bluuebunny/crossref_metadata_2025_split huggingface dataset:
083 Circadian Preference is Associated with Impulsivity at Both the Trait and State Level
Authors: Hasler Brant , Wallace Meredith , Graves Jessica , Pedersen Sarah 
Published: May 2021

Abstract

Introduction
Impulsivity is a multifaceted construct with well-documented risk for substance use problems. A circadian preference towards eveningness has been linked to trait, global impulsivity. Here we extend existing literature by investigating whether eveningness is associated with multiple facets of impulsivity at both trait- and state-level impulsivity. We also examined these associations utilizing daily measures of sleep timing and duration.

Methods
The primary sample included 78 moderate-to-heavy social drinkers (aged 21–35, 100% White men) with circadian preference data (Composite Scale of Morningness: CSM). Five facets of impulsivity were assessed via the UPPS-P, both at baseline (full scale) and up to 6 times per day over 10 days (reduced scale). Daily sleep timing (midsleep) and duration were assessed via self-report over 10 days. Multilevel models were used to examine between- and within-person associations, accounting for covariates and correcting for multiple comparisons.

Results
Between-person models found that eveningness was associated with multiple facets of impulsivity, at trait (lack of perseverance) and state levels (negative urgency, positive urgency, lack of perseverance, and lack of premeditation). However, average midsleep and duration were generally unrelated to impulsivity when accounting for circadian preference. Within-person models in the primary sample largely paralleled the between-person findings. In a larger, more diverse sample (29.1% self-identified as Black, 29.7% female) without CSM data, later midsleep timing was associated with greater mean state-level impulsivity across multiple facets. These effects largely appear to be driven by White women.

Conclusion
A circadian preference for eveningness is strongly associated with multiple facets of impulsivity, at both trait- and state-levels, potentially increasing risk for substance use. This association does not appear to be driven by actual daily sleep timing and/or duration. Future research with objective measures of sleep in larger, more diverse samples will be important to clarify implications for sleep-focused prevention and/or treatment of substance use.

Support (if any)
Supported by grants from NIH (R01AA026249; K01 AA021135), as well as a Foundation Grant from ABMRF/The Foundation for Alcohol Research.
100 Effect of 3 consecutive nights of alcohol on sleep variables: Preliminary report
Authors: Carskadon Mary , Saletin Jared , Gredvig-Ardito Caroline , McGeary John 
Published: May 2021

Abstract

Introduction
The effects of a moderate dose of alcohol one hour before bedtime on sleep have not often been studied nor is the effect across nights well known. We therefore sought to test whether such effects as sleep disruption, increased early-night slow wave sleep (SWS), and reduced early-night REM sleep would be sustained across nights.

Methods
Twenty-five healthy participants (13 male; ages 22–69 yr, mean = 35) reporting moderate drinking kept a fixed sleep schedule (8–9 h TIB, confirmed by actigraphy) for about one week before two 3-night sleep studies in the lab separated by ≥ 3 days. Participants drank either mixer alone or a beverage containing alcohol targeting a breath alcohol content (BrAC) of 0.08% in a counter-balanced order over 45 min ending 1 hr before lights out. Sleep was scored using Rechtschaffen & Kales (1968) rules in 30-sec epochs. Mixed-effects models examined beverage type, study night, and the interaction of beverage and night for 13 variables: sleep efficiency, sleep latency, REM latency, and full-night percent of Stage 1, Stage 2, SWS, and REM sleep; and percent of SWS and REM sleep by thirds of night.

Results
A significant effect of Night was seen for sleep efficiency (F(2,120)=3.79; p=.025) and sleep latency (F(2,120)=5.19;p=.007), both lower on N1, as well as for REM latency, longer on N1 (F(2,120)=6.52;p=.002). REM latency was longer with alcohol (F(1,120)=14.16; p&lt;.000) and no interaction was apparent. St2% was higher (F(1,120)=4.47; p=.037) and REM% lower (F(1,120)=4.41; p=.038) with alcohol, whereas overnight SWS% was unaffected; none showed an effect of night or an interaction. SWS% in the first (F(1,120)=10.51; p=.002) and second thirds (F(1,120)=8.27; p=.005) of the night was higher with alcohol and unaffected in the last third. REM% in the first third alone was higher with alcohol (F(1,120)=10.71; p=.01).

