SLEEP

Antelmi,, Elena;Plazzi,, Giuseppe

doi: 10.1093/sleep/zsaa149pmid: 32910822

Isolated or idiopathic REM sleep behavior disorder (iRBD) is a parasomnia due to the lack of physiological muscle atonia during rapid eye movement (REM) sleep [1]. Its prevalence is rare, but it is probably underdiagnosed [1]. IRBD has a positive predictive value for impending neurodegeneration linked to synucleinopathy [2] and may cause severe injuries and medico-legal consequences to patients and partners [1]. Clonazepam and melatonin have been reported to have some effect on RBD episodes [1]. RBD can be secondary to narcolepsy type 1 (NT1) [1]. Contrary to iRBD, where systematic studies on the efficacy of sodium oxybate (SO) are lacking, in NT1 patients SO (a first-line treatment for NT1) displays a direct effect in enhancing REM sleep muscle atonia, with a dose-dependent effect [3]. The same positive modulation on muscle tone during REM sleep was observed in NT1 children, mirroring the decrease in RBD episodes [4]. Three reports of males with violent, clonazepam-resistant iRBD treated with SO up to 9 gr nightly are available [5, 6]. Patients reported an improvement of symptoms and a significant decrease of nocturnal violent episodes [5, 6], although at video-polysomnography (v-PSG) they presented a persistence of REM sleep without atonia (RSWA) and of RBD episodes [6]. We, therefore, continued to follow-up the two patients of our original description [6] and put on “off-label” SO (6 to 9 gr per night) three additional adult male patients with iRBD, who previously displayed poor response to common treatment. According to the national rules for rare diseases, in all the patients, the cost for SO (Xyrem) was covered by the Italian welfare, since current data suggest that iRBD could be an orphan disease [6]. At 8 and 3 years follow-up, respectively, all continue to refer benefit from SO (although one patient presented an increase of blood pressure that required SO tapering). As formerly observed, at v-PSG follow-up patients did not display an improvement in RSWA in any of them. Nevertheless, patients reported an almost complete disappearance of violent episodes and blunted dream mentation. This observation cautiously allows us to infer that benefit could be related to the decrease of the percentage of REM sleep and the increase of that of slow-wave sleep (SWS), rather than to RSWA modulation. Moreover, all five patients had a longstanding history of RBD (mean disease duration ± SD: 18 years ± 5.8; mean age of the patients 72.6 years old ± 9.8), but none of them presented so far signs of phenoconversion, both at clinical (i.e. motor or cognitive signs) and at neuroimaging (all continued to have negative DATSCAN) follow-up (Table 1). This datum might have some interest in light of recent studies demonstrating how good and sufficient sleep (and particularly SWS) might enhance the clearance of proteins of accumulations that otherwise can likely have detrimental effect on neurodegeneration, favoring pathological proteins’ deposits [7]. Table 1. RBD patients under treatment with sodium oxybate Sex . Age (yrs) . DD . RAI baseline . RAI Post SO . SO dosage gr . DATScan . p-α-syn at skin biopsy . NPS test . Additional NMS . UPDRS-IIII . M 73 23 0.701 0.497* 9 Negative Positive Normal No 0 M 56 14 0.691 0.504* 3 Negative Positive Normal No 0 M 81 23 0.445 0.402† 5 Negative Positive Normal No 0 M 79 20 0.338 0.332† 6 Negative Negative Normal No 0 M 74 10 0.695 0.513† 9 Negative Positive Normal No 3 Sex . Age (yrs) . DD . RAI baseline . RAI Post SO . SO dosage gr . DATScan . p-α-syn at skin biopsy . NPS test . Additional NMS . UPDRS-IIII . M 73 23 0.701 0.497* 9 Negative Positive Normal No 0 M 56 14 0.691 0.504* 3 Negative Positive Normal No 0 M 81 23 0.445 0.402† 5 Negative Positive Normal No 0 M 79 20 0.338 0.332† 6 Negative Negative Normal No 0 M 74 10 0.695 0.513† 9 Negative Positive Normal No 3 DD, disease duration; gr, gram; yrs, years; NMS; Non-motor symptoms (i.e. constipation, urinary symptoms, cognitive symptoms, autonomic symptoms); NPS, neuropsychological test; RAI, REM sleep atonia index; SO, Sodium Oxybate; UPDRS-III, unified Parkinson’s disease rating scale, part III. *8 years follow-up (in patient number 2 SO was tapered from 6 to 3 gr for the increase in blood pressure). †3 years follow-up; p-α-syn: phosphorylated alpha synuclein. Open in new tab Table 1. RBD patients under treatment with sodium oxybate Sex . Age (yrs) . DD . RAI baseline . RAI Post SO . SO dosage gr . DATScan . p-α-syn at skin biopsy . NPS test . Additional NMS . UPDRS-IIII . M 73 23 0.701 0.497* 9 Negative Positive Normal No 0 M 56 14 0.691 0.504* 3 Negative Positive Normal No 0 M 81 23 0.445 0.402† 5 Negative Positive Normal No 0 M 79 20 0.338 0.332† 6 Negative Negative Normal No 0 M 74 10 0.695 0.513† 9 Negative Positive Normal No 3 Sex . Age (yrs) . DD . RAI baseline . RAI Post SO . SO dosage gr . DATScan . p-α-syn at skin biopsy . NPS test . Additional NMS . UPDRS-IIII . M 73 23 0.701 0.497* 9 Negative Positive Normal No 0 M 56 14 0.691 0.504* 3 Negative Positive Normal No 0 M 81 23 0.445 0.402† 5 Negative Positive Normal No 0 M 79 20 0.338 0.332† 6 Negative Negative Normal No 0 M 74 10 0.695 0.513† 9 Negative Positive Normal No 3 DD, disease duration; gr, gram; yrs, years; NMS; Non-motor symptoms (i.e. constipation, urinary symptoms, cognitive symptoms, autonomic symptoms); NPS, neuropsychological test; RAI, REM sleep atonia index; SO, Sodium Oxybate; UPDRS-III, unified Parkinson’s disease rating scale, part III. *8 years follow-up (in patient number 2 SO was tapered from 6 to 3 gr for the increase in blood pressure). †3 years follow-up; p-α-syn: phosphorylated alpha synuclein. Open in new tab Our observation suggests that: (1) SO seems to be efficacious in iRBD patients, at least in those with violent episodes and rich dream mentation; (2) Future systematic multi-centric cohort studies are needed to investigate whether SO may delay neurodegenerative processes, even in light of new literature on the role of SWS in enhancing the proteins’ clearance, potentially preventing neurodegeneration [7]. For hypertensive cases, low-sodium oxibate, for the lower risk factor for triggering high blood pressure, might be more suitable for future clinical trials in adults with iRBD. Acknowledgments We thank the Italian RBD patients association. Funding No targeted funding reported. Author Contributions E.A.: (1) Research project: Organization and (2) Manuscript: A. Review and Critique. GP: (1) Research project: A. Conception, B. Organization, C. Execution and (2) Manuscript: A. Writing of the first draft, B. Review and Critique. Conflict of interest statement. E.A. reports nothing to disclose. G.P. has served as consultant for UCB, Bioprojet, Jazz, Idorsia. References 1. American Academy of Sleep Medicine . The International Classification of Sleep Disorders: Diagnostic & Coding Manual . 3rd ed. Rochester, MN : American Academy of Sleep Medicine ; 2014 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 2. Postuma RB , et al. Risk and predictors of dementia and parkinsonism in idiopathic REM sleep behaviour disorder: a multicentre study . Brain. 2019 ; 142 ( 3 ): 744 – 759 . Google Scholar Crossref Search ADS PubMed WorldCat 3. Mayer G , et al. ; International Xyrem Study Group . Sodium oxybate treatment in narcolepsy and its effect on muscle tone . Sleep Med. 2017 ; 35 : 1 – 6 . Google Scholar Crossref Search ADS PubMed WorldCat 4. Antelmi E , et al. The spectrum of REM sleep-related episodes in children with type 1 narcolepsy . Brain. 2017 ; 140 ( 6 ): 1669 – 1679 . Google Scholar Crossref Search ADS PubMed WorldCat 5. Shneerson JM . Successful treatment of REM sleep behavior disorder with sodium oxybate . Clin Neuropharmacol. 2009 ; 32 ( 3 ): 158 – 159 . Google Scholar Crossref Search ADS PubMed WorldCat 6. Moghadam KK , et al. Sodium oxybate for idiopathic REM sleep behavior disorder: a report on two patients . Sleep Med. 2017 ; 32 : 16 – 21 . Google Scholar Crossref Search ADS PubMed WorldCat 7. Holth JK , et al. The sleep-wake cycle regulates brain interstitial fluid tau in mice and CSF tau in humans . Science. 2019 ; 363 ( 6429 ): 880 – 884 . Google Scholar Crossref Search ADS PubMed WorldCat © Sleep Research Society 2020. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail [email protected]. 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Kempler,, Liora;Sharpe, Louise, A;Marshall, Nathaniel, S;Bartlett, Delwyn, J

doi: 10.1093/sleep/zsaa101pmid: 32453835

Abstract Study Objectives Poor sleep is commonly problematic during pregnancy and postpartum and is associated with depression. This trial investigated the efficacy of prenatal brief, group sleep psychoeducation in improving postpartum maternal sleep, and depression. Methods A total of 215 healthy expectant first-time mothers were cluster randomized (1:1) to receive either a 2 × 1.5 h psychoeducation intervention and a set of booklets, or a set of booklets only. Participants completed questionnaires during pregnancy (pre-intervention), and 6 weeks and 4 months postpartum. A post hoc subset of questionnaires was collected at 10 months postpartum. The primary hypothesis was the intervention group would have improved postpartum sleep quality, and reduced levels of insomnia symptoms, fatigue, and daytime sleepiness compared to the control group. Secondary outcomes included depression, anxiety, and stress. Results Linear mixed model analyses failed to confirm a group by time interaction on primary or secondary outcomes across all time points. There was no effect of the intervention on outcomes at 6 weeks, or 10 months postpartum. A significant time by group interaction was found at 4 months, favoring the intervention for sleep quality (p = 0.03) and insomnia symptoms (p = 0.03), but not fatigue or daytime sleepiness. Conclusions Prenatal sleep psychoeducation did not produce a sustained effect on maternal sleep throughout the postpartum period. There was little evidence of benefits on depressive symptoms. Clinical Trial Registration ACTRN12611000859987 psychoeducation, sleep, mothers, infants, depression, randomized controlled trial, stress, anxiety Statement of Significance Most research in infant and maternal sleep has focused on treating infant sleep, rather than preventing poor maternal sleep. This brief group psychoeducational intervention was administered during the third trimester as a novel approach to reduce or prevent postpartum sleep problems. The program provided healthy expectant mothers with knowledge about normal sleep changes during pregnancy and throughout the postpartum year. This brief program did not produce sustained improvements in maternal sleep. However, benefits in sleep were observed on sleep quality and insomnia symptoms at 4 months’ postpartum. Our sample of highly educated mothers had low rates of sleep difficulties and antenatal depression and this may have limited the benefits observed. Introduction Pregnancy involves numerous physical and psychological changes [1], such as frequent urination, nausea, fatigue, discomfort, increased fetal movement, and temperature variability, all of which negatively impact sleep [2, 3]. Sleep disturbance is experienced by up to 75% of women by their third trimester [4] and is associated with an increased risk of perinatal depression [5]. Irrespective of prenatal sleep, sleep is necessarily interrupted in the newborn phase when infants have short sleep durations and require feeding and care throughout the night. Approximately 36%–45% of parents report their infant (6–12 months) has “sleep problems” [6], which results in less sleep opportunity for many mothers. Insomnia is also common, with a prevalence of up to 40% in the first 2 years postpartum [7]. Insomnia is a risk factor for postpartum depression (PPD) [8, 9], which affects 22% of women in Australia [10]. In addition to the negative impact on the mother, PPD is associated with behavioral problems and cognitive deficits in the children of mothers with depression. Hence, sleep disturbance is a potentially modifiable risk factor for PPD, and there is evidence that improving sleep can improve symptoms of depression [11]. However, there are few large randomized controlled trials (RCT) testing a brief psychosocial intervention in a group context that target maternal sleep in healthy samples and include a measure of depression. Of those that exist, many provide the program after birth rather than during pregnancy, and do not include information about maternal insomnia and sleep architecture with video references [11–13]. A systematic review of non-pharmacological interventions delivered during pregnancy to improve sleep was conducted in 2013 [14]. Only seven papers were identified, and only three were tested in RCTs. Acupuncture, massage, relaxation, aerobic exercise, and mindfulness were included in these interventions and most studies did not determine whether the benefits continued following childbirth [14]. One large RCT delivered in the immediate postpartum period observed that a psychoeducation intervention was ineffective in improving maternal and infant sleep or other health outcomes in the first postpartum months [15]. More recently, Taylor et al. [16] assessed a sleep education program delivered during pregnancy and in the early postpartum. Results indicated that participants who received the sleep program had more consistent bedtime strategies, which was associated with fewer self-control problems at 3.5 years and longer sleep duration from 1 to 5 years [17]. Two other studies found benefits of a cognitive behavioral sleep program for women with insomnia during pregnancy [18, 19]. There is a clear need for evidence-based approaches to help manage sleep disturbance in the perinatal period for expectant mothers. This study intended to bridge that gap. Aim and hypothesis The aim of this study was to design and test a novel group sleep psychoeducational intervention (2 × 1.5 h workshops) delivered during the third trimester of a woman’s first pregnancy. The intervention targeted healthy nondepressed expectant mothers and provided information about the changes to sleep that occur during pregnancy and the sleep architecture of infants throughout their first year postpartum. Despite the natural deterioration in sleep after childbirth, we hypothesized that the intervention would improve sleep outcomes compared to the control group, throughout the postpartum period. As there is currently no independent measure assessing all aspects of sleep, the following four measures were nominated as co-primary outcomes: sleep quality, insomnia symptoms, daytime sleepiness, and fatigue. We assessed outcomes at three key time points, namely 6 weeks, 4 months, and 10 months postpartum. If the predicted benefits in sleep were observed, we hypothesized that there would be a corresponding reduction in depressive symptoms in the intervention group compared to the control group. Method Design The study involved a cluster RCT design with a 1:1 allocation ratio and included 215 healthy women expecting their first baby. Eligible women completed their first set of questionnaires during their third trimester of pregnancy. They were then randomized in clusters of 4–8 to either the intervention or control group. The intervention group received a 2 × 1.5 h group program about maternal and infant sleep in pregnancy and the first year postpartum and a set of booklets; whereas the control group received the booklets but no psychoeducation. All participants were followed up over the phone at 3 and 6 weeks postpartum. The full set of questionnaires, assessing sleep, depression, anxiety, stress, self-efficacy, coping, bonding, and infant temperament were administered at 6 weeks and 4 months postpartum. At 10 months postpartum, a subset of questionnaires were readministered, assessing sleep quality, insomnia symptoms, depression, anxiety, and stress only. This time point was added into the study after recruitment commenced in order to examine whether long-term outcomes were influenced by the intervention. The data collection time points were selected, as they are key change times in sleep architecture and development of the infant [20]. At 6 weeks postpartum, mothers are still partially sleep deprived and feeding frequently, but are also more likely to respond to questionnaires than in the initial few weeks. At 4 months postpartum, the acute sleep deprivation associated with the newborn period starts to abate and infant sleep consolidates