Genetics and Cognitive Vulnerability to Sleep Deprivation in Healthy Subjects: Interaction of ADORA2A, TNF-α and COMT Polymorphisms

Several genetic polymorphisms differentiate between healthy individuals who are more cognitively vulnerable or resistant during total sleep deprivation (TSD). Common metrics of cognitive functioning for classifying vulnerable and resilient individuals include the Psychomotor Vigilance Test (PVT), Go/noGo executive inhibition task, and subjective daytime sleepiness. We evaluated the influence of 14 single-nucleotide polymorphisms (SNPs) on cognitive responses during total sleep deprivation (continuous wakefulness for 38 h) in 47 healthy subjects (age 37.0 ± 1.1 years). SNPs selected after a literature review included SNPs of the adenosine-A2A receptor gene (including the most studied rs5751876), pro-inflammatory cytokines (TNF-α, IL1-β, IL-6), catechol-O-methyl-transferase (COMT), and PER3. Subjects performed a psychomotor vigilance test (PVT) and a Go/noGo-inhibition task, and completed the Karolinska Sleepiness Scale (KSS) every 6 h during TSD. For PVT lapses (reaction time >500 ms), an interaction between SNP and SDT (p < 0.05) was observed for ADORA2A (rs5751862 and rs2236624) and TNF-α (rs1800629). During TSD, carriers of the A allele for ADORA2A (rs5751862) and TNF-α were significantly more impaired for cognitive responses than their respective ancestral G/G genotypes. Carriers of the ancestral G/G genotype of ADORA2A rs5751862 were found to be very similar to the most resilient subjects for PVT lapses and Go/noGo commission errors. Carriers of the ancestral G/G genotype of COMT were close to the most vulnerable subjects. ADORA2A (rs5751862) was significantly associated with COMT (rs4680) (p = 0.001). In conclusion, we show that genetic polymorphisms in ADORA2A (rs5751862), TNF-α (rs1800629), and COMT (rs4680) are involved in creating profiles of high vulnerability or high resilience to sleep deprivation. (NCT03859882).

Concordance of multiple methods to define resiliency and vulnerability to sleep loss depends on Psychomotor Vigilance Test metric

Study Objectives Sleep restriction (SR) and total sleep deprivation (TSD) reveal well-established individual differences in Psychomotor Vigilance Test (PVT) performance. While prior studies have used different methods to categorize such resiliency/vulnerability, none have systematically investigated whether these methods categorize individuals similarly. Methods 41 adults participated in a 13-day laboratory study consisting of 2 baseline, 5 SR, 4 recovery, and one 36h TSD night. The PVT was administered every 2h during wakefulness. Three approaches (Raw Score [average SR performance], Change from Baseline [average SR minus average baseline performance], and Variance [intraindividual variance of SR performance]), and within each approach, six thresholds (±1 standard deviation and the best/worst performing 12.5%, 20%, 25%, 33%, and 50%) classified Resilient/Vulnerable groups. Kendall’s tau-b correlations examined the concordance of group categorizations of approaches within and between PVT lapses and 1/reaction time (RT). Bias-corrected and accelerated bootstrapped t-tests compared group performance. Results Correlations comparing the approaches ranged from moderate to perfect for lapses and zero to moderate for 1/RT. Defined by all approaches, the Resilient groups had significantly fewer lapses on nearly all study days. Defined by the Raw Score approach only, the Resilient groups had significantly faster 1/RT on all study days. Between-measures comparisons revealed significant correlations between the Raw Score approach for 1/RT and all approaches for lapses. Conclusion The three approaches defining vigilant attention resiliency/vulnerability to sleep loss resulted in groups comprised of similar individuals for PVT lapses but not for 1/RT. Thus, both method and metric selection for defining vigilant attention resiliency/vulnerability to sleep loss is critical.

How Mentally Fatiguing Are Consecutive World Padel Tour Matches?