Conclusion
These findings show only modest effects of pre-sleep alcohol consumption (targeting 0.08% BrAC) on subsequent sleep in healthy drinkers, with no evidence of a cumulative impact across three nights. We aim to increase the sample size and examine effects on next-day cognitive function in subsequent analyses.

Support (if any)
R01AA025593
102 Single-Prolonged Stress as a Model of PTSD in Mice: Effects on Sleep and Anxiety
Authors: Gutowsky Katelyn , Jones Carolyn , Lim Miranda 
Published: May 2021

Abstract

Introduction
Sleep problems are common in humans with post-traumatic stress disorder (PTSD). Rapid eye movement (REM) sleep is involved in processing emotional memories; it is often disrupted in those with PTSD, and may be related to increased anxiety. Single prolonged stress (SPS) is a protocol used to model PTSD in rats, however little is known about how this model impacts sleep in mice. Prior research suggests SPS produces short term disturbances in REM sleep and increases in anxiety-like behavior, but further validation of this model is needed to understand how SPS impacts sleep and anxiety-like behaviors in mice specifically, as they have greater potential for transgenic manipulation

Methods
C57BL6/J mice underwent a SPS protocol in which they were tube-restrained for 2 hours, followed by a 15 minute forced swim in a group, ether exposure until loss of consciousness, and 10 days of social isolation. Following SPS, mice were tested for anxiety-like behavior in a light-dark box and sleep was measured from surgically implanted EEG and EMG leads. Time spent in wake, REM sleep, and non-REM sleep was quantified for 24 continuous hours in SPS and Control mice.

Results
There were no significant effects of SPS on the amount of time spent in any vigilance state, or in sleep-wake transitions. However, SPS-exposed mice showed significantly more anxiety-like behavior. EEG power spectra were analyzed in relevant frequency bands during each sleep state, and exploratory analyses were conducted

Conclusion
Minimal effects on sleep macroarchitecture were seen in mice 10 days after SPS. It is possible that sleep disturbances seen immediately after trauma exposure (such as in prior studies in rats) may have diminished over time. Further studies will need to include additional timepoints and analysis of sleep microarchitecture following SPS, and in other mouse models of PTSD, in order to more comprehensively examine changes in sleep.

Support (if any)
VA CDA #IK2 BX002712, Portland VA Research Foundation, Medical Research Foundation
113 Effects of Sleep on Intrusive Symptoms and Emotion Reactivity in a Laboratory-Based Film Analogue Study
Authors: Nguyen Anna Marie , Campbell Rebecca , Vance Abigail , Leen-Feldner Ellen 
Published: May 2021

Abstract

Introduction
Recent literature highlights the need to focus on the impact of intrusive symptoms as a possible risk factor for the development and maintenance of PTSD. Cognitive and sleep models also contribute to the further understanding of intrusive symptoms. Further emotion work emphasizes that disgust is an emotion closely associated with the emergence of posttraumatic stress symptomology following traumatic events.

Methods
This study utilized a film eliciting disgust to examine the effects of acute sleep deprivation on the intensity of intrusive symptoms and emotion reactivity. Forty-nine college students were randomly assigned to sleep as usual or an acute sleep deprivation after watching a disturbing film. It was hypothesized that, relative to the control group, participants who were acutely sleep deprived would report higher frequency of intrusive symptoms and higher negative valence.

Results
Findings were partially consistent with hypotheses. There were no group or interaction effects on intrusive symptoms, although participants across both groups reported significant decreases in negative valence and intrusive symptoms across the study (F(1, 47) = 10.30, p &lt; 0.01). There was a significant interaction effect between sleep group and self-reported negative valence, where individuals in the sleep deprived group reported significantly higher valence than individuals in the control group, despite significant decreases in negative valence over time (F(1, 48) = 7.869, p &lt; 0.01).

