequent NMDA-dependent plasticity ( 1094 , 1099 ).

Quite a number of studies in this context have been criticized as they employed the “flower-pot” method to deprive the animal from REM sleep. In this procedure, the rat rests on a small platform (i.e., the flower pot) surrounded by water and, owing to complete muscle atonia, falls into the water whenever REM sleep starts ( 119 , 387 , 569 ). Awakenings induced in this way are highly stressful and may per se impair later memory performance ( 953 ). However, impairing effects of REM sleep deprivation on memory retention have been also demonstrated with less stressful procedures like gentle head lifting ( 263 ), mild touching ( 596 ), or after pharmacological REM suppression ( 720 , 721 , 1098 , 1108 ). Ponto-geniculo-occipital (PGO) waves which occur associated with REMs in rats and cats have been proposed as a mechanism promoting plastic processes underlying memory formation during REM sleep ( 259 , 260 ) (see sect. IV F ).

The focus on REM sleep as the sleep stage that supports memory has also been criticized by several studies revealing concomitant or even selective increases in non-REM sleep after the animal's exposure to enriched environments or other learning procedures ( 389 , 493 , 495 , 510 , 529 , 540 , 645 , 1092 , 1104 , 1168 ). Non-REM sleep was even proposed as a factor that could explain REM sleep-deprivation-induced memory deficits ( 992 ), and increases in nonREM sleep after fear conditioning correlated with the learned fear response on the next day ( 62 ). In addition, in some cases learning decreased subsequent REM sleep ( 795 , 1029 – 1031 , 1100 ), and this could be accompanied by a concurrent increase in non-REM sleep ( 754 ).

Compared with the findings in rats, evidence for a role of REM sleep in memory processing in humans is surprisingly inconsistent. Most studies failed to find effects of selective REM sleep deprivation on the retention of declarative memories when simple verbal materials (word lists, word pairs, etc.) were used ( 88 , 185 , 192 , 343 , 344 , 729 , 918 , 1094 , 1191 ). Only with more complex declarative materials (meaningless sentences, stories, etc.) did REM sleep deprivation impair declarative memory in some studies ( 350 , 1192 ). In narcoleptics, isolated periods of REM sleep facilitated memory for complex associative information, compared with periods of non-REM sleep or wakefulness ( 1054 ), and learning a topographical map increased subsequent REM sleep in healthy participants ( 365 ). Furthermore, changes in REM sleep patterns were found during intensive study periods (e.g., student exams, see Refs. 1103 and 811 , but see Ref. 559 ). More consistent evidence for an involvement of REM sleep was obtained for tasks with a strong procedural memory component, like learning a foreign language or Morse code (e.g., Refs. 488 , 656 , 657 , 758 , 1249 ). Increases in REM sleep were observed, for example, during training of unfamiliar patterns in motor coordination, like trampolining ( 149 , 150 ) and adaptation to distorted vision by a set of lenses (e.g., Refs. 20 , 232 , 658 , 659 , 1356 , but see also Refs. 19 and 1358 ). REM sleep increased also in infants who learned a head turning response, in contrast to infants who did not learn the response ( 891 ). In a motor finger sequence tapping task, the amount of REM sleep after learning predicted sleep-dependent improvement in this task ( 383 ). Although less clear than in rats, some evidence in humans has also been provided for a REM sleep window of task specific memory processing ( 1103 , 1114 ): memory for the procedural Tower of Hanoi task was revealed to be most strongly correlated with increases in REMs in the second REM episode of postlearning sleep, whereas the improvement in the mirror tracing task correlated with the number of REMs in the fourth REM sleep period. Good learners with higher IQ showed greater increases in REM sleep ( 1114 ). In another study, REM sleep in the last quarter of an 8-h period of sleep, together with the time in non-REM sleep in the first quarter, was highly predictive for learning success in a visual texture discrimination task ( 1157 ).

Compared with declarative learning paradigms, tasks with a strong procedural memory component appeared to be also more sensitive to the detrimental effects of REM sleep deprivation. Karni et al. ( 622 ) were the first to show that sleep after training a visual texture discrimination task substantially reduced discrimination thresholds on the task, indicating a critical importance of sleep for gains in skills that occur offline after training has finished. The overnight reduction in discrimination thresholds was prevented by selective REM sleep deprivation, whereas awakening from non-REM sleep had no effect. Comparing the effects of REM sleep and non-REM sleep deprivation on tasks with strong procedural components (verbal word fragmentation, priming, Tower of Hanoi, Corsi block tapping) and declarative memory tasks (verbal word recognition, visual-spatial learning), Smith and colleagues ( 230 , 1093 , 1094 ) found that total REM sleep deprivation or deprivation of the last two REM episodes of postlearning sleep selectively impaired performance on the procedural memory tasks at the retest session 1 wk later. A similar impairment specific to procedural memory was also revealed after alcohol-induced REM sleep suppression ( 1107 ).

Based on these findings, Smith ( 1094 , 1096 ) suggested that in humans, REM sleep is involved in the processing of procedural memory, whereas REM sleep plays no role in the formation of declarative memories, particularly with respect to simple learning tasks. However, REM sleep deprivation experiments in humans, like in animals, have been criticized due to possible confounding effects of stress on memory formation ( 119 ). In addition, the idea that REM sleep contributes to memory formation has been questioned based on two more fundamental concerns ( 387 , 1076 , 1250 , 1253 ): one was that the large differences in time spent in REM sleep between species, e.g., ferrets spend more than 6 h per day in REM sleep, whereas humans only 2 h, do not translate into any obvious and systematic differences in capabilities to form memories. However, comparisons between species in learning capabilities are per se rather difficult and inconclusive. The second more critical concern is that even the complete absence of any REM sleep, e.g., during treatment with antidepressants, does not lead to any obvious impairment of memory formation (e.g., Refs. 962 and 1252 , but see Refs. 138 and 1310 for discrepant results in rodents), suggesting that at least the overt EEG characteristics of REM sleep are not necessary for successful memory consolidation. On the other hand, the increase in serotonin and catecholamine levels induced by antidepressant intake might compensate for the memory-impairing effects of REM-sleep suppression, as these neurotransmitters have been implicated in memory consolidation processes ( 815 , 842 , 1011 ). To summarize, although REM sleep may benefit procedural memory consolidation, this effect appears to be linked to specific conditions and to underlying, REM-sleep associated biological and molecular mechanisms that are so far unknown.

C. Sleep and the Erasure of Information: Accounts of Emotional Memory

The idea that sleep might be involved in the erasure or filtering of information has been put forward by several authors (e.g., Refs. 210 , 236 , 362 , 369 , 854 ). In particular in 1983, Crick and Mitchison ( 236 ) proposed, based on a neurocomputational model of associative learning, that dreaming during REM sleep helps to forget “parasitic modes” of activity, thus ensuring an efficient mode of operation of the brain during waking. Such parasitic modes of activity particularly occurred after stimulation overload and included “fantasy” (i.e., the net produces far-fetched and bizarre associations), “obsession” (i.e., iterates the same response, irrespective of input) and “hallucination”-like responses (i.e., responds to inappropriate input signals). As a solution to this problem, the authors proposed a “reverse learning” mechanism during REM sleep-dreaming that dampens synaptic weights to reduce the probability of these parasitic activity modes and thereby also enhances the efficacy and storage capacity of the network. Thus, according to this account, dreaming reduces unwanted and bizarre forms of representations in memory, which enhances new learning the next day as well as retrieval of memories acquired before sleep ( 237 ). In simulation studies, repeated unlearning procedures indeed improved the learning capability of the network and retrieval of recently learned patterns, but concurrently weakened more remote memories ( 564 , 1237 ).

Although computational scientists agree in that a mechanism is necessary that limits the strength of synaptic weights in artificial neural networks, empirical evidence for the proposed function of REM sleep, specifically with regard to the removal of “unwanted modes” of activation, is so far lacking. Several authors have reported an influence of REM sleep on emotional reactivity or mood ( 490 , 491 , 1243 ), but a specific influence on “obsessive” or “hallucination”-like behaviors has not yet been tested. In contrast, after awakening from REM sleep, the brain appears to remain in a “hyperassociative” mode, in which weak semantic primes produced distinctly stronger priming effects than during consolidated wakefulness or after awakenings from non-REM sleep ( 1154 ). Similarly, REM sleep awakenings also resulted in a 32% increase in a complex anagram solving task ( 1288 ), and conversely, the need for creative thinking increased subsequent REM sleep ( 730 ). Furthermore, priming before naps filled with REM sleep had a much stronger impact on later creative answers compared with naps without REM or quiet resting, indicating that REM sleep improves creative problem solving instead of reducing creativity ( 163 ). In volunteers asked to voluntarily suppress “unwanted memories,” sleep and particularly REM sleep appeared to counteract this suppression as reflected by an improved retrieval for previously suppressed items, rather than to enhance forgetting of these unwanted memories ( 381 ). Finally, repetitive nightmares, which are highly prevalent in patients suffering from posttraumatic stress disorders (PTSD), do not lead to forgetting of the traumatic event, but are rather associated with increased severity of the disorder and considered a risk factor in the development of PTSD ( 75 , 1240 , 1331 ). Overall, these findings speak against the view posed by Crick and Mitchison ( 236 ).

Of note, in other computational models the mechanisms limiting the strength of synaptic weights in the neural network have been linked to non-REM rather than REM sleep ( 210 ). A recent version of this idea is the “synaptic homeostasis hypothesis” ( 1203 , 1204 ) which will be discussed in section IV B and assumes that a global downscaling in the strength of synaptic connections takes place during SWS to prevent saturation and to reduce place and energy demands, thereby preparing the network for the encoding of new information during succeeding wakefulness ( 565 , 566 ).

If the emotional tone of a memory is considered an “unwanted activation,” then the “sleep to forget sleep to remember” (SFSR) hypothesis recently proposed by Walker and van der Helm ( 1282 , 1284 ) bears some similarities with the ideas about REM sleep by Crick and Mitchison. The SFSR hypothesis assumes that REM sleep after an (aversive) emotional experience strengthens the content of the respective representations in memory, but simultaneously reduces the emotional tone associated with this memory, i.e., reduces the emotional response when the memory is retrieved. The process is not restricted to one night after encoding, but would continue during multiple nights. In depressed patients showing enhanced REM sleep, according to the SFSR hypothesis, this enhancement would bias strengthening of memories towards increased storage of negative contents, while suppression of REM sleep through antidepressants counteracts this bias. The impairment of mood associated with this REM-related process might indicate that the attenuation of emotional tone by REM sleep is not functional in these patients. Similarly, in traumatized patients, increased nightmare frequency would point to a failure to attenuate memory-associated emotions during REM sleep.

Consistent with the theory, several studies have shown that emotional memories are particularly strengthened across sleep ( 560 , 574 , 732 , 861 , 895 , 898 , 1274 , 1276 , 1278 , 1294 ), in particular when containing high amounts of REM sleep ( 179 , 478 , 479 , 1274 ). The enhancing effect of postencoding sleep on emotional memories was detectable even after several years ( 1276 ). Duration and latency of REM sleep significantly correlated with the later recall emotional memories ( 861 ). In the context of fear learning, sleep promoted the generalization of extinction learning ( 878 ), increased intersession habituation to emotional stimuli ( 879 ), and in both humans and rodents, the deprivation of REM sleep impaired fear extinction ( 421 , 1084 , 1133 ). Further support for the SFSR hypothesis was provided by functional magnetic resonance imaging studies indicating that the strengthening of negative emotional memories by sleep is accompanied by a reduced amygdala activation, i.e., a diminished emotional response during retrieval ( 1147 , 1231 ).

However, there are also contradicting findings. For example, two studies have shown that memory recall of negative pictures is less impaired by sleep deprivation after encoding compared with neutral pictures, suggesting a reduced dependency of negative memories on sleep-dependent consolidation processes ( 46 , 1147 ). With respect to emotional reactivity, one study showed that REM sleep-rich sleep amplified subjectively experienced aversion to previously viewed emotional pictures ( 1273 ). Conversely, selective REM sleep deprivation after picture viewing reduced arousal ratings to negative pictures presented again on the next morning ( 704 ). Others found that emotional reactivity decreased across wakefulness, but was preserved during sleep, with the preserving effect on emotional reactivity being specifically linked to REM sleep ( 60 , 481 ). In addition, amygdala responses to correctly recognized emotional objects (with reference to neutral objects) increased rather than decreased after sleep, compared with an assessment after a wake retention interval ( 896 ). The increased amygdala response was accompanied by a stronger connectivity among limbic regions after sleep in this study. Sleep after fear conditioning in humans increases the conditioned response and the associated amygdala activity, with this enhancement being positively associated with postlearning REM sleep (Menz, Rihm, Born, Kalisch, Pape, Marshall, and Büchel, unpublished observation). Sleep likewise facilitated the generalization of implicit fear responses ( 678 ). In animals, REM sleep has been consistently associated with a strengthening of conditioned fear memories (see sect. II B ; for reviews, see, e.g., Refs. 538 , 807 , 1092 , 1094 ). Furthermore, a recent study points towards an involvement of adrenergic activity during SWS instead of REM sleep for the consolidation of emotional information ( 482 ). In conclusion, it is still an open question whether the consolidation of emotional memories actually differs in quality from that of neutral declarative memories, or whether it is the same consolidation process that is simply enhanced or accelerated by the emotional arousal that is attached to the representation at encoding.

D. The Dual Process Hypothesis

The dual processes hypothesis assumes that different sleep stages serve the consolidation of different types of memories ( 428 , 765 , 967 , 1096 ). Specifically, it has been assumed that declarative memory profits from SWS, whereas the consolidation of nondeclarative memory is supported by REM sleep. The hypothesis received support mainly from studies in humans, particularly from those employing the “night-half paradigm.” This paradigm, originally developed by Ekstrand and co-workers ( 341 , 408 , 1340 ), basically compares retention performance across retention intervals that cover either the early or late half of nocturnal sleep. Whereas in the early sleep condition, participants learn (to criterion) the memory tasks in the evening and then sleep for 3–4 h before a later recall test, in the late sleep condition, participants first sleep for ∼3 h (to satisfy the need of SWS) and then are subjected to the learning phase, followed by the late night retention sleep. Due to the circadian rhythm, early nocturnal sleep contains most of SWS, whereas late nocturnal sleep is dominated by REM sleep. Time in stage 1 or 2 sleep usually does not differ between early and late sleep retention conditions. The approach thus allows for comparing the effects of sleep rich in SWS versus REM sleep, elegantly avoiding possible confounding effects resulting from stressful repeated awakenings accompanying standard procedures of selective sleep deprivation. To control possible confounds of the circadian rhythm, the effects of early and late sleep retention periods are typically compared additionally with the effects of wake retention periods that cover the same early and late phases of the night. These two wake control conditions are also necessary to ensure that depth of encoding during the learning phase is comparable between the conditions, because prior sleep is known to influence encoding capabilities ( 328 , 342 , 486 , 515 , 548 , 1190 , 1232 , 1345 ), and learning is preceded by prior sleep only in the late sleep condition, but not in the early sleep condition.

Yarush et al. ( 1340 ) were the first to report a beneficial effect of SWS-rich early sleep on declarative memory (word pairs), compared with retention performance across a REM sleep-rich late sleep or across corresponding wake intervals, and these findings were replicated in a later study of the same group ( 408 ). A similar benefit for declarative (paired-associates) memories was revealed when controlling for circadian influences by placing SWS-rich and REM sleep-rich sleep periods at the same circadian phase, between 3 and 7 a.m. ( 63 ). While these studies quite compellingly showed that declarative memory for neutral materials is enhanced by SWS, emotional declarative memories appear to additionally benefit from REM-rich late sleep ( 1273 , 1274 ). Building on these early studies, Plihal and Born ( 930 , 931 ) not only demonstrated a benefit for declarative memories (word pairs, spatial information) from early SWS-rich sleep, but also demonstrated that late REM sleep-rich retention sleep selectively improved procedural and implicit memories (mirror tracing skill, word-stem priming), compared with corresponding wake-retention intervals. Later studies replicated beneficial effects of late REM sleep-rich sleep on implicit memories (e.g., faces, masked stimuli) ( 1248 , 1277 ), and altogether these findings fit well with the notion of an involvement of REM sleep in procedural and implicit memory processes as revealed by standard REM sleep deprivation procedures (described in sect. II B ).

Improving the effects of early SWS-rich sleep on declarative memory appeared to be overall more consistent with free (or cued) recall measures than with recognition measures of memory ( 265 , 326 , 667 , 966 ; see Ref. 297 , for a review), possibly reflecting that unlike recollection, familiarity based recognition taps to a greater extent implicit components of these memories ( 1343 , 1344 ). Two studies found enhanced recollection (of words) after early, SWS-rich sleep, compared with a late REM-rich sleep or wake intervals, while familiarity-based recognition measures remained unaffected ( 265 , 326 ). In a third study measuring recognition right after free recall, this effect was not replicated, although SWS-rich sleep still enhanced free recall of the declarative materials ( 966 ).

Overall, studies using the night-half paradigm have provided substantial evidence for the dual processes hypothesis, such as hippocampus-dependent declarative memories preferentially profiting from SWS, whereas nondeclarative aspects of memory, such as procedural, implicit, and emotional, additionally profiting from REM sleep ( 121 , 428 ). The hypothesis has been challenged by findings showing that procedural tasks like visuomotor adaptation and visual texture discrimination also benefit from SWS ( 10 , 433 , 581 , 582 ). However, training such skills does not proceed entirely detached from declarative memory mechanisms, especially at an initial stage of training (e.g., Refs. 198 , 533 , 1013 , 1014 , 1042 ). Such declarative components might have mediated the strong benefits for skills from SWS in those studies ( 910 ).

A major weakness of the night-half paradigm is that it ignores possible contributions of stage 2 sleep to memory. Although the amounts of stage 2 sleep were comparable in the early and late sleep conditions of the studies reported above, sleep in this sleep stage may have substantially differed between the two phases, for example, with regard to spindle density ( 440 , 441 , 550 ), heart rate, or levels of neuromodulators like catecholamines and cortisol ( 95 , 124 , 131 , 313 , 554 , 960 ). There is consistent evidence for an involvement of stage 2 sleep and sleep spindles in motor learning ( 139 , 396 , 822 , 862 , 921 ), and memory for a simple motor task is impaired after selective stage 2 sleep deprivation ( 1112 , 1117 ). Training on motor tasks increased the time spent in stage 2 and the density of fast (but not slow) spindles ( 399 , 837 , 920 , 921 , 1173 , 1174 ), and sleep stage 2 duration and fast spindle density also predicted sleep-dependent improvements in a finger tapping task ( 59 , 961 , 1285 , 1286 ). Based on these findings, Smith and co-workers ( 846 , 1114 ) proposed that especially simple motor tasks require stage 2 sleep, whereas complex motor tasks may require REM sleep. However, sleep stage 2 spindle activity and spindle counts were found to similarly correlate with the overnight retention of verbal and visuospatial memories, suggesting that spindles and stage 2 sleep are also involved in declarative memory formation ( 213 , 214 , 1016 ).

E. The Sequential Hypothesis

The sequential hypothesis stresses the importance of the cyclic succession of SWS (or non-REM sleep) and REM sleep for memory formation, with the sleep stages serving complementary functions in this process. The sequential hypothesis originally assumed that in a first processing step during SWS, nonadaptive memories were weakened and adaptive responses were strengthened, whereas during the second processing step during REM sleep, the adaptive memories would be integrated and stored in preexisting knowledge networks ( 23 , 460 , 461 ). A series of studies in rats provided evidence for the hypothesis ( 24 , 25 , 27 , 28 ), for example, reporting high positive correlations between the number of SWS periods followed by REM sleep with memory performance on a two-way active avoidance task ( 698 ). In contrast, the number of SWS periods followed by wakening correlated negatively with performance, providing indirect support for a weakening of memories during SWS, if not followed by REM sleep. In addition, it was argued that SWS led to a global depotentiation of synaptic connections due to the slow EEG frequency as well as the absence of important transcriptional factors. Then, during subsequent REM sleep, the high-frequency EEG and hippocampal theta activity support the strengthening of synaptic connections ( 461 ). Further elaboration of the hypothesis integrated an additional stage of transitional sleep characterized by a sudden mixing of theta and alpha waves with the previous delta waves ( 23 , 929 ). SWS-REM sleep sequences comprising a short period of transitional sleep accurately predicted whether rats learned the active avoidance tasks, whereas in the absence of interposed transitional sleep, rats did not reach the learning criterion ( 26 , 760 , 1254 ).

The sequential hypothesis has also received some support from studies in humans. The overnight improvement on a visual texture discrimination task was best predicted by the time in SWS in the first quarter and the time in REM sleep in the last quarter of the night ( 1157 ). In nap studies, discrimination thresholds in the same task improved only after a longer 90-min nap containing both non-REM and REM sleep, but not after a shorter 60-min nap solely containing non-REM sleep ( 808 ). Similarly, studies adopting the night-half paradigm revealed the greatest improvement in texture discrimination thresholds after a whole night of sleep contained both early SWS-rich and late REM-rich sleep, whereas early SWS-rich sleep per se had only intermediate effects, and REM-rich late sleep was completely ineffective ( 433 ). Experimentally fragmenting sleep such that the cyclic structure was disturbed strongly impaired overnight-retention of words, whereas the same degree of fragmentation did not impair word recall when the sleep cycles were preserved ( 378 ). In the elderly, sleep cycle organization predicted their capacity for overnight-retention of words ( 796 ). It has been also suggested that differences in memory retention between early and late sleep in the night-half paradigm actually reflect differences in the cyclic organization of sleep rather than in the amount of SWS and REM sleep ( 379 ). Indeed, it appears that many findings support the sequential hypothesis, although this hypothesis has rarely been subjected to direct testing.

F. The Active System Consolidation Hypothesis

The hypothesis that sleep supports the formation of long-term memory in an active system consolidation process has been elaborated in several previous reviews ( 293 , 345 , 734 , 828 , 957 , 958 , 984 , 988 , 1284 , 1305 ). The hypothesis integrates aspects of both the dual-process view and the sequential hypothesis. Central to the “active system consolidation” hypothesis is the assumption that memory consolidation during sleep originates from the repeated reactivation of newly encoded memory representations. These reactivations occur during SWS and mediate the redistribution of the temporarily stored representations to long-term storage sites where they become integrated into preexisting long-term memories ( FIGURE 3 A ). The slow oscillations during SWS drive the repeated reactivation of hippocampal memory representations during sharp wave-ripples (SW-Rs; FIGURE 1 B ) in the hippocampus together with thalamo-cortical spindles, which are involved in inducing enduring plastic changes in cortical areas ( FIGURE 3 B ) . Thus reactivation and integration of temporarily stored memories into long-term stores accompany a qualitative reorganization (transformation) of the memory representation (system consolidation) that needs to be stabilized in a synaptic consolidation process assumed to take place during succeeding periods of REM sleep. In claiming that memory consolidation during sleep is an active process, this hypothesis contrasts with accounts that sleep only passively or opportunistically supports consolidation processes mainly by providing a time of reduced interference ( 809 , 1332 ).

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A model of active system consolidation during sleep. A : during SWS, memories newly encoded into a temporary store (i.e., the hippocampus in the declarative memory system) are repeatedly reactivated, which drives their gradual redistribution to the long-term store (i.e., the neocortex). B : system consolidation during SWS relies on a dialogue between neocortex and hippocampus under top-down control by the neocortical slow oscillations (red). The depolarizing up phases of the slow oscillations drive the repeated reactivation of hippocampal memory representations together with sharp wave-ripples (green) and thalamo-cortical spindles (blue). This synchronous drive allows for the formation of spindle-ripple events where sharp wave-ripples and associated reactivated memory information becomes nested into succeeding troughs of a spindle (shown at larger scale). In the black-and-white version of the figure, red, green, and blue correspond to dark, middle, and light gray, respectively. [Modified from Born and Wilhelm ( 125 ).]

Compared with the previously discussed hypotheses, the “active system consolidation” account is more concerned with identifying and integrating the neural mechanisms mediating the beneficial effect of sleep on memory consolidation, going far beyond the simple differentiation of sleep stages. In this regard, electrophysiological, neurochemical, and genetic conditions are considered that will be discussed in detail in the next sections. In fact, at a purely behavioral level, numerous studies have demonstrated that sleep after learning benefits declarative ( 17 , 18 , 60 , 63 , 79 , 290 , 326 , 327 , 343 , 348 , 350 , 376 , 408 , 427 , 431 , 478 , 479 , 547 , 560 , 571 , 574 , 592 , 603 , 682 , 732 , 735 , 861 , 898 , 930 , 932 , 959 , 1069 , 1172 , 1192 , 1217 , 1274 , 1276 , 1278 , 1282 , 1302 ) as well as nondeclarative kinds of procedural memory ( 135 , 147 , 164 , 311 , 322 , 374 , 383 , 435 , 447 , 563 , 602 , 631 , 664 , 679 , 733 , 930 , 995 , 1003 , 1286 , 1287 , 1330 ). However, these behavioral findings have but shed little light on the putative processes of memory reactivation and reorganization mediating the consolidation process during sleep.

1. Reorganization of procedural and declarative memories during sleep

With regard to procedural memory, findings that sleep after training of perceptual and motor skills like visual texture discrimination and finger sequence tapping can produce significant improvement (i.e., a gain in skill at a later retesting) have pointed towards an active reprocessing of skill representations occurring during sleep that sharpens the respective representations ( 47 , 57 , 147 , 274 , 275 , 291 , 322 , 383 , 384 , 433 , 582 , 622 , 631 , 664 , 665 , 679 , 762 , 770 , 808 , 810 , 863 , 930 , 1003 , 1153 , 1157 , 1286 , 1287 , 1290 , 1318 ). However, sleep-dependent gains may not equally occur for all types of skills (e.g., Refs. 221 , 1002 , 1015 ). Moreover, recent studies have shown that such gains in skill that are typically measured with reference to the performance level at the end of training, can occur also within a few hours after training in the absence of sleep, and may thus partly reflect a recovery process that is independent of sleep ( 364 , 573 , 1002 – 1004 ). Furthermore, some studies reported that no sleep-dependent gains in procedural motor tasks occur when circadian and homeostatic influences (e.g., time of day, time since sleep) are controlled ( 164 , 991 , 995 ). Also, a mere gain, for example, of speed in finger sequence tapping could be explained solely on the basis of synaptic consolidation processes that strengthen connections formed during training without reorganizing the memory representation. However, convincing behavioral clues for a reorganization of skill representations during sleep come from investigations of sequence-finger tapping skills that show that sleep favors the emergence of an effector independent representation, i.e., sleep benefited pressing the sequence of target keys independent of whether the sequence was tapped with the right or left hand, whereas the sequenced tapping movements per se appeared to benefit also from a wake retention interval ( 218 , 1330 ). Additionally, sleep enhanced sequence-finger tapping performance when learning occurred by observation ( 1234 ) or motor imagery ( 274 ), a further hint towards qualitative changes in the skill representations induced by sleep.

In connection with declarative memory processes, evidence for an active consolidation process during sleep that leads to a qualitative reorganization of memory representations has been provided by studies showing that sleep preferentially supports memory for the “gist” in the learned material, thereby supporting processes of abstraction, inference, and insight. Thus, in adults and children, sleep promoted the integration of newly learned spoken words into existing knowledge networks as measured by a lexical competition task ( 333 , 334 , 530 ; but see Ref. 735 ), with this effect being associated with increased spindle activity ( 1175 ). Sleep likewise promoted grammar-related abstraction processes in language learning tasks in infants ( 469 , 589 ). When participants learned a hierarchy of pairwise presented elements (e.g., A > B and D > E), sleep after learning improved the ability to infer the correct relation between the most distant elements (A > E) ( 345 ). Comparable results were obtained after a nap using relations between different faces paired with the same object, whereby the amount of SWS correlated with the accuracy of relational memory ( 705 , 706 ). Sleep also increased the production of false memories in the Deese-Roediger-McDermott (DRM) paradigm ( 1007 ), in which in the learning phase participants listened to lists of semantically related words (e.g., nurse, patient, hospital, sick, medicine, etc.), whereas the semantic “topic” of the list is not presented (the “critical lure,” e.g., doctor). Compared with wake retention conditions, after sleep subjects showed a greater tendency to “falsely” recall the critical lure, in line with the notion that sleep promotes processes of abstraction and the extraction of the gist information from the list ( 294 , 897 ). However, the effect of sleep in the DRM paradigm was less consistent when recall of the critical lure was tested with a recognition procedure rather than by free recall ( 255 , 296 , 373 ; see Ref. 1159 for an overview).

Using the Number Reduction Task ( 1189 ), Wagner et al. ( 1275 ) showed that sleep facilitates the gain of insight, i.e., explicit knowledge of a hidden structure that was embedded in strings of digits which the subjects had to process before sleep. Subsequent studies of the same group specified that the gain of insight depended on the occurrence of spindle activity during early SWS-rich nocturnal sleep ( 1346 – 1350 ). Very similar results were obtained with an approach combining the classical Serial Reaction Time Task (SRTT) with a so-called “generation task.” On the SRTT, the subject is trained to press as fast and as accurately as possible different keys corresponding to the changing positions of a cue. Unknown to the subject, the changes in the cue position follow a repeating sequence. Typically, during training subjects acquire an implicit knowledge of this sequence as indicated by faster responses to cue positions that follow the sequence compared with responses to random positions. Training does not lead to an immediate formation of explicit sequence knowledge, as the subjects typically cannot deliberately reproduce the SRTT sequence, when explicitly asked to do so right after training (in a generation task). Yet, significant explicit sequence knowledge developed when SRTT training was followed by nocturnal sleep ( 324 , 382 ). Generation task performance in the wake control group in these studies remained at chance level. The sleep-induced extraction of explicit sequence knowledge from an implicitly trained SRTT was particularly pronounced in children, where this extraction process was correlated with enhanced EEG slow oscillation activity during posttraining sleep ( 1321 ). Similarly, in adults performing on a statistical learning task, sleep promoted the abstraction of probabilistic regularities in tone sequences, with this effect being associated with increased SWS during postlearning sleep ( 337 ). Complementary evidence for a neuronal reorganization of memory representations by sleep was provided by studies imaging brain activation during learning and retrieval. In experiments by Takashima et al. ( 1170 ), participants napped for 90 min after studying pictures. Recognition of the pictures was tested 1, 30, and 90 days later. Activation of the hippocampus gradually decreased over time, whereas cortical activation in ventromedial prefrontal areas increased. The duration of SWS during the nap predicted the reduction in hippocampal activity and was also associated with better recognition performance on day 1. A second study of this group confirmed that one night of sleep decreases hippocampal activation and increases activation in neocortex areas ( 1169 ). In another study ( 427 ), the sleep-induced improvement in word pair memories, at a retrieval test 2 days later, was accompanied by an increased functional connectivity between the hippocampus and the medial prefrontal cortex, although contrary to Takashima et al.'s findings, hippocampal activation per se was enhanced at recall in the sleep group. At a follow-up test 6 mo later, the sleep group exhibited increased cortical activation compared with the subjects who stayed awake on the night after word pair learning. Sleep following learning spatial memories of a virtual town produced increased activation in the striatum at a recall test 3 days later, when compared with a group deprived of sleep after learning ( 873 ). Activation in the right striatum positively predicted way finding in the virtual town only in the sleep group, and also functional connectivity between the striatum and hippocampus was modulated by sleep after learning. Similarly, the sleep-enhancing effect of statistical learning ( 337 ) was accompanied by a shift in brain activation from the medial temporal lobe to the striatum ( 336 ). A sleep-induced reorganization of neuronal representations was also revealed for emotional memories (pictures) after 1 night ( 896 ) or 3 days after learning ( 1147 ). In the later study, the reorganization led to increased hippocampal and cortical activation as well as increased connectivity between the medial frontal cortical and hippocampal areas, compared with a control group, which stayed awake on the night after learning. Some of the effects of sleep on brain activation during recall of emotional memories were still observed 6 mo later ( 1148 ). Finally, sleep-dependent changes in brain activity indicative for a reorganization of memory representations have also been reported for procedural tasks like sequences finger tapping and visual texture discrimination ( 384 , 1292 , 1293 ).

2. Selectivity of memory consolidation during sleep

As another key feature, the “active system consolidation” concept implies that memory consolidation during sleep is selective. It is rather unlikely that off-line consolidation strengthens recently acquired memory traces and their synaptic connections globally, because such global and unselective strengthening would inevitably produce a system overflow. In support of this notion, a growing body of experiments indicates that sleep does not equally benefit all memories, although the mechanisms determining whether or not a certain memory gains access to offline consolidation during sleep are currently not well understood.

Several factors have been identified. First, sleep-dependent gains in skills are more robust under explicit learning conditions (i.e., the subjects are aware about the skill to be acquired) ( 383 , 664 , 1285 ) compared with implicit learning conditions ( 385 , 1003 , 1122 , 1128 ). As learning of declarative memories is explicit (and often intentional), these results suggest that explicit encoding favors access to memory consolidation during sleep. Second, the initial memory strength might affect consolidation during sleep, although the available data are not consistent. Stickgold ( 1151 ) proposed that sleep mainly benefits memories encoded at an intermediate memory strength, and that the effect of sleep on memories with varying initial encoding levels follows an U-shaped curve ( 1320 ). Contrasting this view, sleep after learning preferentially strengthened memory for word stimuli weakened by interference learning ( 325 , 343 ) or retrieval-induced forgetting ( 1 ) or for very difficult motor movements ( 679 ). On the other hand, other studies revealed greater benefits from sleep for strongly encoded memories ( 523 , 1172 ) or only in well-performing subjects ( 1038 , 1215 ). Moreover, it is well known that the encoding of emotional events results in stronger memories. Some studies indeed demonstrated that sleep preferentially consolidates emotional over neutral memories ( 574 , 602 , 861 , 896 , 898 , 1276 ); however, others failed to reveal such effects ( 60 , 168 , 732 , 1147 , 1148 ).

A third factor that was consistently found to favor the sleep-dependent strengthening of a memory is the relevance of an encoded memory for an individual's future plans ( 218 , 298 , 380 , 1056 , 1236 , 1319 ). In a sequence-finger tapping task, sleep preferentially improved the goal-related aspects (i.e., the target keys) rather than the movement-related aspects per se (i.e., the tapping executed with specifically the left or right hand) ( 218 , 1330 ). When subjects were promised an extra monetary award after learning (and before sleep) for performing well on one of two equally trained sequences of a sequence-finger tapping task the next day, the sleep-dependent gain on this sequence was greater than for the other sequence that was not associated with reward ( 380 ). Importantly, before the actual retest the next day, the participants were informed that both sequences were equally rewarded to ensure that motivation to perform well was equal for both sequences. Likewise, sleep-associated benefits for declarative memories (e.g., visual-spatial and verbal paired associates) were significantly greater in subjects who were informed before sleep that they would need the materials at a later recall test, than in subjects who were not informed in this way ( 1319 ), and only in the informed subjects later recall performance correlated with SWA during postlearning sleep. A similar selective effect of sleep was reported after learning two sets of picture-location association when only one set was labeled as relevant for later recall testing ( 1236 ). Importantly, in all of these experiments, the expectancy about the later relevance of the memories was induced after the encoding phase, eliminating possible differences in memory strength related to relevance at encoding. Also, when participants were instructed to remember some and to forget other items during learning, sleep preferentially strengthened the to-be-remembered items ( 330 , 381 , 968 ). However, sleep also appeared to counteracted instructed, i.e., “directed” forgetting effects in these studies ( 2 , 330 , 381 ). Similar to the preferential influence on relevant memories, sleep after encoding benefited the memory to perform an intended action at a designated time ( 298 , 1056 ), suggesting an enhancing effect of sleep also on prospective memory for future plans.

Collectively these findings indicate that consolidation processes acting during sleep are driven by motivational factors and specifically strengthen those memories, which are relevant for our goals and future behavior. However, the mechanisms underlying this selection process are unclear. Prefrontal cortex executive functions mediate the processing of anticipatory aspects of behavior and, in collaboration with the hippocampus, these prefrontal regions also regulate the implementation of anticipated memory retrieval as well as the allocation of relevance and expectancies to a memory ( 219 , 511 , 820 , 938 ). In rats, prefrontal cell assemblies that fired during learning when EEG theta coherence between the prefrontal cortex and hippocampus was increased showed a distinctly increased probability to be reactivated during subsequent SWS ( 73 ). Thus theta coherence in the prefrontal-hippocampal circuitry during the encoding of explicit memories might be a critical factor that tags these memories for later consolidation during sleep, with the prefrontal-hippocampal circuitry integrating also emotional and reward-predicting aspects of the encoded events ( 74 , 423 , 782 ). Whether theta coherence likewise mediates tagging that occurred after actual encoding of the memory, i.e., in experiments where subjects were instructed about the future relevance of the learned material after the learning phase ( 380 , 1319 ) remains to be investigated. However, theta-related tagging during encoding might represent a mechanism likewise mediating the preferential consolidation of emotional contents and reward-associated behaviors during sleep, as the network activated by theta also spans brain regions implicated in the processing of emotional stimuli and reward, such as the amygdala and ventral tegmental area, in addition to the prefrontal-hippocampal axis ( 423 , 728 , 887 ).

This view of theta-related tagging of memories integrates a growing number of rodent studies indicating that sleep favors the consolidation of memories that essentially depend on hippocampal functions. In rats and mice, sleep specifically supported consolidation of contextual fear conditioning which is well known to involve the hippocampal function, but did not alter consolidation of cued fear conditioning that does not require the hippocampal function (e.g., Refs. 476 , 165 , but see Ref. 676 ). In a spatial maze task, mice that were sleep deprived after learning shifted from a hippocampus-dependent to a striatum-dependent response strategy (e.g., Refs. 498 and 500 ; for related results, see Refs. 94 , 1106 , 1115 ). Likewise, in studies of novel object recognition, novel place object recognition, and temporal order tasks, retention of the place and time of an event was found to require sleep after the learning phase, whereas the retention of the event per se (item recognition) did not require sleep (e.g., Refs. 92 and 593 , but see Ref. 882 ). Hints that consolidation of memories during sleep depend on hippocampal activation during prior learning have also been provided by human studies using functional magnetic resonance imaging (fMRI) ( 16 , 733 , 968 ). In addition, some human studies revealed that, compared with item memory, context memory, a core function of the hippocampus, is particularly sensitive to the beneficial effects of sleep ( 602 , 732 , 1128 , 1230 ). Interestingly, a recent human study indicated that working memory capacity, a function that in a healthy brain is most closely linked to a cooperative activation of the prefrontal cortex and hippocampus, is strongly correlated with sleep-dependent benefits for hippocampus-dependent declarative memories (word pair associates) ( 374 ). In conclusion, activation of the prefrontal-hippocampal axis in the theta rhythm during the wake encoding process might be the main factor that predisposes a memory for the system consolidation process that takes place during subsequent sleep ( 64 , 782 ).

3. A model of active system consolidation during sleep

The findings discussed above have been used to essentially refine the model of active system consolidation, mainly with respect to memories that are explicitly encoded, via activation of prefrontal-hippocampal circuitry, in the hippocampus-dependent declarative memory system. Basically, the model assumes that during wakefulness the various aspects of an experienced episode are encoded into cortical networks with the different parts of the new memory representation bound together by areas in the medial temporal lobe, especially the hippocampus. During sleep, reactivation of the episodic memory originating from hippocampal networks results in the activation of the different memory parts also at the cortical level, thereby successively strengthening cortico-cortical connections and transforming the temporary representations into long-term memories ( 158 ). Because resources for the strengthening of synaptic connections are less available during SWS, reactivations during SWS might only tag the involved cortical synapses for later strengthening during subsequent REM sleep, in accordance with the sequential hypothesis ( 989 ).

Processes of abstraction, insight, and integration promoted by sleep in this model are thought to be a consequence of reactivation-induced reorganization of memory representation. Thus the repeated reactivation of episodic memory representations during sleep may be capable of identifying and extracting invariant features in the learning material simply because commonalities between acquired memories overall are more frequently reactivated during sleep. Indeed, such extraction processes might facilitate the creation of prototypes and the development of cognitive schemes, i.e., memories less dependent on specific contexts in which they were learned ( 166 , 734 ), and also less sensitive to interference ( 291 , 295 ). Efficacy of this process is probably enhanced as the reactivation of new representations also spreads to closely associated older memory representations, whereby reactivations simultaneously prime the gradual integration of new memories into networks of preexisting old memories. Moreover, reactivation-induced reorganization of memory representations during SWS might enhance the accessibility of memories such that implicitly acquired regularities are strengthened and, after sleep, become accessible to explicit assessment. For this sleep-induced gain of explicit memory, the strengthening of ventromedial prefrontal cortical connections might be particularly important ( 256 , 1321 ).