with at least some circadian realignment [21]. The final follow up of 10 months postpartum, was selected as nighttime feeds in full-term infants can be safely phased out if the mothers choose; however, it also captures within 12 months when Australian parental leave usually ends. A subset of participants (n = 76) wore actiwatches for 1 week during the third trimester of pregnancy and at 4 months postpartum. Intervention description The program involved a total of 3 h of group psychoeducation delivered in 2 × 1.5 h face-to-face sessions 2 weeks apart, and provided information about the science behind sleep, normal sleep changes during pregnancy and postpartum, the association between sleep and perinatal depression, and strategies on how to manage these changes. The information offered to each group during the presentations and in the booklets is outlined in Figure 1. Delivery included a presentation with videos of mothers sharing their personal experiences with their infants of various ages, footage of infants cycling between quiet and active sleep, and real mothers using settling routines. An underlying premise was to highlight individual experience and enable mothers to make informed decisions about how to manage their sleep and their infant’s sleep during this unpredictable time. Fathers and non-pregnant partners were encouraged to attend, however, only 53/104 attended one or both sessions and we did not consider this further in the analyses. Figure 1. Open in new tabDownload slide Program and booklet content. Content provided in the presentations to the intervention group (in 2 × 1.5 hour workshops) and the booklets given to all participants. Figure 1. Open in new tabDownload slide Program and booklet content. Content provided in the presentations to the intervention group (in 2 × 1.5 hour workshops) and the booklets given to all participants. Participants Participants were recruited from prenatal classes at the Royal Prince Alfred Hospital, Sydney Australia, the University of Sydney, the study Facebook page and via word of mouth. All participants provided signed informed consent. Eligibility criteria were women over 18 years of age and in their third trimester of pregnancy with their first baby. Participants were excluded if they had a history of major depression. Measures Participants received an email with a link to their questionnaire. Primary outcomes There were four co-primary outcomes: sleep quality measured by the Pittsburgh Sleep Quality Index (PSQI) [22], insomnia indicated by the Insomnia Severity Index (ISI) [23], fatigue using the Multidimensional Assessment of Fatigue (MAF) [24], and daytime sleepiness measured by the Epworth Sleepiness Scale (ESS) [25]. Secondary outcomes Secondary outcomes were depressive symptoms measured by the Edinburgh Postnatal Depression Scale (EPDS) [26] and anxiety and stress measured by the relevant scales of the Depression, Anxiety, and Stress Scales (DASS) [27]. Other outcomes that were investigated as possible mechanisms or processes by which the intervention affected the primary and secondary outcomes included self-efficacy using the generalized Self-Efficacy Scale (SES) [28], bonding using the Maternal-to-Infant Bonding Scale (MIBS) [29], coping skills using the Brief Coping Scale (COPE) [30] which has 14 subscales with 2 items in each subscale and infant temperament using the Infant Characteristics Questionnaire (ICQ) [31]. A Generalized Feeding Questionnaire (GFQ) and a Generalized Sleep Questionnaire (GSQ) were designed by the researchers to obtain information about type of feeding methods and daily sleep habits including napping. These measures were included at 6 weeks and 4 months’ postpartum to explore other mechanisms of the intervention and potential effects. The questionnaires used in this study are listed in Supplementary Table 1, with an “X” marking the time point that each questionnaire was completed and are described in detail in Supplementary Table 2. All questionnaires that were chosen had been validated and found to be reliable in community samples, but only the MAF had been validated to measure sleep-related outcomes in an obstetric sample [24]. More recently, publications have validated the PSQI, the ESS, and the DASS during pregnancy [32–34]. Nevertheless, the EPDS [35] is still the most widely used and well-validated measure of depression in obstetric samples. Implementation and blinding The random allocation sequence was generated by an independent researcher (N.S.M.) who played no role in patient enrollment. Participants were randomized by clusters once we had a sufficient number of women to form a group (between 4 and 8 women). The group allocation was concealed in opaque securely sealed individual envelopes and opened by the researcher (L.K.) and all women in that cluster were randomized together. Hence, all women from a single parenting class were assigned to the same cluster, but that cluster also included other women who were recruited from other sources. It was not possible to blind the participants or for L.K. to be blinded as she was delivering the program and hence meeting participants. Statistical analysis When the study was originally designed, there was no 10-month follow-up included. Therefore, all of the statistical analyses were based on 4 months postpartum as the primary endpoint. The 10-month follow-up was added into the study early into the recruitment period, to review longer-term effects. Past sleep programs using the sleep questionnaires of the current study (the PSQI and ISI) ranged from Cohen’s d = 0.35–0.88 [36, 37]. On the basis of these data, we estimated the most conservative effect size we would need was only 41 participants per group in order to achieve 80% power at the 0.05 significance level. Using a cluster design, the sample size requirement increased according to the following formula: 1 + (m − 1) × ICC, where ICC is the intra-cluster correlation and m is the average number of patients per group [38]. We aimed for an average cluster group size of 6, and conservatively took the middle of the range for the ICC of 0.42, requiring 2.6 times the number of participants. To ensure sufficient power to find differences in maternal sleep quality at an effect size of at least 0.4, we needed 107 participants per group (n = 214). A linear mixed model analysis was used to test whether the treatment changed each outcome measure across all post intervention time points. In addition, we analyzed outcomes at each time point. This type of analysis provides information about a general effect over multiple time points and typically, this is the main interest of a study when a linear effect over time is expected, such as general improvement. However, in the circumstance of the peripartum period, the different experiences that arise with changes in infant sleep made each individual time point of interest, particularly given that the 10-month follow-up was introduced into the study following initial design. Fixed effects were time (6 week, 4 months, and 10 months), group (intervention and control), and their interaction. We used the least square means (LSM) option in the mixed model to quantify the differences between each treatment at each specific time point. Participants and their assigned clusters were classed as having random intercepts. Uncorrected pairwise comparisons were used based on estimated marginal means. We also calculated effect sizes using Cohen’s d, using the mean difference divided by the pooled standard deviations of assessment during pregnancy. To assess clinical significance, we used dichotomous clinical cutoffs appropriate for each questionnaire. Given the high proportion of women that met criteria for poor sleep quality (PSQI > 5) in this cohort (71.1%), the upper quartile of 10 was used to determine a clinically significant level of poor sleep quality. Pearson’s chi squared was used to assess differences in proportions of clinical cases in each group. A mediation analyses was conducted using regression analyses and Sobel Tests were conducted to investigate whether the superior sleep outcomes in the intervention group were mediated by changes in self-efficacy. Given the number of comparisons for changes in the COPE questionnaire due to its structure, including 14 subscales, we used the Bonferroni adjustment for the number of comparisons and only considered those analyses with a p < 0.003 to be significant. Results Participant characteristics Participants were recruited from September 2012 to June 2014 and followed up until July 2015. The study was discussed with 251 women, of whom 239 were recruited. A total of 119 participants were recruited from prenatal classes at the Royal Prince Alfred Hospital in New South Wales, Australia and 120 made contact through print and social media advertising. A total of 215 (86% recruitment rate from those expressing interest) completed assessment and were randomized. Eight withdrew from the study due to poor infant health (n = 3) and insufficient time to complete the study questionnaires (n = 5). A flowchart showing participant data collection can be found in Figure 2: Participant flowchart. This sample was highly educated (Table 1) and the average age of participants at the time of the babies’ birth was 33.3 years. As seen in Table 1, questionnaires during pregnancy indicated that sleep and depression levels were equivalent between groups and within the normal range for pregnant women [39], prior to the intervention. There was one twin birth in the intervention group but participant characteristics were not different between the intervention and control groups during pregnancy. Table 1. Demographics: percentages, least square means, and standard errors on important outcomes at pregnancy pre-intervention for mothers and 6 weeks postpartum for infants Mothers’ characteristics . Control n = 107 . Intervention n = 108 . Between group p-value .  Age 33.3 (4.0) 33.3 (4.0) Nationality 0.49  Australia/New Zealand 65.4% 69.4%  Other 34.6% 30.6% Education 0.31  High school or Diploma 9.3% 8.3%  Tertiary 90.7% 91.7% Sleep during pregnancy  PSQI 7.9 (0.4) 7.5 (0.4) 0.4  ISI 8.0 (0.5) 7.3 (0.5) 0.29  ESS 6.5 (0.4) 7.1 (0.4) 0.32  MAF 22.7 (0.9) 23.2 (0.9) 0.7 Depression during pregnancy  EPDS 5.6 (0.4) 5.0 (0.4) 0.31 Infant characteristics n = 107 n = 104 Gender 0.29  Female 52.3% 47.6%  Male 47.7% 52.4% Feeding method 0.14  Breastfeeding 83.5% 80.4%  Formula feeding 4.9% 3.9%  Mixed 7.8% 13.7% Temperament 89.3 (1.76) 84.9 (1.76) 0.92 Mothers’ characteristics . Control n = 107 . Intervention n = 108 . Between group p-value .  Age 33.3 (4.0) 33.3 (4.0) Nationality 0.49  Australia/New Zealand 65.4% 69.4%  Other 34.6% 30.6% Education 0.31  High school or Diploma 9.3% 8.3%  Tertiary 90.7% 91.7% Sleep during pregnancy  PSQI 7.9 (0.4) 7.5 (0.4) 0.4  ISI 8.0 (0.5) 7.3 (0.5) 0.29  ESS 6.5 (0.4) 7.1 (0.4) 0.32  MAF 22.7 (0.9) 23.2 (0.9) 0.7 Depression during pregnancy  EPDS 5.6 (0.4) 5.0 (0.4) 0.31 Infant characteristics n = 107 n = 104 Gender 0.29  Female 52.3% 47.6%  Male 47.7% 52.4% Feeding method 0.14  Breastfeeding 83.5% 80.4%  Formula feeding 4.9% 3.9%  Mixed 7.8% 13.7% Temperament 89.3 (1.76) 84.9 (1.76) 0.92 PSQI, Pittsburgh Sleep Quality Index; ISI, Insomnia Severity Index; MAF, multidimensional assessment of fatigue; ESS, Epworth Sleepiness Scale; EPDS, Edinburgh Postnatal Depression Scale. Open in new tab Table 1. Demographics: percentages, least square means, and standard errors on important outcomes at pregnancy pre-intervention for mothers and 6 weeks postpartum for infants Mothers’ characteristics . Control n = 107 . Intervention n = 108 . Between group p-value .  Age 33.3 (4.0) 33.3 (4.0) Nationality 0.49  Australia/New Zealand 65.4% 69.4%  Other 34.6% 30.6% Education 0.31  High school or Diploma 9.3% 8.3%  Tertiary 90.7% 91.7% Sleep during pregnancy  PSQI 7.9 (0.4) 7.5 (0.4) 0.4  ISI 8.0 (0.5) 7.3 (0.5) 0.29  ESS 6.5 (0.4) 7.1 (0.4) 0.32  MAF 22.7 (0.9) 23.2 (0.9) 0.7 Depression during pregnancy  EPDS 5.6 (0.4) 5.0 (0.4) 0.31 Infant characteristics n = 107 n = 104 Gender 0.29  Female 52.3% 47.6%  Male 47.7% 52.4% Feeding method 0.14  Breastfeeding 83.5% 80.4%  Formula feeding 4.9% 3.9%  Mixed 7.8% 13.7% Temperament 89.3 (1.76) 84.9 (1.76) 0.92 Mothers’ characteristics . Control n = 107 . Intervention n = 108 . Between group p-value .  Age 33.3 (4.0) 33.3 (4.0) Nationality 0.49  Australia/New Zealand 65.4% 69.4%  Other 34.6% 30.6% Education 0.31  High school or Diploma 9.3% 8.3%  Tertiary 90.7% 91.7% Sleep during pregnancy  PSQI 7.9 (0.4) 7.5 (0.4) 0.4  ISI 8.0 (0.5) 7.3 (0.5) 0.29  ESS 6.5 (0.4) 7.1 (0.4) 0.32  MAF 22.7 (0.9) 23.2 (0.9) 0.7 Depression during pregnancy  EPDS 5.6 (0.4) 5.0 (0.4) 0.31 Infant characteristics n = 107 n = 104 Gender 0.29  Female 52.3% 47.6%  Male 47.7% 52.4% Feeding method 0.14  Breastfeeding 83.5% 80.4%  Formula feeding 4.9% 3.9%  Mixed 7.8% 13.7% Temperament 89.3 (1.76) 84.9 (1.76) 0.92 PSQI, Pittsburgh Sleep Quality Index; ISI, Insomnia Severity Index; MAF, multidimensional assessment of fatigue; ESS, Epworth Sleepiness Scale; EPDS, Edinburgh Postnatal Depression Scale. Open in new tab Figure 2. Open in new tabDownload slide Participant flowchart. Ctrl, control; Interv, Intervention; RPA, Royal Prince Alfred Hospital; SM, social media; Uni, University; NB: intervention was delivered in 2 × 1.5 h workshops. Figure 2. Open in new tabDownload slide Participant flowchart. Ctrl, control; Interv, Intervention; RPA, Royal Prince Alfred Hospital; SM, social media; Uni, University; NB: intervention was delivered in 2 × 1.5 h workshops. Post-intervention results Table 2 presents descriptive values including LSM, standard errors and confidence intervals on all outcomes at all time points. The actigraphy data failed to show any effect of the intervention on objective sleep parameters (all ps > 0.05). Actigraphy methods and results are available in the Supplementary File 1. Table 2. Results: least square means, standard errors, and confidence intervals in each group on each outcome at each time point . Pregnancy . 6 weeks postpartum . 4 months postpartum . 10 months postpartum . . LSM Score ± SEM . LSM Score ± SEM . LSM Score ± SEM . LSM Score ± SEM . Time . (95% CI) . (95% CI) . (95% CI) . (95% CI) . Treatment Control Intervention Control Intervention Control Intervention Control Intervention PSQI 7.9 ± 0.4 7.5 ± 0.4 9.7 ± 0.4 9.3 ± 0.4 8.9 ± 0.4 7.7 ± 0.4 7.3 ± 0.4 6.4 ± 0.4 (7.2–8.7) (6.7–8.2) (9.0–10.5) (8.5–10.0) (8.1–9.7) (6.8–9.5) (6.4–8.1) (5.6–7.3) ISI 8.0 ± 0.5 7.3 ± 0.5 7.4 ± 0.5 7.5 ± 0.5 8.2 ± 0.5 6.6 ± 0.5 7.4 ± 0.5 6.4 ± 0.5 (7.1–9.0) (6.4–8.3) (6.5–8.4) (6.5–8.4) (7.2–9.2) (5.7–7.6) (6.4–8.4) (5.4–7.4) MAF 22.7 ± 0.9 23.2 ± 0.9 25.8 ± 0.9 26.3 ± 0.9 25.0 ± 0.9 22.6 ± 0.9 (21.0–24.5) (21.4–25.0) (24.0–27.6) (24.5–28.1) (23.1–26.90) (20.8–24.5) ESS 6.5 ± 0.4 7.1 ± 0.4 8.0 ± 0.4 9.0 ± 0.4 6.2 ± 0.4 6.7 ± 0.4 (5.7–7.4) (6.3–7.9) (7.2–8.9) (8.2–9.8) (5.3–7.0) (5.9–7.6) EPDS 5.6 ± 0.4 5.0 ± 0.4 6.0 ± 0.4 5.9 ± 0.4 6.2 ± 0.4 5.1 ± 0.4 5.6 ± 0.4 5.0 ± 0.4 (4.8–6.4) (4.2–5.8) (5.2–6.8) (5.1–6.7) (5.4–7.0) (4.3–5.9) (4.7–6.4) (4.2–5.9) DASS 2.7 ± 0.4 2.8 ± 0.5 3.2 ± 0.5 2.7 ± 0.5 4.2 ± 0.5 3.4 ± 0.5 3.5 ± 0.5 3.5 ± 0.5 Depression (1.9–3.6) (1.9–3.7) (2.4–4.1) (1.8–3.6) (3.3–5.2) (2.5–4.3) (2.5–4.4) (2.6–4.5) DASS 3.3 ± 0.3 3.5 ± 0.4 2.3 ± 0.4 2.3 ± 0.4 2.6 ± 0.4 2.1 ± 0.4 1.7 ± 0.4 1.7 ± 0.4 Anxiety (2.6–4.0) (2.9–4.2) (1.6–3.0) (1.6–3.0) (1.9–3.3) (1.4–2.8) (1.0–2.5) (1.0–2.4) DASS 10.2 ± 0.69 8.8 ± 0.69 10.3 ± 0.71 10.2 ± 0.71 11.0 ± 0.73 10.7 ± 0.72 9.1 ± 0.73 9.9 ± 0.74 Stress (8.8–11.5) (7.5–10.2) (9.0–11.7) (8.8–11.6) (9.5–12.4) (9.3–12.1) (7.6–10.5) (8.4–11.4) SES 32.3 ± 0.7 32.3 ± 0.7 30.8 ± 0.7 31.8 ± 0.7 29.1 ± 0.7 31.7 ± 0.7 (30.9–33.6) (30.9–33.6) (29.4–32.1) (30.4–33.2) (27.7–30.5) (30.3–33.1) ICQ 89.3 ± 1.8 84.9 ± 1.8 83.1 ± 1.8 79.1 ± 1.8 (85.8–92.7) (81.5–88.4) (79.5–86.7) (75.6–82.6) MIBS 2.0 ± 0.2 2.2 ± 0.2 1.9 ± 0.2 1.8 ± 0.2 (1.6–2.4) (1.7–2.6) (1.5–2.3) (1.4–2.3) COPE 1 3.7 ± 0.1 3.6 ± 0.1 3.4 ± 0.1 3.2 ± 0.1 3.3 ± 0.1 3.5 ± 0.1 Self-distraction (3.4–4.0) (3.4–3.9) (3.2–3.7) (3.0–3.5) (3.1–3.6) (3.2–3.8) COPE 2 5.2 ± 0.2 5.1 ± 0.2 5.6 ± 0.2 6.1 ± 0.2 5.2 ± 0.2 5.6 ± 0.2 Active coping (4.8–6.0) (4.7–5.8) (5.2–6.0) (5.7–6.5) (4.8–5.6) (5.2–6.0) COPE 3 2.3 ± 0.1 2.2 ± 0.1 2.2 ± 0.1 2.1 ± 0.1 2.1 ± 0.1 2.2 ± 0.1 Denial (2.2–2.5) (2.1–2.4) (2.1–2.4) (2.0–2.3) (2.0–2.2) (2.0–2.3) COPE 4 2.0 ± 0.04 2.1 ± 0.04 2.1 ± 0.05 2.2 ± 0.04 2.2 ± 0.05 2.2 ± 0.05 Substance use (1.9–2.1) (2.0–2.1) (2.0–2.2) (2.0–2.1) (2.1–2.3) (2.1–2.3) COPE 5 5.3 ± 0.2 5.5 ± 0.2 6.1 ± 0.2 6.3 ± 0.2 5.5 ± 0.2 5.7 ± 0.2 Emotional support (5.0–5.6) (5.2–5.8) (5.7–6.4) (5.9–6.6) (5.2–5.9) (5.4–6.0) COPE 6 5.2 ± 0.2 5.0 ± 0.2 6.0 ± 0.2 6.2 ± 0.2 5.5 ± 0.2 5.6 ± 0.2 Instrumental support (4.9–5.6) (4.7–5.4) (5.6–6.3) (5.7–6.4) (5.1–5.8) (5.2–5.9) COPE 7 2.4 ± 0.1 2.3 ± 0.1 2.1 ± 0.1 2.3 ± 0.1 2.2 ± 0.1 2.3 ± 0.1 Behavioural disengagement (2.2–2.5) (2.1–2.4) (2.0–2.3) (2.2–2.4) (2.1–2.4) (2.2–2.4) COPE 8 3.7 ± 0.1 4.0 ± 0.2 3.9 ± 0.2 4.1 ± 0.2 3.9 ± 0.2 4.2 ± 0.2 Venting (3.4–4.0) (3.7–4.3) (3.6–4.2) (3.84–4.4) (3.6–4.2) (3.9–4.5) COPE 9 5.0 ± 0.2 5.0 ± 0.2 5.1 ± 0.2 5.4 ± 0.2 5.0 ± 0.2 5.1 ± 0.2 Positive reframing (4.6–5.3) (4.6–5.4) (4.7–5.5) (5.0–5.8) (4.7–5.4) (4.7–5.5) COPE 10 5.0 ± 0.2 5.1 ± 0.2 5.2 ± 0.2 5.6 ± 0.2 5.2 ± 0.2 5.3 ± 0.2 Planning (4.6–5.4) (4.7–5.5) (4.8–5.6) (5.2–6.0) (4.8–5.6) (4.9–5.7) COPE 11 4.3 ± 0.2 4.3 ± 0.2 4.3 ± 0.2 4.4 ± 0.2 4.1 ± 0.2 4.7 ± 0.2 Humor (3.9–4.6) (4.0–4.7) (4.0–4.7) (4.1–4.8) (3.7–4.5) (4.3–5.0) COPE 12 5.5 ± 0.2 5.7 ± 0.2 6.0 ± 0.2 6.2 ± 0.2 5.7 ± 0.2 5.9 ± 0.2 Acceptance (5.1–5.9) (5.3–6.1) (5.6–6.3) (5.8–6.6) (5.3–6.1) (5.5–6.3) COPE 13 3.0 ± 0.1 3.1 ± 0.1 2.6 ± 0.1 2.7 ± 0.1 2.7 ± 0.1 2.6 ± 0.1 Religion (2.8–3.3) (2.9–3.4) (2.4–2.9) (2.4–2.9) (2.4–3.0) (2.3–2.9) COPE 14 2.9 ± 0.1 2.8 ± 0.1 3.1 ± 0.1 3.3 ± 0.1 3.3 ± 0.1 3.4 ± 0.1 Self-blame (2.6–3.1) (2.6–3.1) (2.8–3.4) (3.1–3.6) (3.1–3.6) (3.1–3.6) . Pregnancy . 