bluuebunny/crossref_metadata_2025 huggingface dataset:
054 Characterization of Sleep Phenotypes and Sleep-Dependent Memory Consolidation in a Mouse Model of Fragile X Syndrome
Authors: Martinez Jessy , Popke Donald , Donnelly Marcus , Torres Daniel , Clawson Brittany , Jiang Sha , Aton Sara
Published: May 2021

Introduction

Fragile X syndrome (FXS) is a neurodevelopmental disorder caused by disruption of Fmr1 gene function, leading to intellectual disability. FXS individuals report increased incidence of sleep disruptions such as loss of NREM sleep, irregular sleep/wake cycles, and circadian rhythm disturbances that warrant pharmacological intervention. Since sleep has critical roles in the promotion of memory consolidation, it is unknown whether disrupted cognitive function in FXS is exacerbated by abnormal sleep. We characterized the link between sleep loss phenotypes and cognition in FXS mice (Fmr1 KO). We hypothesized that normalizing sleep in Fmr1 KO mice could improve sleep-dependent cognitive function. Because direct activation of G-protein inward rectifying potassium (GIRK) channels by ML297 has been found to promote NREM sleep, we tested how ML297 affected sleep and memory consolidation phenotypes in Fmr1 KO mice.

Methods

Wild type (WT) and Fmr1 KO were implanted with electrodes for electroencephalogram/electromyogram (EEG/EMG) recording of wakefulness, NREM and REM sleep. Sleep-dependent memory consolidation was measured using single-trial contextual fear conditioning (CFC). ML297 or vehicle was administered after CFC training to measure the effects on sleep and fear memory consolidation.

Results

Fmr1 KO mice showed reduced sleep in the hours following CFC learning compared to wild type littermates, and reduced contextual fear memory consolidation. Post-CFC sleep deprivation disrupted memory consolidation in wild type littermates, but not Fmr1 KO mice. Both NREM sleep time and NREM bout length were reduced in Fmr1 KO mice, and preliminary data suggest reduced NREM delta (0.5–4 Hz) power in the prefrontal cortex. These deficits were present at baseline and also following CFC. Post-CFC training administration of ML297 rescued NREM sleep and contextual fear memory deficits in Fmr1 KO mice.

Conclusion

Our study showed a strong link between NREM sleep loss and cognitive deficits in Fmr1 KO mice. Critically, normalization of NREM sleep through direct activation of GIRK channels rescues cognitive deficits seen in Fmr1 KO mice, suggesting a new therapeutic approach to treating cognitive deficits associated with FXS.

Support (if any)

This work was supported by a Rackham Merit Fellowship to JDM.
056 Effects of Simulated Shift Schedules on Visual Search
Authors: Hudson Amanda , Hinson John , Whitney Paul , Crooks Elena , Shattuck Nita , Matsangas Panagiotis , Van Dongen Hans , Honn Kimberly
Published: May 2021

Introduction

Visual search is important in many operational tasks, such as passive sonar monitoring in naval operations. Shift work can contribute to fatigue and task performance impairment; in particular, backward rotating shift schedules have been shown to impair vigilant attention performance. However, the impact on visual search performance, above and beyond impaired vigilant attention, is unknown. We investigated the effects of two distinct shift work schedules using a visual search task with properties of real-life visual search performance.

Methods

N=13 adult males (ages 18–39) completed a 6-day/5-night laboratory study with an acclimation day, four simulated shift days, and a recovery day. Shift days involved either a 5h-on/15h-off backward rotating schedule (n=8) or a 3h-on/9h-off fixed schedule (n=5). The visual search task was performed once per shift at varying time of day depending on shift. Participants viewed search arrays where stimuli consisted of colored letters of different shapes. Over three trial blocks of 24 trials each, participants determined if a target was present or absent among 1, 5, 15, or 30 distractors. Similarity between targets and distractors was manipulated between blocks, such that targets differed from distractors by color only, shape only, or either color or shape but not both. For each distinct target feature block, and separately for presence or absence of a target, slopes of response times regressed against number of stimuli were calculated to quantify visual search rates. Mixed-effects ANOVA was used to analyze visual search rates by shift schedule and shift day.

Results

There were no significant effects of shift schedule (all p>0.30), shift day (all p>0.13), or their interaction (all p>0.22) on visual search rates.

Conclusion

Previous work showed degraded vigilant attention in the shift schedules considered here, especially in the backward rotating schedule, which may compromise operational performance. However, while our sample may have been too small to have adequate statistical power, we failed to identify specific impairments in visual search with statistical significance. It remains to be determined whether greater levels of fatigue, such as could be induced by total sleep deprivation, would reveal significant visual search deficits.

Support (if any)

Naval Postgraduate School award N62271-13-M-1228
057 Age-related changes in sleep impact learning-related functional connectivity in the cortico-striatal-hippocampal system
Authors: Smith Dylan , Fang Zhuo , Carrier Julie , Doyon Julien , Fogel Stuart
Published: May 2021

Introduction

Older adults do not consolidate newly learned motor sequences with the same efficiency compared to younger adults, and there is evidence that enhanced consolidation by sleep is also impaired with age. It is known that brain activity in the hippocampal-cortical-striatal network is important for off-line consolidation of motor-sequences, however, the intricacies of how communication within this network is altered by sleep in order to facilitate consolidation is not known.

Methods

In this study, 37 young and 49 older individuals underwent resting state MRI before training on a MSL task, as well as after training, and then once again, after either a nap or a period of awake rest.