It is currently unknown whether mental fatigue occurs throughout a WPT competition and whether consecutive matches affect how mentally fatiguing a match is perceived to be. The objective was to quantify the effects of successive professional matches on mental fatigue. A total of 14 professional players (9 males, Mage = 25, 5 females, Mage = 21) participated during qualified rounds of a WPT with three eliminatory matches: Match 1 (morning) and 2 (afternoon) on day 1 (n = 14), Match 3 (morning) on day 2 (n = 6). Mental fatigue and motivation, with scales, and reaction time, with a 3-min Psychomotor Vigilance Test, were measured at two time intervals (pre and post matches (<30 min)). To analyze the evolution of these variables, a two-way repeated measures MANOVA was performed. An increase in mental fatigue from pre- to post-matches was observed (p < 0.01), with an accumulation of mental fatigue between matches played on day 1 (p < 0.01), maximizing the mental fatigue perceived during Match 2. Padel matches impair motivation and reaction time (p = 0.04), without effects between successive matches, which reinforced the idea that mental fatigue may impair padel performance (i.e., reaction time). Coaches should use training interventions and recovery strategies to counteract/avoid the accumulation of mental fatigue during professional tournaments.

101 Measurement of Tapping During the Interstimulus Interval as a Validation Metric for the 3-Minute Psychomotor Vigilance Test

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.

Effects of a Snack on Performance and Errors During a Simulated 16-h Night Shift: a Pilot Study

Background: Night shift workers might not eat due to their busy schedules during the night shift. However, diet may not only satisfy hunger, but also affect performance and errors.The aim of this study was to clarify the effect of a snack on performance and errors during 2-day, 16-h simulated night shifts. Methods: A randomized repeated-measure crossover study was performed to investigate subjective and cognitive performance in 15 healthy female adults (mean age, 21.7 years) after they consumed a snack (352 kcal) during a simulated night shift (16:00 to 09:00). The participants were kept awake from wake up in the morning to the next day at 09:00. Subjects were tested for performance on the Uchida-Kraepelin test, as well as for subjective feeling, body temperature, psychomotor vigilance test, and heart rate variability, before and after they consumed the snack. One day before the experiment, all participants wore an actigraphy monitoring device to determine their sleep state. Results: There was no difference between the snack condition and the skipping condition in sleep states the day before the experiment. On the day of the experiment, between 16:00 and 09:00, subjective sleepiness, fatigue, and body temperature were not different between the two conditions. Subjects showed a significant improvement in performance on the Uchida-Kraepelin test and total errors on the psychomotor vigilance test, the primary outcome measure, when they consumed a snack compared with the skipping condition. In addition, the snack condition was associated with decreased high-frequency power, decreased low-frequency power/high-frequency power ratio, and increased heart rate, in the vagally mediated heart rate variability indices, which may reflect a higher ability to modulate cognitive and behavioral processes. Conclusions: These results suggest that providing a snack to shift workers during night shifts might improve work safety and efficiency.Trial registration: This study was registered with the University Hospital Medical Information Network-Clinical Trials Registry (UMIN-CRT registry ID: UMIN 000034345).

Response speed measurements on the psychomotor vigilance test: how precise is precise enough?

Study Objectives The psychomotor vigilance test (PVT) is frequently used to measure behavioral alertness in sleep research on various software and hardware platforms. In contrast to many other cognitive tests, PVT response time (RT) shifts of a few milliseconds can be meaningful. It is, therefore, important to use calibrated systems, but calibration standards are currently missing. This study investigated the influence of system latency bias and its variability on two frequently used PVT performance metrics, attentional lapses (RTs ≥500 ms) and response speed, in sleep-deprived and alert participants. Methods PVT data from one acute total (N = 31 participants) and one chronic partial (N = 43 participants) sleep deprivation protocol were the basis for simulations in which response bias (±15 ms) and its variability (0–50 ms) were systematically varied and transgressions of predefined thresholds (i.e. ±1 for lapses, ±0.1/s for response speed) recorded. Results Both increasing bias and its variability caused deviations from true scores that were higher for the number of lapses in sleep-deprived participants and for response speed in alert participants. Threshold transgressions were typically rare (i.e. &lt;5%) if system latency bias was less than ±5 ms and its standard deviation was ≤10 ms. Conclusions A bias of ±5 ms with a standard deviation of ≤10 ms could be considered maximally allowable margins for calibrating PVT systems for timing accuracy. Future studies should report the average system latency and its standard deviation in addition to adhering to published standards for administering and analyzing the PVT.