Conclusion
Possible mechanisms that may contribute to the significant difference in valence may be due to higher order emotion regulation strategies that are compromised due to sleep loss. However, the significant decreases in negative valence and intrusive symptoms over time may be due to methodological factors or the type of sleep manipulation. Further work can address these challenges by using a larger sample size or examining the effects of chronic, partial sleep deprivation.
Support (if any):
116 Behavioral Attention Raw Scores Best Differentiate Cognitive Resilience and Vulnerability to Sleep Loss
Authors: Yamazaki Erika , Brieva Tess , Casale Courtney , Antler Caroline , Goel Namni 
Published: May 2021

Abstract

Introduction
There are substantial, stable individual differences in cognitive performance resulting from sleep restriction (SR) and total sleep deprivation (TSD). The best method for defining cognitive resilience and vulnerability to sleep loss remains an unanswered, yet important question. To investigate this, we compared multiple approaches and cutoff thresholds to define resilience and vulnerability using the 10-minute Psychomotor Vigilance Test (PVT).

Methods
Forty-one healthy adults (ages 21-49; mean±SD, 33.9±8.9y; 18 females) participated in a 13-night experiment [2 baseline nights (10h-12h time-in-bed, TIB), 5 SR nights (4h TIB), 4 recovery nights (12h TIB), and 36h TSD]. The PVT was administered every 2h during wakefulness. PVT lapses (reaction time [RT]&gt;500 ms) and 1/RT (response speed) were measured. Resilient and vulnerable groups were defined by three approaches: average performance during SR1-5, average performance change from baseline to SR1-5, and variance in performance during SR1-5. Within each approach, resilient/vulnerable groups were defined by +/- 1 standard deviation and by the top and bottom 12.5%, 20%, 25%, 33%, 50%. Bias-corrected and accelerated bootstrapped t-tests compared PVT performance between the resilient and vulnerable groups during baseline and SR1-5. Kendall’s tau correlations compared the ranking of individuals in each group.

Results
T-tests revealed that the resilient and vulnerable PVT lapses groups, defined by all three approaches, had significantly different mean PVT lapses at all cutoffs. Resilient and vulnerable PVT 1/RT groups, defined by raw scores and by change from baseline, had significantly different mean PVT 1/RT at all cutoffs. However, resilient/vulnerable PVT 1/RT groups defined by variance only differed at the 33% and 50% cutoffs. Notably, raw scores at baseline significantly differed between resilient/vulnerable groups for both PVT measures. Variance vs. raw scores and variance vs. change from baseline had the lowest correlation coefficients for both PVT measures.

Conclusion
Defining resilient and vulnerable groups by raw scores during SR1-5 produced the clearest differentiation between resilient and vulnerable groups at every cutoff threshold for PVT lapses and response speed. As such, we propose that using PVT raw score is the optimal approach to define resilient and vulnerable groups for behavioral attention performance during sleep loss.

Support (if any)
ONR Award No.N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243;NIH R01DK117488
128 Raw Scores Best Differentiate Resilience and Vulnerability to Sleep Loss for Cognitive Throughput and Working Memory
Authors: Brieva Tess , Casale Courtney , Yamazaki Erika , Antler Caroline , Goel Namni 
Published: May 2021

Abstract

Introduction
Substantial individual differences exist in cognitive deficits due to sleep restriction (SR) and total sleep deprivation (TSD), but the best approach to define such resilience and vulnerability remains a critical question. We compared multiple approaches and cutoff thresholds to define resilience and vulnerability using the Digit Symbol Substitution Task (DSST) and the Digit Span Task (DST).

Methods
Forty-one healthy adults (mean±SD ages,33.9±8.9y) 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 DSST [measuring cognitive throughput] and DST [measuring working memory] were administered every 2h during wakefulness. Resilient/vulnerable groups were defined by average performance (DSST: number correct; DST: total correct from forward and backward versions) during SR1-5, average performance change from baseline during SR1-5, and variance in performance during SR1-5. Within each approach, groups were defined by +/-1 standard deviation (SD) and the top and bottom 12.5%, 20%, 25%, 33%, 50%. Bias-corrected and accelerated bootstrapped t-tests compared performance between resilient and vulnerable groups during baseline and SR1-5. Kendall’s tau correlations compared the ranking of individuals in each group.