Results

Preliminary analysis showed a significant difference in functional communication (FC) in the hippocampal-cortical-striatal network, with younger subjects showing increased FC compared to younger individuals. Follow-up analyses revealed this effect was driven by younger subjects who showed an increase in FC between striatum and motor cortices, as well as older subjects who showed decreased FC between hippocampus, striatum, and precuneus. Therefore, an opposite effect of sleep was observed in younger vs. older participants, where young participants primarily showed increased communication in the striatal-motor network and older participants showed decrease in key nodes of the default mode network.

Conclusion

This shows that changes to sleeps’ ability to optimize functional communication may disrupt sleep-enhanced MSL consolidation in old age.

Support (if any)

Canadian Institutes of Health Research
058 The interactions of Sleep, Heart Rate Variability and Aging on an Emotional Directed Forgetting Memory Task
Authors: Sattari Negin , Upadhya Anisha , Vinces Karla , Mednick Sara
Published: May 2021

Introduction

The ability to forget information plays an important role in our daily lives. Sleep plays a role in memory formation and as we age, sleep-quality and memory decrease. For emotional memory a drop in preference for negative stimuli is presented with aging. Heart-rate variability (HRV), a measurement of cardiac autonomic-activity, has been related to cognitive processes. It is unknown how HRV impacts sleep-dependent memory updates in older adults. Here, we investigated HRV and sleep-related emotional memory updates in the context of aging using a Directed-Forgetting (DF) paradigm.

Methods

We tested younger [N=105,18-25yr] and older adults [N=119,60-85yr]. Subjects encoded a DF Word-Paired task, in which either negatively/neutrally-valenced word-pairs were cued to-be-remembered (Retain) or forgotten (Alter) for a later test. They then took a polysomnographically-recorded (PSG) nap including HRV. Next, recognition was tested. Memory for both Retain and Alter words was measured. We compared memory, sleep-quality measured by Sleep-Efficiency (SE) and HRV, measured by normalized High-Frequency (HFnu), an indicator of parasympathetic activity. Bivariate correlations were used to measure the associations.

Results

Younger adults showed greater performance on both Retain and Alter word-pairs (p<.001) with being able to better forget Alter word-pairs only for the negative-condition (p<.001). Younger adults had a higher SE (p<.001) and a higher HRV-HFnu in both Stage2 (p=.02) and Stage3 (p =.03). Only for older adults in the neutral-condition, we found correlations between memory and sleep [Retain: r(20)=.52, p=.01; Alter: r(20) =.51, p=.01]. Finally, among younger adults, in Stage 2, memory was related to HFnu for both neutral [Retain: r(17) = .46, p=.05] and negative-condition [Retain: r(25) = -.41, p =.03; Alter: r(25) = -.39, p =.05]. No correlations were found for older adults(all ps>.11).

Conclusion

Our result indicate a possible loss of the ability to intentionally forget irrelevant information among older adults and a role for the saliency to forget irrelevant items among younger adults. In addition, aging brain may benefit from sleep only for the neutrally-valenced items; the memory biased seen in aging. Finally, for younger adults HRV may be related to memory updates and its role depends on specific sleep stages however this association is faded away with aging.

Support (if any):
072 Sleep Spindle Harmonics in Insomnia
Authors: Goldstein Michael , Haack Monika , Mullington Janet
Published: May 2021

Introduction

Prior research has reported NREM spectral EEG differences between individuals with insomnia and good-sleeper controls, including elevated high-frequency EEG power (beta/gamma bands, ~16-50Hz) and, to a lesser extent, elevations in sleep spindle parameters. However, the mechanisms driving these differences remain unclear. Harmonics have been observed in EEG data as spectral peaks at multiples of a fundamental frequency associated with an event (e.g., for a 14Hz spindle, the 2nd harmonic is expected to be a peak at 28Hz). Thus far, there has been very limited application of this idea of spectral harmonics to sleep spindles, even though these patterns can indeed be seen in some existing literature. We sought to build on this literature to apply spectral harmonic analysis to better understand differences between insomnia and good sleepers.

Methods

15 individuals with insomnia disorder (DSM-5 criteria, 13 female, age 18–32 years) and 15 good-sleeper controls (matched for sex, age, and BMI) completed an overnight polysomnography recording in the laboratory and subsequent daytime testing. Insomnia diagnosis was determined by a board-certified sleep specialist, and exclusion criteria included psychiatric history within past 6 months, other sleep disorders, significant medical conditions, and medications with significant effects on inflammation, autonomic function, or other psychotropic effects.

Results

Consistent with prior studies, we found elevated sleep spindle density and fast sigma power (14-16Hz). Despite no difference in beta or gamma band power when averaged across NREM sleep, time-frequency analysis centered on the peaks of detected spindles revealed a phasic elevation in spectral power surrounding the 28Hz harmonic peak in the insomnia group, especially for spindles coupled with slow waves. We also observed an overall pattern of time-locked delay in the 28Hz harmonic peak, occurring approximately 40 msec after spindle peaks. Furthermore, we observed a 42Hz ‘3rd harmonic’ peak, not yet predicted by the existing modeling work, which was also elevated for insomnia.

Conclusion

In conjunction with existing mathematical modeling work that has linked sleep spindle harmonic peaks with thalamic relay nuclei as the primary generators of this EEG signature, these findings may enable novel insights into specific thalamocortical mechanisms of insomnia and non-restorative sleep.

