Novel Electrophysiological Signatures of Learning and Forgetting in Human REM Sleep.

Despite the known behavioral benefits of rapid eye movement (REM) sleep, discrete neural oscillatory events in human scalp electroencephalography (EEG) linked with behavior have not been discovered. This knowledge gap hinders mechanistic understanding of the function of sleep, as well as the development of biophysical models and REM-based causal interventions. We designed a detection algorithm to identify bursts of activity in high-density, scalp EEG within theta (4-8 Hz) and alpha (8-13 Hz) bands during REM sleep. Across 38 nights of sleep, we characterized the burst events (i.e., count, duration, density, peak frequency, amplitude) in healthy, young male and female human participants (38; 21F) and investigated burst activity in relation to sleep-dependent memory tasks: hippocampal-dependent episodic verbal memory and non-hippocampal visual perceptual learning. We found greater burst count during the more REM-intensive second half of the night (p < .05), longer burst duration during the first half of the night (p < .05), but no differences across the night in density or power (p > .05). Moreover, increased alpha burst power was associated with increased overnight forgetting for episodic memory (p < .05). Furthermore, we show that increased REM theta burst activity in retinotopically specific regions was associated with better visual perceptual performance. Our work provides a critical bridge between discrete REM sleep events in human scalp EEG that support cognitive processes, and the identification of similar activity patterns in animals models that allow for further mechanistic characterization.Significance Statement Current understanding of sleep and its role cognitive processes is incomplete due to a lack of discrete electrophysiological events in human rapid eye movement (REM) sleep detectable via scalp EEG. Our work remedies this gap in knowledge by designing an open-source, computational approach to identify electrophysiological alpha and theta burst events in REM sleep. Additionally, we provide evidence that these burst events are functionally important for learning and memory. Defining burst events in human REM will contribute to the development of a comprehensive mechanistic model of how sleep as a whole, and REM specifically, facilitate cognitive processes, and provide a deeper understanding of the fundamental electrophysiological properties of REM sleep that are distinct from non-REM sleep.

Evidence of an active role of dreaming in emotional memory processing shows that we dream to forget.

Dreaming is a universal human behavior that has inspired searches for meaning across many disciplines including art, psychology, religion, and politics, yet its function remains poorly understood. Given the suggested role of sleep in emotional memory processing, we investigated whether reported overnight dreaming and dream content are associated with sleep-dependent changes in emotional memory and reactivity, and whether dreaming plays an active or passive role. Participants completed an emotional picture task before and after a full night of sleep and they recorded the presence and content of their dreams upon waking in the morning. The results replicated the emotional memory trade-off (negative images maintained at the cost of neutral memories), but only in those who reported dreaming (Dream-Recallers), and not in Non-Dream-Recallers. Results also replicated sleep-dependent reductions in emotional reactivity, but only in Dream-Recallers, not in Non-Dream-Recallers. Additionally, the more positive the dream report, the more positive the next-day emotional reactivity is compared to the night before. These findings implicate an active role for dreaming in overnight emotional memory processing and suggest a mechanistic framework whereby dreaming may enhance salient emotional experiences via the forgetting of less relevant information.

A classification-based generative approach to selective targeting of global slow oscillations during sleep.

Background: Given sleep's crucial role in health and cognition, numerous sleep-based brain interventions are being developed, aiming to enhance cognitive function, particularly memory consolidation, by improving sleep. Research has shown that Transcranial Alternating Current Stimulation (tACS) during sleep can enhance memory performance, especially when used in a closed-loop (cl-tACS) mode that coordinates with sleep slow oscillations (SOs, 0.5-1.5Hz). However, sleep tACS research is characterized by mixed results across individuals, which are often attributed to individual variability. Objective/Hypothesis: This study targets a specific type of SOs, widespread on the electrode manifold in a short delay ("global SOs"), due to their close relationship with long-term memory consolidation. We propose a model-based approach to optimize cl-tACS paradigms, targeting global SOs not only by considering their temporal properties but also their spatial profile. Methods: We introduce selective targeting of global SOs using a classification-based approach. We first estimate the current elicited by various stimulation paradigms, and optimize parameters to match currents found in natural sleep during a global SO. Then, we employ an ensemble classifier trained on sleep data to identify effective paradigms. Finally, the best stimulation protocol is determined based on classification performance. Results: Our study introduces a model-driven cl-tACS approach that specifically targets global SOs, with the potential to extend to other brain dynamics. This method establishes a connection between brain dynamics and stimulation optimization. Conclusion: Our research presents a novel approach to optimize cl-tACS during sleep, with a focus on targeting global SOs. This approach holds promise for improving cl-tACS not only for global SOs but also for other physiological events, benefiting both research and clinical applications in sleep and cognition.