Results
T-tests showed significant differences between resilient/vulnerable groups at all raw score cutoffs for DSST and DST performance during SR and at baseline. Change from baseline t-tests showed significant differences during SR between the DSST groups only at 12.5%, 20%, and SD whereas DST t-tests showed significant differences at all cutoffs. Variance t-tests revealed a significant difference between the DSST groups only at 25% during SR. For the DSST, the variance vs. change from baseline comparison at all cutoffs and between raw score vs. change from baseline for the SD cutoff showed moderate correlations, and for the DST, the raw score vs. change from baseline correlation was moderate for 25% and 33%.

Conclusion
The resilient/vulnerable groups defined by raw score were more consistent than those defined by change from baseline or variance, and raw score did not track these approaches well. As such, raw score is the optimal approach to define cognitive throughput and working memory performance resiliency/vulnerability during sleep loss.

Support (if any)
ONR Award No. N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243;NIH R01DK117488
132 Impact of Experimentally Shortened Sleep on Meal Timing in Adolescents
Authors: Duraccio Kara , Whitacre Catharine , Beebe Dean 
Published: May 2021

Abstract

Introduction
Short sleep increases the risk for obesity in adolescents. One potential mechanism relates to when eating occurs, rather than what or how much is eaten. This study investigated the impact of shortened sleep on meal timing and late evening eating in adolescents.

Methods
93 healthy 14- to17-year-olds (62% female) completed a within-subject counterbalanced experimental sleep manipulation, engaging in shortened sleep (SS; 6.5 hours/night in bed) or healthy sleep (HS; 9.5 hours/night in bed), each for five nights, with order randomized. Sleep timing was verified through wrist-worn actigraphy. During each sleep period, adolescents completed 2-3 dietary recalls. Repeated-measure T-tests assessed the sleep manipulation effect on averaged times of the first and last eating episode, number of eating episodes after 8:00pm, and range of the daily eating period.

Results
Youth averaged 2.2 hours/night longer sleep during HS than SS (p&lt;.001). The timing of the first eating episode was similar across conditions, relative to the clock (SS=08:51, HS=08:52) and to time since waking (SS=1.8hr, HS=1.9hr). The timing of the last eating episode averaged later on the clock during SS (20:34) than HS (19:38; p&lt;.001), resulting in a longer eating period (SS=11.7hr, HS=10.8hr, p&lt;.001). Youth averaged more eating episodes after 8:00pm during SS (0.87) than HS (0.59, p&lt;.001). The gap between last eating episode and sleep onset was larger in SS (4.1hr) than HS (2.8hr; p&lt;.001). Notably, on average, adolescents last eating episodes during SS (20:34) were earlier than sleep onset in either condition, and were even 2 hours earlier than when they fell asleep during HS (M=22:30). In exploratory analyses, these effects did not systematically vary by experimental order of the sleep conditions, family income, or participant age, sex, or norm-referenced body mass index.

Conclusion
Shortened sleep resulted in adolescents eating later and lengthening the daily period of time in which they ate, despite typically stopping eating well before sleep onset during healthy sleep. Late evening eating and long daily eating periods have been strongly associated with weight gain, which may help explain the link between shortened sleep and increased obesity risk in adolescents.

Support (if any)
R01HL120879
133 The Effects of Chronic Sleep Restriction on Human Oculomotor Behavior
Authors: Evans Erin , Tyson Terence , Costedoat Gregory , Stone Leland 
Published: May 2021

Abstract

Introduction
Oculomotor behavioral metrics change according to time awake and circadian phase following a distinct pattern of impairment. Acute sleep deprivation (ASD) causes large decreases in pursuit initiation and steady-state gain, and a compensatory increase in saccadic rate without any systematic change in saccadic size. It also causes large deficits in visual processing of direction and speed, and impaired saccade dynamics. Such deterioration likely reflects changes in both higher cortical and brainstem function, explaining in-part how sleep loss and circadian misalignment affect cognition. It is unclear how oculomotor behavior might change according to chronic sleep restriction (CSR). Here, we measured the same eye-movement metrics during CSR.

