Differential associations of early- and late-night sleep with functional brain states promoting insight to abstract task regularity

Delayed bedtime on non-school days associates with higher weight and waist circumference in children: Cross-sectional and longitudinal analyses with Mendelian randomisation

Sleep duration has been linked with obesity in population-based studies. Less is known about bedtimes and, especially, if discrepancy between bedtimes on school and non-school days associate with adiposity in children. The associations of self-reported bedtimes with the body mass index z-score (BMIz) and waist-to-height ratio (WtHr) were examined among children with a mean (SD) age of 11.2 (0.85) years in cross-sectional (n = 10,245) and longitudinal (n = 5085) study settings. The causal relationship of whether BMIz contributes to bedtimes, was further examined in a subset of 1064 participants by exploiting Mendelian randomisation (MR). After adjusting for sleep duration and other confounders, every 0.5 h later bedtime on non-school nights and a delay in bedtime in non-school nights compared with school nights associated with 0.048 (95% CI 0.027; 0.069) and 0.08 (95% CI 0.056; 0.105) higher BMIz as well as 0.001 (95% CI 0; 0.002) and 0.004 (95% CI 0.003; 0.005) with higher WtHr, respectively. Moreover, every 0.5-h delay in bedtime in non-school nights compared with school nights associated with 0.001 (95% CI 0; 0.002) greater increase in WtHr in the 2.5 years follow-up. Thus, a 2-h delay in bedtime at the age of 11 years corresponds with a 0.6 cm increase in waist circumference. The MR analysis did not indicate an opposite causal relationship: higher BMIz was not causing delayed bedtimes. Later bedtime on non-school days and discrepancy in bedtimes associated with increased BMIz and WtHr, while longitudinally these predicted higher WtHr, independently of sleep duration. Promoting early bedtimes, especially on weekends, should be considered in obesity prevention among school-aged children.

Interbeat interval-based sleep staging: work in progress toward real-time implementation

Objective. Cardiac activity changes during sleep enable real-time sleep staging. We developed a deep neural network (DNN) to detect sleep stages using interbeat intervals (IBIs) extracted from electrocardiogram signals.Approach. Data from healthy and apnea subjects were used for training and validation; 2 additional datasets (healthy and sleep disorders subjects) were used for testing. R-peak detection was used to determine IBIs before resampling at 2 Hz; the resulting signal was segmented into 150 s windows (30 s shift). DNN output approximated the probabilities of a window belonging to light, deep, REM, or wake stages. Cohen's Kappa, accuracy, and sensitivity/specificity per stage were determined, and Kappa was optimized using thresholds on probability ratios for each stage versus light sleep.Main results. Mean (SD) Kappa and accuracy for 4 sleep stages were 0.44 (0.09) and 0.65 (0.07), respectively, in healthy subjects. For 3 sleep stages (light+deep, REM, and wake), Kappa and accuracy were 0.52 (0.12) and 0.76 (0.07), respectively. Algorithm performance on data from subjects with REM behavior disorder or periodic limb movement disorder was significantly worse, with Kappa of 0.24 (0.09) and 0.36 (0.12), respectively. Average processing time by an ARM microprocessor for a 300-sample window was 19.2 ms.Significance. IBIs can be obtained from a variety of cardiac signals, including electrocardiogram, photoplethysmography, and ballistocardiography. The DNN algorithm presented is 3 orders of magnitude smaller compared with state-of-the-art algorithms and was developed to perform real-time, IBI-based sleep staging. With high specificity and moderate sensitivity for deep and REM sleep, small footprint, and causal processing, this algorithm may be used across different platforms to perform real-time sleep staging and direct intervention strategies.Novelty & Significance(92/100 words) This article describes the development and testing of a deep neural network-based algorithm to detect sleep stages using interbeat intervals, which can be obtained from a variety of cardiac signals including photoplethysmography, electrocardiogram, and ballistocardiography. Based on the interbeat intervals identified in electrocardiogram signals, the algorithm architecture included a group of convolution layers and a group of long short-term memory layers. With its small footprint, fast processing time, high specificity and good sensitivity for deep and REM sleep, this algorithm may provide a good option for real-time sleep staging to direct interventions.

Sleep onset is a creative sweet spot

The ability to think creatively is paramount to facing new challenges, but how creativity arises remains mysterious. Here, we show that the brain activity common to the twilight zone between sleep and wakefulness (nonrapid eye movement sleep stage 1 or N1) ignites creative sparks. Participants (N = 103) were exposed to mathematical problems without knowing that a hidden rule allowed solving them almost instantly. We found that spending at least 15 s in N1 during a resting period tripled the chance to discover the hidden rule (83% versus 30% when participants remained awake), and this effect vanished if subjects reached deeper sleep. Our findings suggest that there is a creative sweet spot within the sleep-onset period, and hitting it requires individuals balancing falling asleep easily against falling asleep too deeply.

Ill-Defined Problem Solving Does Not Benefit From Daytime Napping

The main goal of the present study was to explore the role of sleep in the process of ill-defined problem solving. The results of previous studies indicate that various cognitive processes are largely dependent on the quality and quantity of sleep. However, while sleep-related memory consolidation seems to be well-grounded, with regard to the impact of sleep on problem solving, existing research yields mixed and rather inconclusive results. Moreover, this effect has been mainly tested using simple and well-defined, common laboratory problems, such as the remote associate test (RAT), crossword and anagram puzzles, numeric and logic problems, etc. What is lacking is research on the effect of sleep on solving more complex and more real-life oriented ill-defined problems. In the present study, we hypothesized that sleep can improve performance in solving this kind of problems. The study involved 40 participants, randomly assigned to two experimental conditions: sleep group and waking group. The experimental protocol comprised three stages: problem presentation, retention interval, and testing stage. The problem was presented to the participants in the form of an interactive computer game concerning a complex, elaborate crime story. During the retention interval, the participants—depending on the condition—took a nap or stayed awake; sleeping participants underwent polysomnography recording, while waking participants performed activities not related to the experimental problem. In the testing stage, participants tried to solve the presented problem. The solutions generated were assessed both for quality (reasonableness, consistency, and story recall) and creativity (fluency, flexibility, originality, and elaboration). Contrary to expectations, we found no effect of sleep on ill-defined problem solving. Neither quality nor creativity of the solutions generated by the participants was higher in the nap group than in the waking group. There were also no performance improvements with regard to any sleep stage or incidence of dreams. Our study adds to a growing body of evidence that sleep probably might provide an incubation gap, but not a facilitating environment serving the purpose of problem solving, at least with regard to ill-defined problems.

The effect of complex cognitive training on subsequent night sleep

Using a nap design, we have recently shown that training at a complex cognitive task at bedtime improves objective sleep quality by reducing sleep fragmentation. In order to extend our findings to nighttime sleep, here we assess the impact of a multi-componential cognitive task at bedtime on the subsequent sleep episode of subjects reporting habitual bad sleep, allegedly characterized by high sleep fragmentation. In a within-subjects design, 20 subjective bad sleepers underwent polysomnographic recording in three conditions: (a) baseline sleep (BL); (b) post-training sleep (TR), preceded by a complex ecological task, i.e. a modified version of the word game Ruzzle; (c) post-active control sleep (AC), preceded by a control task. Sleep in TR was more organized (higher number of cycles and longer time spent in cycles) and showed lower microarousal frequency than in AC and BL. As for sleep continuity (total and brief awakening frequency) and other stability measures (state transition and functional uncertainty period frequency, time in functional uncertainty), both TR and AC showed significant improvements compared with BL. Arousal frequency was also reduced in TR relative to BL. Our results show a clear impact of cognitive training on subsequent night sleep, basically consisting of an increase in sleep continuity, stability and organization. In our sample of bad sleepers, these post-training changes end up representing a notable sleep improvement, also consistently reflected in subjective sleep quality perception. Therefore, ecological pre-sleep cognitive training should be further studied as an easily accessible complementary approach in standard therapies for sleep-disordered populations.

Sleep and the extraction of hidden regularities: A systematic review and the importance of temporal rules

As part of its role in memory consolidation, sleep has been repeatedly identified as critical for the extraction of regularities from wake experiences. However, many null results have been published as well, with no clear consensus emerging regarding the conditions that yield this sleep effect. Here, we systematically review the role of sleep in the extraction of hidden regularities, specifically those involving associative relations embedded in newly learned information. We found that the specific behavioral task used in a study had far more impact on whether a sleep effect was discovered than either the category of the cognitive processes targeted, or the particular experimental design employed. One emerging pattern, however, was that the explicit detection of hidden rules is more likely to happen when the rules are of a temporal nature (i.e., event A at time t predicts a later event B) than when they are non-temporal. We discuss this temporal rule sensitivity in reference to the compressed memory replay occurring in the hippocampus during slow-wave-sleep, and compare this effect to what happens when the extraction of regularities depends on prior knowledge and relies on structures other than the hippocampus.

Transcranial Current Stimulation During Sleep Facilitates Insight into Temporal Rules, but does not Consolidate Memories of Individual Sequential Experiences

Slow-wave sleep (SWS) is known to contribute to memory consolidation, likely through the reactivation of previously encoded waking experiences. Contemporary studies demonstrate that when auditory or olfactory stimulation is administered during memory encoding and then reapplied during SWS, memory consolidation can be enhanced, an effect that is believed to rely on targeted memory reactivation (TMR) induced by the sensory stimulation. Here, we show that transcranial current stimulations (tCS) during sleep can also be used to induce TMR, resulting in the facilitation of high-level cognitive processes. Participants were exposed to repeating sequences in a realistic 3D immersive environment while being stimulated with particular tCS patterns. A subset of these tCS patterns was then reapplied during sleep stages N2 and SWS coupled to slow oscillations in a closed-loop manner. We found that in contrast to our initial hypothesis, performance for the sequences corresponding to the reapplied tCS patterns was no better than for other sequences that received stimulations only during wake or not at all. In contrast, we found that the more stimulations participants received overnight, the more likely they were to detect temporal regularities governing the learned sequences the following morning, with tCS-induced beta power modulations during sleep mediating this effect.

Individual Differences in Slow-Wave-Sleep Predict Acquisition of Full Cognitive Maps

Accumulating evidence suggests that sleep, and particularly Slow-Wave-Sleep (SWS), helps the implicit and explicit extraction of regularities within memories that were encoded in a previous wake period. Sleep following training on virtual navigation was also shown to improve performance in subsequent navigation tests. Some studies propose that this sleep-effect on navigation is based on explicit recognition of landmarks; however, it is possible that SWS-dependent extraction of implicit spatiotemporal regularities contributes as well. To examine this possibility, we administered a novel virtual navigation task in which participants were required to walk through a winding corridor and then choose one of five marked doors to exit. Unknown to participants, the markings on the correct door reflected the corridor’s shape (from a bird’s eye view). Detecting this regularity negates the need to find the exit by trial and error. Participants performed the task twice a day for a week, while their overnight sleep was monitored. We found that the more time participants spent in SWS across the week, the better they were able to implicitly extract the hidden regularity. In contrast, the few participants that explicitly realized the regularity did not rely on SWS to do so. Moreover, the SWS effect was strictly at the trait-level: Baseline levels of SWS prior to the experimental week could predict success just as well, but day-to-day variations in SWS did not predict day-to-day improvements. We propose that our findings indicate SWS facilitates implicit integration of new information into cognitive maps, possibly through compressed memory replay.

Sleep Spindles in the Right Hemisphere Support Awareness of Regularities and Reflect Pre-Sleep Activations

Study Objectives

The present study explored the sleep mechanisms which may support awareness of hidden regularities.

Methods

Before sleep, 53 participants learned implicitly a lateralized variant of the serial response-time task in order to localize sensorimotor encoding either in the left or right hemisphere and induce implicit regularity representations. Electroencephalographic (EEG) activity was recorded at multiple electrodes during both task performance and sleep, searching for lateralized traces of the preceding activity during learning. Sleep EEG analysis focused on region-specific slow (9-12 Hz) and fast (13-16 Hz) sleep spindles during nonrapid eye movement sleep.

Results

Fast spindle activity at those motor regions that were activated during learning increased with the amount of postsleep awareness. Independently of side of learning, spindle activity at right frontal and fronto-central regions was involved: there, fast spindles increased with the transformation of sequence knowledge from implicit before sleep to explicit after sleep, and slow spindles correlated with individual abilities of gaining awareness. These local modulations of sleep spindles corresponded to regions with greater presleep activation in participants with postsleep explicit knowledge.

Conclusions

Sleep spindle mechanisms are related to explicit awareness (1) by tracing the activation of motor cortical and right-hemisphere regions which had stronger involvement already during learning and (2) by recruitment of individually consolidated processing modules in the right hemisphere. The integration of different sleep spindle mechanisms with functional states during wake collectively supports the gain of awareness of previously experienced regularities, with a special role for the right hemisphere.

Sleep on your memory traces: How sleep effects can be explained by Act-In, a functional memory model

Numerous studies have explored the effect of sleep on memory. It is well known that a period of sleep, compared to a similar period of wakefulness, protects memories from interference, improves performance, and might also reorganize memory traces in a way that encourages creativity and rule extraction. It is assumed that these benefits come from the reactivation of brain networks, mainly involving the hippocampal structure, as well as from their synchronization with neocortical networks during sleep, thereby underpinning sleep-dependent memory consolidation and reorganization. However, this memory reorganization is difficult to explain within classical memory models. The present paper aims to describe whether the influence of sleep on memory could be explained using a multiple trace memory model that is consistent with the concept of embodied cognition: the Act-In (activation-integration) memory model. We propose an original approach to the results observed in sleep research on the basis of two simple mechanisms, namely activation and integration.

Increased Performance Variability as a Marker of Implicit/Explicit Interactions in Knowledge Awareness

Only some, but not all, individuals who practice tasks with dual structure, overt and covert, are able to comprehend consciously a hidden regularity. The formation of implicit representations of regularity has been proposed to be critical for subsequent awareness. However, explicit knowledge also has been predicted by the activation of executive control systems during task encoding. The present study analyzed performance patterns in participants who could comprehend task regularity and those who could not at delayed recall. Specifically, the role of practice-based knowledge of sequence for individual awareness was focused on. A lateralized variant of the visual serial response time task (SRTT) comprising structured and random blocks was practiced in implicit conditions by 109 participants before and after 10-h retention, with explicit knowledge about covert sequence tested thereafter. Sequence learning was quantified using the normalized difference between response speed in regular and subsequent random blocks. Patterns of performance dynamics were evaluated using response speed, response variability, and error rate. Major results demonstrate that (1) All participants who became aware of the sequence (solvers), gained practice-based sequence knowledge at learning or after retention, (2) Such knowledge also was accumulated during learning by participants who remained fully unaware about covert task structure, (3) Only in explicit solvers, however, was sequence-specific learning accompanied by a prominent increase in performance variability. (4) Specific features and dynamics of performance patterns distinguished different cognitive modes of SRTT processing, each of which supported subsequent knowledge awareness. It is concluded that a behavioral precursor of sequence awareness is the combination of speeded sequence processing and increased performance variability, pointing to an interaction between implicit and explicit processing systems. These results may contribute to refine the evaluation of online and offline learning of tasks with dual structure, and to extend understanding of increased behavioral variability in both normal and pathological conditions.

Labile sleep promotes awareness of abstract knowledge in a serial reaction time task

Sleep has been identified as a critical brain state enhancing the probability of gaining insight into covert task regularities. Both non-rapid eye movement (NREM) and rapid eye movement (REM) sleep have been implicated with offline re-activation and reorganization of memories supporting explicit knowledge generation. According to two-stage models of sleep function, offline processing of information during sleep is sequential requiring multiple cycles of NREM and REM sleep stages. However, the role of overnight dynamic sleep macrostructure for insightfulness has not been studied so far. In the present study, we test the hypothesis that the frequency of interactions between NREM and REM sleep stages might be critical for awareness after sleep. For that aim, the rate of sleep stage transitions was evaluated in 53 participants who learned implicitly a serial reaction time task (SRTT) in which a determined sequence was inserted. The amount of explicit knowledge about the sequence was established by verbal recall after a night of sleep following SRTT learning. Polysomnography was recorded in this night and in a control night before and was analyzed to compare the rate of sleep-stage transitions between participants who did or did not gain awareness of task regularity after sleep. Indeed, individual ability of explicit knowledge generation was strongly associated with increased rate of transitions between NREM and REM sleep stages and between light sleep stages and slow wave sleep. However, the rate of NREM-REM transitions specifically predicted the amount of explicit knowledge after sleep in a trait-dependent way. These results demonstrate that enhanced lability of sleep goes along with individual ability of knowledge awareness. Observations suggest that facilitated dynamic interactions between sleep stages, particularly between NREM and REM sleep stages play a role for offline processing which promotes rule extraction and awareness.

After being challenged by a video game problem, sleep increases the chance to solve it

In the past years many studies have demonstrated the role of sleep on memory consolidation. It is known that sleeping after learning a declarative or non-declarative task, is better than remaining awake. Furthermore, there are reports of a possible role for dreams in consolidation of declarative memories. Other studies have reported the effect of naps on memory consolidation. With similar protocols, another set of studies indicated that sleep has a role in creativity and problem-solving. Here we hypothesised that sleep can increase the likelihood of solving problems. After struggling to solve a video game problem, subjects who took a nap (n = 14) were almost twice as likely to solve it when compared to the wake control group (n = 15). It is interesting to note that, in the nap group 9 out 14 subjects engaged in slow-wave sleep (SWS) and all solved the problem. Surprisingly, we did not find a significant involvement of Rapid Eye Movement (REM) sleep in this task. Slow-wave sleep is believed to be crucial for the transfer of memory-related information to the neocortex and implement intentions. Sleep can benefit problem-solving through the generalisation of newly encoded information and abstraction of the gist. In conclusion, our results indicate that sleep, even a nap, can potentiate the solution of problems that involve logical reasoning. Thus, sleep's function seems to go beyond memory consolidation to include managing of everyday-life events.

Insights into sleep's role for insight: Studies with the number reduction task

In recent years, vibrant research has developed on “consolidation” during sleep: To what extent are newly experienced impressions reprocessed or even restructured during sleep? We used the number reduction task (NRT) to study if and how sleep does not only reiterate new experiences but may even lead to new insights. In the NRT, covert regularities may speed responses. This implicit acquisition of regularities may become explicitly conscious at some point, leading to a qualitative change in behavior which reflects this insight. By applying the NRT at two consecutive sessions separated by an interval, we investigated the role of sleep in this interval for attaining insight at the second session. In the first study, a night of sleep was shown to triple the number of participants attaining insight above the base rate of about 20%. In the second study, this hard core of 20% discoverers differed from other participants in their task-related EEG potentials from the very beginning already. In the third study, the additional role of sleep was specified as an effect of the deep-sleep phase of slow-wave sleep on participants who had implicitly acquired the covert regularity before sleep. It was in these participants that a specific increase of EEG during slow-wave sleep in the 10-12 Hz band was obtained. These results support the view that neuronal memory reprocessing during slow-wave sleep restructures task-related representations in the brain, and that such restructuring promotes the gain of explicit knowledge.

Dreaming and insight

This paper addresses claims that dreams can be a source of personal insight. Whereas there has been anecdotal backing for such claims, there is now tangential support from findings of the facilitative effect of sleep on cognitive insight, and of REM sleep in particular on emotional memory consolidation. Furthermore, the presence in dreams of metaphorical representations of waking life indicates the possibility of novel insight as an emergent feature of such metaphorical mappings. In order to assess whether personal insight can occur as a result of the consideration of dream content, 11 dream group discussion sessions were conducted which followed the Ullman Dream Appreciation technique, one session for each of 11 participants (10 females, 1 male; mean age = 19.2 years). Self-ratings of deepened self-perception and personal gains from participation in the group sessions showed that the Ullman technique is an effective procedure for establishing connections between dream content and recent waking life experiences, although wake life sources were found for only 14% of dream report text. The mean Exploration-Insight score on the Gains from Dream Interpretation questionnaire was very high and comparable to outcomes from the well-established Hill (1996) therapist-led dream interpretation method. This score was associated between-subjects with pre-group positive Attitude Toward Dreams (ATD). The need to distinguish “aha” experiences as a result of discovering a waking life source for part of a dream, from “aha” experiences of personal insight as a result of considering dream content, is discussed. Difficulties are described in designing a control condition to which the dream report condition can be compared.

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.

Increased Alpha (8–12 Hz) Activity during Slow Wave Sleep as a Marker for the Transition from Implicit Knowledge to Explicit Insight

The number reduction task (NRT) allows us to study the transition from implicit knowledge of hidden task regularities to explicit insight into these regularities. To identify sleep-associated neurophysiological indicators of this restructuring of knowledge representations, we measured frequency-specific power of EEG while participants slept during the night between two sessions of the NRT. Alpha (8–12 Hz) EEG power during slow wave sleep (SWS) emerged as a specific marker of the transformation of presleep implicit knowledge to postsleep explicit knowledge (ExK). Beta power during SWS was increased whenever ExK was attained after sleep, irrespective of presleep knowledge. No such EEG predictors of insight were found during Sleep Stage 2 and rapid eye movement sleep. These results support the view that it is neuronal memory reprocessing during sleep, in particular during SWS, that lays the foundations for restructuring those task-related representations in the brain that are necessary for promoting the gain of ExK.

Electrophysiological traces of visuomotor learning and their renormalization after sleep

OBJECTIVE

Adapting movements to a visual rotation involves the activation of right posterior parietal areas. Further performance improvement requires an increase of slow wave activity in subsequent sleep in the same areas. Here we ascertained whether a post-learning trace is present in wake EEG and whether such a trace is influenced by sleep slow waves.

METHODS

In two separate sessions, we recorded high-density EEG in 17 healthy subjects before and after a visuomotor rotation task, which was performed both before and after sleep. High-density EEG was recorded also during sleep. One session aimed to suppress sleep slow waves, while the other session served as a control.

RESULTS

After learning, we found a trace in the eyes-open wake EEG as a local, parietal decrease in alpha power. After the control night, this trace returned to baseline levels, but it failed to do so after slow wave deprivation. The overnight change of the trace correlated with the dissipation of low frequency (<8 Hz) NREM sleep activity only in the control session.

CONCLUSIONS

Visuomotor learning leaves a trace in the wake EEG alpha power that appears to be renormalized by sleep slow waves.

SIGNIFICANCE

These findings link visuomotor learning to regional changes in wake EEG and sleep homeostasis.