III. MEMORY REACTIVATIONS DURING SLEEP

The assumption that memories are reactivated during the consolidation phase is an integral part of standard consolidation theory as well as of the “active system consolidation” view of the memory function of sleep ( 418 , 780 , 800 ) (see sect. II F ). Reactivations of memory representations are thought to transform new memories that are still labile and prone to decay into stable memories that are preserved for the long-term. In the standard two-stage memory model, comprised of a fast encoding temporary store (hippocampus) and a slow-learning long-term store (neocortex, see sect. I B3 ), reactivations are regarded as critical for distributing the newly encoded memories to long-term storage sites. These reactivations occur repeatedly and offline (during sleep) to enable the gradual integration of the gist of the new representations into preexisting long-term memory networks, without overwriting these older memories. There is now ample evidence that reactivations of memories occur during sleep.

A. Animal Models

1. reactivation of hippocampal place cells during sleep.

Most reactivation studies in animals examined single unit or multiunit activity from hippocampal place cells in rats. Place cells code for the position of the animal in space relative to certain landmarks, i.e., they fire at substantial rates whenever the animal enters a certain spatial field ( 152 , 868 ). Running along a particular path leads to a specific sequence of place cell firing, which reoccurs when the animal runs along the same path at different times, indicating that the pattern of firing in place cell assemblies is linked to the memory of the spatial environment. Pavlides and Winson ( 893 ) were the first to show that hippocampal place cells exhibit signs of memory reactivation during sleep. They recorded spiking activity from pairs of hippocampal cells with nonoverlapping place fields. When the rats were not allowed to enter the place field of one of the two cells, firing rates of the exposed cell increased relative to the unexposed cell, and this pattern reoccurred during sleep (SWS and REM sleep) after the wake experience.

In a classic study, Wilson and McNaughton ( 1325 ) recorded multiple pairs of hippocampal CA1 place cells, which owing to their overlapping place fields exhibited correlated activity while the rat was running along a track to obtain a food reward. During subsequent SWS, the correlation pattern of these place cells was strikingly similar to the pattern observed during the awake performance. Importantly, the place cell assemblies did not exhibit such firing patterns during sleep before running along the track. Reactivation of firing patterns during sleep after the performance on spatial tasks was likewise obtained in the dentate gyrus ( 1065 ). Based on regression analyses, the activity pattern observed during awake task performance was estimated to explain 10–30% of the variance in spiking activity in the respective place cells during subsequent sleep ( 673 ). Importantly, further studies showed that cells contributing to reactivations are not only coactivated, but a temporal order of spiking in the assembly is also preserved ( 1089 ). In fact, entire spike trains observed during a task performed during prior waking reemerged in the same temporal order during succeeding sleep, with this “replay” running at a faster speed, i.e., in a time-compressed form ( 715 , 716 , 843 ).

Results from initial studies showing reactivation of assembly firing patterns during sleep were criticized because the tested animals were highly overtrained in the spatial tasks ( 538 , 1204 ). Thus, when rats repeatedly perform stereotypical behaviors in a familiar environment, also the firing patterns during pretraining sleep periods become more similar to postsleep periods, and this similarity is reduced when the rat learns a new behavior ( 551 ). This discussion motivated several studies examining reactivations during sleep after rats had engaged in exploratory behavior involving the encoding of new information. These studies uniformly confirmed that signs of assembly reactivation occur in the hippocampus and several other regions of the brain also during sleep after exploration ( 870 , 871 , 985 ). Reactivations could last for more than 24 h, although in the earlier of these studies the data analysis was criticized on methodological grounds ( 1180 ). Neuron assemblies linked to place fields that were longer or more frequently explored during waking showed stronger reactivation during succeeding sleep ( 870 , 871 ). Replay of hippocampal place cell assembly firing during sleep has been observed in many further studies, with the important findings summarized as follows (for detailed reviews, see Refs. 865 , 1163 ): 1 ) during SWS, firing patterns observed during waking are replayed at a much faster (10–20 times) rate, suggesting a time-compressed form of memory reactivation ( 160 , 551 , 606 , 715 , 843 , 1090 ); 2 ) replay activity is typically most prominent within the first 20–40 min of sleep or rest after learning and decays later ( 65 , 673 , 951 , 1065 , 1090 ), although in a few cases signs of hippocampal reactivation persisted for more than 24 h ( 985 ); and 3 ) in hippocampal neuron assemblies, reactivations are mostly observed in conjunction with SW-R ( 673 , 849 , 871 ), which are prominent oscillatory phenomena of the hippocampal EEG and occur in an irregular fashion mainly during SWS but also during quiet wakefulness (see sect. IV D ).

2. Reactivation in nonhippocampal areas

If memory reactivations during sleep promote the redistribution of representations from temporary hippocampal to long-term stores, as it is assumed by the active system consolidation view, neuronal reactivations should also occur in other structures than the hippocampus. In fact, in rats, conjointly with reactivations in hippocampal assemblies, reactivations have been observed in the parietal ( 951 ) and visual cortex during sleep after performance on spatial tasks, whereby replay in the visual cortex tended to slightly follow replay in the hippocampus ( 606 ). Similar to hippocampal replay, reactivation of spatiotemporal patterns during sleep in the medial prefrontal cortex was compressed by a factor of 6–7 ( 361 ). Furthermore, medial prefrontal reactivation correlated with the density of down-to-upstate transition during slow waves in simultaneously recorded cells, K-complexes, and low-voltage spindles in local field potentials ( 608 ), supporting the notion of a functional association between memory reactivations, slow-waves, and sleep spindles supporting consolidation processes during sleep (see sect. IV). In addition, after learning of a new rule in a Y-maze task, reactivation of learning-related patterns in the medial frontal cortex during sleep in distinct bouts mostly followed hippocampal SW-R events with a slight delay of 40 ms ( 926 ), i.e., a temporal relationship consistent with the notion of hippocampal replay guiding neocortical replay. Neuronal replay activity during SWS was also revealed in subcortical regions including the ventral striatum where it was linked to the learning of place-reward associations ( 914 ). Obtaining rewards during learning was associated with strong firing in specific striatal cell firing patterns, which reemerged during succeeding SWS in close association with hippocampal SW-R. Concurrent replay patterns in the hippocampus occurred shortly before striatal reactivations ( 699 , 700 ) and, in contrast to hippocampal replay, reactivation in the striatum did not decay during the 40 min recording period ( 914 ). These findings indicate a leading role of hippocampal memory replay for replay in neocortical and striatal areas. In addition, they are well in line with the idea that redistributions of memory representations during sleep originates from the reactivation of recently acquired memories in the hippocampus, thereby spreading the memory traces to neocortical and striatal sites that may serve as long-term stores.

3. Reactivation during REM sleep

In a few studies, signs of neuronal reactivations have been observed also during REM sleep ( 536 , 544 , 744 , 893 , 935 , 936 ). Interestingly, with the rat growing more familiar with certain parts of a track, respective reactivations of hippocampal firing patterns showed a shift in the phase of the EEG theta cycle such that their occurrence was more likely during troughs of the theta cycle rather than during peaks as typically observed during wakefulness. As spike induced plasticity is known to depend on the theta phase, these data suggest that REM sleep may help erasing episodic memory information once it becomes familiar ( 110 , 935 ). No reactivation was observed when rats were allowed to visit a novel portion of the track. Louie and Wilson ( 744 ) revealed reactivation patterns during REM sleep that showed even greater similarities with patterns that had emerged during REM sleep preceding the performance on the spatial task (circular track), which was possibly related to the fact that the rats were highly familiar with the task which they had performed over several days. Others did not find any hints at experience-dependent neuronal pattern reactivation during REM sleep (e.g., Ref. 132 , 673 ). Indeed, most reactivation studies in rats did not extend their analyses to periods of REM sleep. Thus, although the presence of consistent reactivations of assembly firing patterns during REM sleep is also suggested by modeling approaches ( 521 ), empirical evidence overall remains ambiguous in this regard.

4. Reactivation and behavioral relevance

So far, surprisingly few studies have aimed to examine the functional significance of firing pattern reactivations during SWS for memory consolidation. In this regard, first clues were provided by studies of old rats in which impaired hippocampal reactivation patterns during rest were associated with reduced capabilities for forming spatial memory in the Morris water maze task ( 448 , 449 ). In rats learning goal locations in a spatial task, reactivation of goal-related firing patterns during SW-Rs after learning was predictive for later memory recall ( 335 ). Importantly, blockade of NMDA receptors critical for spatial learning eliminated the tendency to reactivate goal-related firing patterns during SW-Rs, and impaired later memory recall. However, it remained unclear whether reactivations occurred during sleep or waking, as sleep was not recorded in this study.

Direct evidence for a contribution of SW-R-associated reactivations of assembly firing patterns for memory was provided by Nakashiba et al. ( 849 ), who employed a genetic approach to transiently block hippocampal CA3 output, which strongly reduced the occurrence of SW-R events. The blockade resulted in a marked reduction of hippocampal pattern reactivations during sleep and also reduced subsequent retrieval performance in a context fear-conditioning task. The acquisition of spatial memory was likewise impaired when hippocampal SW-Rs in resting rats were suppressed by electrical stimulation of hippocampal afferents ( 340 , 458 ). In the latter study, most (84%) of the single pulse stimulations of the ventral hippocampal commissure occurred in fact during SWS. While these studies provide solid evidence that SW-R events, which act as a carrier wave for neuronal memory reactivations, are behaviorally relevant for memory consolidation during sleep, direct disruption of memory reactivations and its impairing consequences for memory has not yet been demonstrated.

5. Reactivating memories during sleep by cueing

Cueing procedures represent an important tool in examining the functional significance of memory reactivation during sleep. In this approach, contextual cues are associated with the learning materials, which are redelivered during subsequent sleep to reactivate at least parts of the acquired memory representations. Hennevin and co-workers ( 534 , 538 ) were the first to extensively apply this approach to rats. They trained rats on an active avoidance-conditioning task where a mild electrical shock to the ear was associated with a strong aversive foot shock. Redelivery of the mild ear shock during the first six periods of posttraining REM sleep significantly increased recall of avoidance performance after sleep and also increased the time in REM sleep ( 519 ). The delivery of the mild ear-shock was ineffective when the food-shock had been associated with a tone (rather than a mild ear-shock) at training, excluding unspecific effects of stimulus presentation during sleep. Cueing during waking did not affect learning, whereas cueing during SWS and also cueing of old, remote memories during REM sleep acquired 25 days before reduced fear memories ( 517 , 518 ), indicating that cueing of fear memories during sleep strengthen these memories only when cueing is conducted during REM sleep and as long as these memories are fresh. The effects could be connected to cue-specific arousal responses mediated by the mesencephalic reticular formation (MRF) as later studies of this group showed that MRF stimulation during post training REM sleep, but not during waking or SWS, also enhanced memory though in a different task (6-unit spatial discrimination maze) ( 535 ). Recordings of single cells in the hippocampus confirmed that re-exposure to the cue during REM sleep activated firing patterns similar to those observed during training of fear conditioning ( 756 ). Cue-induced firing patterns during SWS were not investigated in these studies. Increased neural responses were also observed after cue reexposure during REM sleep in the medial geniculate body of the auditory thalamus and, for aversive cues, in the lateral amygdala ( 542 , 543 , 755 ). Taken together, these studies by Hennevin's group indicate that learning-induced plasticity can be reexpressed by cueing during REM sleep, whereas expression of conditioned responses was not consistently observed during SWS ( 541 ). Indeed, inasmuch as all of these cueing studies involved a highly emotional, mostly aversive learning component, the REM specificity of the observed effects fits well with the notion that this sleep stage is critically involved in the processing of emotional memories ( 1273 , 1294 ) (see sect. II C ). Most recently, Bender and Wilson ( 75 ) specifically examined whether cueing during non-REM sleep is capable of reactivating hippocampal place cell activity implicated in prior learning. Rats learned by reward to associate an auditory stimulus (sound L or sound R) to either the left or right portion of a linear track (length: 1.5 m). Reexposure to the sounds during subsequent non-REM sleep biased reactivation of hippocampal place cells such that sound L preferentially activated place cells with a left-side place field, and sound R preferentially activated place cells with a right-sided place field. The effect was stronger in the early compared with the later portion of non-REM sleep and did not only pertain to firing rates of individual place cells but the task-related sounds effectively reactivated the temporal structure of the replayed events. Interestingly, cueing did not increase the overall number of reactivations, suggesting a capacity-limiting mechanism for replay activity during sleep ( 626 ).

In sum, the findings in rats have compellingly demonstrated that newly encoded memories are spontaneously reactivated during SWS, whereby reactivations in the hippocampus appear to lead reactivation in other neocortical and striatal regions. Consistent with predictions from computational models ( 613 , 800 ), there is now also evidence that hippocampal activity during SWS (i.e., SW-R, in conjunction with accompanying neuronal reactivations of memory representations) serve the strengthening of these memories, and that these reactivation patterns can be cued by task-related stimuli presented during sleep. Moreover, experimentally enforced memory reactivations during REM sleep were consistently revealed to strengthen emotional memories. However, it remains unclear how exactly this strengthening is achieved by spontaneous or cued reactivations.

B. Human Studies

1. signs of reactivation in pet, fmri, and eeg recordings.

Signs of memory trace reactivation during sleep in humans have been mostly reported using imaging of brain activation with positron emission tomography (PET) or functional magnetic resonance imaging (fMRI). Compared with the multiunit recordings applied in corresponding studies in rodents, these brain imaging techniques suffer from the disadvantage that temporal and spatial resolution is very low. Generally, during non-REM sleep, brain activation and connectivity are distinctly reduced (up to 40%) compared with wakefulness, particularly in prefrontal cortical areas, the anterior cingulum, and several subcortical structures like the basal ganglia ( 134 , 764 , 766 , 768 , 1335 ). However, distinct increases in activity occur associated with sleep spindles or slow waves ( 250 , 1036 , 1132 ). During REM sleep, some brain areas including temporal-occipital cortical regions, the hippocampus and amygdala, exhibit activation comparable to that during waking, whereas activity in others is relatively reduced (e.g., parietal and prefrontal cortices) ( 189 , 253 , 764 , 769 , 1335 ). With regard to memory processes, Maquet and colleagues ( 767 , 912 ) were the first to identify learning-dependent brain activation during sleep using PET. During REM sleep following a 4-h training on a SRTT under implicit conditions (i.e., with the subject being unaware of an underlying regular sequence to be tapped), activation was enhanced in several areas including the bilateral striatum, parietal and premotor cortices, compared with a control group which had performed on a random sequence before sleep, indicating that the activation effects were specifically linked to procedural memory formation. Also, SRTT training changes functional connectivity patterns during subsequent REM sleep ( 707 ). Learning a perceptual skill (texture discrimination) was revealed to reactivate blood oxygen-dependent (BOLD) signal in the trained region of area V1 in the visual cortex during subsequent non-REM sleep, with the magnitude of reactivation predicting improvement visual discrimination skills at retest ( 1351 ).

Clear signs of reactivation during SWS were obtained (by PET) also following hippocampus-dependent spatial learning on a virtual navigation task ( 911 ). Learning to navigate, as expected, activated hippocampal and parahippocampal areas, and the same areas were again activated during subsequent SWS, with the size of hippocampal reactivation predicting navigation performance at a later retest. No signs of reactivation occurred during REM sleep. These findings are convergent with results on replay of firing patterns in hippocampal cell assemblies observed during SWS in rats, and suggest that a similar process of hippocampal memory reactivation occurs also in humans. Learning of face-scene associations induced combined reactivation (in fMRI) in hippocampal and face/scene selective visual cortical areas that occurred particularly during sleep spindles ( 84 ), when functional connectivity between hippocampus and neocortex is generally increased ( 36 ). Notably, reactivations did not only occur in synchrony with spindles, but their size appeared to be also modulated by spindle amplitude, a pattern of findings which is altogether consistent with the view that spindles mediate hippocampo-neocortical interactions during declarative memory processing. Using surface EEG, enhanced EEG coherence, though in different frequency bands, was found during the learning of word pairs and during subsequent SWS, where the coherence effects concentrated on the up-state of the slow oscillations ( 831 ).

Although the available studies provide initial evidence that reactivation of brain areas can be identified in PET, fMRI, and also EEG recordings, more consistently so during SWS than REM sleep, they need to be better characterized using, for example, higher resolution imaging or multivariate pattern classifiers to determine more accurately when and where they occur.

2. Reactivating memories during sleep by cueing

Cueing has also been used in humans to examine the function of memory reactivations during sleep. In early studies using a related approach, participants learned Morse codes and the same codes were acoustically presented again (at a low nonwaking intensity) during subsequent REM sleep ( 331 , 489 ). Reexposure to the codes during REM sleep containing phasic REMs (i.e., acute eye movements) increased performance the next day compared with reexposure during tonic REM sleep without REMs or a no stimulation control condition. In another study, subjects acquired a set of complex rules in the presence of a loud ticking alarm clock and were then reexposed to the ticking sound during phasic REM sleep. At retest after 1 wk, these subjects showed a distinct improvement (by 23%) in memory for the rules compared with control subjects who had not acquired the rules in the presence of the ticking clock ( 1109 ).

Because spontaneous reactivations (of hippocampus-dependent memory) are much more consistently observed during SWS than REM sleep, recent studies have turned towards the examination of cue-induced reactivations during SWS. In a study of ours ( 959 ), we used an olfactory stimulus (the scent of roses) to reactivate visuospatial memories for card-pair locations known to involve the hippocampus ( 1121 ). Odors were used because they do not affect the sleep architecture ( 178 ) and are also known for their strong potency to activate associated memories ( 197 ). The participants learned the card-pair locations in the presence of the odor, with the odor being reexposed during subsequent SWS ( FIGURE 4 A ) , REM sleep or while the subject remained awake. At a later retest, participants recalled significantly more card locations after reexposure of the odor during SWS compared with the other two conditions. The participants were not aware of the odor presentation during sleep. Further controls specified that the memory-enhancing effect of odor exposure during SWS critically depended on whether or not the odor was present during the prior learning phase, indicating that the memory enhancement was caused by a reactivation of odor-associated memories and not simply by unspecific effects of odor-exposure during SWS ( FIGURE 4 B ) . Functional magnetic imaging confirmed that odor presentation during SWS activated the hippocampus, again only when participants had received the odor also during prior learning ( FIGURE 4 E ; Ref. 959 ). Interestingly, hippocampal activation during odor-induced reactivation in SWS was distinctly stronger than during wakefulness, suggesting that during SWS, the hippocampus is more sensitive to stimuli capable of reactivating memories compared with wakefulness. Subsequent experiments (Rihm, Diekelmann, Born, and Rasch, unpublished observation) showed that the improvement in recall was significantly reduced, if the odor presented during SWS differed from that during learning ( FIGURE 4 C ) , and so were also associated increases in EEG delta (1.5–4.5 Hz) and spindle (13–15 Hz) activity during sleep, suggesting that olfactory-induced reactivations of memory representations in the hippocampus can promote oscillatory activity facilitating plastic changes in thalamocortical circuits (see sect. IV, A and C ). Recent experiments show that odor-induced reactivations during sleep can even induce the generation of more creative solutions to a problem encountered before sleep ( 999 ).

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Odor-induced reactivations during SWS benefit memory consolidation. A : procedures: participants learned a visuospatial memory task (card-pair locations) in the presence of an odor. During subsequent SWS, they were either reexposed to the same odor serving as a cue to induce memory reactivations, or received an odorless vehicle. After sleep, retrieval was tested (in the absence of the odor). B : odor-induced reactivation of memories during SWS distinctly increased memory for card-pair locations, compared with vehicle condition. In a control experiment, retention of card-pairs remained unchanged when the odor was not administered during learning, excluding unspecific effects of odor exposure on memory processing during sleep. [Modified from Rasch et al. ( 959 ), with permission from American Association for the Advancement of Science.] C : only reexposure during SWS to the same odor as during learning effectively enhanced card-pair memory (congruent odor condition), whereas an odor different from that administered during learning (incongruent condition) was not effective. (Data from Rihm et al., unpublished observation). D : odor-induced reactivations during SWS immediately stabilized memories against interference (induced by learning an interference card-pair task shortly after reactivations during SWS). In contrast and consistent with reconsolidation theory, odor-induced reactivations during wakefulness destabilized memories, as indicated by an impaired card-pair recall when reactivations during waking were followed by learning an interference card-pair task. [Data from Diekelmann et al. ( 295 ).] E : odor-induced reactivations of memories during SWS activated the left hippocampus as revealed by functional magnetic resonance imaging (fMRI). Values are means ± SE: * P ≤ 0.05; ** P ≤ 0.01; *** P ≤ 0.001. [Modified from Rasch and Born ( 957 ), with permission from Elsevier.]

To differentially reactivate individual memory traces during postlearning SWS, Rudoy et al. ( 1018 ), rather than using a global context cue, paired the location of different cards (showing objects and animals) with a characteristic sound (e.g., meow for a cat). During a posttraining nap, only half of the sounds were administered again to reactivate respective place-object associations. At later retrieval, memory for the reactivated associations was significantly better than for the nonreactivated associations. In a subsequent study, reactivation of the characteristic sounds was revealed to be associated with increased activation in the right parahippocampal cortex ( 1235 ). Cueing during sleep was also effective for skill memories. Antony et al. ( 40 ) trained participants to play two different melodies on a piano keyboard. When only one of the melodies was presented again during a period of posttraining SWS, performance on just this reactivated melody was improved at a later retest, compared with performance on the nonreactivated melody. Taken together, in showing that experimentally induced reactivation of declarative and also procedural memories robustly enhance later recall of the memories, these studies demonstrate a causal role of such reactivations for memory consolidation. Thus the cueing of memory during sleep has provided valuable insight into the function of sleep-associated reactivations, stimulating to exploit this approach, in future research, also for specifying the sequels of reactivations for the representational reorganization memories undergone during sleep-associated system consolidation.

3. Memory reactivations and dreaming

It is unclear whether neuronal signs of memory reactivation during sleep are in any way linked to the recall of dreams after awakening from sleep. Highly vivid and emotional dreams are typically reported after awakenings from REM sleep, whereas more thoughtlike dream reports can be obtained after awakenings from non-REM sleep ( 199 , 554 ). Subjectively, reported dreams often cover an extended time period involving a sequence of events, whereas signs of memory reactivation are usually restricted to brief intervals in the range of several 100 ms. However, time perception during dreams might be compressed, as has been reported for replay of hippocampal neuron assemblies during SWS. Nevertheless, although semantic features from past experience are often included, only a very small portion of dream reports (1–2%) incorporate genuinely episodic memories experienced during presleep waking ( 405 , 1050 ). Also, experimental cueing of memories during sleep did not produce specific dream reports of task-related themes ( 959 , 1018 ; Rihm et al., unpublished observation). On the other hand, several experiments showed that specific waking behaviors can influence subsequent dream content ( 1152 ). When participants played an emotionally engaging ski computer game, 30% of dreams reported upon awakenings (from non-REM sleep stage 1 and 2) shortly after sleep onset contained elements related to the computer game ( 1304 ). The same group also reported a link between dream mentation during non-REM sleep and memory consolidation after a nap ( 1303 ), although the effect was based on only four subjects who actually reported task-related mentation.

Overall, there is so far no convincing evidence for a direct link between the reactivation of newly encoded memory representations during sleep as evidenced by the recording of neuronal activity and reported dreams. Although it cannot be excluded that aside from consolidating memory, these reactivations occasionally trigger certain fragments of memory that then are incorporated into dreams.

C. Memory Reactivations in the Wake State

1. animal studies.

Reactivations of hippocampal cell firing patterns occur also during waking when the animal rests after task performance or during brief pauses of active behavior like exploration or running in a maze ( 191 , 268 , 556 , 673 , 870 ; see Ref. 175 for a review). They also occur conjointly with SW-Rs that are observed at somewhat lower rates during wakefulness than during SWS ( 159 , 191 , 870 ). Different from sequenced reactivations in hippocampal assemblies during SWS which always occur in a forward direction, replay of sequences during waking can occur in both forward and backward directions ( 243 , 406 ). In a task requiring the rat to run back and forth on the same elevated linear track, replay in the reverse order occurred mainly at the end of a run, whereas replay in a forward direction transpired in the anticipatory period before a new run ( 288 , 492 ). Wake reactivations were found to be particularly precise when the animal explores a novel environment, and the precision decreases when the animal becomes more and more familiar with the spatial task ( 191 ). In larger experimental environments, both forward and reverse replay can occur over multiple SW-R events thereby covering longer distances ( 268 ). In addition to current learning experience, more remote learning experiences are also reactivated, indicating that wake reactivations do not depend on the actual perceptual input ( 621 ). Also, new path sequences that had never been experienced can be constructed during replay, which could facilitate short-cut learning and the creation of an allocentric cognitive map. However, the exact role of wake reactivations for memory formation and their behavioral relevance has not been thoroughly studied so far. One study reported that both the number of SW-Rs during learning a spatial task as well as during subsequent rest were predictive for later memory performance ( 335 ).

2. Human studies

Signs of spontaneous memory reactivations during the wake state were also observed in humans. FMRI recordings in subjects performing on a vigilance task indicated biased brain activation depending on whether the subjects had performed before on either a procedural serial reaction task or a spatial navigation ( 913 ). Prior performance on the procedural tasks produced relative enhanced activation in striatal and supplementary motor areas, whereas prior navigation performance enhanced activation in temporal lobe regions including the hippocampus. On a shorter time scale, application of multivariate pattern classifiers revealed that MEG responses to sensory inputs (pictures of indoor and outdoor scenes) were replayed during the 5-s delay in a working memory task ( 422 ). The strength of the replay was modulated by the MEG theta rhythm, with the amount of theta phase coordination predicting working memory performance on the pictures. Using an interference A–B A–C paradigm, Kuhl et al. ( 674 ) observed distinct hippocampal activation during learning of the new A–C object associations (interfering with the first learned A–B associations), which was predictive for later remembering the first-learned A–B associations, suggesting that reactivation of old associations during new learning prevented forgetting. Overall, these findings provide first hints that neuronal signs of spontaneously occurring memory reactivations in the wake state can be identified also in humans.

3. Comparing cueing of memories during wakefulness and sleep

Whereas the reactivation of memories during wakefulness, as it occurs for example during rehearsal, can strengthen these memories in the long run ( 623 , 1006 ), the reactivation transfers the representation into an transient unstable state such that memory is in need of reconsolidation ( 470 , 718 , 731 , 844 , 1033 ). Thus, according to the “reconsolidation” concept, memories exist either in an active or inactive state. Consolidation transforms active but unstable memories into passive but stable memories, and reactivation renders these memories again susceptible to interfering influences. There is consistent evidence from animal and human studies that experimentally induced memory reactivations can invoke a transient labilization of respective traces, and also the underlying neural and molecular mechanisms have been partly characterized ( 403 , 404 , 588 , 590 , 639 , 1043 , 1118 ; for reviews, see e.g., Refs. 14 , 844 , 1208 ). Indeed, the destabilization of long-term memories after reactivation might be highly adaptive because it presents the opportunity to update memories with respect to new experiences ( 330 , 513 , 718 , 957 ).

Whether sleep differentially acts on processes of consolidation and reconsolidation is presently not known ( 1034 , 1155 , 1156 , 1285 ). Yet, of more immediate relevance in this context is the question: Does reactivation during SWS like reactivation during waking, transiently destabilize memories? Indeed, in line with the “sequential hypothesis,” it has been proposed that reactivation during non-REM sleep destabilizes memories, which in turn are stabilized during subsequent REM sleep ( 293 , 1156 ). The transient destabilization upon reactivation during SWS could ease the integration of the newly acquired memory into preexisting, neocortical knowledge networks ( 957 ). However, recent experiments appeared to refute these hypotheses. In this study by Diekelmann et al. ( 295 ), memories of card-pair locations were reactivated either during postlearning SWS or wakefulness using a contextual odor cue. To probe stability of the reactivated memory trace, after reactivation (and in the sleep group after being awakened) the participants learned an interference task using the same card pairs but locations that differed from the originally learned task. As expected from reconsolidation studies (e.g., Refs. 844 , 845 , 1043 ), reactivation during wakefulness destabilized memories rendering them susceptible to interference learning, as indicated by impaired memory recall for the originally learned card-pair locations after interference learning ( FIGURE 4 D ) . In sharp contrast, reactivation during SWS had an immediate enhancing effect on the originally learned card-pair locations, although the participants also in this condition learned the interference locations right after reactivation. Note, in this condition memories were reactivated during the first period of SWS not followed by any REM sleep, arguing against the sequential occurrence of REM sleep as another prerequisite for the stabilizing effects of reactivations during SWS. Additional fMRI recordings showed that whereas memory reactivations during SWS mainly resulted in activation of the hippocampus and posterior cortical brain areas, reactivating memories during wakefulness primarily induced activation of the lateral prefrontal cortex ( 295 ).

In showing that the consequences of memory reactivation depend on the brain state with opposing effects induced during waking and SWS, these data refute “opportunistic” theories of sleep-associated memory consolidation ( 809 ) assuming that reactivation-induced consolidation processes do not basically differ between sleep and wakefulness, apart from the fact that sleep protects the processes from external interference (see sect. II A ). Yet, the mechanisms mediating reactivation-induced stabilization and destabilization in the respective brain states are unclear. One factor could be the cholinergic tone which is high during waking but at a minimum during SWS. Cholinergic activity might thus act as a switch that shifts information flow from the prefronto-hippocampal direction prevailing during wakefulness, into the opposite direction during SWS, i.e., from hippocampal to neocortical networks ( 520 , 957 ) (see sect. V B2 ). Related to this, (prefrontal) capacities of explicit encoding and retrieval monitoring available during waking might be critical for whether reactivations actually produce a destabilization of memory traces ( 839 , 1308 ). In combination, these experiments provide emergent evidence for a key role reactivation plays in all phases of memory formation that essentially depends on the brain state.

IV. SLEEP-SPECIFIC ELECTRICAL OSCILLATIONS

Sleep and sleep stages are characterized by specific field potential rhythms of brain activity. Neocortical slow oscillations and SWA, thalamo-cortical spindles and hippocampal SW-R have been associated with processes of memory consolidation during SWS and might support the reactivation and redistribution of memory representations during this sleep stage. Theta rhythms and PGO waves have been proposed to support REM sleep-dependent processes of consolidation and might support enduring synaptic plastic changes during this sleep stage.

A. Slow Oscillations and SWA

1. generation, propagation, and homeostatic regulation.

During human slow wave sleep (SWS), the EEG shows predominant slow wave activity (SWA), which is defined by the 0.5- to 4.0-Hz frequency band and includes the <1-Hz slow oscillations with a peak frequency of 0.8 Hz ( 7 , 830 ). “Delta” activity refers to the 1- to 4-Hz band of SWA. Slow oscillations comprise alterations between periods of neuronal membrane depolarization accompanied by sustained firing (“up-states”) and periods of membrane hyperpolarization associated with neuronal silence (“down-state”). Steriade's group was the first to demonstrate and to provide an in-depth analysis of slow oscillations on the level of intracellular and local field potential recordings in anesthetized cats ( 228 , 1137 , 1138 , 1140 , 1143 – 1145 ). Later studies confirmed that also during natural SWS, cortical neurons are indeed depolarized and fire during the depth-negative (surface-positive) field potential of the slow oscillation half-wave and are hyperpolarized and silent during the depth-positive (surface-negative) half-wave, whereas neurons are depolarized and tonically fire during waking and REM sleep ( 552 , 1143 , 1146 , 1199 ). Virtually every cortical neuron, both excitatory as well as inhibitory neuron, engages in the slow oscillation with the firing patterns showing high synchrony across cellular populations ( 33 , 188 , 1139 , 1146 , 1261 , 1268 ). The widespread synchronization of cortical and thalamo-cortical networks during non-REM sleep is considered the major function of the slow oscillation, providing a global time frame whereby the network is clocked and reset by the hyperpolarizing phase and neuronal processing is limited to the subsequent depolarizing up-phases ( 224 , 282 , 748 , 828 , 832 , 1138 , 1143 ). Indeed, phase-locked electrical and transmagnetic stimulation has revealed the depolarizing up-phase as a period of distinctly enhanced neuronal network excitability ( 83 , 792 , 1200 ). In the scalp EEG, the negative peak of the slow oscillation coincides with the beginning of the down-to-up state transition ( 281 , 993 , 1261 ), whereas the depolarizing phase of sustained firing correlates with the positive EEG deflection ( 33 , 998 , 1268 ). K-complexes during non-REM sleep stage 2 appear to represent isolated slow waves ( 181 ).

The cellular mechanisms of slow oscillation generation are not fully understood. Whereas the up-state reflects some balance between excitatory and inhibitory neuronal activity ( 505 , 1017 , 1074 ), the hyperpolarizing down-state represents a period of disfacilitation that does not contain active inhibition ( 229 , 1146 ). Thus a central question is how up-states are initiated when all neurons are hyperpolarized and silent during the down-state? As a mechanism, hyperpolarization-activated depolarizing currents ( I h ) that depolarize a subset of layer 5 cortical neurons have been discussed ( 1028 ). However, the I h is not strong in cortical neurons and may increase firing only in conditions of increased extracellular K + concentration, e.g., during seizures ( 1194 ). Hence, the more likely explanation is that up-states are triggered by the occasional summation of miniature EPSPs as a residual synaptic activity resulting from stimulus processing during prior wakefulness, and formed mainly by activation of T-type Ca 2+ currents in combination with a persistent Na + sodium inward current ( 1193 , 1198 ). Amplitude and frequency of miniature EPSPs are low under baseline conditions. However, large neuronal constellations, particularly after intense encoding during prior waking, might provide a sufficient number of synapses enabling the generation of repetitive up-states. Moreover, extracellular Ca 2+ concentrations are increased during the down-state, which enhances synaptic efficacy, such that a single spike generated in this condition may effectively excite the whole network ( 238 , 790 ), in particular when such spikes originate from neurons in deep cortical layers with larger numbers of synaptic contacts to other neurons ( 188 ). Once initiated, the up-state is likely amplified by activities of intrinsic currents such as persistent Na + and high-threshold Ca 2+ currents. Contributions of glia cells to the regulation of Ca 2+ concentrations and excitability in neighboring neurons are likely ( 30 , 31 , 506 , 672 ). Synchronization of activity during depolarizing states might be partially achieved via corticocortical glutamatergic synaptic connections, implicating contributions of NMDA and AMPA receptor activation to establish long-range synchrony ( 32 , 360 , 586 ). Induction of the hyperpolarizing slow oscillation down-phase has been mainly linked to synaptic depression, activation of Ca 2+ -dependent and of Na + -dependent K + currents, and the inactivation of persistent Na + currents generally disfacilitating neuronal excitability (e.g., 68, 96, 391, 392, 437, 1053). Recent results suggest that active inhibition is also involved ( 190 , 1027 ), which may be mediated by a particular set of cortical interneurons preferentially firing towards the end of the up-state ( 950 ). During waking and REM sleep, the expression of down-states might be suppressed mainly due to enhanced cholinergic activity in these states.

The slow oscillation is generated in cortical networks and can occur in isolated cortical slices ( 239 , 1028 , 1198 ). Slow oscillations originating from thalamo-cortical neurons ( 586 ) vanish when the thalamus is isolated from cortical inputs ( 1200 ), indicating the slow oscillation is a primary cortical phenomenon ( 993 ). Nevertheless, the slow oscillation of the intact brain likely reflects an interaction between cortical and thalamic networks ( 239 , 1196 ). High-density EEG recordings in humans as well as depth recordings in epileptic patients and cats indicate that the slow oscillation behaves like a travelling wave, which originates most frequently in the frontal regions and propagates towards posterior regions, although other origins and directions of propagation occur ( 188 , 748 , 791 , 841 , 859 , 1261 , 1314 ). Interhemispheric connections contribute to the propagation ( 825 ). EEG-based source modeling located the main origins of the travelling waves in the cingulate gyrus and the left insula ( 841 , 993 ). These estimates roughly correspond with results from PET and fMRI studies likewise indicating frontal as well as midline structures as major sources of slow waves, i.e., the bilateral medial and inferior frontal cortices, precuneus and the posterior cingulate cortex ( 250 , 252 , 557 ). Interestingly, the largest slow waves (>140 μV) were associated with activation in the parahippocampal gyrus, cerebellum, and brain stem, whereas smaller waves were more related to activation changes in frontal areas ( 788 ).

Traditionally, SWA is regarded as a marker of the homeostatically regulated sleep pressure, which increases after prolonged sleep deprivation and decreases from early to late sleep ( 111 , 113 , 994 , 1269 ). It has been argued that the decrease in SWA across sleep reflects differences in the homeostatic regulation between <1 Hz slow oscillations and 1–4 Hz delta oscillations ( 6 , 171 , 1207 ). However, as distinct qualitative differences between both frequency bands have not been confirmed, the decrease in SWA across sleep appears to be most parsimoniously explained by a decrease in the incidence of high-amplitude slow waves ( 1267 ). Independent of the decrease in amplitude, the slope of the slow waves also decreases from early to late sleep, possibly reflecting a decrease in the synchrony and speed of recruitment of neurons at the down-to-up state transition ( 87 , 360 , 994 , 1267 ).

2. The relation between SWA and memory benefits

There is now convergent evidence that SWA and the slow oscillations represent a central mechanism conveying the beneficial effect of SWS on memory consolidation, in particular in the declarative memory system (see sect. II D ). In animals, the encoding and learning of information during waking produced consistent increases in SWA in respective cortical networks during subsequent SWS ( 624 , 1263 ). Thus hemispheric differences in SWA during sleep in rats were related to the preferential use of the left or right paw during the day ( 1265 ). When rats had to rely on their whiskers during the activity period in darkness, subsequent SWA was higher in the somato-sensory cortex compared with a situation in which additional light was available during the active period ( 1341 ). In birds, sensory deprivation of one eye while watching a documentary about birds increased SWA and the slope of slow waves only in the hyperpallium (a primary visual region) connected to the stimulated eye ( 726 ).

In humans, intense learning of declarative memories (word pairs) enhanced amplitudes of the slow oscillation up-states as well as coherence in the SWA frequency band during succeeding SWS ( 829 , 831 ). Also, slopes of the down-to-up state of the slow oscillations were steeper after learning. Regarding procedural skills, training on a visuomotor adaptation task increased the amplitude of slow waves during subsequent SWS ( 581 , 582 ). The increase was locally restricted to the motor areas mainly involved in prior training and correlated with the overnight improvement on the task. Increases in local SWA were even observed when learning took place in the morning, suggesting that the training-induced changes in SWA do not depend on the time between training and subsequent sleep ( 751 ). Conversely, arm immobilization during daytime resulted in reduced SWA over the contralateral motor cortex ( 581 ). Correspondingly, reducing SWA during sleep by the presentation of tones was revealed to suppress the sleep-dependent improvement in visuomotor adaptation skills ( 691 , 692 ) and also in texture discrimination skills ( 10 ).

Several studies examined the effects of repetitive transcranial magnetic stimulation (rTMS) as a tool to directly induce synaptic potentiation, on subsequent SWA. Application of 5 Hz rTMS over the motor cortex induced an immediate potentiation of the TMS-induced cortical response which was followed by a marked (40%) increase in SWA in the same cortical region during subsequent sleep ( 580 ). Paired associative TMS stimulation (PAS) before sleep produced increases or decreases in subsequent SWS, depending on whether the protocol successfully induced long-term potentiation-like increases or long-term depression-like decreases in cortical excitability as measured by motor evoked potentials ( 583 ). In another study, similar effects of PAS stimulation on SWA were associated with local changes in slow spindle activity ( 85 ). Changes in frontal SWA after PAS appeared to extend even into succeeding REM sleep ( 443 ). Overall, these findings support the view that SWA (as well as the amplitude and down-to-up state slope of the slow oscillation during SWS) reflect the intensity and amount of encoding during prior wakefulness, with some studies also indicating an association of these measures with later retrieval.

