SLEEP

Kim, Sun-Young; Lee, Kyung Hwa; Lee, Hayoung; Jeon, Jeong Eun; Kim, Soohyun; Lee, Mi Hyun; Lee, Jooyoung; Jeon, Sehyun; Oh, Seong-Min; Kim, Seog Ju; Lee, Yu Jin

doi: 10.1093/sleep/zsac219pmid: 36107968

Study ObjectivesThis study investigated the altered neural function involved in emotional interference and its role in linking sleep disturbance and depressive/anxiety symptoms in rotating shift workers.MethodsSixty rotating shift workers and 61 controls performed the emotional Stroop task in three blocks (emotional-related, sleep-related, and neutral words) during functional magnetic resonance imaging (fMRI) assessments. Sleep disturbance and depressive/anxiety symptoms were assessed using self-report measures and sleep diaries. Actigraphy was used to assess the sleep and circadian variables. fMRI scans were performed to compare brain activation during the emotional Stroop task. The proposed moderating models were tested using the PROCESS macro in SPSS software.ResultsA significant condition effect on reaction time was detected. Regardless of the group, reaction times were longer in the negative emotional word and sleep-related conditions than in the neutral word condition. Whole-brain analysis revealed that rotating shift workers show greater neural activation in the left dorsolateral prefrontal cortex (DLPFC) compared with controls while performing the emotional Stroop task with negative emotional words. Sleep disturbance was more strongly associated with depressive symptoms when activation of the left DLPFC was higher during the emotional Stroop task with negative words.ConclusionsThe left DLPFC may play important roles in increased sensitivity to emotional information, possibly due to circadian misalignment, and has moderating effects on the association between sleep disturbance and depressive symptoms in rotating shift workers. These findings will help to identify possible brain regions where interventions can be performed to correct sleep and mood problems in rotating shift workers.

Heidenreich, Sebastian; Ross, Melissa; Chua, Gin Nie; Seboek Kinter, Dalma; Phillips-Beyer, Andrea

doi: 10.1093/sleep/zsac204pmid: 36054921

Study ObjectivesTo elicit the trade-offs patients are willing to make between benefits and risks of medications for chronic insomnia, with the purpose of allowing a patient-centric interpretation of clinical trial data.MethodsA discrete choice experiment (DCE) was included in the two placebo-controlled phase III trials that evaluated the efficacy and safety of daridorexant. The DCE design was informed by a two-phase qualitative study, followed by qualitative and quantitative pilot testing before fielding. Relative attribute importance (RAI) and acceptable trade-offs between benefits and risks were obtained using a mixed logit model.ResultsPreferences were elicited from 602 trial participants (68.1% female, aged 58.6 ± 14.5 years). Preferences were most affected by daytime functioning (RAI = 33.7%) as a treatment benefit and withdrawal symptoms (RAI = 27.5%) as a risk. Patients also valued shorter sleep onset (RAI = 6.4%), longer sleep maintenance (RAI = 5.4%), reduced likelihood of abnormal thoughts and behavioral changes (RAI = 11.3%), reduced likelihood of dizziness/grogginess (RAI = 9.2%), and reduced likelihood of falls at night (RAI = 6.5%). Patients were willing to make trade-offs between these attributes. For example, they would accept an additional 18.8% risk of abnormal thoughts and behavioral changes to improve their daytime functioning from difficult to restricted and an additional 8.1% risk of abnormal thoughts and behavioral changes to avoid moderate withdrawal effects.ConclusionsPatients with insomnia were willing to make trade-offs between multiple benefits and risks of pharmacological treatments. Because patients valued daytime functioning more than sleep latency and duration, we recommend that functional outcomes and sleep quality be considered in treatment development and evaluation.