Support (if any)

NIH 5T32HL007901-22
077 Human activity levels reflect circadian influences independent of sleep/wake behavior
Authors: Klerman Elizabeth , McHill Andrew , Brown Lindsey , Czeisler Charles
Published: May 2021

Introduction

Actigraphy is a non-invasive method that allows long-term recordings of activity, light, and other variables in diverse environments. In real-world settings, activity usually has a 24-hour rhythm that may arise from sleep/wake-associated behavior and/or circadian rhythmicity. We tested for an independent circadian component using data from people living on non-24 hours “days” in the laboratory.

Methods

Data are from five inpatient studies with tightly-controlled forced desynchrony (FD) conditions. Participants (19–34 yo) were healthy by history, physical exam, laboratory tests of blood and urine, and clinical polysomnography, and did not report using prescription medicines. Caffeine-containing substances were prohibited during the study. Protocol 1: 7 participants (3 F) T-cycle (i.e., FD sleep-wake cycle duration) = 42.85h; Rest:Activity ratio 1:3.3. Protocol 2: 8 participants (3 F) T cycle =42.85h; Rest:Activity 1:2. Protocol 3: 9 participants (3 F) T cycle =28.0h; Rest:Activity 1:2. Protocol 4: 7 participants (3 F) T cycle =20.0h; Rest:Activity ratio 1:3.3. Protocol 5: 7 participants (5 F) T cycle =20.0h; Rest:Activity 1:2. At all times except during showers, participants wore an actiwatch that measured activity levels and light. Melatonin period and phase 0 (i.e., fit maximum) were computed using non-orthogonal spectral analyses. Data were analyzed relative to 3-hr Circadian Phase bins (1/8 of computed circadian period for each individual) and 3-hr Wake Duration bins. Activity data were summarized using Zero-Inflated-Poison-based statistics for each Circadian*Wake Duration bin for each individual and then across individuals within each study. Repeated measures ANOVA were conducted. Statistics were performed using SAS.

Results

For all protocols, there were significant differences (all p<0.007) by individual participant, by Circadian Phase, and by Wake Duration bin, but not by the interaction term (Circadian Phase* Wake Duration). Highest levels of activity were at Circadian Phase 7.5–10.5 (~10am–1pm) and lowest values at Circadian Phase -1.5–1.5 (~midnight–3 am). Activity values were lowest at scheduled sleep times.

Conclusion

Circadian rhythms independent of sleep/wake behaviors influence activity levels and may be an important component of analyses. In individuals living on non-24-hr days (e.g., some blind people and some sighted people with Non-24-hr Sleep Disorder), it may be possible to derive circadian-based metrics.

Support (if any)

NIH K24-HL105664, P01-AG009975, T32-HL007901, K01-HL146992.
087 Gender differences in attitudes towards sleep moderate sleep hygiene behaviors
Authors: Zendels Philip , Ruggiero Aria , Peach Hannah , Gaultney Jane
Published: May 2021

Introduction

Attitudes towards sleep have been shown to be a predictor for sleep hygiene. Sleep hygiene is the set of behaviors and conditions that promote optimal sleep, such as avoiding arousing nighttime activities, avoiding eating too close before bed, having a dark and quiet bedroom, and having a regular sleep schedule. Previous literature indicates that there are gender differences in health attitudes. This study examined whether gender differences in sleep attitudes may explain differences in sleep hygiene.

Methods

A sample of 172 (101 males, 71 females) individuals completed surveys through Amazon’s Mechanical Turk. Sleep attitudes were assessed using the Charlotte Attitudes Towards Sleep Scale (CATS; Peach & Gaultney, 2017). Sleep hygiene was measured using the Sleep Hygiene Practice Scale (SHPS; Lin et al; 2007; Yang et al., 2010). Males were dummy coded as 0. Other data were collected surrounding sleep outcomes, health behaviors, and demographics. Linear regression analyses were ran to examine the impact of Sleep attitudes, gender, and an interaction term on each subscale of the SHPS.

Results

Sleep attitudes significantly predicted each of the components of the SHPS: arousal, eating, environment, and time (b = -3.44, -2.93, -3.80, -3.04; p<.01 for each). Gender significantly predicted sleep hygiene behaviors for eating (b = -10.35, p<.05) and environment (b = -15.40, p<.05) only. The interaction term also significantly predicted sleep hygiene eating behaviors (b = 1.70 p<.05) and environmental conditions (b = 2.91, p<.05). These findings suggest that more favorable sleep attitudes lead to better sleep hygiene behaviors, and women tend to have better eating and environment related sleep hygiene behaviors. Graphs of the interactions indicated males’ sleep attitudes associated with greater differences in sleep hygiene practices, in that positive sleep attitudes predicted better eating and environment elements of sleep hygiene.

Conclusion

This exploratory research suggested that men’s sleep-related behaviors may be more sensitive to the role of sleep attitudes. Future research should explore causes for gender differences in sleep attitudes and seek ways to improve behaviors and outcomes that are most relevant for specific demographic groups.

Support (if any)

Psychological Sciences department funding
088 Irregular Sleep Patterns Predict Worse Sleep Quality and Poorer Psychosocial and Academic Outcomes
Authors: Gao Chenlu , Do Alexander , Scullin Michael
Published: May 2021

Introduction

While the consequences of short sleep duration are well understood, relatively little is known about how irregular sleep durations are related to daily functioning. We utilized actigraphy and self-report methods to investigate the correlates of intraindividual sleep variability in two populations.