Direct evidence for a causal role of slow oscillations on sleep-dependent memory consolidation is provided by studies experimentally inducing slow oscillations by transcranial direct current stimulation (tDCS; FIGURE 5 A ). In humans, tDCS that oscillated at a very low frequency (0.003 Hz; 30-s on/30-s off) and was applied to the prefrontal cortex during early nocturnal non-REM sleep, increased endogenous SWA, and produced a significant improvement in the overnight retention of word pair memories ( 785 ). Effects on word pair memories were even more consistent with tDCS oscillating at 0.75 Hz, i.e., a frequency mimicking the endogenous slow oscillations ( FIGURE 5 B , Ref. 783 ). This type of stimulation applied during early non-REM sleep specifically enhanced <1 Hz slow oscillations and additionally increased frontal slow spindle activity (10–12 Hz). tDCS at the same frequency (0.75 Hz) during late REM-rich sleep was ineffective in enhancing both endogenous slow oscillations and overnight retention of word pairs. Further controls ensured that the effects depended on the frequency of the oscillating stimulation and on the brain state ( FIGURE 5 C ) : tDCS at the 5 Hz theta frequency during early non-REM sleep had an immediate suppressing rather than enhancing effect on endogenous slow oscillation and frontal slow spindle activity, and impaired overnight retention of word pairs ( 784 ). Applying the 0.75 Hz slow oscillatory stimulation during wakefulness induced a widespread increase in theta (4–8 Hz), rather than slow oscillation activity, and this increase in theta activity was associated with a significant improvement in the encoding of declarative memories, rather than affecting retention of these memories ( FIGURE 5 D , Ref. 643 ). These results agree with findings that reveal increased cortical excitability following oscillatory electrical stimulation in the wake state ( 83 , 484 ) and provided initial hints at a causal role of EEG theta activity for the encoding of new memories in humans ( 646 , 647 , 687 ). In fact, combination changes observed after tDCS at different frequencies seem to indicate that the cortical networks oscillating at the theta frequency during encoding of hippocampus-dependent memories are functionally linked to the networks that oscillate at the slow oscillation frequency during subsequent SWS to consolidate these memories.

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Probing the functional relevance of slow oscillatory activity for memory processes by transcranial direct current stimulation (tDCS). A : procedures: participants learned declarative and nondeclarative tasks before sleep and recall was tested in the next morning. During early postlearning non-REM sleep, tDCS oscillating at different frequencies was applied via electrodes attached bilaterally over the prefrontal cortex and to the mastoids. In a sham control condition, no current was applied. B and C : effects of tDCS depend on frequency of the oscillating stimulation. B : tDCS during non-REM sleep oscillating at the 0.75 Hz slow oscillation frequency (SO-tDCS) increased endogenous slow oscillation activity (0.5–1 Hz) at all recording sites and slow frontal spindle activity (8–12 Hz), and these effects were associated with an enhanced retention (consolidation) of declarative memory (for word pairs) across sleep, compared with the sham condition. [Data from Marshall et al. ( 783 ).] C : in contrast, tDCS oscillating at 5 Hz (theta-tDCS) decreased slow oscillation activity at all recording sites and slow frontal spindle activity, and these effects were associated with an impaired retention of declarative memory across sleep. [Data from Marshall et al. ( 784 ).] D : effects of SO-tDCS depend on brain state: when applied during waking (rather than during non-REM sleep), tDCS induced a widespread increase in 4–8 Hz theta and 16–14 Hz beta activity, rather than slow oscillation activity, and these increases were associated with an enhanced encoding of declarative memory (for words), particularly in later learning trials (R5, R6), whereas consolidation across the wake retention interval remained unaffected (not shown). Values are means ± SE: * P ≤ 0.05; ** P ≤ 0.01. [Data from Kirov et al. ( 643 ).]

B. The Synaptic Homeostasis Hypothesis

1. the concept.

An influential concept proposed by Tononi and Cirelli ( 1203 , 1204 ) assumes that the slow waves of SWS serve primarily to globally down-scale the strength of synapses that were potentiated in the course of encoding of information during prior waking. According to this concept, linear downscaling across synapses enhances memory indirectly as this process nullifies the strength of connections that were only weakly potentiated during wakefulness leading to the improvement of the signal-to-noise ratio for more strongly encoded memory representations. Synaptic downscaling can be considered a nonspecific process complementing active system consolidation during sleep ( 293 ).

The synaptic homeostasis hypothesis originated from Borbely's “two process” model of sleep ( 5 , 8 , 111 , 112 ), assumes that, apart from a circadian process C, sleep and specifically SWS is regulated by a homeostatic process S, which rises during waking and declines during sleep. The most important marker of process S is SWA during non-REM sleep, which reliably increases as a function of time awake and shows an exponential decrease in the course of subsequent non-REM sleep. The synaptic homeostasis hypothesis basically links process S to processes of synaptic plasticity ( 1203 , 1204 ). It relies on four key assumptions. First, it is assumed that wakefulness in general is a state of information intake and encoding that induces processes of LTP in cortical networks resulting in a net increase in synaptic weights. Second, it is assumed that SWA is a direct marker of the amount of synaptic potentiation during prior wakefulness such that “the higher the amount of synaptic potentiation in cortical circuits during wakefulness [is], the higher [is] the increase in slow wave activity during subsequent sleep” (p. 144 in Ref. 1203 ). Third, it is assumed that SWA is implicated in the downscaling of synaptic weights. As synapses are predominantly potentiated during wakefulness, such process would inevitably lead to increasing energy and space demands for permanently increased synaptic weights, ultimately saturating the network thus impairing further encoding of information. SWA and in particular the <1 Hz slow oscillations are assumed to proportionally downscale the potentiated synapses by a long-term depression-like mechanism, as neuronal firing at frequencies <1 Hz is known to preferentially induce long-term depression ( 630 , 789 ). Moreover, down-states of neuronal silence following up-states of enhanced firing increase the probability that presynaptic input is not followed by any postsynaptic output, i.e., a mechanism leading to further depotentiation of the network. Also, the neuromodulatory milieu during SWS is characterized by low levels of acetylcholine, norepinephrine, and serotonin and therefore favors processes of depotentiation. The downscaling process, and in parallel the propensity for SWA, is self-limiting as synapses are gradually depotentiated, reaching a constant homeostatic level at the end of sleep. A computational model confirmed that a decrease in synaptic strength can fully account for the gradual decrease of SWA from the beginning to the end of the sleep period ( 360 ). Specifically, the model predicted a decrease in the incidence of high-amplitude slow waves, a decrease in slope, as well as an increase in the number of multipeak waves from early to late sleep, which was confirmed in related animal and human studies ( 994 , 1269 ). Fourth, the synaptic homeostasis hypothesis assumes that memories are enhanced by sleep as a by-product of synaptic downscaling. During learning, correct (signal) as well as erroneous information (noise) is encoded, although the latter at a weaker strength. The proportional downscaling of synapses during SWS reduces the weights of weaker synapses below a threshold, making them completely ineffective, whereby the signal-to-noise ratio and the subsequent recall of the memory representation is enhanced.

The concept, and specifically the association of synaptic potentiation during wakefulness and subsequent SWA, was confirmed by several studies measuring markers of synaptic plasticity like plasticity-related genes [brain-derived neotrophic factor (BDNF), activity-regulated cytoskeleton-associated protein (arc), Homer, nerve-growth factor-induced gene A (NGFI-A)] ( 584 ). In rats, learning a reaching task increased the protein expression of two activity-dependent genes c- fos and arc in the motor cortex and increased subsequent SWA in the same brain region ( 510 ). Suppressing expression of LTP-related genes in rats by chronic lesion of the noradrenergic system produced a strong reduction in the homeostatic SWA response (see, e.g., Refs. 205 and 457 , for similar results in flies). Levels of postsynaptic glutamatergic AMPA receptors containing the glutamate receptor (GluR1)-1 subunit reliably indicate synaptic plasticity and were revealed to be high during wakefulness and distinctly lower (by 40%) during sleep ( 1264 ). Correspondingly, results from a recent study suggest that discharge patterns of pyramidal neurons during SWS promote the removal of synaptic Ca 2+ -permeable AMPA receptors in the somatosensory cortex of juvenile rats ( 701 ). Procedures assumed to induce local net increases in synaptic potentiation (e.g., the cortical application of BDNF and KCl) induced cortical spreading depression ( 81 ) and increased SWA during subsequent sleep, whereas BDNF receptor inhibition decreased SWA ( 366 , 367 ). Importantly, in demonstrating that the encoding of information during waking enhances subsequent SWA in specific brain regions, these studies provide compelling evidence that SWA is regulated locally in addition to its global regulation by brain stem and diencephalic structures ( 672 , 859 , 975 , 1267 ).

Supplementary evidence for sleep-associated synaptic downscaling was obtained in two recent studies measuring synaptic growth. Using staining techniques in fruit flies, Bushey et al. ( 156 ) showed that synapse size or number increased after a few hours of wakefulness and decreased only when flies were allowed to sleep. Increased synaptic growth due to an enriched wake experience (12 h spent together with 100 other flies) led to an increase in sleep with the time spent asleep after enriched experience being negatively correlated with spine density, suggesting that sleep renormalized synapses after potentiation during wakefulness. In juvenile mice in vivo, two-photon microscopy in the sensorimotor cortex revealed a net decrease in spine growth across periods of sleep, although there was also significant spine growth at the same time ( 772 ). In adult mice, no similar changes were observed.

Further support for a net increase in synaptic weights across the wake period and a decrease across sleep derives from measures of cortical excitability and actual firing rates which should be increased with net increases in synaptic strength. Indeed, in rats, slope and amplitude of the electrically evoked cortical potential as indicators of synaptic strength progressively increased during sustained wakefulness and decreased after sleep, and these changes were correlated with SWA ( 1264 ). In humans, cortical excitability as measured by a combined TMS/EEG study was increased after sustained wakefulness and decreased after sleep ( 71 ), although another study reported a decrease in TMS-induced cortical excitability after 40 h of sleep deprivation ( 445 ). Firing rates of cortical neurons were high after periods of sustained wakefulness and decreased during sleep, with the decrease correlating with SWA during sleep ( 1268 ). Miniature excitatory postsynaptic currents (mEPSC) are considered residual activity resulting from prior potentiating of synapses in the course of information encoding. Frequency and amplitude of mEPSCs were increased in the frontal cortex slices of mice and rats after prolonged wakefulness compared with slices obtained after sleep ( 740 ).

There is also evidence that, together with increased net synaptic potentiation, wakefulness leads to a gradual increase in energy demands, e.g., glucose uptake, of the brain. Indeed, cerebral metabolism measured by 2-deoxyglucose uptake increased after periods of wakefulness and decreased after sleep in mice ( 1265 ). However, decreases in cerebral metabolic rates after prolonged waking have also been observed in rats ( 363 ). In humans, cerebral glucose uptake measured by PET did not appear to be decreased when measured 2–4 h after sleeping ( 157 ). Moreover, decreases rather than increases in metabolic rates were observed after 24 h of sleep deprivation ( 1186 , 1336 ), although it cannot be excluded that distinctly extended periods of wakefulness trigger separate compensatory processes characterized by the occurrence of local slow waves in the waking state and by reduced metabolic demands ( 1204 , 1267 ).

2. Critical issues

Although the synaptic homeostasis hypothesis integrates a wide variety of findings, especially on SWA, concerns have been raised about both the concept of downscaling in general, and specifically about how the theory explains sleep-dependent memory benefits. In light of clear evidence that learning during wakefulness relies both on LTP and LTD-like mechanisms (e.g., Refs. 222 , 629 ) “ … it is thus most improbable that sleep need - to the extent this is determined by learning - is determined solely by Hebbian LTP (or any other single form of synaptic plasticity)” (p. 4 in Ref. 410 ). Also, molecular markers of synaptic potentiation like arc, BDNF or Calmodulin-dependent-kinase (CaMK) IV are involved in both the potentiation and depression of synaptic strength or other forms of non-Hebbian scaling ( 1026 , 1067 ), questioning that the relative increase and decrease of these markers across sleep and wakefulness, respectively, is actually related to one specific form of synaptic plasticity. Furthermore, there is evidence that protein synthesis involved in LTP stabilization is enhanced during sleep, with the rate of synthesis linked particularly to SWS ( 848 , 956 , 1245 ) (see also effects of sleep on ocular dominance plasticity discussed in section VII A3 ). Recently, Chauvette and colleagues ( 187 ) showed in vivo that somatorsensory evoked potentials in cats were enhanced after a short period of SWS (about 5–10 min) compared with the previous wake period. Further experiments in vitro confirmed that the SWS-related enhancement in synaptic strength is a calcium-dependent postsynaptic mechanism which requires (i) the hyperpolarizing down-state of the slow oscillation and (ii) the coactivation of AMPA and NMDA receptors, suggesting that SWS is linked to synaptic potentiation rather than down-scaling ( 118 ). Related to that, although stimulation at the ∼1 Hz slow oscillation frequency typically induces LTD in vitro, in vivo such stimulation can remain ineffective or even induce LTP (e.g., Refs. 497 , 919 ). In fact, the original concept of synaptic scaling as a self-tuning process of neuron networks assumes that periods of reduced synaptic activity, like sleep, produce net up-scaling rather than downscaling of synapses ( 409 , 1219 , 1220 ).

The memory-benefit from sleep, according to the synaptic homeostasis theory, reflects an increased signal-to-noise ratio for the memorized representation that occurs as a byproduct of proportional synaptic downscaling, shifting weakly potentiated synapses below a threshold thus nullifying their weight. So far, there is no experimental data supporting the existence of such a threshold, nor for the assumption that synaptic scaling in the cortical network is proportional ( 293 ). Compared with older memories, new memories appear to be generally more labile, i.e., linked to weak synaptic weights and thus would be at a greater risk of being erased during sleep. This prediction contradicts experimental findings pointing towards the preferential consolidation of weaker over stronger representations during sleep ( 325 , 343 , 344 , 375 , 679 ). In classic interference paradigms, learning a second list of words after a first list weakens memory for the first list learned due to retroactive interference. Contrary to predictions from the synaptic homeostasis theory, the benefit from sleep for the first (weaker) list of words learned was significantly stronger than for the second list learned ( 325 , 343 ). There is indeed a lack of any behavioral data indicating sleep-associated forgetting of irrelevant memories ( 293 ).

Signs of reactivations of neuronal memory representations observed during SWS, in the synaptic homeostasis view, reflect ongoing activity in hypermetabolic traces induced by intense learning in these networks, without any relation to memory consolidation ( 869 ). This contrasts with findings indicating that an external triggering of memory reactivations during sleep enhances memory performance the next day ( 295 , 959 , 1018 ) and that spontaneous memory reactivations during sleep correlate with later memory performance ( 335 , 448 , 449 ). Consequently, findings of a sleep-dependent reorganization of neuronal memory representations also represent a challenge to the synaptic homeostasis theory inasmuch as this implies that some parts of a representation undergo up-scaling during sleep while others are down-scaled (see sect. III). As any synaptic downscaling per se cannot explain the memory consolidating effects of sleep, reactivation-based consolidation has been proposed as a core mechanism driving active system consolidation during SWS, including basically local processes of up-scaling and synaptic strengthening. However, these processes might be complemented and even enhanced by an unspecific process of synaptic downscaling that acts on a global scale to maintain overall synaptic homeostasis ( 293 ).

3. SWA enhances subsequent encoding

Importantly in the synaptic homeostasis theory, sleep-associated synaptic down-scaling primarily serves to renormalize synaptic weights in networks that, in the course of the encoding of information during prior wakefulness, were potentiated to close to saturation. Therefore, rather than support the consolidation of memory, synaptic down-scaling during SWS is expected to ease the encoding of new information during subsequent wakefulness. Indeed, there is convergent evidence for such action in humans and rats, i.e., an impairment of learning when they were deprived of sleep ( 195 , 328 , 500 , 634 , 759 , 1291 , 1345 ). Fittingly, in rats, sleep deprivation also hampered the induction of LTP in the hippocampal regions ( 170 , 778 , 1161 , 1179 ), the expression of plasticity-related genes ( 496 ) as well as excitability of the hippocampal neurons ( 801 , 802 ). Selective deprivation of REM sleep appears to be sufficient for producing an impairment in LTP induction ( 269 , 270 , 596 , 636 , 743 , 970 , 1010 ). In humans, the capacity to encode episodic memories deteriorates across the day and is restored after a period of sleep ( 759 ). Prior sleep deprivation impairs verbal learning ability and reduces temporal lobe activation during learning ( 328 , 329 ). Interestingly, the ability to encode pictures after a nap was already substantially diminished when SWA during napping was selectively attenuated by contingent mild acoustic stimulation leaving the gross sleep architecture intact ( 1232 , 1233 ). The impairing effect on picture encoding was accompanied by reduced hippocampus activation during learning. Surprisingly, no change in activation in relevant neocortical areas was found, and suppressing SWA also did not impair the learning of a motor skill task. Complementary effects, i.e., enhanced encoding on hippocampus-dependent declarative tasks in the absence of changes in motor skill learning, were revealed after enhancing sleep SWA by transcranial direct current stimulation (oscillating at the 0.75 Hz slow oscillation frequency) ( 39 ). However, no signs of improved encoding were revealed after a drug-induced enhancement of SWA by sodium oxybate ( 1297 ). After 36 h of total sleep deprivation, encoding of emotional pictures was strongly impaired, with this effect sparing negative emotional pictures ( 1291 ). Interestingly, amygdala responses during the viewing of aversive pictures increased after 35 h of sleep deprivation ( 1345 ). Collectively, these initial data corroborate the idea that SWA during sleep refreshes capacities for the encoding of information possibly by a synaptic down-scaling-like mechanism. It remains unclear why the effect apparently predominates in hippocampal networks that encode information in declarative tasks, although the hippocampus itself does not generate slow oscillations ( 597 ).

C. Spindles

1. spindle generation, fast and slow spindles.

Spindle activity refers to regular EEG oscillatory activity which occurs in a frequency range between ∼10 and 15 Hz and expresses in human non-REM sleep stage 2 as discrete, waxing and waning spindles lasting 0.5–3 s ( 440 ). Spindles occurring during stage 2 sleep low-voltage EEG can be temporally locked to a vertex sharp wave or a K-complex. Spindles are also present during SWS and superimposed on delta activity, and then form less clearly discrete spindles ( 38 , 53 , 303 , 426 , 440 , 442 , 785 , 1083 , 1221 , 1355 ). Although in the beginning of SWS spindle activity can reach levels similar to those in stage 2 sleep, on average spindle activity in SWS is lower than during stage 2 sleep. There is a reciprocal relationship between spindles and SWA such that while SWA progressively decreases across nocturnal sleep in humans, sleep spindles and power in the 12–15 Hz (“sigma”) band tend to increase ( 11 , 442 ). Sleep deprivation typically reduces spindle activity during subsequent recovery sleep, together with an increase in SWA ( 114 , 300 – 302 ), and similar reciprocal changes, i.e., decreases in SWA conjoint with increases in spindle activity, are observed after administration of GABA A receptor agonistic drugs (see sect. V A2 ).

Spindle activity originates in the thalamus from mutual interactions between GABAergic neurons of the nucleus reticularis ( 670 ) which function as pacemaker, and glutamatergic thalamo-cortical projections which mediate the synchronized and widespread propagation of spindles to cortical regions ( 227 , 440 , 1139 , 1195 ). The isolated reticular thalamic nucleus is indeed able to generate spindles, whereas the thalamus isolated from the reticular thalamus is not ( 1141 , 1142 ). Thalamic generation of spindles is linked to activation of T-type calcium channels ( 45 , 67 , 1162 ) and calcium-dependent small-conductance channels ( 1326 ). Within neocortical networks, spindle activity is probably associated with a massive calcium influx into pyramidal cells ( 1061 ). Repeated spindle-associated spike discharges efficiently triggered LTP in neocortical synapses in in vitro models ( 1012 , 1197 ). In vivo, synchronous spindle activity occurred more reliably in neocortical synaptic networks that had been previously potentiated through tetanic stimulation ( 1315 ). Correspondingly in humans, the local expression of spindle activity during non-REM sleep increased or decreased depending on whether LTP-like or LTD-like plasticity had been induced through transcranial magnetic stimulation (TMS) before sleep ( 85 ). These observations suggest that spindles occur preferentially in potentiated synaptic networks and may contribute to maintaining this potentiation.

Studies in humans have consistently revealed the presence of two kinds of spindles: fast spindles (∼13–15 Hz) show a more widespread distribution concentrating over the central and parietal cortex, whereas slow spindles (∼10–12 Hz) show a more focused topography over the frontal cortex and are more pronounced during SWS than stage 2 sleep ( 34 , 440 , 826 , 1184 ). The two types of spindles differ in many aspects, including their circadian and homeostatic regulation, pharmacological reactivity, as well as their age-related changes ( 440 ). With the use of EEG and MEG, neocortical sources of the classic fast spindles have been located in the precuneus, and for slow spindles in the prefrontal cortex (Brodman areas 9 and 10) ( 34 , 763 ). Combined EEG/fMRI recordings revealed that both spindles are associated with increased activity in the thalamus, anterior cingulate, and insula cortices ( 1036 ). However, slow spindles were associated with increased activation in the superior frontal gyrus, whereas fast spindles recruited medial frontal, midcingulate, sensorimotor, and supplementary motor cortical areas. Importantly, fast spindles were also associated with increased activation in the hippocampus, suggesting a particular relationship between classic fast spindles and hippocampus-dependent memory processes during sleep. It has been suspected that slow spindle activity reflects predominant coupling among cortical networks, whereas fast spindles may be more closely related to thalamocortical coupling ( 317 ). Indeed, optogenetic stimulation of reticular thalamic neurons can induce spindles in the neocortex, that are not accompanied by thalamic spindles ( 507 ), suggesting an active role of the neocortex in the expression of spindle oscillations. Despite such evidence, it remains a matter of debate whether fast and slow spindles actually reflect different neural processes or are simply the modulation of a single spindle generator ( 440 , 788 ). The picture is complicated by recent MEG data as well as intracranial recordings from neurological patients that identified multiple local generators for neocortical spindles ( 38 , 52 , 276 , 859 ). Importantly, this research revealed that in the neocortex, single spindles typically express as a local phenomena and are restricted to specific regions and circuits, regardless of whether or not they are synchronized in phase with central thalamic spindle generation.

2. Relationship between spindles and memory

Many studies in humans and animals have indicated a robust association between spindle activity and memory processing during sleep. Effects appear to be particularly consistent for the classic fast spindles. However, not many studies differentiated both types of sleep spindles. Intense learning of declarative memories (word pairs or virtual maze) increases the number of spindles (12–15 Hz) during subsequent sleep, particularly in the early part of the night ( 432 , 812 ). In another similar study, only participants who exhibited enhanced spindle activity (11.5–16 Hz) after word pair learning (in comparison with sleep after a non-learning control session) also showed a significant overnight improvement of verbal memory ( 1037 ). Encoding of a difficult list of abstract words produced an increase in the power and density of spindles (11.27–13.75 Hz) compared with sleep after encoding a list of easier, concrete words ( 1044 ). In rats, robust increases in sleep spindles (12–15 Hz) were observed after learning odor-reward associations ( 354 ) and after avoidance training ( 397 ). Furthermore, indicators of sleep spindle expression consistently correlated with the amount of overnight retention of declarative memories ( 86 , 213 , 214 , 235 , 446 , 562 , 822 , 1016 , 1024 , 1037 , 1044 , 1057 ). Interestingly, spindle activity (11–15 Hz) also correlated with signs of overnight lexical integration of newly learned information, suggesting that spindles contribute to the integration of new memories into existing neocortical knowledge networks ( 1175 ). Moreover, spindles (13–15 Hz) during a postlearning nap predicted sleep-dependent memory improvement for contextual aspects of episodic memories known to most closely depend on the hippocampal function ( 1230 ). Using combined EEG/fMRI recordings, Bergmann et al. ( 84 ) observed conjoint reactivations in relevant neocortical and hippocampal regions that occurred in temporal synchrony with spindle events (12–14 Hz) during non-REM sleep after learning of face-scene associations. The strength in reactivations covaried with spindle amplitude. Together these findings provide first hints that spindles are implicated in the hippocampo-neocortical exchange of memory information mediating active system consolidation during sleep. Interestingly, spindle activity following exposure to a novel spatio-tactile experience in rats predicted immediate early gene activity (Arc expression) in the somatosensory cortex during later REM sleep, suggesting that neocortical parts of representations become tagged for later synaptic strengthening during spindles ( 989 ).

Spindles also appear to be involved in the consolidation of skills, especially of simple motor (e.g., pursuit rotor task, simple sequential finger tapping) and visuomotor skills ( 1114 ). Increases of spindles (13–15 Hz) and stage 2 sleep are particularly observed following training of tasks like figure tracing ( 59 , 394 – 396 , 399 , 837 , 920 , 921 , 1173 , 1174 ), which were also revealed to be vulnerable to stage 2 sleep deprivation ( 1112 , 1117 ). In some studies the increase in spindle activity was topographically restricted to the cortex areas most strongly involved in skill performance, i.e., in the contralateral motor cortex after motor tapping training ( 862 ) and in the parietal cortex after training in a visuospatial skill ( 214 ). Also, correlations between different parameters of spindle activity or stage 2 sleep and overnight improvements in motor skills have been consistently reported ( 562 , 862 , 1216 , 1286 , 1287 ), and these correlations were revealed for fast rather than slow spindles, in the studies distinguishing these two spindle types ( 59 , 962 , 1174 ).

In spite of the quite robust correlational evidence for the involvement of spindles in memory processing, a causal role of spindles in memory consolidation has not yet been demonstrated, as this requires the selective experimental manipulation of spindle activity. Approaches to increase spindle activity pharmacologically by administration of GABA A receptor agonistic drugs do not appear to be suitable in this regard, as they concurrently decrease SWA (see sect. V A2 ). In another approach, neurofeedback training to voluntarily increase EEG power in the 11.6–16 Hz sigma frequency band produced a small increase in sigma power also during subsequent sleep ( 86 ). This increase, however, was not associated with any change in the overnight retention of declarative memories.

D. Sharp Wave-Ripples

Hippocampal sharp waves are fast, depolarizing events generated in CA3 that become superimposed by ripple activity [i.e., high-frequency local field potential oscillations (100–300 Hz) originating in CA1] to form SW-R events ( 159 , 162 , 196 , 241 , 459 , 832 , 1342 ). SW-Rs occur mainly during SWS but also during nonexploratory wakefulness (e.g., drinking, grooming, and quiet wakefulness). Ripples arise from an interaction between inhibitory interneurons and pyramidal cells via synaptic (glutamatergic, GABAergic) connections and gap junctions ( 162 ). Hippocampal stimulation protocols that induce LTP concurrently facilitate the generation of SW-Rs in CA3, and SW-Rs during sleep can be initiated by neurons whose recurrent connectivity had been transiently potentiated during preceding wakefulness ( 70 ). SW-Rs may conversely promote LTP and spike time-dependent plasticity in hippocampal circuits ( 97 , 159 , 241 , 640 , 777 ). Modulation of neuronal connectivity during ripples might be specific to local circuits, because firing during single ripples involves only small subpopulations of pyramidal cells and is highly variable across multiple succeeding ripples ( 241 , 1342 ). Most importantly, SW-Rs during SWS typically accompany the reactivation of neuron ensembles active during the preceding wake experience (see sect. III A ).

In rats, learning of an odor-reward association task produced a strong and long-lasting (up to 2 h) increase in the magnitude of ripples and the number of ripple events during subsequent SWS ( 355 ). Similarly, after a spatial learning task, increases in ripple density during postlearning sleep were significantly correlated with the formation of associative spatial memories ( 955 ). In epileptic humans, the number of rhinal ripples during a nap correlated positively with the consolidation of previously acquired picture memories ( 50 ). Two recent studies in rats demonstrated a causal role of SW-Rs in memory consolidation ( 340 , 458 ). In both studies, emergent ripple events were selectively disrupted by electrical stimulation during the rest period after learning, without disturbing sleep, which distinctly impaired consolidation of the acquired spatial memories. In conclusion, there is now good evidence that SW-Rs are critically involved in the consolidation of hippocampus-dependent memories during sleep.

E. Slow Oscillations, Spindles, and SW-R Interact

The fine-tuned temporal relationship between the neocortical slow oscillations, thalamic spindles, and hippocampal SW-Rs originates from a top-down control of the slow oscillation on the two other events. In addition to the neural synchronization in the neocortex, the synchronizing effects of slow oscillations spread to thalamic and hippocampal as well as other brain regions involved in off-line memory consolidation. At the neocortical level, the slow oscillation in terms of EEG rhythms strongly modulates the amplitude of faster beta and gamma frequencies which is reduced to a minimum during the hyperpolarizing down-phase of the slow oscillation ( 224 , 240 , 828 , 829 ). Generation of spindles in the thalamus and generation of SW-Rs in the hippocampus are distinctly suppressed during the down-phase of the slow oscillation, and this is followed by a rebound in spindle and SW-R activity during the succeeding depolarizing up-state ( 66 , 215 , 597 , 829 – 832 , 925 , 1088 ). As for spindles, only the classic fast spindles display the strong phase synchronization with the emergent depolarizing up-state of the slow oscillation. Slow frontal spindles, in contrast, tend to follow fast spindles by 200–500 ms and thus occur already in the up-to-down state transition of the slow oscillation ( 38 , 826 ). As for the modulation of SW-Rs, this appears to be entirely driven by cortical inputs, as the hippocampus itself does not generate slow oscillations ( 597 ). However, membrane potentials with a slight delay (of ∼50 ms) follow the up- and down-states of neocortical slow oscillations, particularly in dentate gyrus and CA1 ( 502 , 503 , 1333 ). In parallel, neocortical up states might time-lock spontaneous activity in the medial entorhinal cortex, a major gateway between the neocortex and hippocampus ( 501 ). There is also evidence for a slow oscillation modulation of locus coeruleus burst activity with preferential locus coeruleus firing during the down-to-up state transition of the slow oscillation ( 353 , 727 ).

Importantly, prior learning appears to strengthen the top-down control of slow oscillations on spindles and ripples. In humans, intense learning of vocabulary did not only increase the slope of the down-to-up state transition of the slow oscillation during succeeding non-REM sleep, but concurrently enhanced fast spindle activity, with this increase concentrating on the up-states of the slow oscillation, whereas no changes were observed in hyperpolarizing down-states ( 826 , 829 ). Interestingly, these analyses also revealed that learning promoted the occurrence of trains of several succeeding slow oscillations. In these trains, fast spindles were not only driven by the depolarizing slow oscillation up-state but appeared to feed themselves back to enforce the succeeding slow oscillation, as well as the likelihood of associated slow frontal spindles. The enhancement of such slow oscillation-spindle cycles might be a key mechanism whereby fast spindles initiate the consolidation of newly learned materials during sleep ( 826 ). Learning also increases hippocampal SW-Rs ( 355 , 955 ), and it is likely that learning-induced increases in slow oscillations strengthen parallel to the synchronization of enhanced SW-R activity and the depolarizing up-state of the slow oscillation, which remains to be demonstrated.

SW-Rs are also temporally coupled to spindles ( 1075 , 1088 ) which can only be partly explained by the common driving impact of the slow oscillation on both phenomena ( 829 ). Event-correlation histograms derived from intracranial recordings in rat and humans revealed that ripples are associated with a rise in spindle activity that starts shortly before ripple onset and then outlasts the ripple ( 215 , 216 , 829 , 832 ). The rise in spindle activity was even more persistent (up to 2 s) when the rats had performed on a learning task prior to sleep ( 829 ). Moreover, fine-grained temporal analyses revealed that the co-occurrence of spindles and ripples leads to the formation of so-called “spindle-ripple events” where individual ripple events become temporally nested into succeeding troughs of a spindle ( 216 , 1075 , 1317 ). As ripples accompany assemble reactivations in the hippocampus, spindle-ripple events might represent a mechanism serving the sequenced transfer of reactivated memory information towards neocortical sites ( 827 , 1087 ). Importantly, the formation of spindle-ripple events is restricted to classic fast spindles, which (like SW-Rs) are driven by the depolarizing down-to-up state transition of the slow oscillation. In contrast, the slow frontal spindles typically occur 200–500 ms later in the slow oscillation cycle (i.e., at the up-to-down state transition), and thus also tend to follow hippocampal SW-Rs with the same delay ( 216 ). In contrast to fast spindles, the neocortex might be “functionally deafferented” from its hippocampal inputs during frontal slow spindles ( 925 ). Taken together, these data suggest a looplike scenario during sleep after learning. While thalamo-cortical sleep spindles enforce the generation of hippocampal ripples, ripple events in turn feed back to sustain the ongoing generation of spindle activity ( 828 ), and possibly also slow oscillation activity, although this is presently unclear ( 635 , 1087 ). Such looplike coordination goes along with an enhanced formation of spindle-ripple events whereby ripples (together with the reactivated hippocampal memory information they carry) are fed exactly into the excitatory phases of the spindle cycle. By still reaching neocortical networks during the depolarizing up-phase of the slow oscillation, the spindle-ripple event may thus serve as an effective mechanism for transferring hippocampal memory information towards neocortical long-term stores. Indeed, fast spindles do not only phase-lock hippocampal ripples but also neocortical gamma-band activity as an indicator of coherent information processing in local neocortical networks ( 52 ). Spindle-gamma coupling might be linked to facilitate synaptic plastic processes underlying the storage of information in neocortical circuitry ( 1012 ).

In sum, there is now growing evidence for a dialogue between neocortex and hippocampus mediating the system consolidation of hippocampus-dependent memory, which is orchestrated by a fine-tuned interaction between oscillatory field potential activities. In this dialogue, the neocortical slow oscillation provides a top-down temporal frame that synchronizes the reactivation of hippocampal memories with the simultaneous occurrence of fast spindles to enable the formation of spindle-ripple events. Spindle-ripple events occurring during the slow oscillation up-states, conversely, provide a bottom-up mechanism for the transfer of reactivated memory information from the hippocampus to the neocortex where they might effectively support plastic synaptic processes underlying the storage of this information.

F. PGO Waves and Theta-Rhythm of REM Sleep

1. pgo waves.

PGO waves are driven by an intense burst of synchronized activity that propagates from the pontine tegmentum to the lateral geniculate nucleus and visual cortex. They occur in temporal association with rapid eye movements in rats and cats. They are not readily identifiable in the human EEG, though fMRI studies revealed activations in pontine tegmentum, thalamus, primary visual cortex, putamen, and limbic areas associated with rapid eye movements during REM sleep, which might be linked to PGO waves ( 824 , 1312 ). PGO waves tend to occur phase-locked to theta oscillations ( 618 ). Like theta activity, PGO waves have been proposed as a mechanism supporting synaptic plasticity in the regions they reach ( 258 , 264 ). Training on an active avoidance task is followed by a robust increase in PGO wave density for 3–4 h after training, and changes in PGO wave density were also proportional to the improvement in task performance between initial training and post-sleep retest ( 259 , 261 , 1225 ). Moreover, PGO wave density during posttraining REM sleep is correlated with increased activity of plasticity-related immediate early genes and brain-derived neurotrophic factors in the dorsal hippocampus. These increases were abolished after selective elimination of the PGO wave generating cells in the brain stem and enhanced after cholinergic stimulation of these cells ( 261 , 1225 ).

2. Theta activity

Theta activity, i.e., periods of synchronized activity in the 4–8 Hz frequency band, is a hallmark of tonic REM sleep (REM sleep without actual rapid eye movements) ( 161 ) and correlates with rapid eye movements and PGO waves ( 618 – 620 ). Primary generating mechanisms appear to be located within the hippocampus, i.e., in CA1 ( 472 ), although extrahippocampal input, mainly from the septum, contributes. In rodents, theta predominates in the hippocampus and associated areas where it is likewise seen during awake exploratory behaviors. In humans, EEG theta activity during REM sleep is less coherent and persistent in hippocampal regions and is seen as well in neocortical areas, again also during wakefulness ( 174 , 860 , 1222 , 1223 ). Theta activity in waking is considered a condition favoring the encoding of new information and associated synaptic plastic processes in hippocampal networks ( 64 , 371 , 612 , 1251 ). Burst stimulation of CA1 inputs can induce LTP or LTD depending on whether it arrives at the peak or trough, respectively, of ongoing theta oscillations ( 561 , 892 ). Likewise, LTP and LTD, respectively, can result from slight differences in the phase of theta oscillations between pre- and postsynaptic neurons ( 514 ). Theta activity also modulates the amplitude of high-frequency gamma oscillations (∼40 Hz), i.e., a rhythm likewise thought to favor neuronal encoding and spike time-dependent plastic processes ( 173 , 242 , 420 ), with the theta-phase coupling of such faster oscillations differing between phasic and tonic REM periods ( 133 ). Phase-locking of gamma band activity during coherent theta activity in prefrontal-hippocampal circuitry is thought to underlie the successful (explicit) encoding of hippocampus-dependent memory information during wakefulness ( 72 , 74 , 220 , 423 ).

Evidence for an involvement of theta activity during REM sleep in memory consolidation is overall meager. Rats showed increased theta activity during REM sleep after training on an avoidance task ( 397 ). However, after fear conditioning, mice exhibited reduced REM sleep theta ( 529 ). Interindividual variability in fear learning across sleep was related to bidirectional changes in theta coherence between the amygdala, medial frontal cortex, and the hippocampus during REM sleep ( 941 ). In the two studies reporting signs of firing pattern reactivation in hippocampal neuron assemblies during REM sleep after performance on spatial tasks, these reactivations were specifically linked to ongoing theta activity, with reactivations expressing either during a specific phase of the theta cycle or linked to a specific amplitude modulation of ongoing theta activity ( 744 , 935 ) (see sect. III A3 ). In humans, scalp-recorded EEG theta activity was enhanced during REM sleep following learning of paired associates ( 399 ) and was correlated with consolidation of emotional memories (specifically over the right prefrontal cortex relative to the left) ( 861 ). However, the overnight reduction in amygdala activation in response to emotional pictures presented before and after sleep was correlated with gamma rather than theta activity during intervening REM sleep ( 1231 ). In patients with Alzheimer's disease, theta activity during not only REM but also SWS was faster compared with age-matched controls, and fast theta activity correlated with better overnight memory formation ( 572 ).

While these data so far do not speak for any essential function of REM sleep theta activity in memory consolidation, the characteristics of theta activity as well as faster EEG frequencies during REM sleep point out an important feature relevant to putative memory processing during this sleep stage. Compared with wakefulness or SWS, EEG activity during REM sleep shows reduced coherence between limbic-hippocampal and neocortical circuitry in a wide range of frequencies including theta and gamma ( 51 , 174 , 293 ). Similarly, within hippocampal circuitry, gamma band activity shows reduced coherence across the CA3 and CA1 regions during tonic REM sleep compared with activity during wake exploration ( 834 ), suggesting altogether diminished coordinated information flow between hippocampal input and output regions and between the hippocampus and neocortex during tonic REM sleep. At the same time, local information processing might be enhanced, as evidenced by increased theta and gamma synchrony between dentate and CA3 to levels even higher than during wakefulness, as well as by the general high levels of theta and fast EEG frequencies in hippocampus and neocortex during REM sleep. It has been argued that such high levels of local information processing in the absence of coordinate long-range communication, together with the specific neurochemical milieu during REM sleep, represent conditions that favor processes of synaptic consolidation ( 293 ). In fact, a recent study showed that brain activation during intervening REM sleep periods might contribute to an overall downscaling of neuronal firing rates observed across sleep ( 485 ). While discharges in hippocampal CA1 neurons increased during single non-REM episodes, firing rates decreased from pre-REM non-REM periods to post-REM non-REM periods, with the decrease being correlated with theta power during intervening REM sleep. Interestingly, the general decrease in firing rate during REM sleep was accompanied by an increase in firing synchrony and a decrease in the variability of cell firing specifically during ripple events. Specifically, across non-REM-REM-non-REM triplets, the discharge rate of hippocampal neurons decreased between ripple events and increased during ripple events, and this increase in synchrony was likewise correlated with theta power during intervening REM sleep. These data suggest that REM sleep theta is not only involved in an unspecific synaptic downscaling but also in reorganizing and shaping hippocampal memory representations ( 118 ).

V. NEUROCHEMICAL SIGNALING AND MEMORY CONSOLIDATION DURING SLEEP

Sleep and sleep stages are characterized by a specific neurochemical milieu of neurotransmitters and hormones (see FIGURE 1 C ), some of which contribute to memory consolidation by favoring processes of synaptic consolidation (i.e., synaptic LTP or synaptic LTD and depotentiation) or processes of system consolidation. Neurochemical studies of sleep-associated plasticity have taken two distinct approaches: 1 ) they have examined signals known to be essential for synaptic plasticity, such as glutamatergic synaptic transmission and the cascade of intracellular signaling mediating LTP, LTD, as well as changes in synaptic morphology thought to underlie long-term memory; or 2 ) they have examined signals known to be essentially involved in the regulation of sleep.