Blume, Christine; Niedernhuber, Maria; Spitschan, Manuel; Slawik, Helen C; Meyer, Martin P; Bekinschtein, Tristan A; Cajochen, Christian

doi: 10.1093/sleep/zsac199pmid: 35998110

Presleep exposure to short-wavelength light suppresses melatonin and decreases sleepiness with activating effects extending to sleep. This has mainly been attributed to melanopic effects, but mechanistic insights are missing. Thus, we investigated whether two light conditions only differing in the melanopic effects (123 vs. 59 lx melanopic EDI) differentially affect sleep besides melatonin. Additionally, we studied whether the light differentially modulates sensory processing during wakefulness and sleep. Twenty-nine healthy volunteers (18–30 years, 15 women) were exposed to two metameric light conditions (high- vs. low-melanopic, ≈60 photopic lx) for 1 h ending 50 min prior to habitual bed time. This was followed by an 8-h sleep opportunity with polysomnography. Objective sleep measurements were complemented by self-report. Salivary melatonin, subjective sleepiness, and behavioral vigilance were sampled at regular intervals. Sensory processing was evaluated during light exposure and sleep on the basis of neural responses related to violations of expectations in an oddball paradigm. We observed suppression of melatonin by ≈14% in the high- compared to the low-melanopic condition. However, conditions did not differentially affect sleep, sleep quality, sleepiness, or vigilance. A neural mismatch response was evident during all sleep stages, but not differentially modulated by light. Suppression of melatonin by light targeting the melanopic system does not automatically translate to acutely altered levels of vigilance or sleepiness or to changes in sleep, sleep quality, or basic sensory processing. Given contradicting earlier findings and the retinal anatomy, this may suggest that an interaction between melanopsin and cone-rod signals needs to be considered.Clinical Trial Registry: German Clinical Trials Register, DRKS00023602, https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00023602.

Otaiku, Abidemi I

doi: 10.1093/sleep/zsac206pmid: 36037514

Study ObjectivesParkinson’s disease (PD) is associated with abnormalities of sleep macro- and microstructure as measured using polysomnography (PSG). Whether these abnormalities precede the development of PD is unknown. This study investigated the association between PSG measured sleep abnormalities in older adults and the risk of incident PD.MethodsA total of 2,770 men from the ancillary sleep study of the Osteoporotic Fractures in Men Study (MrOS), a population-based cohort from the United States, who were free from PD baseline and underwent overnight PSG, were included in this longitudinal analysis. Incident PD was based on a clinical diagnosis from a medical professional. Multivariable logistic regression was used to estimate odds ratios (OR) for incident PD by quartiles of PSG measures, with adjustment for sociodemographic characteristics, medical comorbidities, and lifestyle factors.ResultsDuring a median follow-up of 9.8 years, 70 (2.5%) cases of incident PD were identified. Longer total sleep time, lower rapid eye movement sleep (REM) percentage, a lower α/θ ratio during non-REM sleep and higher minimum oxygen saturations during REM sleep, were each associated with an increased risk of developing PD. Conversely, a higher awakening index was associated with a decreased risk of developing PD. The OR for the highest risk quartiles compared to the lowest risk quartiles, ranged from 2.1 to 3.7 (p’s < .05). The associations remained significant when cases occurring within the first two years of follow-up were excluded from the analyses.ConclusionsMacro- and micro-structural sleep abnormalities precede the development of PD by several years and can identify individuals at high risk of developing PD in the future.

Liu, Zhen-Zhen; Jia, Cun-Xian; Liu, Xianchen

doi: 10.1093/sleep/zsac221pmid: 36108086

Study ObjectivesInsomnia symptoms, excessive daytime sleepiness (EDS), and suicidal behavior are prevalent among adolescents. Growing studies have shown that both insomnia symptoms and EDS are associated with suicidal behavior. However, little is known about the pathways between insomnia symptoms, EDS, and suicidal behavior. This study aimed to examine the longitudinal mediating effect of EDS on insomnia-suicidal behavior link in a large sample of Chinese adolescents.MethodsParticipants were 7072 adolescents (Mean age = 14.58 years, 50.0% males) who were surveyed at baseline and were followed up 1 year later in the Shandong Adolescent Behavior and Health Cohort study. A self-administered questionnaire was used to measure insomnia symptoms, daytime sleepiness, sleep duration, social jetlag, suicidal behavior, and adolescent and family demographics.ResultsThe prevalence of insomnia symptoms and EDS at baseline were 14.3% and 21.1%, respectively. Adolescents with insomnia symptoms or EDS at baseline were more likely to report suicidal behavior at 1-year follow-up compared to adolescents without insomnia symptoms or EDS. Path analyses showed that EDS played a significant mediation role between insomnia symptoms and suicidal behavior (including any suicidal behavior, suicidal thought, suicide plan, and suicide attempt) before and after adjusting for adolescent and family factors, sleep duration, social jetlag, and prior suicidal behavior.ConclusionInsomnia symptoms and EDS were associated with increased risk of subsequent suicidal behavior. The association between insomnia symptoms and suicidal behavior was mediated by EDS. These findings highlight the importance of assessment and treatment of insomnia and daytime sleepiness for suicide prevention in adolescents.

