Abstract Introduction REM sleep is critical for health and cognitive functioning. Recent studies have identified electrophysiological REM burst events (REM bursts) in theta (4-8Hz) and alpha (8-16Hz) frequency bands associated with cognitive performance, suggesting REM bursts as neural markers of cognitive processes. Still, intra-individual characteristics of REM bursts are unknown. Here, we conducted an in-depth analysis of REM burst events across the night and examined their intra-subject reliability across four nights of sleep. Methods 86 healthy young adults (67F; 18-35 years) slept in-lab for four nights with polysomnography. A validated REM burst detection algorithm was used to identify EEG theta and alpha bursts. Using linear mixed models, we examined burst features (count, power, duration, density) across four quartiles of sleep and relations between time in REM and bursts in each night. We also calculated intraclass correlation coefficients (ICCs) for burst features and REM minutes across four nights of sleep per subject. Results Both REM theta and alpha burst count increased across each quartile, but burst power decreased over the night. Alpha burst density was lowest during quartile 1 compared with the rest of the night. Theta burst duration increased from quartile 1 and 2 to 3 and 4, while alpha burst duration did not vary. REM minutes positively predicted count and density measures, but negatively predicted power and duration measures. Exclusively in quartiles 1 and 2, REM minutes positively predicted alpha burst density and negatively predicted alpha burst power, indicating potential change in REM physiology from early to late night. Intraclass correlations from alpha and theta bursts showed that power and duration are highly reliable within-subject (ICC≈0.9), while counts, density, and REM minutes are moderately reliable (ICC≈0.5), showing similar levels of reliability levels as non-REM sleep spindles. Conclusion REM bursts were shorter, higher in power, and negatively correlated with the amount of REM sleep in the first half of night, while density increased across the night. We also identified burst features as trait-like, with intra-individual reliability comparable to existing spindles results. These results may provide foundational knowledge for studying REM bursts as neural markers of REM functions. Support (if any) RF1AG061355 (Baker/Mednick). K08HD107161 (Simon).

Abstract Introduction Tailored Lighting Interventions (TLI) that target the circadian system can improve sleep in individuals with mild cognitive impairment (MCI). Yet, whether these sleep benefits translate to improved cognitive function is not known. To address this gap, we examined how at-home TLI impacted longitudinal trajectories of sleep-dependent episodic and working memory in older adults with MCI. Methods We conducted a randomized, placebo-controlled, longitudinal at-home TLI trial in adults with MCI. Participants (n=61; 69.7±8.0 years; 29F) were randomly assigned to the active TLI or placebo condition, with all participants completing four at-home visits at weeks 1 (baseline), 13, 25, and 37 (follow-up). At each visit, participants completed episodic and working memory tasks before and after a full night of sleep at-home monitored by actigraphy. Episodic memory was assessed via the Word Pair Association task, while working memory was assessed via the operation span (OSPAN) task. Sleep-dependent memory for each task was defined as the over-night change in performance. Linear mixed effects models evaluated main and interaction effects of group (TLI vs. placebo) and time (visit) on sleep-dependent memory. Significant interaction effects were further examined using post-hoc t-tests. Results The TLI group showed significantly greater sleep-dependent memory at the follow-up visit compared with placebo (p < .05). For both tasks, mixed linear models yielded a significant interaction of visit x group (WPA: p = .02, t = -2.33; OSPAN: p = .03, t = -2.19), with post-hoc t-tests identifying significant differences between groups at visit 4, with greater sleep-dependent memory in the TLI group (WPA: p = .003, t = 3.16; OSPAN: p = .04, t = 2.17). No significant main effects of visit or group were observed (p > .05), suggesting that the TLI specifically contributed to long-term benefits in sleep-dependent memory. Conclusion Our findings demonstrate that circadian entrainment can be done at-home to improve sleep-dependent memory in individuals with MCI. This approach holds translational relevance for developing accessible, sleep-focused, at-home strategies to mitigate age-related cognitive decline. Support (if any) The NIA provided funding under grant number R01AG062288 and R01AG034157.