A. LTP/LTD During Sleep

Synaptic LTP and LTD are considered basic neurophysiological mechanisms underlying the formation of memory. LTP and LTD have been mainly studied in excitatory glutamatergic synapses but occur also in inhibitory GABAergic synapses. In glutamatergic synapses, LTP is induced via activation of postsynaptic NMDA receptors and subsequent calcium (Ca 2+ ) influx, which leads to activation of calcium-sensitive kinases like Ca 2+ /calmodulin kinase II (CaMKII) and protein kinase C (PKC) that in turn activate transcription factors and immediate early genes that can eventually lead to an altered protein synthesis and resculpturing of synapses ( 4 , 983 ). An early and late phase of LTP and LTD is discriminated with only late LTP (>3 h) requiring new protein synthesis. Maintenance of LTP, i.e., the development of late LTP from early LTP, is assumed to require a “tagging” of synapses due to associative heterosynaptic stimulation within a certain time window (∼30 min) following LTP induction ( 419 ). Heterosynaptic tagging can involve noradrenergic and dopaminergic transmission, but also the activation of mineralocorticoid receptors. The expression of BDNF and PKM-ζ are further important signals not only supporting induction of LTP but in particular for mediating the emergence of late LTP ( 746 , 850 , 1020 ).

It is likely that memory consolidation during sleep involves both synaptic and system consolidation processes ( 1305 ) (see sect. I B ). Sleep might enhance memory by directly favoring late LTP and LTD in synaptic networks that were potentiated during the preceding wake phase. Alternatively, this form of synaptic consolidation may occur as part of a system consolidation process in which newly encoded memories are reactivated and redistributed to other networks during sleep where they subsequently undergo synaptic consolidation (see sect. II F ). Considering that reactivation and redistribution of memory representations constitute a central mechanism of memory consolidation during sleep, then the basic question arises whether these reactivations during sleep can newly induce LTP and LTD or merely serve to support the maintenance of LTP and LTD induced during prior waking. These issues are presently far from being clear. Nevertheless, investigations of sleep-associated changes in the molecular signals that mediate the induction and maintenance of LTP and LTD have provided some important clues about possible contributions of sleep to persisting plastic synaptic changes. As discussed in section IV B , several studies show an increase in extracellular glutamate concentrations as well as molecular markers of LTP across wake periods and a decrease after sleep periods, particularly after SWS ( 204 , 205 , 207 , 257 , 457 , 607 , 940 , 1264 ), suggesting that the induction of new LTP is more likely during wakefulness than sleep. While experimental induction of LTP and LTD is feasible during wakefulness and REM sleep, induction of long-lasting changes was less likely (although not impossible) during SWS ( 130 ). Sleep might, nonetheless, be critically involved in processes supporting the long-term maintenance of synaptic changes, particularly in those involving protein synthesis. More importantly, most of these studies assessed expression of the relevant signals on a global scale in large regions of the brain and cortex, and the extent of encoding of information, i.e., learning during wakefulness before sleep, was not systematically varied ( 989 ). Hence, this approach leaves open the basic question of how sleep specifically affects newly formed memory traces and underlying synaptic plasticity in discrete neuronal circuits.

1. Glutamate and intracellular PKA signaling

Blocking of ionotropic glutamate receptors during postlearning sleep was used as a pharmacological tool to study whether sleep-dependent memory consolidation involves the reactivation of glutamatergic synapses. Consistent with this hypothesis, in humans, blocking of NMDA or AMPA receptors by infusion of ketamine or caroverine during post-learning retention sleep completely abolished overnight gains on a visual texture discrimination task ( 435 ), presumably mediated by local synaptic plastic changes in the visual cortex ( 1049 , 1051 ). Surprisingly, ketamine or caroverine administration during retention sleep did not impair consolidation of hippocampus-dependent declarative memories (word pairs) (unpublished observation). However, in these studies consolidation of word pair memories during sleep distinctly benefited from administration of d -cycloserine, a partial agonist of the NMDA receptor that acts at the glycine binding site to enhance Ca 2+ influx. These data suggest that the consolidation of hippocampus-dependent memory involves the reactivation of glutamatergic synapses, although the exact mechanisms of glutamatergic reactivation might differ between memories with primary hippocampal and primary neocortical representations.

In animals, sleep-dependent plastic changes were also revealed to critically depend on glutamatergic and LTP-related mechanisms ( 411 , 1066 ). In the developing cortex of cats, ocular dominance plasticity induced by monocular deprivation of one eye requires sleep ( 77 , 412 , 413 ). These sleep-dependent changes are prevented by blocking NMDA receptors or cAMP-dependent protein kinase (PKA) during sleep after monocular experience ( 48 ) (see sect. VII A3 ). Blockade of NMDA and PKA signaling was associated with reduced activation of the kinases CaMKII and ERK as well as phosporylation of GluR1 at Ser831, i.e., processes that are critical to the insertion of AMPA receptors into the postsynaptic membrane and the strengthening and maintenance of LTP. In mice, treatment with the selective phosphodiesterases (PDE) 4 inhibitor rolipram immediately and 2.5 h after single-trial contextual fear conditioning prevented the impairing effect of sleep deprivation on retention of the freezing response to the conditioned context ( 1246 ). These observations corroborate the concept that impaired memory retention following sleep deprivation results from the disruption of the cAMP-PKA-CREB pathway that supports the formation of late LTP. Impaired signaling in this pathway originates from increases in PDE activity during sleep deprivation, with rolipram counteracting this process ( 477 , 498 – 500 , 546 , 1246 ). Based on this research, Abel and colleagues ( 546 ) suggested that cAMP-PKA dependent consolidation in the hippocampus occurs as a “sort of molecular replay” of the same molecular events recruited during prior encoding of information during wakefulness. This notion fits well to a model proposed earlier by Ribeiro and Nicolelis ( 988 ), suggesting that memory encoding during waking involves calcium-dependent genetic processes which trigger plasticity-related gene regulation during subsequent sleep. Similar ideas have been formulated by Benington and Frank ( 77 ) as well Datta and colleagues ( 261 ). The molecular replay may mainly occur during REM sleep enabled through coactivation of muscarinic receptors in the presence of high global cholinergic and low serotonergic activity, although it may similarly occur in local circuitry during non-REM sleep ( 475 , 546 ).

Also, cAMP-PKA-dependent memory formation for contextual fear conditioning is selectively sensitive to the inhibition of PKA and protein synthesis when this inhibition occurs within specific time windows after training, i.e., immediately or 4 h after training ( 128 ). Correspondingly, specific time windows of increased REM sleep and sensitivity to REM sleep deprivation have been observed after learning of hippocampus-dependent tasks (Morris water maze with hidden platform, radial maze) ( 1115 ), and blockade of NMDA receptors shortly after these REM sleep windows disrupted sleep-dependent consolidation of these tasks ( 1094 , 1099 ) (see sect. II B ).

GABAergic inhibition via interneurons is embedded in almost all central nervous networks and contributes essentially to both the induction of sleep via brain stem and hypothalamic mechanisms, and the local control of LTP and LTD at glutamatergic synapses. GABAergic agonists, like benzodiazepines that are also used clinically to improve sleep, were consistently revealed to impair LTP as well as LTP-related plasticity in glutamatergic synapses via activation of the ionotropic GABA A receptor (e.g., Refs. 225 , 277 , 549 , 575 , 633 , 1008 , 1270 ). In addition, GABAergic synapses themselves undergo plastic changes that predominate during early development, but some of which can be observed in the mature brain as well ( 683 ). Yet, it is presently unclear how these processes contribute to hippocampal memory formation.

In humans and rats, systemic administration of agonistic modulators of the GABA A receptor, including barbiturates, benzodiazepines, and zolipedem, reduces sleep latency and consistently increases both sleep in non-REM stage 2 and sleep spindles, but typically simultaneously reduces SWS and SWA ( 684 ). This pattern diverges from the concordant increase in both spindles and SWA typically associated with memory processing during sleep following intense learning. Interestingly, nonspecifically enhancing availability of GABA in the synaptic cleft by administration of the GABA reuptake inhibitor tiagabin induced profound increases in SWS and SWA paralleled by marked declines in light sleep and REM sleep in both rats and humans, leaving spindle activity unaffected ( 686 , 793 , 1299 , 1301 ). Selective GABA A or GABA B receptor agonists like gaboxadol, muscimol, or γ-hydroxybutyrate produced similarly robust increases in SWS and SWA, while effects on spindle activity differed between drugs ( 304 , 684 , 685 , 702 , 1228 , 1297 , 1298 , 1300 ). At the network level, GABA A agonistic drugs like diazepam in slice preparations suppressed the generation of SW-Rs that are known to accompany the neuronal reactivation of newly encoded memory representations during SWS. The effect was dose-dependent and not observed following the GABA A -receptor modulator zolpidem ( 655 ). Conversely, the GABA A agonist muscimol and also zolpidem impaired sleep-dependent plastic changes in the visual cortex in a developmental model of synaptic plasticity in cats [i.e., ocular dominance plasticity (ODP); see sect. VII A3 for details] ( 48 , 411 , 1059 ), suggesting that GABA plays a critical role for plastic processes occurring during sleep.

GABAergic effects on sleep-dependent consolidation of memory in humans have not been thoroughly investigated thus far, which is surprising given that GABA-agonistic substances like benzodiazepines are widely used for the treatment of sleep disorders. Interestingly, there is rather consistent evidence that benzodiazepines administered after the encoding of information can enhance memory for this information in waking subjects, possibly by preventing the encoding of new interfering information after learning ( 247 , 888 , 1313 , 1332 ). However, the few studies concentrating on memory consolidation during sleep point towards opposite effects. Early experiments in healthy volunteers suggested an impairing influence of the benzodiazepine trialzolam and the non-benzodiazepine zopiclone administered after learning before sleep on the retention of words ( 1040 , 1082 ), whereas one study did not find an impairing effect of the hypnotics zolpidem and triazolam on sleep-dependent memory consolidation of words and nonwords ( 813 ). Administration of the GABA B agonists sodium oxybate or baclofen before a nap increased SWS, without affecting sleep-dependent declarative memory consolidation of word pairs and face-location associations ( 1255 ). In rats, administration of the GABA A agonists eszopiclone and zolipem after learning before sleep impaired contextual memory tested 24 h later ( 577 ). Two more recent studies in humans showed similarly impairing effects of triazolam and zolpidem on sleep-dependent consolidation of finger sequence tapping skills ( 835 , 836 ). This is remarkable, as the impairment in motor skill consolidation was paralleled by robust increases in the amount of non-REM sleep stage 2 and spindle activity as well as REM sleep in these studies. Sleep-dependent consolidation of motor skill memories was also impaired following administration of the GABA reuptake inhibitor tiagabin, and this impairment was paralleled by a substantial increase in SWS and SWA ( 370 ). No effects on declarative memory consolidation (word pairs) were found in this study. The negative findings after benzodiazepine or tiagabin administration during postlearning sleep suggest that EEG phenomena-like spindles and SWA do not represent mechanisms that per se are sufficient to support sleep-dependent memory consolidation. Yet it is not clear whether pharmacologically induced spindles and SWA are functionally equivalent to their endogenous counterparts.

B. Neurotransmitters Involved in Sleep Regulation

Most neurotransmitters and neuromodulators involved in sleep regulation are also involved in processes of memory and plasticity, speaking in favor of a high degree of overlap and functional interaction between these processes. The regulation of sleep and wakefulness is based on a balanced interaction between wake-promoting networks centered in the upper brain stem and lateral hypothalamus and sleep-promoting networks in the anterior hypothalamus, which mutually inhibit each other to enable transitions into the respective brain states depending on a switchlike mechanism ( 146 , 262 , 1032 ). The wake-promoting network includes mainly cholinergic neurons of peduncolopontine and laterodorsal tegemental nuclei (PPT, LDT), the noradrenergic locus coeruleus (LC), and the serotonergic dorsal and median raphe nucleus, as well as histaminergic neurons in the hypothalamic tuberomammillary nucleus (TMN), which have widespread projections to the lateral hypothalamus, basal forebrain, and cerebral cortex. Activity in this network is reinforced by orexin A and B (hypocretin 1 and 2) producing neurons in the posterior lateral hypothalamus (adjacent to the TMN). The sleep-promoting network includes mainly the ventrolateral (VLPO) and median (MnPO) preoptic nuclei of the hypothalamus, which contain neurons releasing GABA and galanin to inhibit the wake-promoting network at all levels.

The sleep-promoting network is stimulated by neurons sensing astrocytic adenosine that accumulates extracellularly as a rundown product of cellular metabolism at least in some parts of the brain ( 78 , 506 , 942 , 1160 ). Signaling of adenosine via A1 receptors, which are diffusely distributed in the brain, may directly inhibit neurons of the wake-promoting arousal system (e.g., Refs. 741 , 867 , 884 ). A2a receptors located close to the VLPO mediate a direct sleep-inducing effect that is counteracted by A2a-receptor blockers like caffeine ( 578 , 1035 ).

Transitions between non-REM and REM sleep are mediated by a balanced interaction in brainstem pontine networks between GABAergic neurons in the sublaterodorsal region (precoeruleus) that fire during REM sleep (REM-on) and GABAergic neurons in the periaqueductal gray matter and the adjacent lateral pontine tegmentum (vlPAG, LPT) that fire during Non-REM sleep to inhibit the REM-on neurons. This core REM switch is in turn modulated by noradrenergic LC neurons and serotonergic neurons of the dorsal raphe nucleus that act on both sides of the switch to inhibit REM sleep and cholinergic neurons of the PPT and LDT promoting REM sleep. Via the same core REM switch, hypothalamic orexin neurons inhibit, whereas VLPO neurons promote REM sleep ( 1032 ).

As a result of this regulation of non-REM and REM sleep, widespread changes in activity occur also for the wake promoting neuromodulators, mainly activity of acetylcholine, norepinephrine, and serotonin. Cholinergic activity reaches a minimum during SWS, whereas during REM sleep it reaches levels well comparable or even higher compared with cholinergic activity during wakefulness. Noradrenergic and serotonergic activity reaches a minimum during REM sleep, and is at an intermediate level during SWS. At the neurohormonal level, early periods of SWS are associated with a strong activation of somatotropic activity, whereas late periods of REM sleep are characterized by an increased release of corticosteroids from the adrenals.

1. Effects of wake- versus sleep-promoting signals on LTP and LTD

Wake-promoting signals have been mainly found to support LTP induction and maintenance. The wake-promoting neuropeptide orexin robustly facilitated LTP induction in the ventral tegmental area (VTA) and in the hippocampus, directly and via LC noradrenergic release, respectively ( 115 , 1296 ). Supporting effects on the induction and maintenance of LTP were also consistently revealed for histamine ( 677 , 750 ), norepinephrine, as well as acetylcholine ( 140 , 585 , 598 , 1187 , 1309 ). Indeed, norepinephrine and acetylcholine are also important for the induction of late LTP ( 12 , 1295 ) and were often found to synergistically act to enhance LTP ( 140 , 1309 ) as well as LTD ( 642 ). Serotonergic activity displaying sleep-dependent fluctuations similar to those of noradrenergic activity appears to influence LTP and LTD in a less homogeneous way, with its effects strongly depending on the brain region, receptor subtype activated, and the type of stimulation used to induce LTP or LTD ( 98 , 576 , 601 , 628 , 654 , 675 ).

The major sleep-promoting factor adenosine was also revealed to contribute to the regulation of synaptic plasticity. Via activation of A1 receptors extracellular adenosine attenuates hippocampal LTP, whereas A2A receptor activation mediates an enhancing effect ( 233 , 816 , 972 ). A2A receptor activation is also critically involved in the enhancing effects of BDNF on hippocampal LTP ( 402 ). On the other hand, LTP as well as basal excitatory synaptic transmission in orexin neurons of the lateral hypothalamus can be diminished by activation of adenosine A1 receptors ( 1339 ). It has been proposed that under basal firing conditions, glia-derived extracellular adenosine mainly activates A1 receptors leading to a diffuse inhibition of synaptic transmission, whereas with high-frequency synaptic stimulation A2A receptors are activated by adenosine which is formed locally by ecto-nucleotidases from synaptically released ATP and overrides A1 receptor-mediated effects ( 245 ). LTP induced at hippocampal Schaffer collateral synapses can be reversed by low-frequency (1–2 Hz) stimulation mimicking basal conditions as they could occur during SWS, and this depotentiation appears to be essentially mediated through diffuse activation of A1 receptors ( 576 ). The depotentiation triggered in this way by direct neocortical inputs might represent a mechanism helping to reset synaptic transmission in the hippocampus, thus preparing these networks for further encoding of information ( 599 ).

2. Neuromodulation associated with sleep and SWS, and memory processing

The neuromodulatory changes characterizing SWS are indeed the same that accompany the induction of sleep. Studies specifically targeting the role of neurochemical conditions during SWS for memory processing have so far concentrated on astrocytic adenosine signaling, the minimum levels of cholinergic activity and intermediate-level, pulsatile activity of noradrenergic systems during SWS, as well as on SWS-related changes in hormonal systems.

Regarding astrocytic adenosine, Halassa and co-workers ( 393 , 506 ) showed that genetic inhibition of gliatransmission in mice attenuates the accumulation of sleep pressure as indicated by decreased sleep time, decreased duration of non-REM sleep bouts, and decreased SWA activity in response to sleep deprivation. Despite decreased SWA, these mice, unlike wild-type mice, did not exhibit signs of impaired recognition memory on an object recognition task when they were sleep deprived after training. Infusion of the adenosine A1 receptor antagonist 8-cyclopentyl-1,3- dimethylxanthine (CPT) suppressed sleep only in the wild-type mice and mimicked the transgenic phenotype with regard to both sleep and memory effects. Parallel effects were demonstrated for the sleep deprivation-induced impairment of hippocampal late-phase LTP. Recently, Schmitt and colleagues ( 1047 ) showed that the level of the adenosine A1 receptor activation increases during normal and prolonged wakefulness in mice, and that this increase affects synaptic transmission in the hippocampus as well as network activity in the cortex. These effects were prevented by genetic inhibition of gliatransmission. Taken together, these findings indicate that astrocytic adenosine, beyond its promoting effect on sleep, via activation of A1 receptors is also involved in mediating the impairing effects of sleep deprivation on memory consolidation.

Suppression of cholinergic activity during SWS alleviates tonic inhibition of hippocampal CA3 and CA1 feedback neurons, thereby it enables spontaneous reactivations of the hippocampal networks and of the memory information encoded in these networks, as well as the transfer of the reactivated information to neocortical networks ( FIGURE 6 A ; Refs. 520 , 522 , 782 ). Consistent with this concept of low acetylcholine enabling systems consolidation of declarative memories, increasing cholinergic tone during a period of SWS-rich sleep by administration of the cholinesterase inhibitor physostigmine completely blocked the sleep-associated consolidation of word pair memories ( FIGURE 6 B ; Refs. 429 , 963 ). Some subjects showed slight decreases in SWA after physostigmine, but these were unrelated to the impairment in declarative memory consolidation, suggesting that cholinesterase inhibition primarily affected memory processing in hippocampal rather than thalamo-cortical circuitry. Conversely, blocking cholinergic receptors in waking subjects by simultaneous administration of nicotinic and muscarinic receptor antagonists improved the consolidation of declarative memories during wakefulness, but concomitantly decreased the ability to encode new information ( 963 ). These results strongly support the notion that high acetylcholine levels are critical for successful encoding, whereas low acetylcholine levels facilitate consolidation of memories, suggesting that acetylcholine might function as a switch between brain modes of encoding and consolidation as established during waking and SWS, respectively ( 522 ). Manipulating solely nicotinic or muscarinic receptor activity remained ineffective in these studies ( 864 , 963 ). Since the cholinergic neurons mediating the recurrent inhibition in hippocampus receive strong inhibitory GABAergic inputs, strengthening this GABAergic activity should enhance hippocampal memory reactivation. This mechanism could explain why postlearning administration of benzodiazepines improves memory consolidation in waking subjects but not during SWS when cholinergic activity is already minimal ( 454 , 813 ).

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Influence of cholinergic activity on memory consolidation during wakefulness and sleep. A : concept: during active waking, acetylcholine (ACh) levels are high. Information encoded by neocortical structures flows through the entorhinal cortex and dentate gyrus (DG) into hippocampal region CA3 (connections less sensitive to modulation by ACh; thick arrows). Connections suppressed by ACh modulation (dashed arrows) to region CA1, entorhinal cortex, and association cortex are strong enough to mediate immediate retrieval, but do not overwhelm the feed-forward connectivity, ensuring efficient encoding. In contrast, during SWS, ACh levels are low, and memories are reactivated in region CA3 during sharp wave-ripples (SW-Rs). These waves of activity flow back through region CA1 to entorhinal cortex and neocortex, enabling an efficient redistribution of memory representation (system consolidation) underlying long term memory storage. [Adapted from Hasselmo ( 520 ), with permission from Elsevier.] B : in accordance with the model, increasing cholinergic tone in humans by administration of the acetylcholineesterase inhibitor physostigmine during postlearning SWS impairs consolidation of declarative memory (word pairs) during sleep, compared with placebo. In contrast, combined blockade of muscarinic and nicotinic cholinergic receptors during a postlearning wake interval (by administration of scopolamine and mecamylamine) enhanced consolidation of declarative memory (word pairs) during this wake interval. Simultaneously, the combined receptor blockade impaired new encoding (of numbers). Values are means ± SE: * P ≤ 0.05; ** P ≤ 0.01. [Data from Gais and Born ( 429 ) and Rasch et al. ( 963 ).]

Noradrenergic activity during SWS, arising from the LC as the brain's main source of norepinephrine, seems to be particularly related to the depolarizing up-state of the slow oscillations as LC firing has been found to be entrained to these up-states ( 353 ). Moreover, a specific sleep-related window of increased LC burst activity ∼120 min after learning of odor-reward associations has been identified in rats ( 356 ). In humans, suppressing noradrenergic LC output by administration of the alpha2-autoreceptor agonist clonidine during a SWS-rich retention period reduced consolidation of odor memories, whereas the retention of these memories was enhanced when availability of epinephrine during sleep was increased ( 434 ). In a subsequent study, clonidine infused during a SWS-rich period of retention sleep appeared to impair specifically emotional memory processing ( 1031 ). Whereas temporal order of emotional stories was better remembered compared with neutral stories in the placebo condition, clonidine blocked this superiority of emotional memory consolidation during SWS-rich sleep. Together, these findings suggest that bursts of noradrenergic activity during SWS are particularly important for the consolidation of memories that involve both a strong amygdala-mediated emotional component as well as a hippocampus-mediated declarative component, as keeping temporal order in the events of an episode represents a key function of the hippocampal formation (e.g., Refs. 285 , 722 , 761 ). Phasic burst of noradrenergic LC activity can enforce plasticity-related immediate early gene activity ( 207 , 208 ), and thereby contribute to LTP maintenance in circuitries that were potentiated during prior encoding. In the case of odor and emotional memories, these representations may partly reside in the basolateral amygdala as a main target of noradrenergic influences ( 804 ).

3. Hormonal modulation associated with sleep and SWS, and memory processing

SWS is associated with the inhibition of glucocorticoid release from the hypothalamus-pituitary-adrenal (HPA) system and a profound surge in the release of growth hormone releasing hormone (GHRH) and GH from the somatotropic axis ( 122 , 123 , 1271 ). While activation of the latter system might support hippocampal memory processing through brain-borne GHRH ( 430 , 509 ), inhibition of the former system affects limbic regions mainly via cortisol feedback that is downregulated during SWS. Low cortisol concentrations during SWS-rich sleep benefit declarative memory consolidation by preventing activation of glucocorticoid receptors that mediate an inhibitory influence on hippocampal LTP and output from CA1 ( 648 , 649 , 666 ). Accordingly, increasing glucocorticoid levels during sleep by postlearning administration of cortisol or dexamethasone impaired consolidation of memory for word pairs and for the temporal order in stories ( 932 , 933 , 1323 ). In addition, spontaneously increased nocturnal cortisol levels around midnight were found to be correlated with impaired retrieval of declarative memories in healthy subjects and patients with primary insomnia ( 58 ). However, a certain basal release of cortisol is necessary during SWS to sufficiently occupy mineralocorticoid receptors, which bind cortisol with a distinctly higher affinity and support the transition of early into late LTP in hippocampus. Accordingly, lowering cortisol levels in humans during early nocturnal SWS below baseline levels by pharmacological blockade of cortisol synthesis impaired sleep-dependent declarative memory consolidation ( 1272 ).

Melatonin is a circadian hormone that is released by the pineal gland during the night and, in diurnal animals and humans, substantially contributes to the entrainment of sleep to the night-time phase ( 212 ). Melatonin inhibits hippocampal LTP ( 223 , 877 , 1171 , 1307 ) and has also been found to impair acquisition of hippocampus-dependent spatial memories in rats ( 372 ) and of an active avoidance task in diurnal zebrafish ( 971 ), although divergent findings were obtained in humans ( 997 ). Whereas these observations point to an impairing influence of melatonin on the encoding of memory, there is preliminary evidence from fMRI experiments in humans that melatonin might enhance processes of hippocampal memory consolidation during sleep: parahippocampal activity patterns during retrieval of word-pair memories were found following a nap showed some similarity with those obtained when napping was replaced with administration of melatonin ( 471 ).

4. Neuro- and hormonal modulation associated with REM sleep, and memory

Cholinergic activity during REM sleep is high and comparable with that during waking, which might be particularly relevant for the consolidation of procedural memories during this sleep stage. Blocking muscarinic cholinergic receptors during “REM sleep windows” (i.e., time periods after learning in which REM sleep deprivation effectively impaired consolidation of respective materials, see sect. II B ) by administration of scopolamine consistently impaired memory in a habit learning version of the radial arm maze ( 720 , 721 ). Infusion of scopolamine into the dorsal striatum, a structure specifically involved in habit and skill learning, was particularly effective. In humans, the combined blockade of nicotinic and muscarinic receptors during REM-rich late sleep impaired off-line consolidation of a motor skill (finger sequence tapping) without affecting declarative memories ( 961 ). Conversely, increasing the availability of acetylcholine during post-training sleep by administration of an acetylcholinesterase inhibitor enhanced sleep-related benefits in a skill learning task ( 570 ). Anticholinergic treatment during wakefulness after learning remained ineffective ( 961 ). Taken together, these results identify high cholinergic tone as an important factor contributing to the off-line consolidation of procedural skills during REM sleep, in combination with other unknown processes. The effects are consistent with a role of acetylcholine in synaptic consolidation, e.g., by promoting activity of plasticity-related immediate early genes ( 475 , 615 , 1182 ) and maintenance of LTP in cortico-striatal networks ( 742 ).

There is some evidence that the increase in cholinergic activity during REM sleep is accompanied by an increased dopaminergic activity originating from ventral tegmental, rather than striatal, areas which innervate a network of further regions such as the nucleus accumbens and medial prefrontal cortex ( 724 , 757 ). Although it is not unlikely that such REM-related increases in dopamine activity modulate the effects of memory reprocessing during sleep (e.g., Ref. 73 ), the involvement of dopaminergic activity in sleep-associated memory consolidation has not been thoroughly examined thus far. Legault et al. ( 721 ) reported that the dopamine receptor blocker flupenthixol infused into the dorsal striatum 0–4 h after learning during “REM sleep windows” exerted the same impairing effect on habit consolidation as observed after scopolamine. In humans, so far only patients (with early Parkinson disease and schizophrenia) have been examined in this context with dopaminergic drugs that were partly rather unspecific ( 465 , 776 ). No consistent effects of the treatments on overnight retention of procedural or declarative memories were found in these patients.

Whether the very low levels of noradrenergic and serotonergic activity during REM sleep, perhaps in a permissive way, also contribute to memory consolidation, is currently not clear. Selective serotonin or norepinephrine reuptake inhibitors (SSRI or SNRI) that enhance availability of these monoamines in the synaptic cleft are commonly used for antidepressant therapy and, thereby, produce a substantial reduction in REM sleep. However, clinical observations revealed no clear memory impairments in patients treated with these drugs ( 22 ). Also, a more systematic clinical study of the effects of the SSRI citalopram and the SRNI reboxetine in moderately depressed patients failed to reveal any association of REM sleep diminution after the reuptake inhibitors, with decreases in overnight retention of declarative memories (word lists) or procedural skills (mirror tracing) ( 466 ). In healthy humans, administration of the SSRI fluvoxamine and the SNRI reboxetine during a postlearning period of REM-rich sleep likewise did not lead to any impairment in the sleep-dependent consolidation of mirror tracing or finger sequence tapping skills, although after the SNRI REM sleep was almost completely suppressed ( 962 ). On the contrary, sleep-dependent gains in finger sequence tapping accuracy were even significantly greater following the reuptake inhibitors than after placebo. The greater gains in accuracy after SNRI and SSRI administration were additionally correlated with increases in non-REM sleep spindle density. These findings challenge a role of phenotypic REM sleep for procedural memory consolidation. If any substantial contribution of REM sleep to procedural memory consolidation exists, increasing noradrenergic or serotonergic tone can apparently compensate for it. Both monoamines support synaptic remodeling via increasing activity of plasticity related immediate early genes ( 205 , 208 , 475 ).

It has been proposed that the inhibition of noradrenergic activity during REM sleep enhances procedural types of memory representations in cortico-striatal circuitry by enabling spontaneous reactivations in cortical networks that during wakefulness are under tonic inhibitory control by noradrenergic neurons ( 520 ). Similarly, it has been suggested that REM sleep allows for a replay of amygdala-dependent emotional memories that, in the absence of noradrenergic activation, decrease the emotional tone in these memories, whereas their information content is maintained ( 1294 ) (see sect. II C ). Additionally, the decreasing effect on emotional tone could be further enhanced by glucocorticoids, the levels of which are distinctly increased during late-night REM sleep and which distinctly diminish emotional memory consolidation ( 1272 ). However, there is presently little empirical support for these theories. Thus, overall, the role of noradrenergic and serotonergic inhibition during REM sleep for memory processing remains enigmatic.

VI. GENETIC APPROACHES TO SLEEP-DEPENDENT MEMORY FORMATION

Sleep is genetically controlled. Although environmental factors clearly contribute, a large part of the interindividual differences in sleep architecture is likely due to genetic factors. The search for genes involved in sleep regulation has received a great boost in recent years with the discovery of quiescent states in simple organisms like the fruit fly and worms that fulfill the criteria for sleep: a reversible and repeatedly occurring period of reduced responsiveness and relative inactivity which is homeostatically regulated (i.e., deprivation of sleep leads to subsequent longer sleep periods) ( 209 ). In these organisms, molecular methodology can be applied much more efficiently and at low costs mainly due to the reduced number of genes and neurons and the high reproduction rate. However, there are also important caveats, as certain sleep stages (e.g., REM sleep) and oscillatory brain activity characterizing sleep in mammals and humans cannot be discriminated in these simple organisms. Also, although typically homologs of genes exist between species, they can be much more differentiated in complex animals. For example, one Shaker gene in the fly has 16 homologs in rodents ( 202 ). These limitations underscore the importance of genetic studies in rats and mice and optimally in humans.

Gene expression studies, mainly in flies and mice, show an upregulation of genes specifically during sleep, including genes involved in protein synthesis and synaptic depotentiation. In addition, several genes related to neural plasticity and synaptic potentiation are upregulated during sleep depending on prior learning and exposure to novel experiences. Also, genes have been identified that exhibit joint regulatory actions on sleep and memory. Intriguingly, short-sleeping genotypes often also show impairments in learning and memory.

1. Differential gene expression during sleep and wakefulness

Early studies suggesting that gene expression differs between sleep and wakefulness mainly examined overall changes in mRNA and protein synthesis. First experiments showed that an injected radioactive substance was incorporated into newly synthesized RNA at a faster rate during sleep than during wakefulness ( 462 , 1257 ). Similarly, labeled proteins in rats increased after 90 min of sleep compared with 90 min of sleep deprivation ( 103 ). In rats and monkeys, the rate of protein synthesis correlated positively with the amount of non-REM sleep ( 848 , 956 ). Analysis of several hundreds of proteins by mass spectrometry ( 309 ) revealed that protein levels in the mouse cerebral cortex were generally decreased after sleep deprivation compared with sleep. These studies indicate that sleep favors protein synthesis, thus pointing towards a differential expression of genes during sleep and wakefulness. Indeed, blocking protein synthesis during sleep impairs sleep-dependent consolidation of ocular dominance plasticity (ODP) in the visual cortex of cats ( 1060 ) (see sect. VII A3 ), indicating that the increased protein synthesis during sleep has functional consequences for plastic processes underlying memory formation.

More recent studies have directly examined gene expression with molecular techniques like microarrays that indicate changes in transcription of the genome, i.e., the up- or downregulation for a great number of gene transcripts (for reviews, see Refs. 201 , 752 ). Cirelli and colleagues ( 204 ) examined gene expression in the cerebellar cortex of rats which were killed after 8-h periods of sleep, wakefulness, or sleep deprivation. Five percent of the examined transcripts were differentially regulated between sleep versus wakefulness or sleep deprivation. Sleep was associated with a great number of upregulated gene transcripts, and this number was indeed comparable with that during wakefulness, despite the behaviorally “inactive” state of sleep.

Importantly, one category of gene transcripts upregulated during sleep is involved in synaptic plasticity. This includes mRNA expression levels of calmodulin-dependent protein kinase IV (CAMK4), a gene that is implicated in synaptic depression and long-term memory consolidation ( 617 ), and the expression of several other genes associated with depotentiation and depression of synaptic strength. In contrast, during wakefulness, expression of genes that are involved in LTP is upregulated, including genes coding for Arc, c-Fos, NGFI-A, and BDNF ( 206 , 939 , 940 ). Based essentially on these gene expression patterns, the synaptic homeostasis hypothesis has been proposed assuming that wakefulness is associated with prevailing synaptic potentiation in cortical networks, whereas processes of synaptic depotentiation and depression predominate during sleep to desaturate the network ( 1204 ) (see sect. IV B ). Subsequent microarray studies in the mouse and fruit fly largely confirmed that expression of several genes is differentially regulated during sleep and waking and that these genes are related to synaptic plasticity, response to cellular stress, energy and lipid metabolism, and membrane trafficking ( 201 , 610 , 752 , 753 , 771 , 1357 ). In particular, the expression of genes involved in macromolecular synthesis increased during sleep, supporting a role for sleep in the synthesis of proteins and lipids (e.g., fats, sterols, vitamins) ( 753 ). Conversely, sleep deprivation strongly attenuates the expression of genes related to protein synthesis and thus downregulated translation in the mouse hippocampus, mainly mediated by key regulators of protein synthesis like mammalian target of rapamycin (mTOR) ( 1247 ). In the developing visual cortex in cats, expression of LTP-promoting genes (e.g., Arc, BDNF) was likewise found to decrease across sleep in the visual cortex as in adult rodents ( 1060 ) (see sect. VII A3 ). Notably, however, translation of the corresponding proteins at the same time was increased particularly during early sleep, suggesting that the first hours of sleep might be a period of accelerated protein synthesis in these networks.

Gene expression studies identified Homer1a as a core gene of sleep loss, as its expression was consistently upregulated in different strains of mice after 6 h of sleep deprivation ( 771 ). Homer1a expressing cells also overexpressed three further genes in response to sleep loss, which all appear to be implicated in intracellular calcium homeostasis, and may function to protect and recover neurons from glutamate-induced hyperactivity during periods of extended wakefulness. Even after controlling for possible confounding influences of increased glucocorticoids due to the sleep deprivation procedure, Homer1a (as well as plasticity-related genes including Arc and Fos) was still specifically associated with sleep loss ( 833 ). In the later study, several other genetic pathways that were previously associated with sleep homeostasis (e.g., circadian clock genes, see Ref. 415 ) were no longer affected by sleep deprivation in this study, indicating that effects of stress and glucocorticoids need to be carefully controlled in gene expression studies examining sleep homeostasis.

2. Experience-dependent local regulation of genes

Apart from the global regulation of gene expression during sleep and wakefulness, experience-dependent upregulation of plasticity-related genes has also been observed during sleep. Using fluorescence in situ hybridization in hippocampal cells in rats, Marrone and co-workers ( 781 ) found that exploration of a new environment increased the number of hippocampal CA1 neurons showing induction of the immediate early genes Arc and Homer1a both during exploration as well as during rest after exploration. However, others failed to reveal increased protein levels of c-Fos or Arc in cortical motor areas in rats during sleep following exploration or following training of skilled reaching, a task known to induce LTP in motor areas ( 510 , 584 ). Still, expression of the immediate early gene coding for BDNF increased following exploratory behavior during wakefulness, and the amount of exploration during waking (% recording time) was correlated with subsequent increases in BDNF measured in the frontal and parietal cortex after a short 10-min period of non-REM sleep, as well as with the amount of SWA during this non-REM sleep period ( 584 ).

Distinct local upregulation of plasticity-related genes and proteins following novelty and learning experiences has been revealed to be specifically related to REM sleep ( 261 , 1022 , 1225 ). Learning of a two-way active avoidance task in rats increased phosphorylation of the cAMP response element-binding protein (CREB), expression of Arc protein as well as mRNA expression of Arc, BDNF and early growth response-1 (Egr-1, also known as Zif268) in the dorsal hippocampus and amygdala during a 3-h postlearning sleep period. Elimination of pontine brain stem cells that generate P-waves hallmarking REM sleep suppressed retention of the avoidance response as well as learning-dependent increases in the expression of phosphorylated CREB, Arc protein, and mRNA of Arc, BDNF, and Egr-1 in dorsal hippocampus and amygdala ( 261 ). Conversely, cholinergic stimulation of P-waves by microinjection of carbachol into respective pontine regions increased expression of these proteins and mRNAs in the dorsal hippocampus, and was also associated with improved retention of the active avoidance response ( 263 , 794 ). These findings indicate that consolidation of an active avoidance response during REM sleep critically relies on P-wave-induced upregulation of synaptic plasticity-related genes in limbic regions, specifically in the dorsal hippocampus.

Exposure to novel tactile stimuli as well as experimental induction of hippocampal LTP during wakefulness produced an upregulation of Arc and Egr-1 in neocortical and hippocampal regions during subsequent REM sleep ( 986 , 987 , 989 ). The reinduction of both immediate early genes during REM sleep after novelty exposure was only transient in hippocampal regions, and more pronounced and persistent in the neocortex, where it was particularly strong in the somatosensory areas most activated by the previous tactile novelty experience ( 986 , 989 ). Cortical expression of Arc was correlated to EEG spindle activity during prior non-REM sleep, possibly favoring immediate early gene activity during REM via Ca 2+ -dependent mechanisms ( 989 ). Spindles likely promote massive calcium influx into cortical pyramidal cells and through activation of CaMKII may set the stage for the expression of immediate early genes during ensuing REM sleep ( 283 , 1061 ). Both Arc as well as Egr-1 are known to interact with CaMKII in synaptic remodeling during LTP. Ribeiro et al. ( 989 ) related the REM sleep-associated increase in immediate early gene expression occurring preferentially in cortical over hippocampal regions to waves of synaptic plasticity that hippocampus-dependent memories undergo during successive non-REM-REM sleep cycles (see FIGURE 7 , A AND B ). Reactivation of hippocampal memory representations during non-REM sleep stimulate the redistribution of these memories to neocortical areas where they are synaptically consolidated during ensuing REM sleep ( 293 ). Across several waves of plasticity associated with the non-REM-REM sleep cycles, memories become stored mainly within neocortical networks while they fade out in the hippocampus, the latter coinciding with the fading of reinduction of immediate early gene activity during postencoding REM sleep in hippocampal areas ( FIGURE 7 C ) .

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Different time courses of plasticity in the hippocampus and neocortex. A : concept: the hippocampus undergoes a few plasticity waves before fading out. These plasticity waves are probably enough for memories to remain in the hippocampus for weeks or months. In contrast, the cerebral cortex undergoes plasticity waves for a much longer period of time, leading to many more cycles of memory reinforcement and years-old memories. B : in single neuron recordings in rats, long-lasting firing rate increases after novel spatio-tactile stimulation (EXP) occurred during SWS in primary somatosensory cortex (S1), but not in the hippocampus (HP) or primary visual cortex (V1). Increased neuronal activity persisted for hours after experience offset during SWS in S1. Shown are the normalized firing rates during concatenated SWS episodes spanning an entire representative experiment. Ticks at the bottom indicate SWS episode boundaries. C : model of memory propagation from hippocampus to neocortex during sleep. Via thalamo-cortical inputs (not shown), episodic and spatial memories are acquired during waking as new synaptic changes (red) distributed over hippocampocortical networks of neurons ( top panel ). The recurrence of cortical plasticity during subsequent sleep causes the stabilization and propagation of new synaptic changes in the neocortex. Conversely, the fast decay of sleep-dependent plasticity in the hippocampus generates a net outflow of information, gradually flushing memories to associated cortical networks over time. [Modified from Ribeiro et al. ( 989 ).]

3. Deletion and gene knockout studies

In flies, several genes that regulate the amount of sleep have been identified by mutagenesis screenings. Whereas wild-type flies sleep ∼8–10 h/day, flies with a loss-of-function mutation in the genes Shaker (Sh) or hyperkinetic (Hk) sleep only 2–4 h/day ( 154 , 202 , 203 ). Shaker codes for the alpha subunit of a specific K + channel (alpha-subunit of a tetrameric potassium channel that mediates a voltage-activated fast inactivating IA current), and hyperkinetic codes for a regulatory beta-subunit of this channel that interacts with the alpha-subunits encoded by Shaker ( 202 , 1052 ). Shaker-modulated K + channels have homologs in vertebrates (Kv.1 and other Shaker-like channels, Kv.2, Kv.3, Kv.4, etc.). In both flies and mammals, they play a major role in membrane repolarization and likely also modulate EEG oscillatory phenomena like spindles ( 35 , 155 , 244 , 320 , 357 , 358 , 717 , 934 , 1266 ). Importantly, both Shaker and hyperkinetic mutants showed marked impairments in learning and memory, as well as a reduced life span. Similarly, reduced sleep and life span have been reported for flies with a loss of the gene Sleepless (SSS) which codes for glycosylphosphatidylinositol-anchored protein with unknown function, with the effects of this mutation possibly in part also mediated by Shaker channels ( 202 , 272 , 653 , 1306 , 1337 ). However, its influence on learning and memory has not been investigated so far.

There is strong evidence for an involvement of clock genes in sleep-dependent plasticity. Clock genes regulate the circadian rhythm and affect sleep ( 681 ). In mammals, genetic variations in both clock genes activating circadian rhythm, like Clock (Clk) and cycle (cyc), and clock genes with repressing functions on circadian rhythm, like period (per) and Cryptochrome (Cry), have been linked to variations in sleep ( 663 , 703 , 851 , 1073 , 1328 , 1329 ), whereas in flies only the activators Clk and cyc affect sleep phenotypes ( 531 ). Per, which is regulated by CREB, plays a key role in long-term memory formation in Drosophila ( 1023 ). Recent studies in fruit flies by Shaw and colleagues revealed that per, together with other clock genes, also regulates sleep-dependent memory formation ( 314 , 438 ). These studies first established that waking experience, i.e., a socially enriched environment where the flies were housed in groups of ∼40, compared with social isolation increased sleep time and sleep bout duration ( 315 ). Sleep need likewise increased after a learning task (conditioned suppression of courtship behavior), and this increase in sleep was critical for successful recall of the conditioned behavior 48 h later ( 314 , 438 ). Remarkably, flies mutant in the clock gene per failed to show this experience-dependent increase in sleep need and in parallel showed no long-term memory after 48 h. Similar results were observed in flies with mutations in the rutabaga gene (involved in cAMP signaling) and blistered (involved in synaptic LTP and contextual habituation). Rescue of the genes in clock neurons of the flies reestablished the experience-dependent increase in sleep as well as long-term memory ( 314 ).

Several other genes and transcripts have been identified that not only strongly influence sleep quantity, but also are involved in major pathways regulating synaptic plasticity, like CREB ( 477 , 532 , 916 ), the extracellular signal-regulated kinase/mitogen-activated protein kinase (ERK/MAPK), and the epidermal growth factor (EGF) (e.g., Ref. 401 , for a review see Ref. 202 ). However, direct links between sleep and memory formation have not been demonstrated for most of these signals. An exception is the gene bunched, a regulator of the transmembrane receptor Notch which is involved in both sleep homeostasis and learning in flies ( 1062 ). Overexpression of the Notch ligand Delta as well as the introduction of a Notch gain-of-function allele reduced sleep rebound and prevented impairments in new learning (aversive phototaxic suppression) induced by prior sleep deprivation. Interestingly, the effects on sleep and memory of Notch signaling were mainly observed in glia cells, pointing to a critical role of neuron-glia interactions for regulating sleep-dependent learning benefits ( 1338 ). Similar effects of Notch signaling were observed in worms ( Caenorhabditis elegans ) ( 1085 ).

Knockout procedures have also been used in numerous studies as a genetic tool to examine the impact of neurotransmitter systems on sleep regulation (reviewed in Ref. 202 ; see also sect. V B ) and oscillatory EEG activity during sleep (reviewed in Ref. 37 ; see also sect. IV). For example, a genetic region on chromosome 13 (containing several genes) was identified by quantitative trait loci analysis (QTL) that explained almost 50% of the variance in the rebound in non-REM sleep EEG delta activity after sleep deprivation in rodents ( 414 ). However, for none of these genes have any links to sleep-dependent memory formation been examined in a more systematic manner.

3. Heat-induced sleep in transgenic animals

Gerstner and co-workers ( 450 – 452 ) engineered heat-responsive transgenic flies to examine the role of fatty-acid binding proteins (Fabps) for sleep-associated memory consolidation. Fabps bind small lipids and act as transporters in various cells and tissues. Of the nine mammalian genes, mRNA coding for Fabp7 is expressed in the brain, and in rodents its expression follows a circadian rhythm. In flies, mRNA expression coding for the Fabp7 homolog dFabp also shows a circadian rhythm, with elevated levels during the night. Inducing mRNA expression of genes coding for Fabp7 or dFabp in temperature-sensitive transgenic flies by elevating ambient temperature from 20 to 30°C increased sleep and, if temperature was raised after learning, had an enhancing effect on long-term memory for a conditioned olfactory avoidance response. While baseline sleep was lowered in the transgenic flies, the heat-induced increase in sleep was correlated with the gain in memory, altogether suggesting an important role for Fabps in mediating memory consolidation during sleep ( 451 ).

Using a similar approach, Donlea et al. ( 316 ) introduced a temperature-sensitive cation channel in neurons of Drosophila . Activating the channel by raising temperature above 31°C resulted in a sleeplike state. Inducing 4 h of sleep in this way after massed trials on a courtship conditioning procedure enhanced retention of the conditioned behavior at a retest 48 h later, compared with flies that did not sleep after training. The effect cannot be explained by an activation of the temperature-gated neurons per se, because sleep deprivation during periods of high temperature did not enhance long-term memory. Of note, temperature-induced sleep also benefited new learning on the next day, fitting the idea that memory consolidation during sleep is concurrently linked to processes preparing the brain for the future encoding of new information ( 828 ).

These studies exemplify the rapid development of new genetic tools that can be used as the “remote control” of sleep ( 316 ) and, in this way, to study the function of sleep for memory consolidation. First attempts conducted to control sleep optogenetically in mice have revealed that fragmenting postlearning sleep by activating hypothalamic hypocretin/orexin neurons impaired consolidation in a novel object recognition task ( 9 , 1009 ). Future developments of these transgenic techniques will enable the reliable activation of specific neuronal memory traces, thus providing promising tools to a more fine-grained analysis of the fate of a memory trace during sleep ( 739 ).

1. Stability and heritability of sleep

Human sleep and particularly oscillatory EEG activity during sleep is highly heritable and very stable within individuals ( 689 ). In addition, large interindividual differences in the sleep EEG exist, which are more than 10 times higher than those, observed within subjects across multiple nights of sleep. Already in 1937, Geyer ( 453 ) reported a higher similarity in sleep profiles for monozygotic compared with dizygotic twins. Systematic twin studies using large samples indicate that genetic differences account for 30–45% of the variance in subjective sleep quality and sleep disturbances ( 186 , 526 , 889 ) and for ∼50% in physiological measures of sleep stages ( 137 , 737 , 738 ). Highest heritability values are observed for REM density (h 2 = 64–95%) as well as EEG spectral power particularly in the alpha (8–11 Hz) and spindle (11–15 Hz) frequency bands (h 2 = 76–96%) ( 29 , 266 , 444 , 736 , 1227 ). Similar heritability estimates (h 2 = 40–60%) have been reported for sleep amount and sleep organization in rodents ( 1165 , 1166 , 1226 ), with even higher estimates of genetic control for oscillatory electrical activity during sleep ( 37 , 414 , 416 , 417 ).

Genetic factors also contribute to sleep differences between ethnicities (e.g., Ref. 1019 , for a meta-analysis) and gender (e.g., Ref. 976 ). Women sleep longer and still have a higher SWS percentage than men, whereas men show increased non-REM sleep stages 1 and 2 ( 424 , 587 , 652 , 952 ). In a study in 2,600 participants aged 37 to 92, gender explained 14.6% of the variance in SWS percentage and 10.9% of stage 2 sleep percentage ( 976 ).

Additionally to the strong heritability, the human sleep EEG is remarkably stable within individuals across multiple nights ( 151 , 1176 , 1177 , 1201 ). Stability as indicated by intraclass coefficients is highest for SWS (73%), but also significant for other parameters like the amount of REM sleep (48%), stage 2 sleep (56%), and total sleep duration (46%). Power in the delta frequency band (0.75–4.5 Hz) shows particularly high stability (78–89%). Interindividual differences in delta power were on average ∼10 times greater than the rebound in delta activity observed following sleep deprivation ( 818 , 1213 ). The interindividual differences in the sleep EEG are remarkably robust also against sleep disturbances, first night effects, prior sleep deprivation, and the administration of sleep-promoting agents ( 881 , 1213 ).

2. Genetics, sleep and cognitive function

Specific genetic markers of sleep have been identified mainly in studies of different populations with disordered sleep, such as fatal familial insomnia, narcolepsy, restless legs syndrome, and circadian rhythm disorders ( 266 , 299 , 638 , 954 , 1058 , 1167 ). However, the relationships of these genetic markers to sleep-dependent memory processes are entirely unknown so far, except one observation indicating that a single nucleotide polymorphism (SNP) in the prion protein gene (PRNP) implicated in fatal familial insomnia in human and sleep regulation in mice ( 579 ) affects learning performance in healthy participants ( 886 ).

Studies in healthy humans revealed clear associations between sleep and genes involved in regulating circadian rhythm, mainly the clock gene PER3 that can occur in a different number of repetitions, so-called “variable number of tandem repeats” (VNTR) polymorphisms. Homozygosis for the 5-repeat allele (PER3 5/5 ) has been repeatedly associated with morning preferences (morning types) compared with homozygotes for the 4-repeat allele (PER3 4/4 ) and heterozygotes (PER3 4/5 ) ( 41 , 61 , 349 , 609 , 874 , 917 ), as well as with increased SWS percentage and SWA during non-REM sleep and increased theta power during REM sleep and wakefulness ( 1256 ). In addition, PER3 5/5 carriers were more vulnerable to sleep deprivation in the early morning hours (e.g., Refs. 1256 and 483, but see Ref. 467 ) and exhibited a stronger decrease in brain activity assessed by fMRI in prefrontal areas from morning to evening ( 1244 ). Altogether, these findings suggest that circadian genotypes contribute to differences in the allocation of cognitive resources, thus possibly also affecting memory processing during sleep.

With regard to noncircadian genes, Landolt and co-workers identified a genetic difference (G->A transition at codon 22) in the gene encoding the adenosine metabolizing enzyme (adenosine deaminase, ADA) that was significantly associated with SWS duration and SWA, in particular in the <2 Hz range (e.g., Refs. 54 , 798 , 982 ; but see Ref. 797 ). A-allele carriers who exhibited more SWS also showed poorer performance in focused attention (as indicated by the d2 test), but not in several other tests of short-term memory and executive functions (verbal, figural memory, digit span, Stroop test. etc.) (see Ref. 688 for a review).

Differences in the widely researched functional Val158Met polymorphism in the gene coding for the catechol- O -methlytransferase (COMT) were associated with more global alterations in alpha peak frequency as well as in spectral power in the 11–13 Hz band, occurring during REM, non-REM sleep, and also during wakefulness ( 104 , 105 ). Investigation of the Val66Met polymorphism, i.e., a valine to methionine amino acid substitution at codon 66 of the BDNF gene, revealed that Met-allele carriers (compared with Val/Val homozygotes) exhibit less SWS and reduced delta and theta activity during non-REM sleep in baseline nights as well as in response to sleep deprivation ( 54 ). BDNF is particularly expressed in the prefrontal cortex and the hippocampus in humans ( 927 ), and the Val66Met BDNF polymorphism has been repeatedly associated with a range of cognitive functions including memory (e.g., see Ref. 614 , for a meta-analysis). Unfortunately, none of these studies included direct tests of sleep-dependent memory formation.

3. Genetic studies on sleep-dependent memory consolidation

Studies mainly on flies indicated that similar genes are involved in sleep regulation and memory processing, and that memory and plasticity-related genes are expressed differentially during sleep and wakefulness. No such studies are available in humans. In addition, no genome-wide association study in humans with regard to sleep and memory parameters has been conducted thus far, although such studies would be highly informative. In fact, presently, direct evidence for a genetic contribution to the sleep-dependent formation of memory is entirely lacking.

There is a strong genetic influence on sleep parameters (discussed above) and also on memory formation. Twin studies have estimated that 50% of the variance in learning performance, typically measured after short retention periods of 5–30 min, is due to genetic factors ( 127 , 885 , 1164 , 1262 ). Against this background, as both sleep and memory measures strongly differ among individuals, a putative genetic determination of sleep-dependent memory consolidation should basically express itself in significant correlations between the respective sleep and memory parameters on the interindividual level. Hence, the demonstration that sleep and memory processes share significant portions of their interindividual variance represents a first and most important step towards the demonstration of any genetic contribution to sleep-associated memory formation, although it does not prove such contribution because the associations can basically result from parallel, but independent, influences of the putative genetic factor on the sleep and memory processes of interest. Also, it cannot be concluded from interindividual sleep-memory associations whether a putative genetic factor primarily affects sleep to change memory processing or, conversely, affects memory processing to change sleep, although clues can be derived from temporal relationships. For example, Dionne et al. ( 310 ) showed in a longitudinal twin study that longer and more consolidated sleep at the age of 6 mo predicted better language development at 18 and 30 mo. As the type of sleep was highly heritable, the findings suggest a sleep-mediated genetic influence on later language proficiency.

With regard to non-REM sleep, spindles have been consistently revealed to be associated with memory-related parameters and thus represent a most promising candidate mediating a genetic link between sleep and memory. In children and adults, spindle number and density strongly correlate with IQ scores ( 106 , 395 , 439 , 847 , 1038 , 1039 ) as well as overnight retention of memories ( 213 , 1057 , 1230 ). Abnormally large spindles or the absence of spindles were observed in mentally retarded and dyslexic children ( 93 , 148 , 400 , 455 , 456 , 1070 , 1071 ). Contrasting with the findings in healthy humans, elevated baseline spindles predicted poor performance on a two-way shuttle box avoidance task in rats ( 397 ). However, learning-dependent increases in sleep spindles only occurred in rats that successfully learned the task, and these increases were positively correlated with post-sleep improvements in performance. Based on these findings, Fogel and Smith ( 398 ) proposed a curvilinear relationship between sleep spindles and capabilities of learning, with high spindle numbers reflecting either highly efficient or pathological memory processing in thalamocortical systems.

Although the importance of SWA and slow oscillation for memory processing is well established (see sect. IV A ), interindividual correlations between these EEG oscillations and learning capabilities and intelligence have been less frequently reported. This is remarkable given the great interindividual variance and intraindividual stability of these sleep EEG parameters (discussed above). Several studies reported correlations between SWS or SWA and the overnight retention of declarative memory ( 55 , 58 , 464 , 1319 ) as well as procedural motor skills ( 581 , 582 ). However, because in these studies the sleep EEG was assessed after the learning phase in highly homogenous subject samples, learning-induced changes in EEG activity could not be clearly dissociated from trait-dependent variance.

Historically, memory function has been much more often associated with parameters of REM sleep than non-REM sleep. Evolutionary increases in encephalization are positively associated with sleep time allocated to REM sleep, even when the effects of phylogenetic similarity between species are controlled (e.g., Ref. 725 , but see Refs. 1077 , 1078 ). Analyses of sleep in seven inbred strains of mice revealed a high correlation between the relative and absolute time in REM sleep and the performance level avoidance conditioning or maze learning across strains ( 880 ). In humans, mentally retarded children generally exhibit less REM sleep, longer REM sleep latencies, and less REM density relative to normal controls ( 182 – 184 , 211 , 368 , 369 , 425 , 922 – 924 , 1045 ). However, correlations between REM sleep and IQ measures in healthy children and adults revealed mixed results ( 126 , 153 , 922 , 923 , 1183 ). In some studies, gifted children tended to sleep longer and had more stage 2 sleep, compared with controls ( 153 , 1183 ). In addition to baseline amounts of REM sleep, the magnitude of learning-induced increases in REM sleep parameters have been linked to capabilities of learning in animals ( 1110 ) and humans ( 1114 ).

The studies discussed so far examined associations between sleep and memory in rather small samples (12–30 participants). Together with the testing of multiple sleep parameters (like amounts of SWS, REM sleep, spindle numbers, density, etc.) and with multiple measures of memory and learning capabilities, this can lead to an overestimation of the strength of associations due to multiple comparisons. To overcome such limitations, in recent study, we assessed sleep (at home by portable polysomnography) together with measures of pictorial and procedural motor learning and overnight memory retention in a large sample of 855 young healthy participants (Ackermann, Pappassotiropoulos, De Quervain, and Rasch, unpublished results). In accordance with previous studies, the number of fast spindles (13–15 Hz) showed a positive (but rather small) correlation with short-term recall (tested after 10 min) of pictures. However, this correlation was entirely explained by gender differences, as women remembered more pictures and exhibited also an increased number of spindles. Short-term recall of pictures was also correlated with the percentage of REM sleep ( r = 0.09), with this correlation surviving control for gender. Also, with regard to overnight retention of pictures, the only significant correlation was revealed for time in REM sleep: increased REM sleep was associated with diminished overnight retention of pictures ( r = −0.13), independently of whether these pictures were emotional or neutral. No such associations were observed for SWS-related measures, spindles, or sleep length. Overall, these data indicate a surprisingly weak association between interindividual differences in sleep and memory parameters. Specifically, they suggest that the genetic determination of sleep, particularly of SWS-related measures and spindles, does not substantially contribute to sleep-dependent memory formation processes, as these parameters do not appear to be associated with each other on the interindividual level. It is important to note, however, that the lack of association between sleep and memory parameters among subjects does not at all rule out a basic role of sleep in memory formation. For example, one individual can display higher SWA than others due to neuroanatomical and related genetic factors in the absence of any differences in memory among these individuals, yet increasing SWA in this same individual might still lead to a distinct improvement in memory consolidation during sleep.

VII. DEVELOPMENTAL ASPECTS OF SLEEP-RELATED BRAIN PLASTICITY

Only a handful of studies have been conducted to explore memory-consolidating functions of sleep during early postnatal periods, infancy, and adolescence in humans. The developmental approach might be particularly important for understanding the mechanisms underlying sleep-dependent memory consolidation, mainly for two reasons. First, compared with adults, infants and children sleep longer and deeper. In parallel, during early life the brain exhibits particularly strong plasticity shaping memory systems and underlying neuronal circuit formation. During limited periods early during development, the brain is particularly sensitive to certain experience that instructs neuronal circuits to represent respective information as very persistent memories determining performance throughout later life ( 650 ). However, the mechanisms underlying neuronal plasticity during such critical periods differ from that during adulthood. As a consequence of maturational processes, critical period plasticity is linked to a distinct biochemical milieu, a change in the subunit composition of NMDA receptors in forebrain synapses from a predominance of NR2B subunits at birth to a prevalence of synaptic NR2A subunits towards the end of the critical period, a specific adjustment of the balance between excitatory and inhibitory inputs to neuronal networks, and eventually also to different features of spike timing-dependent plasticity ( 332 , 352 , 545 , 838 , 1212 ). The specific characteristics of plasticity during critical periods prevent a straightforward generalization of the effects of sleep on plasticity in the adult brain. Nevertheless, it is striking that studies of the developing brain, including those targeting critical period plasticity, show an influence of sleep on the formation of memory that is even more profound than that observed in the adult brain.

A. Early Development: Animal Models

Filial imprinting in domestic chicks, song learning in zebra finches, and ocular dominance plasticity in the visual cortex of cats have been studied as models of developmental brain plasticity and memory formation in animals in conjunction with sleep. Recent research has indicated that the formation of these three types of memory indeed critically depends on sleep.

1. Filial imprinting

Imprinting is a very strong and early form of social recognition memory, often studied in the domestic chick. Under natural conditions, during imprinting the chick learns the characteristics of its mother to selectively follow her rather than any other adult. In the laboratory, the chick is repeatedly exposed to a moving object, and so acquires a preference for this object over an alternative, novel stimulus, with this preference used as measure of the imprinting memory ( 107 , 567 ). Imprinting over repeated training sessions leads to a gradual increase in the number of neurons in the intermediate and medial mesopallium (IMM, also termed intermediate and medial hyperstriatum) that selectively respond to the imprinting stimulus which is correlated with the chick's preference for the imprinting stimulus ( 144 , 568 ). Imprinting increases also the amount of activated CaMKII, the size of dendritic spine postsynaptic densities, and a delayed upregulation of NMDA receptors in the IMM, without changing the number of synapses per se ( 129 , 567 , 799 ).

An involvement of sleep in imprinting was suggested by an early study ( 1120 ) in which a single imprinting session starting 12 h after ecclosion produced a significant increase in the number of episodes and the amount of REM sleep, whereas after pseudo-imprinting REM sleep decreased. In a different study, changing the imprinting object from one day to the other produced a bias towards increased left hemispheric sleep, possibly related to increased consolidation processes in the left hemisphere ( 102 ). Unilateral sleep typically covers ∼1–2% of sleep time in chicks. Results from a more recent study compellingly demonstrated a critical involvement of sleep in the consolidation of the imprinting stimulus ( 600 ). In this study, one group of chicks was allowed to sleep undisturbed after imprinting training for 6 h before being retested and then was subjected to a 6-h period of (slightly) disturbed sleep. Final testing after the second sleep period revealed a stable memory for the imprinting stimulus together with an overall doubled number of IMM neurons responding to the imprinting stimulus (compared to imprinting training before sleep). In contrast, chicks that experienced disturbed sleep in the first 6-h interval after training and undisturbed sleep in the second 6-h interval, at final testing did not exhibit any significant memory for the imprinting stimulus and showed a strong decrease in the number of IMM neurons responsive to the imprinting stimulus. In addition, chicks with undisturbed sleep after imprinting displayed an increase in EEG SWA covering the 0–6 Hz frequency band during this period. These findings point to a particular importance of SWS, occurring in a window ∼1.3–5 h after stimulus exposition, for forming a stable memory for the imprinting stimulus. Interestingly, tracking of individual IMM neurons revealed a pattern suggesting that sleep shortly after imprinting training stabilizes the memory representation by supporting the recovery of responsiveness of cells that for any reasons had ceased to respond to the imprinting stimulus in the course of training or shortly afterwards, with this effect requiring some hours to occur ( 600 , 1150 ). The mechanisms of this sleep effect are unclear. Given that the IMM corresponds to parts of the mammalian neocortex receiving significant input from the hippocampus ( 819 , 979 ), such inputs during sleep may drive the stabilization of IMM representations, although hippocampal neurons do not appear to selectively respond to the imprinting stimulus after training ( 600 , 858 ).

2. Song learning in birds

Song learning in birds was one of the first models used to systematically study the role of sleep in developmental learning. It bears great similarity with speech learning in human infants ( 321 , 774 ). The surprising discovery that sleep is essential to a bird's acquisition of a song fostered a major advance in this field ( 773 , 774 ). Song learning in birds has been mainly studied in zebra finch males which develop their song between day 30 (after hatching), when they start producing unstructured sounds, and day 90, when they exhibit a well-developed song to be used as a complex social signal ( 321 ). Song learning is based on an innate predisposition to imitate vocalizations, and requires exposure to a song model. Under experimental conditions, the birds are typically subjected to a standard protocol, i.e., they are first raised by females who do not sing and then, at the appropriate age, gain limited access to a tutor song by a form of instrumental conditioning, e.g., by pecking a certain key ( 774 , 1181 ). Song learning has been conceptualized as a two-stage process ( 660 , 661 ): first, the bird acquires a sensory model (“template”) of the tutored song which in a second sensorimotor step the bird gradually learns to imitate, whereby auditory feedback is essential for this process.

Derégnaucourt et al. ( 279 ) provided compelling evidence that the sensorimotor phase of song development is driven by sleep. Monitoring the structure of the song and its syllable features by frequency-based automated song analyses across the whole period of song development in juvenile zebra finches, they revealed that song structure profoundly deteriorated after nocturnal sleep compared with the evening before sleep, and was regained only after intense morning singing ( FIGURE 8 A ) . The effects occurred on the background of a gradual day-to-day increase in song structure and were not seen in adult birds. Interestingly, the young birds showing the greatest morning deterioration in song structure achieved the best final imitation of the tutored song at the end of the 45-day monitoring period, suggesting a functional role of the sleep-related deterioration in singing behavior for the overall learning process. Deterioration of song structure occurred also after induction of sleep during the daytime by the administration of melatonin excluding confounding effects by the circadian rhythm.

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Sleep-dependent formation and reactivation of song memory in birds. A : the measure of Wiener entropy variance (EV) of song structures reveals a continuous improvement in song structure in young birds over the 45-day developmental period starting with the first exposure to the tutor song (start of training). In spite of this overall improvement, during early development (day 46, middle panel ) overnight sleep induces an acute decrease in song performance as indicated by a stronger deviation in song structure in the morning after sleep compared with presleep performance. This overnight decrease is not any more present in the end of the learning period (day 89, right panel ). Bottom panel indicates continuous tracking of EV values over the 45-day period. [Modified from Derégnaucourt et al. ( 280 ), with permission from Nature Publishing Group.] B : neuronal trace of an arcopallium (RA) neuron emitting 10 distinct bursts of 2–7 spikes/burst (”singing“). The bursts are precisely timed to when the bird sang a song whose motif consisted of a sequence of five syllables (see spectrograph, frequency vs. time representation; top ). For each song bout, the sequence of syllables and the structure of each spike burst (timing of spikes and numbers of spikes) were highly reliable. The same pattern of spike-bursts reoccurred during recording during sleep. [Modified from Dave and Margoliash ( 267 ), with permission from American Association for the Advancement of Science.]

The neuronal mechanisms during sleep that mediate the deterioration in morning song structure are not clear. The major auditory and song system pathways of the songbird brain have been described in terms of a functional hierarchy. The caudal medial nidopallium (NCM) and the caudal mesopallium (CM) receiving inputs from primary auditory structures contribute to the formation of auditory representations likely including those forming the sensory template of the tutored song ( 108 , 463 ). These structures represent a major source of the inputs to the vocal control “song system” comprising chiefly the nucleus HVC (“high vocal control”) and the robust nucleus of the arcopallium (RA) representing the motor cortex analog of the song system ( 774 ). Along with the deterioration of song structure, HVC neurons show a decline in burst activation across sleep in juvenile songbirds.

In adult songbirds, RA premotor neurons have been identified that show replay during sleep, i.e., patterns of burst activity during sleep which, in terms of their temporal sequence and spike sequence structure within each burst, are very similar to those observed during singing ( FIGURE 8 B ) . The replay in RA neurons is driven by input from HVC neurons ( 267 , 504 ) which, in juvenile songbirds, show a decline in burst activation across sleep, in parallel with the deterioration of song structure ( 271 ). Several studies by Margoliash's group provided evidence that the replay activity during sleep indeed reflects a processing of memory representations producing changes in the representation and also in subsequent song performance. Thus, in juvenile zebra finches, the first exposure to a tutor song distinctly enhanced high-frequency bursting in RA neurons during subsequent sleep, with corresponding changes in singing occurring not until the following day ( 1063 ). Moreover, the sequence structure of spiking within bursts identified during sleep was significantly correlated with that observed while the bird was listening to the tutor song during the preceding wake phase. Both the increase in burst occurrence and burst structure in RA after initial exposure to the tutor song also required that the bird receive auditory feedback from its own singing, as these increases were drastically reduced when the birds were experimentally deprived from auditory feedback or were surgically muted.

The findings suggested a model where RA burst activity during sleep and the forming of sensorimotor representations of a song requires a twofold of inputs during waking, i.e., on the one hand, auditory feedback activity from actual singing during waking that serves a permissive role in structuring night-time RA bursting and organizing the song representation and on the other hand activity related to the sensory template of the tutor song ( 774 , 775 ). Given that RA burst activity during sleep reflects sensory experience of the tutored song during the wake phase, reactivated “template” activity during sleep may drive plastic changes in the song representation that, as they occur under unsupervised conditions (i.e., in the absence of actual auditory feedback), may manifest themselves in a deterioration of song structure in the morning after sleep ( 280 , 463 , 856 , 857 ). Early during song development, reactivated sensory representations and sensorimotor feedback as experienced during actual singing are poorly correlated, resulting in large differences in singing between morning and evening performance which disappear with increased learning. In addition to modifying sensorimotor representations, reactivations during sleep may also strengthen the song template ( 136 ). Whether SWS or REM sleep is more important for the processing of song memories during sleep cannot be answered, as RA burst activity appeared to be equally affected in both sleep stages ( 1063 ). In addition, it remains unclear whether sleep actually benefits reorganization of memory traces between brain structures as assumed for episodic memories in humans ( 964 , 965 ). Although this model as proposed by Margoliash and co-workers ( 774 , 775 ) aims to explain developmental song learning, it may account also for song learning and the maintenance of learned songs in adult songbirds. There is evidence for song replay and for systematic changes in RA burst activity patterns across sleep in adult zebra finches as well ( 969 ). Interestingly, RA burst activity during sleep in some of these cases appeared to be more similar to that observed during singing after sleep than before sleep, suggesting a kind of “preplay” that occurs during sleep and creates new song features ( 323 ).

3. Ocular dominance plasticity

Ocular diminance plasticity (ODP) has been studied mainly in cats as a developmental model of synaptic plasticity that is induced by specific stimulus conditions, i.e., monocular deprivation ( 77 , 411 ). In critical periods during development (in cats between about postnatal days 28 and 40), blocking vision in one eye causes a massive rewiring of cortical circuitry in favor of the open eye. ODP is sleep-dependent inasmuch as sleep following a period of monocular deprivation resulted in an almost twofold increase in synaptic remodeling in visual cortical areas, whereas after a similar waking interval in complete darkness, a tendency for erasure of the effects induced by the preceding monocular deprivation was observed ( 412 ). The beneficial effect of sleep on consolidation of ODP mainly leads to a strengthening of the cortical responses to stimulation of the nondeprived eye ( 48 ). Inactivating the sleeping visual cortex by administration of the Na + channel blocker lidocaine or the GABA A agonist muscimol inhibited ODP, indicating that postsynaptic activity during sleep is required for consolidating the plastic changes induced by a monocular experience ( 411 , 605 ). Sleep-dependent ODP is prevented by blocking glutamatergic NMDA receptors or cAMP-dependent protein kinase (PKA) during sleep after monocular deprivation ( 48 ). Blockade of NMDA and PKA signaling was associated with reduced activation of the kinases CaMKII and ERK as well as phosphorylation of GluR1 at Ser831, i.e., processes that are critical to the insertion of AMPA receptors into the postsynaptic membrane and the strengthening and maintenance of LTP. Most recently, Seibt et al. ( 1060 ) demonstrated the critical dependence of ODP on protein synthesis during sleep, as respective plastic changes in visual cortex could be prevented by inhibition of mTOR-mediated protein synthesis through rapamycin. Rapamycin had no effect on plasticity induced during wakefulness. Furthermore, phosphorylation of regulators of protein synthesis as well as translation of plasticity-related mRNAs (e.g., Arc and BDNF) was increased during sleep, overall speaking for the notion that sleep is a period of enhanced protein synthesis in these networks. ODP was paralleled by increased multiunit activity during both SWS and REM sleep, and was correlated to the time spent in non-REM sleep ( 412 ). Of the GABA A agonistic substances zaleplon, eszopiclone, zolpidem, and triazolam, only zolpidem impaired sleep-dependent ODP, although all substances distinctly reduced REM sleep and increased non-REM sleep ( 48 , 1059 ). The differential efficacy of these substances in diminishing ODP may be related to differences in their pharmacodynamics and selectivity for binding specific GABA A receptor subtypes, but is independent of their profound changes in sleep architecture, as determined by standard EEG sleep recordings. ODP was impaired following application of the atypical hypnotic trazodone, probably acting via blocking 5-HT 2c receptors during sleep ( 48 ). Since serotonin activity is at a minimum during REM sleep and at intermediate levels during non-REM sleep, this finding suggests that processes during non-REM are more important for the expression of ODP. Interestingly, there is evidence that ODP critically depends on the T-type calcium channel function ( 1224 ), which also plays an essential role in the generation of spindles and slow oscillations during non-REM sleep ( 282 , 283 ).

In sum, this research indicates that sleep plays an essential role for behaviors that are formed during critical or sensitive periods in early postnatal life. The mechanisms mediating synaptic plasticity during critical periods in essential aspects differ from those during later life ( 838 ) and differences in the regulation of synaptic plasticity may still exist between juveniles and adults ( 772 ). Yet, this does not necessarily imply that the manner in which sleep contributes to critical period plasticity also basically differs from the effect of sleep on memory formation in the adult brain. Sleep during the early postnatal period and in adults mainly differs in quantity of total sleep, time in SWS and REM sleep, intensity of SWS and SWA, etc., suggesting that the impact of sleep on memory formation during early development is overall stronger than during adulthood, rather than differing in quality. This view may account especially for developmental learning processes that are less clearly linked to an early postnatal critical period but extend towards adolescence, like song learning in birds or language learning in children. Indeed, the power of sleep in forming memory during early life and the fact that these memories are the first to be formed may suffice to explain the unique strength of these memories being difficult to overwrite or reverse.

B. Early Development: Human Studies

Investigations of the effects of sleep on memory formation during early development in humans focused on declarative and procedural memory processes similar to those examined in adults, rather than on behaviors linked to sensitive periods, like social imprinting and amblyopia as a consequence of monocular deprivation. Overall, these studies point towards the particular relevance of SWS for sleep-dependent memory consolidation in children. In adults, SWS has been revealed to play a causal role for the consolidation of hippocampus-dependent declarative memories ( 783 , 957 ), but may play a similar role for explicitly acquired procedural memory (e.g., Ref. 691 , see also sect. IV A ).

Infants and children sleep longer than adults. However, compared with adults, the proportion of REM sleep in children increases only during the first 6 mo of life, whereas the proportion of SWS remains enhanced throughout development until adolescence ( 480 , 866 ). The EEG during SWS also shows typical changes during development: the amplitude and slope of slow waves as well as SWA (i.e., spectral power in the 0.5–4 Hz band, including both <1 Hz slow oscillation and 1–4 Hz delta activity) increases until the beginning of puberty at the age of 10–12 years to levels distinctly higher than in adults, and thereafter starts to decrease ( 169 , 604 , 680 ). Several studies indicated that sleep disturbances and sleep loss impairs learning and school performance in children (for reviews, see Refs. 44 , 246 , 286 , 662 , 1178 ). Considering the importance of SWS specifically for the consolidation of hippocampus-dependent memory in adults, it was hypothesized that the effect of sleep on the consolidation of declarative memories is even greater during development ( 1321 ).

1. Consolidation of declarative memory

Backhaus et al. ( 56 ) reported beneficial effects of sleep on the consolidation of declarative memories (word pairs) in 9- to 12-yr-old children. The children's recall performance after sleep was not only better than that after the wake-retention intervals, but also compared with recall performance tested before sleep, suggesting a genuine gain of declarative memory that is produced by intervening sleep in children. Retention of word pairs across the sleep interval was positively correlated with the time spent in non-REM sleep and negatively with the time in REM sleep. Similarly, several other studies demonstrated profits from sleep in children between 7 and 14 years of age for word memories as well as for the integration of novel words into lexical knowledge ( 141 , 530 , 945 ). Similarly, in 7- and 12-yr-old children, memory for novel words profited from an off-line consolidation interval involving sleep ( 141 ). Sleep in children also preferentially enhanced emotional declarative memories (recognition of emotional pictures), whereas no effect of sleep on procedural memory consolidation (mirror tracing) was observed ( 947 ). A comparison of healthy children (10–16 years) with age-matched children with attention deficit/hyperactivity disorder (ADHD) revealed a superior sleep-dependent benefit for the retention of pictures in the healthy children ( 948 ). Although time in SWS did not differ between the groups, a significant positive correlation between slow oscillation power during non-REM sleep was only observed in the healthy children, possibly reflecting impaired functionality of this rhythm in ADHD children. In 14- to 16-yr-old adolescents, the restriction of sleep (to up to 5 h for 4 consecutive nights) did not impair either retention of word pairs or procedural memories for mirror tracing skill ( 1259 ). However, the adolescents showed a remarkable increase in the proportion of SWS during the restriction period, suggesting a high capability of the adolescent's brain to compensate effects of sleep restriction by flexibly increasing the sleep depth.

A beneficial effect of sleep in children (6–8 years) was found also for the consolidation of visuospatial memories in a two-dimensional object-location task known to involve the hippocampal function ( 1318 ). Unexpectedly, in this study comprising a comparison of memory performance in adults, the size of the sleep effect was closely comparable between children and adults, although the amount of SWS during experimental nights was on average more than twofold higher in the children. However, the direct comparison of retention rates for declarative materials between children and adults is generally hampered by the fact that consolidation can be sped up depending on the availability of associative schemas in long-term memory that can integrate new declarative information ( 1210 , 1211 ). In the adult brain, more of such schemas may be available than in the child's brain. Thus, irrespective of this confound, these studies overall point towards a particular relevance of the high amounts of SWS and associated SWA for declarative memory consolidation during development.

2. Consolidation of procedural memory

In contrast to the strong beneficial effects of non-REM sleep on the consolidation of declarative memory in children, studies examining procedural memories consistently demonstrated that in children, unlike in adults, posttraining sleep does not produce a robust improvement of skill. This finding is surprising considering that the acquisition of skills like learning to walk and to speak, to write, and to ride a bicycle represent major challenges of childhood. Also, the neuroanatomic structures underlying skill learning mature quite early during development, i.e., within the first 3 years of life ( 180 , 468 ). In fact, the majority of studies on sleep-dependent procedural memory formation in children indicated an impairing rather than improving effect of sleep on skill ( 385 , 947 , 949 , 1318 ), remarkably similar to findings in juvenile song birds where overnight sleep likewise deteriorated performance on the tutored song ( 280 ). In the respective studies, the sleep-dependent overnight gains in skill were measured with reference to performance before sleep rather than with reference to performance changes across a corresponding wake interval, which excludes that the absent gain of skill across sleep in children is due to a relatively greater capability in children to stabilize memories during wakefulness ( 318 ). Interestingly, sleep restored daytime deficits in procedural memory (serial reaction time task) in children with attention-deficit hyperactivity disorder, while again no overnight improvements were observed in healthy children ( 949 ).

One factor explaining the missing overnight gain in skill in children might be their fairly low initial skill levels. In adults, the level of skill performance at learning has repeatedly been demonstrated to modulate sleep-dependent memory consolidation, whereby benefits from sleep appeared to be most robust with intermediate performance levels ( 16 , 297 , 679 , 1151 ). In children, skill performance is generally much slower, less accurate, and less automated than in adults, particularly in the initial stage of training ( 318 , 385 , 1185 ), which gives rise to the question of whether sleep enhances skill memories also in children if presleep performance is improved to a level comparable with that in adults. Indeed, children (aged 4–6 years) who received high amounts of training in a finger-tapping task did develop a significant gain in tapping skill across sleep, whereas no such gain was observed in low performing children who had received the standard amount of training ( 1320 ). In the adults of this study, only the group with minimum training showed a consistent benefit from the nap. In combination, the findings in children and adults suggest that across age groups, the likelihood that sleep produces a distinct gain in skill performance is greatest when presleep performance is at an intermediate level. Owing to their generally quite low performance of skills, children reach levels of strength at which sleep-dependent benefits directly translate into benefits in behavioral speed and accuracy only after extensive levels of training.

3. Children extract more explicit knowledge from implicitly trained tasks than adults

Alternatively, the lack of sleep-dependent gains in skill performance in children might be ascribed to a competitive interaction between declarative and procedural memory systems ( 385 , 1318 ). Particularly in the initial stages of training, contributions of explicit (i.e., conscious) learning mechanisms involving prefrontal-hippocampal circuitry can be essential for skill acquisition, with the parallel storing of procedural and hippocampus-dependent declarative aspects favoring interactions and competitions between the two memory systems ( 16 , 43 , 937 , 1042 ), which can result in an impaired implicit task performance (e.g., slowing of reaction times) ( 1064 , 1135 ). Importantly, this competitive interaction between the memory systems might extend to processes of consolidation during sleep ( 143 , 147 , 273 , 1000 ). On the basis of the assumption that SWS predominantly supports consolidation of hippocampus-dependent declarative memory, it was argued that sleep in children, because of its great amounts of SWS, preferentially strengthens hippocampus-dependent explicit aspects in a skill representation, thereby hampering implicit performance. A recent study indeed confirmed a striking superiority of sleep in 8- to 11-yr-old children, compared with adult's sleep, to extract explicit declarative sequence knowledge from a coarse motor SRTT that was trained under implicit conditions (i.e., without being aware of the underlying sequence) before sleep ( 1322 ). On the generation task after retention sleep requiring the subjects to deliberately generate the eight-element sequence of cue positions underlying the SRTT trained before sleep, the children clearly outperformed the adults. The children's performance was also distinctly better than in control children who performed the generation task after training before the sleep interval. Superior explicit knowledge after sleep correlated with the amount of SWA in both age groups, in line with studies in adults indicating that SWS is critically involved in the extraction of explicit knowledge ( 1347 ).

Consistent with the notion that sleep in children particularly benefits explicit task aspects and the extraction of abstract knowledge, 15-mo-old infants already showed a sleep-dependent benefit for extraction of grammatical rules during language learning ( 469 ). The results were confirmed in a second study of this group employing an extended retention interval of 24 h ( 589 ). Both studies used the infant's orienting response towards the auditory word-strings to assess memory recall. Orienting is a well-known hippocampus-dependent function ( 1119 ), suggesting that the retrieval test tapped hippocampus-dependent declarative aspects of the memory, although this issue deserves further examination.

Together, the findings of a facilitating effect of sleep during development on the generation of hippocampus-dependent explicit knowledge about sequences and grammatical rules are well in line with the assumption of an active system consolidation established during sleep ( 293 , 1321 , 1327 ). According to this concept, processes during SWS supporting the redistribution of memories from temporary hippocampal to extrahippocampal long-term storage sites, do not only strengthen these memory representations, but also bring about qualitative transformations, manifesting themselves behaviorally in increased knowledge about invariant repeating and relevant features in the learned materials. Due to increased SWS in children, such transformations could be stronger during development than during adulthood.

C. Sleep and Memory Formation in Aged Rats and Humans

Sleep undergoes characteristic changes in the course of aging. Older adults sleep less and awaken more frequently during the night. Beginning already around the age of 40, there is a distinct decrease in SWS ( 167 , 866 ). Non-REM sleep in the elderly is characterized by decreases in SWA, especially over the prefrontal cortex, as well as decreases in spindle density ( 176 , 177 , 690 , 786 ). The time in REM sleep, on the other hand, remains relatively unchanged in late life. However, there is a significant decrease in the density of phasic REMs in elderly persons ( 254 ).

1. Hippocampus-dependent declarative memory

Considering that the major age-related changes in sleep architecture concern SWS and associated SWA, prominent impairments in the consolidation, especially of hippocampus-dependent declarative memory, may be expected ( 512 ). Consistent with this notion, aging rats at rest after spatial memory encoding showed weaker reactivations of temporal firing patterns in hippocampal neuron ensembles, and this was associated with diminished spatial memory performance in the Morris Water maze at the final testing day (e.g., Ref. 448 , see sect. III A4 ). The weakening of temporally sequenced reactivation patterns in aged rates did not appear to be a consequence of age-related decreases in SWS. Interestingly, simple reactivation patterns in CA1 cell ensembles, not taking into account the relative temporal order of cell-pair firing, was comparable between aged and young rats ( 449 ).

In humans, comparing retention of word pairs across nocturnal sleep between young (18–25 years) and middle-aged adults (48–55 years), Backhaus et al. ( 55 ) revealed a significantly lower retention of word pairs in the group of middle-aged participants. The impairment was observed specifically for a retention interval that covered early nocturnal sleep, which is typically dominated by SWS. SWS in this interval was strikingly lower in the middle-aged than young subjects. Also, percentages of time spent in SWS, but not in REM sleep, were strongly correlated with later retention performance. Overall, these data strongly suggest that the age-related deficits in declarative memory consolidation during sleep are due to a decline in SWS. In line with these findings, in aged individuals (69–80 years), the improvement in the recollection of hippocampus-dependent episodic memories (for personally experienced events) after retention periods of nocturnal sleep, with reference to recollection after daytime wakefulness, was less pronounced than in young (19–29 years) subjects ( 21 ). A recent study reported no beneficial effect of sleep on memory and no correlation between SWS and consolidation of word pairs across sleep in elderly subjects (60–84 years), whereas a positive correlation with SWS and a sleep-dependent memory improvement was observed in younger participants (18–22 years) ( 1055 ) A diminished capability to consolidate hippocampus-dependent memory (for word pairs) was not confirmed in a recent study by Wilson et al. ( 1324 ) including, besides young subjects, middle-aged (30–55 years) as well as old (55–70 years) participants. With reference to retention rates across daytime wake intervals, sleep improved retention rates in all age groups to the same extent. Although overall the findings point towards a diminished sleep-dependent consolidation of declarative memory in the aged due to deficits in SWS, the direct comparison of benefits from sleep between age groups might be confounded by the fact that age also diminishes memory retention across wakefulness, which is typically used as reference in these studies.

2. Procedural memory

With regard to procedural memory, several studies relying on different versions of the SRTT revealed age-related impairments in the formation of motor skill memories during sleep. Comparing older (45–80 years) and younger adults (18–24 years) on the SRTT under implicit and explicit learning conditions, Spencer et al. ( 1127 ) found that only the younger but not the older subjects distinctly improved in performance in both task versions across overnight sleep, compared with performance changes across a daytime retention period of wakefulness. Another study revealed sleep-dependent performance gains on a deterministic SRTT in middle-aged (35–55 years), but not in 55–70 years old participants ( 1324 ). In contrast, using a finger sequence tapping task, one study reported that elderly subjects (60–79 years) maintained their performance across 24 h including sleep, while their performance was impaired after 12 h of wakefulness, suggesting a role for sleep in optimal motor consolidation also in the elderly ( 1214 ). However, the overnight improvements typically observed in young subjects did not occur in these elderly participants. Sleep also did not benefit the performance of older (50–75 years) subjects on a continuous motor tracking task where the moving target to be tracked could follow a repeated sequence or move randomly similar to the SRTT ( 1079 – 1081 ). Interestingly, age-matched stroke patients in this study did show an improvement in tracing accuracy selectively across the sleep retention interval for both explicit and implicit performance conditions. Finally, sleep also did not improve performance in a second-order probabilistic SRTT in old subjects (60–80 years) ( 852 ). However, sleep-dependent benefits are also not consistently revealed in younger subjects for this task, possibly due to the great complexity of the underlying sequence rules associated with a higher degree of implicitness at learning ( 852 , 1122 , 1123 ).

To what extent the age-related decreases in sleep benefits for skill learning are related to specific alteration in sleep, in particular of SWS, is presently unclear as most of the relevant studies in aged subjects did not include polysomnographic recordings. REM sleep does not appear to be involved as neither experimental REM sleep deprivation nor REM sleep augmentation (i.e., the rebound after deprivation) produced any significant performance change in aged subjects on a procedural mirror tracing task ( 570 ). Nevertheless, like in young adults, high cholinergic activation during REM sleep seems to contribute to sleep-dependent consolidation of procedural memory also in aged individuals, as administration of the acetylcholinesterase inhibitor donezipil in aged subjects, together with an increase in REM density, significantly enhanced sleep-dependent gains in mirror tracing performance ( 570 , 961 ). The contributions of impaired SWS to the age-related decline in procedural memory consolidation remain to be specified in future studies.

VIII. SLEEP-DEPENDENT MEMORY CONSOLIDATION IN THE IMMUNE SYSTEM

A. memory formation in the immune system.

The immune system forms long-lasting memories for an antigen in a multistep process. The mounting of a so-called adaptive immune response is at the core of this process. Once antigens invade the organism at certain barriers, they are taken up and processed by antigen presenting cells (APC). APC carry the antigenic information to secondary lymphoid organs where they present fragments of the processed antigen to naive T cells, which become only activated if they express the specific cognate receptor that can recognize this particular antigenic epitope. The T cell recognizing its antigen, together with the APC, form the so-called immunological synapse. Activation of the T cell is linked to the release of different proinflammatory cytokines by APC, among which interleukin-12 (IL-12) is central for the subsequent differentiation of the activated T cell into T-helper 1 (Th1) cells. T-cell growth, proliferation, and differentiation is additionally supported by the release of IL-2, whereas the release of IL-10 in this context plays an antagonistic role favoring differentiation of Th2 cells and the recruitment of immediate but preformed humoral immune responses. Th1 cells also support the differentiation of B cells and their production of antigen-specific antibodies. Whereas most effector cells differentiated during acute infection do not survive for a long time after the infection, there is a subset of differentiated T and B cells that maintains the antigenic memory for the long-term. These memory cells mediate a speedy and more effective immune response upon reencounter of the antigen ( 13 , 1258 ).

There are apparent differences between memory formation in the immune system and in the central nervous system. Most obvious, in the immune system cells migrate to act in different tissues and body compartments and, unlike neurons, they often show strong proliferation in response to (antigenic) stimulation. These conditions may partly explain why memory formation in the immune system takes much longer (several days to weeks) than in the central nervous system. Nevertheless, immunological memory, like neurobehavioral memory, is formed as part of an adaptive organismic response to an environmental stressor, and shares the basic subprocesses of encoding, consolidation, and retrieval ( 641 , 695 ). Also, consolidation includes a cell-cell interaction in which the antigenic information is transferred from a temporary to a long-term store, with both stores represented by different cellular networks. Specifically, in this conceptual approach (see FIGURE 9 A ), encoding in the immune system would denote the uptake of antigenic information by APC. Consolidation would refer to processes at the immunological synapse that is subsequently formed between APC and T cells ( 338 , 473 ) and comprise the transfer of the antigenic information from the APC, only serving as temporary store, to the T cells serving as long-term store. Recall would refer to the faster and facilitated immune response when the antigen is re-encountered.

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Sleep supports the initiation of an adaptive immune response. A : concept: the invading antigen is taken up and processed by antigen presenting cells (APC) which present fragments of the antigen to T helper (Th) cells, with the two kinds of cells forming an “immunological synapse.” The concomitant release of interleukin (IL)-12 by APC induces a Th1 response that supports the function of antigen-specific cytotoxic T cells and initiates production of antibodies by B cells. This response finally generates long-lasting immunological memory for the antigen. Sleep, in particular slow wave sleep (SWS), and the circadian system act in concert to generate a proinflammatory hormonal milieu with enhanced growth hormone and prolactin release and reduced levels of the anti-inflammatory stress hormone cortisol. The hormonal changes in turn support the early steps in the generation of an adaptive immune response in the lymph nodes. In analogy to neurobehavioral memory formed in the central nervous system, the different phases of immunological memory might be divided in an encoding, a consolidation, and a recall phase. In both the central nervous system and the immune system, sleep specifically supports the consolidation stage of the respective memory types. [Modified from Besedovsky et al. ( 90 ).] B : sleep enhances the hepatitis A virus (HAV)-specific T helper (Th) cell response to vaccination (three shots at weeks 0, 8, and 16, vertical syringes) in two groups of human subjects who either slept (black circle, thick line) or stayed awake (white circle, thin line) in the night following inoculations. The immune response as indicated by the frequency of CD40L+ HAV-specific Th cells (percentage of total Th cells) at weeks 18–20 ( left panel ) and particularly at week 52 ( right panel ) is strongly predicted by the amount of slow wave activity (averaged across the three postinoculation nights). Values are means ± SE: * P ≤ 0.1; ** P ≤ 0.05; *** P ≤ 0.01. [Data from Lange et al. ( 694 ).]

B. Effects of Sleep on the Formation of an Adaptive Immune Response

1. experimental vaccination.

Vaccination as an experimental model of infection has been used as a straightforward approach to comprehensively assess effects of sleep on the formation of immunological memory mainly in humans where this approach quite consistently revealed an enhancing effect of sleep on the adaptive immune response and measures of antigenic memory. Human volunteers who slept normally in the first night after a single vaccination against hepatitis A, 4 weeks after the vaccination displayed a twofold increase in antigen-specific antibody titers, compared with participants who stayed awake on the first night after vaccination ( 697 ). Similar effects were found in recent experiments using repeated and combined inoculations with hepatitis A and B antigens ( FIGURE 9 B , Ref. 694 ). In this study, nocturnal sleep after the inoculations doubled blood counts of antigen-specific Th cells, as marker of antigenic memory. During antigen reencounter, these Th cells stimulate the production of antibodies specifically directed against hepatitis A and B. Moreover, sleep profoundly increased the proportion of T cells producing proinflammatory and Th1-cytokines like IL-2, interferon-γ (IFN-γ), and tumor necrosis factor-α (TNF-α). The effects persisted over 1 year. In a further study, short sleep duration after hepatitis B vaccination predicted a decreased antibody response to the immunization ( 946 ). Together, these studies provide a compelling demonstration that nocturnal sleep after vaccination suffices to strengthen the evolvement of an adaptive, i.e., memory-forming immune response to a clinically relevant extent. A strengthening role of sleep for immunological memory formation is also supported by observations of reduced total IgG under conditions of sleep restriction (to 4 h/night) for several days after vaccination against influenza ( 1130 ).

In contrast to studies in humans, which examined effects of sleep on primary immune response developing to the first encounter of an antigen, studies in animals have so far concentrated on secondary immune responses, i.e., the effect of sleep on the recall of an antigenic memory that was already established during a preceding primary response. Overall, these studies revealed inconsistent results with sleep, compared with conditions of sleep deprivation, enhancing or weakening markers of recall, or showing no effect ( 145 , 980 , 981 , 1206 ). Because of their focus on secondary immune responses, these animal studies basically cannot be compared with the studies of primary responses in humans.

The strengthening effects of sleep on the human primary immune response to infection were revealed with sleep occurring within the first 36 h after inoculation. This time window suggests that sleep affects the cascade of responses to the viral challenge at a rather early stage. Indeed, research has identified two main targets that are affected by sleep in this process, i.e., 1 ) the interactions between APC and T cells at the immunological synapse and 2 ) the migration of these cells to secondary lymphoid tissues.

2. Sleep supports APC-T cell interactions

There is converging evidence that sleep promotes Th1 immune responses as an immediate sequel of APC-T cell interactions. Thus sleep in humans enhances the production of IL-12 by monocytes and premyolid dendritic cells (pre-mDC) which represent the most important precursors of mature professional APC circulating in blood ( 307 , 696 ). Concurrently, sleep diminishes the production of anti-inflammatory IL-10 by monocytes ( 696 ). Furthermore, several reports indicated an enhancing influence of sleep on T-cell production of IL-2 which acts to further promote T-cell growth, proliferation, and differentiation ( 120 , 595 ), although other studies failed to reveal such effects when examining IL-2 production in specific T-cell subpopulations ( 109 , 308 ). Sleep increased proliferation of Th cells as well as the activity of natural regulatory T (nTreg) cells ( 109 ). Concurrent upregulation of nTreg activity might reflect a counterregulatory response preventing overshooting of Th1 responses. Moreover, early nocturnal sleep increased the ratio in IFN-γ/IL-4 production by Th cells, indicating a shift in the Th1/Th2 cytokine balance towards preponderant production of Th1 cytokines ( 308 ). In patients with allergic rhinitis, wheal reactions to a skin prick test and spontaneous IgE production of peripheral blood mononuclear cells were increased after a night of total sleep deprivation, indicating a shift from Th1 to prevailing Th2 immune defense ( 637 ). Finally, there is evidence that sleep increases blood concentrations of IL-7, a cytokine fostering differentiation of T memory cells ( 76 ). Together, these findings speak for the notion that sleep specifically benefits APC-T cell interactions towards the development of predominant Th1 immune responses.

3. Effects of sleep on the migration of T cells and APC to lymphoid tissues

Sleep appears to affect also the migratory pattern of leukocytes, although the trafficking of T cells as well as APC has not been studied thoroughly in the context of sleep. Cells centrally involved in the formation of adaptive immunity, like naive and central memory T cells, show a circadian rhythm with the highest numbers of circulating cells at night in humans. This circadian rhythm is mainly driven by cortisol which upregulates the chemokine receptor CXCR4 on these cells and thereby facilitates their redistribution to the bone marrow during daytime ( 305 , 693 ). On top of this circadian rhythm, nocturnal sleep, compared with wakefulness, induces a slight but significant reduction in circulating naive and central memory T cells ( 695 ), which indeed might reflect a redistribution of these cells to lymph nodes that is specific to sleep. In rodents, counts of lymphocytes as well as percentages of Th cells showed a variation across the 24-h period that ran in parallel in blood, spleen, and lymph nodes, with peak counts reached during the rest period, suggesting a quick redistribution of T cells from the vascular compartment to secondary lymphatic tissues. Two studies, one in sheep ( 289 ) and the other in humans ( 351 ), provided more direct evidence that the efflux of lymphocytes from lymph nodes is reduced during sleep. In rats, lymphocyte counts in lymph nodes indeed increased after recovery sleep that followed a period of sleep deprivation ( 1354 ). The mechanisms underlying this sleep-induced homing of T cells to lymph nodes are not clear. Homing to the lymph node via high endothelial venules relies on adhesion molecules and chemokine receptors (CD62L, CCR7, and CD11a) expressed on the T-cell membrane. In addition to the suppression of cortisol release during early nocturnal SWS, sleep may ease T-cell trafficking to lymph nodes via sympathetic and peptidergic innervation of lymph nodes and influences on cytokine release ( 117 , 234 , 876 ).

In combination, these data support the view that sleep favors an accumulation of T cells in lymph nodes, and there might be a parallel effect on APC migration ( 307 , 627 ). These migratory effects probably occur in concert with synergistic actions of the circadian rhythm and eventually might serve to enhance the rate of APC-Th cell interactions in secondary lymphoid tissues during sleep. In contrast to sleep, wakefulness appears to support the recruitment of cells in circulating blood that have high cytotoxic effector potential, like proinflammatory monocytes, terminally differentiated cytotoxic T cells, and cytotoxic natural killer (NK) cells, and serve the immediate immune defense against pathogens invading the organism preferentially during the activity period (e.g., Refs. 42 , 287 ).

4. Contribution of SWS to memory formation in the immune system

Although effects of the experimental manipulation of specific sleep stages on the adaptive immune response have not, thus far, been systematically assessed, there is converging though indirect evidence that the strengthening effects of sleep on the formation of long-term antigenic memory originates from processes during SWS. The sleep-induced increase in antigen-specific Th cells after hepatitis A vaccination was strongly correlated with the amount SWS and EEG SWA on the night after vaccination ( 693 ). The increase in percentage of hepatitis A virus specific antigen-specific Th cells measured 18–20 wk after vaccination was correlated with r = 0.72, and that measured 1 year later with a coefficient of r = 0.94 with SWA. Other measures, including REM sleep-related parameters, were not predictive of this increase. Corresponding findings were obtained in rabbits, where the amount of SWS predicted the survival rate after a pathogen challenge ( 1205 ). Moreover, in humans, early nocturnal SWS-rich sleep is associated with a shift towards Th1 cytokine activity predominating over Th2 activity which is replaced by a shift towards the predominance of Th2 cytokine production during late sleep when SWS is fading and REM sleep becomes prevalent ( 308 ). In fact, peaks in Th1 cytokine activity and, more generally, in proinflammatory cytokine activity during the rest period were mainly detected during the early SWS-dominated portion of sleep, in humans as well as in animals in different tissues including the brain ( 487 , 671 ) and lymph nodes ( 359 ), but also in blood and circulating leukocytes ( 109 , 120 , 172 , 306 , 308 , 487 , 696 , 749 , 1158 ). Interestingly, some of these cytokines were found, conversely, to promote SWS, pointing towards a positive-feedback loop between the central nervous and immune systems ( 693 ).

5. Endocrine mediation of immunological memory formation during sleep

The strengthening effect of sleep on immunological memory formation has been ascribed to the endocrine milieu as it is generated specifically during SWS. In humans, early nocturnal SWS is associated with an increased release of the immune-supportive hormones prolactin and growth hormones while release of the immunosuppressive glucocorticoids is suppressed ( 117 ). Sleep, specifically SWS, contributes to the generation of this unique endocrine pattern in concert with circadian mechanisms ( 91 , 1125 , 1126 , 1129 ). Not only SWS, but also the enhanced release of GH and prolactin, together with the simultaneously suppressed cortisol levels observed during SWS-rich sleep after hepatitis A vaccination, were revealed to be highly predictive for the number of antigen-specific Th cells detected in blood 4 wk and 1 year after vaccination ( 696 ). In fact, blocking of mineralocorticoid receptors during early SWS-rich sleep produces an enhancement in naive T-helper cell counts ( 89 ). Moreover, in vivo as well as in vitro studies identified high prolactin and low cortisol levels as factors most strongly contributing to the nocturnal upsurge of IL-12 production ( 307 , 696 ). GH and prolactin are well known to promote T-cell proliferation and differentiation as well as Th1 cytokine activity ( 193 , 217 , 436 , 814 ), and to develop adjuvant-like actions when given shortly after vaccination ( 1124 , 1136 ). Low glucocorticoid levels during SWS-rich sleep contribute to these effects as these hormones are highly potent anti-inflammatory agents suppressing proinflammatory and Th1 cytokine activity ( 693 ). Probably, other factors (orexin, leptin, autonomic innervation of lymphoid tissues, etc.) add to this mediation of sleep effects, but these are as of yet entirely unexplored. Overall, these findings corroborate the view that during SWS, which dominates the early night, an endocrine milieu is established that supports the initiation of an adaptive immune response as a basis for the formation of long-lasting antigenic memory. Conversely, there are hints that owing to an overshooting proinflammatory cytokine response, the immediate defense of antigen is impaired during sleep, particularly during SWS ( 508 , 627 , 693 , 779 ).

It is important to note here that sleep, and particularly SWS, has been likewise identified as a factor critically involved in the formation of long-term memories in the neurobehavioral domain ( 292 ). These findings indicate that memory formation during sleep in both the immune and the central nervous system, beyond conceptual analogies, shares causal mechanisms. This, indeed, conveys the idea that forming long-term memory is a general function of sleep, which eventually serves to strategically adapt the organism to environmental stressors in entirely different domains.

IX. CONCLUDING REMARKS

For more than a century it has been known that memory benefits from sleep, and research in this field has put forward different explanations for this phenomenon. Here we aimed to establish a review covering the progress of research in this field of sleep and memory in its entireness, simultaneously taking into account the vastly differing approaches that have been adopted to clarify the mechanism mediating the memory benefit from sleep. Whereas initially it was commonly assumed that sleep improves memory in a passive manner, by protecting it from being overwritten by interfering external stimulus inputs, the current theorizing assumes an active consolidation of memories that is specifically established during sleep, and basically originates from the reactivation of newly encoded memory representations. In parallel, the perspective on the researched memory has changed. Whereas initial research largely concentrated on the stability of memory, demonstrating how sleep contributes to the persistence of a memory in all of its qualities, recent research has begun to concentrate on the dynamics of memory formation, systematically examining the changes a memory representation undergoes during sleep-dependent consolidation. The active system consolidation process assumed to take place during sleep leads to a transformation and a qualitative reorganization of the memory representation, whereby the “gist” is extracted from the newly encoded memory information and integrated into the long-term knowledge networks.

Most recently, the focus of research in the field has broadened, indicating that sleep benefits memory not only in the neurobehavioral domain, but also in the formation of immunological long-term memories, stimulating the idea that forming long-term memories represents a general function of sleep. There are first cues that sleep-dependent memory formation in the immune and central nervous system share common mechanisms, that in both domains appear to be linked to SWS. Also in the immune system, sleep appears to support the intercellular reorganization of memory representations such that during the APC-T cell interaction the epitopic information is extracted from the antigen to be stored by T cells. Certain features of active system consolidation, like cell assembly reactivation of neurobehavioral representations, can occur also during wakefulness, but with different consequences. The effective reorganization of the representation requires a specific milieu of neurotransmitter and endocrine activity as it is established only during sleep, specifically during SWS. Subsequent REM sleep may then be involved in strengthening the reactivated and reorganized representations on a molecular and synaptic level. Thus sleep and wakefulness appear to be associated with different and mutually exclusive modes of memory processing, with sleep favoring processes of memory consolidation that are incompatible with the efficient encoding and retrieval of stimuli, as required while coping with environmental demands in the wake phase.

This work was supported by Deutsche Forschungsgemeinschaft Grant SFB 654 Plasticity and Sleep and Swiss National Foundation Grant PP00P1_133685.

DISCLOSURES

No conflicts of interest, financial or otherwise, are declared by the authors.

ACKNOWLEDGMENTS

We thank Maren Cordi, Anja Otterbein, Ursula Rasch, Julia Rihm, Thomas Schreiner, Sarah Schoch, and Manuela Steinauer for assistance in preparing the manuscript and Sandra Ackermann-Wohlgemuth, Luciana Besedovsky, Susanne Diekelmann, and Ines Wilhelm for helpful comments on earlier versions of the manuscript. In particular, we thank Drs. Ted Abel, Igor Timofeev, and Paul Shaw for very constructive comments and corrections.

Addresses for reprint requests and other correspondence: J. Born, Institute of Medical Psychology and Behavioral Neurobiology, Univ. of Tuebingen, Gartenstraße 29, 72074 Tuebingen, Germany (e-mail: [email protected] ); or B. Rasch, Institute of Psychology, Div. of Biopsychology, Univ. of Zurich, Binzmühlestrasse 14, Box 5, CH-8050 Zurich Switzerland (e-mail: [email protected] ).

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How the brain consolidates memory during deep sleep

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Using a computational model, a new study explains how the hippocampus influences synaptic connections in the cortex -

Research strongly suggests that sleep, which constitutes about a third of our lives, is crucial for learning and forming long-term memories. But exactly how such memory is formed is not well understood and remains, despite considerable research, a central question of inquiry in neuroscience.

Neuroscientists at the University of California (UC), Riverside report in the Journal of Neuroscience that they now may have an answer to this question. Their study provides for the first time a mechanistic explanation for how deep sleep (also called slow-wave sleep) may be promoting the consolidation of recent memories.

See Also: Sleep in a dish: Researchers isolate smallest unit of sleep to date

During sleep, human and animal brains are primarily decoupled from sensory input. Nevertheless, the brain remains highly active, showing electrical activity in the form of sharp-wave ripples in the hippocampus and large-amplitude slow oscillations in the cortex, reflecting alternating periods of active and silent states of cortical neurons during deep sleep. Traces of episodic memory acquired during wakefulness and initially stored in the hippocampus are progressively transferred to the cortex as long-term memory during sleep.

Using a computational model, the UC Riverside researchers provide a link between electrical activity in the brain during deep sleep and synaptic connections between neurons. They show that patterns of slow oscillations in the cortex, which their model spontaneously generates, are influenced by the hippocampal sharp-wave ripples and that these patterns of slow oscillations determine synaptic changes in the cortex. (Change in synaptic strength is widely believed to underlie learning and memory storage in the brain). The model shows that the synaptic changes, in turn, affect the patterns of slow oscillations, promoting a kind of reinforcement and replay of specific firing sequences of the cortical neurons—representing a replay of specific memory.

"These patterns of slow oscillations remain even without further input from the hippocampus," said Yina Wei, a postdoctoral researcher and the first author of the research paper. "We interpret these results as a mechanistic explanation for the consolidation of specific memories during deep sleep, whereby the memory traces are formed in the cortex and become independent of the hippocampus."

Wei explained that according to the biologically realistic network model the researchers used, input from the hippocampus reaches the cortex during deep sleep and influences how the slow oscillations are initiated and propagated in the cortical network.

"Input from the hippocampus—the sharp-wave ripples—determines the spatial and temporal pattern of these slow oscillations," she said. "By influencing the nature of these oscillations, this hippocampal input activates selective memories during deep sleep and causes a replay of specific memories. During such memory replay, the corresponding synapses are strengthened for long-term storage in the cortex. These results suggest the importance of the hippocampal sharp-wave ripple events in transferring memory information to the cortex."

Learn More: Weekend catch-up sleep can reduce diabetes risk associated with sleep loss

Normal sleep, during which brain activity remains high, is made up of non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep. NREM and REM sleep alternate in each of the 4-5 cycles during an eight-hour sleep period. Each cycle consists of NREM sleep followed by REM sleep, and roughly lasts 90-110 minutes. NREM sleep has three stages, Stage 3 being deep sleep. Deep sleep, which makes up at least 20 percent of a person's total sleep time, occurs mostly in the first third of the night.

"In our model, even weak and spatially localized input from the hippocampus influenced the spatiotemporal pattern of slow oscillations and led to a persistent change of synaptic efficacy between neurons," Wei said. "Further, our model makes predictions that can be tested experimentally, including specific interventions to suppress or augment memory consolidation processes."

Note: Material may have been edited for length and content. For further information, please contact the cited source.

University of California, Riverside Original reporting by: Iqbal Pittalwala

Publication

Wei Y, Krishnan GP, Bazhenov M. Synaptic Mechanisms of Memory Consolidation during Sleep Slow Oscillations. Journal of Neuroscience, Published April 13 2016. doi: 10.1523/JNEUROSCI.3648-15.2016

The brain stores at least 3 copies of every memory

A new study in mice suggests that the brain creates multiple copies of memories, which enables it to regulate how they change over time.

Swirls of fluorescent magenta are shown against a black background. There are also spots of white dotted along the swirls.

Memories evolve throughout our lifetimes , changing as we learn and experience new things and as we recall a memory repeatedly. And then, memories degrade as we age.

Previously, scientists thought that this malleability was the result of changes in the brain cells that originally encoded the memory, and they believed these cells stored just one copy of every memory in the brain. However, new research suggests that might not be true.

The scientists found that, in rodents, the brain stores at least three copies of a given memory, encoding it in multiple places in the organ.

These copies are encoded by different groups of neurons in the hippocampus , a brain region critical for learning and memory. The copies vary in terms of when they're created, how long they last and how modifiable they are through time.

Related: How accurate are our first childhood memories?

In the new study, published Aug. 16 in the journal Science , the scientists showed that, as mice encode new memories, they first create so-called early-born neurons. These neurons are responsible for storing a long-term copy of the memory that is initially weak but becomes stronger over time.

Next comes middle-ground neurons, which are more stable from the outset, followed by late-born neurons that from the beginning encode very strong copies of a memory. However, that strength fades over time.

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Researchers uncovered these findings by examining the activity of different groups of neurons in the hippocampus after mice had completed various memory tasks. These tasks involved learning to avoid harmful situations, such as receiving an electric shock to their feet, before being confronted with the same task later on.

The way these three groups of neurons operate on different timescales may help explain how the brain regulates memories over time, the study authors suggested. However, it is still unclear how exactly these neurons interact with each other to facilitate this, study co-author Flavio Donato , an assistant professor of neurobiology at the University of Basel in Switzerland, told Live Science.

Notably, the memories stored by late-born neurons were more plastic, or malleable, than those of early-born neurons, the team found. This suggests that at the start of memory formation — when early-born neurons reign — the information stored remains fairly stable over time, while memories stored later on are more easily warped by new information.

If the same phenomenon happens in humans, this finding could someday lead to the development of new therapies for specific disorders, Donato said. For example, in post-traumatic stress disorder (PTSD), people experience intrusive memories, meaning unwanted, distressing memories of a traumatic event. Perhaps a drug could be designed that preferentially activates late-born neurons, which are more plastic and thus more receptive to psychotherapy, he said.

— 'Muscle memories' get 'zipped and unzipped' in the brain, like computer files

— Secret inner workings of cells revealed through self-assembling 'memory' chains

— How does the brain store memories?

In the case of people with memory loss due to dementia, meanwhile, another type of drug could stimulate the activity of early-born neurons, whose data is stored more rigidly. Broadly speaking, such treatments would manipulate the properties of a memory by selecting which type of neuron is used to encode it in the brain, Donato explained.

"I feel like we now have biological entry points to modulate the plasticity of memory in a way that might allow us to push it towards being more or less plastic, in order to preserve it or to basically re-write it," Donato said.

Ever wonder why some people build muscle more easily than others or why freckles come out in the sun ? Send us your questions about how the human body works to [email protected] with the subject line "Health Desk Q," and you may see your question answered on the website!

Emily is a health news writer based in London, United Kingdom. She holds a bachelor's degree in biology from Durham University and a master's degree in clinical and therapeutic neuroscience from Oxford University. She has worked in science communication, medical writing and as a local news reporter while undertaking journalism training. In 2018, she was named one of MHP Communications' 30 journalists to watch under 30. ( [email protected] )

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RSC Advances

Improving the optical properties of magnesium spinel chromites through ni and cu substitutions for optoelectronic applications.

* Corresponding authors

a Laboratory of Physical Chemistry of Materials, Physics Department, Faculty of Sciences of Monastir, Monastir University, 5019 Monastir, Tunisia E-mail: [email protected]

b Laboratory of Advanced Multifunctional Materials and Technological Applications, Faculty of Science and Technology of Sidi Bouzid, University Campus Agricultural City, University of Kairouan, Sidi Bouzid 9100, Tunisia

c Department of Mechanical Engineering, College of Engineering, Prince Sattam bin Abdulaziz University, Alkharj 16273, Saudi Arabia

d Laboratory of Applied Fluid Mechanics, Processes Engineering and Environment, National Engineering School of Sfax, University of Sfax, Tunisia

In this study, we investigated the optoelectronic performance of magnesium spinel chromites with nickel (Ni) and copper (Cu) substitutions. Using the sol–gel method, we synthesized two spinel chromites: Mg 0.6 Ni 0.4 Cr 2 O 4 (MNCO) and Mg 0.6 Cu 0.4 Cr 2 O 4 (MCCO). We extensively characterized these samples to analyze their thermal, structural, elastic, and optical properties. Structural analysis reveals good agreement between the calculated and refined structural parameters, which supports the proposed cation distributions for the samples. By calculating stiffness constants from force constants, we derived elastic moduli such as bulk modulus, longitudinal modulus, and rigidity modulus. MCCO exhibited lower values for these moduli, as well as the Debye temperature, compared to MNCO. Both samples displayed a brittle mechanical nature according to the Pugh ratio, while the Poisson ratio remained constant at 0.25, indicating isotropic elasticity. UV-vis-NIR spectroscopy revealed that MNCO has higher band-gap ( E g ) and Urbach ( E u ) energies than MCCO. Further analysis of refractive index, penetration depth, extinction coefficients, nonlinear optical parameters, optical conductivity, and optical dielectric constants highlighted the promising optoelectronic applications of the synthesized materials. Our study found that the band-gap energy values of the as-synthesized samples were smaller than reported values for MgCr 2 O 4 spinel chromites, indicating that Ni and Cu substitutions offer an opportunity to extend the sunlight absorption range of magnesium chromites.

Graphical abstract: Improving the optical properties of magnesium spinel chromites through Ni and Cu substitutions for optoelectronic applications

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S. Heni, S. Hcini, M. L. Bouazizi, L. HajTaieb, A. Dhahri and H. B. Bacha, RSC Adv. , 2024, 14 , 26340 DOI: 10.1039/D4RA03342F

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Unlocking the Memory Vault: Dopamine, Novelty, and Memory Consolidation in the Hippocampus

First Online: 26 April 2024

Cite this chapter

Tomonori Takeuchi 3 , 4 , 5

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Most everyday memories, including numerous episodic memories formed automatically in the hippocampus, are forgotten. However, some memories are retained for extended periods through a memory stabilization process known as cellular or initial memory consolidation. Notably, in both animals and humans, the retention of everyday memories is enhanced during novel experiences occurring shortly before or after memory encoding, a process known as synaptic tagging and capture (STC). A growing body of evidence suggests that dopamine signaling via D 1 /D 5 receptors in the hippocampus is crucial for the persistence of synaptic plasticity and memory, highlighting its significant role in novelty-associated memory enhancement. This chapter presents an overview of key findings related to the persistence of synaptic plasticity and memory in the hippocampus through hippocampal D 1 /D 5 receptor dependency, with special emphasis on the emerging role of the locus coeruleus (LC) in novelty-associated dopamine-dependent memory consolidation. Furthermore, two distinct dopaminergic systems are explored (the ventral tegmental area (VTA)-hippocampal and LC-hippocampal systems), and the specialization mechanisms of each system in different memory consolidation processes are discussed. Additionally, the anatomical and molecular foundations of D 1 /D 5 receptor-mediated signaling in the LC-hippocampal system are examined. Finally, the molecular mechanisms possibly underlying distinct novelty-associated memory enhancement are discussed, including the involvement of plasticity-related proteins (PRPs) in the stabilization of structural and functional changes at potentiated synapses, culminating in initial memory consolidation in the hippocampus.

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Acknowledgments

This study was supported by grants from the Novo Nordisk Foundation Young Investigator Award 2017 (NNF17OC0026774), Lundbeckfonden (DANDRITE-R248-2016-2518), and the PROMEMO Center for Proteins in Memory, a Center of Excellence funded by the Danish National Research Foundation (DNRF133). I would like to thank Kristoffer Højgaard and Taichi Hiraga for their contributions to the scientific discussion. I am grateful for the support from Yasunari Koyama (KOYAMA Medical and Welfare Group), Daisaku Sawada (Sugamo Sougou Chiryoin), Shinji Adachi (DAIWA KIKO CO. LTD.), and the Tokyo Future Lions Club.

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Published: 21 August 2024

Replay-triggered brain-wide activation in humans

Qi Huang 1 , 2 na1 ,
Zhibing Xiao ORCID: orcid.org/0000-0002-0457-0233 1 , 2 na1 ,
Qianqian Yu 3 na1 ,
Yuejia Luo 1 , 3 na1 ,
Jiahua Xu 2 ,
Yukun Qu 1 , 2 ,
Raymond Dolan ORCID: orcid.org/0000-0001-9356-761X 1 , 4 , 5 ,
Timothy Behrens 5 , 6 , 7 &
Yunzhe Liu ORCID: orcid.org/0000-0003-0836-9403 1 , 2

Nature Communications volume 15 , Article number: 7185 ( 2024 ) Cite this article

Metrics details

Cognitive neuroscience
Learning and memory

The consolidation of discrete experiences into a coherent narrative shapes the cognitive map, providing structured mental representations of our experiences. In this process, past memories are reactivated and replayed in sequence, fostering hippocampal-cortical dialogue. However, brain-wide engagement coinciding with sequential reactivation (or replay) of memories remains largely unexplored. In this study, employing simultaneous EEG-fMRI, we capture both the spatial and temporal dynamics of memory replay. We find that during mental simulation, past memories are replayed in fast sequences as detected via EEG. These transient replay events are associated with heightened fMRI activity in the hippocampus and medial prefrontal cortex. Replay occurrence strengthens functional connectivity between the hippocampus and the default mode network, a set of brain regions key to representing the cognitive map. On the other hand, when subjects are at rest following learning, memory reactivation of task-related items is stronger than that of pre-learning rest, and is also associated with heightened hippocampal activation and augmented hippocampal connectivity to the entorhinal cortex. Together, our findings highlight a distributed, brain-wide engagement associated with transient memory reactivation and its sequential replay.

Flexible reuse of cortico-hippocampal representations during encoding and recall of naturalistic events

Replay, the default mode network and the cascaded memory systems model

Episodic memory retrieval success is associated with rapid replay of episode content

Introduction.

Imagine tackling a complex puzzle, and then, during moments of rest, your brain spontaneously begins to piece together the solution. This process mirrors a neural phenomenon known as ‘replay’, characterized by the fast reactivation of experiences in sequence 1 , 2 . A replay sequence may repeat past experiences, but may also predict the future 3 , 4 , or even reorganize experiences for flexible behavior 5 , 6 , 7 , such as solving a complex puzzle 8 . Replay is also thought to promote hippocampal-cortical dialogue in general 9 , 10 , 11 , 12 , but its exact spatial and temporal dynamics are unclear.

Replay, first identified in the rodent hippocampus during sleep 1 , 2 , has subsequently been observed during wakeful rest and while on-task, and is now considered to serve a broad spectrum of cognitive functions 13 , 14 . Initial studies suggested that replay (during sleep) plays a crucial role in consolidating past experiences 9 , 10 , 11 , 12 . Further research has extended the recognized functions of replay beyond mere memory consolidation 13 . Replay assists in reorganizing experiences, for instance, by spontaneously representing rules or identifying shortcuts in a maze 5 , 6 , 7 . It supports reminiscing about past experiences 6 , 15 , 16 , understanding the present 5 , 17 , and planning for the future 3 , 4 . Consequently, replay has been detected not only in the hippocampus but in other brain regions as well 18 , including the visual cortex 19 and entorhinal cortex (EC) 20 . These replays occur either in coordination with, or independently from, hippocampal replay. However, the restricted spatial coverage of invasive neural recordings means that the comprehensive pattern of whole-brain activation associated with replay events remains largely uncharted.

In humans, noninvasive neuroimaging has found evidence for memory reactivation 21 , 22 , 23 and more recently, sequential replay 6 , 24 , findings that align with those observed in rodent studies 25 . During planning, Kurth-Nelson, et al 26 reported fast neural sequences with a transition speed of 40 ms lag using Magnetoencephalography (MEG). The time compression seen in these sequences resembled those observed in rodent replay 10 , 12 . During rest, reverse replay of past experiences, has been selectively linked to value learning. This was identified using both MEG 3 , 6 and Electroencephalography (EEG) 27 , a finding consistent with the animal literature 16 . While M/EEG provides valuable insights into the rapid dynamics of replay, it does not offer precise information about the source of replay signals.

Human functional magnetic resonance imaging (fMRI) has been used to localize sequential neural replay to specific brain regions 24 , 28 , 29 . Wittkuhn and Schuck 28 , employed fMRI to index the sequence of predictive probabilities within a time repetition (TR), reporting on-task sub-second activations of visual stimuli in the occipital-temporal cortex. During rest, Schuck and Niv 24 reported positive correlation between the frequencies of transitions between decoded states and the expected distances between these states in the hippocampus. However, fMRI is limited in its ability to discern the directionality and speed of replay 28 , characteristics that are likely important given that previous human M/EEG studies 3 , 6 , 30 , 31 , as well as animal research 24 , 28 , 29 , have shown a correspondence to different functional aspects of replay. To date, no study has been able to simultaneously record replay events and capture high spatial resolution, whole-brain activity in humans (cf. related work by Higgins, et al. 32 on MEG source localization).

While replay is thought to be related to a variety of cognitive functions, it is useful to consider these as broadly serving two general aspects: the offline formation of a cognitive map during rest and sleep, and the on-task utilization of this map for guiding behaviour 25 . Here, a ‘cognitive map’ is used in its most general sense, referring to a structured mental representation of experiences, without distinguishing between narrative or schema 17 , 33 , 34 . Understanding the dynamics of replay in relation to broader brain activation is crucial, especially the role of the default mode network (DMN). The DMN, a set of brain regions, shows increased activity during rest 35 or internal cognition tasks, such as mental simulation or imagination 36 , and is hypothesized to encode our world knowledge, or cognitive map 37 , 38 , 39 . However, the interplay between replay dynamics and the DMN during both task and rest remains underexplored. This is due to the temporal transience of hippocampal replay and the spatial distribution of the DMN, with neither M/EEG nor fMRI alone being sufficient to capture these neural processes simultaneously.

In the current study, we examine memory reactivation of task-related items and their sequential replay during mental simulation and wakeful rest. Task-related reactivation refers to the spontaneous reactivation of past experiences, recognized through decoding models, while sequential replay is defined as the sequential reactivation of those experiences. The simultaneous use of EEG-fMRI recording offers a unique opportunity to explore these phenomena in greater depth 40 , 41 , 42 . The fine temporal resolution of EEG captures fast neural replay and provides timestamps of replay events, enabling the probing of brain-wide activation with fMRI. We focus on the whole-brain activation and the hippocampal functional connectivity to other brain regions at the times of transient replay events, both during rest and while on-task.

Task and analysis pipeline

With simultaneous EEG-fMRI recordings, subjects were tasked with mentally connecting dots that were separate in experience but could be linked together based on a learnt relational structure. This cognitive map is a one-dimensional line. Previous studies have shown that a similar task, with two sequences (comprised of six pairwise associations), elicits offline reactivations during rest, which can be detected using either MEG 6 or EEG 27 . The current task is modified to include a directional cue to test if replay directionality during mental simulation is subject to explicit instruction. The task is also simplified to contain only one sequence of four objects.

The task starts with a functional localizer session (Fig. 1 ), used to train decoders, during which subjects were presented with one of four images. They were encouraged to think about the image’s semantic content and were later asked to determine whether the following text matched the preceding image. As in previous studies, subjects were unaware of task-related information during the functional localizer session. This session was used to train decoders for both EEG 27 and fMRI signals 28 . After this, three pairwise associations were presented in a randomized order (e.g., 1 → 2, 3 → 4, 2 → 3), and subjects were required to mentally link the associations into a sequence (i.e., 1 → 2 → 3 → 4), a process we term sequence learning. Only those subjects who achieved at least 90% accuracy in the last learning run proceeded to the cued mental simulation task. A resting state period was included both before (PRE Rest) and after learning (POST Rest). After that, subjects were asked to mentally simulate the sequence in either a forward or reverse order, based on the cue (1 →, forward; ← 4, backward). Our subsequent analyses included 33 subjects who completed all task sessions with simultaneous EEG-fMRI recording.

Subjects, undergoing simultaneous EEG-fMRI recordings, were required to construct a sequence by learning pairwise associations of four discrete visual stimuli. They were then cued to mentally simulate the learned sequence in either a forward or reverse order. As in previous replay studies 3 , 6 , 30 , 32 , 45 , 46 , stimuli were first presented in a random order during functional localizer phase, prior to learning. We included a resting state both before (PRE Rest) and after learning (POST Rest) and this allowed us to measure changes in spontaneous neural activity induced by learning.

Utilizing the fine spatiotemporal resolution offered by simultaneous EEG-fMRI, our goal is to determine when and where neural replay occurs in the brain. This involves indexing fast replay events through EEG and imaging replay-aligned brain-wide activation in fMRI. In brief, our analysis pipeline comprises five steps (Fig. 2 ). First, we train neural decoding models for each image based on EEG data from the functional localizer session. These models are then applied to decode their neural reactivations during mental simulation and offline resting time. After decoding, we quantify the strength of sequential reactivations (or replay) in a sequence (e.g., 1 → 2 → 3 → 4), separately for forward and reverse order 43 . If there is significant evidence for replay, we can calculate when such replay occurs and the strength of this evidence. To model this replay probability in fMRI, we convolve it with a canonical hemodynamic response function (HRF), and down-sample it to match the temporal resolution of the fMRI signal. Replay probability can then be encoded as an additional psychological condition using a general linear model (GLM) in fMRI. In addition to localizing replay, we can model its psychophysiological interaction (PPI) 44 to explore how functional connectivity between a region of interest (ROI, e.g., the hippocampus) and other brain regions changes as a function of replay probability. Notably, this analysis pipeline is not restricted to replay; we can investigate the spatiotemporal dynamics of any task reactivations in the same way.

a EEG-based stimuli classifiers were trained using whole-brain channel features during the functional localizer and later used to decode stimuli reactivations during specific task phases, such as rest or during mental simulation. b Temporal Delayed Linear Modelling (TDLM) was applied to the decoded time series to measure the sequential reactivation of states (e.g., visual stimuli) separately for forward and reverse order 43 . c After identifying a time lag of interest (e.g., the peak of sequenceness), we derived an EEG-based replay probability time course. This was then convolved with the hemodynamic response function (HRF) and down-sampled to match the fMRI time resolution, serving as an additional regressor in an fMRI-based GLM analysis. d Based on the new GLM, we determined when (via EEG) and where (via fMRI) replay occurs. e Using an fMRI-derived ROI (green trace, hippocampus), this EEG-based replay probability can be used (by multiplying with ROI neural activity) to detect changes in functional connectivity with other brain regions as a function of replay probability (i.e., psychophysiological interaction, PPI). Data shown here (decoding, EEG replay and coupled fMRI pattern) are from representative subjects. Results are presented with P unc . < 0.01 for illustrative purpose and reported using the MNI coordinate system.

EEG-based and fMRI-based neural decoding

During the functional localizer, subjects were instructed to press key ‘1’ when a text matched the semantic content of its preceding image (congruent condition), and ‘2’ otherwise (incongruent condition). The mean behavioural accuracy was 94.57 ± 0.70%, where chance level is 50%. Following the analysis step outlined above, we trained four separate one-vs-rest logistic regression classifiers based upon EEG data from correct trials, one for each image. As in previous M/EEG-based replay studies 3 , 6 , 26 , 27 , 30 , 32 , 43 , 45 , 46 , we trained EEG decoding models using all available channels as features at a single time bin (10 ms) and tested performance at all time points from 200 ms prior to the stimulus onset to 800 ms post onset (Fig. 3a ). The peak cross-validated decoding accuracy was observed at 210 ms post stimulus onset (46.25 ± 0.95%, compared with a chance level of 25%, t (32) = 22.41, P < 0.001). To further examine the sensitivity of the classifiers to each image, we analyzed the time course of predicted probability separately for each image (Fig. 3b ). All image classifiers showed above-chance probability in predicting the images they were trained on (dark grey lines) and not for other images (lighter grey lines). Based on these results, the image classifiers were trained at 210 ms post stimulus onset for our subsequent EEG-based replay analysis. Note, similar decoding accuracy and temporal dynamics were observed in a pilot subject who performed under both standalone EEG and simultaneous EEG-fMRI settings, indicating consistent neural dynamics across both settings (Supplementary Fig. 1a ).

a The mean cross validated decoding accuracy of EEG-based classifiers. As in previous studies 3 , 6 , 27 , 30 , 32 , 45 , 46 , classifiers were trained independently at each time point and tested on all time points, starting from 200 ms before stimulus onset to 800 ms post onset (10 ms time bin) during the functional localizer task (left panel). Decoding accuracy peaked at 210 ms post-stimulus onset. n = 33. b The time course (−200 – 800 ms) of mean EEG-based decoding probability trained and tested at the same post-stimulus onset (black line), separately for each stimulus. The dark grey lines represent the decoding probability of a particular classifier for a given image (black line represent the mean probability across subjects), while the light grey lines represent the mean decoding probability of the same classifier for other images. n = 33. c Feature selection procedure in fMRI-based decoding. Following Wittkuhn and Schuck 28 , we selected the subject-specific anatomical masks combined with thresholding t-maps ( t > 3) to identify voxels that selectively response to functional localizer. Note that the result presented here was selected from a representative subject for illustrative purpose only. d The time course (in TR, starting from stimulus onset) of mean fMRI-based decoding probability trained and tested at the same post-stimulus time, separately for each stimulus. The dark grey lines represent the decoding probability of a particular classifier for a given image (black line represent the mean probability across subjects), while light grey lines represent the mean decoding probability of the same classifier for other images. n = 33. Source data are provided as a Source Data file.

Contrary to the fine temporal resolution offered by EEG, fMRI is better suited for localizing where in the brain a specific cognitive process unfolds. In fMRI, we found significant activation in the visual cortex when an image was on-screen, with also class-specific activation patterns observed (Supplementary Fig. 2a ). Moreover, heightened activation was detected in the temporal cortex and anterior cingulate cortex (ACC) when semantic text was presented (Supplementary Fig. 2b ). We decoded images from the fMRI signal as in Wittkuhn and Schuck 28 . We first performed feature selection based on anatomical mask and functional t map (Fig. 3c ), and found a peak cross-validated decoding accuracy of 83.39 ± 1.77% (compared with a chance level of 25%, t (32) = 38.00, P < 0.001), at the 4 th TR post stimulus onset (Fig. 3d ), consistent with Wittkuhn and Schuck 28 .

In both EEG and fMRI-based decoding, the predicted probability for the true stimulus significantly exceeded the chance level (25%, EEG: all t (32) ≥ 8.47, P < 0.001; fMRI: all t (32) ≥ 20.63, P < 0.001, Fig. 3b & d ). A key advantage of simultaneous EEG-fMRI recording is its capability to examine activations in response to the same event. We observed a significant positive correlation across subjects between the decoding accuracies of EEG and fMRI classifiers (robust correlation, r = 0.49, P = 0.004, see Supplementary Fig. 1c ), consistent with them capturing the same cognitive processes.

Spatiotemporal dynamics of neural replay during mental simulation

Human replays have been found to spontaneously reorganize experience in a manner that corresponds to a learnt relational structure 3 , 6 , 26 , 27 , 30 , 32 , 45 , 46 . In a sequence learning session, subjects learnt to form a linear sequence consisting of four images based on three pairwise associations experienced in randomized order. During learning, images from pairwise associations were presented serially with heightened activation in visual cortex, dorsal lateral prefrontal cortex (DLPFC) and hippocampus was evident at the onset of the 1 st compared to the 2 nd image (Supplementary Fig. 3a ). Over the course of learning, hippocampal engagement by the 2 nd image increased ( β = 0.015 ± 0.005, P < 0.001, Supplementary Fig. 3b ), consistent with its role in associative learning 38 , 47 . Behavioral performance significantly increased with learning experience ( F (32) = 19.04, P < 0.001), aligning with heightened hippocampal activity observed in the second item over trials. During the probe phase, a target image was first presented on the screen, then subjects were asked to think of which image comes next. A probe image was then shown, and subjects were required to determine if it was correct or not. We found significant activation in the ACC, DLPFC and insular cortex at the onset of the target image (Supplementary Fig. 3c ), and higher ACC activation to the probe image for correct vs. error response trials (Supplementary Fig. 3d ). Across all subjects, the mean accuracy of the probe test was 93.86 ± 1.20%, indicating successful learning of the sequence. The mean accuracy of the last run was 99.49 ± 0.35% across subjects; no subjects were excluded from the analysis based on the criterion of > 90% accuracy.

After sequence learning, subjects were instructed to mentally simulate the sequence in either forward or reverse order based on a given cue. Subsequently, they were required to identify whether a probe image was part of the sequence. The average behavioral accuracy was 93.59 ± 0.82%. Subjects were also asked to rate the vividness of their subjective experience on a scale from 1 to 4. All participants reported a high level of vividness with a mean rating of 3.35 ± 0.07. No significant difference was found between forward and backward mental simulation in terms of behavioral accuracy ( t (32) = -0.98, P = 0.335) or vividness rating ( t (32) = 1.50, P = 0.143).

At the beginning of each simulation trial, a directional cue, “1 → ” or “←4” appeared. If replay can be modulated by explicit instruction, we would predict a shift in the direction of replay (if it exists) that aligns with the cued instruction. By contrast, if replay corresponds to a more unconscious and spontaneous process, then we would expect the direction of replay to be independent of the cue.

In the human neuroimaging literature to date, there are two ways to quantify task-related sequential reactivations or replay during task. One is Temporal Delayed Linear Modelling (TDLM) 43 , which calculates the mean sequenceness over all time bins, independently at different speeds (time lags), separately for forward and reverse direction. This method is mainly used in M/EEG studies 3 , 6 , 27 , 30 , 32 , 45 , 46 but can, in principle, be applied to fMRI data 43 . The second method uses fMRI data 28 , and calculates the regression slope that predicts the position of a state based on the rank of the state probability at each time bin (or TR in fMRI terminology). Figure 4a , provides an illustration separately for the TDLM method and fMRI-based regression method. In addition, another method for detecting fMRI-based off-task replay is from Schuck and Niv 24 , calculates the similarity between an hypothesized transition matrix (state distances) and an empirical transition matrix (transition frequency between states) within a brain region of interest (ROI) during rest. In principle, this method can also be applied to the cued mental simulation session.

a The illustration of two analysis methods for detecting replays. TDLM 43 is used primarily with MEG 3 , 6 , 30 , 32 , 45 , 46 , and more recently also with EEG 27 . The other is a regression method, as per Wittkuhn and Schuck 28 and primarily used with fMRI 29 . Please note that this panel is solely for illustrative purposes. For results based on actual data, refer to Supplementary Fig. 5 and Panel c . b EEG-based replay with TDLM, separately for forward (cued “ $1\to$ ”, top row) and backward (cued “ $\leftarrow 4$ ”, bottom row) mental simulation conditions. There were significant forward (but not reverse) replays during both forward and backward mental simulation. Sequence strength on the peak time lag (30 ms) is shown on the right, separately for forward and backward mental simulation conditions (two-sided paired t -test, forward condition: t (32) = 2.80, P = 0.009; backward condition: t (32) = 3.09, P = 0.004). The grey dash line represents the permutation threshold, defined as the 95 th percentile of the permutated transitions of interest controlling for multiple compariso n s. n = 33. c fMRI-based neural sequence with regression method 28 , separately for forward and backward mental simulation conditions. There was no significant evidence for sequential activation in the correct order (all P corr. ≥ 0.06, two-sided one-sample t -test against zero, FDR corrected). The bar plot in the upper right corner shows mean slope coefficients for each period (two-sided paired t -test, forward condition: t (32) = 1.14, P = 0.260; backward condition: t (32) = −0.175, P = 0.862). None of these coefficients were significantly different compared to zero. See Supplementary Fig. 5 for assessing fMRI replay using TDLM, as well as results from single subject for illustration purpose. n = 33. d The parametric modulation of EEG-based replay probability in the whole-brain fMRI during mental simulation showed significant activations in hippocampus and mPFC. We use whole-brain FWE correction at the cluster level ( P < 0.05) with a cluster-inducing voxel threshold of P unc . < 0.001. e The psychophysiological interaction (PPI) between hippocampal activity (anatomically defined) and EEG-based replay probability revealed significant functional connectivity change in mPFC, PCC and visual cortex. See Supplementary Fig. 7c-d for mPFC-based PPI results. We use whole-brain FWE correction at the cluster level ( P < 0.05) with a cluster-inducing voxel threshold of P unc . < 0.01. Each dot is one subject. The grey lines connect results from the same subject. Shaded areas in b and c show SEM across subjects. Error bars in b and c show SEM across subjects. * P < 0.05, ** P < 0.01, ns., not significant. Abbreviation: HPC - hippocampus. Source data are provided as a Source Data file.

Using TDLM on the EEG-based decoding, we indeed found selective significant forward replay from 30 to 50 ms time lag in cue “1 → ” trials (peak at 30 ms lag, β = 0.021 ± 0.012, Fig. 4b upper panel), as well as forward replay from 20 to 40 ms time lag in cue “←4” trials (peak at 30 ms lag, β = 0.023 ± 0.012, Fig. 4b bottom panel). As the subjects’ task experience increased, their replay strength during mental simulation increased ( t (32) = 4.18, P < 0.001). However, vividness ratings of this simulation, elicited as a subjective measure, were found uncorrelated with replay strength ( t (31) = -0.55, P = 0.585). Extending the time lag scale to 2000 ms, to identify replay events at longer timescales, failed to reveal additional replay events (either forward or backward) beyond those detected at a peak of 30 ms (see Supplementary Fig. 4 ).

In fMRI-based decoding, we also applied TDLM method to the fMRI-based data. While there was a suggestion of replay in some individuals, no significant fMRI-based replays were found across subjects (Supplementary Fig. 5 ). Likewise, using the regression method 28 , we did not observe any significant regression slope in either time bin or condition (all P corr . ≥ 0.06, two-sided one-sample t -test against zero, Fig. 4c ), nor was there any significant difference between the 1 st and 2 nd periods (forward: t (32) = 1.14, P = 0.260; backward: t (32) = -0.175, P = 0.862, two-sided paired t -test, Fig. 4c ). Similarly, applying the method from Schuck and Niv 24 obtained non-significant fMRI-based replay (Supplementary Fig. 6a-b ).

To determine where in the brain on-task neural replay occurs, we identified putative replay events at 30 ms time lag and modelled these in a GLM to predict the fMRI signal. After convolving replay events with the HRF and down-sampling, the replay probability time series was modelled as a parametric modulator of the 10 s mental simulation regressor. We found that the occurrence of replay was associated with activations in both the hippocampus and medial prefrontal cortex (mPFC, Fig. 4d , see also Supplementary Fig. 7a for activations of the mental simulation regressor). This result is consistent with previous findings on MEG replay source localization 3 , 6 , 30 , 31 , 45 , suggesting that human replay, as is the case in rodents 11 , 48 , 49 , originates from hippocampus. We also explored brain-wide activation related to single-item reactivation and found increased activity in both the hippocampus and mPFC (see Supplementary Fig. 7b ), the same regions that exhibited higher activation in relation to sequence replay events.

We next investigated how functional connectivity between the hippocampus and other brain regions (e.g., DMN) changes in relation to variations in replay probability 44 . As replay probability increased, there was a significant increase in hippocampal-seed connectivity with DMN, including the mPFC 50 , 51 , 52 , 53 , 54 , and the posterior cingulate cortex (PCC) 53 , 55 , as well as the visual cortex 56 , 57 (see Fig. 4e ). We also explored changes in mPFC-based functional connectivity as a function of replay probability. This revealed significant increases in connectivity between the mPFC-seed and other DMN regions, including the PCC and angular gyrus, as well as with the visual cortex, but not with the hippocampus (see Supplementary Fig. 7c-d ). These results are consistent with a flow of replay information from the hippocampus to the mPFC, and subsequently to other DMN regions and the visual cortex. However, we acknowledge that PPI analyses do not allow for causal or directional inference.

Spatiotemporal dynamics of learning-induced task reactivation during rest

The findings detailed above indicate that simultaneous EEG-fMRI can index when and where of on-task replay. We next applied this analysis pipeline to rest periods, where, unlike task data, there are no obvious timestamps for specific cognitive processes. Nevertheless, identifying spontaneous reactivation or replay during rest can provide naturalistic timing information for modelling resting-state activity 25 . We assumed the absence of significant task-related replay during the PRE Rest period, given subjects had not yet experienced the visual stimuli or acquired any structural knowledge. In contrast, during the POST Rest period, after sequence learning, we predicted the presense of replay 6 . However, our TDLM analysis did not find evidence of replay in either EEG or fMRI-based decoding during the PRE or POST Rest period (Supplementary Fig. 8 ). Similarly, using Schuck and Niv 24 method to detect replay using fMRI, we found no significant evidence of replay in either the PRE or POST Rest period (see Supplementary Fig. 6c ).

The relatively simple sequence setup in the current study, which only involved one sequence as opposed to two sequences used in Liu, et al. 6 , might entail less of a need for sequential replay during rest 3 , 58 , 59 . Next, we analyzed the mean reactivation strength of task-related stimuli, without requiring them to be in sequence. Mean reactivation probabilities were calculated by averaging across all time points and all task stimuli during each rest period. We found that the mean reactivation strength of stimuli, regardless of their sequential order, was significantly higher in the POST Rest compared to the PRE Rest period ( t (31) = 2.75, P = 0.010, two-sided paired t -test; Fig. 5a ), suggesting enhanced task reactivations following learning.

a In EEG-based task reactivations, there was a significant higher reactivation strength during POST than PRE Rest (two-sided paired t -test: t (31) = 2.75, P = 0.010). n = 32, excluding an outlier (beyond three deviation of the mean). b Parametric modulation of EEG-based reactivation probability in whole-brain fMRI during POST Rest showed significant activations in bilateral anterior hippocampus (whole-brain FWE correction at the cluster level ( P < 0.05) with a cluster-forming voxel threshold of P unc . < 0.001). c ROI analysis. The EEG-based reactivation explained hippocampal activation (anatomically defined) during POST Rest, and it was stronger from PRE to POST Rest in hippocampus (two-sided one-sample t -test: PRE: t (32) = 1.08, P corr . = 0.287; POST: t (32) = 3.83, P corr . < 0.001; two-sided paired t -test: POST vs. PRE: t (32) = 2.44, P corr . = 0.030; FDR corrected). n = 33 . d , The task reactivation-aligned BOLD signal in hippocampus during POST Rest. Upon alignment to the onsets of task-related reactivation, we observed a significant increase in hippocampal BOLD activity, peaking at the 2 nd TR post-EEG-based reactivation (two-sided one-sample t -test: TR = 1: t (32) = 2.57, P = 0.015; TR = 2: t (32) = 3.02, P = 0.005), and also found at onset of fMRI-based reactivation (TR = 0: two-sided one-sample t -test: t (32) = 2.45, P = 0.020). n = 33. e The PPI between hippocampal activity (anatomically defined) and EEG-based reactivation probability showed increased functional connectivity with EC (anatomical mask depicted in blue) during POST Rest. We thresholded at P unc . < 0.01, K > 10 for visualization. f ROI analysis. The PPI revealed a significant increase in hippocampal-seed connectivity with the EC (anatomically defined) during POST Rest when EEG-based reactivation increased. (two-sided one-sample t -test: PRE: t (32) = 1.48, P = 0.148; POST: t (32) = 2.75, P = 0.010). n = 33. g There was a positive correlation between EEG-based and fMRI-based reactivation in explaining hippocampal activity during POST Rest, but not PRE Rest (robust correlation, PRE: r = 0.09, P = 0.602; POST: r = 0.38, P = 0.029). n = 33. The solid lines reflect the robust linear fit. Each dot is one subject. The grey lines connect results from the same subject. Shaded areas in d show SEM across subjects. Error bars in a, c and f show SEM across subjects. * P < 0.05, ** P < 0.01, *** P < 0.001, ns., not significant. Abbreviation: HPC - hippocampus. Source data are provided as a Source Data file.

We did not find significant correlations between single-item reactivation strength during POST rest and any behavioural performance measures. This includes sequence learning task performance ( r = -0.09, P = 0.619), cued mental simulation task performance ( r = -0.09, P = 0.607), and vividness ratings ( r = -0.13, P = 0.456). The same was true for PRE rest (all r < 0.15, P > 0.5). These null results may be due to a ceiling effect and limited variability in behavioural performance. Participants consistently demonstrated high accuracy in the last run of the sequence learning task (99.49 ± 0.35%), overall sequence learning (93.86 ± 1.20%), cued mental simulation (93.59 ± 0.82%), and vividness ratings (3.35 ± 0.07).

To explore offline reactivation-triggered whole-brain activity patterns, we applied our analysis pipeline to task-related reactivation during rest by summarizing the EEG-based reactivation across stimuli. These reactivation events were then convolved with the HRF, and the ensuing reactivation time series was used as a regressor to explain resting-state fMRI signals. We found that higher reactivation strength correlated with increased hippocampal activation during POST Rest (Fig. 5b ; hippocampal ROI analysis, t (32) = 3.83, P corr . < 0.001, two-sided one-sample t -test), while no activation was identified during PRE Rest, at either the whole-brain or the hippocampal ROI level ( t (32) = 1.08, P corr . = 0.287). Moreover, hippocampal activation was significantly stronger during POST Rest compared to PRE Rest ( t (32) = 2.44, P corr . = 0.030, two-sided paired t -test, Fig. 5c ). As a control analysis, we found that no EEG-based reactivation explained activity in the primary motor cortex (M1), during either PRE or POST Rest or their differences (all t (32) ≤ 1.20, P ≥ 0.24).

When we applied the same analysis pipeline to fMRI-based task reactivation, we found no significant increase in reactivation for POST Rest compared to PRE Rest ( t (32) = 0.92, P corr . = 0.363; Supplementary Fig. 9a ). However, consistent with EEG-based findings, the strength of fMRI-based reactivation correlated with increased hippocampal activation during POST Rest (see Supplementary Fig. 9b-c ; hippocampus ROI analysis: t (32) = 2.87, P corr . = 0.021), but not during PRE Rest ( t (32) = 1.25, P corr . = 0.329). Interestingly, by aligning to the onset of EEG-based reactivation, a significant increase in hippocampal BOLD activity was observed, peaking at the 2 nd TR post-reactivation ( t (32) = 3.02, P = 0.005), indicating a higher sensitivity than that of fMRI-based reactivation (Fig. 5d ). This finding suggests that EEG provide an effective means for localizing the timing of spontaneous task reactivation during rest.

We have also examined functional connectivity between the hippocampus and other brain regions as a function of task reactivation during rest (Fig. 5e ). The PPI analysis revealed a significant increase in hippocampal-seed connectivity with EC during POST Rest when EEG-based reactivation increased (ROI analysis, t (32) = 2.75, P = 0.010, Fig. 5f ), but not during PRE Rest ( t (32) = 1.48, P = 0.148). No significant results were found when the analysis is done based on fMRI-based reactivation (all t (32) ≤ 1.41, P ≥ 0.17).

The differences between EEG- and fMRI-based task reactivation raise an intriguing question as to their relationship. While EEG and fMRI-based reactivation time series themselves were not correlated, nor there was a systematic temporal relationship between them either during task or rest (Supplementary Fig. 10 ), we found a significant positive correlation of predicted hippocampal activity with EEG-based reactivation and that of fMRI during POST Rest (Fig. 5g , robust correlation, r = 0.38, P = 0.029), not PRE Rest ( r = 0.09, P = 0.602). This suggests offline task reactivation may align EEG and fMRI-based representation in hippocampus.

The simultaneous EEG-fMRI analysis framework offers a powerful method to probe the brain-wide patterns associated with temporally transient events, such as memory reactivation and its sequential replay. During task, we show that this combined pipeline can detect fast replay events, as reflected in EEG signals (with a 30 ms time lag), and localize these to the hippocampus, as revealed by simultaneous fMRI. We found that replay during mental simulation is a spontaneous process, one that operates independent of explicit task instruction. An increase in EEG indexed replay strength was associated not only with significant fMRI activations in hippocampus and mPFC, but also with a significant increase in hippocampal-seed connectivity with the DMN and visual cortex. During rest, we observed a marked increase in EEG-based task-related reactivation from pre- to post-learning periods. Here, an increase in reactivation strength was associated with a significant increase in hippocampal activation and increased hippocampal-seed connectivity with the EC.

These results align with previous work in humans. The decoding accuracy and dynamics of simultaneous EEG mirrored those observed in the standalone EEG condition (Supplementary Fig. 1a ), as well as previous M/EEG decoding findings 6 , 27 . In fMRI-based decoding, we followed the methods described by Wittkuhn and Schuck 28 and achieved similar decoding performance, albeit not detecting replay events in fMRI. During mental simulation, we found 30-ms-time-lag on-task replay in EEG, a finding in keeping with previously reported human replay speed 3 , 6 , 26 , 27 , 46 . We further showed the EEG-based replay was associated with activation of hippocampus and mPFC, revealed by fMRI, a finding consistent with previous fMRI replay findings 24 and MEG source localization 3 , 6 , 30 , 31 , 46 .

A key advance in our analysis pipeline is its ability to detect brain-wide activation in coordination with replay. During mental simulation, we found hippocampal and DMN activation associated with sequence replay, as well as reactivation in general. The hippocampal connectivity with the DMN also increased as a function of replay strength. The DMN is proposed to encode an internal model of the world 25 , 34 . When initializing replay, this increase suggests a query from the hippocampus to a putative cognitive map, possibly serving to align experiences into an ordered structure. Here, it is intriguing to note that our results are consistent with findings from Kaplan, et al. 60 , who combined electrophysiological recordings of the hippocampus with whole-brain fMRI in anesthetized monkeys. They also align with rodent studies where widespread activation of cortical regions in the DMN is associated with the onset of hippocampal SWRs, a biomarker of replay 61 , 62 . These findings collectively suggest possible cross-species relationships between hippocampal replay and the DMN.

The direction of replay is found to be independent of explicit instruction. Likewise, there was no significant evidence for replay with time lags exceeding 100 ms, even when extending the analysis to an upper time lag limit of 2000 ms. In previous studies, replay direction was found to change with task demands, such as value learning 3 , 6 , probe questions 31 , and decision-making versus memory preservation 30 . A common feature of these prior studies is that a shift in replay direction served a specific computational goal. For instance, replay shifted from forward to backward only for a sequence paired with a reward outcome, but not for a neutral sequence 6 , where reverse replay is hypothesized to support credit assignment 3 , 63 . In the current study, verbal instruction alone does not entail any computational demand and the instruction-independent replay pattern suggests that on-task replay may in fact be spontaneous, independent of volition, and conscious mental effort. Consistent with this, we also found no correlation between the subjective rating of vividness of mental simulation and replay strength. While it could be a ceiling effect of vividness rating, it is also possible that the content of replay is more at a semantic level where imagery vividness is likely to exert little impact on replay quality 64 .

During rest, we found that task reactivation associated with increased hippocampal activity in the POST compared to the PRE Rest period, with enhanced hippocampal connectivity to the EC (Fig. 5d-f ). The EC is thought to encode task relational structures 65 , a concept supported by the presence of grid cells in animal research 66 and grid-like coding in human fMRI studies 37 , 38 . It is noteworthy that compared to the mental simulation period, cross-regional communication during rest was predominantly confined to functional connectivity between the hippocampus and EC, rather than with the DMN. This suggests that on-task replay and off-task reactivation, manifest different brain dynamics that is suggestive of serving distinct cognitive functions 14 . In the rodent literature, on-task replay is associated with memory retrieval and planning 49 , whereas rest replay or reactivation is more linked to memory consolidation 9 . Although the exact mechanism is unclear, the increased connectivity between the hippocampus and EC during rest reactivation may relate to their coordinated activity. For instance, Ólafsdóttir, et al. 67 found evidence that coordinated grid and place cell replay during rest in rodents supports memory consolidation. The differing functions of replay and reactivation pose an intriguing question, where recent theoretical work has attempted to provide a unified account 63 . We anticipate that simultaneous EEG-fMRI will provide a promising tool for testing these theoretical predictions, especially in the context of human studies.

Despite a marked increase in task-related reactivation from the PRE to the POST Rest period, significant sequential reactivation—or replay—during rest remained elusive. A key challenge in detecting replay during rest might be the relatively diminished quality of EEG signals obtained during simultaneous EEG-fMRI recordings. Rest replay tends to manifest as a temporally dispersed signal, occurring in bursts 32 , in contrast to the temporally localized and robust signals induced by cue-based simulations. Nevertheless, EEG alone has successfully detected rest replay using a task similar to Liu, et al. 6 , where significant reverse replay during rest was linked to value learning, underscoring the capacity of EEG to detect rest replay signals 27 . Another plausible explanation for the absence of rest-replay is the relatively simple sequence set-up, where subjects had less need to replay a sequence that is already well learnt. This contrast with the more demanding task features in Liu, et al. 6 and Yu, et al. 27 . This conjecture is supported by the near-perfect behavioral performance in the final run of sequence learning and is also consistent with Wimmer, et al. 31 , who reported enhanced mean reactivation, but not sequential replay, for well-encoded memories. However, it is also the case that stronger replay has been observed for sequences that have been more robustly encoded. For instance, the more time an animal spends within two place fields, the more frequently a corresponding place cell pair is reactivated during sleep 68 . Conversely, other findings in the animal literature indicate that replay is more readily observed in novel compared to familiar tracks 10 , with a higher reactivation probability in novel environments 69 . This raises an intriguing question regarding the relationship between learning performance and replay strength, particularly considering previous human studies report replay tends to prioritize weakly encoded memories 58 . It is conceivable that the learning experience and replay strength follow an inverted U-shaped curve, where the strongest replay occurring for intermediate learning experience 31 , 70 , a possibility that warrants more detailed investigation.

Comparing EEG and fMRI-based decoding, we found higher decoding accuracy for fMRI-based classifiers compared to EEG, possibly due to the much larger feature size. However, the reactivation/replay analysis on the fMRI signal alone was less effective. During mental simulation, following Wittkuhn and Schuck 28 , we found a qualitatively similar pattern to EEG-based replay, but this was non-significant in the fMRI signal (Fig. 4c ). During rest, we found a chance level of decoding accuracy in hippocampus, and non-significant replay in hippocampus and mPFC, when applying the Schuck and Niv 24 method (Supplementary Fig. 6 ). This might reflect that mental imagery is a degraded, fuzzy experience, difficult to detect (Pearson, 2019), and these fMRI-based replay methods 24 , 28 , 43 are not optimized for discerning replay events in the current study.

A question arising is whether EEG-based and fMRI-based analyses capture overlapping or independent cognitive processes. We found a significant positive correlation between decoding accuracy of EEG and fMRI classifiers during the functional localizer session, suggesting they capture a common process. However, at the level of reactivation dynamics, we found no temporal correlations between EEG and fMRI, neither during mental simulation nor during rest (Supplementary Fig. 10 ). When we probed the relationship between EEG and fMRI-based reactivation in explaining hippocampal activation, we found a positive correlation during POST but not PRE Rest (Fig. 5g ). This suggests that despite the different temporal dynamics between EEG and fMRI activity, spontaneous task-related reactivations align in the hippocampus. Furthermore, a stronger hippocampal BOLD activity when aligning to the onsets of EEG-based, as opposed to fMRI-based, reactivations (Fig. 5d ), suggests that EEG may be more sensitive for localizing the timing of spontaneous task reactivations. Together, these findings imply that simultaneous EEG-fMRI can capture spontaneous cognitive processes, even when these are temporally transient or spatially distributed.

Lastly, research on reactivation and its sequential replay in humans is relatively nascent and has been significantly influenced by studies in rodents 25 . While our human findings largely align with the rodent literature, it is also important to note the differences, as discussed above. The definition of replay or reactivation in humans predominantly refers to a representational frame of reference (e.g., ‘brain representation of a face’) as opposed to a neuronal level framework (e.g., place cells) in rodent studies. This distinction has implications for interpreting replay results, particularly regarding their brain-wide propagation. It is conceivable that representation results may not directly correspond to findings at the neuronal level, and vice versa. Future investigations, recording simultaneous neuronal activity in specific regions of the human brain, will be valuable in addressing this issue. For example, Staresina, et al. 71 used intracranial electroencephalography combined with multiunit activity recordings from the human hippocampus and surrounding medial temporal lobe areas. They report a triple coupling between slow oscillations, spindles, and ripples, orchestrating neuronal processing for systemic consolidation during sleep, thereby validating results from rodent studies 72 .

In conclusion, using simultaneous EEG-fMRI, our study provides empirical validation of an analysis pipeline for studying replay and reactivation alongside whole-brain activation. Identifying the putative replay/reactivation events in EEG provides a unique timestamp for imaging brain-wide activation in fMRI. This same analysis pipeline helps bridge between disparate research areas and provides for a comprehensive understanding of the functions of replay in relation to human cognition. This opens exciting new possibilities for future studies, such as investigating hippocampal replay and grid-like coding during cognitive-map-based computation 6 , 37 , as well as a richer examination of memory consolidation during sleep 25 . It enables a more sophisticated understanding of the entorhinal-hippocampal-prefrontal systems underlying inference and generalization.

Participants

A total of 40 healthy adults were recruited for the study. All participants had normal or corrected-to-normal vision and no history of psychiatric or neurological disorders. They were screened for magnetic resonance imaging (MRI) eligibility prior to participation. The experiment was approved by the Medical Ethics Committee of Shenzhen University Medical School (reference number: PN-202300012), and all subjects provided written informed consent. After excluding subjects with excessive head motion (FD > 0.2) or incomplete participation, 33 subjects were included in the full analysis (age: 22.91 ± 0.33 years, 17 females, 16 males). None of the subjects reported any prior experience with the stimuli or the behavioral task.

Overview of the task design

After completing preparatory work, subjects were taken into the MRI scanner. We began with a short brain localizer, followed by an 8-min anatomical scan and a 5-min resting-state scan, during which subjects were asked to stay awake and focus on a white fixation cross presented on a grey screen. Then, the subjects underwent a series of task sessions: functional localizer, sequence learning, and cued mental simulation. We acquired four functional localizer runs of approximately 12 min each, three sequence learning runs of 6 min each. After sequence learning, we acquired a further 5-min resting-state, again with subjects’ eye open. Finally, we acquired three cued mental simulation runs of about 10 min. The entire experiment lasted, on average, between 2 and 3 h.

Functional localizer

The functional localizer session was designed to train neural decoders on task states. The experiment utilized four visual stimuli (face, scissor, zebra, and banana), which were previously shown to elicit object-specific neural patterns in human brain 3 , 45 . Subjects were presented with one of four images for 1 sec and encouraged to consider its semantic content. Following this, a word was displayed for 1 sec, after a blank interval of 1-2 secs. Subjects then determined whether the word matched the preceding image, pressing ‘1’ for matches and ‘2’ for non-matches. Key positions were counterbalanced across subjects. Trials were separated with intervals of 1-3 secs to ensure adequate time delay between them. For incorrect responses, subjects received visual feedback for 1 sec. Each visual stimulus was shown 72 times, followed by both matching and non-matching semantic stimuli, totaling 288 trials evenly split between corresponding and non-corresponding pairs. Stimuli were presented in a pseudo-random order, avoiding more than two consecutive presentations of the same stimulus. We provided visual feedback on the accuracy and timeliness of responses. Incorrect responses prompted feedback for 1 sec with instructions to press the correct button. If no response was made within the allotted time, a “Response timeout. Please answer promptly.” message was displayed. For correct responses, no additional feedback was given, and the task moved to the next trial. Subjects achieving over 90% accuracy received a ¥20 bonus. The task comprised four blocks, each lasting about 12 min, totalling approximately 48 min for the entire task phase.