Soose, Ryan J; Araujo, Matheus; Faber, Kevin; Roy, Asim; Lee, Kent; Ni, Quan; Srivastava, Jaideep; Strollo, Patrick J

doi: 10.1093/sleep/zsac049pmid: 35245933

Study ObjectivesUpper airway stimulation (UAS) therapy is effective for a subset of obstructive sleep apnea (OSA) patients with continuous positive airway pressure (CPAP) intolerance. While overall adherence is high, some patients have suboptimal adherence, which limits efficacy. Our goal was to identify therapy usage patterns during the first 3 months of therapy to enable targeted strategies for improved adherence.MethodsTherapy data was retrieved from 2098 patients for three months after device activation. Data included mean and standard deviation (SD) of hours of use, therapy pauses, hours from midnight the therapy was turned ON and OFF, percentage of missing days, and stimulation amplitude. Cluster analysis was performed using Gaussian mixture models that categorized patients into six main groups.ResultsThe six groups and their prevalence can be summarized as Cluster 1A: Excellent Use (34%); Cluster 1B: Excellent Use with variable timing (23%); Cluster 2A: Good Use with missing days and late therapy ON (16%), Cluster 2B: Good Use with missing days, late therapy ON, and early therapy OFF (12%); Cluster 3A: Variable Use with frequent missing days (8%); Cluster 3B: Variable Use with frequent pauses (7%). Most patients (85%) are excellent or good users with mean therapy use >6 hours per night.ConclusionsCluster analysis of early UAS usage patterns identified six distinct groups that may enable personalized interventions for improved long-term management. Differentiation of the patient clusters may have clinical implications with regard to sleep hygiene education, therapy discomfort, comorbid insomnia, and other conditions that impact adherence.

Olivares, María José; Toledo, Camilo; Ortolani, Domiziana; Ortiz, Fernando C; Díaz, Hugo S; Iturriaga, Rodrigo; Del Río, Rodrigo

doi: 10.1093/sleep/zsac166pmid: 35878762

The autonomic nervous system (ANS) plays an important role in the coordination of several physiological functions including sleep/wake process. Significant changes in ANS activity occur during wake-to-sleep transition maintaining the adequate cardiorespiratory regulation and brain activity. Since sleep is a complex homeostatic function, partly regulated by the ANS, it is not surprising that sleep disruption trigger and/or evidence symptoms of ANS impairment. Indeed, several studies suggest a bidirectional relationship between impaired ANS function (i.e. enhanced sympathetic drive), and the emergence/development of sleep disorders. Furthermore, several epidemiological studies described a strong association between sympathetic-mediated diseases and the development and maintenance of sleep disorders resulting in a vicious cycle with adverse outcomes and increased mortality risk. However, which and how the sleep/wake control and ANS circuitry becomes affected during the progression of ANS-related diseases remains poorly understood. Thus, understanding the physiological mechanisms underpinning sleep/wake-dependent sympathetic modulation could provide insights into diseases involving autonomic dysfunction. The purpose of this review is to explore potential neural mechanisms involved in both the onset/maintenance of sympathetic-mediated diseases (Rett syndrome, congenital central hypoventilation syndrome, obstructive sleep apnoea, type 2 diabetes, obesity, heart failure, hypertension, and neurodegenerative diseases) and their plausible contribution to the generation of sleep disorders in order to review evidence that may serve to establish a causal link between sleep disorders and heightened sympathetic activity.