Methods
Twelve healthy participants (6 females) kept a fixed wake-time sleep-wake schedule, at home for four weeks (weeks 1 and 3 = 9h time in bed (TIB); weeks 2 and 4 randomized to 5h or 9h TIB; actigraphy confirmed). Following weeks two and four, participants completed a 13-hour laboratory visit under dim light (&lt;15 lux), where they maintained a semi-recumbent posture and were provided with hourly isocaloric snacks. A visual tracking task was performed hourly to assess pursuit and saccadic responses and visual motion processing. Performance metrics were computed using MATLAB, including pursuit gain (eye speed/target speed), the rate and amplitude of catch-up saccades, and the accuracy and precision of direction and speed processing.

Results
As expected, we found a small but significant (t(11)=-2.17, p&lt;0.03) reduction in pursuit gain (mean+/-SEM: -0.028 +/-0.013 with a large (t(11)=2.96, p&lt;0.01) increase in saccadic rate (0.37 +/-0.13 Hz). However, surprisingly, we found a significant (t(11)=-2.52, p&lt;0.03) decrease in the amplitude of catch-up saccades (-0.15 +/-0.06 deg). The only systematic alteration to visual motion processing was a small reduction in horizontal-vertical asymmetry, which was previously observed with ASD.

Conclusion
A week of CSR to 5h is associated with only mild impairment in smooth pursuit eye movements with little impact on visual motion processing. However, CSR caused a maladaptive decrease in saccade amplitude that was not observed during ASD. Eye-movement metrics reveal differential neurological effects of CSR versus ASD.

Support (if any)
Force Health Protection Program, ONR (SAA2402925-1), NASA Human Research Program, and agreement NNX17AE07A
136 Sleep and Porphyromonas gingivalis K-Capsular IgG Serotypes: A Study in the Old Order Amish
Authors: Spector Anna , Wadhawan Abhishek , Constantine Niel , Ryan Kathy , Mohyuddin Iqra , Dagdag Aline , Brenner Lisa , Daue Melanie , Schifferle Robert , Ernst Robert , Mitchell Braxton , Reynolds Mark , Postolache Teodor 
Published: May 2021

Abstract

Introduction
Sleep problems and periodontal disease have a bidirectional relationship and are independently linked with depression, dementia, and metabolic disease. Inadequate sleep can worsen inflammation, a hallmark of periodontal disease, and the activation of the immune system can alter sleep/wake cycles. A key player in periodontal disease is Porphyromonas gingivalis, a bacteria that can translocate to the brain and induce miRNA’s. Antibodies to P. gingivalis capsular virulence factors, K1-7, have been used to estimate P. gingivalis virulence. This study was conducted to explore cross-sectional associations between seropositivity of K serotypes of P. gingivalis and measures of self-reported impairment in sleep. If identified, these links would provide a rationale to initiate causality and mediation studies. We hypothesized that sleep impairment is positively associated with P. gingivalis K IgG serointensity.

Methods
880 Old Order Amish aged 44.8 (SD: 17.2 years); 360 men (40.91%), 520 women (59.09%) responded to an adapted Pittsburgh-Sleep-Quality-Index questionnaire. IgG serointensity to 7 K-capsular P. gingivalis serotypes were measured with ELISAs. We tested for the association of log-transformed serotype IgG intensity and positivity (successively defined as within the top 5% and 25% for each serotype) with sleep parameters (as binary and continuous variables) using linear and logistic regressions, adjusting for age and sex.

Results
We confirmed no hypothesized associations between any of the sleep problems on the PSQI and K serotype serointensity and seropositivity. Exploratory analysis returned a negative association of log-transformed K3 IgG with daytime sleepiness (p=0.01); however, this did not resist adjustment for multiple comparisons and was inconsistent with the direction of the hypothesis.

Conclusion
Strengths of the study include the reduced smoking prevalence in the Amish and the relatively homogenous lifestyle, reducing confounding. The results imply P. gingivalis serotypes are not associated with sleep disturbance. Limitations are self-reporting of sleep, cross-sectional approach and limited generalizability. Results do not support an association between P. gingivalis K serotypes and sleep-problems.

Support (if any)
MVM-CoRE