Methods

In Study 1, N=699 adults (mean age=38.04, SD=11.65; 44.78% female) completed online questionnaires on sleep, work status, and time management. In Study 2, N=100 college students (mean age=19.08, SD=1.26; 50% female; 50% underrepresented minorities) wore actiwatches for two one-week sessions (1 month apart) and reported sleep, psychosocial functions, and academic outcomes. In both studies, variability of sleep duration was defined as the individual standard deviation (ISD) of sleep durations.

Results

In Study 1, after controlling for mean sleep duration, greater variability in sleep duration was related to shiftwork (F=8.68, p=.003), younger age (r=-.15, p<.001), male gender (F=4.43, p=.036), and greater vulnerability to stress (r=.15, p=.001). Beyond mean sleep duration, variability in sleep duration also predicted poorer sleep quality (r=.25, p<.001), greater sleepiness during the day (r=.10, p=.014), poorer time management (r=-.09, p=.031), lower work efficiency (r=-.14, p=.002), and more procrastination (r=.11, p=.009). In Study 2, variability in actigraphy-defined sleep duration increased as the semester progressed (ISD=1.25±0.55 hours at T1; ISD=1.57±0.80 hours at T2, t=4.64, p<.001). After controlling for mean sleep duration, sleep variability was associated with greater perceived stress (r=.31, p=.002 at T1), poorer sleep quality (r=.39, p<.001 at T1; r=.30, p=.003 at T2), and lower expected grades (r=-.27, p=.01 at T2). Sleep variability was unrelated to depression or perceptions of one’s overall health.

Conclusion

Keeping regular sleep habits appear just as important as average sleep duration to psychosocial, work, and academic outcomes. Sleep interventions should incorporate specific guidelines on how to promote regular sleep and employers should institute work schedules that promote regularity.

Support (if any)

This work is supported by the National Science Foundation, Psi Chi Honor Society, and the American Psychological Association.
090 Strategies for Dealing with or Ameliorating Excessive Sleepiness: Beliefs and Attitudes of People with Daytime Sleepiness
Authors: Featherston Breanna , Kapoor Ashna , Wills Chloe , Tubbs Andrew , Grandner Michael
Published: May 2021

Introduction

Sleepiness impacts health and functioning, but despite available treatments, many do not seek care. Beliefs and attitudes about treatments for sleepiness and other sleep problems may be useful to know in designing and targeting interventions.

Methods

N=28 participants with excessive daytime sleepiness (ESS>=10) but no other major medical problems were recruited from the community. They were administered an Epworth Sleepiness Scale and Fatigue Severity Scale at baseline, and asked about a wide range of beliefs/attitudes about mitigating sleepiness, and whether they Strongly Agree(SA), Agree(A), Disagree(D), or Strongly Disagree(SD) with them. Ordinal logistic regressions examined agreement associated with baseline sleepiness and fatigue, adjusted for age, sex, and race/ethnicity (nominal significance p<0.05).

Results

When asked which strategies are helpful for dealing with or fixing daytime sleepiness, baseline agreement was as follows: Just “power through it” (SA:9%,A:55%,D:32%,SD:5%). Caffeine (SA:18%,A:55%,D:27%). Vigorous exercise (SA:9%,A:36%,D:55%). Mild or moderate movement or exercise (SA:14%,A:82%,D:5%). Trying to get better sleep at night (SA:36%,A:64%). Eating or drinking something to help “wake you up” (SA:27%,A:45%,D:23%,SD:5%). Napping (SA:27%,A:64%,D:9%). Giving up and letting yourself be sleepy (SA:9%,A:42%,D:45%,SD:5%). Improve your diet/eat healthy (SA:42%,A:55%,D:5%). Relaxing activities at night (SA:27%,A:68%,D:5%). Meditation, breathing exercises, or other relaxation techniques (SA:45%,A:45%,D:9%). Watching TV, browsing the internet, or other distracting activities (SA:5%,A:36%,D:45%,SD:14%). Just keep moving (SA:9%,A:55%,D:42%,SD:5%). Setting alarms (SA:18%,A:68%,D:14%). Take prescription medication to improve sleep (SA:5%,A:27%,D:42%,SD:27%). Take over-the-counter medication to improve sleep (SA:5%,A:27%,D:59%,SD:9%). Take prescription stimulant medication (SA:5%,A:32%,D:45%,SD:18). Take over-the-counter stimulant medication (SA:5%,A:27%,D:55%,SD:14%). Take prescription medication that reduces daytime sleepiness (SA:5%,A:36%,D:41%,SD:18%). Take over-the-counter medication that reduces daytime sleepiness (SA:5%,A:27%,D:50%,SD:18%). Those with higher levels of baseline sleepiness were more likely to endorse the following as good strategies to handle daytime sleepiness, “Take over-the-counter medication to improve sleep” (oOR=1.55, p=0.04), “Take prescription medication to improve sleep” (oOR=1.49, p=0.01), and “napping” (oOR=2.55, p=0.03). Those with higher baseline fatigue were less likely to endorse “just ‘powering_through’” (oOR=0.81, p=0.02) as a good strategy of handling daytime sleepiness.

Conclusion

Real-world beliefs and attitudes about ways of mitigating effects of sleepiness range from medical to behavioral. Those with greater baseline sleepiness may be more amenable to medication.

