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.

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.

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.

Brainhack: Developing a culture of open, inclusive, community-driven neuroscience.

Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress.

Transient cholinergic enhancement does not significantly affect either the magnitude or selectivity of perceptual learning of visual texture discrimination.

Perceptual learning (PL), often characterized by improvements in perceptual performance with training that are specific to the stimulus conditions used during training, exemplifies experience-dependent cortical plasticity. An improved understanding of how neuromodulatory systems shape PL promises to provide new insights into the mechanisms of plasticity, and by extension how PL can be generated and applied most efficiently. Previous studies have reported enhanced PL in human subjects following administration of drugs that increase signaling through acetylcholine (ACh) receptors, and physiological evidence indicates that ACh sharpens neuronal selectivity, suggesting that this neuromodulator supports PL and its stimulus specificity. Here we explored the effects of enhancing endogenous cholinergic signaling during PL of a visual texture discrimination task. We found that training on this task in the lower visual field yielded significant behavioral improvement at the trained location. However, a single dose of the cholinesterase inhibitor donepezil, administered before training, did not significantly impact either the magnitude or the location specificity of texture discrimination learning compared with placebo. We discuss potential explanations for discrepant findings in the literature regarding the role of ACh in visual PL, including possible differences in plasticity mechanisms in the dorsal and ventral cortical processing streams.

Timing between Cortical Slow Oscillations and Heart Rate Bursts during Sleep Predicts Temporal Processing Speed, but Not Offline Consolidation.

Central and autonomic nervous system activities are coupled during sleep. Cortical slow oscillations (SOs; <1 Hz) coincide with brief bursts in heart rate (HR), but the functional consequence of this coupling in cognition remains elusive. We measured SO-HR temporal coupling (i.e., the peak-to-peak interval between downstate of SO event and HR burst) during a daytime nap and asked whether this SO-HR timing measure was associated with temporal processing speed and learning on a texture discrimination task by testing participants before and after a nap. The coherence of SO-HR events during sleep strongly correlated with an individual's temporal processing speed in the morning and evening test sessions, but not with their change in performance after the nap (i.e., consolidation). We confirmed this result in two additional experimental visits and also discovered that this association was visit-specific, indicating a state (not trait) marker. Thus, we introduce a novel physiological index that may be a useful marker of state-dependent processing speed of an individual.

Coupling of autonomic and central events during sleep benefits declarative memory consolidation.

While anatomical pathways between forebrain cognitive and brainstem autonomic nervous centers are well-defined, autonomic-central interactions during sleep and their contribution to waking performance are not understood. Here, we analyzed simultaneous central activity via electroencephalography (EEG) and autonomic heart beat-to-beat intervals (RR intervals) from electrocardiography (ECG) during wake and daytime sleep. We identified bursts of ECG activity that lasted 4-5 s and predominated in non-rapid-eye-movement sleep (NREM). Using event-based analysis of NREM sleep, we found an increase in delta (0.5-4 Hz) and sigma (12-15 Hz) power and an elevated density of slow oscillations (0.5-1 Hz) about 5 s prior to peak of the heart rate burst, as well as a surge in vagal activity, assessed by high-frequency (HF) component of RR intervals. Using regression framework, we show that these Autonomic/Central Events (ACE) positively predicted post-nap improvement in a declarative memory task after controlling for the effects of spindles and slow oscillations from sleep periods without ACE. No such relation was found between memory performance and a control nap. Additionally, NREM ACE negatively correlated with REM sleep and learning in a non-declarative memory task. These results provide the first evidence that coordinated autonomic and central events play a significant role in declarative memory consolidation.

The impact of frequent napping and nap practice on sleep-dependent memory in humans.

Napping benefits long-term memory formation and is a tool many individuals use to improve daytime functioning. Despite its potential advantages, approximately 47% of people in the United States eschew napping. The goal of this study was to determine whether people who endorse napping at least once a week (nap+) show differences in nap outcomes, including nap-dependent memory consolidation, compared with people who rarely or never nap (nap-). Additionally, we tested whether four weeks of nap practice or restriction would change sleep and performance profiles. Using a perceptual learning task, we found that napping enhanced performance to a greater degree in nap+ compared with nap- individuals (at baseline). Additionally, performance change was associated with different electrophysiological sleep features in each group. In the nap+ group, spindle density was positively correlated with performance improvement, an effect specific to spindles in the hemisphere contralateral to the trained visual field. In the nap- group, slow oscillatory power (0.5-1 Hz) was correlated with performance. Surprisingly, no changes to performance or brain activity during sleep emerged after four weeks of nap practice or restriction. These results suggest that individual differences may impact the potential benefits of napping on performance and the ability to become a better napper.

