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Declarative memory retrieval is thought to involve reinstatement of neuronal activity patterns elicited and encoded during a prior learning episode. Furthermore, it is suggested that two mechanisms operate during reinstatement, dependent on task demands: individual memory items can be reactivated simultaneously as a clustered occurrence or, alternatively, replayed sequentially as temporally separate instances. In the current study, participants learned associations between images that were embedded in a directed graph network and retained this information over a brief 8 min consolidation period. During a subsequent cued recall session, participants retrieved the learned information while undergoing magnetoencephalographic recording. Using a trained stimulus decoder, we found evidence for clustered reactivation of learned material. Reactivation strength of individual items during clustered reactivation decreased as a function of increasing graph distance, an ordering present solely for successful retrieval but not for retrieval failure. In line with previous research, we found evidence that sequential replay was dependent on retrieval performance and was most evident in low performers. The results provide evidence for distinct performance-dependent retrieval mechanisms, with graded clustered reactivation emerging as a plausible mechanism to search within abstract cognitive maps.

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Both, the hippocampal formation and the neocortex are contributing to declarative memory, but their functional specialization remains unclear. We investigated the differential contribution of both memory systems during free recall of word lists. In total, 21 women and 17 men studied the same list but with the help of different encoding associations. Participants associated the words either sequentially with the previous word on the list, with spatial locations on a well-known path, or with unique autobiographical events. After intensive rehearsal, subjects recalled the words during functional magnetic resonance imaging (fMRI). Common activity to all three types of encoding associations was identified in the posterior parietal cortex, in particular in the precuneus. Additionally, when associating spatial or autobiographical material, retrosplenial cortex activity was elicited during word list recall, while hippocampal activity emerged only for autobiographically associated words. These findings support a general, critical function of the precuneus in episodic memory storage and retrieval. The encoding-retrieval repetitions during learning seem to have accelerated hippocampus-independence and lead to direct neocortical integration in the sequentially associated and spatially associated word list tasks. During recall of words associated with autobiographical memories, the hippocampus might add spatiotemporal information supporting detailed scenic and contextual memories.

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The human brain has evolved to acquire novel information rapidly while serving the need to store long-term memories in a stable and lasting form. Presenting interfering information directly after learning can lead to forgetting of the original material. It has been suggested that sleep aids the stabilization of new memories and protects them from interference. Here, we aim to replicate in two separate experiments the claim that sleep protects memories from retroactive interference (Current Biology, 16, 2006 and 1290; PLoS ONE, 4, 2009 and e4117). We let participants study wordlists before letting them sleep for an afternoon nap or for a full night. In a control condition, subjects stayed awake for the same amount of time. After the consolidation interval, participants learnt an interfering wordlist and were tested on memory of the original wordlist. Sleep did not stabilize memory for the original wordlist in either study. We discuss our findings in the light of recent advances in computational neuroscience, and conclude that the stabilizing effect of sleep against interference has been overestimated.

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While in the past much of our knowledge about memory representations in the brain has relied on loss-of-function studies in which whole brain regions were temporarily inactivated or permanently lesioned, the recent development of new methods has ushered in a new era of downright "engram excitement." Animal research is now able to specifically label, track, and manipulate engram cells in the brain. While early studies have mostly focused on single brain regions like the hippocampus, recently more and more evidence for brain-wide distributed engram networks is emerging. Memory research in humans has also picked up pace, fueled by promising magnetic resonance imaging (MRI)-based methods like diffusion-weighted MRI (DW-MRI) and brain decoding. In this review, we will outline recent advancements in engram research, with a focus on human data and neocortical representations. We will illustrate the available noninvasive methods for the detection of engrams in different neocortical regions like the medial prefrontal cortex and the posterior parietal cortex and discuss evidence for systems consolidation and parallel memory encoding. Finally, we will explore how reactivation and prior knowledge can lead to and enhance engram formation in the neocortex.