Abstract Introduction Sleep disturbances are common in individuals with mild cognitive impairment (MCI) and are associated with worse cognition, mood, and risk of dementia progression. Our group previously showed that a tailored lighting intervention (TLI) designed to maximally affect the circadian system improves sleep and mood in individuals with Alzheimer’s disease and related dementia living in controlled environment, but its impact in community-dwelling individuals with MCI remains unclear. Methods We conducted a randomized, placebo-controlled trial of a home-based TLI in adults with MCI and sleep disturbances (N = 61; mean age = 69.7 years). Participants were randomly assigned to receive either active or placebo lighting for 24 weeks, with assessments at baseline, weeks 13, 25, and 37. Outcomes included sleep quality (Pittsburgh Sleep Quality Index [PSQI]), actigraphy-based sleep metrics, mood (Geriatric Depression Scale [GDS]), quality of life (Dementia Quality of Life [DQoL]), and cognition (ADAS-Cog and Montreal Cognitive Assessment [MoCA]). Circadian-effective light exposure was measured using a Daysimeter and summarized as area under the curve (AUC) for morning circadian stimulus (CS). Analyses used intention-to-treat and instrumental variable models to estimate causal effects of achieved light exposure. Results The active condition produced greater improvement in ADAS-Cog memory scores versus placebo (p = 0.035) and a trend toward improvement in total ADAS-Cog (p = 0.142). Morning CS AUC was higher in the active group by week 25 (0.021 vs –0.030; p = 0.025). Higher light exposure was associated with better sleep outcomes at week 25, including percent sleep (–0.21 vs –1.29 percentage points; p = 0.042), percent wake (–0.10 vs 1.19 percentage points; p = 0.023), and wake after sleep onset (2.19 vs 8.23 minutes; p = 0.031). Effects at week 37 were similar but not statistically significant. Conclusion A home-based light intervention for individuals with MCI was feasible and showed potential benefits for memory and sleep. Measuring actual light exposure was essential for understanding treatment effects. Support (if any) The NIA provided funding under grant number R01AG062288 and R01 AG034157.

Abstract Introduction Sleep scoring algorithms have advanced efficiency in evaluating sleep staging, however they have primarily been developed for adult populations. The present preliminary study seeks to evaluate whether a spectral scoring approach, previously validated in adults, is also valid in pediatric populations. While this approach was designed to capture dominant spectral patterns, rather than replicate specific visual stages, visual-spectral comparisons are essential to evaluate how these methods correspond. Methods Participants (n = 22, 9-13 years, 15F) completed overnight sleep studies monitored by polysomnography at the Children’s Hospital of Orange County. Visual scoring was determined by registered polysomnographic technologists and confirmed by board certified sleep physicians following AASM guidelines. Spectral activity within each epoch was assessed for the following: Wake (40–95 Hz), Light (∼11– 15.5 Hz), Hi Deep (1–3 Hz), Lo Deep (0.1–1 Hz), and REM (∼17–26 Hz). For each 30-sec epoch, the conditional probability of the participant being in each stage was calculated and the stage with maximal probability was assigned as the spectral score. Paired t-tests compared time spent in each stage for visual vs. spectral scores. Visually scored stage 2 (N2) was compared to spectral Light sleep. Visually scored slow wave sleep (SWS) was compared to spectral Lo Deep. Visually scored REM and total sleep time (TST) were compared to their spectrally scored equivalents. Results Overall, time in sleep stages was consistent between visual and spectral scoring, with high similarity for time spent in REM sleep, SWS, and TST (e.g. no significant differences, p>.05). However, participants spent less time in spectrally scored Light Sleep compared to visually scored N2 (p=.04, t=-2.44). Conclusion These results provide preliminary evidence that spectral scoring is a promising method for efficiently scoring pediatric sleep. Ongoing analyses will further evaluate the discrepancy between visual Light Sleep and spectral N2. Given that this method assigns stages based on dominant spectral features within each epoch, this spectral approach provides additional, clinically relevant information that is not readily captured through traditional visual scoring. This may be particularly valuable for assessing sleep physiology in pediatric clinical populations, where developmental differences in EEG patterns may complicate visual scoring. Support (if any) CHOC Internal Grant and K08HD107161

Abstract Introduction Sleep supports emotion regulation by preferentially consolidating emotional memories while attenuating reactivity. We have shown that dream recall plays an active role by increasing negative over neutral memories and reducing reactivity. In women, fluctuating reproductive hormones across the menstrual cycle influence sleep features implicated in emotional memory, yet whether menstrual phases influence how dreams shape emotional processing remains unknown. This study investigates how dreams shape sleep-dependent emotional processing across the menstrual cycle in naturally cycling women. Methods 128 women (Mage = 32.85 ±11.93 years) completed up to four visits across verified menstrual phases (menses, late-follicular, mid-luteal, late-luteal). At each visit, participants performed the Emotional Picture Task with negative and neutral IAPS images in the evening (Test 1) and the next morning (Test 2). Participants rated old/new, arousal, and valence of images shown at each test. Dream reports were collected upon waking prior to Test 2. Linear mixed-effects models tested main and interaction effects of menstrual phase and dream recall. Results The menstrual cycle altered how dreaming shaped overnight emotional memory. Dream recall typically benefited the emotional trade-off effect —favoring consolidation of negative relative to neutral images (Δd′; t(410)=1.95, p=0.05)—but this pattern reversed during the late-luteal phase (dream × menstrual cycle: t(381)=-2.29, p=0.02). Dreaming showed independent effects on emotional reactivity. Higher valence and arousal ratings for negative images during Test 1 predicted greater dream recall (valence: t(344)=2.05, p=0.04; arousal: t(327)=2.04, p=0.04). Additionally, the more negatively participants rated the images at Test 1, the more negative their dreams tended to be (t(166)=-2.11, p=0.04). Dream recall was linked to reduced next-morning emotional reactivity (valence: t(413)=-2.89, p=0.004; arousal: t(413)=-2.65, p=0.01), with stronger reductions following more negative dreams (β=0.15, t(182)=2.86, p=0.005). Conclusion Menstrual cycle phase influenced how dreams shaped overnight emotional memory. Negative waking experiences increased dream recall and shaped dream content—and recalling dreams, especially negative ones, reduced emotional reactivity and typically strengthened emotional memory—but this benefit disappeared in the late-luteal phase when there are declining reproductive hormones. These findings suggest a novel interaction between the menstrual cycle and dreaming, showing that hormonal fluctuations reshape how sleep and dreams regulate emotional experience and memory. Support (if any) RF1AG061355 (Baker/Mednick)

Understanding spatial navigation and memory formation is critical to exploring how humans learn and adapt in complex environments. To investigate these processes, we conducted an experiment using the Minecraft Memory and Navigation Task, collecting detailed three-dimensional (3D) path data in a virtual open-world setting. Statistically, we developed a novel methodology to convert complex high-dimensional 3D movement data into functional representations, enabling standardized comparisons and analyses across participants and environments. We applied techniques such as functional clustering and regression to identify navigation patterns and their relationships with cognitive map development and memory retention. Our analysis uncovered two significant insights: first, participants who adopted moderately exploratory behaviors during training demonstrated superior retention of object locations; second, inefficient navigation strategies were strongly linked to poorer spatial memory and navigation performance. These findings highlight the effectiveness of our methodology in advancing the study of navigation behaviors and cognitive processes in dynamic 3D environments.

Abstract Introduction Some studies have reported that the menstrual cycle and fluctuating sex hormones influence sleep microarchitecture, which may in turn shape sleep-dependent memory processes. We examined within-person menstrual cycle dynamics on spatial memory and navigation performance. The Minecraft Memory and Navigation (MMN) task was administered to participants during four distinct menstrual phases: menses, late follicular, midluteal (ML), and late luteal. Our goal was to determine whether variations in sex hormones and sleep spindles across the cycle predicted changes in spatial memory and navigation performance. Methods Forty-three reproductive-aged female participants (18–35yrs) completed the MMN task four times, once per menstrual phase. During training, participants learned the locations of 12 objects across two training trials. Immediate pre-sleep and delayed post-sleep recall were tested by being teleported to random points in the environment and then navigating to remembered object-locations and placing the objects. Training analyses evaluated environment exploration, measured as the area of the environment covered while searching for objects. MNN test analyses examined spatial memory accuracy (Euclidean distance between true and indicated object-locations), navigation metrics (area explored and total steps), and we evaluated sigma power (12-15Hz). All within-subject variables were z-transformed and linear models fit. Results We found menstrual cycle effects on navigation, with participants in the ML, compared to other phases, spending more time during the exploration period (p=0.02), but showing worse spatial memory accuracy for the objects at immediate recall (p=0.018). However, overnight changes in navigation performance or spatial memory did not vary by menstrual cycle phase. A main effect of parietal sigma activity (12–15Hz) predicted greater overnight spatial memory accuracy improvement (p=0.034). Conclusion During the high-hormone ML phase, participants showed more exploratory navigation during learning but performed worse on the immediate pre-sleep test, which could reflect broader spatial search ability and challenges to retain specific spatial locations. We also found that sigma power was associated with overnight spatial memory performance gains, but not overnight changes in navigation behaviors. Despite the role of spindles in overnight spatial memory improvement on this real-world, immersive spatial navigation task, we did not find that the menstrual cycle modulated these sleep-dependent memory processes. Support (if any) RF1AG061355 (Baker/Mednick) K08HD107161 (Simon)

BackgroundLight offers a promising approach to sleep and circadian disturbances in mild cognitive impairment (MCI).ObjectiveTest a home-based lighting intervention in people living with MCI.MethodsIn a randomized, placebo-controlled trial, 61 participants received active or placebo light for 24 weeks, with assessments for cognition, sleep, depression, and quality of life at baseline, week 13, 25, and, post-intervention, at week 37. Light exposure was measured as area under the curve (AUC) for morning circadian stimulus (CS).ResultsActive light participants (mean age 69.7 years; 52% male; mean MoCA 21.4) showed greater improvement in ADAS-Cog memory scores than placebo (p = 0.035), higher morning CS AUC by week 25 (0.021 vs -0.030; p = 0.025), and better sleep percent (-0.21 vs -1.29; p = 0.042), wake percent (-0.10 vs 1.19; p = 0.023), and wake after sleep onset (2.19 vs 8.23 min; p = 0.031).

Understanding the intricate relationship between menstrual phases, sleep, and mood remains a critical area of investigation. Existing literature often falls short by relying on small sample sizes or failing to comprehensively evaluate women using a within-subject design across multiple time points of the menstrual cycle. Furthermore, few studies incorporate assessments of daily mood, sleep, and hormonal fluctuations. This study addresses these gaps by examining how menstrual phases modulate the interplay between mood and sleep in healthy young women. We collected daily sleep and mood self-report measures in 60 healthy young women (18-35 years old). Each participant completed in-lab visits during four menstrual phases (Menses, Pre-Ovulation, Mid-Luteal, and Late-Luteal), and hormone levels were collected through saliva samples. Objective sleep was examined at each visit via polysomnography (PSG) recordings. Using Pearson’s correlations, we examined the relationship between self-reported scores of positive, negative, and angry mood, hormone levels, and objective sleep at each menstrual phase. Key findings indicate that reduced Stage 2 sleep is associated with negative emotions (less positive and more negative mood, r=-0.48,p< 0.001), particularly during the Mid-luteal phase. Progesterone was positively correlated (r=.16,p=0.04) with negative mood, and progesterone and testosterone was positively correlated (r=.21,p=0.009,r=.23,p=0.017) with Stage 2 sleep across all menstrual phases. Furthermore, positive mood consistently correlated with increased REM sleep across all menstrual phases(MS:r=.35,p=0.04, PO:r=.56,p=0.001,ML:r=.29,p=0.04,LL:r=.40,p=0.03) These results underscore the dynamic interdependence between menstrual cycle phases, mood, and sleep. Our findings suggest that hormonal fluctuations modulate these relationships, providing a nuanced understanding of sleep and mood variability in young women. RF1AG061355. KS supported by NICHD/NIH K08HD107161 PM supported by NIH-NHLBI R21HL170255

Despite extensive evidence on the roles of nonrapid eye movement (NREM) and REM sleep in memory processing, a comprehensive model that integrates their complementary functions remains elusive due to a lack of mechanistic understanding of REM's role in offline memory processing. We present the REM Refining and Rescuing (RnR) Hypothesis, which posits that the principal function of REM sleep is to increase the signal-to-noise ratio within and across memory representations. As such, REM sleep selectively enhances essential nodes within a memory representation while inhibiting the majority (Refine). Additionally, REM sleep modulates weak and strong memory representations so they fall within a similar range of recallability (Rescue). Across multiple NREM-REM cycles, tuning functions of individual memory traces get sharpened, allowing for integration of shared features across representations. We hypothesize that REM sleep's unique cellular, neuromodulatory, and electrophysiological milieu, marked by greater inhibition and a mixed autonomic state of both sympathetic and parasympathetic activity, underpins these processes. The RnR Hypothesis offers a unified framework that explains diverse behavioral and neural outcomes associated with REM sleep, paving the way for future research and a more comprehensive model of sleep-dependent cognitive functions.

Studies have reported that NREM sleep, specifically spindle activity (12-15Hz), varies across the menstrual cycle. However, menstrual-related changes to REM sleep or its electrophysiological markers remain unclear. Animal studies suggest that periods of high estrogen and low progesterone may reduce low-frequency EEG activity (≤7Hz) via their effects on GABA. We recently investigated EEG burst activity during REM sleep within the theta (4-8Hz) and alpha (8-16Hz) frequency bands (‘REM bursts’) in relation to sleep-dependent memory. Here, we examined whether changes in sex hormones across the menstrual cycle affect REM burst activity. We hypothesize decreased theta burst activity during the high-estrogen, low-progesterone phase (pre-ovulation) compared to other menstrual phases. 35 healthy women (18-35yrs) slept in-lab with polysomnography during four phases of their menstrual cycle: menses(low hormones), pre-ovulation(high estrogen), mid-luteal(high progesterone and estrogen), and late-luteal(decreasing hormones). At each visit, we collected hormone levels (estrogen/progesterone/testosterone) via saliva samples. Using a validated REM burst detection algorithm, we identified REM theta and alpha bursts and normalized each burst metric across the four visits to measure their relative levels for each individual. We used linear-mixed-models, with pre-ovulation as the reference phase, to assess menstrual phase differences in burst characteristics and performed Pearson’s correlations between sex hormone levels and burst features. We successfully identified REM alpha and theta bursts in human scalp EEG, replicating prior findings of these events. Additionally, we showed menstrual cycle differences in REM sleep and REM burst characteristics. During pre-ovulation, we found 1) fewer alpha and theta burst occurrences, 2) greater alpha and theta burst power, 3) greater theta burst density, and 4) reduced REM sleep. Additionally, independent of menstrual phase, progesterone levels negatively predicted theta and alpha density, and testosterone positively predicted time in REM. In late-luteal, testosterone positively predicted theta power. No significant correlations with estrogen were found. This study replicated the finding of discrete EEG events during REM sleep, i.e., theta and alpha bursts. Furthermore, we show that REM bursts are modulated by menstrual phases and hormonal fluctuations across the cycle. Next steps will be to examine how these REM sleep changes affect cognition. RF1AG061355 (Baker & Mednick); K08 HD107161 (Simon).

Sleep is dynamic across the lifespan, influenced by brain maturation, neurophysiology, hormones, and cognitive processes. Sleep behaviors influenced by physiological and external factors can also impact sleep health. As sleep plays a mechanistic role in health across the lifespan, understanding when and how to intervene to benefit health is essential. Recent research has advanced our understanding of sleep across three domains: patterns, neurophysiology, and behaviors. Highlights include (1) Early childhood nap cessation is thought to relate to medial temporal lobe network maturation and underlie long-term hippocampal-dependent memory development. (2) Chronotype misalignment is a key factor in sleep deficits and social jetlag. (3) Older adult daytime sleep has complex effects on health, at times beneficial while others, potentially maladaptive. (4) Longitudinal sleep oscillation trajectories are starting to be investigated and indicate neurophysiology could be interpreted as indicative of brain maturation in development. (5) In adults, sleep quality and macrostructure trajectories show high variability, emphasizing distinctive traits in shaping sleep and its lifespan trajectories. (6) Neighborhood and socioeconomic factors influence sleep health across all ages. (7) In older adults, associations between loneliness and poor sleep are being unpacked. This recent research, while comprehensively describing our current understanding of sleep trajectories across the lifespan, emphasizes the need to expand current approaches to longitudinal measurement studies that cross age-spans. Expanding will enhance our ability to mechanistically determine the temporal and causal relations between the multiple dimensions of sleep (i.e., patterns, behaviors, and physiology) and outcomes in sleep health.

Recent research has identified mechanisms in REM sleep involved in hippocampal-dependent memory consolidation, specifically with forgetting, and implicated REM alpha bursts in these processes. Additionally, REM sleep rescues non-hippocampal memories weakened by interference, although it is unclear if this also applies to hippocampal memories. We investigated whether REM alpha burst activity impacted forgetting differentially for weak vs. strong hippocampal memories, with the prediction that greater burst activity would predict more forgetting for strong memories. We utilized a validated burst detection algorithm to identify alpha bursts (8-13 Hz) during overnight REM sleep in young adults (n = 24, 18 – 35 years). Before and after sleep, participants completed a Face-Name Association (FNA) task, with interference introduced via an AB-AC paradigm to a proportion of the face-name pairs prior to sleep. Accuracy was measured pre-sleep, post-sleep, and for the overnight difference (post-sleep - pre-sleep). We correlated REM alpha burst power and performance for weak (pairs with interference) and strong (pairs without interference) memories. Participants exhibited forgetting overnight for both weak and strong memories (p <.05), with no difference in the extent of forgetting between the two (p >.05). REM alpha burst power predicted forgetting for strong memories (p <.05), with areas of significance concentrated in the posterior right hemisphere, a hemisphere involved in whole face processing. In contrast, no significant association was found between REM alpha burst power and weak memories (p >.05), or with any task performance metric pre-sleep or post-sleep (p <.05). We demonstrate that REM alpha bursts impact strong and weak hippocampal-dependent memories differently, with increased burst activity predicting more forgetting for strong memories, but not weak. These results suggest that alpha bursts are involved in peak normalization during REM sleep, a process where strong and weak memories are leveled such that they are more equally available at retrieval. National Science Foundation (BCS1439210) and National Institute on Aging (RF1AG061355)

Studies examining how the female menstrual cycle and fluctuating sex hormones affect sleep have demonstrated a significant increase in non-rapid eye movement sigma frequency band (12-15Hz) in the luteal phase. However, existing literature often falls short by relying on small sample sizes, not employing within-subject designs, or failing to comprehensively evaluate multiple menstrual phases. The goal of our study was to conduct a comprehensive, within-subject, investigation of sleep and sex hormones across the menstrual cycle (menses, pre-ovulation, mid-luteal, and late-luteal) in young, healthy women. Thirty-five young, healthy women participated in a study to monitor their menstrual cycles for several months. We collected four in-lab, high-density electroencephalography sleep recordings during each of the four phases of their menstrual cycle, plus measured sex hormones. At each phase, we calculated the EEG power density ratio (PDR) in frequency bins 1-29Hz as power density (µV²/Hz) divided by mean power density across the four phases. We extracted sigma PDR for frequencies between 12-15Hz, and detected spindles. Sigma PDR and spindle density were compared across menstrual phases using repeated-measures-ANOVAs, with Bonferroni-corrections plus Pearson’s correlations between sleep/sex hormones. Across phases, sigma PDR varied significantly, lowest during pre-ovulation (p=0.005) and highest during mid-luteal (p=0.004). Results were similar for spindle density (p=0.005). Topographically, frontal regions showed greater cycle-related modulation in sigma power and fast spindle density compared to central, parietal, and occipital regions. Sex hormones impacted spindles. During pre-ovulation, progesterone negatively associated with sigma PDR (p=0.038), while during mid-luteal both progesterone (p=0.023) and estrogen (p=0.005) positively correlated with sigma PDR. Testosterone did not significantly predict sigma (p>.05). Spindle density correlated with progesterone in all phases except menses (pre-ovulation:p=0.34; mid-luteal:p=0.045; late-luteal:p=0.008), with especially strong correlations in the central channels (pre-ovulation:p=0.019; mid-luteal:p=0.043; late-luteal:p=0.008). Our results demonstrate a significant shaping of sigma/spindle activity by the modulation of sex hormones across the menstrual cycle. Given the importance of spindles for sleep-dependent memory consolidation, future research should examine how these changes affect cognitive functioning and underlying brain networks. Research supported by RF1AG061355. KS supported by NICHD/NIH K08HD107161 PM supported by NIH-NHLBI R21HL170255

Alarmingly few studies track sleep and cognition across the phases of the menstrual cycle. Existing studies fail to encapsulate the complex interactions that arise by neglecting to study each of the four hormone phases within a cycle-haver, or limiting the number of variables(e.g. sleep/cognition or menstrual cycle/cognition). This creates a gap in knowledge about how the menstrual cycle affects sleep-dependent cognitive processes. We aim to fill this gap by specifically examining how the menstrual cycle modulates sleep-dependent working memory (WM). Healthy young women (18-35yrs; n=65) completed four experimental visits at specific phases of their menstrual cycle: menses, pre-ovulation, mid-luteal, and late luteal. Each visit, women slept overnight with in-lab polysomnography and completed a WM operation span (OSPAN) task before and after sleep. We used linear-mixed-models (LMMs) to predict post-sleep WM performance, with menstrual phase, sleep variables (i.e. sleep architecture and spindle (12-15Hz) density), and pre-sleep WM as predictors. Pearson’s correlations were performed to examine the relationship between sleep and post-sleep performance, with a median split to divide participants into high and low pre-sleep performance groups. We found a significant interaction between pre-sleep WM performance and phase (p< 0.05) where pre-sleep performance moderated menstrual phase effects on post-sleep WM, with greater improvements during pre-ovulation compared with mid-luteal. No sleep variables contributed to this interaction. Next, we examined the predictability of each sleep variable on WM performance irrespective of phase. We found an interaction between pre-sleep performance and minutes in REM (p< 0.05) and spindle density (p< 0.05). Correlations demonstrated that lower REM sleep and spindle density (both p< 0.01) predicted better post-sleep WM improvement only in low pre-sleep WM performers. Our results show that sleep-dependent WM varies across the menstrual cycle, with greater WM during the high estrogen, pre-ovulation phase. While sleep variables did not impact the phase-WM interaction, we found that sleep independently contributed to working memory through our REM and spindle density results. Though we have yet to demonstrate a three-way link, our sleep variable results support previous work showing a negative correlation between NREM sigma and WM. - RF1AG061355 - Dr. Simon was funded by K08 HD107161

Hormonal fluctuations across the menstrual cycle modulate sleep architecture and NREM sleep spindles (sigma activity: 12-15 Hz), but their role in sleep-dependent memory consolidation is unclear. Studies report that emotional memory consolidation is modulated by sleep spindles and estrogen, independently. Yet, their combined effects on memory remains unknown. To address this gap, we investigated how hormonal fluctuations (estrogen and progesterone) across the menstrual cycle affect spindle density and emotional memory consolidation. Using a within-subject repeated measures design, fifty-eight healthy women (18–35yr), who were contraceptive-free and with regular menstrual cycles, were assessed in a sleep lab at four timepoints of their menstrual cycle, in random order: low hormones (menses), high estrogen (pre-ovulation), high estrogen and progesterone (mid-luteal), and falling hormones (late-luteal). Participants were tested on an Emotional Pictures Task (neutral and negative) with emotional reactivity (subjective valence and arousal) measured pre and post each night of in-lab sleep, with difference scores calculated and sex hormones measured at each visit. Linear mixed effects models were utilized with the high-estrogen phase (pre-ovulation) as the reference category. Menstrual phase status impacted emotional memory and reactivity. Compared to the high-estrogen phase, 1) pre-sleep memory (d’) for neutral pictures was higher at mid-luteal (b=.199, p=.014); 2) arousal for negative images was reduced at menses post-sleep (b=-.377, p=.026) and for difference score (b=-.436, p=.012); and 3) d’ difference score for neutral images was significantly reduced at mid-luteal (b=-.249, p=.038). Additionally, during the high-estrogen phase, there was a significantly different relation to memory (but not arousal) compared to other phases, with spindle density associated with fewer false alarms and greater d’ for both negative and neutral images (all p’s<.05). During the high-estrogen phase, women showed less arousal for negative images and better memory for neutral images. Similarly, negative and neutral memory was significantly enhanced by spindles during the high-estrogen phase. Estrogen-related changes in sleep spindles may shift the female brain to be more emotionally resilient during the pre-ovulation phase. This study was supported by the National Institutes of Health (NIH) grants RF1AG061355 (Baker/Mednick) and NICHD K08 HD107161 supported KS.

Sleep patterns and their variability across time are influenced by biological and behavioral factors. Sex differences in sleep, including variability potentially influenced by hormonal fluctuations during the menstrual cycle in females, remain poorly understood. This study compared sleep and its monthly variability between females with ovulatory menstrual cycles and males, using polysomnography (PSG) and wearables. We compared 14 males and 14 females (age range:18-32 Years). Ovulation was confirmed with urine. Data included four in-lab PSG recordings, scheduled for males at 1-week intervals and for females specifically targeting 4 menstrual windows:1) menses, 2) ovulation, 3) mid-luteal, 4) late-luteal. Oura ring gen2 data were collected through the study. The means and monthly variability of each sleep variable (monthly ranges for PSG data and standard deviations for wearable data) were calculated within each participant. These individual averages and variability measures were compared by sex were performed using Wilcoxon tests, and results are presented as the median ± half the interquartile range. In PSG measures, sleep latency to stage N2 was longer in females (2.44 ± 1.06 min) compared to males (1.33 ± 0.46 min, p = 0.045). Fewer awakenings were observed in females (20.5 ± 2.83) than in males (23.5 ± 3.00, p = 0.042). In wearable measures, the time awake at night was shorter in females (63.55 ± 12.00 min) than in males (83.00 ± 7.64 min, p < 0.01). Wearable-derived sleep efficiency was higher in females (87.79 ± 2.66%) compared to males (82.70 ± 1.56%, p < 0.01). Wearable-detected deep sleep was also higher in females (136.14 ± 18.66 min) than in males (89.89 ± 15.15 min, p < 0.01). No sex differences were found in the monthly variability for any of the PSG and wearable sleep measures. Using multiple nights of PSG data and a continuous month of wearable data, we found sex differences in sleep measure levels, with indications of more objective sleep disturbances in males than in females. However, no sex-differences in monthly variability was observed. These findings highlight the importance of considering sex-specific factors when assessing and interpreting sleep patterns and variability. RF1AG061355 (Baker/Mednick), K08HD107161 supported KS.