6 weeks postpartum . 4 months postpartum . 10 months postpartum . . LSM Score ± SEM . LSM Score ± SEM . LSM Score ± SEM . LSM Score ± SEM . Time . (95% CI) . (95% CI) . (95% CI) . (95% CI) . Treatment Control Intervention Control Intervention Control Intervention Control Intervention PSQI 7.9 ± 0.4 7.5 ± 0.4 9.7 ± 0.4 9.3 ± 0.4 8.9 ± 0.4 7.7 ± 0.4 7.3 ± 0.4 6.4 ± 0.4 (7.2–8.7) (6.7–8.2) (9.0–10.5) (8.5–10.0) (8.1–9.7) (6.8–9.5) (6.4–8.1) (5.6–7.3) ISI 8.0 ± 0.5 7.3 ± 0.5 7.4 ± 0.5 7.5 ± 0.5 8.2 ± 0.5 6.6 ± 0.5 7.4 ± 0.5 6.4 ± 0.5 (7.1–9.0) (6.4–8.3) (6.5–8.4) (6.5–8.4) (7.2–9.2) (5.7–7.6) (6.4–8.4) (5.4–7.4) MAF 22.7 ± 0.9 23.2 ± 0.9 25.8 ± 0.9 26.3 ± 0.9 25.0 ± 0.9 22.6 ± 0.9 (21.0–24.5) (21.4–25.0) (24.0–27.6) (24.5–28.1) (23.1–26.90) (20.8–24.5) ESS 6.5 ± 0.4 7.1 ± 0.4 8.0 ± 0.4 9.0 ± 0.4 6.2 ± 0.4 6.7 ± 0.4 (5.7–7.4) (6.3–7.9) (7.2–8.9) (8.2–9.8) (5.3–7.0) (5.9–7.6) EPDS 5.6 ± 0.4 5.0 ± 0.4 6.0 ± 0.4 5.9 ± 0.4 6.2 ± 0.4 5.1 ± 0.4 5.6 ± 0.4 5.0 ± 0.4 (4.8–6.4) (4.2–5.8) (5.2–6.8) (5.1–6.7) (5.4–7.0) (4.3–5.9) (4.7–6.4) (4.2–5.9) DASS 2.7 ± 0.4 2.8 ± 0.5 3.2 ± 0.5 2.7 ± 0.5 4.2 ± 0.5 3.4 ± 0.5 3.5 ± 0.5 3.5 ± 0.5 Depression (1.9–3.6) (1.9–3.7) (2.4–4.1) (1.8–3.6) (3.3–5.2) (2.5–4.3) (2.5–4.4) (2.6–4.5) DASS 3.3 ± 0.3 3.5 ± 0.4 2.3 ± 0.4 2.3 ± 0.4 2.6 ± 0.4 2.1 ± 0.4 1.7 ± 0.4 1.7 ± 0.4 Anxiety (2.6–4.0) (2.9–4.2) (1.6–3.0) (1.6–3.0) (1.9–3.3) (1.4–2.8) (1.0–2.5) (1.0–2.4) DASS 10.2 ± 0.69 8.8 ± 0.69 10.3 ± 0.71 10.2 ± 0.71 11.0 ± 0.73 10.7 ± 0.72 9.1 ± 0.73 9.9 ± 0.74 Stress (8.8–11.5) (7.5–10.2) (9.0–11.7) (8.8–11.6) (9.5–12.4) (9.3–12.1) (7.6–10.5) (8.4–11.4) SES 32.3 ± 0.7 32.3 ± 0.7 30.8 ± 0.7 31.8 ± 0.7 29.1 ± 0.7 31.7 ± 0.7 (30.9–33.6) (30.9–33.6) (29.4–32.1) (30.4–33.2) (27.7–30.5) (30.3–33.1) ICQ 89.3 ± 1.8 84.9 ± 1.8 83.1 ± 1.8 79.1 ± 1.8 (85.8–92.7) (81.5–88.4) (79.5–86.7) (75.6–82.6) MIBS 2.0 ± 0.2 2.2 ± 0.2 1.9 ± 0.2 1.8 ± 0.2 (1.6–2.4) (1.7–2.6) (1.5–2.3) (1.4–2.3) COPE 1 3.7 ± 0.1 3.6 ± 0.1 3.4 ± 0.1 3.2 ± 0.1 3.3 ± 0.1 3.5 ± 0.1 Self-distraction (3.4–4.0) (3.4–3.9) (3.2–3.7) (3.0–3.5) (3.1–3.6) (3.2–3.8) COPE 2 5.2 ± 0.2 5.1 ± 0.2 5.6 ± 0.2 6.1 ± 0.2 5.2 ± 0.2 5.6 ± 0.2 Active coping (4.8–6.0) (4.7–5.8) (5.2–6.0) (5.7–6.5) (4.8–5.6) (5.2–6.0) COPE 3 2.3 ± 0.1 2.2 ± 0.1 2.2 ± 0.1 2.1 ± 0.1 2.1 ± 0.1 2.2 ± 0.1 Denial (2.2–2.5) (2.1–2.4) (2.1–2.4) (2.0–2.3) (2.0–2.2) (2.0–2.3) COPE 4 2.0 ± 0.04 2.1 ± 0.04 2.1 ± 0.05 2.2 ± 0.04 2.2 ± 0.05 2.2 ± 0.05 Substance use (1.9–2.1) (2.0–2.1) (2.0–2.2) (2.0–2.1) (2.1–2.3) (2.1–2.3) COPE 5 5.3 ± 0.2 5.5 ± 0.2 6.1 ± 0.2 6.3 ± 0.2 5.5 ± 0.2 5.7 ± 0.2 Emotional support (5.0–5.6) (5.2–5.8) (5.7–6.4) (5.9–6.6) (5.2–5.9) (5.4–6.0) COPE 6 5.2 ± 0.2 5.0 ± 0.2 6.0 ± 0.2 6.2 ± 0.2 5.5 ± 0.2 5.6 ± 0.2 Instrumental support (4.9–5.6) (4.7–5.4) (5.6–6.3) (5.7–6.4) (5.1–5.8) (5.2–5.9) COPE 7 2.4 ± 0.1 2.3 ± 0.1 2.1 ± 0.1 2.3 ± 0.1 2.2 ± 0.1 2.3 ± 0.1 Behavioural disengagement (2.2–2.5) (2.1–2.4) (2.0–2.3) (2.2–2.4) (2.1–2.4) (2.2–2.4) COPE 8 3.7 ± 0.1 4.0 ± 0.2 3.9 ± 0.2 4.1 ± 0.2 3.9 ± 0.2 4.2 ± 0.2 Venting (3.4–4.0) (3.7–4.3) (3.6–4.2) (3.84–4.4) (3.6–4.2) (3.9–4.5) COPE 9 5.0 ± 0.2 5.0 ± 0.2 5.1 ± 0.2 5.4 ± 0.2 5.0 ± 0.2 5.1 ± 0.2 Positive reframing (4.6–5.3) (4.6–5.4) (4.7–5.5) (5.0–5.8) (4.7–5.4) (4.7–5.5) COPE 10 5.0 ± 0.2 5.1 ± 0.2 5.2 ± 0.2 5.6 ± 0.2 5.2 ± 0.2 5.3 ± 0.2 Planning (4.6–5.4) (4.7–5.5) (4.8–5.6) (5.2–6.0) (4.8–5.6) (4.9–5.7) COPE 11 4.3 ± 0.2 4.3 ± 0.2 4.3 ± 0.2 4.4 ± 0.2 4.1 ± 0.2 4.7 ± 0.2 Humor (3.9–4.6) (4.0–4.7) (4.0–4.7) (4.1–4.8) (3.7–4.5) (4.3–5.0) COPE 12 5.5 ± 0.2 5.7 ± 0.2 6.0 ± 0.2 6.2 ± 0.2 5.7 ± 0.2 5.9 ± 0.2 Acceptance (5.1–5.9) (5.3–6.1) (5.6–6.3) (5.8–6.6) (5.3–6.1) (5.5–6.3) COPE 13 3.0 ± 0.1 3.1 ± 0.1 2.6 ± 0.1 2.7 ± 0.1 2.7 ± 0.1 2.6 ± 0.1 Religion (2.8–3.3) (2.9–3.4) (2.4–2.9) (2.4–2.9) (2.4–3.0) (2.3–2.9) COPE 14 2.9 ± 0.1 2.8 ± 0.1 3.1 ± 0.1 3.3 ± 0.1 3.3 ± 0.1 3.4 ± 0.1 Self-blame (2.6–3.1) (2.6–3.1) (2.8–3.4) (3.1–3.6) (3.1–3.6) (3.1–3.6) Significant differences between groups are marked with bolded text. LSM, Least square mean; SEM, Standard error of the mean; PSQI, Pittsburgh Sleep Quality Index; ISI, Insomnia Severity Index; MAF, multidimensional assessment of fatigue; ESS, Epworth Sleepiness Scale; EPDS, Edinburgh Postnatal Depression Scale; DASS, Depression, Anxiety, and Stress Scale; SES, Self-Efficacy Scale; ICQ, Infant Characteristics Questionnaire; MIBS, Maternal-to-Infant Bonding Scale. Open in new tab Table 2. Results: least square means, standard errors, and confidence intervals in each group on each outcome at each time point . Pregnancy . 6 weeks postpartum . 4 months postpartum . 10 months postpartum . . LSM Score ± SEM . LSM Score ± SEM . LSM Score ± SEM . LSM Score ± SEM . Time . (95% CI) . (95% CI) . (95% CI) . (95% CI) . Treatment Control Intervention Control Intervention Control Intervention Control Intervention PSQI 7.9 ± 0.4 7.5 ± 0.4 9.7 ± 0.4 9.3 ± 0.4 8.9 ± 0.4 7.7 ± 0.4 7.3 ± 0.4 6.4 ± 0.4 (7.2–8.7) (6.7–8.2) (9.0–10.5) (8.5–10.0) (8.1–9.7) (6.8–9.5) (6.4–8.1) (5.6–7.3) ISI 8.0 ± 0.5 7.3 ± 0.5 7.4 ± 0.5 7.5 ± 0.5 8.2 ± 0.5 6.6 ± 0.5 7.4 ± 0.5 6.4 ± 0.5 (7.1–9.0) (6.4–8.3) (6.5–8.4) (6.5–8.4) (7.2–9.2) (5.7–7.6) (6.4–8.4) (5.4–7.4) MAF 22.7 ± 0.9 23.2 ± 0.9 25.8 ± 0.9 26.3 ± 0.9 25.0 ± 0.9 22.6 ± 0.9 (21.0–24.5) (21.4–25.0) (24.0–27.6) (24.5–28.1) (23.1–26.90) (20.8–24.5) ESS 6.5 ± 0.4 7.1 ± 0.4 8.0 ± 0.4 9.0 ± 0.4 6.2 ± 0.4 6.7 ± 0.4 (5.7–7.4) (6.3–7.9) (7.2–8.9) (8.2–9.8) (5.3–7.0) (5.9–7.6) EPDS 5.6 ± 0.4 5.0 ± 0.4 6.0 ± 0.4 5.9 ± 0.4 6.2 ± 0.4 5.1 ± 0.4 5.6 ± 0.4 5.0 ± 0.4 (4.8–6.4) (4.2–5.8) (5.2–6.8) (5.1–6.7) (5.4–7.0) (4.3–5.9) (4.7–6.4) (4.2–5.9) DASS 2.7 ± 0.4 2.8 ± 0.5 3.2 ± 0.5 2.7 ± 0.5 4.2 ± 0.5 3.4 ± 0.5 3.5 ± 0.5 3.5 ± 0.5 Depression (1.9–3.6) (1.9–3.7) (2.4–4.1) (1.8–3.6) (3.3–5.2) (2.5–4.3) (2.5–4.4) (2.6–4.5) DASS 3.3 ± 0.3 3.5 ± 0.4 2.3 ± 0.4 2.3 ± 0.4 2.6 ± 0.4 2.1 ± 0.4 1.7 ± 0.4 1.7 ± 0.4 Anxiety (2.6–4.0) (2.9–4.2) (1.6–3.0) (1.6–3.0) (1.9–3.3) (1.4–2.8) (1.0–2.5) (1.0–2.4) DASS 10.2 ± 0.69 8.8 ± 0.69 10.3 ± 0.71 10.2 ± 0.71 11.0 ± 0.73 10.7 ± 0.72 9.1 ± 0.73 9.9 ± 0.74 Stress (8.8–11.5) (7.5–10.2) (9.0–11.7) (8.8–11.6) (9.5–12.4) (9.3–12.1) (7.6–10.5) (8.4–11.4) SES 32.3 ± 0.7 32.3 ± 0.7 30.8 ± 0.7 31.8 ± 0.7 29.1 ± 0.7 31.7 ± 0.7 (30.9–33.6) (30.9–33.6) (29.4–32.1) (30.4–33.2) (27.7–30.5) (30.3–33.1) ICQ 89.3 ± 1.8 84.9 ± 1.8 83.1 ± 1.8 79.1 ± 1.8 (85.8–92.7) (81.5–88.4) (79.5–86.7) (75.6–82.6) MIBS 2.0 ± 0.2 2.2 ± 0.2 1.9 ± 0.2 1.8 ± 0.2 (1.6–2.4) (1.7–2.6) (1.5–2.3) (1.4–2.3) COPE 1 3.7 ± 0.1 3.6 ± 0.1 3.4 ± 0.1 3.2 ± 0.1 3.3 ± 0.1 3.5 ± 0.1 Self-distraction (3.4–4.0) (3.4–3.9) (3.2–3.7) (3.0–3.5) (3.1–3.6) (3.2–3.8) COPE 2 5.2 ± 0.2 5.1 ± 0.2 5.6 ± 0.2 6.1 ± 0.2 5.2 ± 0.2 5.6 ± 0.2 Active coping (4.8–6.0) (4.7–5.8) (5.2–6.0) (5.7–6.5) (4.8–5.6) (5.2–6.0) COPE 3 2.3 ± 0.1 2.2 ± 0.1 2.2 ± 0.1 2.1 ± 0.1 2.1 ± 0.1 2.2 ± 0.1 Denial (2.2–2.5) (2.1–2.4) (2.1–2.4) (2.0–2.3) (2.0–2.2) (2.0–2.3) COPE 4 2.0 ± 0.04 2.1 ± 0.04 2.1 ± 0.05 2.2 ± 0.04 2.2 ± 0.05 2.2 ± 0.05 Substance use (1.9–2.1) (2.0–2.1) (2.0–2.2) (2.0–2.1) (2.1–2.3) (2.1–2.3) COPE 5 5.3 ± 0.2 5.5 ± 0.2 6.1 ± 0.2 6.3 ± 0.2 5.5 ± 0.2 5.7 ± 0.2 Emotional support (5.0–5.6) (5.2–5.8) (5.7–6.4) (5.9–6.6) (5.2–5.9) (5.4–6.0) COPE 6 5.2 ± 0.2 5.0 ± 0.2 6.0 ± 0.2 6.2 ± 0.2 5.5 ± 0.2 5.6 ± 0.2 Instrumental support (4.9–5.6) (4.7–5.4) (5.6–6.3) (5.7–6.4) (5.1–5.8) (5.2–5.9) COPE 7 2.4 ± 0.1 2.3 ± 0.1 2.1 ± 0.1 2.3 ± 0.1 2.2 ± 0.1 2.3 ± 0.1 Behavioural disengagement (2.2–2.5) (2.1–2.4) (2.0–2.3) (2.2–2.4) (2.1–2.4) (2.2–2.4) COPE 8 3.7 ± 0.1 4.0 ± 0.2 3.9 ± 0.2 4.1 ± 0.2 3.9 ± 0.2 4.2 ± 0.2 Venting (3.4–4.0) (3.7–4.3) (3.6–4.2) (3.84–4.4) (3.6–4.2) (3.9–4.5) COPE 9 5.0 ± 0.2 5.0 ± 0.2 5.1 ± 0.2 5.4 ± 0.2 5.0 ± 0.2 5.1 ± 0.2 Positive reframing (4.6–5.3) (4.6–5.4) (4.7–5.5) (5.0–5.8) (4.7–5.4) (4.7–5.5) COPE 10 5.0 ± 0.2 5.1 ± 0.2 5.2 ± 0.2 5.6 ± 0.2 5.2 ± 0.2 5.3 ± 0.2 Planning (4.6–5.4) (4.7–5.5) (4.8–5.6) (5.2–6.0) (4.8–5.6) (4.9–5.7) COPE 11 4.3 ± 0.2 4.3 ± 0.2 4.3 ± 0.2 4.4 ± 0.2 4.1 ± 0.2 4.7 ± 0.2 Humor (3.9–4.6) (4.0–4.7) (4.0–4.7) (4.1–4.8) (3.7–4.5) (4.3–5.0) COPE 12 5.5 ± 0.2 5.7 ± 0.2 6.0 ± 0.2 6.2 ± 0.2 5.7 ± 0.2 5.9 ± 0.2 Acceptance (5.1–5.9) (5.3–6.1) (5.6–6.3) (5.8–6.6) (5.3–6.1) (5.5–6.3) COPE 13 3.0 ± 0.1 3.1 ± 0.1 2.6 ± 0.1 2.7 ± 0.1 2.7 ± 0.1 2.6 ± 0.1 Religion (2.8–3.3) (2.9–3.4) (2.4–2.9) (2.4–2.9) (2.4–3.0) (2.3–2.9) COPE 14 2.9 ± 0.1 2.8 ± 0.1 3.1 ± 0.1 3.3 ± 0.1 3.3 ± 0.1 3.4 ± 0.1 Self-blame (2.6–3.1) (2.6–3.1) (2.8–3.4) (3.1–3.6) (3.1–3.6) (3.1–3.6) . Pregnancy . 6 weeks postpartum . 4 months postpartum . 10 months postpartum . . LSM Score ± SEM . LSM Score ± SEM . LSM Score ± SEM . LSM Score ± SEM . Time . (95% CI) . (95% CI) . (95% CI) . (95% CI) . Treatment Control Intervention Control Intervention Control Intervention Control Intervention PSQI 7.9 ± 0.4 7.5 ± 0.4 9.7 ± 0.4 9.3 ± 0.4 8.9 ± 0.4 7.7 ± 0.4 7.3 ± 0.4 6.4 ± 0.4 (7.2–8.7) (6.7–8.2) (9.0–10.5) (8.5–10.0) (8.1–9.7) (6.8–9.5) (6.4–8.1) (5.6–7.3) ISI 8.0 ± 0.5 7.3 ± 0.5 7.4 ± 0.5 7.5 ± 0.5 8.2 ± 0.5 6.6 ± 0.5 7.4 ± 0.5 6.4 ± 0.5 (7.1–9.0) (6.4–8.3) (6.5–8.4) (6.5–8.4) (7.2–9.2) (5.7–7.6) (6.4–8.4) (5.4–7.4) MAF 22.7 ± 0.9 23.2 ± 0.9 25.8 ± 0.9 26.3 ± 0.9 25.0 ± 0.9 22.6 ± 0.9 (21.0–24.5) (21.4–25.0) (24.0–27.6) (24.5–28.1) (23.1–26.90) (20.8–24.5) ESS 6.5 ± 0.4 7.1 ± 0.4 8.0 ± 0.4 9.0 ± 0.4 6.2 ± 0.4 6.7 ± 0.4 (5.7–7.4) (6.3–7.9) (7.2–8.9) (8.2–9.8) (5.3–7.0) (5.9–7.6) EPDS 5.6 ± 0.4 5.0 ± 0.4 6.0 ± 0.4 5.9 ± 0.4 6.2 ± 0.4 5.1 ± 0.4 5.6 ± 0.4 5.0 ± 0.4 (4.8–6.4) (4.2–5.8) (5.2–6.8) (5.1–6.7) (5.4–7.0) (4.3–5.9) (4.7–6.4) (4.2–5.9) DASS 2.7 ± 0.4 2.8 ± 0.5 3.2 ± 0.5 2.7 ± 0.5 4.2 ± 0.5 3.4 ± 0.5 3.5 ± 0.5 3.5 ± 0.5 Depression (1.9–3.6) (1.9–3.7) (2.4–4.1) (1.8–3.6) (3.3–5.2) (2.5–4.3) (2.5–4.4) (2.6–4.5) DASS 3.3 ± 0.3 3.5 ± 0.4 2.3 ± 0.4 2.3 ± 0.4 2.6 ± 0.4 2.1 ± 0.4 1.7 ± 0.4 1.7 ± 0.4 Anxiety (2.6–4.0) (2.9–4.2) (1.6–3.0) (1.6–3.0) (1.9–3.3) (1.4–2.8) (1.0–2.5) (1.0–2.4) DASS 10.2 ± 0.69 8.8 ± 0.69 10.3 ± 0.71 10.2 ± 0.71 11.0 ± 0.73 10.7 ± 0.72 9.1 ± 0.73 9.9 ± 0.74 Stress (8.8–11.5) (7.5–10.2) (9.0–11.7) (8.8–11.6) (9.5–12.4) (9.3–12.1) (7.6–10.5) (8.4–11.4) SES 32.3 ± 0.7 32.3 ± 0.7 30.8 ± 0.7 31.8 ± 0.7 29.1 ± 0.7 31.7 ± 0.7 (30.9–33.6) (30.9–33.6) (29.4–32.1) (30.4–33.2) (27.7–30.5) (30.3–33.1) ICQ 89.3 ± 1.8 84.9 ± 1.8 83.1 ± 1.8 79.1 ± 1.8 (85.8–92.7) (81.5–88.4) (79.5–86.7) (75.6–82.6) MIBS 2.0 ± 0.2 2.2 ± 0.2 1.9 ± 0.2 1.8 ± 0.2 (1.6–2.4) (1.7–2.6) (1.5–2.3) (1.4–2.3) COPE 1 3.7 ± 0.1 3.6 ± 0.1 3.4 ± 0.1 3.2 ± 0.1 3.3 ± 0.1 3.5 ± 0.1 Self-distraction (3.4–4.0) (3.4–3.9) (3.2–3.7) (3.0–3.5) (3.1–3.6) (3.2–3.8) COPE 2 5.2 ± 0.2 5.1 ± 0.2 5.6 ± 0.2 6.1 ± 0.2 5.2 ± 0.2 5.6 ± 0.2 Active coping (4.8–6.0) (4.7–5.8) (5.2–6.0) (5.7–6.5) (4.8–5.6) (5.2–6.0) COPE 3 2.3 ± 0.1 2.2 ± 0.1 2.2 ± 0.1 2.1 ± 0.1 2.1 ± 0.1 2.2 ± 0.1 Denial (2.2–2.5) (2.1–2.4) (2.1–2.4) (2.0–2.3) (2.0–2.2) (2.0–2.3) COPE 4 2.0 ± 0.04 2.1 ± 0.04 2.1 ± 0.05 2.2 ± 0.04 2.2 ± 0.05 2.2 ± 0.05 Substance use (1.9–2.1) (2.0–2.1) (2.0–2.2) (2.0–2.1) (2.1–2.3) (2.1–2.3) COPE 5 5.3 ± 0.2 5.5 ± 0.2 6.1 ± 0.2 6.3 ± 0.2 5.5 ± 0.2 5.7 ± 0.2 Emotional support (5.0–5.6) (5.2–5.8) (5.7–6.4) (5.9–6.6) (5.2–5.9) (5.4–6.0) COPE 6 5.2 ± 0.2 5.0 ± 0.2 6.0 ± 0.2 6.2 ± 0.2 5.5 ± 0.2 5.6 ± 0.2 Instrumental support (4.9–5.6) (4.7–5.4) (5.6–6.3) (5.7–6.4) (5.1–5.8) (5.2–5.9) COPE 7 2.4 ± 0.1 2.3 ± 0.1 2.1 ± 0.1 2.3 ± 0.1 2.2 ± 0.1 2.3 ± 0.1 Behavioural disengagement (2.2–2.5) (2.1–2.4) (2.0–2.3) (2.2–2.4) (2.1–2.4) (2.2–2.4) COPE 8 3.7 ± 0.1 4.0 ± 0.2 3.9 ± 0.2 4.1 ± 0.2 3.9 ± 0.2 4.2 ± 0.2 Venting (3.4–4.0) (3.7–4.3) (3.6–4.2) (3.84–4.4) (3.6–4.2) (3.9–4.5) COPE 9 5.0 ± 0.2 5.0 ± 0.2 5.1 ± 0.2 5.4 ± 0.2 5.0 ± 0.2 5.1 ± 0.2 Positive reframing (4.6–5.3) (4.6–5.4) (4.7–5.5) (5.0–5.8) (4.7–5.4) (4.7–5.5) COPE 10 5.0 ± 0.2 5.1 ± 0.2 5.2 ± 0.2 5.6 ± 0.2 5.2 ± 0.2 5.3 ± 0.2 Planning (4.6–5.4) (4.7–5.5) (4.8–5.6) (5.2–6.0) (4.8–5.6) (4.9–5.7) COPE 11 4.3 ± 0.2 4.3 ± 0.2 4.3 ± 0.2 4.4 ± 0.2 4.1 ± 0.2 4.7 ± 0.2 Humor (3.9–4.6) (4.0–4.7) (4.0–4.7) (4.1–4.8) (3.7–4.5) (4.3–5.0) COPE 12 5.5 ± 0.2 5.7 ± 0.2 6.0 ± 0.2 6.2 ± 0.2 5.7 ± 0.2 5.9 ± 0.2 Acceptance (5.1–5.9) (5.3–6.1) (5.6–6.3) (5.8–6.6) (5.3–6.1) (5.5–6.3) COPE 13 3.0 ± 0.1 3.1 ± 0.1 2.6 ± 0.1 2.7 ± 0.1 2.7 ± 0.1 2.6 ± 0.1 Religion (2.8–3.3) (2.9–3.4) (2.4–2.9) (2.4–2.9) (2.4–3.0) (2.3–2.9) COPE 14 2.9 ± 0.1 2.8 ± 0.1 3.1 ± 0.1 3.3 ± 0.1 3.3 ± 0.1 3.4 ± 0.1 Self-blame (2.6–3.1) (2.6–3.1) (2.8–3.4) (3.1–3.6) (3.1–3.6) (3.1–3.6) Significant differences between groups are marked with bolded text. LSM, Least square mean; SEM, Standard error of the mean; PSQI, Pittsburgh Sleep Quality Index; ISI, Insomnia Severity Index; MAF, multidimensional assessment of fatigue; ESS, Epworth Sleepiness Scale; EPDS, Edinburgh Postnatal Depression Scale; DASS, Depression, Anxiety, and Stress Scale; SES, Self-Efficacy Scale; ICQ, Infant Characteristics Questionnaire; MIBS, Maternal-to-Infant Bonding Scale. Open in new tab Sleep outcomes Pittsburgh Sleep Quality index There was no significant effect of the intervention on overall postpartum sleep quality (mean difference = 0.8; 95% CI [−0.1 to 1.7]; p = 0.07). Sleep quality reduced for both groups at 6 weeks postpartum (control group: mean difference −1.82; p < 0.001; intervention group: mean difference = −1.81; 95% CI [−2.64 to −0.98]; p < 0.001) with no group difference at 6 weeks postpartum. An interaction effect was found between 6 weeks and 4 months postpartum, with the intervention group reporting improved sleep quality compared to the control group at 4 months postpartum (mean difference = 1.27; 95% CI [0.12 to 2.41]; p = 0.03). A small effect size of d = 0.33 was found. There was no difference between groups at 10 months. Figure 3 depicts the PSQI scores at each time point. Figure 3. Open in new tabDownload slide Sleep quality rating. Mean PSQI scores and standard errors for each group at each time point indicating no differences between groups during pregnancy (pre-intervention), 6 weeks postpartum, or 10 months postpartum but significantly better sleep quality in the intervention group at 4 months postpartum (p = 0.03). There was no overall treatment effect in the postpartum period (p = 0.07, df = 1/325, F = 3.31). Asterisk indicates p < 0.05. Higher scores indicate poorer sleep quality. PSQI: Pittsburgh Sleep Quality Index; Interv, intervention. Figure 3. Open in new tabDownload slide Sleep quality rating. Mean PSQI scores and standard errors for each group at each time point indicating no differences between groups during pregnancy (pre-intervention), 6 weeks postpartum, or 10 months postpartum but significantly better sleep quality in the intervention group at 4 months postpartum (p = 0.03). There was no overall treatment effect in the postpartum period (p = 0.07, df = 1/325, F = 3.31). Asterisk indicates p < 0.05. Higher scores indicate poorer sleep quality. PSQI: Pittsburgh Sleep Quality Index; Interv, intervention. Insomnia Severity Index As seen in Figure 4, there was no significant effect of the intervention on overall postpartum insomnia symptoms (mean difference = 0.89; 95%CI [−0.23 to 1.99]; p = 0.12). Insomnia symptoms did not differ between groups at 6 weeks postpartum. There was an interaction effect where the intervention group’s insomnia symptoms improved more than the control group at 4 months’ postpartum (mean difference 1.55; 95% CI [1.66 to 2.93; p = 0.03]). A small effect size (Cohen’s d = 0.33) was found. There was no difference between the groups at the 10-month follow-up time point. Figure 4. Open in new tabDownload slide Insomnia rating. Mean ISI scores and standard errors for each group at each time point indicating no differences between groups during pregnancy (pre-intervention), 6 weeks postpartum, or 10 months postpartum but significantly fewer insomnia symptoms in the intervention group at 4 months postpartum (p = 0.03). There was no overall treatment effect in the postpartum period (p = 0.12, df = 1/356, F = 2.42). Asterisk indicates p < 0.05. Higher scores indicate more insomnia symptoms. ISI, Insomnia Severity Index; Interv, intervention. Figure 4. Open in new tabDownload slide Insomnia rating. Mean ISI scores and standard errors for each group at each time point indicating no differences between groups during pregnancy (pre-intervention), 6 weeks postpartum, or 10 months postpartum but significantly fewer insomnia symptoms in the intervention group at 4 months postpartum (p = 0.03). There was no overall treatment effect in the postpartum period (p = 0.12, df = 1/356, F = 2.42). Asterisk indicates p < 0.05. Higher scores indicate more insomnia symptoms. ISI, Insomnia Severity Index; Interv, intervention. Multidimensional Assessment of Fatigue and Epworth Sleepiness Scale The MAF and ESS questionnaires indicated that all mothers became significantly more fatigued and sleepy between pregnancy and 6 weeks postpartum. There were no group differences over the postpartum time frame (MAF mean difference = 0.81; 95% CI [−1.57 to 3.2]; p = 0.5). By 4 months postpartum, the intervention group’s fatigue scores had decreased from the 6 week follow-up (p < 0.001). In contrast, the control group’s fatigue scores remained at the 6 week mark and were higher than their prenatal measure (p = 0.03). All mothers’ daytime sleepiness scores reduced at 4 months postpartum with no group differences detected at any time or when taken altogether (ESS mean difference = −0.77; 95% CI [−1.98 to 0.43]; p = 0.21). These scores can be viewed in Figures 5 and 6. Figure 5. Open in new tabDownload slide Fatigue rating. Mean MAF scores and standard errors for each group at each time point indicating no differences between groups in the postpartum period. There was no overall treatment effect in the postpartum period (p = 0.5, df = 1/181, F = 0.45). Higher scores indicate more fatigue. MAF, Multidimensional assessment of fatigue; Interv, intervention. Figure 5. Open in new tabDownload slide Fatigue rating. Mean MAF scores and standard errors for each group at each time point indicating no differences between groups in the postpartum period. There was no overall treatment effect in the postpartum period (p = 0.5, df = 1/181, F = 0.45). Higher scores indicate more fatigue. MAF, Multidimensional assessment of fatigue; Interv, intervention. Figure 6. Open in new tabDownload slide Sleepiness rating. Mean ESS scores and standard errors for each group at each time point indicating no postpartum differences between groups. There was no overall treatment effect in the postpartum period (p = 0.21, df = 1/181, F = 1.61). Higher scores indicate more sleepiness. ESS, Epworth Sleepiness Scale; Interv, intervention. Figure 6. Open in new tabDownload slide Sleepiness rating. Mean ESS scores and standard errors for each group at each time point indicating no postpartum differences between groups. There was no overall treatment effect in the postpartum period (p = 0.21, df = 1/181, F = 1.61). Higher scores indicate more sleepiness. ESS, Epworth Sleepiness Scale; Interv, intervention. Generalised Sleep Questionnaire At 6 weeks postpartum, participants in the intervention group napped more often than those in the control group (mean difference = −0.38; 95% CI [−0.72 to −0.028]; p = 0.04). There were no differences in naps across the overall time frame (mean difference = −0.26; 95% CI [−0.57 to 0.05]; p = 0.1). There was a main effect of time on napping, with napping frequency decreasing in both groups by 4 months postpartum (F = 30.95, p < 0.0001). Groups did not differ on measures of sleep location, ratings of their infants’ sleep, or number of reported night wakes. Depression, anxiety, and stress outcomes Edinburgh Postnatal Depression Scale There was no main effect of time or treatment on depression scores and no interactions at any time. A trend in favor of the intervention group was found at the 4 months postpartum time point (mean difference 1.05; 95% CI [−0.12 to 2.21; p = 0.08]). We also conducted post hoc analyses where participants were grouped by prenatal depression scores and only participants in the range (EPDS ≥ 10) were included (n = 26; intervention group n = 10; control group n = 16). Effects of the intervention indicated a main effect of time (F = 5.64, p < 0.01) showing no group differences during pregnancy (pre-intervention), or at 6 weeks or 10 months postpartum. Results approached an interaction effect at 4 months postpartum (mean difference 3.79, 95% CI [−0.06 to 7.09] p = 0.05) with a medium effect size (Cohen’s d = 0.78) and the control group showing further deterioration than the intervention group. Depression, Anxiety and Stress Scale No group differences in depression, anxiety, or stress were found at any time or across the time frame. Anxiety scores dropped significantly in both groups between pregnancy and at 6 weeks postpartum (control group: mean difference 0.99; 95% CI [0.28 to 1.71]; p < 0.01; intervention group: mean difference 1.28; 95% CI [0.56 to 2.00] p < 0.001) and remained stable at 4 months. Clinical significance Using the more stringent cutoff of the upper interquartile range, post hoc analyses indicated a significant difference in the proportion of clinical cases of poor sleep quality at 4 months postpartum. Those in the control group were almost twice as likely to score above 10 on the PSQI than those in the intervention group (42% in the control group versus 22% in the intervention group p < 0.01; Pearson chi-squared = 7.77). No group differences were found using the more established cut-off of five. In addition, almost four times as many participants in the control group met criteria for clinical insomnia (ISI > 15) than those in the intervention group (control group = 11.8%, intervention group = 3.1%; Pearson’s chi-squared = 5.21; p < 0.02) at 4 months postpartum. Proportions of clinical cases did not differ between groups on the other measures (see Table 3). Table 3. Clinical significance: difference between the proportion of clinical cases on primary outcomes in each group at 4 months postpartum Outcome measure . Control . Intervention . Pearson chi-squared . Sig. . PSQI—Poor sleep quality (>5) 73.9% 68.5% 0.612 0.434 PSQI—Poor sleep quality (>10) 42% 22.5% 7.765 0.005* ISI—Clinical insomnia (>15) 11.8% 3.1% 5.308 0.021* ESS—Clinically sleepy (>10) 20.9% 23.2% 0.140 0.708 MAF—Clinically fatigued (>26) 42.9% 36.1% 0.903 0.342 Outcome measure . Control . Intervention . Pearson chi-squared . Sig. . PSQI—Poor sleep quality (>5) 73.9% 68.5% 0.612 0.434 PSQI—Poor sleep quality (>10) 42% 22.5% 7.765 0.005* ISI—Clinical insomnia (>15) 11.8% 3.1% 5.308 0.021* ESS—Clinically sleepy (>10) 20.9% 23.2% 0.140 0.708 MAF—Clinically fatigued (>26) 42.9% 36.1% 0.903 0.342 PSQI, Pittsburgh Sleep Quality Index; ISI, Insomnia Severity Index; ESS, Epworth Sleepiness Scale; MAF, Multidimensional Assessment of Fatigue. Significant differences between groups are marked with bolded text and an asterisk. Open in new tab Table 3. Clinical significance: difference between the proportion of clinical cases on primary outcomes in each group at 4 months postpartum Outcome measure . Control . Intervention . Pearson chi-squared . Sig. . PSQI—Poor sleep quality (>5) 73.9% 68.5% 0.612 0.434 PSQI—Poor sleep quality (>10) 42% 22.5% 7.765 0.005* ISI—Clinical insomnia (>15) 11.8% 3.1% 5.308 0.021* ESS—Clinically sleepy (>10) 20.9% 23.2% 0.140 0.708 MAF—Clinically fatigued (>26) 42.9% 36.1% 0.903 0.342 Outcome measure . Control . Intervention . Pearson chi-squared . Sig. . PSQI—Poor sleep quality (>5) 73.9% 68.5% 0.612 0.434 PSQI—Poor sleep quality (>10) 42% 22.5% 7.765 0.005* ISI—Clinical insomnia (>15) 11.8% 3.1% 5.308 0.021* ESS—Clinically sleepy (>10) 20.9% 23.2% 0.140 0.708 MAF—Clinically fatigued (>26) 42.9% 36.1% 0.903 0.342 PSQI, Pittsburgh Sleep Quality Index; ISI, Insomnia Severity Index; ESS, Epworth Sleepiness Scale; MAF, Multidimensional Assessment of Fatigue. Significant differences between groups are marked with bolded text and an asterisk. Open in new tab Infant characteristics There were equal numbers of male and female infants per group. Parent ratings of infant temperament did not differ although the intervention group tended to rate their infants as having a better temperament than the control group at 6 weeks postpartum (ICQ, p = 0.08). Mothers’ ratings of infant sleep did not differ between groups (p = 0.57) with 48% of participants rating their infant’s sleep as “good,” 47% as “okay”, and 5% as “poor” at 6 weeks of age. Similar ratings on night-wakings were found between groups (p = 0.37) with two-thirds of the infants waking three to five times per night. Responses to feeding experience did not differ between groups with 93% of participants reporting either a good or adequate feeding pattern. Other measures Self-Efficacy Scale Groups did not differ in self-efficacy during pregnancy, at 6 weeks postpartum, or across the overall time frame. At 4 months postpartum, the intervention group displayed higher levels of self-efficacy than the control group (mean difference −2.6, 95% CI [−4.59 to −0.61], p = 0.01) with a small effect size (Cohen’s d = 0.39). Scores on the SES in each group at each time point can be seen in Figure 7. Figure 7. Open in new tabDownload slide Self-efficacy rating. Mean SES scores and standard errors for each group at each time point indicating no differences between groups during pregnancy (pre-intervention) or 6 weeks but a significant difference at 4 months postpartum (p = 0.01). There was no overall treatment effect across the postpartum period (p = 0.10, df = 1/404, F = 2.65). Asterisk indicates p < 0.05. Higher scores indicate better self-efficacy. SES, Self-Efficacy Scale; Interv, Intervention. Figure 7. Open in new tabDownload slide Self-efficacy rating. Mean SES scores and standard errors for each group at each time point indicating no differences between groups during pregnancy (pre-intervention) or 6 weeks but a significant difference at 4 months postpartum (p = 0.01). There was no overall treatment effect across the postpartum period (p = 0.10, df = 1/404, F = 2.65). Asterisk indicates p < 0.05. Higher scores indicate better self-efficacy. SES, Self-Efficacy Scale; Interv, Intervention. Bonding scores were predominantly within a normal range of 0–2 between mothers and babies [29] and no between group differences were identified. Similarly, mothers’ reports of infant temperaments were equivalent. Coping strategies were also measured with 14 strategies of interest, but none of them reached the more stringent level of significance required (p < 0.003) once we corrected for multiple comparisons (all p’s > 0.03). Completers and non-completers There was a significant difference between “completers” (those who completed their final set of questionnaires) and non-completers (those who did not complete their final questionnaires) on sleep quality (p = 0.03) and insomnia symptoms (p = 0.03) where those who had poorer sleep during pregnancy (prior to randomization) were less likely to complete the study. The proportion of participants completing the study in each group was equivalent, at 82%. No other differences were identified. Discussion Effects of the intervention on primary outcomes Consistent effects of the intervention were not observed on the primary outcomes across the 10 months’ postpartum period. As expected, sleep in both intervention and control groups deteriorated from pregnancy to 6 weeks postpartum on all primary outcomes except insomnia scores, which remained stable. At 4 months postpartum, improvements in sleep quality and insomnia symptoms were observed in the intervention group compared to the control group. There was no impact on daytime sleepiness or fatigue. Final follow up at 10 months postpartum showed no differences between the groups, but improvements in sleep quality and insomnia symptoms that were consistent with a return to prenatal sleep were observed in both groups. Although the effect sizes were small, clinical levels of poor sleep quality were more common in the control group (42%) compared with the intervention group (22.5%) at 4 months postpartum. Similarly, there were fewer participants with clinical insomnia in the intervention group (3.1%) compared with the control group (11.8%) at this time. Effects of the intervention on secondary outcomes Results indicated no postpartum group differences for depressive symptoms, anxiety, and stress. This is likely explained by the predominantly non-depressed maternal sample. Sleep interventions can improve depression but in a previous meta-analysis investigating a perinatal sample, few studies examined this relationship and there was evidence of publication bias [11]. Our post hoc analysis of a small subgroup of women who had clinically significant depression scores during pregnancy indicated near significant effects demonstrating that the intervention group had fewer depressive symptoms at 4 months postpartum than the control group, with a medium effect size. Although only suggestive, we would argue that trials of this intervention for women who are at a higher risk for PPD are warranted. Effects of intervention on other outcomes We measured outcomes that might be either a potential process of intervention change (e.g. increased self-efficacy or improved coping) or a consequence of improved sleep (e.g. maternal infant bonding). There was little evidence that the intervention produced consistent changes in any of these outcomes across time in comparison to the control group. There was only one variable on which differences between the groups were observed at 4 months’ postpartum. The control group experienced a reduction in self-efficacy at 4 months’ postpartum, whereas the intervention group maintained their prenatal self-efficacy levels, resulting in higher self-efficacy in the intervention group than the control group at this time. We initially thought self-efficacy could predict treatment outcomes; however, a mediation analysis failed to confirm this. None of the coping strategies improved significantly more in the intervention group compared to the control group once Bonferroni corrections were applied. Comparison with other sleep-focused interventions Recently, Lee et al. [40] found a 4 week cognitive behavioral sleep-focused program in the final month of pregnancy produced benefits on both objective and subjective sleep outcomes at 1–2 months postpartum, but these are difficult to evaluate given the lack of randomization. Perhaps more relevant to the present study, Lee and Gay conducted 2 RCTs of sleep hygiene during pregnancy [41]. They found that the same program was efficacious in one sample with disadvantaged mothers, but not in more socioeconomically advantaged mothers. Our sample was highly educated and slightly older than average and is similar to the more advantaged group, which may explain our less robust results. Hence, it is important that future research strive for more representative samples, or specifically target disadvantaged mothers. Other studies delivered to healthy expectant mothers in the perinatal period have failed to find robust effects on the sleep of mothers and their infants [15]. One meta-analysis found 9 RCTs of psychosocial sleep interventions in the postnatal period and demonstrated a small impact on total sleep time of the infant, but no difference in number of nighttime awakenings [11]. As such, the impact of these interventions on maternal sleep is unclear. There was also an effect of sleep-based interventions on maternal depressive symptoms. These results indicate a potential for reducing depression through improving sleep and a need for further research. Another more recent meta-analysis including a greater range of non-pharmacological interventions found positive effects on postpartum maternal sleep, particularly following massage and exercise interventions, but no effect on depression [13]. Targeted interventions for women