Support (if any)

This work was supported by Jazz Pharmaceuticals
092 Effect of the dual orexin receptor antagonist (DORA) daridorexant on behaviour upon awakening in rats and dogs
Authors: Bergamini Giorgio , Roch Catherine , Durkin Sean , Steiner Michel
Published: May 2021

Introduction

The ability to be fast alert and to interact with the environment without motor impairment upon waking up, is a critical feature of natural sleep. DORAs represent a new class of insomnia medications that specifically inhibit the wake-promoting effects of orexin neuropeptides. Daridorexant is a potent and selective DORA under late stage development for the treatment of insomnia. Here, we assessed the impact of sleep-promoting doses of daridorexant on rats’ and dogs’ behaviour upon forced awakening. Zolpidem (a positive GABAA receptor modulator) was used as active comparator in rats because of its known negative impact on motor functions.

Methods

Rats were woken up at different time points after oral administration of daridorexant (10, 30, 100 mg/kg) or zolpidem (30, 100 mg/kg) during their inactive phase, and repeatedly subjected to two motor tasks: 1) the rotating rod test (lasting 120 sec, at each time point) assessing gross motor skills and coordination, and 2) the forepaw grip strength test assessing fine motor skills and muscle strength. Dogs were presented with food as an external, salient stimulus, three hours after administration of daridorexant in gelatin capsules (10, 30 or 90 mg/dog) during their active phase. Behaviour and signs of muscle weakness, after having woken up, were assessed by manual inspection of video recordings and concomitant electroencephalogram/electromyogram recordings.

Results

In both the rotarod and grip tests, daridorexant treatment had no effect on motor behavior at any dose or time point tested, while zolpidem significantly reduced the time spent on the rotarod and the grip strength in a dose and time-dependent manner (N=12/group; p<0.001;) (e.g. at 30 min post-dose, time spent on the rotarod was 84, 79–89 and 10–19 sec for vehicle, daridorexant and zolpidem, respectively). Dogs treated with daridorexant were able to wake up easily upon food presentation. They behaved and ate normally and did not show any signs of muscle weakness.

Conclusion

The type of sleep promoted by daridorexant is surmountable in rats and dogs and similar to physiological sleep. It allows animals to easily wake up, to behave normally without motor impairment and to respond efficiently to the environmental conditions.

Support (if any)

Funded by Idorsia Pharmaceuticals Ltd
093 Sleep and Mood Across the Menstrual Cycle in Young Women
Authors: Shuster Alessandra , Zhang Jing , Simon Katharine , Sattari Negin , Albinni Benedetta , Alzueta Elisabet , Dulai Teji , de Zambotti Massimiliano , Baker Fiona , Mednick Sara
Published: May 2021

Introduction

Sleep modulates mood, with adequate, good-quality sleep associated with a more positive mood. Additionally, sleep is affected by the menstrual cycle in women, with better self-reported sleep quality during non-menses, compared with pre- and during menses. The present study examines the interaction between self-reported sleep quality and mood across the menses (pre/during menses) and non-menses portion of the menstrual cycle.

Methods

10 female participants (27± 2.96) completed sleep and mood diaries over a two month period, answering questions about their nighttime sleep quality and daily mood, and also marking days of menstruation. Each morning participants indicated on a 7-point scale how their overall sleep quality was the previous night. Then, each evening, participants indicated on a 7-point scale the extent to which they felt specific positive and negative moods throughout the day. Time lag cross-correlation and linear regression models were used to determine directionality of sleep quality and mood, and interaction between sleep quality, menses status, and mood levels.

Results

Better sleep quality was associated with better mood, with prior sleep quality predicting mood, but prior mood did not predict sleep (cross-correlation time lag = 0) across menses and non-menses. Additionally, on average women reported higher levels of sleep quality and better mood during the non-menses portion of their cycle. During the non-menses portion, women reported higher levels of positive mood (happy, pleased, and joyful), which were mediated by level of sleep quality. Women also reported lower levels of negative mood (depression, frustration, anxiety, unhappiness, and overwhelmed feelings), which were similarly mediated by level of sleep quality.

Conclusion

These findings are consistent with the hypothesis that sleep modulates mood, which fluctuates over the course of a woman’s menstrual cycle. Moreover, our data suggest that perceived sleep quality impacts mood, and that the menstrual cycle affects both sleep quality and mood

Support (if any)

Supported by: RF1AG061355 (Baker/Mednick)
094 Bedtime Hunger Predicts Reduced Sleep Efficiency That Night in Users of a Consumer Sleep Measurement Device
Authors: Dixon Bonnie , Liu Siwei , German J , Nordahl Thomas
Published: May 2021

Introduction

It has long been suspected that bedtime hunger can potentially disturb sleep. The neural circuits that control sleep are now known to receive signals of appetite and energy balance via hypocretin/orexin neurons from the lateral hypothalamus. But there remains need to specifically identify how, and under what conditions, appetite mechanisms affect human sleep. This study documents the relationship between bedtime hunger and subsequent sleep efficiency in users of a consumer sleep measurement device.

Methods

The Zeo headband (sold to the public during 2009–2013) used detected electrical potentials to periodically calculate the most probable stage of sleep or wake. Users uploaded sleep records online and could track nightly conditions — including bedtime hunger — on provided rating scales. De-identified summary data from these nightly records were aggregated into a research registry. We extracted the sleep records with bedtime hunger ratings and analyzed them using multilevel modeling to identify within-person and between-person relationships between bedtime hunger and sleep efficiency. We decomposed bedtime hunger ratings into person-mean hunger and nightly hunger, the difference between each night’s hunger rating and the person’s mean.