Sequence learning

In sequence learning session, subjects were required to build a 4-item sequence (e.g., A → B → C → D) by mentally connecting three pairwise experiences (i.e., A $\to$ B, B $\to$ C, C $\to$ D). The task comprised three runs, each including an associative learning (with three learning pairs) and a probe test. During learning, each trial started with a 300 ms fixation, then stimuli within the learning pair were presented sequentially, one for 1.5 s, with a 1-3 s interval between stimuli. The interval between learning pairs was 5 s. Each learning pair was repeated three times in a run. Subjects were asked to learn associations between stimuli, and their memory performances were probed in the following test. During test, a target stimulus with ‘->…->…?’ cue was presented for 4 s, and subjects were asked to imagine all images that followed the target image. Then after a 1-3 s interval with a blank screen, subjects were presented with a probe stimulus for 2 s. Subjects pressed key ‘1’ if the probe stimulus followed the target stimulus in the sequence, and ‘2’ otherwise. Key positions were counterbalanced between subjects, and no feedback was given during probe trials. There were 12 probe trials per learning run. The mapping between stimuli (face, scissor, zebra, and banana) and states (A, B, C, D) was fixed within subject but randomized across subjects. Subjects were allowed to proceed if they achieved at least 90% accuracy on the last learning run. Each block, consisting of one learning session and one test session, lasts about 6 min. With three blocks per subject, the total duration for this phase is approximately 18 min.

Cued mental simulation

Subjects were directed to mentally simulate the image sequence for 10 s, in either a forward (1 →) or a reverse direction ( ← 4) based upon a directional cue. Then, following a 1-3 s inter-stimulus interval, a probe image was displayed for 2 s. Subjects were required to determine whether the probe image was within the learnt sequence or not. To promote attentive processing, we created four lure probe images of the same content with the original ones, but with subtle difference (e.g., orientation of the zebra head, colour of the banana, etc), as well as four new images with different content. The probe images were randomly displayed, with half necessitating a key ‘1’ response if they were in the sequence, and the remaining half requiring a key ‘2’ response if they were not. Key positions ‘1’ and ‘2’ were counterbalanced between subjects. No feedback was provided during probe trials to prevent additional learning. We found no difference of performance in differentiating original and lure images ( t (32) = 0.62, P = 0.541), suggesting the subjects were attentive. After probe test, participants were asked to assess the vividness of their recently performed mental simulation. The task comprised of 96 trials, equally split between forward and backward conditions. Each block, consisting of 32 trials, lasted about 10 min. With three blocks per subject, the total duration for an individual amounted to approximately 30 min.

EEG data acquisition

EEG was recorded simultaneously with fMRI data using an MR-compatible EEG amplifier system (BrainAmps MR-Plus, Brain Products, Germany), along with a specialized electrode cap (BrainCap). The recording was done using 64 channels using the international 10/20 system, with the reference channel positioned at FCz. A drop-down rear electrode was utilized to record electrocardiographic (ECG) activity. EEG data was recorded at a sample rate of 1000 Hz, with the impedance of all channels was kept below 10 kΩ throughout the experiment. To synchronize the EEG and fMRI recordings, the BrainVision recording software (BrainProducts, Germany) was utilized to capture triggers from both the MRI scanner and a stimulus presentation software developed using PsychoPy 73 .

MRI data acquisition

All MRI data were acquired using a 64-channel head coil on a research-dedicated 3-Tesla Siemens Magnetom Prisma MRI scanner. For the functional scans, whole-brain images were acquired using a segmented k-space and steady-state T2*-weighted multi-band (MB) echo-planar imaging (EPI) single-echo gradient sequence that is sensitive to the BOLD contrast. This measures local magnetic changes caused by changes in blood oxygenation that accompany neural activity (sequence specification: 46 slices in interleaved ascending order; anterior-to-posterior (A–P) phase-encoding direction; TR = 1300 ms; echo time (TE) = 24 ms; voxel size = 3 × 3 × 3 mm; matrix = 64 × 64; field of view (FOV) = 192 × 192 mm 2 ; flip angle (FA) = 67°; distance factor = 0%; MB acceleration factor 2). Slices were tilted for each subject by 30° forwards relative to the rostro-caudal axis to improve the quality of fMRI signal from the hippocampus. For each functional run, the task began after acquisition of the first four volumes (i.e., after 5.2 s) to avoid partial saturation effects and allow for scanner equilibrium. We also recorded two functional runs of resting-state fMRI data, one before and one after the functional localizer and sequence learning task runs. Each resting-state run was about 5 min in length, during which 237 functional volumes were acquired. High-resolution T1-weighted (T1w) anatomical Magnetization Prepared Rapid Gradient Echo (MPRAGE) sequences were obtained from each subject to allow registration and brain-surface reconstruction (sequence specification: 192 slices; TR = 2300 ms; TE = 2.26 ms; FA = 8°; inversion time (TI) = 1000 ms; matrix size = 192 × 256; FOV = 192 × 256 mm 2 ; voxel size = 1 × 1 × 1 mm).

EEG data preparation and preprocessing

EEG data collected inside MRI scanner was contaminated by imaging, ballistocardiographic and ocular artifacts. We utilized an Average Artefact Subtraction (AAS) 74 algorithm provide by the BrainVision Analyzer software (BrainProducts, Germany) to remove imaging artifacts. Following this, several preprocessing steps were undertaken, which involved removing residual physiological artifacts through the use of EEGLAB 75 and custom MATLAB scripts. We follow the same pre-processing pipeline for previous MEG/EEG based replay analysis 6 , 27 , 43 . Specifically, we downsampled the EEG data to a frequency of 100 Hz and applied 1 Hz high pass and 40 Hz low pass finite impulse response (FIR) filters. Due to poor signal quality, channel AF3 was excluded from further analysis, and the ECG channel was also excluded. To reduce dependence on the reference electrode position, the average of all electrodes was subtracted from each electrode. The data was then segmented into epochs extending from −200 ms before to 800 ms after the onset of functional localizer stimulus, and from -0.2 s before to 10 s after the onset of the mental simulation cue. To promote better decoding performance, baseline correction was omitted (see Supplementary Fig. 1b ). Epochs showing residual MR artifacts were detected and removed from the dataset, with an average of 8.88 ± 0.39 (mean ± SEM) trials excluded for the functional localizer task and 9.12 ± 0.17 trials for the mental simulation task. Subsequently, Independent Component Analysis (ICA) was applied to the EEG data to isolate physiological artifacts from eye movements, muscle activity, and ballistocardiogram. These artifact-related ICs were carefully labeled and manually removed. EEG data during rest underwent the same preprocessing steps.

MRI data preparation and preprocessing

Results in this manuscript come from preprocessing performed using fMRIPrep 21.0.2 (Esteban, et al. 76 ; RRID:SCR_016216), which is based on Nipype 1.6.1 (Gorgolewski, et al. 77 ; RRID:SCR_002502). Many internal operations of fMRIPrep use Nilearn 0.8.1 (Abraham, et al. 78 , RRID:SCR_001362), mostly within the functional processing workflow. For more details of the pipeline, see https://fmriprep.readthedocs.io/en/latest/workflows.html .

Conversion of data to the brain imaging data structure standard

To facilitate further analysis and sharing of data, all study data were arranged according to the Brain Imaging Data Structure (BIDS) specification using dcm2bids tool, which is freely available from https://unfmontreal.github.io/Dcm2Bids/ .

Anatomical data preprocessing

One T1-weighted (T1w) image was found within the input BIDS dataset. The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with N4BiasFieldCorrection 79 , distributed with ANTs 2.3.3 (RRID:SCR_004757) 80 , and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a Nipype implementation of the antsBrainExtraction.sh workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using fast (FSL 6.0.5.1:57b01774, RRID:SCR_002823) 81 . Brain surfaces were reconstructed using recon-all (FreeSurfer 6.0.1, RRID:SCR_001847) 82 , and the brain mask estimated previously was refined with a custom variation of the method to reconcile ANTs-derived and FreeSurfer-derived segmentations of the cortical gray-matter of Mindboggle (RRID:SCR_002438) 83 . Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with antsRegistration (ANTs 2.3.3), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: ICBM 152 Nonlinear Asymmetrical template version 2009c [RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym] 84 .

Functional data preprocessing

For each of the 12 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of fMRIPrep . Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using mcflirt (FSL 6.0.5.1:57b01774) 85 . BOLD runs were slice-time corrected to 0.612 s (0.5 of slice acquisition range 0 s - 1.23 s) using 3dTshift from AFNI (RRID:SCR_005927) 86 . The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as preprocessed BOLD in original space , or just preprocessed BOLD . The BOLD reference was then co-registered to the T1w reference using bbregister (FreeSurfer) which implements boundary-based registration 87 . Co-registration was configured with six degrees of freedom. Several confounding time-series were calculated based on the preprocessed BOLD : framewise displacement (FD), DVARS and three region-wise global signals. FD was computed using two formulations following Power (absolute sum of relative motions) 88 and Jenkinson (relative root mean square displacement between affines) 85 . FD and DVARS are calculated for each functional run, both using their implementations in Nipype (following the definitions by Power, et al.) 88 . The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction ( CompCor ) 89 . Principal components are estimated after high-pass filtering the preprocessed BOLD time-series (using a discrete cosine filter with 128 s cut-off) for the two CompCor variants: temporal (tCompCor) and anatomical (aCompCor). tCompCor components are then calculated from the top 2% variable voxels within the brain mask. For aCompCor, three probabilistic masks (CSF, WM and combined CSF + WM) are generated in anatomical space. The implementation differs from that of Behzadi et al. in that instead of eroding the masks by 2 pixels on BOLD space, the aCompCor masks are subtracted a mask of pixels that likely contain a volume fraction of GM. This mask is obtained by dilating a GM mask extracted from the FreeSurfer’s aseg segmentation, and it ensures components are not extracted from voxels containing a minimal fraction of GM. Finally, these masks are resampled into BOLD space and binarized by thresholding at 0.99 (as in the original implementation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the k components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each 90 . Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers.

The BOLD time-series were resampled into standard space, generating a preprocessed BOLD run in MNI152NLin2009cAsym space . First, a reference volume and its skull-stripped version were generated using a custom methodology of fMRIPrep . The BOLD time-series were resampled onto the following surfaces (FreeSurfer reconstruction nomenclature): fsnative , fsaverage . All resamplings can be performed with a single interpolation step by composing all the pertinent transformations (i.e., head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using antsApplyTransforms (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels 91 . Non-gridded (surface) resamplings were performed using mri_vol2surf (FreeSurfer).

Multivariate EEG pattern analysis

Lasso-regularized logistic regression models were trained on EEG data elicited by direct presentations of the images. The preprocessed data from 62 channels were used as input features for the model, which was implemented using the lassoglm function in MATLAB. Each model k had a vector of n + 1 coefficients: one slope for each channel and one intercept. To prevent overfitting, we applied L1 regularization with a lambda coefficient of 0.001. To evaluate the performance of the model, 5-fold cross-validation was employed. The data were randomly divided into five equal-sized subsets, and the model was trained on four subsets and tested on the remaining subset. This process was repeated five times, with each subset serving as the test set once. Decoding accuracy was calculated as the number of correctly classified images divided by the total number of images. We performed decoding at one subject and one time point at a time, repeating the process several times to obtain decoding accuracy for all subjects throughout the entire epoch. We calculated the mean decoding accuracy across subjects to identify the peak decoding accuracy time point at the group level. This accuracy was then compared to a chance baseline of 25% using a two-sided one-sample t -test. We then selected the models corresponding to the time point with the highest accuracy to decode replay or mental simulation.

Multivariate fMRI pattern analysis

All fMRI pattern classification analyses were conducted using open-source packages from the Python (v.3.9.13) modules Nilearn (v.0.10.0) 78 and scikit-learn (version 1.1.2) 92 . All multiple comparison correction in fMRI analysis were performed using FMRIB Software Library (FSL, https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/ ) 93 .

Feature selection

Follow Wittkuhn and Schuck 28 , we combined a functional ROI approach using thresholded t-maps and anatomical masks to identify image-responsive voxels located within a specific brain region. We ran four first-level general linear models (GLMs) for each subject, with one for each of the four cross-validation folds to identify voxels that showed significant activation in response to functional localizer by thresholding t-maps. A first-level GLM was fitted to the training set data (e.g., data from run 2 to 4) of each cross-validation fold and modelled the visual stimulus onset of all corrected trials of functional localizer (1 s for all events). We included wrong trials as a regressor of no interest. All the parameters of GLM analysis were consistent with those utilized in other GLMs (see detail in GLM analysis part). These anatomical masks were created based on automated anatomical labelling for brain-surface reconstructions of individual T1w-reference images using Freesurfer 82 , 94 , 95 , including the cuneus, lateral occipital sulcus, superior parietal lobule, pericalcarine gyrus, lingual gyrus, inferior parietal lobule, fusiform gyrus, inferior temporal gyrus, the middle temporal gyrus (cf. 31 , 96 ), as well as hippocampus, entorhinal cortex, and para hippocampal gyrus. Only gray-matter voxels were included in the masks 97 . Voxels with t-values above or below a threshold of t = 3 in the anatomical mask for the left-out run (e.g., run 1) of the classification analysis were selected and set to 1 to create the final binarized masks.

Leave-one-run-out cross validation procedure

We performed fMRI pattern classification using a leave-one-run-out cross-validation approach, where three task runs (e.g., run 2 to 4) were used for training and the left-out runs (e.g., run 1) used for testing. We trained and tested the classifiers on data obtained from the trials where subjects responded correctly. Four independent one-vs-rest logistic regression classifiers were trained, one for each of the four stimulus classes ( face , scissor , zebra , banana ) and relabeled all other classes to a common other category. This process was repeated four times to ensure each task run served as the test set once. All the identical parameter settings were the consistent with those set by Wittkuhn and Schuck 28 .

To identify the reactivation probability during mental simulation and rest period, we used all the data from functional localizer runs to train the classifiers. We created a new binarized mask for each subject by taking the intersection of the four binarized masks used for cross-validation. The classifiers with the same identical parameter settings as above were trained on the fMRI data. The classifiers were applied to the data during mental simulation (8 volumes per trial), and to data from resting sessions (230 volumes per rest session).

Temporally delayed linear modelling

We used Temporally Delayed Linear Modelling (TDLM) to measure spontaneous sequential reactivation of four states, either during the mental simulation or rest 43 . At each time bin during the mental simulation and the two resting sessions, we applied four classifiers to the EEG data and another four classifiers to the fMRI data. Each of these modality data sets contained three [time × state] reactivation probability matrices from the mental simulation (three runs of mental simulation) and two reactivation probability matrices from the resting sessions (PRE and POST Rest).

In a first step, we aimed to identify evidence of state-to-state transitions at a given time lag $\varDelta t$ , by regressing a time-lagged copy of one state, ${X}_{j}$ , onto another, ${X}_{k}$ . In other words, the values of all states ${X}_{k}$ at time t are used in a single multilinear model to predict the value of the single state ${X}_{j}$ at time $t+\varDelta t$ :

In the second step, we tested for the strength of a particular hypothesized sequence, specified as a transition matrix, T:

$\beta$ is the [state × state] empirical transition matrix obtained from previous formula by ordinary least squares regression. ${T}_{r}$ is hypothesized transition matrix. In our study, the transition matrices include a forward transition matrix, a backward transition matrix, a diagonal matrix and a constant matrix. Sequenceness, denoted as $Z(r)$ , reflected the strength of hypothesized transitions in the empirical matrices, which describe the degree to which representations were reactivated in a task-defined sequential order 3 , 6 , 30 , 31 , 45 . ${Z}_{F}$ and ${Z}_{B}$ represented the forward and backward sequenceness, respectively. By repeating this regression at each time lag, we obtained time courses of sequenceness as a function of time lag. In our research, EEG had a time resolution of 10 ms, the smallest time lag in EEG-based TDLM (Fig. 4b , Supplementary Fig. 8 ), while fMRI had a time resolution of 1.3 s (1 TR), the smallest time lag in fMRI-based TDLM (Fig. 4a , Supplementary Fig. 5 & 8 ). Notably, because it is a linear modelling framework applied directly to concatenated, rather than individual replay onsets, TDLM does not distinguish effects of whole sequences from individual duplets. Essentially, it evaluates the average replay strength across all duplets during the whole period of interest.

We employed a non-parametric permutation-based method to test for statistical significance in this study. For each permutation, sequenceness was averaged across trials within each subject, and then across subjects. The null distribution was generated by randomly shuffling the rows and columns of the ${T}_{F}$ (forward predictor matrix), with ${T}_{B}$ (backward predictor matrix) being its transpose, and recalculating the second-level analysis for each shuffle. The permutations covered all possible combinations. For each permutation, the peak absolute mean sequence strength across participants and lags was calculated, controlling for multiple comparisons across lags. In the original, unpermuted data, sequence strength was deemed significant at a peak-level FWE < 0.05 if its value surpassed 95% of the peaks within the permutations. This method has been rigorously validated by simulation studies and empirical data in prior research 3 , 6 , 31 , 43 . As previous human studies have only found evidence for replay with relatively short lags 3 , 6 , 30 , 43 , 45 , we visualized results up to a lag of 600 ms in EEG. To explore whether replay can be observed at longer timescales, we extended the scale of time lag to 2000 ms. Simultaneously, we investigated the possibility of detecting replay in fMRI by computing the sequenceness using TDLM with a lag of up to 8 TR.

Identifying reactivation and replay onsets

To investigate the neural mechanisms underlying replay and task-related reactivation during the mental simulation and rest, we identified the onset of replay and task reactivation for subsequent parametric modulation and psychophysiological interaction analyses (Fig. 2b ) 30 , 45 . We used classifiers trained on functional localizer to decode the reactivation probability of each visual stimulus, resulting in a [time × state] reactivation matrix. Our analysis revealed a time lag of 30 ms between stimuli that provided the strongest evidence of replay transitions in the cued mental simulation task, as determined by TDLM (Fig. 4b ). Next, we identified time points during mental simulation, where strong reactivation of one stimulus (e.g., A) was followed 30 ms later by strong reactivation of a structurally-adjacent stimulus (e.g., B). We first generated a matrix Orig as

where X is the [time × state] reactivation matrix, and T is the task transition matrix. The transition matrix T defines the mapping between the task state corresponding to column i in X , and column i in Orig (specifically, column i in Orig is the reactivation time course of the state that ‘precedes’ state i in T ). We then shifted each column of X by ${\Delta }_{t}$ = 30 ms, to generate another matrix Proj ,

where row i of Proj correspond to row i + 30 ms of X . Multiplying Proj and Orig elementwise, and summing over the columns of the resulting matrix, giving a total of k states, to obtain a long [time × 1] vector, R . Each element in the R indicates the strength of two-state replay at a given moment in time.

Based on this approach, we calculated forward and backward replay probability onsets for each time point during mental simulation. The replay probability in our study was formed by 30-ms-time-lag forward replay in both forward and backward mental simulation conditions. We convolved the replay probability onsets with the HRF, and downsampled it to the same temporal resolution with fMRI signal. The resulting replay probability onset was an EEG-based replay probability onset regressor.

The same analysis pipeline was applied to EEG-based task reactivation probability during both PRE Rest and POST Rest periods. To compare reactivation strength between PRE and POST Rest, we averaged reactivation probabilities across all time points and task stimuli for each rest period, defining this as the mean reactivation strength for each period. Recognizing that spontaneous thoughts could lead to spurious reactivation in both PRE and POST Rest periods. Thus, as a chance level cannot be established a priori, we opt to use the reactivation level during the PRE Rest period as a benchmark for assessing reactivation during the POST Rest.

Detecting neural sequence in task-based fMRI patterns

We employed Wittkuhn and Schuck’s method 28 to measure neural sequence in fMRI data during mental simulation. In brief, this involves identifying the relationship between image position in the sequence and task reactivation probability based on an fMRI decoding classifier. Task reactivation probability during cued mental simulation was normalized by dividing them by their trial-wise sum for each stimulus. Subsequently, we conducted a linear regression between the serial position of four images and their normalized decoding probabilities at every TR. The slopes of linear regression were averaged at the subject level for each task condition and each TR. The sign of the mean regression slopes was flipped so that positive values indicate forward ordering and negative values indicate backward ordering. We also performed the two-sided one-sample t-tests to compare the mean regression slope coefficients against zero for each TR and adjusted their P values by multiple comparison correction (Fig. 4a for illustration, Fig. 4c for observed data).

It is worth noting that in our study, the [time × state] reactivation probability matrices came from the sequential mental simulation, not sequential visual presentation. Since we could not identify the time at which subjects imagined each image or the speed of imagination, we cannot predict probability differences between two time-shifted events by sinusoidal response functions from Wittkuhn and Schuck 28 . The junction of the 1 st and 2 nd period was defined as the point at which the regression slope crossed y = 0 in the forward mental simulation in the positive to negative direction (e.g., junction = 5.2 TR), with the 1 st period preceding the junction (e.g., 1 st period = [1, 2, 3, 4, 5] TR) and the 2 nd period following it (e.g., 2 nd period = [6, 7, 8] TR). Slope coefficients were averaged for each task condition and period (Fig. 4a for illustration, the bar plots in the upper right corner of the Fig. 4c for emperical data). We conducted the two-sided one-sample t-tests to compare the mean regression slopes against zero for each task condition and period.

Detecting sequential replay in rest-based fMRI patterns

We employed Schuck and Niv’s method 24 to measure sequential replay in fMRI data acquired during rest. This method involves identifying the relationship between transition frequency between states and state distances. Similar to the aforementioned training of decoding classifiers (refer to Multivariate fMRI pattern analysis section), hippocampus and mPFC anatomical masks were created based on automated anatomical labelling for brain-surface reconstructions of individual T1w-reference images using Freesurfer 82 , 94 , 95 . We selected the corresponding labels of the bilateral medial orbitofrontal, rostral anterior cingulate, and superior frontal regions for the anatomical mask of mPFC, and the corresponding labels of the bilateral hippocampus regions for the anatomical mask of hippocampus. Considering that these two masks consist of small-quantity voxels, we didn’t employed the thresholded t-map to select image-responsive voxels 28 . Based on the decoding accuracy, the classifiers trained in mPFC mask at the 5 th TR after stimulus onset were chosen to predict the probabilities during mental simulation and rest (see Supplementary Fig. 8 ).

For each task condition (forward and backward mental simulation) and each rest session (PRE and POST Rest), we selected 230 TRs time series of decoding probabilities. This resulted in 229 state transitions for each condition, allowing us to calculate the transition frequency. Similar to Schuck and Niv 24 , We conducted a logistic mixed-effects analysis to examine the effects of state distances (hypothesized transition matrix) on transition frequency between states (empirical transition matrix) while simultaneously excluding the different sources of between- and within-participant variability. To compare the models, we employed a likelihood ratio test, comparing a logistic regression model that solely included random effects to a model that also incorporated the state distances regressor.

GLM analysis

We performed the GLMs to capture the significant event related activations in various sessions: functional localizer (GLM 1), sequence learning (GLM 2, GLM 3), cued mental simulation (GLM 4) and rest periods (GLM 5 for EEG-based reactivation, GLM 6 for fMRI-based reactivation). The fMRI data were smoothed with a 6 mm FWHM kernel before group-level statistics were performed in the GLMs. All images underwent high pass filtering in the temporal domain (width 128 s), and autocorrelation of the hemodynamic responses was modelled using an AR (1) model. We included nuisance regressors estimated during preprocessing with fMRIprep: the six rigid-body motion-correction parameters estimated during realignment (three translation and rotation parameters, respectively), mean White Matter , and mean Cerebral Spinal Fluid . The effect of the experimental conditions on regional blood oxygenation level-dependent responses was estimated with the GLMs. For the group-level analysis, a one-sample t-test was conducted using the whole brain as the volume of interest, and paired t-test was conducted to compare the difference of whole brain activation between PRE Rest and POST Rest. All whole-brain analyses, with the exception for those mentioned otherwise, were thresholded and displayed using a cluster-wise family-wise error (FWE) correction P < 0.05, with cluster-forming threshold P unc . < 0.001 at the voxel level, as reported by FSL.

GLM 1: the activation of images and semantic text in the functional localizer

GLM 1 was employed to find the activation of images and semantic text in the functional localizer session. Each run was modelled with ten regressors, including four regressors to model the onsets of four images, four regressors to model the onsets of four semantic text in correct response trials, one regressor to model the onsets of semantic texts in wrong response trials, and another regressor modelling the onsets of response. To obtain the mean activation of visual processing and semantic processing, we averaged the effect of four images and four semantic texts, respectively (Supplementary Fig. 2a , right panel for images, and Supplementary Fig. 2b , left panel for semantic texts). Furthermore, we identified the specific activation of stimuli by contrasting a specific image or semantic text with the other three images and texts (Supplementary Fig. 2a , left panel for images, and Supplementary Fig. 2b , right panel for semantic texts).

GLM 2: the contrast of 1 st and 2 nd image during sequence learning

GLM 2 was used to examine the differences of activation between the first and second images during sequence learning session. Each run was modelled with two regressors: (1) the onsets of the first image, (2) the onsets of the second image. We contrasted the effect of the first image with that of the second image in the first level GLM (Supplementary Fig. 3a ).

GLM 3: the activation of target image and probe image in sequence probe test

GLM 3 was designed to investigate the activation of target image and probe image in the sequence probe test. Each run was modelled with four regressors: (1) the onsets of target image (Supplementary Fig. 3c ), (2) the onsets of the response, (3) the onsets of correct probe image in all correct response trials, (4) the onsets of wrong probe image in all correct response trials. To access the effect of the wrong probe image in the probe test, we contrasted the effect of probe images between the wrong probe image and correct probe image in the first level GLM (Supplementary Fig. 3d ).

Reactivation and replay onsets modulation analysis

Glm 4: the neural correlates of eeg-based replay during mental simulation.

To investigate the neural correlates of replay events during mental simulation, we performed a GLM 4 with three regressors. The first regressor represented the onsets of EEG-based replay probability events (See Identifying reactivation and replay onsets ). We added two more regressors to isolate the unique brain activations associated with replay. The second regressor modelled the duration of mental simulation in all correct-response trials, while the third regressor modelled the duration of mental simulation in all wrong-response trials. These two regressors were modelled as boxcar functions with a duration of 10 s for all trials. We orthogonalized the first two regressors in GLM 4 to remove any shared variances so that the regression coefficients reflected the unique contribution of each regressor in explaining the variances in neural signals.

GLM 5: the neural correlates of EEG-based task reactivation in the resting states

To investigate the neural correlates of EEG-based task reactivation during rest, we conducted a GLM 5. We summed the [time * state] task-related reactivation probabilities across four task stimuli for each time point, resulting in a [time × 1] array of EEG-based reactivation probability onsets. We convolved the [time × 1] reactivation probability onsets with the HRF, and downsampled it to the same temporal resolution with fMRI signal. We added it as a psychological regressor to the design matrix of the GLM 5.

GLM 6: the neural correlates of fMRI-based task reactivation in the resting states

To investigate the neural correlates of fMRI-based task reactivation during rest, we performed a GLM 6. We summed the [time * state] task-related reactivation probabilities across four task stimuli for each time point, resulting in a [time × 1] array of fMRI-based reactivation probability onsets. As the fMRI-based reactivation itself has HRF properties, we added it as a psychological regressor to the design matrix of the GLM 6 without HRF convolution.

ROI analysis

The purpose of ROI analysis in our study is to identify the increased activation during PRE and POST Rest. The beta values at the subject-level for further statistical inference were averaged across all voxels within each ROI. The hippocampus ROI in our study was anatomically defined using a high-resolution probabilistic atlas of Harvard-Oxford Atlas 98 . The primary motor cortex ROI in our study was anatomically defined using a high-resolution probabilistic atlas of Juelich Histological Atlas 99 . In the further ROI analysis, any voxels that have any probability of being in the hippocampus and primary motor cortex were included in the ROIs. Two-sided one sample t-tests were performed on beta values for each ROI, rest ression and modality, while two-sided paired t-tests were conducted between rest sessions (PRE versus POST Rest) and modalities (EEG versus fMRI). Additionally, we defined entorhinal cortex ROI by applying 40% threshold to the Juelich Histological Atlas for PPI analysis between hippocampal activity and task reactivation during rest.

PPI analysis

We performed whole-brain PPI analyses using nilearn during mental simulation and rest periods. The first analysis aimed to study replay-triggered brain-wide activation during mental simulation. To achieve this, we used the same hippocampus ROI as the ROI analysis . The first PPI model included three regressors for replay onsets: (1) BOLD timeseries extracted from hippocampus, (2) the EEG-based replay probability, (3) the product of the above two regressors (Fig. 4e ).

The second whole-brain PPI analysis aimed to study EEG- and fMRI-based task reactivation aligned brain-wide activation during rest periods. This PPI model included three regressors for task reactivation onsets: (1) BOLD timeseries extracted from hippocampus, (2) the EEG- or fMRI-based task reactivation probability, and (3) the product of the above two regressors (Fig. 5e ).

Cross-correlation

Cross-correlation measures the similarity between two signals as a function of the time lag applied to one of the signals. In the context of EEG-based and fMRI-based decoding probability, during task and rest, we employed the cross-correlation by sliding the EEG time series across the fMRI time series at different time lags and computing the correlation coefficient at each lag. To ensure compatibility between the two signals, we downsampled the EEG time series to the same temporal resolution with fMRI time series before calculating the cross-correlation. We used the cross-correlation function from Liu, et al. 43 and the time lag ranging from 1 to 8 TR in our analysis. The peak of the cross-correlation coefficient indicated the point at which the two signals demonstrated the highest degree of similarity. We also performed the two-sided one-sample t-tests to compare the cross-correlation coefficients against zero for each TR and adjusted their P values by multiple comparison correction (Supplementary Fig. 10 ).

Statistical analysis

Sample sizes were not determined using statistical methods, but were compared with those reported in previous research on replay 3 , 6 , 28 , 32 . Statistical comparisons were performed using with appropriate inferential methods, as indicated in the figure captions. In cases where multiple hypothesis testing was applicable, we applied the correction method to correct for it 100 , 101 .

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The behavioural data, first-level and second-level fMRI statistical images, and EEG decoded time series generated in this study have been deposited in the Zenodo database. They can be found at https://zenodo.org/records/12547774 . Source data are provided with this paper.

Code availability

The analysis code can be found at https://gitlab.com/liu_lab/EEG-fMRI-replay.git .

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Acknowledgements

This study is supported by the National Science and Technology Innovation 2030 Major Program (2022ZD0205500), the National Natural Science Foundation of China (32271093), the Beijing Natural Science Foundation (Z230010), the Major Project of the National Social Science Foundation (20&ZD153), the Fundamental Research Funds for the Central Universities, and the Shenzhen-Hong Kong Institute of Brain Science – Shenzhen Fundamental Research Institutions (2022SHIBS0003). The authors also thank the Magnetic Resonance Imaging Center at Shenzhen University for providing the MRI scanning.

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These authors contributed equally: Qi Huang, Zhibing Xiao, Qianqian Yu, Yuejia Luo.

Authors and Affiliations

State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China

Qi Huang, Zhibing Xiao, Yuejia Luo, Yukun Qu, Raymond Dolan & Yunzhe Liu

Chinese Institute for Brain Research, Beijing, China

Qi Huang, Zhibing Xiao, Jiahua Xu, Yukun Qu & Yunzhe Liu

School of Psychology, Center for Brain Disorders and Cognitive Science, Shenzhen University, Shenzhen, China

Qianqian Yu & Yuejia Luo

Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London, UK

Raymond Dolan

Wellcome Centre for Human Neuroimaging, UCL, London, UK

Raymond Dolan & Timothy Behrens

Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK

Timothy Behrens

Sainsbury Wellcome Centre for Neural Circuits and Behaviour, UCL, London, UK

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Conceptualization, Y.L., Q.H., Z.X., Q.Y., R.D., and T.B.; Investigation, Q.H., Z.X., Q.Y., Y.L., J.X., Y.Q., and Y.Luo.; Writing – Original Draft, Y.L., Q.H., Z.X.; Writing – Review & Editing, Y.L., Q.H., Z.X., R.D., and T.B.

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Correspondence to Yunzhe Liu .

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Huang, Q., Xiao, Z., Yu, Q. et al. Replay-triggered brain-wide activation in humans. Nat Commun 15 , 7185 (2024). https://doi.org/10.1038/s41467-024-51582-5

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Mechanisms of systems memory consolidation during sleep
Scientific Reports (2024) Long-term memory formation is a major function of sleep. Based on evidence from neurophysiological and behavioral studies mainly in humans and rodents, we consider the ...
Memory and Sleep: How Sleep Cognition Can Change the Waking Mind for
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.
Neurochemical mechanisms for memory processing during sleep ...
This set of studies demonstrates that active systems consolidation of declarative memory during sleep likely recruits a mode of plasticity not essentially relying on glutamatergic transmission in ...
Brain Rhythms During Sleep and Memory Consolidation: Neurobiological
Sleep can benefit memory consolidation. The characterization of brain regions underlying memory consolidation during sleep, as well as their temporal interplay, reflected by specific patterns of brain electric activity, is surfacing. Here, we provide an overview of recent concepts and results on the mechanisms of sleep-related memory consolidation. The latest studies strongly impacting future ...
Offline memory consolidation during waking rest
The memory benefit of offline waking rest is comparable to the effect of post-learning sleep, and has been demonstrated for a wide array of types of learning and memory. Periods of offline rest ...
PDF Memory consolidation as an adaptive process
Memory consolidation, the process of stabilizing new memories for the long-term, involves mechanisms that can both prioritize and transform new experiences. In general, memory consolidation can refer to mechanisms at two differ-ent levels: cellular consolidation.
Sleep—A brain-state serving systems memory consolidation
7. These findings call for a reassessment of the precise conditions mediating the sleep consolidation effect. Sleep is a brain state that is characterized by a large-scale organization of neuronal activity that captures virtually all brain regions, the neocortex as well as hippocampus, thalamus, hypothalamus, and brainstem regions. 8.
Memory consolidation during sleep involves context reinstatement in
Consolidation is thought to rely on memory reactivation, which, like contextual reinstatement, involves the selective activation of memory-specific neural circuits. 11. , 12. The extent to which contextually related memories are reinstated over the course of consolidation during sleep remains unclear.
Coordinating what we've learned about memory consolidation: Revisiting
Growing discrepancies between neurobiochemical findings and traditional consolidation theories led to the development of another theory - a "unified theory" (Dash et al., 2004; see Box 2).The central tenet of this theory is that local (e.g., subregional or regional) intra-neocortical and intra-hippocampal cellular consolidation occurs concurrently, and rapidly, after de novo learning in ...
The role of sleep in declarative memory consolidation--direct evidence
Memory consolidation has been suggested to occur predominantly during sleep. Very recent findings, however, suggest that important steps in memory consolidation occur also during waking … Two step theories of memory formation assume that an initial learning phase is followed by a consolidation stage.
Memory processes during sleep: beyond the standard consolidation theory
Alternatively, it has been suggested that consolidation may occur during waking state as well and that the role of sleep is rather to restore encoding capabilities of synaptic connections (synaptic downscaling theory). Here, we review the experimental evidence favoring and challenging these two views and suggest an integrative model of memory ...
Memory Consolidation
The concept of rapid cortical tagging (Lesburgueres et al. 2011; Tse et al. 2011) suggests that, even if the hippocampus is normally engaged for a short period for "binding" disparate cortical networks during memory encoding and the early stages of consolidation, a memory trace of some kind is rapidly formed in the cortex. The cortical ...
Memory consolidation during sleep: a neurophysiological perspective
We suggest that neocortico-hippocampal transfer of information and the modification process in neocortical circuitries by the hippocampal output take place in a temporally discontinuous manner associated with the wake-sleep cycle. Loading the hippocampus with neocortical information may happen fast during the aroused state of the hippocampus ...
Unlocking the Memory Vault: Dopamine, Novelty, and Memory Consolidation
cellular or initial memory consolidation. Notably, in both animals and humans, the retention of everyday memories is enhanced during novel experiences occurring shortly before or after memory encoding, a process known as synaptic tagging and capture (STC). A growing body of evidence suggests that dopamine signaling via D 1 /D 5
Memory consolidation and reconsolidation: what is the role of sleep
Memory consolidation and reconsolidation reflect molecular, cellular and systems-level processes that convert labile memory representations into more permanent ones, available for continued reactivation and recall over extended periods of time. Here, we discuss the complexities of consolidation and reconsolidation, and suggest they should be viewed not as all-or-none phenomena, but as a ...
Memory Consolidation
These considerations suggest a relationship between Müller and Pilzecker's (1900) view of consolidation and the time course of forgetting. More specifically, the fact that a memory trace hardens in such a way as to become increasingly resistant to interference even as the trace fades may help to explain the general shape of the forgetting ...
About Sleep's Role in Memory
As learning of declarative memories is explicit (and often intentional), these results suggest that explicit encoding favors access to memory consolidation during sleep. Second, the initial memory strength might affect consolidation during sleep, although the available data are not consistent.
How the brain consolidates memory during deep sleep
How the brain consolidates memory during deep sleep. Using a computational model, a new study explains how the hippocampus influences synaptic connections in the cortex -. Research strongly suggests that sleep, which constitutes about a third of our lives, is crucial for learning and forming long-term memories.
Memory processes during sleep: beyond the standard consolidation theory
Two-step theories of memory formation suggest that an initial encoding stage, during which transient neural assemblies are formed in the hippocampus, is followed by a second step called consolidation, which involves re-processing of activity patterns and is associated with an increasing involvement of the neocortex. Several studies in human subjects as well as in animals suggest that memory ...
Memory reactivations during sleep: a neural basis of dream experiences
Memory reactivations: pioneering work in rodents. The first empirical evidence for memory reactivations during sleep came from rodent studies. By recording from the hippocampus during maze exploration, these studies showed that cells that were coactive during exploration had correlated activity patterns during subsequent periods of non-rapid eye movement (NREM) sleep [7] and that the temporal ...
Electrophysiological mechanisms of human memory consolidation
Abstract. Consolidation stabilizes memory traces after initial encoding. Rodent studies suggest that memory consolidation depends on replay of stimulus-specific activity patterns during fast ...
Sleep resets neurons for new memories the next day
While everyone knows that a good night's sleep restores energy, a new study finds it resets another vital function: memory. While everyone knows that a good night's sleep restores energy, a new ...
Memory consolidation as an adaptive process
We rely on our long-term memories to guide future behaviors, making it adaptive to prioritize the retention of goal-relevant, salient information in memory. In this review, we discuss findings from rodent and human research to demonstrate that active processes during post-encoding consolidation support the selective stabilization of recent experience into adaptive, long-term memories. Building ...
The brain stores at least 3 copies of every memory
The brain creates at least three copies of any given memory, new research suggests. This includes those encoded by so-called early-born neurons, pictured above in magenta in a cross section of a ...
Divergent recruitment of developmentally defined neuronal ...
Nevertheless, memory expression at remote times hinges on the activity of LBNs both during acquisition and within a specific window during consolidation. Thus, offline reactivation of the transient memory trace supported by the activity of LBNs might play an instructive role in the maintenance and expression of the EBN-dependent memory.
Improving the optical properties of magnesium spinel chromites through
In this study, we investigated the optoelectronic performance of magnesium spinel chromites with nickel (Ni) and copper (Cu) substitutions. Using the sol-gel method, we synthesized two spinel chromites: Mg 0.6 Ni 0.4 Cr 2 O 4 (MNCO) and Mg 0.6 Cu 0.4 Cr 2 O 4 (MCCO). We extensively characterized these samples to analyze their thermal, structural, elastic, and optical properties.
Reproductive Rights in the US Wildfire Crisis
The 78-page report, "Reproductive Rights in the US Wildfire Crisis: Insights from Health Workers in Oregon State," finds that the US government needs to do more to address the growing threat ...
Unlocking the Memory Vault: Dopamine, Novelty, and Memory Consolidation
(b) We suggest that such common novelty activates the VTA-hippocampal (HPC) system, triggering initial memory consolidation in the HPC and leading to enhanced systems memory consolidation between the HPC and prefrontal cortex (PFC). This process, aided by increased sharp wave ripple-related reactivations, supports the long-term retention of ...
Replay-triggered brain-wide activation in humans
The consolidation of discrete experiences into a coherent narrative shapes the cognitive map, providing structured mental representations of our experiences. In this process, past memories are ...
Tracking Generative AI: How Evolving AI Models Are Impacting Legal
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