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Persons in the completely locked-in state (CLIS) suffering from amyotrophic lateral sclerosis (ALS) are deprived of many zeitgebers of the circadian rhythm: While cognitively intact, they are completely paralyzed, eyes mostly closed, with artificial ventilation and artificial nutrition, and social communication extremely restricted or absent. Polysomnographic recordings in eight patients in CLIS, however, revealed the presence of regular episodes of deep sleep during night time in all patients. It was also possible to distinguish an alpha-like state and a wake-like state. Classification of rapid eye movement (REM) sleep is difficult because of absent eye movements and absent muscular activity. Four out of eight patients did not show any sleep spindles. Those who have spindles also show K-complexes and thus regular phases of sleep stage 2. Thus, despite some irregularities, we found a surprisingly healthy sleep pattern in these patients.

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A single infusion of ketamine has sustained antidepressant effects and significantly de-creases the risk of suicide,and the effects can last up for 7-10 days,but the underlying mechanism is un-clear. The mechanism was reviewed underlying the antidepressant effects of ketamine,and found that ket-amine may exert its antidepressant effect by regulating sleep/wake cycle,synaptic pruning,molecular path-ways,and neural circuits for treatment-refractory depression. Further studies are needed to investigate the ge-netic,molecular mechanisms underlying the sustained antidepressant effect of ketamine,and the associated imaging findings through in vivo imaging of animals and imaging genetics techniques,explore the optimal time for administration of ketamine,and then provide accurate scientific basis for enhancing its anti-depressant effect.

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Biological data sets are typically characterized by high dimensionality and low effect sizes. A powerful method for detecting systematic differences between experimental conditions in such multivariate data sets is multivariate pattern analysis (MVPA), particularly pattern classification. However, in virtually all applications, data from the classes that correspond to the conditions of interest are not homogeneous but contain subclasses. Such subclasses can for example arise from individual subjects that contribute multiple data points, or from correlations of items within classes. We show here that in multivariate data that have subclasses nested within its class structure, these subclasses introduce systematic information that improves classifiability beyond what is expected by the size of the class difference. We analytically prove that this subclass bias systematically inflates correct classification rates (CCRs) of linear classifiers depending on the number of subclasses as well as on the portion of variance induced by the subclasses. In simulations, we demonstrate that subclass bias is highest when between-class effect size is low and subclass variance high. This bias can be reduced by increasing the total number of subclasses. However, we can account for the subclass bias by using permutation tests that explicitly consider the subclass structure of the data. We illustrate our result in several experiments that recorded human EEG activity, demonstrating that parametric statistical tests as well as typical trial-wise permutation fail to determine significance of classification outcomes correctly.

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Solving a novel problem and finding innovative solutions requires a flexible and creative recombination of prior knowledge. It is thought that setting a problem aside before giving it another try aids problem-solving. The underlying mechanisms of such an incubation period could include unconscious processing that fosters spreading activation along associated networks and the restructuring of problem representations. Recently, it has been suggested that sleep may also support problem-solving by supporting the transformation and restructuring of memory elements. Since the effect of sleep on problem-solving has been mainly tested using the Remote Associates Test, we chose three different tasks-classical riddles, visual change detection, and anagrams-to examine various aspects of problem-solving and to pinpoint task-specific prerequisites for effects of sleep or incubation to emerge. Sixty-two participants were given two attempts to solve the problems. Both attempts either occurred consecutively or were spaced apart by a 3-hour incubation interval that was spent awake or asleep. We found that a period of incubation positively affected solutions rates in classical riddles, but not in visual change detection or anagram solving. Contrary to our hypothesis, spending the incubation period asleep, did not yield any additional benefit. Our study thus supports the notion that a period of letting a problem rest is beneficial for its solution and confines the role of sleep to memory transformations that do not directly impact on problem-solving ability.

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During creative problem solving, initial solution attempts often fail because of self-imposed constraints that prevent us from thinking out of the box. In order to solve a problem successfully, the problem representation has to be restructured by combining elements of available knowledge in novel and creative ways. It has been suggested that sleep supports the reorganization of memory representations, ultimately aiding problem solving. In this study, we systematically tested the effect of sleep and time on problem solving, using classical insight tasks and magic tricks. Solving these tasks explicitly requires a restructuring of the problem representation and may be accompanied by a subjective feeling of insight. In two sessions, 77 participants had to solve classical insight problems and magic tricks. The two sessions either occurred consecutively or were spaced 3 h apart, with the time in between spent either sleeping or awake. We found that sleep affected neither general solution rates nor the number of solutions accompanied by sudden subjective insight. Our study thus adds to accumulating evidence that sleep does not provide an environment that facilitates the qualitative restructuring of memory representations and enables problem solving.

