The precuneus as a central node in declarative memory retrieval.

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.

Evidence against a large effect of sleep in protecting verbal memories from interference.

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.

Multivariate classification of neuroimaging data with nested subclasses: Biased accuracy and implications for hypothesis testing.

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.

Rapid and independent memory formation in the parietal cortex.

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.

Decoding cognitive concepts from neuroimaging data using multivariate pattern analysis.

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.

Sleep-mediated memory consolidation depends on the level of integration at encoding.

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.

Night sleep in patients with vegetative state.

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.

Classification based hypothesis testing in neuroscience: Below-chance level classification rates and overlooked statistical properties of linear parametric classifiers.

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.

Deprivation and Recovery of Sleep in Succession Enhances Reflexive Motor Behavior.

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.

Strengthening procedural memories by reactivation in sleep.

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.

Reactivation strength during cued recall is modulated by graph distance within cognitive maps.

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.

Neural correlates of performance variability during motor sequence acquisition.

During the initial training of a motor sequence, performance becomes progressively faster but also increasingly reproducible and consistent. However, performance temporarily becomes more variable at mid-training, reflecting a change in the motor representation and the eventual selection of the optimal performance mode (Adi-Japha et al., 2008). At the cerebral level, whereas performance speed is known to be related to the activity in cerebello-cortical and striato-cortical networks, the neural correlates of performance variability remain unknown. We characterized the latter using functional magnetic resonance imaging (fMRI) during the initial training to the Finger Tapping Task (FTT), during which participants produced a 5-element finger sequence on a keyboard with their left non-dominant hand. Our results show that responses in the precuneus decrease whereas responses in the caudate nucleus increase as performance becomes more consistent. In addition, a variable performance is associated with enhanced interaction between the hippocampus and fronto-parietal areas and between the striatum and frontal areas. Our results suggest that these dynamic large-scale interactions represent a cornerstone in the implementation of consistent motor behavior in humans.

The memory function of noradrenergic activity in non-REM sleep.

There is a long-standing assumption that low noradrenergic activity during sleep reflects mainly the low arousal during this brain state. Nevertheless, recent research has demonstrated that the locus coeruleus, which is the main source of cortical noradrenaline, displays discrete periods of intense firing during non-REM sleep, without any signs of awakening. This transient locus coeruleus activation during sleep seems to occur in response to preceding learning-related episodes. In the present study, we manipulate noradrenergic activity during sleep in humans with either the α2-autoreceptor agonist clonidine or the noradrenaline reuptake inhibitor reboxetine. We show that reducing noradrenergic activity during sleep, but not during wakefulness, impairs subsequent memory performance in an odor recognition task. Increasing noradrenergic availability during sleep, in contrast, enhances memory retention. We conclude that noradrenergic activity during non-REM sleep interacts with other sleep-related mechanisms to functionally contribute to off-line memory consolidation.

Spontaneous neural activity during human slow wave sleep.

Slow wave sleep (SWS) is associated with spontaneous brain oscillations that are thought to participate in sleep homeostasis and to support the processing of information related to the experiences of the previous awake period. At the cellular level, during SWS, a slow oscillation (<1 Hz) synchronizes firing patterns in large neuronal populations and is reflected on electroencephalography (EEG) recordings as large-amplitude, low-frequency waves. By using simultaneous EEG and event-related functional magnetic resonance imaging (fMRI), we characterized the transient changes in brain activity consistently associated with slow waves (>140 microV) and delta waves (75-140 microV) during SWS in 14 non-sleep-deprived normal human volunteers. Significant increases in activity were associated with these waves in several cortical areas, including the inferior frontal, medial prefrontal, precuneus, and posterior cingulate areas. Compared with baseline activity, slow waves are associated with significant activity in the parahippocampal gyrus, cerebellum, and brainstem, whereas delta waves are related to frontal responses. No decrease in activity was observed. This study demonstrates that SWS is not a state of brain quiescence, but rather is an active state during which brain activity is consistently synchronized to the slow oscillation in specific cerebral regions. The partial overlap between the response pattern related to SWS waves and the waking default mode network is consistent with the fascinating hypothesis that brain responses synchronized by the slow oscillation restore microwake-like activity patterns that facilitate neuronal interactions.

Memory Engrams in the Neocortex.

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.

Sleep-related hippocampo-cortical interplay during emotional memory recollection.

Emotional events are usually better remembered than neutral ones. This effect is mediated in part by a modulation of the hippocampus by the amygdala. Sleep plays a role in the consolidation of declarative memory. We examined the impact of sleep and lack of sleep on the consolidation of emotional (negative and positive) memories at the macroscopic systems level. Using functional MRI (fMRI), we compared the neural correlates of successful recollection by humans of emotional and neutral stimuli, 72 h after encoding, with or without total sleep deprivation during the first post-encoding night. In contrast to recollection of neutral and positive stimuli, which was deteriorated by sleep deprivation, similar recollection levels were achieved for negative stimuli in both groups. Successful recollection of emotional stimuli elicited larger responses in the hippocampus and various cortical areas, including the medial prefrontal cortex, in the sleep group than in the sleep deprived group. This effect was consistent across subjects for negative items but depended linearly on individual memory performance for positive items. In addition, the hippocampus and medial prefrontal cortex were functionally more connected during recollection of either negative or positive than neutral items, and more so in sleeping than in sleep-deprived subjects. In the sleep-deprived group, recollection of negative items elicited larger responses in the amygdala and an occipital area than in the sleep group. In contrast, no such difference in brain responses between groups was associated with recollection of positive stimuli. The results suggest that the emotional significance of memories influences their sleep-dependent systems-level consolidation. The recruitment of hippocampo-neocortical networks during recollection is enhanced after sleep and is hindered by sleep deprivation. After sleep deprivation, recollection of negative, potentially dangerous, memories recruits an alternate amygdalo-cortical network, which would keep track of emotional information despite sleep deprivation.