Human hippocampal replay during rest prioritizes weakly learned information and predicts memory performance.

The hippocampus replays experiences during quiet rest periods, and this replay benefits subsequent memory. A critical open question is how memories are prioritized for this replay. We used functional magnetic resonance imaging (fMRI) pattern analysis to track item-level replay in the hippocampus during an awake rest period after participants studied 15 objects and completed a memory test. Objects that were remembered less well were replayed more during the subsequent rest period, suggesting a prioritization process in which weaker memories-memories most vulnerable to forgetting-are selected for replay. In a second session 12 hours later, more replay of an object during a rest period predicted better subsequent memory for that object. Replay predicted memory improvement across sessions only for participants who slept during that interval. Our results provide evidence that replay in the human hippocampus prioritizes weakly learned information, predicts subsequent memory performance, and relates to memory improvement across a delay with sleep.

Quantifying sleep architecture dynamics and individual differences using big data and Bayesian networks.

The pattern of sleep stages across a night (sleep architecture) is influenced by biological, behavioral, and clinical variables. However, traditional measures of sleep architecture such as stage proportions, fail to capture sleep dynamics. Here we quantify the impact of individual differences on the dynamics of sleep architecture and determine which factors or set of factors best predict the next sleep stage from current stage information. We investigated the influence of age, sex, body mass index, time of day, and sleep time on static (e.g. minutes in stage, sleep efficiency) and dynamic measures of sleep architecture (e.g. transition probabilities and stage duration distributions) using a large dataset of 3202 nights from a non-clinical population. Multi-level regressions show that sex effects duration of all Non-Rapid Eye Movement (NREM) stages, and age has a curvilinear relationship for Wake After Sleep Onset (WASO) and slow wave sleep (SWS) minutes. Bayesian network modeling reveals sleep architecture depends on time of day, total sleep time, age and sex, but not BMI. Older adults, and particularly males, have shorter bouts (more fragmentation) of Stage 2, SWS, and they transition less frequently to these stages. Additionally, we showed that the next sleep stage and its duration can be optimally predicted by the prior 2 stages and age. Our results demonstrate the potential benefit of big data and Bayesian network approaches in quantifying static and dynamic architecture of normal sleep.

To Nap, Perchance to DREAM: A Factor Analysis of College Students' Self-Reported Reasons for Napping.

Although napping has received attention because of its associations with health and use as a method to understand the function of sleep, to our knowledge no study has systematically and statistically assessed reasons for napping. Using factor analysis, we determined the underlying structure of reasons for napping in diverse undergraduates (N = 430, 59% female) and examined their relationships with self-reported sleep, psychological health, and physical health. The five reasons for napping can be summarized using the acronym DREAM (Dysregulative, Restorative, Emotional, Appetitive, and Mindful). Only Emotional reasons for napping were uniformly related to lower well-being. The use of factor analysis raises possibilities for future research, including examining the stability, structure, and psychological and physical health processes related to napping throughout the lifespan.

Perceptual learning induces changes in early and late visual evoked potentials.

Studies of visual cortical responses following visual perceptual learning (VPL) have produced diverse results, revealing neural changes in early and/or higher-level visual cortex as well as changes in regions responsible for higher cognitive processes such as attentional control. In this study, we investigated substrates of VPL in the human brain by recording visual evoked potentials with high-density electroencephalography (hdEEG) before (Session 1) and after (Session 2) training on a texture discrimination task (TDT), with two full nights of sleep between sessions. We studied the following event-related potential (ERP) components: C1 (early sensory processing), P1 and N1 (later sensory processing, modulated by top-down spatial attention), and P3 (cognitive processing). Our results showed a significant decrease in C1 amplitude at Session 2 relative to Session 1 that was positively correlated with the magnitude of improvement in behavioral performance. Although we observed no significant changes in P1 amplitude with VPL, both N1 amplitude and latency were significantly decreased in Session 2. Moreover, the difference in N1 latency between Session 1 and Session 2 was negatively correlated with behavioral improvement. We also found a significant increase in P3 amplitude following training. Our results suggest that VPL of the TDT task may be due to plasticity in early visual cortical areas as well as changes in top-down attentional control and cognitive processing.