Schechtman, Eitan

doi: 10.1093/sleep/zsac214pmid: 36112506

Dear Editor, Far from being an idle state, sleep plays restorative and transformative functions that are critical to both body and mind. As researchers and clinicians, we strive to evaluate sleep’s effectiveness in completing these functions. However, this endeavor is challenged by the scarcity of function-specific biomarkers. Instead, we focus on the efficiency of sleep, using superficial measures such as total time asleep, number of arousals, or time spent at a specific sleep stage. In this letter, I will argue for the need to consider the cognitive content of sleep (i.e. the reactivated neurocognitive representations) in our clinical and scientific evaluation of sleep. Embracing recently developed methods for measuring and biasing the contents of sleep is a crucial step towards a deeper understanding of cognitive processing during sleep, paving the way toward improved diagnoses and treatments. As a starting point, consider three employees, working together at an office under a meticulous manager. All three follow the same schedule—they arrive and leave at the same times, the number and durations of their breaks are comparable, and they spend similar amounts of time on their computers and filing documents (Figure 1a, top). Based on this information, their manager may assume that they are equally effective. However, only Employee A is truly hard-working and task oriented. Employee B works ineffectively and is easily distracted, whereas Employee C is engaged in active workspace sabotage. Just like superficial measures of workspace efficiency would prove futile in evaluating the employees’ performance, so too do superficial measures of sleep efficiency fail to distinguish adaptive from maladaptive sleep (Figure 1a, bottom). Sleep effectiveness—like workspace effectiveness—must be judged in the context of function, incorporating sleep structure as well as substance. Figure 1. Open in new tabDownload slide The cognitive contents of sleep as a novel dimension for evaluating sleep function. (a) A manager tasked with evaluating three employees would be hard-pressed to do so based only on superficial features such as hours of attendance and timing of breaks (top). In this example, whereas one employee is hard-working (green), the others are unfocused (blue) or subversive (red). Similarly, judging the function of sleep based on sleep efficiency alone is impossible (bottom). The cognitive contents of sleep could prove useful in evaluating sleep for research and diagnosis. Illustrations by Storyset (free for use with attribution; https://storyset.com/business). (b) Different approaches for revealing sleep contents. Left—correlating wake-related patterns of brain activity to patterns of reactivation during slow wave-spindle complexes. In this example, the patient’s neural responses during sleep are correlated with wake-related responses to images linked with negative emotions (upper image) rather than positive emotions. Right—considering the correlation structure within patterns of sleep-related brain activity as a biomarker for adaptive vs maladaptive content processing. In this example, the patient’s neural responses to neutral words (e.g. “pencil”) are more similar to those evoked by negative words (e.g. “blood”) relative to positive ones (e.g. “flower”), suggesting a negative bias in processing during sleep. In both examples, thicker arrows signify stronger correlations. The notion that sleep involves latent cognitive content—and that its content is linked with the function of sleep—rests on two pillars. The first line of evidence builds on dream reports. Although the history of attributing meaning to dreams goes back millennia, Freud’s “The Interpretation of Dreams” is perhaps the most notable attempt at decoding sleep-related contents to improve well-being [1]. Freud didn’t associate dream content with the function of sleep but rather speculated that dreams protect sleep against the workings of the inner psyche. More recent (and scientifically falsifiable) theories posit that dream contents play a role in the function of sleep (e.g. supporting memory consolidation and creativity [2]). However, dream reports are generally an inaccessible and unreliable method for monitoring the cognitive contents of sleep. Reports can only be collected upon awakening and are limited to conscious recollection, whereas some underlying cognitive processes may not require consciousness. The second line of evidence suggesting that cognitive contents play a role in the function of sleep comes from the literature on memory consolidation. Overwhelming evidence from humans and nonhuman animals implicates sleep in the reactivation of memories, thereby strengthening them [3]. Unlike dreams, revealing the cognitive contents of memory reactivation does not rely on self-report or require awakenings. Whereas early work in nonhuman animals monitored reactivation on the cellular level (e.g. [4]), recent technological and methodological advancements in human cognitive neuroscience have allowed researchers to monitor memory reactivation noninvasively and in real time. By leveraging neuroimaging techniques such as electroencephalography (EEG), magnetoencephalography, electrocorticography, and functional magnetic resonance imaging, together with computational approaches such as multivariate pattern analysis, the neural correlates of memory processing during sleep can be deciphered to reveal patterns of activity linked with the representations of memories. In general, two approaches can be used to unravel these representations (Figure 1b). First, patterns of brain activity could be identified during wake and then used as a template for interpreting the activity