0144 Relationship Between Sleep EEG K-Complex Slow Wave Coupling and Next-Day Thalamic Activity During Psychomotor Vigilance Test in Obstructive Sleep Apnea

Introduction There is large inter-individual variability in the relationship between obstructive sleep apnea (OSA) severity and lapses in vigilance as measured using psychomotor vigilance test (PVT). We have previously shown that overnight sleep EEG K-complex slow wave coupling (∆SWAK) exhibits a dose-responsive relationship with next-day lapses in vigilance in OSA on and off treatment. We hypothesized that a variable thalamic dysfunction in OSA explains difference in lapses in vigilance and alterations in ∆SWAK across individuals. Methods Five newly diagnosed severe OSA subjects (mean apnea-hypopnea index [AHI4%=57.1±22.8/hr.]) with excessive daytime sleepiness (Epworth Sleepiness Scale=11±3.4) underwent nocturnal polysomnography followed by PVT testing within a 3T SKYRA MRI scanner. The PVT task inside the scanner (PVT-fMRI) was adapted to match the gold standard PVT-192 device. Each fMRI scanning session consisted of 2 10-min PVT runs interleaved with 2 control conditions wherein the subject pressed the response button at random intervals absent of a visual stimulus. fMRI data was analyzed in 2-step procedure (individual time-series followed by group analysis) using Analysis of Functional Neuroimages (AFNI) software package. To estimate thalamic activity during PVT-fMRI, parameter estimates of the %change in blood-oxygen-level-dependent (BOLD) signal using the contrast PVT-Control were used as the primary metric. The region of interest was limited to the bilateral thalamus using the Eickhoff-Zilles macro labels from the MNI N27 template. Results In a preliminary test, PVT performance for the subjects inside the scanner was not significantly different from that outside the scanner (PVTLapsesfMRI=7.3±2.1 vs. PVTLapsesPVT192=6.4±3.6 mean±std; PVTLapses=reaction time &gt; 500 ms.). Within subjects, a trend toward lower thalamic recruitment was observed during PVT-fMRI (-0.17±0.2%; p=0.1). Further, lower thalamic activity during PVT-fMRI also showed a trend to lower overnight ∆SWAK (mean -1.2±1.4) values (r = 0.61, p = 0.17). Conclusion In severe OSA subjects with excessive daytime sleepiness, we observed a trend to reduced thalamic activity during daytime PVT. Overnight EEG K-complex slow wave coupling showed a similar trend with next-day thalamic activity during PVT, however the small sample size may have limited our ability to detect this association with statistical significance. Support AASM Foundation 199-FP-18; NIH K24HL109156

Self-Reported Sleep Need, Subjective Resilience, and Cognitive Performance Following Sleep Loss and Recovery Sleep

Objective Individuals vary in response to sleep loss: some individuals are “vulnerable” and demonstrate cognitive decrements following insufficient sleep, while others are “resistant” and maintain baseline cognitive capability. Physiological markers (e.g., genetic polymorphisms) have been identified that can predict relative vulnerability. However, a quick, cost-effective, and feasible subjective predictor tool has not been developed. The objective of the present study was to determine whether two factors—“subjective sleep need” and “subjective resilience”—predict cognitive performance following sleep deprivation. Methods Twenty-seven healthy, sleep-satiated young adults participated. These individuals were screened for sleep disorders, comorbidities, and erratic sleep schedules. Prior to 40 hours of in-laboratory total sleep deprivation, participants were questioned on their subjective sleep need and completed a validated resilience scale. During and after sleep deprivation, participants completed a 5-minute psychomotor vigilance test every 2 hours. Results Both subjective resilience and subjective sleep need individually failed to predict performance during sleep loss. However, these two measures interacted to predict performance. Individuals with low resilience and low sleep need had poorer cognitive performance during sleep loss. However, in individuals with medium or high resilience, psychomotor vigilance test performance was not predicted by subjective sleep need. Higher resilience may be protective against sleep loss-related neurobehavioral impairments in the context of subjective sleep need. Conclusions Following sleep loss (and recovery sleep), trait resilient individuals may outperform those with lower resiliency on real-world tasks that require continuous attention. Future studies should determine whether the present findings generalize to other, operationally relevant tasks and additional cognitive domains.