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Multivariate pattern analysis (MVPA) methods are now widely used in life-science research. They have great potential but their complexity also bears unexpected pitfalls. In this paper, we explore the possibilities that arise from the high sensitivity of MVPA for stimulus-related differences, which may confound estimations of class differences during decoding of cognitive concepts. We propose a method that takes advantage of concept-unrelated grouping factors, uses blocked permutation tests, and gradually manipulates the proportion of concept-related information in data while the stimulus-related, concept-irrelevant factors are held constant. This results in a concept-response curve, which shows the relative contribution of these two components, i.e. how much of the decoding performance is specific to higher-order category processing and to lower order stimulus processing. It also allows separating stimulus-related from concept-related neuronal processing, which cannot be achieved experimentally. We applied our method to three different EEG data sets with different levels of stimulus-related confound to decode concepts of digits vs. letters, faces vs. houses, and animals vs. fruits based on event-related potentials at the single trial level. We show that exemplar-specific differences between stimuli can drive classification accuracy to above chance levels even in the absence of conceptual information. By looking into time-resolved windows of brain activity, concept-response curves can help characterize the time-course of lower-level and higher-level neural information processing and detect the corresponding temporal and spatial signatures of the corresponding cognitive processes. In particular, our results show that perceptual information is decoded earlier in time than conceptual information specific to processing digits and letters. In addition, compared to the stimulus-level predictive sites, concept-related topographies are spread more widely and, at later time points, reach the frontal cortex. Thus, our proposed method yields insights into cognitive processing as well as corresponding brain responses.

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Acetylcholine is a major modulator of learning and memory, and its availability varies across the sleep-wake cycle. In this issue of Neuron, Papouin et al. (2017) describe a D-serine-dependent pathway involving astroglia by which the transmitter tunes the hippocampus toward memory encoding during wakefulness.

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Polysomnographic recording of night sleep was carried out in 15 patients with the diagnosis vegetative state (syn. unresponsive wakefulness syndrome). Sleep scoring was performed by three raters, and confirmed by means of a spectral power analysis of the electroencephalogram, electrooculogram and electromyogram. All patients but one exhibited at least some signs of sleep. In particular, sleep stage N1 was found in 13 patients, N2 in 14 patients, N3 in nine patients, and rapid eye movement sleep in 10 patients. Three patients exhibited all phenomena characteristic for normal sleep, including spindles and rapid eye movements. However, in all but one patient, sleep patterns were severely disturbed as compared with normative data. All patients had frequent and long periods of wakefulness during the night. In some apparent rapid eye movement sleep episodes, no eye movements were recorded. Sleep spindles were detected in five patients only, and their density was very low. We conclude that the majority of vegetative state patients retain some important circadian changes. Further studies are necessary to disentangle multiple factors potentially affecting sleep pattern of vegetative state patients.

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There is robust evidence that sleep facilitates declarative memory consolidation. Integration of newly acquired memories into existing neocortical knowledge networks has been proposed to underlie this effect. Here, we test whether sleep affects memory retention for word-picture associations differently when it was learned explicitly or using a fast mapping strategy. Fast mapping is an incidental form of learning that references new information to existing knowledge and possibly allows neocortical integration already during encoding. If the integration of information into neocortical networks is a main function of sleep-dependent memory consolidation, material learned via fast mapping should therefore benefit less from sleep. Supporting this idea, we find that sleep has a protective effect on explicitly learned associations. In contrast, memory for associations learned by fast mapping does not benefit from sleep and remains stable regardless of whether sleep or wakefulness follows learning. Our results thus indicate that the need for sleep-mediated consolidation depends on the strategy used for learning and might thus be related to the level of integration of newly acquired memory achieved during encoding.

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Previous evidence indicates that the brain stores memory in two complementary systems, allowing both rapid plasticity and stable representations at different sites. For memory to be established in a long-lasting neocortical store, many learning repetitions are considered necessary after initial encoding into hippocampal circuits. To elucidate the dynamics of hippocampal and neocortical contributions to the early phases of memory formation, we closely followed changes in human functional brain activity while volunteers navigated through two different, initially unknown virtual environments. In one condition, they were able to encode new information continuously about the spatial layout of the maze. In the control condition, no information could be learned because the layout changed constantly. Our results show that the posterior parietal cortex (PPC) encodes memories for spatial locations rapidly, beginning already with the first visit to a location and steadily increasing activity with each additional encounter. Hippocampal activity and connectivity between the PPC and hippocampus, on the other hand, are strongest during initial encoding, and both decline with additional encounters. Importantly, stronger PPC activity related to higher memory-based performance. Compared with the nonlearnable control condition, PPC activity in the learned environment remained elevated after a 24-h interval, indicating a stable change. Our findings reflect the rapid creation of a memory representation in the PPC, which belongs to a recently proposed parietal memory network. The emerging parietal representation is specific for individual episodes of experience, predicts behavior, and remains stable over offline periods, and must therefore hold a mnemonic function.

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Multivariate pattern analysis (MVPA) has recently become a popular tool for data analysis. Often, classification accuracy as quantified by correct classification rate (CCR) is used to illustrate the size of the effect under investigation. However, we show that in low sample size (LSS), low effect size (LES) data, which is typical in neuroscience, the distribution of CCRs from cross-validation of linear MVPA is asymmetric and can show classification rates considerably below what would be expected from chance classification. Conversely, the mode of the distribution in these cases is above expected chance levels, leading to a spuriously high number of above chance CCRs. This unexpected distribution has strong implications when using MVPA for hypothesis testing. Our analyses warrant the conclusion that CCRs do not well reflect the size of the effect under investigation. Moreover, the skewness of the null-distribution precludes the use of many standard parametric tests to assess significance of CCRs. We propose that MVPA results should be reported in terms of P values, which are estimated using randomization tests. Also, our results show that cross-validation procedures using a low number of folds, e.g. twofold, are generally more sensitive, even though the average CCRs are often considerably lower than those obtained using a higher number of folds. Hum Brain Mapp 37:1842-1855, 2016. © 2016 Wiley Periodicals, Inc.

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Sleep deprivation impairs inhibitory control over reflexive behavior, and this impairment is commonly assumed to dissipate after recovery sleep. Contrary to this belief, here we show that fast reflexive behaviors, when practiced during sleep deprivation, is consolidated across recovery sleep and, thereby, becomes preserved. As a model for the study of sleep effects on prefrontal cortex-mediated inhibitory control in humans, we examined reflexive saccadic eye movements (express saccades), as well as speeded 2-choice finger motor responses. Different groups of subjects were trained on a standard prosaccade gap paradigm before periods of nocturnal sleep and sleep deprivation. Saccade performance was retested in the next morning and again 24 h later. The rate of express saccades was not affected by sleep after training, but slightly increased after sleep deprivation. Surprisingly, this increase augmented even further after recovery sleep and was still present 4 weeks later. Additional experiments revealed that the short testing after sleep deprivation was sufficient to increase express saccades across recovery sleep. An increase in speeded responses across recovery sleep was likewise found for finger motor responses. Our findings indicate that recovery sleep can consolidate motor disinhibition for behaviors practiced during prior sleep deprivation, thereby persistently enhancing response automatization.

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Numerous studies examine the effect of a night's sleep on memory consolidation, but few go beyond this short time-scale to test long-lasting effects of sleep on memory. We investigated long-term effects of sleep on typical memory tasks. During the hours following learning, participants slept or stayed awake. We compared recall performance between wake and sleep conditions after delays of up to 6 days. Performance develops in two distinct ways. Word pair, syllable, and motor sequence learning tasks benefit from sleep during the first day after encoding, when compared with daytime or nighttime wakefulness. However, performance in the wake conditions recovers after another night of sleep, so that we observe no lasting effect of sleep. Sleep deprivation before recall does not impair performance. Thus, fatigue cannot adequately explain the lack of long-term effects. We suggest that the hippocampus might serve as a buffer during the retention interval, and consolidation occurs during delayed sleep. In contrast, a non-hippocampal mirror-tracing task benefits significantly from sleep, even when tested after a 4-day delay including recovery sleep. This indicates a dissociation between two sleep-related consolidation mechanisms, which could rely on distinct neuronal processes.

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STUDY OBJECTIVES: Many studies have found that sleep benefits declarative memory consolidation. However, fundamental questions on the specifics of this effect remain topics of discussion. It is not clear which forms of memory are affected by sleep and whether this beneficial effect is partly mediated by passive protection against interference. Moreover, a putative correlation between the structure of sleep and its memory-enhancing effects is still being discussed. DESIGN: In three experiments, we tested whether sleep differentially affects various forms of declarative memory. We varied verbal content (verbal/nonverbal), item type (single/associate), and recall mode (recall/recognition, cued/free recall) to examine the effect of sleep on specific memory subtypes. We compared within-subject differences in memory consolidation between intervals including sleep, active wakefulness, or quiet meditation, which reduced external as well as internal interference and rehearsal. PARTICIPANTS: Forty healthy adults aged 18-30 y, and 17 healthy adults aged 24-55 y with extensive meditation experience participated in the experiments. RESULTS: All types of memory were enhanced by sleep if the sample size provided sufficient statistical power. Smaller sample sizes showed an effect of sleep if a combined measure of different declarative memory scales was used. In a condition with reduced external and internal interference, performance was equal to one with high interference. Here, memory consolidation was significantly lower than in a sleep condition. We found no correlation between sleep structure and memory consolidation. CONCLUSIONS: Sleep does not preferentially consolidate a specific kind of declarative memory, but consistently promotes overall declarative memory formation. This effect is not mediated by reduced interference.

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There is robust evidence that sleep facilitates procedural memory consolidation. The exact mechanisms underlying this process are still unclear. We tested whether an active replay of prior experience can underlie sleep effects on procedural memory. Participants learned a finger-tapping task in which key presses were associated with tones during practice. Later, during a consolidation interval spent either sleeping or awake, we presented auditory cues to reactivate part of the learned sequence. We show that reactivation strengthens procedural memory formation during sleep, but not during wakefulness. The improvement was restricted to those finger transitions that were cued. Thus, reactivation is a very specific process underpinning procedural memory consolidation. When comparing periods of sleep with and without reactivation, we find that it is not the time spent in a specific stage of sleep per se, but rather the occurrence of reactivation that mediates the effect of sleep on memory consolidation. Our data show that longer sleep time as well as additional reactivation by cueing during sleep can enhance later memory performance.

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In repeated visual search tasks, facilitation of reaction times (RTs) due to repetition of the spatial arrangement of items occurs independently of RT facilitation due to improvements in general task performance. Whereas the latter represents typical procedural learning, the former is a kind of implicit memory that depends on the medial temporal lobe (MTL) memory system and is impaired in patients with amnesia. A third type of memory that develops during visual search is the observers' explicit knowledge of repeated displays. Here, we used a visual search task to investigate whether procedural memory, implicit contextual cueing, and explicit knowledge of repeated configurations, which all arise independently from the same set of stimuli, are influenced by sleep. Observers participated in two experimental sessions, separated by either a nap or a controlled rest period. In each of the two sessions, they performed a visual search task in combination with an explicit recognition task. We found that (1) across sessions, MTL-independent procedural learning was more pronounced for the nap than rest group. This confirms earlier findings, albeit from different motor and perceptual tasks, showing that procedural memory can benefit from sleep. (2) Likewise, the sleep group compared with the rest group showed enhanced context-dependent configural learning in the second session. This is a novel finding, indicating that the MTL-dependent, implicit memory underlying contextual cueing is also sleep-dependent. (3) By contrast, sleep and wake groups displayed equivalent improvements in explicit recognition memory in the second session. Overall, the current study shows that sleep affects MTL-dependent as well as MTL-independent memory, but it affects different, albeit simultaneously acquired, forms of MTL-dependent memory differentially.

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