during sleep. For example, patients suffering from major depressive disorders could be exposed to stimuli linked with positive and negative affects during wake, establishing the patterns of EEG activity linked with these two types of stimuli. Then, the activity observed during sleep may be classified as one or the other, potentially providing a biomarker for maladaptive processing during sleep (Figure 1b, left). Neural patterns identified during wake have been successfully used to identify content reactivation during sleep in a few recent studies, demonstrating the potential of this method (e.g. [5–8]). The second approach to unraveling content during sleep does not rely on data collected during wake. Instead, it considers the data collected during sleep and uses the similarities (or lack thereof) between different observed patterns as its dependent variable. For example, patients suffering from major depressive disorder may be exposed to negative, positive, and neutral words during sleep (Figure 1b, right). The activity observed following the neutral words will be correlated to that following the negative and positive words to determine whether the patients’ information processing during sleep in negatively biased. Methods for monitoring cognitive processing during sleep based exclusively on sleep data have been successfully used in a number of recent studies (e.g. [9–12]). To date, most studies monitoring and decoding the cognitive contents of sleep have been limited to memory reactivation in healthy participants. However, these methods’ potential extends far beyond these realms. Sleep-related abnormalities are common in most psychiatric and neurological disorders, including major depressive disorder, post-traumatic stress disorder, and Alzheimer’s disease. Different hypotheses may drive studies that focus on specific sleep stages and classes of reactivated content in both healthy and clinical populations. Some variations of the methods used to monitor content reactivation focus on time-locked responses to specific events occurring during sleep. For example, studies using targeted memory reactivation, the unobtrusive presentation of stimuli during sleep to bias memory consolidation [13], have analyzed neural activity time-locked to the onset of stimuli presented during sleep (e.g. [6, 10, 12]). However, other studies have focused on undisturbed sleep by focusing on spontaneous sleep-specific events, such as slow wave-spindle complexes (e.g. Figure 1b, left) [5], or even avoided relying on time-locked responses altogether [9]. Whereas most research has used content-specific methods to study memory reactivation, it is within reason to assume that actions, emotions, and intentions are also represented and reactivated within the sleeping brain. Unraveling the involvement of these higher-order cognitive constructs in sleep processing is the logical next step in evaluating sleep’s contribution to psychiatric and neurological disorders [3]. Combining novel methods to reveal sleep contents with manipulations to bias reactivation during sleep, such as targeted memory reactivation, may open new paths for discovery and treatment. Incorporating these methods in the lab and clinic holds the promise to revolutionize the way we evaluate sleep’s contribution to health, and specifically mental health. Acknowledgments This work was supported by National Institute of Health (USA) grant K99-MH122663. I would like to thanks James Antony for helpful comments. Financial disclosure None. Non-financial disclosure None. References 1. Freud S . The interpretation of dreams. New York : Random House, 1900 . Google Scholar 2. Zadra A , Stickgold R. When Brains Dream: Exploring the Science and Mystery of Sleep . New York: WW Norton ; 2021 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 3. Paller KA , et al. Memory and sleep: how sleep cognition can change the waking mind for the better . Annu Rev Psychol. 2021 ; 72 : 123 – 150 . Google Scholar Crossref Search ADS PubMed WorldCat 4. Wilson MA , et al. Reactivation of hippocampal ensemble memories during sleep . Science (New York, NY). 1994 ; 265 ( 5172 ): 676 – 679 . Google Scholar Crossref Search ADS WorldCat 5. Schreiner T , et al. Endogenous memory reactivation during sleep in humans is clocked by slow oscillation-spindle complexes . Nat Commun. 2021 ; 12 ( 1 ): 3112 . doi:10.1038/s41467-021-23520-2. Google Scholar Crossref Search ADS PubMed WorldCat 6. Cairney SA , et al. 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Published online March 29, 2022 , doi:10.1101/2022.03.28.486140, 2022.03.28.486140, preprint: not peer reviewed. Google Scholar OpenURL Placeholder Text WorldCat 11. Arzi A , et al. Dynamic auditory remapping across the sleep–wake cycle . bioRxiv . 2021 , Published online February 17, 2021.02.16.431383, doi:10.1101/2021.02.16.431383, preprint: not peer reviewed. Google Scholar OpenURL Placeholder Text WorldCat 12. Wang B , et al. Targeted memory reactivation during sleep elicits neural signals related to learning content . J Neurosci. 2019 ; 39 ( 34 ): 6728 – 6736 . doi:10.1523/jneurosci.2798-18.2019. Google Scholar Crossref Search ADS PubMed WorldCat 13. Oudiette D , et al. The role of memory reactivation during wakefulness and sleep in determining which memories endure . J Neurosci. 2013 ; 33 ( 15 ): 6672 – 6678 . doi:10.1523/jneurosci.5497-12.2013. Google Scholar Crossref Search ADS PubMed WorldCat © The Author(s) 2022. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For permissions, please e-mail: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/pages/standard-publication-reuse-rights) © The Author(s) 2022. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For permissions, please e-mail: [email protected]

Harbison, Susan T

doi: 10.1093/sleep/zsac147pmid: 36111812

Selective breeding is a classic technique that enables an experimenter to modify a heritable target trait as desired. Direct selective breeding for extreme sleep and circadian phenotypes in flies successfully alters these behaviors, and sleep and circadian perturbations emerge as correlated responses to selection for other traits in mice, rats, and dogs. The application of sequencing technologies to the process of selective breeding identifies the genetic network impacting the selected trait in a holistic way. Breeding techniques preserve the extreme phenotypes generated during selective breeding, generating community resources for further functional testing. Selective breeding is thus a unique strategy that can explore the phenotypic limits of sleep and circadian behavior, discover correlated responses of traits having shared genetic architecture with the target trait, identify naturally-occurring genomic variants and gene expression changes that affect trait variability, and pinpoint genes with conserved roles.

Mayeli, Ahmad; Wilson, James D; Donati, Francesco L; LaGoy, Alice D; Ferrarelli, Fabio

doi: 10.1093/sleep/zsac193pmid: 35981865

Study ObjectivesSleep spindles are waxing and waning EEG waves exemplifying the main fast oscillatory activity occurring during NREM sleep. Several recent studies have established that sleep spindle abnormalities are present in schizophrenia spectrum disorders, including in early-course and first-episode patients, and those spindle deficits are associated with some of the cognitive impairments commonly observed in these patients. Cognitive deficits are often observed before the onset of psychosis and seem to predict poor functional outcomes in individuals at clinical high-risk for psychosis (CHR). Yet, the presence of spindle abnormalities and their relationship with cognitive dysfunction has not been investigated in CHR.MethodsIn this study, overnight high-density (hd)-EEG recordings were collected in 24 CHR and 24 healthy control (HC) subjects. Spindle density, duration, amplitude, and frequency were computed and compared between CHR and HC. Furthermore, WM was assessed for both HC and CHR, and its relationship with spindle parameters was examined.ResultsCHR had reduced spindle duration in centro-parietal and prefrontal regions, with the largest decrease in the right prefrontal area. Moderation analysis showed that the relation between spindle duration and spindle frequency was altered in CHR relative to HC. Furthermore, CHR had reduced WM performance compared to HC, which was predicted by spindle frequency, whereas in HC spindle frequency, duration, and density all predicted working memory performance.ConclusionAltogether, these findings indicate that sleep spindles are altered in CHR individuals, and spindle alterations are associated with their cognitive deficits, thus representing a sleep-specific putative neurophysiological biomarker of cognitive dysfunction in psychosis risk.