who are experiencing infant sleep or feeding problems are currently associated with the greatest benefits on maternal depressive symptoms [41, 42]. This is paralleled in participants with prenatal insomnia [19]. The failure of the program in this study to make clear improvements to depressive symptoms is not surprising given the modest sleep improvements and the low levels of depressive symptoms in the sample. The trajectory of sleep in the postpartum period Developmentally, healthy infant sleep in the first 6 weeks involves multiple wakes during the night [43]. Therefore, the universal nature of sleep deprivation at this early stage may be more impervious to intervention. Between 6 weeks and 4 months postpartum, infants develop a circadian rhythm, leading them to longer sleep cycles, with fewer wakes [43], increasing parents’ opportunity for sleep, yet with considerable variation. Only 58% achieve eight uninterrupted hours sleep at 4 months postpartum and 19%–33% mothers reported fewer than five uninterrupted hours of sleep. By 10 months postpartum, almost all infants (90%) sleep at least 5 h uninterrupted [44]. In this sample, it may be that there was insufficient variation in sleep to observe differences between the groups at either 6 weeks or 10 months. Consistent with this suggestion, another study which implemented a sleep education program during pregnancy and 3-weeks postpartum, found more consistent use of bedtime strategies in the intervention group at 4 and 6 months postpartum compared to the control group [17]. It may be that the 4–6 month postpartum period is more sensitive to the effects of behavioral interventions. Nevertheless, it remains possible that the finding of an effect at 4 months postpartum is a spurious post hoc finding. Study limitations This was an open label study and neither participants nor the researchers were blind to group allocation. Further, although we used actigraphy in a proportion of participants, the remaining outcomes are subjective self-report questionnaires. The interventions were not matched for time or attention; therefore, it is possible that the benefits reflect a placebo effect. Arguably the most significant limitation is the nature of our sample. The fact that a large proportion of the sample was recruited through advertisement resulted in a highly educated, high functioning sample who were metropolitan based. The mean age of participants at the time of first birth was 33.3, compared with a mean of 28.9 in Australia [45]. It is therefore unclear how well these results would generalize to a non-metropolitan community setting. Although we encouraged partners to attend, we were not able to collect their data but would like to include this in future research. Those with poorer sleep during pregnancy were less likely to complete the study, thus, although the program may be effective for sleep quality and insomnia, it is unclear how those with poorer sleep would respond. Methods need to be developed to engage and retain at-risk individuals. Preventative trials generally have smaller effect sizes than treatment trials [46], and this was the case for those significant findings that were observed on two of our primary outcomes. We took a pragmatic approach by including four primary outcomes due to the multifaceted nature of sleep and the lack of evidence about which aspect of sleep was likely to be most affected. We believe this approach may be considered a strength; allowing exploration of how the program impacts on various sleep outcomes and daytime function but we acknowledge the possible inflation of Type I errors. These limitations notwithstanding, this RCT with 215 women had a low overall attrition rate at 10 months postpartum (82.7%). Unselected women pregnant with their first child were recruited, which likely limited the scope for positive effects on some outcomes. Significant improvement in sleep quality and reduced insomnia in the intervention group at 4 months postpartum highlights some potential benefits of this brief prenatal program. Conclusion In summary, our preventative group psychoeducation intervention was not able to produce sustained improvements in sleep across the full postpartum period. The only significant effects of the interventions were observed at 4 months postpartum for sleep quality and insomnia, and these changes were clinically significant at this time. While these results may suggest that it is premature for the wide adoption of such an approach, it should be noted that the intervention itself is brief and the low rates of attrition indicated it was acceptable to the women who took part. It is easily administered and could be simply integrated into available prenatal care delivered by hospital midwives or other health professionals as an inexpensive adjunct. Further research testing such approaches in women at higher risk of insomnia and/or depression are needed, but the indication of a potentially positive effect on depression for those who present with clinically significant levels of depression is encouraging. Interventions that improve sleep quality and reduce the risk of insomnia amongst first-time mothers are important, and therefore future research of this or similar programs is warranted. Ethical Approval The trial was registered with the Australian New Zealand Clinical Trials Registry (ANZCTR): ACTRN12611000859987, and ethics approval was granted by the Sydney Local Health Network Ethics Review Committee (RPAH zone) and the University of Sydney Human Research Ethics Committee (HREC): X11-0036 & HEC/11/RPAH/49. 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Zhang,, Jing;Yetton,, Ben;Whitehurst, Lauren, N;Naji,, Mohsen;Mednick, Sara, C

doi: 10.1093/sleep/zsaa084pmid: 32330272

Abstract Study Objectives: Nonrapid eye movement sleep boosts hippocampus-dependent, long-term memory formation more so than wake. Studies have pointed to several electrophysiological events that likely play a role in this process, including thalamocortical sleep spindles (12–15 Hz). However, interventional studies that directly probe the causal role of spindles in consolidation are scarce. Previous studies have used zolpidem, a GABA-A agonist, to increase sleep spindles during a daytime nap and promote hippocampal-dependent episodic memory. The current study investigated the effect of zolpidem on nighttime sleep and overnight improvement of episodic memories. Methods: We used a double-blind, placebo-controlled within-subject design to test the a priori hypothesis that zolpidem would lead to increased memory performance on a word-paired associates task by boosting spindle activity. We also explored the impact of zolpidem across a range of other spectral sleep features, including slow oscillations (0–1 Hz), delta (1–4 Hz), theta (4–8 Hz), sigma (12–15 Hz), as well as spindle–SO coupling. Results: We showed greater memory improvement after a night of sleep with zolpidem, compared to placebo, replicating a prior nap study. Additionally, zolpidem increased sigma power, decreased theta and delta power, and altered the phase angle of spindle–SO coupling, compared to placebo. Spindle density, theta power, and spindle–SO coupling were associated with next-day memory performance. Conclusions: These results are consistent with the hypothesis that sleep, specifically the timing and amount of sleep spindles, plays a causal role in the long-term formation of episodic memories. Furthermore, our results emphasize the role of nonrapid eye movement theta activity in human memory consolidation. zolpidem, memory, sleep spindles, spindle, SO coupling, theta Statement of Significance Sleep spindles have emerged as one of the key electrophysiological markers for memory consolidation during sleep, yet interventional studies that directly probe the causal relation are scarce. It also remains under-investigated if other oscillations, such as theta, contribute to memory consolidation during nonrapid eye movement sleep. Using a within-subject, double-blind, placebo-controlled paradigm, this study pharmacologically manipulated a range of spectra features over a night of sleep in order to identify the key mechanism of sleep-dependent memory consolidation. Our results suggest a functional role of both sigma and theta in which optimizing sleep spindles while preserving theta activity may be a goal of future sleep interventions to enhance memory consolidation. Introduction Converging evidence from both cellular and behavioral research suggests an essential role of sleep in memory consolidation [1–3]. For hippocampal-dependent episodic memories, studies have shown greater performance improvement following the first half of the night rich in slow-wave sleep (SWS), compared to the second half of the night with majority rapid eye movement (REM) sleep [4, 5]. In addition, neocortical slow oscillations (SOs, 0.5–1 Hz) and thalamocortical spindles (12–15 Hz) have emerged as two prominent features of non-REM (NREM) sleep associated with episodic memory consolidation [6]. Several studies have shown correlations between postsleep memory improvement and SOs, and causally increasing SOs using stimulation interventions have shown significant increases in hippocampal-dependent episodic memories in both animals [7] and humans [8, 9]. Studies investigating the role of sleep spindles in memory consolidation have also shown positive associations [10, 11]. Clemens et al. [12] reported correlations between spindles and overnight verbal memory retention, but not visual skill learning. In another study, spindle density in stage 2 sleep increased after episodic memory encoding but not after a nonlearning task where participants were instructed to count letters containing curved lines [13]. The important role of sleep spindles in sleep-dependent memory consolidation is further suggested in targeted memory reactivation (TMR) studies [14, 15]. Specifically, cues associated with memory reactivation optimally benefitted declarative memory when presented outside of spindle refractory periods [14], and TMR cues can increase fast spindle power [15]. In addition, Lustenberger et al. [16] have enhanced spindle activity using transcranial alternating current stimulation, which led to an increase in motor memory consolidation, but not declarative memory. In the current study, we used pharmacology to manipulate spindle activity and investigated the relationship between changes in memory performance and multiple sleep features. More recently, the temporal coupling between spindles and SOs has been suggested to reflect hippocampal-thalamocortical communication during NREM sleep, and that this coordinated activity pattern may underlie the formation and protection of long-term memories [17, 18]. Accordingly, several studies suggest that coincident spindle–SO coupling leads to greater memory, optimally when spindle activity occurs during the SO up-state [19–22]. Optogenetically stimulating spindle activity during the up-state of SOs in rodents improved contextual fear-conditioning, compared with random or no stimulation [23]. A recent study examining the coupling of sigma and SO in relation to functional magnetic resonance imaging (fMRI) brain activity and memory performance in young and older adults [24] reported that spindle–SO coupling predicted postsleep episodic memory improvement in both age groups. However, older adults showed altered timing between spindle–SO coupling and decreased coupling over the frontal pole, which may contribute to decreases in overnight memory retention in older compared to young adults. Additionally, optimal spindle–SO timing may compensate for a lower total number of spindles and support memory [25]. Specifically, patients with schizophrenia showed reduced spindle number and density but similar spindles–SO coupling compared to healthy individuals, and magnitude of coordination between spindles and SOs was correlated with overnight improvement on a hippocampal-dependent memory task [25]. Experimental approaches to probe the causal nature of sleep spindles are scarce. Spindles can be pharmacologically increased with zolpidem (Ambien), a GABA-A agonist with a half-life of approximately 1.5–3.2 h and peak plasma concentration 1.6 h after ingestion [26]. Our group has shown that administering zolpidem during a morning nap increases spindle density compared with placebo and a comparison hypnotic, sodium oxybate [27]. Additionally, zolpidem improved episodic memory, but decreased perceptual learning, and had no effect on motor learning, compared with placebo [27]. Verbal memory performance was significantly correlated with spindle density in zolpidem and placebo, but not with sodium oxybate [27]. Furthermore, zolpidem increased the coincidence of the spindle–SO coupling measured by phase angle, which also correlated with memory improvement [21]. Similarly, eszopiclone, a GABA-based non-benzodiazepine, increased spindles and the association between spindles and motor learning [28]. Given the small number of studies that test the impact of directly manipulating spindles on memory, and that zolpidem has only been tested in a morning nap, more research is needed. The current study investigated the impact of zolpidem on sleep physiology across a night of sleep, specifically on spindle activity and spindle–SO coupling, and overnight episodic memory consolidation of word-paired associates (WPA). In addition, zolpidem is known to reduce theta oscillation for both men and women [29], yet the effect on memory is unknown. Specifically, a study using zolpidem as a treatment for insomnia showed that zolpidem decreased theta power and increased sigma power with no change in SO in patients [30]. Another study showed that zolpidem decreased band power between 3.75 and 10 Hz for sleep-deprived participants, with theta power having the largest reduction [31]. Therefore, a secondary goal of the current study was to investigate a range of sleep features that could be altered by zolpidem, focusing on theta activity, but also including SO and delta, and their relation to memory performance. Taking into consideration the short half-life of zolpidem [26], we divided the night of sleep into four quartiles in order to assess the effect of zolpidem on sleep physiology during peak plasma periods, as well as across the whole night. We hypothesized that, compared to placebo, zolpidem would show increased performance on an episodic memory task, as well as greater spindle density and spindle–SO coupling during the first two quartiles of sleep due to the pharmacokinetics of zolpidem. Methods Participants Thirty-six healthy adults (Mage = 21.00 ± 2.97 years, 19 females) with no history of neurological, psychological, or other chronic illnesses were recruited for the study. All participants signed informed consent, which was approved by the Western Institutional Review Board and the University of California, Riverside Human Research Review Board. Exclusion criteria included irregular sleep/wake cycles (defined as having a habitual bedtime after 02:00 am and a habitual wake time after 10:00 am), sleep disorder, personal or familial history of diagnosed psychopathology, substance abuse/dependence, loss of consciousness greater than 2 min or a history of epilepsy, current use of psychotropic medications, and any cardiac or respiratory illness that may affect cerebral metabolism, which was determined during an in-person psychiatric assessment with trained research personnel. Additionally, all participants underwent a medical history and physical appointment with a staff physician to ensure their physical well-being. All participants were naïve to or had limited contact with (<2 lifetime use and no use in last year) the medication used in the study. Participants received monetary compensation and/or course credit for participating in the study. Procedure This study employed a double-blind, placebo-controlled, within-subject design, in which every participant experienced both zolpidem and placebo. The order of drug conditions was counterbalanced with at least a 1-week interval between each experimental visit to allow for drug clearance. Participants reported to the laboratory at 7:30 am and began encoding the paired associates verbal memory task at 8:00 am. This research was a part of a larger study that investigated the independent and combined effects of a psychostimulant (dextroamphetamine) and a hypnotic (zolpidem) on sleep and cognition. In this parent study, we tested the daytime administration of the stimulant, which is the reason participants encoded word-pairs in the morning and then tested three times across 24 h [32]. Participants returned to the laboratory at 9:00 pm at which point a second memory test was given. After testing, participants were prepared for nighttime sleep, which included a 32-channel polysomnography recording. Once in bed and directly before lights out, participants ingested either 10 mg of zolpidem or placebo. Participants were woken up at 9:00 am the next morning and provided a standardized breakfast. At 10:30 am, participants completed the memory task and were permitted to leave the laboratory after being cleared by study personnel (Figure 1). Figure 1. Open in new tabDownload slide Study protocol. Participants encoded 60 word-pairs at 8:00 am and trained with 70% criterion, followed by an immediate test of 20 pairs. In the evening, participants returned to the laboratory and were tested on another set of 20 pairs. Before sleep, participants ingested a dose of zolpidem or placebo. In the morning, participants were tested on the remaining set of 20 pairs. Participants returned to the laboratory after 1-week washout period and performed the same protocol with a different drug. The order of the drug was counterbalanced between participants. Figure 1. Open in new tabDownload slide Study protocol. Participants encoded 60 word-pairs at 8:00 am and trained with 70% criterion, followed by an immediate test of 20 pairs. In the evening, participants returned to the laboratory and were tested on another set of 20 pairs. Before sleep, participants ingested a dose of zolpidem or placebo. In the morning, participants were tested on the remaining set of 20 pairs. Participants returned to the laboratory after 1-week washout period and performed the same protocol with a different drug. The order of the drug was counterbalanced between participants. Task The WPA task consisted of an encoding phase and three retrieval phases. Encoding consisted of viewing 60 pairs of words, each presented vertically stacked and shown twice in random order. Every word pair was presented for 1,000 ms followed by a fixation cross for 100 ms. We trained participants to criterion using a test in which participants were shown one word of the pair and were required to type in the missing word. Feedback was provided and participants had to achieve 70% accuracy to finish the training. For testing, the 60 word-pairs were divided into three sets of 20 pairs; one set was tested at each test session and the order was counterbalanced. Three retrieval tests were conducted: (1) immediately after encoding (09:00 am, test 1), (2) before sleep (09:00 pm, test 2), and (3) the morning after sleep (10:30 am, test 3). No feedback was provided during testing. Sleep recording EEG data were acquired using a 32-channel cap (EASEYCAP GmbH) with Ag/AgCI electrodes placed according to the international 10–20 System (Jasper, 1958). Twenty-two electrodes were scalp recordings and the remaining electrodes were used for electrocardiogram, electromyogram (EMG), electrooculogram (EOG), ground, an online common reference channel (at FCz location, retained after re-referencing), and mastoid (A1 & A2) recordings. The EEG was recorded with a 1,000 Hz sampling rate and was re-referenced to the contralateral mastoid (A1 & A2) postrecording (midline electrodes were re-referenced to the average of two mastoids [33]). Data were preprocessed using BrainVision Analyzer 2.0 (BrainProducts, Munich, Germany). Eight scalp electrodes (F3, F4, C3, C4, P3, P4, O1, and O2), the EMG, and EOG were used in the scoring of the nighttime sleep data. High-pass filters were set at 0.3 Hz and low-pass filters at 35 Hz for EEG and EOG. Raw data were visually scored in 30-s epochs into wake, stage 1, stage 2, SWS, and REM sleep according to the Rechtschaffen & Kales’ manual [34]. After staging, all epochs with artifacts and arousals were identified rejected by visual inspection before spectral analyses. Minutes in each sleep stage and sleep latencies (the number of minutes from lights out until the initial epoch of sleep, stage 2, SWS, and REM) were calculated. Additionally, wake after sleep onset (WASO) was calculated as total minutes awake after the initial epoch of sleep, and sleep efficiency (SE) was computed as total time spent asleep after lights out (~11:00 pm) divided by the total time spent in bed (~11:00 pm–9:00 am) × 100. Spindle detection For each electrode, we first found the peak frequency fp of stage 2 and SWS power spectrum within the 9–15 Hz band. For the electrodes with no power spectrum peak in this range, the average of peak frequencies from other EEG electrodes was considered as fp. Next, we calculated the time series of average EEG energy, E(t), after convolving the signals by complex Morlet wavelets ψt=Aexp(-t2/2σt2) exp (i2πf0t), where f0 is in range [fp − 1.5 fp +1.5] Hz with 0.1 Hz steps, A=σtπ-1/2, σt=1/2πσf, σ f=f0/10, i=-1, and wavelet duration is in range −5σt < t < 5σt. Spindles were detected at each EEG electrode by applying a thresholding algorithm on E2(t). The threshold was defined as four times the mean amplitude of E2(t) of all artifact-free 30-s epochs. A spindle event was identified whenever E2(t) exceeded the threshold for a minimum of 250 ms. Finally, the detected spindles were refined if the estimated frequency of each spindle fell in the range of 9–15 Hz. In order to estimate a spindle frequency, the zero-crossings of the high-passed filtered (2 Hz) version of EEG in the candidate spindle intervals were first detected. Then, the spindle frequency was estimated as fest=(N-1)/2∆T, where N is the number of zero-crossings and ΔT is the time difference between the last and first zero-crossings within a candidate spindle interval. This method is based on Wamsley’s method [22, 35–39] which has been cited in a meta-analysis of spindle detectors as a high performer [40–42], with a few minor modifications. Specifically, Wamsley employed a fixed range of wavelets to filter the data for spindle activity across electrodes, while our method extracts a peak frequency in a possible range for spindle activity for each electrode. This allows a more flexible window to account for all spindle activities. After identifying candidate spindle events, we also do a refinement based on number of zero-crossings for candidate spindles. Calculating spindle detection thresholds individually for both conditions risks the possibility that the threshold might differ significantly between conditions. If the spindle amplitudes were higher in one condition, a higher detection threshold would be used for that condition, biasing the density values to lower numbers. To address this issue, we have acquired individual threshold at each channel for each condition averaged across participants and performed paired t-test between the two conditions. No significant difference was found at any electrode between the two conditions (Supplementary Figure 2), suggesting spindle values detected from this method were not biased by drug condition. Another issue associated with the individual thresholds used in the spindle detector is the possibility to bias slow spindles as slower frequencies display higher spectral power. To address this issue, we acquired individual peak-frequencies at each channel for each condition averaged across participants and performed paired t-test between the two conditions. No significant difference between the two conditions was found at any channel, suggesting slow spindles were not biased by this detection method (Supplementary Figure 3). SO detection SO events were detected based on the algorithm developed by Massimini et al. [43]. The EEG signals were first filtered (zero-phase bandpass, 0.1–4 Hz). Then, the SO events were detected based on a set of criteria for down- to up-state amplitude (>140 µV), down-state amplitude (<−80 µV), and duration of down-state (between 0.3 and 1.5 s) and up-state (<1 sec) (see the study of Dang-Vu et al. [44] for more details). SO–spindle modulation index Coupling between the phase of SO and amplitude of sigma power (12–15 Hz) during stage 2 and SWS was measured by modulation index (MI) as described by Canolty et al. [45] and adapted by our group [21]. First, we based the MI analysis on detected SO events (described in the SO detection section) in order to eliminate spurious EEG coupling from the entire sleep stage. In addition, we narrowed our analysis to frontal electrodes (F3 and F4), which have been reported to show the strongest SO activity [43]. To calculate the MI, we applied a Hilbert Transformation to SO and sigma power within the SO event windows to construct the composite complex-valued signal of the amplitude of sigma power and the phase of the SO: Z[n]=asigma[n]expi∅SOn. The normalized mean of this composite vector across trials is the raw MI. Higher MI values indicate less variability in the timing between spindle amplitude peak and a certain phase of the SO. To account for overestimation of MI due to noise, a normalized MI was calculated. A distribution of surrogate MI values was generated randomly, with mean μ and standard deviation σ and the normalized MI was computed as MIraw−μ/σ. We also measured the phase angle of the composite signal Z[n], which is the SO phase at which the amplitude tends to peak. For each SO event, the SO phase during the peak sigma amplitude was calculated. A value of zero for SO phase (∅SO = 0) represents the negative peak of the oscillation (SO trough), and a positive value suggests the up-state of the SO. Here, we set the phase angle of zero as the trough of the SO, in contrast to Niknazar et al. [21], where the phase angle of zero was the positive peak of the SO. MI and phase angle were computed for zolpidem and placebo separately to determine the consistency and preference of the temporal relationship between the phase of SO and the amplitude of spindles. If MI or phase angle variables were significantly different between drug conditions, we computed Pearson’s r between memory performance and the variable to determine if such a difference plays a role in behavioral change. Power spectrum estimation To examine whether other sleep frequency bands might account for memory changes, we analyzed the following sleep frequency bands: sigma (11–15 Hz), theta (4–7 Hz), delta (1–4 Hz), and SO (0–1 Hz). The EEG epochs that were contaminated by muscle and/or other artifacts were rejected using a simple out-of-bounds test (with a ±200 µV threshold) on high-pass filtered (0.5 Hz) version of the EEG signals. The EEG power spectra were computed using the Welch method (4 s Hanning windows with 50% overlap) on the artifact-free 30-s epochs. Then, the estimated power spectra were averaged within each participant/sleep condition/stage/quartile/electrode. Statistical analysis Data reduction Eight participants (6 females) did not complete both visits due to scheduling conflicts, which resulted in 28 participants being included in the analyses. Behavioral data To assess the impact of zolpidem on memory, we examined memory performance using two difference scores (24-h retention: test 3 − test 1, and overnight retention: test 3 − test 2). We conducted a two-sample paired t-test for each difference score comparing placebo and zolpidem conditions. Our primary hypothesis was that the improvement in memory in the zolpidem condition compared to placebo is correlated with a corresponding increase in spindle-related activity. To test this hypothesis, we calculated a change score for spindle density from zolpidem to placebo for each electrode, within each sleep stage, averaged within each quartile. We then calculated Pearson’s r between the memory difference scores and spindle difference scores and the sleep frequency bands. Benjamini–Hochberg correction with false discovery rate set as 5% was used to control for multiple comparisons. Power spectrum estimation To examine the effect of zolpidem on the sleep frequency bands, we performed paired t-tests on the estimated power spectra averaged among all the electrodes for zolpidem and placebo from 0 to 30 Hz in 0.5 frequency bins, corrected by Benjamini–Hochberg correction test [46]. To investigate the spatial distribution of sleep frequency bands, we then performed paired t-tests for each sleep frequency band at each electrode. To control for multiple comparison, we performed Benjamini–Hochberg correction test [46] and cluster-based permutation [47]. Cluster-based permutation Cluster-based permutation tests have been widely used in the field of fMRI studies to control for multiple comparison problems [48]. Maris and Oostenveld [47] developed a method to incorporate cluster-based permutation tests in EEG data, which is used in the current analysis. This technique increases the statistical power to find a drug effect, while sacrificing spatial specificity (i.e. we cannot say which electrode had the significant effect). To test a significant drug effect within each sleep stage and each quartile, we performed a paired t-tests on each electrode site “sample” pair. Clusters were identified if one or more than one adjacent electrode reached significance level (p < 0.05) in the same direction. Within each cluster, t-statistics were summed, and the max of the summed t-stats across all samples was calculated thereby creating the “real” cluster-level statistic. Then, the assigned drug condition to each electrode data point was randomly shuffled, and a “permuted” cluster-level statistic was calculated using the same above procedure. We repeatedly shuffled and calculated the “permuted” cluster-level statistic 2,000 times to get the expected distribution of the cluster-level statistic if there was no drug effect (permutation distribution). The real cluster-level statistic was then compared with permutation distribution and drug was considered to have a significant effect when it was larger than 98.75% of the shuffled t-values after correcting for multiple comparisons (i.e. 100 – 5/number of quartiles). The reported statistic is the number of times the real cluster-level statistic happens in the 2,000 permutations. Results Behavioral data For the placebo condition, participants took 2.89 ± 1.37 trials to reach the 70% criterion. For the zolpidem condition, participants took 2.61 ± 1.03 trials to reach the 70% criterion. Participants took similar amount of trials to reach the criterion between conditions, t27 = 1.31, p = 0.20. Two conditions did not differ in performance during test 1 (t27 = −1.09, p = 0.29) or test 2 (t27 = −1.00, p = 0.32), suggesting a comparable baseline (Supplementary Figure 1). Similar to prior reports [24], memory recollection was improved in the zolpidem group compared to the placebo group. Specifically, participants in zolpidem condition had better verbal memory retention both at the 24-h retention (t27=2.40, p = 0.02) and overnight retention (t27 = 2.64, p = 0.01) tests (Figure 2). Figure 2. Open in new tabDownload slide Behavioral results. Participants performed better after taking zolpidem than placebo both for 24-h retention (t27=2.40, p = 0.02) and overnight retention (t27 = 2.64, p = 0.01). *Statistically significant at p < 0.05 Figure 2. Open in new tabDownload slide Behavioral results. Participants performed better after taking zolpidem than placebo both for 24-h retention (t27=2.40, p = 0.02) and overnight retention (t27 = 2.64, p = 0.01). *Statistically significant at p < 0.05 Sleep data No differences in sleep architecture were detected except for zolpidem showing more SWS and less REM sleep (Table 1), similar to our prior finding [27]. Specifically, total sleep time, WASO, SE, and time spent in stage 1 and stage 2 were not significantly different between two conditions (p > 0.05). Zolpidem condition had significant more SWS (p < 0.05) and less REM sleep (p < 0.05) compared to placebo. When examined by quartile (Supplementary Table 1), we found that the placebo condition had significantly more stage 2 (p < 0.05) and less SWS (p < 0.05) during quartile 1 as well as more REM during quartile 2 (p < 0.05). Table 1. Sleep Architecture Sleep stage . Placebo . Zolpidem . p . TST (min) 536.18 (47.92) 537.71 (39.15) 0.83 N1 (min) 14.46 (8.44) 13.32 (11.73) 0.59 N1 (%) 2.74 (1.62) 2.55 (2.28) 0.63 N2 (min) 283.66 (53.44) 288.61 (46.66) 0.44 N2 (%) 52.87 (8.61) 53.75 (8.19) 0.40 N3 (min) 110.09 (37.99) 121.66 (41.68) 0.02 N3 (%) 20.64 (7.32) 22.58 (7.46) 0.02 REM (min) 127.95 (32.07) 113.66 (29.06) 0.01 REM (%) 23.76 (5.26) 21.01 (4.58) 0.00 WASO 31.30 (27.60) 25.73 (26.00) 0.11 SE 92.39 (5.64) 93.26 (4.89) 0.18 Sleep stage . Placebo . Zolpidem . p . TST (min) 536.18 (47.92) 537.71 (39.15) 0.83 N1 (min) 14.46 (8.44) 13.32 (11.73) 0.59 N1 (%) 2.74 (1.62) 2.55 (2.28) 0.63 N2 (min) 283.66 (53.44) 288.61 (46.66) 0.44 N2 (%) 52.87 (8.61) 53.75 (8.19) 0.40 N3 (min) 110.09 (37.99) 121.66 (41.68) 0.02 N3 (%) 20.64 (7.32) 22.58 (7.46) 0.02 REM (min) 127.95 (32.07) 113.66 (29.06) 0.01 REM (%) 23.76 (5.26) 21.01 (4.58) 0.00 WASO 31.30 (27.60) 25.73 (26.00) 0.11 SE 92.39 (5.64) 93.26 (4.89) 0.18 Means ± SD. TST, total sleep time; N1, stage 1 sleep; N2, stage 2 sleep; N3, stage 3 sleep/slow-wave sleep; REM, nonrapid eye movement sleep; WASO, wake after sleep onset; SE, sleep efficiency. Open in new tab Table 1. Sleep Architecture Sleep stage . Placebo . Zolpidem . p . TST (min) 536.18 (47.92) 537.71 (39.15) 0.83 N1 (min) 14.46 (8.44) 13.32 (11.73) 0.59 N1 (%) 2.74 (1.62) 2.55 (2.28) 0.63 N2 (min) 283.66 (53.44) 288.61 (46.66) 0.44 N2 (%) 52.87 (8.61) 53.75 (8.19) 0.40 N3 (min) 110.09 (37.99) 121.66 (41.68) 0.02 N3 (%) 20.64 (7.32) 22.58 (7.46) 0.02 REM (min) 127.95 (32.07) 113.66 (29.06) 0.01 REM (%) 23.76 (5.26) 21.01 (4.58) 0.00 WASO 31.30 (27.60) 25.73 (26.00) 0.11 SE 92.39 (5.64) 93.26 (4.89) 0.18 Sleep stage . Placebo . Zolpidem . p . TST (min) 536.18 (47.92) 537.71 (39.15) 0.83 N1 (min) 14.46 (8.44) 13.32 (11.73) 0.59 N1 (%) 2.74 (1.62) 2.55 (2.28) 0.63 N2 (min) 283.66 (53.44) 288.61 (46.66) 0.44 N2 (%) 52.87 (8.61) 53.75 (8.19) 0.40 N3 (min) 110.09 (37.99) 121.66 (41.68) 0.02 N3 (%) 20.64 (7.32) 22.58 (7.46) 0.02 REM (min) 127.95 (32.07) 113.66 (29.06) 0.01 REM (%) 23.76 (5.26) 21.01 (4.58) 0.00 WASO 31.30 (27.60) 25.73 (26.00) 0.11 SE 92.39 (5.64) 93.26 (4.89) 0.18 Means ± SD. TST, total sleep time; N1, stage 1 sleep; N2, stage 2 sleep; N3, stage 3 sleep/slow-wave sleep; REM, nonrapid eye movement sleep; WASO, wake after sleep onset; SE, sleep efficiency. Open in new tab As shown in Figure 3, when averaged across all electrodes, zolpidem showed (1) decreases in power in theta frequencies in stage 2 (quartiles 1 and 2) and SWS (quartile 1) and (2) increases in sigma frequencies in stage 2 (quartiles 2), after correcting for multiple comparisons. Importantly, expected peak plasma concentrations occur during quartiles 1 and 2. Figure 3. Open in new tabDownload slide Power spectrum for zolpidem and placebo in stage 2 and SWS (slow wave sleep) divided into four quartiles, averaged across electrodes. *Statistically significant for the mean across all electrodes following Benjamini–Hochberg correction for multiple comparisons (p < 0.05). Figure 3. Open in new tabDownload slide Power spectrum for zolpidem and placebo in stage 2 and SWS (slow wave sleep) divided into four quartiles, averaged across electrodes. *Statistically significant for the mean across all electrodes following Benjamini–Hochberg correction for multiple comparisons (p < 0.05). Sigma Electrode-based power spectrum estimation Zolpidem shows significantly greater sigma activity in stage 2 (33% of electrodes) and SWS (10% of electrodes), compared to placebo. After correction for multiple comparisons across electrodes using the Benjamini–Hochberg correction, seven electrodes remained significant in quartile 2 and five electrodes in quartile 4 in stage 2, as shown in Figure 4, A. The range of increase is between 14% and 16%. Figure 4. Open in new tabDownload slide Topographic plots of the estimated marginal mean difference in spectral power by 4 quartiles between zolpidem and placebo for (a) sigma frequency (11-15Hz) at stage 2; (b) theta frequency (4-7Hz) at stage 2 and SWS; (c) delta frequency (1-4Hz) at stage 2 and SWS. Q1: quartile 1; Q2: quartile 2; Q3: quartile 3; Q4: quartile 4; dif: difference in the estimated marginal mean between zolpidem and placebo. *Statistically significant at this electrode following Benjamini–Hochberg correction for multiple comparisons. Figure 4. Open in new tabDownload slide Topographic plots of the estimated marginal mean difference in spectral power by 4 quartiles between zolpidem and placebo for (a) sigma frequency (11-15Hz) at stage 2; (b) theta frequency (4-7Hz) at stage 2 and SWS; (c) delta frequency (1-4Hz) at stage 2 and SWS. Q1: quartile 1; Q2: quartile 2; Q3: quartile 3; Q4: quartile 4; dif: difference in the estimated marginal mean between zolpidem and placebo. *Statistically significant at this electrode following Benjamini–Hochberg correction for multiple comparisons. Cluster-based permutation for power spectrum estimation Cluster-based permutation tests confirmed the individual electrode analysis, where the zolpidem group exhibited significantly greater sigma in stage 2 quartile 1 (p = 13/2,000), quartile 2 (p < 1/2,000), quartile 3 (p < 1/2,000), and quartile 4 (p < 1/2,000). Significance was also detected in SWS quartile 2 (p = 2/2,000) and quartile 3 (p = 42/2,000). Correlation between EEG activity and performance No significant correlations emerged between sigma activity and performance change in either the cluster-based permutation or individual electrode site after Benjamini–Hochberg correction. Spindle density Electrode-based estimation Spindle density was correlated with sigma power (r = 0.36, p < 0.001), and zolpidem showed increases in spindle density in stage 2 (4% electrodes were significant) and SWS (5% electrodes were significant), compared with placebo. However, no comparisons survived Benjamini–Hochberg correction (Supplementary Table 2). Cluster-based permutation for power spectrum estimation No drug effect was detected for spindle density. Correlation between EEG activity and performance For spindle density, three electrodes located at central occipital and left temporal areas displayed a positive correlation with overnight retention at quartile 2 in stage 2 after Benjamini–Hochberg correction (Figure 5). Similarly, overnight retention and cluster-based permutation on spindle density were significantly correlated during stage 2 quartile 2 (p = 33/2,000). Figure 5. Open in new tabDownload slide Topographic plots of Pearson’s r in spectral power change (zolpidem minus placebo) and performance change (zolpidem minus placebo) for overnight retention. *Statistically significant at this electrode following Benjamini–Hochberg correction for multiple comparisons. Figure 5. Open in new tabDownload slide Topographic plots of Pearson’s r in spectral power change (zolpidem minus placebo) and performance change (zolpidem minus placebo) for overnight retention. *Statistically significant at this electrode following Benjamini–Hochberg correction for multiple comparisons. Theta Electrode-based power spectrum estimation When each electrode was considered separately, there was a general decrease in theta power in the zolpidem condition compared to placebo in stage 2 (68% electrodes were significant) and SWS (40% electrodes were significant). As shown in Figure 4, B, after correction for multiple comparisons using Benjamini–Hochberg correction, 23 electrodes remained significant in quartiles 1 and 2, 14 electrodes located in the right hemisphere in quartile 3, and 6 electrodes located in central frontal in quartile 4 for stage 2 remained significant. For SWS, 23 electrodes remained significant in quartiles 1 and 2 and 14 electrodes located in the right hemisphere in quartile 3. Cluster-based permutation for power spectrum estimation The zolpidem group exhibited significantly lower theta in stage 2 (p = 98/2,000). All four quartiles showed significantly decreased theta for zolpidem compared to placebo in stage 2 (p < 1/2,000 for quartiles 1–4) and quartiles 1 (p < 1/2,000) and 2 (p < 1/2,000) in SWS. Correlations between EEG activity and memory performance The correlation between differences in overnight memory performance and theta power was significant during stage 2 quartile 2 (p = 37/2,000) from cluster-based permutation. As shown in Figure 5, after Benjamini–Hochberg correction for multiple comparisons, six electrodes located at right occipital lobe displayed a positive correlation with overnight retention at quartile 2 in stage 2, one electrode remained significant at quartile 3 stage 2, suggesting increased theta power has a positive association with better memory retention. Delta Electrode-based power spectrum estimation When each electrode was considered separately, there was a general decrease in delta power in the zolpidem condition compared to placebo in stage 2 (23% electrodes were significant) and SWS (18% electrodes were significant). After correction for multiple comparisons using Benjamini–Hochberg correction, three frontal electrodes remained significant in stage 2 quartile 2. Nine frontal electrodes were significant in quartile 1 SWS and 19 electrodes for quartile 2 SWS, as shown in Figure 4, C. Cluster-based permutation for power spectrum estimation The zolpidem group exhibited significantly lower delta in stage 2 quartile 1 (p = 4/2,000), quartile 2 (p < 1/2,000), and quartile 3 (p = 7/2,000), as well as quartiles 1 (p < 1/2,000) and 2 (p < 1/2,000) in SWS. Correlations between EEG activity and memory performance The correlation between overnight retention and delta power change was significant in stage 2 quartile 2 using the cluster-based permutation analysis (p < 42/2,000). However, there was no significant correlation between delta power and performance at individual electrodes after Benjamini–Hochberg correction. Slow oscillation Electrode-based power spectrum estimation When each electrode was considered separately, changes in SO did not survive Benjamini–Hochberg correction. Cluster-based permutation for power spectrum estimation No drug effect was detected for SO. Correlations between EEG activity and memory performance No correlation was detected between SO and performance by cluster-based permutation or individual electrode analysis after Benjamini–Hochberg correction. Spindle–SO coupling Similar to Niknazar et al. [21], we found significantly higher phase angle measures for zolpidem (0.60 ± 0.56) compared to placebo (0.40 ± 0.48) at F4 (t27 = −2.18, p = 0.04), but not at F3 (p > 0.05) (Figure 6). Higher phase angle measures indicate the spindles were clustered in the up-state of the SO phase closer to the positive peak (Figure 7). Furthermore, a positive relationship was observed between phase angle and memory performance in zolpidem (r = 0.46, p = 0.01) but not placebo (r = 0.11, p > 0.50) at F4, as shown in Figure 8. These findings are similar to Niknazar et al. [21], which showed spindles clustered in the up-state closer to the positive peak of the SO in the zolpidem condition compared to placebo, as well as the significant correlation between phase angle and memory in the zolpidem condition and only marginal correlation in placebo. A higher positive phase angle and the positive correlation between phase values and memory performance in zolpidem suggest that spindles occurring during the up-state of the SO and closer to the positive peak may be optimal as this phase-locking was associated with better memory enhancement. There was no difference in MI between zolpidem and placebo for F3 or F4 (p > 0.05), nor was there a correlation between MI and memory improvement. Figure 6. Open in new tabDownload slide The phase angle of SO–spindle coupling for placebo and zolpidem. Individual phase angle is marked in circles while the average value for each condition is marked by a plus sign. Compared to placebo, zolpidem condition had a shifted phase angle at F4 (t27 = −2.18, p = 0.04). Figure 6. Open in new tabDownload slide The phase angle of SO–spindle coupling for placebo and zolpidem. Individual phase angle is marked in circles while the average value for each condition is marked by a plus sign. Compared to placebo, zolpidem condition had a shifted phase angle at F4 (t27 = −2.18, p = 0.04). Figure 7. Open in new tabDownload slide Mean and standard deviation of SO–spindle coupling phase angle for zolpidem and placebo on a schematic SO. Figure 7. Open in new tabDownload slide Mean and standard deviation of SO–spindle coupling phase angle for zolpidem and placebo on a schematic SO. Figure 8. Open in new tabDownload slide Memory performance improvement and SO–spindle coupling: phase angle and memory performance are positively associated in zolpidem (r = 0.46, p = 0.01) but not placebo (r = 0.11, p > 0.50) at F4. Figure 8. Open in new tabDownload slide Memory performance improvement and SO–spindle coupling: phase angle and memory performance are positively associated in zolpidem (r = 0.46, p = 0.01) but not placebo (r = 0.11, p > 0.50) at F4. Discussion The current study showed that zolpidem led to higher memory retention after a night of sleep compared to placebo, which adds valuable information regarding the effect of zolpidem on memory. Zolpidem also led to increased sigma power and decreased theta and delta power. Overnight retention in the zolpidem condition was associated with increased spindle density, replicating prior work [27], and theta power, a novel finding. Studies investigating the effect of hypnotics on sleep-dependent memory consolidation have shown mixed results. While our group report a positive effect of zolpidem on declarative memory consolidation here and in a prior study [27], others have observed no effect [49] or even a negative effect [50]. Conflicting findings may be due to methodological differences between studies. Specifically, Hall-Porter et al. [50] used the extended release version of zolpidem with 6–8 h of action and found decreased memory performance after drug administration, while zolpidem used in our study has a half-life of approximately 1.5–3.2 h. Meléndez et al. [49] used the same version and dosage of zolpidem as our study and found no memory differences between zolpidem and the control condition. However, they investigated item-memory while we probed associative memory, which has been shown to engage the hippocampus to a greater extent [51]. The current study builds on this literature by showing that zolpidem administered over a full night of sleep enhances associative memory, replicating a prior result using 90-min daytime naps [27]. The positive correlation between theta power and memory performance suggests that even though zolpidem leads to a decrease in theta power globally, participants who had the least reduction in theta tended to have a better memory retention. Even though we did not find significant increases in spindle density in the zolpidem condition compared to placebo, this relationship has been consistently shown in previous studies [21, 27, 29]. Discrepancies between prior results and the current data may be due in part to algorithm-based spindle detection used here while prior studies used visual inspection to hand count spindles [42, 52]. A positive association between spindle density and memory improvement is consistent with previous findings, adding more support to the theory that sleep spindles are critical for memory consolidation [23]. Even though we did not find a correlation between SO and memory improvement, the coupling of SO and spindles was associated with memory, which is consistent with prior literature [21, 53]. Current models of sleep-dependent memory consolidation may help clarify the role of spindles and spindle–SO coupling in this process. One of predominate views of long-term memory consolidation is the active system consolidation theory, which indicates that memories are consolidated during sleep by reactivating memory traces associated with learning and redistributing them in the neocortex [54]. The three oscillations that are hallmarks of memory reactivation include sharp wave ripples, spindles, and SOs [20, 55]. Sharp wave ripples have been found to be nested into the troughs of faster spindle oscillations [18, 53], and spindles occur to a great extent in the up-state of the SO [9, 17, 23], emphasizing the interdependent role of these three oscillations in relaying information from hippocampus to neocortex [23]. Indeed, the simultaneous occurrence of all three oscillations naturally or by experimental intervention leads to great memory consolidation, compared with the features occurring out of phase with one another [23]. In addition to facilitating the thalamocortical communication [54], spindles have been shown to induce long-term potentiation (LTP), which is a key process in long-term memory consolidation [56, 57]. Interestingly, LTP was only inducible in synchronous pre- and postsynaptic spindles but not asynchronous spindles [56], suggesting that the timing between spindles and occurrence of pre- and postsynaptic events is crucial for memory consolidation [57]. Similar results have been shown in humans where memory improvement was observed when auditory stimuli were applied in phase but not out of phase with SO and spindles [9], and both the study of Niknazar et al. [21] and the current study showed that hippocampus-dependent memory improvement is associated with the coupling of SO and spindles during the up-state of the SO. It has been proposed that the up-state of neocortical SOs leads to depolarization and provides an opportunity for reactivation [6]. In short, the positive association between spindle density, spindle–SO coupling, and declarative memory improvement supports the notion that sleep benefits hippocampus-dependent memory by transferring the information from hippocampus to neocortex, as well as inducing synaptic plasticity. The inhibitory effect of zolpidem on theta power has been previously reported [29–31], and such an effect has been suggested to result from binding to GABA receptors [58]. Specifically, mice with insensitive alpha1GABAA receptors and controls both had decreased sleep latency after taking zolpidem, but only the wild type and not the mutants showed significant power reduction encompassing a broad power band of more than 5 Hz, which suggests that alpha1GABAA receptor is responsible for decreased EEG power while the other two are responsible for promoting sleep [58]. The current study showed that theta power was positively associated with memory improvement, suggesting that engineering zolpidem to preserve theta could potentially create an optimal environment for memory consolidation. Prior studies indicate candidate mechanisms by which theta preservation might be achieved. For example, histaminergic neurons in the hypothalamus are known to promote wakefulness, and increased GABA activity in these areas promotes sleep [59]. Increasing GABA activity only on histamine neurons using zolpidem promoted sleep in mice without reducing EEG power [60]. Specifically, zolpidem-insensitive mice were genetically manipulated to be selectively sensitive to zolpidem in histaminergic neurons in the tuberomammillary nucleus of the hypothalamus, and they experienced the sleep promoting effect of zolpidem without having reduced EEG power [60]. Further pharmacological research may be useful in optimizing the memory-enhancing effects of sleep. Theta oscillation during wakefulness is essential for episodic memory consolidation [61, 62] and has been proposed to integrate information between hippocampus and neocortex [63]. It has been speculated that theta during sleep might have similar functionality as during wakefulness [64]. Specifically, theta oscillation tends to occur in close temporal proximity to synaptic and neuronal changes after memory encoding [65], signaling its role in memory consolidation. Animal studies show that theta peaks signal the depolarization phase of the cell membrane, which leads to increased neuron reactivity to inputs [65, 66] and facilitates the induction of LTP during wakefulness [67]. Such a neural firing pattern is replayed during REM sleep [68], supporting theta’s role in memory consolidation during sleep. Specifically, Poe et al. [68] found that hippocampal cells that were active when animals were in novel places tended to fire during theta peaks in REM sleep, whereas cells that were associated with familiar places tended to fire during the trough of the theta oscillation. Therefore, theta oscillation is implicated in memory consolidation through neural replay during REM sleep [69]. Although the theta that can be measured via scalp EEG in humans is unlikely the same as the hippocampal theta measured in rodents, human memory studies have shown a relation between theta EEG and emotional memory consolidation during REM sleep [70, 71]. In addition, a growing number of TMR studies has explored the role of theta on memory consolidation during NREM sleep [64, 72, 73]. Specifically, Schreiner et al. [72, 74] reported that induced theta power during NREM sleep signals successful memory reactivation. They further showed that the theta power triggered by memory cues shared similar neural signatures between wakefulness and NREM sleep, suggesting a role for theta in stabilizing reactivated memories in both wakefulness and sleep [64, 73]. Our positive correlation between theta and memory suggests that pharmacological modulation of theta power during NREM sleep might also play a role in hippocampal-dependent memory consolidation. The current study has limitations. Even though our discussion on GABA receptor subtypes provides a plausible mechanism and intervention to increase spindles while preserving theta pharmacologically, alternative explanations about how decreased theta contribute to memory retention are possible. For example, it might be the case that as the number of spindles increased, theta power decreased as a simple side effect of the fact that a greater portion of each 30-s epoch was taken up by sigma-frequency activity. In this scenario, participants with “preserved” theta might be those for whom sigma power increased as a result of an increase in the amplitude of spindles, rather than the number or duration of spindles. Here, we showed that spindle density, but not sigma power, is positively correlated with memory, suggesting that the number of spindles contribute to memory, which weakens the possibility that spindle amplitude contributes to memory. However, a specific investigation of how theta and sigma independently and collectively contribute to memory is warranted. Other limitations include small sample size and that the dosage is not based on mg/kg. Factors like sex and BMI could influence the metabolism of zolpidem, which would increase the individual variability of the drug effect [75]. In addition, we were not able to tease apart the specific and potentially independent roles that theta and spindles may play in memory consolidation. Recent findings by Kim et al. [76] have demonstrated that SO and delta activity may support different and even opposing aspects of the memory process. It would be interesting to investigate how theta and spindles may contribute to different aspects of the memory process using a more complex memory task. For example, a memory task that distinguishes sensory-rich content from non-sensory content to investigate the possibility that theta during NREM preferentially enhances sensory-rich information [77, 78]. Taken together, this study demonstrates a positive role of zolpidem on overnight memory performance as well as suggests a role for spindle density and theta frequency power in these performance improvements. Furthermore, it provides additional support for the critical role of sleep spindles as well as the coupling between SO and spindles in memory consolidation. Future studies are needed to tease apart mechanisms behind the role of NREM theta power and spindle–SO coupling on memory consolidation. The work was performed at the University of California, Riverside. Acknowledgments We thank undergraduate research assistants in the laboratory for assistance with data collection. Funding This work was supported by the Office of Naval Research (grant N00014-14-1-0513) and the National Institutes of Health (NIH) (grant R01 AG046646). Financial disclosure: None. Nonfinancial disclosure: None. Conflict of interest statement. None declared. References 1. Diekelmann S . 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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/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Roth, Alicia, J;Dietch, Jessica, R

doi: 10.1093/sleep/zsaa153pmid: 32805031

Despite decades of research demonstrating the efficacy and effectiveness of Cognitive Behavioral Therapy for Insomnia’s (CBT-I) [1], it is still difficult to provide real-world access to CBT-I for the majority of individuals suffering from insomnia. CBT-I has been recommended as the first-line treatment for insomnia by the American College of Physicians [2], European Sleep Research Society [3], American Academy of Sleep Medicine [4], National Institutes of Health (NIH) Consensus State-of-the-Science Conference [5], and Veterans Affairs (VA)/Department of Defense clinical practice guidelines [6], yet CBT-I is underutilized. The demand for CBT-I services is high, with epidemiological estimates that insomnia affects between 10% and 50% of the general population [7], and the supply of qualified CBT-I providers is low. Although the dissemination of CBT-I training is growing, it is difficult to capture the amount of qualified CBT-I practitioners. There are still only 106 certified providers worldwide who carry the Diplomate in Behavioral Sleep Medicine (DBSM) [8] and they are geographically limited [9]. As of a 2016 report, there have been 598 mental health providers who have completed the VA CBT-I competency training [10]. Anecdotally, the authors note that providers within the behavioral sleep medicine community report high referral volume and months-long waitlists, indicating the number of individuals in need of CBT-I far exceeds the number of qualified CBT-I providers. Moreover, there is limited public and healthcare provider knowledge about behavioral treatments for insomnia; many providers are unaware of CBT-I or, if familiar, lack an appropriate referral source. An additional degree of complexity for delivering CBT-I is that research has demonstrated insomnia frequently co-occurs with other mental health problems (e.g. depression, anxiety, chronic pain, posttraumatic stress disorder [PTSD]). Previous work has demonstrated treating the co-occurring condition frequently does not result in remission from insomnia; once insomnia develops, it is unlikely to remit without targeted treatment. On the other hand, numerous meta-analyses suggest treating insomnia via CBT-I can have positive benefits on psychosocial health symptoms, including depression [11, 12], anxiety [12, 13], chronic pain [14], and PTSD [15]. Despite the demonstrated impact of CBT-I on co-occurring psychosocial health conditions, CBT-I is underutilized. In particular, CBT-I may be an effective approach to prevent depressive symptoms for individuals with comorbid insomnia. In the recent study published in SLEEP, Cheng et al. [16] addressed both the challenge of access to CBT-I and the complexity of addressing the prevention of depression by providing an easy to disseminate digital platform to deliver CBT-I (dCBT-I) as well as observing the effects that CBT-I had on depressive symptoms and incident rates of depression over a 1-year period. A previous meta-analysis of 15 randomized controlled trials evaluating internet-delivered CBT-I showed that internet-based CBT-I significantly improved sleep efficiency and total sleep time while decreasing Insomnia Severity Index (ISI) scores and depressive symptoms compared to control conditions [17]. This study [16] added to the growing literature on insomnia treatment in the context of depression prevention by testing the following: (1) The durability of the treatment’s antidepressant effect 1 year post-dCBT-I; (2) Incidence of moderate to severe depression 1 year post-dCBT-I in participants with minimal depression at baseline; and (3) Whether the clinical targets typically addressed by insomnia treatment protect against depression (i.e. do insomnia outcomes predict depression outcomes?). Figure 1 summarizes these key findings. Figure 1. Open in new tabDownload slide Key results from Cheng et al. [16]. Figure 1. Open in new tabDownload slide Key results from Cheng et al. [16]. Participants met DSM-5 criteria for Chronic Insomnia [18] and were excluded if they reported daily or near-daily low mood or anhedonia, and/or suicidality. Depression severity at baseline did not differ between the two treatment groups, and half of the sample reported moderate to severe depressive symptoms. Participants were randomized to dCBT-I or the control condition (online sleep psychoeducation). The dCBT-I program implemented was the Sleepio online CBT-I program, which has demonstrated effectiveness in improving insomnia across several randomized clinical trials [19, 20]. The dCBT-I treatment included the full range of skills typically taught in in-person CBT-I: behavioral interventions (sleep hygiene, sleep restriction, and stimulus control), relaxation strategies (PMR and autogenic training), and cognitive interventions (cognitive therapy and paradoxical intention). Online sleep psychoeducation included six weekly emails from the NIH on sleep topics (basic sleep science, impact of sleep on health problems, effect of substances such as alcohol, caffeine on sleep, creating a sleep-conducive environment) [21]. A total of 358 participants completed dCBT-I and 300 completed the online sleep psychoeducation. The Quick Inventory of Depressive Symptomatology (QIDS-SR) measured depression severity (moderate severity or higher = probable depression; less than or equal to 6 = subclinical depression) and the ISI measured insomnia symptoms (0–7 = no clinically significant insomnia; 8–14 = sub-threshold insomnia; 15–21 = moderately severe clinical insomnia; 22–28 = severe clinical insomnia). Both measures were completed pre-intervention, post-intervention, and at 1-year follow-up. The main treatment outcomes are presented in Figure 1. The authors concluded that dCBT-I has both acute and long-term (1 year) effects on depressive symptomatology. dCBT-I may also serve as a preventative measure to protect against the development of new incidence of depression as the chronicity of insomnia persists. Although the sample data showed a higher rate of insomnia and depression severity than in comparable studies, the authors discuss that this may be in part due to this sample representing a more inclusive sample (e.g. more racial minorities, SES spread, and women). The sample also includes participants drawn from several healthcare settings: primary care offices, hospitals, and insurance subscribers. Limitations discussed by the authors included utilizing clinically validated questionnaires instead of a clinician’s evaluation to assess depression or insomnia, as well as high attrition in the dCBT-I group (though intent to treat analyses were utilized). Further, the study excluded individuals with daily or near-daily low mood and anhedonia (suggesting individuals with one of these symptoms could still be included), and thus the results for improvement in depressive symptoms may not generalize to samples with more severe depressive symptoms at treatment initiation. An additional limitation is that the threshold for new incidence of depression were symptoms in the moderate range of symptom severity, which excluded mild symptoms of depression in the calculations for new incidence of depression. The results from Cheng et al. [16] add to the growing literature demonstrating that effectively treating insomnia with CBT-I may serve an important role in the prevention of comorbid mental health conditions. As recent research shows, behavioral sleep medicine interventions are not siloed interventions that only affect sleep. Moreover, insomnia should be considered a target for improvement rather than a symptom (e.g. depression, other chronic health conditions). In addition to addressing medical comorbidities, brief CBT-I interventions have shown to lower overall healthcare costs [22] and have effects on public health (e.g. increased productivity by reducing absenteeism, reduction of accidents due to fatigue/sleepiness). However, in order to maximize CBT-I’s utility as the front-line treatment for insomnia, the field must address barriers across multiple fronts. Koffel, Bramoweth & Ulmer [23] conducted a narrative review that demonstrated three levels of barriers that interfere with access to CBT-I: systemic, patient-level, and provider-level. Systemic barriers, such as limited number of trained providers or lack of personalized treatment for comorbid conditions, can potentially be addressed by developing models of CBT-I that expand access (e.g. stepped-care models incorporating dCBT-I) and adapting CBT-I for prevention of or treatment in the context of comorbid conditions (e.g. depression). Patient-level barriers, such as lack of knowledge about CBT-I or access to CBT-I referral, can potentially be addressed by raising public health awareness about CBT-I and other behavioral sleep medicine interventions, increasing resources for patient education at the primary care level, and improving referral streams and access to existing CBT-I providers. