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 Spatial navigation is a complex behavior requiring the integration of memory, perception, attention, motor movement, and decision making, and relies on the hippocampus. Since NREM sleep supports hippocampal-dependent memory, NREM may also benefit navigation. Yet prior findings are mixed, likely due to heavy reliance on stationary, two-dimensional desktop tasks. Additionally, many studies lack EEG data to examine the role of sleep electrophysiology, e.g. sleep spindles (12-15Hz), in navigation. To address this gap and the lack of immersive tasks mirroring human navigation, we systematically investigated sleep’s contribution to spatial navigation using a nap paradigm and an immersive, ambulatory virtual reality (VR) task. Methods 60 healthy young adults (18-35y, mean 22.55; 17 wake, 43 nap) completed an ambulatory VR spatial navigation task where participants freely explored a maze to locate and remember 12 object locations. They were tested three times: immediately after encoding (Test 1), at the end of the experimental day (Test 2), and at the follow-up visit (Test 3; 2-3 days later). Between Tests 1 and 2, participants either took an EEG-monitored nap or remained quietly awake, and spindles were detected. Navigation efficiency improvement (i.e. change in proportion of actual to optimal path length) was analyzed using two linear models with sex, age, and group (nap vs. wake; model1) or sleep spindles and total sleep time (model2) as predictors. Results Although there were not group differences directly after a nap, participants who napped right after learning showed greater benefits at the 48–72-hour follow-up. Within the nap group, spindle density (N3) significantly predicted greater improvements in navigation efficiency from Test 2 to 3. Sex and age were not significant predictors in either analysis. Conclusion Using an ambulatory VR navigation task, we found that sleep benefits spatial navigation, with a nap immediately after learning improving navigation efficiency after 48 hours. Navigation efficiency reflects consolidation of a cognitive map, i.e., a mental representation of the environment, and was enhanced specifically through sleep spindles, suggesting that spindles facilitate shortcuts and novel routes to remembered target locations. Future studies should examine how spindles modulate hippocampal engagement during the development of cognitive map representations. Support (if any) UCI Internal Grant

Abstract Introduction The rigidity or fluidity of working memory (WM) capacity has been debated, with some studies suggesting that capacity is strictly limited by the number of to-be-remembered objects. While others indicate that prioritizing certain items in a memory array (and therefore creating more precise representations) can influence the number of items we are able to store. This creates a tradeoff between memory precision and capacity. Sleep, specifically NREM, has been shown to enhance WM using complex sequences or updating tasks (e.g. OSPAN, n-back). However, no studies have examined whether sleep plays a role in the trade-off between capacity and precision. This study bridges this gap by examining how a nap versus wake affects WM capacity and precision measured from a continuous color recall task. Methods 64 healthy adults (18-35 yrs) completed a WM continuous recall task before and after a period of rest. Participants were randomized into three rest groups: a 60-minute NREM nap with polysomnography (PSG), a 90-minute NREM+REM nap with PSG, and an unmonitored active wake period. Using linear models (LMs), we predicted post-sleep WM capacity and precision, with rest group and sleep architecture (total sleep time (TST) and total time spent in NREM and REM) as predictors. Results The NREM+REM group had moderately higher post-nap improvement in precision than the NREM group. There were no significant differences in precision changes between the AW group and either nap group, and no differences in capacity between sessions across the three groups. Time in REM, but not NREM, correlated with precision improvement. Finally, we found that increased REM sleep moderately predicted a tradeoff between improvement in precision over capacity (i.e. capacity change - precision change). Conclusion Our results show that while WM capacity does not improve with a nap, precision is influenced by sleep. We specifically found that REM sleep supports WM precision improvement and the trend toward precision improvement over capacity improvement. Our results provide preliminary evidence supporting a relation between sleep and WM capacity and precision. Support (if any) University of California - Irvine Internal Grant

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)

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)

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).

The COVID-19 Pandemic increased the prevalence and severity of insomnia and depression symptoms. The effects of an insomnia intervention on future insomnia and depression symptoms delivered during an ongoing stressor, which may have precipitated the insomnia symptoms, is unknown. We conducted a two-arm randomized controlled pilot study to evaluate whether an insomnia intervention would improve the trajectory of insomnia and depression symptoms in the context of a global pandemic. Forty-nine individuals with clinically significant insomnia symptoms that emerged after the start of the COVID-19 Pandemic were randomized to one of two groups: one group received four sessions of Cognitive Behavioral Therapy for Insomnia (CBT-I) over five weeks via telehealth, and the other was assigned to a 28-week waitlist control group. Participants completed assessments of insomnia and depressive symptom severity at baseline (week 0) and at weeks 1-6, 12, and 28. Linear mixed-effects models were used to evaluate treatment efficacy. The MacArthur model was used to test whether improvement in insomnia symptoms mediated subsequent improvement in mood. The CBT-I group showed improved trajectories of insomnia (b =-1.03, p<0.005, 95% CI [-1.53, -0.53]) and depressive symptoms (b=-0.47, p=0.007, [-0.80, -0.13]) across the 28 weeks compared to the control group. The rate of improvement of insomnia symptoms during treatment mediated the subsequent improvement in depressive symptom severity following treatment (b=2.10, p=0.024, [0.30, 3.90]). Although the sample size was small, these results underscore the potential CBT-I in the context of an ongoing stressor to not only alleviate insomnia symptoms, but also improve depressive symptoms.

Emotional memories change over time, but the mechanisms supporting this change are not well understood. Sleep has been identified as one mechanism that supports memory consolidation, with sleep selectively benefitting negative emotional consolidation at the expense of neutral memories, with specific oscillatory events linked to this process. In contrast, the consolidation of neutral and positive memories, compared to negative memories, has been associated with increased vagally mediated heart rate variability (HRV) during wakefulness. However, how HRV during sleep contributes to emotional memory consolidation remains unexplored. We investigated how sleep oscillations (i.e., sleep spindles) and vagal activity during sleep contribute to the consolidation of neutral and negative memories. Using a double-blind, placebo-controlled, within-subject, cross-over design, we examined the impact of pharmacological vagal suppression using zolpidem on overnight emotional memory consolidation. Thirty-three participants encoded neutral and negative pictures in the morning, followed by picture recognition tests before and after a night of sleep. Zolpidem or placebo was administered in the evening before overnight sleep, and participants were monitored with electroencephalography and electrocardiography. In the placebo condition, greater overnight improvement for neutral pictures was associated with higher vagal HRV in both Non-Rapid Eye Movement Slow Wave Sleep (NREM SWS) and REM. Additionally, the emotional memory tradeoff (i.e., difference between consolidation of neutral versus negative memories) was associated with higher vagal HRV during REM, but in this case, neutral memories were remembered better than negative memories, indicating a potential role for REM vagal HRV in promoting a positive memory bias overnight. Zolpidem, on the other hand, reduced vagal HRV during SWS, increased NREM spindle activity, and eliminated the positive memory bias. Lastly, we used stepwise linear mixed effects regression to determine how NREM spindle activity and vagal HRV during REM independently explained the variance in the emotional memory tradeoff effect. We found that the addition of vagal HRV in combination with spindle activity significantly improved the model’s fit. Overall, our results suggest that sleep brain oscillations and vagal signals synergistically interact in the overnight consolidation of emotional memories, with REM vagal HRV critically contributing to the positive memory bias.

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.

Abstract Introduction The COVID-19 Pandemic resulted in increases in insomnia risk factors, including elevated sleep reactivity, perceived stress, loneliness, and screen time. Here, we test whether these factors predicted worse subsequent insomnia and depression symptoms as part of a randomized controlled feasibility trial of a brief insomnia intervention early in the pandemic. Methods Forty-nine participants with acute pandemic-onset insomnia symptoms were randomized to receive telehealth Cognitive Behavioral Therapy for Insomnia (CBT-I) over five weeks or to a waitlist control. Participants completed baseline measures of sleep reactivity, perceived stress, loneliness, and screen time. Outcome measures included the Insomnia Severity Index and the Patient Health Questionnaire-9 (minus the sleep item) collected at 12 and 28 weeks. As described in the protocol paper, two likelihood ratio tests (one for insomnia and one for depression) were used to test the hypothesis that the risk factors collectively contribute to subsequent insomnia and depression. Specifically, for each outcome, likelihood ratio tests compared a linear mixed effect model containing the 4-baseline risk factor measures and treatment arm as predictors against a model containing only treatment arm. Considering our joint hypothesis test does not address the significance of individual risk factors, we tested whether each risk factor was predictive in isolation using post-hoc mixed effects models (one for each risk factor across insomnia and depression while still controlling for treatment arm). Benjamini-Hochberg adjustment for multiple comparisons was used across the 8 post-hoc models. Results Collectively, the insomnia risk factors did not predict subsequent insomnia or depression (p’s>0.25, marginal ΔR2’s< 0.518) above what would be predicted by receiving CBT-I or waitlist control. However, when considered in individual models, perceived stress and loneliness were significant predictors of both outcomes (b’s>0.22, adjusted-p’s< 0.012) above and beyond treatment arm. Screen time and sleep reactivity were not significant predictors of either outcome (b’s< 0.13, adjusted-p’s>0.149). Conclusion Although this study was conducted in a relatively small sample, these results suggest that increased loneliness and perceived stress may be associated with worse insomnia and depressive symptoms several months later. Thus, loneliness and perceived stress may represent early intervention targets during periods of acute stress and disruption, like the COVID-19 pandemic. Support (if any)

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)

The menstrual cycle is a loop involving the interplay of different organs and hormones, with the capacity to impact numerous physiological processes, including body temperature and heart rate, which in turn display menstrual rhythms. The advent of wearable devices that can continuously track physiological data opens the possibility of using these prolonged time series of skin temperature data to noninvasively detect the temperature variations that occur in ovulatory menstrual cycles. Here, we show that the menstrual skin temperature variation is better represented by a model of oscillation, the cosinor, than by a biphasic square wave model. We describe how applying a cosinor model to a menstrual cycle of distal skin temperature data can be used to assess whether the data oscillate or not, and in cases of oscillation, rhythm metrics for the cycle, including mesor, amplitude, and acrophase, can be obtained. We apply the method to wearable temperature data collected at a minute resolution each day from 120 female individuals over a menstrual cycle to illustrate how the method can be used to derive and present menstrual cycle characteristics, which can be used in other analyses examining indicators of female health. The cosinor method, frequently used in circadian rhythms studies, can be employed in research to facilitate the assessment of menstrual cycle effects on physiological parameters, and in clinical settings to use the characteristics of the menstrual cycles as health markers or to facilitate menstrual chronotherapy.

Nonmedical use of psychostimulants for cognitive enhancement is widespread and growing in neurotypical individuals, despite mixed scientific evidence of their effectiveness. Sleep benefits cognition, yet the interaction between stimulants, sleep, and cognition in neurotypical adults has received little attention. We propose that one effect of psychostimulants, namely decreased sleep, may play an important and unconsidered role in the effect of stimulants on cognition. We discuss the role of sleep in cognition, the alerting effects of stimulants in the context of sleep loss, and the conflicting findings of stimulants for complex cognitive processes. Finally, we hypothesize that sleep may be one unconsidered factor in the mythology of stimulants as cognitive enhancers and propose a methodological approach to systematically assess this relation.

Most studies about the menstrual cycle are laboratory-based, in small samples, with infrequent sampling, and limited to young individuals. Here, we use wearable and diary-based data to investigate menstrual phase and age effects on finger temperature, sleep, heart rate (HR), physical activity, physical symptoms, and mood. A total of 116 healthy females, without menstrual disorders, were enrolled: 67 young (18-35 years, reproductive stage) and 53 midlife (42-55 years, late reproductive to menopause transition). Over one menstrual cycle, participants wore Oura ring Gen2 to detect finger temperature, HR, heart rate variability (root mean square of successive differences between normal heartbeats [RMSSD]), steps, and sleep. They used luteinizing hormone (LH) kits and daily rated sleep, mood, and physical symptoms. A cosinor rhythm analysis was applied to detect menstrual oscillations in temperature. The effect of menstrual cycle phase and group on all other variables was assessed using hierarchical linear models. Finger temperature followed an oscillatory trend indicative of ovulatory cycles in 96 participants. In the midlife group, the temperature rhythm's mesor was higher, but period, amplitude, and number of days between menses and acrophase were similar in both groups. In those with oscillatory temperatures, HR was lowest during menses in both groups. In the young group only, RMSSD was lower in the late-luteal phase than during menses. Overall, RMSSD was lower, and number of daily steps was higher, in the midlife group. No significant menstrual cycle changes were detected in wearable-derived or self-reported measures of sleep efficiency, duration, wake-after-sleep onset, sleep onset latency, or sleep quality. Mood positivity was higher around ovulation, and physical symptoms manifested during menses. Temperature and HR changed across the menstrual cycle; however, sleep measures remained stable in these healthy young and midlife individuals. Further work should investigate over longer periods whether individual- or cluster-specific sleep changes exist, and if a buffering mechanism protects sleep from physiological changes across the menstrual cycle.