Results

4,284 nightly sleep records with a bedtime hunger rating were provided by 183 people (age 19–77, 68% male, mean: 23 records/person). Sleep efficiency was not related to person-mean hunger (p=0.26), but was inversely related to nightly hunger that night (p=0.01). The model predicted a within-person difference in sleep efficiency between nights with high, versus low, nightly hunger that varied across the people in the sample (mean [range]: -2.4 [-12.5-1.6] percentage points) and correlated positively with typical sleep efficiency (r=0.73, p=0.00) and negatively with unexplained variability in sleep efficiency (r=-0.47, p=0.00).

Conclusion

For the people in this dataset, on average, going to bed hungrier than usual predicted reduced sleep efficiency that night. The effect was strongest in people who tend toward low and variable sleep efficiency. This finding strongly suggests that bedtime hunger can indeed disturb sleep, especially in poor sleepers. Further research is needed to determine who is most affected and to understand implications, such as for weight management, eating disorders, food insecurity, or sleep-supporting foods and dietary practices.

Support (if any)

USDA-NIFA-AFRI and NIH-NCATS
096 The Association Between Sleep Regularity Index and Self-Reported Behavioral and Emotional Symptoms in Adolescents
Authors: Grover Eliza , Wong Patricia , Barker David , Gredvig-Ardito Caroline , Carskadon Mary
Published: May 2021

Introduction

Among adolescents, sleep health has been associated with emotional and mood regulation, cognitive functioning, and behavior. Few studies, however, have examined the Sleep Regularity Index (SRI, Phillips et. al, 2017) and its associations with mental health and well-being in this age group. For this study, we examined whether SRI in 15-16-year-old adolescents would predict internalizing and externalizing symptoms as measured by Youth Self-Report (YSR) scores two years later. We hypothesized that a higher baseline sleep regularity would predict lower internalizing and externalizing YSR scores at the 2-year follow-up.

Methods

The sample included 32 adolescents (14 male) ages 15-16yr (mean = 15.6) at baseline and 2 years later (mean age = 17.7). Actigraphy data and YSR scores were collected at baseline, and YSR was examined at follow-up. Participant’s SRIs were calculated using 24-hour actigraphy data scored for sleep and wake. YSR T-scores of 60 or above indicate borderline clinical internalizing (n = 2) and externalizing (n = 4) symptoms at follow-up. We used linear regression modeling to determine whether baseline SRI predicted YSR scores 2 years later. Covariates included sleep start time, sleep duration, sex, and baseline YSR scores.

Results

At baseline, average SRI and YSR scores were not significantly correlated (internalizing: r = 0.10; externalizing: r = 0.24, p’s > 0.1). SRI score at baseline (mean = 80.5 ± 7.4) significantly predicted YSR internalizing scores (mean = 42 ± 9) at the 2-year follow up (t(26) = 2.57, p = 0.016) but not externalizing scores (mean = 44.8 ± 10.3, t(26) = .78, p = 0.44).

Conclusion

We observed that sleep regularity was associated with internalizing symptoms two years later; however, the association was not in the expected direction: higher SRI was correlated with increased YSR internalizing scores at the 2-year follow-up. As most participants were in a healthy range for YSR scores at both assessments, a possible explanation for this finding is that those with higher SRIs have greater self-awareness in assessing their internal feelings. Future work will examine SRI values and YSR in this sample across 6 assessments acquired at 6-month intervals.

Support (if any)

AA13252 (NIH)
098 Effects of Metameric Display-Light on Alertness, Vigilance and Melatonin
Authors: Schöllhorn Isabel , Stefani Oliver , Spitschan Manuel , Lucas Robert , Cajochen Christian
Published: May 2021

Introduction

Light emitted from visual displays can acutely increase alertness, improve cognitive performance and suppress melatonin in the evening. Here we tested the influence of different melanopic irradiance levels emitted by a metameric display setting on alertness, vigilance and salivary melatonin levels.

Methods

In an ongoing study, 37 healthy, male participants have so far completed a 2-week study protocol. Volunteers were assigned to one of four luminance groups which differed in brightness levels (27 cd/m2 - 280 cd/m2). Illuminance ranged between 7 and 85 lx. Within the four groups each volunteer was exposed to a low melanopic (LM) and a high melanopic condition (HM). The LM and HM differed in melanopic irradiance (ca. 3-fold change), but matched in terms of cone excitation (metamers). Before, during and after the light exposure, volunteers performed a psychomotor vigilance task (PVT). Subjective alertness and melatonin levels were continuously measured in half-hourly intervals throughout scheduled wakefulness in the 17-h in lab study.

Results

Preliminary analysis yielded an overall alerting response in the HM vs. the LM condition (p<0.05) concomitant with a trend of reduced melatonin levels in HM vs. LM (p=0.08). So far, we could not observe a difference in PVT performance for HM and LM (Reaction time responses between 100 and 500 ms). Since we are still lacking statistical power in the ongoing study, we cannot yet satisfactorily interpret interaction effects between melanopic condition and brightness.

Conclusion

Our data indicate that rather low brightness levels of high melanopic display light impacts alertness and melatonin levels in the evening. Thus, metameric low melanopic display light may be a promising method to attenuate activating properties of evening light on circadian physiology without affecting visual appearance.