IntroductionVisual scoring of sleep electroencephalography (EEG) has long been considered the gold standard for sleep staging. However, it has several drawbacks, including high cost, time-intensiveness, vulnerability to human variability, discomfort to patients, lack of visualization to validate the hypnogram, and no acknowledgment of differences between delta and slow oscillation deep sleep. This report highlights a spectral scoring approach that addresses all these shortcomings of visual scoring. Past algorithms have used spectral information to help classify traditional visual stages. The current method used the clearly visible spectral patterns to develop new spectral stages, which are similar to but not the same as visual stages. Importantly, spectral scoring delivers both a hypnogram and a whole-night spectrogram, which can be visually inspected to ensure accurate scoring.MethodsThis study compared traditional visual scoring of 32-channel polysomnography with forehead-only spectral scoring from an EEG patch worn concurrently. The PSG was visually scored by trained technicians and the forehead patch was scored spectrally. Because non-rapid eye movement (NREM) stage divisions in spectral scoring are not based on visual NREM stages, the agreements are not expected to be as high as other automated sleep scoring algorithms. Rather, they are a guide to understanding spectral stages as they relate to the more widely understood visual stages and to emphasize reasons for the differences.ResultsThe results showed that visual REM was highly recognized as spectral REM (89%). Visual wake was only scored as spectral Wake 47% of the time, partly because of excessive visual scoring of wake during Light and REM sleep. The majority of spectral Light (predominance of spindle power) was scored as N2 (74%), while less N2 was scored as Light (65%), mostly because of incorrect visual staging of Lo Deep sleep due to high-pass filtering. N3 was scored as both Hi Deep (13 Hz power, 42%) and Lo Deep (0–1 Hz power, 39%), constituting a total of 81% of N3.DiscussionThe results show that spectral scoring better identifies clinically relevant physiology at a substantially lower cost and in a more reproducible fashion than visual scoring, supporting further work exploring its use in clinical and research settings.

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 nonhippocampal visual perceptual learning. We found greater burst count during the more REM-intensive second half of the night (p < 0.05), longer burst duration during the first half of the night (p < 0.05), but no differences across the night in density or power (p > 0.05). Moreover, increased alpha burst power was associated with increased overnight forgetting for episodic memory (p < 0.05). 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 animal models that allow for further mechanistic characterization.

Abstract Introduction Numerous physiological processes display menstrual cycle variations, including body temperature. The advances in quality and accessibility of wearables facilitate collecting time series of physiological data, including skin temperature during sleep. The cosinor method, frequently used in circadian rhythms biology, may be a useful tool to assess if a menstrual cycle is ovulatory, based on a biphasic temperature rhythm. It could also be used to derive metrics about the cycle, in turn allowing the investigation of rhythm characteristics of females at different reproductive stages. Methods Here, 67 females in the early reproductive (age: 25.5 ± 5.4 years (mean ± SD)) and 53 females in the late reproductive/menopausal transition (age: 47 ± 2.9 years) stages tracked sleep and temperature with an Oura ring 2 across a menstrual cycle. They also reported menses and used an ovulation kit that detects a rise in luteinizing hormone. A cosinor method was fitted to daily skin temperature points extracted during the sleep period, and the fit quality was compared with the ovulation kits results. The cycle metrics were extracted and compared between the two groups with Wilcoxon tests. Results With the cosinor method, a cycle was considered ovulatory when the fit had a r2 > .25, a method that agreed with the ovulation kit in 82% of cases. When the fit quality was r2 > .4, the model was considered sufficiently good to calculate derived metrics. There was no difference in the fit quality, acrophase relative to menses, or amplitude of the rhythm between the early and late reproductive/menopausal transition groups. However, the latter had a higher mesor compared with the early reproductive stage group (p = 0.03). Conclusion The cosinor method can be used to model not only circadian but also menstrual rhythms, allowing identification of ovulatory cycles, and derivation of metrics about menstrual cycle rhythms that can be used to track characteristics within and between individuals over time. The overall higher skin temperature rhythms found in the late reproductive/early menopausal transition group could reflect shifted temperature regulation and more heat dissipation during this stage. Support (if any) National Institutes of Health (NIH) grant RF1AG061355 (Baker/Mednick)