A Multi-Level Interpretable Sleep Stage Scoring System by Infusing Experts' Knowledge Into a Deep Network Architecture.

In recent years, deep learning has shown potential and efficiency in a wide area including computer vision, image and signal processing. Yet, translational challenges remain for user applications due to a lack of interpretability of algorithmic decisions and results. This black box problem is particularly problematic for high-risk applications such as medical-related decision-making. The current study goal was to design an interpretable deep learning system for time series classification of electroencephalogram (EEG) for sleep stage scoring as a step toward designing a transparent system. We have developed an interpretable deep neural network that includes a kernel-based layer guided by a set of principles used for sleep scoring by human experts in the visual analysis of polysomnographic records. A kernel-based convolutional layer was defined and used as the first layer of the system and made available for user interpretation. The trained system and its results were interpreted in four levels from microstructure of EEG signals, such as trained kernels and effect of each kernel on the detected stages, to macrostructures, such as transitions between stages. The proposed system demonstrated greater performance than prior studies and the system learned information consistent with expert knowledge.

Good sleep is a mood buffer for young women during menses.

STUDY OBJECTIVES: We sought to elucidate the interaction between sleep and mood considering menstrual cycle phase (menses and non-menses portions of the cycle) in 72 healthy young women (18-33 years) with natural, regular menstrual cycles and without menstrual-associated disorders. This work fills a gap in literature of examining mood in context of sleep and menstrual cycle jointly, rather than individually. METHODS: Daily subjective measures of sleep and mood, and date of menses were remotely, digitally collected over a 2-month period. Each morning, participants rated their sleep on the previous night, and each evening participants rated the extent of positive and negative mood for that day. Objective sleep was tracked with a wearable (OURA ring) during month 2 of the study. Time-lag cross-correlation and mixed linear models were used to analyze the significance and directionality of the sleep-mood relationship, and how the interaction between menstrual cycle status and sleep impacted mood levels. RESULTS: We found that menstrual status alone did not impact mood. However, subjective sleep quality and menstrual status interacted to impact positive mood (p < .05). After a night of perceived poor sleep quality, participants reported lower positive mood during menses compared to non-menses portions of the cycle, while after a night of perceived good sleep quality participants reported equivalent levels of positive mood across the cycle. CONCLUSIONS: We suggest that the perception of good sleep quality acts as a mood equalizer, with good sleep providing a protective buffer to positive mood across the menstrual cycle.

The Role of Working Memory in Age-Related Emotional Memory Bias.

Aging is accompanied by deterioration in both working memory (WM) and long-term memory (LTM), yet whether these changes are related is not understood. Sleep plays a role in the formation of LTM in young adults, but the findings in older adults are not as clear. The types of memories we store also shift with age as young adults preserve a higher proportion of negative experiences when compared to older adults. The reason for this age-related change in emotional memory bias is also not clear; however, some studies have suggested that WM changes across aging may be an important factor. In the current study, we examined performance in WM and emotional LTM in younger and older adults. We added a daytime nap in half the subjects to examine a possible role of sleep on emotional LTM. In the morning, 93 younger (18-39) and 121 older (60-85) adults completed a WM task. Subjects also encoded neutral or negative word pairs and provided valence and arousal ratings for each pair. After half the subjects took a daytime nap, LTM was examined, and valence and arousal ratings were reassessed. Results indicate that older adults showed worse recognition for negative word pairs compared with neutral, as well as decreased negative valence ratings in the afternoon. This decrease in emotional reactivity was correlated with better LTM performance. In contrast, younger adults performed better on the negative compared to neutral word pairs, with no change in emotional reactivity and no association between emotional reactivity and LTM. In addition, WM was positively related to LTM in younger, but not in older adults. Lastly, no differences were shown across sleep, regardless of age. Our findings suggest that the emotional memory bias may be associated with the emotional saliency of the information in older adults, and with WM capacity in younger adults. Supplementary Information: The online version contains supplementary material available at 10.1007/s42761-022-00134-5.

Understanding the roles of central and autonomic activity during sleep in the improvement of working memory and episodic memory.

The last decade has seen significant progress in identifying sleep mechanisms that support cognition. Most of these studies focus on the link between electrophysiological events of the central nervous system during sleep and improvements in different cognitive domains, while the dynamic shifts of the autonomic nervous system across sleep have been largely overlooked. Recent studies, however, have identified significant contributions of autonomic inputs during sleep to cognition. Yet, there remain considerable gaps in understanding how central and autonomic systems work together during sleep to facilitate cognitive improvement. In this article we examine the evidence for the independent and interactive roles of central and autonomic activities during sleep and wake in cognitive processing. We specifically focus on the prefrontal-subcortical structures supporting working memory and mechanisms underlying the formation of hippocampal-dependent episodic memory. Our Slow Oscillation Switch Model identifies separate and competing underlying mechanisms supporting the two memory domains at the synaptic, systems, and behavioral levels. We propose that sleep is a competitive arena in which both memory domains vie for limited resources, experimentally demonstrated when boosting one system leads to a functional trade-off in electrophysiological and behavioral outcomes. As these findings inevitably lead to further questions, we suggest areas of future research to better understand how the brain and body interact to support a wide range of cognitive domains during a single sleep episode.

Sleep facilitates spatial memory but not navigation using the Minecraft Memory and Navigation task.

Sleep facilitates hippocampal-dependent memories, supporting the acquisition and maintenance of internal representation of spatial relations within an environment. In humans, however, findings have been mixed regarding sleep's contribution to spatial memory and navigation, which may be due to task designs or outcome measurements. We developed the Minecraft Memory and Navigation (MMN) task for the purpose of disentangling how spatial memory accuracy and navigation change over time, and to study sleep's independent contributions to each. In the MMN task, participants learned the locations of objects through free exploration of an open field computerized environment. At test, they were teleported to random positions around the environment and required to navigate to the remembered location of each object. In study 1, we developed and validated four unique MMN environments with the goal of equating baseline learning and immediate test performance. A total of 86 participants were administered the training phases and immediate test. Participants' baseline performance was equivalent across all four environments, supporting the use of the MMN task. In study 2, 29 participants were trained, tested immediately, and again 12 h later after a period of sleep or wake. We found that the metric accuracy of object locations, i.e., spatial memory, was maintained over a night of sleep, while after wake, metric accuracy declined. In contrast, spatial navigation improved over both sleep and wake delays. Our findings support the role of sleep in retaining the precise spatial relationships within a cognitive map; however, they do not support a specific role of sleep in navigation.

Zolpidem Maintains Memories for Negative Emotions Across a Night of Sleep.

Zolpidem, a common medication for sleep complaints, also shows secondary, unexpected memory benefits. We previously found that zolpidem prior to a nap enhanced negative, highly arousing picture memory. As zolpidem is typically administered at night, how it affects overnight emotional memory processing is relevant. We used a double-blind, placebo-controlled, within-subject, cross-over design to investigate if zolpidem boosted negative compared to neutral picture memory. Subjects learned both pictures sets in the morning. That evening, subjects were administered zolpidem or placebo and slept in the lab. Recognition was tested that evening and the following morning. We found that zolpidem maintained negative picture memory compared to forgetting in the placebo condition. Furthermore, zolpidem increased slow-wave sleep time, decreased rapid eye movement sleep time, and increased the fast spindle range in NREM. Our results suggest that zolpidem may enhance negative memory longevity and salience. These findings raise concerns for zolpidem administration to certain clinical populations. Supplementary Information: The online version contains supplementary material available at 10.1007/s42761-021-00079-1.

The space-time profiles of sleep spindles and their coordination with slow oscillations on the electrode manifold.

Sleep spindles are important for sleep quality and cognitive functions, with their coordination with slow oscillations (SOs) potentially organizing cross-region reactivation of memory traces. Here, we describe the organization of spindles on the electrode manifold and their relation to SOs. We analyzed the sleep night EEG of 34 subjects and detected spindles and SOs separately at each electrode. We compared spindle properties (frequency, duration, and amplitude) in slow wave sleep (SWS) and Stage 2 sleep (S2); and in spindles that coordinate with SOs or are uncoupled. We identified different topographical spindle types using clustering analysis that grouped together spindles co-detected across electrodes within a short delay (±300 ms). We then analyzed the properties of spindles of each type, and coordination to SOs. We found that SWS spindles are shorter than S2 spindles, and spindles at frontal electrodes have higher frequencies in S2 compared to SWS. Furthermore, S2 spindles closely following an SO (about 10% of all spindles) show faster frequency, shorter duration, and larger amplitude than uncoupled ones. Clustering identified Global, Local, Posterior, Frontal-Right and Left spindle types. At centro-parietal locations, Posterior spindles show faster frequencies compared to other types. Furthermore, the infrequent SO-spindle complexes are preferentially recruiting Global SO waves coupled with fast Posterior spindles. Our results suggest a non-uniform participation of spindles to complexes, especially evident in S2. This suggests the possibility that different mechanisms could initiate an SO-spindle complex compared to SOs and spindles separately. This has implications for understanding the role of SOs-spindle complexes in memory reactivation.

The effect of interference, offline sleep, and wake on spatial statistical learning.

Statistical learning, the ability of the human brain to uncover patterns organized according to probabilistic relationships between elements and events of the environment, is a powerful learning mechanism underlying many cognitive processes. Here we examined how memory for statistical learning of probabilistic spatial configurations is impacted by interference at the time of initial exposure and varying degrees of wakefulness and sleep during subsequent offline processing. We manipulated levels of interference at learning by varying the time between exposures of different spatial configurations. During the subsequent offline period, participants either remained awake (active wake or quiet wake) or took a nap comprised of either non-rapid eye movement (NREM) sleep only or NREM and rapid eye movement (REM) sleep. Recognition of the trained spatial configurations, as well as a novel configuration exposed after the offline period, was tested approximately 6-7 h after initial exposure. We found that the sleep conditions did not provide any additional memory benefit compared to wakefulness for spatial statistical learning with low interference. For high interference, we found some evidence that memory may be impaired following quiet wake and NREM sleep only, but not active wake or combined NREM and REM sleep. These results indicate that learning conditions may interact with offline brain states to influence the long-term retention of spatial statistical learning.

Autonomic central coupling during daytime sleep differs between older and younger people.

Decreased functioning in the elderly is mirrored by independent changes in central and autonomic nervous systems. Additionally, recent work suggests that the coupling of these systems may also serve an important role. In young adults, Autonomic and Central Events (ACEs), measured in the temporal coincidence of heart rate bursts (HRBs) and increased slow-wave-activity (SWA, 0.5-1 Hz) and sigma activity (12-15 Hz), followed by parasympathetic surge (RRHF) during non-rapid eye movement (NREM) sleep, predicted cognitive improvements. However, ACEs have not been examined in the elderly. Thus, the current study compared ACEs during wake and daytime sleep in older and younger adults and examined associations with working memory improvement before and after a nap. Compared to youngers, older adults showed lower amplitude of ACEs during NREM sleep, but not during wake. Furthermore, while younger adults demonstrated a parasympathetic surge after HRBs, older adults showed an earlier rise and longer maintenance of the RRHF. Taken together, our results demonstrate that autonomic-central coupling declines with age. Pathological aging implicates independent roles for decreased autonomic and central nervous system functioning, the current findings suggest that the coupling of these systems may also deserve attention.

Slow oscillations promote long-range effective communication: The key for memory consolidation in a broken-down network.

A prominent and robust finding in cognitive neuroscience is the strengthening of memories during nonrapid eye movement (NREM) sleep, with slow oscillations (SOs;<1Hz) playing a critical role in systems-level consolidation. However, NREM generally shows a breakdown in connectivity and reduction of synaptic plasticity with increasing depth: a brain state seemingly unfavorable to memory consolidation. Here, we present an approach to address this apparent paradox that leverages an event-related causality measure to estimate directional information flow during NREM in epochs with and without SOs. Our results confirm that NREM is generally a state of dampened neural communication but reveals that SOs provide two windows of enhanced large-scale communication before and after the SO trough. These peaks in communication are significantly higher when SOs are coupled with sleep spindles compared with uncoupled SOs. To probe the functional relevance of these SO-selective peaks of information flow, we tested the temporal and topographic conditions that predict overnight episodic memory improvement. Our results show that global, long-range communication during SOs promotes sleep-dependent systems consolidation of episodic memories. A significant correlation between peaks of information flow and memory improvement lends predictive validity to our measurements of effective connectivity. In other words, we were able to predict memory improvement based on independent electrophysiological observations during sleep. This work introduces a noninvasive approach to understanding information processing during sleep and provides a mechanism for how systems-level brain communication can occur during an otherwise low connectivity sleep state. In short, SOs are a gating mechanism for large-scale neural communication, a necessary substrate for systems consolidation and long-term memory formation.

Tracking Sleep, Temperature, Heart Rate, and Daily Symptoms Across the Menstrual Cycle with the Oura Ring in Healthy Women.

Background and Objective: The ovulatory menstrual cycle is characterized by hormonal fluctuations that influence physiological systems and functioning. Multi-sensor wearable devices can be sensitive tools capturing cycle-related physiological features pertinent to women's health research. This study used the Oura ring to track changes in sleep and related physiological features, and also tracked self-reported daily functioning and symptoms across the regular, healthy menstrual cycle. Methods: Twenty-six healthy women (age, mean (SD): 24.4 (1.1 years)) with regular, ovulatory cycles (length, mean (SD): 28.57 (3.8 days)) were monitored across a complete menstrual cycle. Four menstrual cycle phases, reflecting different hormone milieus, were selected for analysis: menses, ovulation, mid-luteal, and late-luteal. Objective measures of sleep, sleep distal skin temperature, heart rate (HR) and vagal-mediated heart rate variability (HRV, rMSSD), derived from the Oura ring, and subjective daily diary measures (eg sleep quality, readiness) were compared across phases. Results: Wearable-based measures of sleep continuity and sleep stages did not vary across the menstrual cycle. Women reported no menstrual cycle-related changes in perceived sleep quality or readiness and only marginally poorer mood in the midluteal phase. However, they reported moderately more physical symptoms during menses (p < 0.001). Distal skin temperature and HR, measured during sleep, showed a biphasic pattern across the menstrual cycle, with increased HR (p < 0.03) and body temperature (p < 0.001) in the mid- and late-luteal phases relative to menses and ovulation. Correspondingly, rMSSD HRV tended to be lower in the luteal phase. Further, distal skin temperature was lower during ovulation relative to menses (p = 0.05). Conclusion: The menstrual cycle was not accompanied by significant fluctuations in objective and perceived measures of sleep or in mood, in healthy women with regular, ovulatory menstrual cycles. However, other physiological changes in skin temperature and HR were evident and may be longitudinally tracked with the Oura ring in women over multiple cycles in a natural setting.

The role of sleep for episodic memory consolidation: Stabilizing or rescuing?

Prior studies suggest a role for sleep in memory consolidation, with specific contributions from slow oscillations and sleep spindles (Rasch & Born, 2013). However, recent studies failed to replicate sleep's superiority over wake in strengthening memory against interference (Cordi & Rasch, 2021). The goal of the current study is to investigate whether sleep protects newly formed memory from unspecific interference induced by daytime experiences over 24 h, as well as to elucidate the sleep features that are involved. 56 healthy adults were randomly assigned to either the Sleep First or Wake First group. The Sleep First group encoded word pairs at night before sleep, while the Wake First group encoded word pairs in the morning before a day of wakefulness. Memory was tested 30 min, 12 h, and 24 h after encoding for both groups. The Sleep First group performed significantly better 12 h after encoding, replicating prior findings that memory is better after a period of sleep compared to wake. However, after 24 h, the two groups performed similarly. The Wake First group showed a positive correlation between overnight memory improvement and the theta and delta band power during slow wave sleep, an effect not found in the Sleep First group. These correlations suggest the possibility that after a day of waking interference, the brain recruits extra sleep resources to rescue memories from further forgetting. Our results are not consistent with prior studies showing a significant role for sleep in stabilizing memory from future interference, but they may suggest that sleep rescues memories after interference has occurred.

Progressive muscle relaxation increases slow-wave sleep during a daytime nap.

Sleep is critical for health, cognition, and restorative processes, and yet, many experience chronic sleep restriction. Sleep interventions have been designed to enhance overnight sleep quality and physiology. Components of these interventions, like relaxation-based progressive muscle relaxation (PMR), have been studied in isolation and have shown direct effects on sleep architecture, including increasing time in restorative, slow-wave sleep (SWS). These relaxation methods have been understudied in naps, which are effective fatigue countermeasures that reduce deleterious effects of chronic sleep restriction. We hypothesised that PMR should boost SWS in a nap, as compared to an active control. We used a between-subject design in which healthy young adults underwent PMR training or listened to Mozart music (control) prior to a 90-min nap opportunity. We assessed changes in the amount and lateralisation of SWS, as evidence suggests left hemispheric lateralisation may be a proxy for recuperative sleep needs, and changes to state-dependent anxiety and fatigue before and after the nap to assess intervention success. We found PMR participants spent ~10 min more in SWS, equivalent to 125% more time, than the control group, and concomitantly, significantly less time in rapid eye movement sleep. PMR participants also had greater right lateralised slow-wave activity and delta activity compared to the control suggesting a more well-rested brain profile during sleep. Further, pre-sleep anxiety levels predicted nap architecture in the intervention group, suggesting benefits may be impacted by anxiety. The feasibility and accessibility of PMR prior to a nap make this an interesting research avenue to pursue with strong translational application.

Global and non-Global slow oscillations differentiate in their depth profiles.

Sleep slow oscillations (SOs, 0.5-1.5 Hz) are thought to organize activity across cortical and subcortical structures, leading to selective synaptic changes that mediate consolidation of recent memories. Currently, the specific mechanism that allows for this selectively coherent activation across brain regions is not understood. Our previous research has shown that SOs can be classified on the scalp as Global, Local or Frontal, where Global SOs are found in most electrodes within a short time delay and gate long-range information flow during NREM sleep. The functional significance of space-time profiles of SOs hinges on testing if these differential SOs scalp profiles are mirrored by differential depth structure of SOs in the brain. In this study, we built an analytical framework to allow for the characterization of SO depth profiles in space-time across cortical and sub-cortical regions. To test if the two SO types could be differentiated in their cortical-subcortical activity, we trained 30 machine learning classification algorithms to distinguish Global and non-Global SOs within each individual, and repeated this analysis for light (Stage 2, S2) and deep (slow wave sleep, SWS) NREM stages separately. Multiple algorithms reached high performance across all participants, in particular algorithms based on k-nearest neighbors classification principles. Univariate feature ranking and selection showed that the most differentiating features for Global vs. non-Global SOs appeared around the trough of the SO, and in regions including cortex, thalamus, caudate nucleus, and brainstem. Results also indicated that differentiation during S2 required an extended network of current from cortical-subcortical regions, including all regions found in SWS and other basal ganglia regions, and amygdala and hippocampus, suggesting a potential functional differentiation in the role of Global SOs in S2 vs. SWS. We interpret our results as supporting the potential functional difference of Global and non-Global SOs in sleep dynamics.

Competitive dynamics underlie cognitive improvements during sleep.

We provide evidence that human sleep is a competitive arena in which cognitive domains vie for limited resources. Using pharmacology and effective connectivity analysis, we demonstrate that long-term memory and working memory are served by distinct offline neural mechanisms that are mutually antagonistic. Specifically, we administered zolpidem to increase central sigma activity and demonstrated targeted suppression of autonomic vagal activity. With effective connectivity, we determined the central activity has greater causal influence over autonomic activity, and the magnitude of this influence during sleep produced a behavioral trade-off between offline long-term and working memory processing. These findings suggest a sleep switch mechanism that toggles between central sigma-dependent long-term memory and autonomic vagal-dependent working memory processing.