Support (if any)

This project is funded by the Swiss National Science Foundation (SNSF).
101 Measurement of Tapping During the Interstimulus Interval as a Validation Metric for the 3-Minute Psychomotor Vigilance Test
Authors: Deering Sean , Stepnowsky Carl
Published: May 2021

Introduction

The Psychomotor Vigilance Test is a well-validated measure of sustained attention used to assess daytime alertness in sleep research studies.1 It is commonly used in a variety of research settings due to its high sensitivity to sleep loss and absence of learning effects,2 making it an ideal tool to assess objective alertness. As some types of sleep research transition out of controlled laboratory environments, tools like the PVT require modification to maximize their reliability. The validation of the 3-minute version (PVT-B) against the 10-minute PVT is an example of this modification.3 However, considerable work is needed to improve trust in the utility of the PVT-B in and outside of traditional laboratory settings.

Methods

We carefully analyzed data from a mobile-based version of the PVT-B, noting responses that occurred during the interstimulus interval which were termed “wrong taps.” Wrong taps indicated that participants were not performing the task as instructed. In some cases, wrong taps occurred across multiple trials of the same PVT block, indicative of participants repeatedly tapping the screen throughout the task to minimize response times. A comprehensive examination of wrong taps was carried out in order to identify instances where this pattern emerged.

Results

A total of 1,338,538 PVT-B trials from 7,028 participants were examined to determine the number of wrong taps present across all trials. While 91.7% of PVT-B trials were free of wrong taps, 8.3% of PVT-B trials contained 1 or more wrong taps and 5.2% contained 2 or more wrong taps. It appears that a maximum of one wrong tap per trial is acceptable and trials containing 2 or more should be excluded to maximize PVT data quality.

Conclusion

Utilizing a metric like wrong taps can help identify individuals taking the PVT-B who are tapping the screen multiple times prior to stimulus display. Closely examining this metric can help to ensure the validity of PVT-B administrations. Two possible uses of the metric could be to provide feedback during training trials and to remove trials where this strategy was employed.

Support (if any)

This analysis was supported by the VA San Diego Healthcare System Research Service.
110 Physiological correlates of the Epworth Sleepiness Scale reveal different dimensions of daytime sleepiness
Authors: Lok Renske , Zeitzer Jamie
Published: May 2021

Introduction

The Epworth Sleepiness Scale (ESS) is used as a clinical tool for determining excessive daytime sleepiness. However, the behavior and biology that underlie ESS scores remain to be elucidated. The main objective of this analysis is to determine objective behavioral and physiologic correlates of the ESS. Secondarily, we examine the relationship of the ESS to parallel subjective and objective endpoints that could represent measures of daytime sleepiness.

Methods

Using two separate machine learning algorithms, Random Forest and Lasso, we determined the association between ESS scores and 55 sleep and medical variables in individuals who participated in the Sleep Heart Health Study (N=2105). These variables include self-reported sleep characteristics (e.g., habitual sleep length and latency, frequency of not getting enough sleep), polysomnographic sleep measures from a single night, medication use, and mental and physical health status. Additional analyses were conducted on data stratified by age and gender. To investigate the relationship between ESS and other measures of daytime sleepiness, cross-correlation analysis was conducted on the ESS and five variables that could analog daytime sleepiness (feeling unrested, nap duration and frequency, sleep latency, frequency of not getting enough sleep).

Results

Analysis of the main dataset resulted in low explained variance (7.15 - 10.0%), with self-reported frequency of not getting enough sleep as most important predictor (10.3–13.9% of the model variance). Stratification by neither age nor gender significantly improved explained variance. Habitual sleep length was not an important predictor in any model. Cross-correlational analysis revealed low correlation of other daytime sleepiness measures to ESS score.

Conclusion

Data analyses indicate that ESS scores are not well explained by habitual or polysomnographic sleep values, or a variety of other biomedical characteristics. This suggests that there are different, potentially orthogonal dimensions of the concept of “daytime sleepiness” that may be driven by different aspects of sleep physiology. Caution should be used when considering the ESS as a clinical measure given that the physiologic correlates still remain to be elucidated.

Support (if any):
111 Artificial Light-Triggered Sleep Deprivation May Lead to Allodynia in Rodent Model
Authors: Kanegi Skyler , Akopian Armen
Published: May 2021

Introduction

The combination of artificial light and lack of exposure to natural light can delay the circadian clock, dysregulate the circadian cycle, and decrease alertness upon waking. This effect has been especially significant during the COVID-19 pandemic, where overexposure to artificial light at improper hours has contributed to increased rates of clinical insomnia. Artificial light may also contribute to concomitant neurological conditions such as primary headache, but the mechanisms by which light triggers sleep deprivation-induced headache are not well-understood.

Methods

To measure pain sensitivity, we habituated 13 wild-type male mice to von Frey filaments applied to the periorbital area until there was no response to 0.6g stimulus. We then applied 5 lux of continuous dim light to mice during their usual 12-hour dark cycle. The 12-hour light cycle remained unchanged with 200 lux continuous light. Three groups of mice experienced the dim light stimulus for one, three, or five consecutive days. Ambulation and rest activity were measured using SOF-812 Activity Monitor machines. After the experiment concluded, we waited 24 hours and measured mechanical threshold using von Frey filaments at 1, 3, 5, 8, and every 3 days subsequently until mice no longer responded to 0.6g stimulus.

Results

Artificial light triggered changes in circadian behavior including increased number of rest periods during 12-hour dark (dim light) cycle and shortened sleep duration during 12-hour light cycle. Following the artificial light stimulus, there was a significant decrease in mechanical threshold (P<0.05), representing allodynia. The one-day group displayed one day of significant allodynia. The three-day group displayed three days of significant allodynia. The five-day group displayed five days of significant allodynia.