Sleep inspires insight.

Insight denotes a mental restructuring that leads to a sudden gain of explicit knowledge allowing qualitatively changed behaviour. Anecdotal reports on scientific discovery suggest that pivotal insights can be gained through sleep. Sleep consolidates recent memories and, concomitantly, could allow insight by changing their representational structure. Here we show a facilitating role of sleep in a process of insight. Subjects performed a cognitive task requiring the learning of stimulus-response sequences, in which they improved gradually by increasing response speed across task blocks. However, they could also improve abruptly after gaining insight into a hidden abstract rule underlying all sequences. Initial training establishing a task representation was followed by 8 h of nocturnal sleep, nocturnal wakefulness, or daytime wakefulness. At subsequent retesting, more than twice as many subjects gained insight into the hidden rule after sleep as after wakefulness, regardless of time of day. Sleep did not enhance insight in the absence of initial training. A characteristic antecedent of sleep-related insight was revealed in a slowing of reaction times across sleep. We conclude that sleep, by restructuring new memory representations, facilitates extraction of explicit knowledge and insightful behaviour.

Sleep transforms the cerebral trace of declarative memories.

After encoding, memory traces are initially fragile and have to be reinforced to become permanent. The initial steps of this process occur at a cellular level within minutes or hours. Besides this rapid synaptic consolidation, systems consolidation occurs within a time frame of days to years. For declarative memory, the latter is presumed to rely on an interaction between different brain regions, in particular the hippocampus and the medial prefrontal cortex (mPFC). Specifically, sleep has been proposed to provide a setting that supports such systems consolidation processes, leading to a transfer and perhaps transformation of memories. Using functional MRI, we show that postlearning sleep enhances hippocampal responses during recall of word pairs 48 h after learning, indicating intrahippocampal memory processing during sleep. At the same time, sleep induces a memory-related functional connectivity between the hippocampus and the mPFC. Six months after learning, memories activated the mPFC more strongly when they were encoded before sleep, showing that sleep leads to long-lasting changes in the representation of memories on a systems level.

Visual-procedural memory consolidation during sleep blocked by glutamatergic receptor antagonists.

Visual cortex plasticity is enhanced by sleep. It is hypothesized that a reactivation of glutamatergic synapses is essential for this form of plasticity to occur after learning. To test this hypothesis, human subjects practiced a visual texture discrimination skill known to require post-training sleep for improvements to occur. During sleep, glutamatergic transmission was inhibited by administration of the two glutamate antagonists, caroverine and ketamine, targeting the ionotropic NMDA and AMPA receptors. Both substances given during consolidation sleep in a placebo controlled crossover design were able to prevent improvement of the skill measured the next morning. An off-line activation of glutamatergic synapses therefore seems to play a critical part in the consolidation of plastic changes in the visual cortex.

The influence of learning on sleep slow oscillations and associated spindles and ripples in humans and rats.

The mechanisms underlying off-line consolidation of memory during sleep are elusive. Learning of hippocampus-dependent tasks increases neocortical slow oscillation synchrony, and thalamocortical spindle and hippocampal ripple activity during subsequent non-rapid eye movement sleep. Slow oscillations representing an oscillation between global neocortical states of increased (up-state) and decreased (down-state) neuronal firing temporally group thalamic spindle and hippocampal ripple activity, which both occur preferentially during slow oscillation up-states. Here we examined whether slow oscillations also group learning-induced increases in spindle and ripple activity, thereby providing time-frames of facilitated hippocampus-to-neocortical information transfer underlying the conversion of temporary into long-term memories. Learning (word-pairs in humans, odor-reward associations in rats) increased slow oscillation up-states and, in humans, shaped the timing of down-states. Slow oscillations grouped spindle and rat ripple activity into up-states under basal conditions. Prior learning produced in humans an increase in spindle activity focused on slow oscillation up-states. In rats, learning induced a distinct increase in spindle and ripple activity that was not synchronized to up-states. Event-correlation histograms indicated an increase in spindle activity with the occurrence of ripples. This increase was prolonged after learning, suggesting a direct temporal tuning between ripples and spindles. The lack of a grouping effect of slow oscillations on learning-induced spindles and ripples in rats, together with the less pronounced effects of learning on slow oscillations, presumably reflects a weaker dependence of odor learning on thalamo-neocortical circuitry. Slow oscillations might provide an effective temporal frame for hippocampus-to-neocortical information transfer only when thalamo-neocortical systems are already critically involved during learning.