Sleep Benefits Memory for Semantic Category Structure While Preserving Exemplar-Specific Information.

Semantic memory encompasses knowledge about both the properties that typify concepts (e.g. robins, like all birds, have wings) as well as the properties that individuate conceptually related items (e.g. robins, in particular, have red breasts). We investigate the impact of sleep on new semantic learning using a property inference task in which both kinds of information are initially acquired equally well. Participants learned about three categories of novel objects possessing some properties that were shared among category exemplars and others that were unique to an exemplar, with exposure frequency varying across categories. In Experiment 1, memory for shared properties improved and memory for unique properties was preserved across a night of sleep, while memory for both feature types declined over a day awake. In Experiment 2, memory for shared properties improved across a nap, but only for the lower-frequency category, suggesting a prioritization of weakly learned information early in a sleep period. The increase was significantly correlated with amount of REM, but was also observed in participants who did not enter REM, suggesting involvement of both REM and NREM sleep. The results provide the first evidence that sleep improves memory for the shared structure of object categories, while simultaneously preserving object-unique information.

The effect of sex and menstrual phase on memory formation during a nap.

Memory formation can be influenced by sleep and sex hormones in both men and women, and by the menstrual cycle in women. Though many studies have shown that sleep benefits the consolidation of memories, it is not clear whether this effect differs between men and women in general or according to menstrual phase in women. The present study investigated the effect of sex and menstrual cycle on memory consolidation of face-name associations (FNA) following a daytime nap. Recognition memory was tested using a face-name paired associates task with a polysomnographic nap between morning and evening testing. Seventeen healthy women (age: 20.75 (1.98) years) were studied at two time points of their menstrual cycles, defined from self-report and separated by 2weeks (perimenses: -5days to +6days from the start of menses, and non-perimenses: outside of the perimenses phase), and compared with eighteen healthy men (age: 22.01 (2.91) years). Regardless of menstrual phase, women had better pre-nap performance than men. Further, menstrual phase affected post-nap memory consolidation, with women showing greater forgetting in their perimenses phase compared with their non-perimenses phase and men. Interestingly, post-nap performance correlated with electrophysiological events during sleep (slow oscillations, spindles, and temporal coupling between the two), however, these correlations differed according to menstrual phase and sex. Men's performance improvement was associated with the temporal coupling of spindles and slow oscillations (i.e., spindle/SO coincidence) as well as spindles. Women, however, showed an association with slow oscillations during non-perimenses, whereas when they were in their perimenses phase of their cycle, women appeared to show an association only with sleep spindle events for consolidation. These findings add to the growing literature demonstrating sex and menstrual phase effects on memory formation during sleep.

Autonomic activity during sleep predicts memory consolidation in humans.

Throughout history, psychologists and philosophers have proposed that good sleep benefits memory, yet current studies focusing on the relationship between traditionally reported sleep features (e.g., minutes in sleep stages) and changes in memory performance show contradictory findings. This discrepancy suggests that there are events occurring during sleep that have not yet been considered. The autonomic nervous system (ANS) shows strong variation across sleep stages. Also, increases in ANS activity during waking, as measured by heart rate variability (HRV), have been correlated with memory improvement. However, the role of ANS in sleep-dependent memory consolidation has never been examined. Here, we examined whether changes in cardiac ANS activity (HRV) during a daytime nap were related to performance on two memory conditions (Primed and Repeated) and a nonmemory control condition on the Remote Associates Test. In line with prior studies, we found sleep-dependent improvement in the Primed condition compared with the Quiet Wake control condition. Using regression analyses, we compared the proportion of variance in performance associated with traditionally reported sleep features (model 1) vs. sleep features and HRV during sleep (model 2). For both the Primed and Repeated conditions, model 2 (sleep + HRV) predicted performance significantly better (73% and 58% of variance explained, respectively) compared with model 1 (sleep only, 46% and 26% of variance explained, respectively). These findings present the first evidence, to our knowledge, that ANS activity may be one potential mechanism driving sleep-dependent plasticity.

Free-living cross-comparison of two wearable monitors for sleep and physical activity in healthy young adults.

There is a growing need for free-living monitoring of the full 24 h spectrum of behaviors with a single or integrated set of sensors. The validity of field standard wearable monitors in sleep and physical activity have yet to be assessed for the complementary behavior in the context of 24 h continuous monitoring. We conducted a free-living comparison study of the Actigraph GT3X+ (GT3X+) to assess sleep parameters as compared with the Actiwatch-64 (AW-64) and concurrently, the AW-64 to assess sedentary and physical activity behaviors as compared with the GT3X+. Thirty young adults (15 female, 19.2±0.86 years) wore both monitors for 3 consecutive days and 2 consecutive nights. Agreement of sleep, sedentary, and physical activity metrics were evaluated using analyses of variance, intraclass correlation coefficients, Bland-Altman plots with associated confidence limits, mean absolute percentage of errors and equivalence tests. For sleep, the GT3X+ showed high agreement for total sleep time and sleep efficiency, but underestimated wakefulness after sleep onset and sleep onset latency relative to the AW-64. For sedentary behavior and physical activity, the AW-64 showed a moderate agreement for activity energy expenditure, but not for sedentary, light or moderate-vigorous physical activities relative to the GT3X+. Overall our results showed good agreement of the GT3X+ with AW-64 for assessing sleep but a lack of agreement between AW-64 and GT3X+ for physical activity and sedentary behaviors. These results are likely due to the monitor placement (wrist vs hip), as well as the algorithm employed to score the data. Future validation work of existing and emerging technologies that may hold promise for 24 h continuous monitoring is needed.

Heart rate variability during daytime naps in healthy adults: Autonomic profile and short-term reliability.

In healthy individuals, a reduction in cardiovascular output and a shift to parasympathetic/vagal dominant activity is observed across nocturnal sleep. This cardiac autonomic profile, often measured by heart rate variability (HRV), has been associated with significant benefits for the cardiovascular system. However, little is known about the autonomic profile during daytime sleep. Here, we investigated the autonomic profile and short-term reliability of HRV during daytime naps in 66 healthy young adults. Participants took an 80-120 min polysomnographically recorded nap at 1:30 pm. Beat-by-beat RR interval values (RR), high (HF) and low frequency (LF) power, total power (TP), HF normalized units (HF(nu)), and the LF/HF ratio were obtained for 5 min during presleep wakefulness and during nap sleep stages (N2, N3, REM). A subsample of 37 participants took two additional naps with 2 weeks between recordings. We observed lengthening of the RR, higher HF and HF(nu), and lower LF/HF during NREM, compared with REM and wake, and a marked reduction of LF and TP during N3. Intraclass correlation coefficients highlighted a short-term stability of RR and HF ranging across sleep stages between 0.52-0.76 and 0.52-0.80, respectively. Our results suggest that daytime napping in healthy young adults is associated with dynamic changes in the autonomic profile, similar to those seen during nocturnal sleep. Moreover, a reliable intraindividual measure of autonomic cardiac activity can be obtained by just a single daytime nap depending on specific parameters and recording purposes. Nap methodology may be a new and promising tool to explore sleep-dependent, autonomic fluctuations in healthy and at-risk populations.

Automatic detection of rapid eye movements (REMs): A machine learning approach.

BACKGROUND: Rapid eye movements (REMs) are a defining feature of REM sleep. The number of discrete REMs over time, or REM density, has been investigated as a marker of clinical psychopathology and memory consolidation. However, human detection of REMs is a time-consuming and subjective process. Therefore, reliable, automated REM detection software is a valuable research tool. NEW METHOD: We developed an automatic REM detection algorithm combining a novel set of extracted features and the 'AdaBoost' classification algorithm to detect the presence of REMs in Electrooculogram data collected from the right and left outer canthi (ROC/LOC). Algorithm performance measures of Recall (percentage of REMs detected) and Precision (percentage of REMs detected that are true REMs) were calculated and compared to the gold standard of human detection by three expert sleep scorers. REM detection by four non-experts were also investigated and compared to expert raters and the algorithm. RESULTS: The algorithm performance (78.1% Recall, 82.6% Precision) surpassed that of the average (expert & non-expert) single human detection performance (76% Recall, 83% Precision). Agreement between non-experts (Cronbach Alpha=0.65) is markedly lower than experts (Cronbach Alpha=0.80). COMPARISON WITH EXISTING METHOD(S): By following reported methods, we implemented all previously published LOC and ROC based detection algorithms on our dataset. Our algorithm performance exceeded all others. CONCLUSIONS: The automatic detection algorithm presented is a viable and efficient method of REM detection as it reliably matches the performance of human scorers and outperforms all other known LOC- and ROC-based detection algorithms.