Conclusion

Artificial light may trigger circadian dysregulation, and the duration of artificial light exposure seemed to be directly correlated to the duration of allodynia up to one week after the stimulus was removed. We will repeat these experiments and analyze CNS and PNS tissue samples to understand the underlying physiological and biochemical bases of how artificial light triggers sleep deprivation-induced headache. This knowledge could increase our understanding of the pathophysiology and comorbidity of sleep deprivation and headache.

Support (if any)

Funding was received from the National Institute of Health (NS104200).
114 Stability of Gut Microbiome Alpha Diversity During Combined Sleep Restriction and Circadian Misalignment
Authors: Withrow Dana , Gonzalez Antonio , Sprecher Kate , Depner Christopher , Burke Tina , Fleshner Monika , Lowry Christopher , Turek Fred , Vitaterna Martha , Dorrestein Pieter , Knight Rob , Wright Kenneth
Published: May 2021

Introduction

Disturbed gut microbiome diversity has been associated with poor health outcomes and various disease states. We investigated the impact of combined sleep restriction (3h time in bed [TIB] sleep opportunities per day) and circadian misalignment (daytime sleep and nighttime wakefulness) on gut microbiome alpha diversity in healthy young individuals in a controlled laboratory setting.

Methods

Twenty healthy adults (8 female), mean age (±SD) 25.65(±4.2) completed a 39-day protocol consisting of two laboratory visits lasting 4 days each. Two weeks of ambulatory monitoring prior to laboratory visits confirmed ~8h habitual sleep duration per night. Participants consumed energy-balanced diets, identical within participants, 2 days before and during the laboratory visits. The laboratory visits consisted of sleep opportunities as follows: night 1 (8h TIB), night 2 (3h TIB), day 3 (3h TIB) and day 4 (3h TIB). Fecal microbiome samples were obtained at baseline between day 1 and 2, and during sleep and circadian disruption (between day 3 and 4). Alpha diversity measures were calculated using Pielou’s evenness, Faith’s phylogenetic diversity and number of observed OTUs.

Results

Linear mixed models with subject as a random factor and visit as a fixed factor were performed to assess whether any alpha diversity measures changed during sleep and circadian disruption compared to baseline. Alpha diversity did not change significantly between baseline and sleep and circadian disruption (all p > 0.57). Additionally, intraclass correlation coefficients (ICCs) were calculated at baseline and during sleep and circadian disruption to determine if alpha diversity measures showed trait-like stability at both time points. ICCs were substantial to almost perfect (ICC 0.64–0.84) at baseline and substantial (ICC 0.70–0.80) during sleep and circadian disruption.

Conclusion

Four days of combined sleep restriction and circadian misalignment does not appear to alter alpha diversity of gut microbiota species in healthy adults. Further, substantial to almost perfect intraclass correlation coefficients suggest alpha diversity of the human microbiome is stable during combined sleep and circadian perturbation and that examination at the level of microbiota community composition and functional outcomes are needed.

Support (if any)

Office of Naval Research MURI (N00014- 15-1-2809), NIH/NCATS (UL1TR002535), NIH T32 HL149646, CU Undergraduate Research Opportunities.
117 Comparison of Various Methods to Differentiate Resilience and Vulnerability to Sleep Loss Using Self-Rated Measures
Authors: Casale Courtney , Yamazaki Erika , Brieva Tess , Antler Caroline , Goel Namni
Published: May 2021

Introduction

There are robust, trait-like individual differences in subjective perceptions in response to sleep restriction (SR) and total sleep deprivation (TSD). How to best define neurobehavioral resilience and vulnerability to sleep loss remains an open question. We compared multiple approaches and cutoff thresholds for defining resilience and vulnerability using scores on the Karolinska Sleepiness Scale (KSS) and the Profile of Mood States Fatigue and Vigor (POMS-F and POMS-V) subscales.

Methods

Forty-one adults (33.9±8.9y;18 females) participated in a 13-night experiment (two baseline nights [10h-12h time in bed, TIB], 5 SR nights [4h TIB], 4 recovery nights [12h TIB], and 36h TSD). The KSS, POMS-F, and POMS-V were administered every 2h during wakefulness. Resilience and vulnerability were defined by the following: average score during SR1-5, average change from baseline to SR1-5, and variance during SR1-5. Resilient and vulnerable groups were defined by the following cutoffs: the top and bottom 12.5%, 20%, 25%, 33%, 50%, and +/-1 standard deviation. Bias-corrected and accelerated bootstrapped t-tests compared the scores of resilient and vulnerable groups during baseline and across SR1-5. Kendall’s tau correlations compared the ranking of individuals in each group (tau=0.4:moderate,0.7:strong).

Results

Resilient and vulnerable groups for POMS-F, as defined by all three approaches, significantly differed in their scores at all cutoffs during SR. However, only raw score and change from baseline approaches defined significantly different resilient and vulnerable groups during SR for KSS, and only raw score and variance approaches defined significantly different groups during SR for POMS-V. Notably, raw scores at baseline significantly differed between resilient and vulnerable groups for all measures. Correlations revealed moderate to strong associations between all three approaches at all cutoffs for POMS-F, between raw score and change from baseline approaches for KSS, and between raw score and variance approaches for POMS-V.

Conclusion

Defining resilience and vulnerability on self-rated measures by change from baseline was comparable to using raw score for KSS and POMS-F, whereas defining these groups by variance was comparable for POMS-F and POMS-V. Differences across methods may be due to the differential impact of SR on these various distinct subjective states.

Support (if any)

ONR Award No. N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243;NIH R01DK117488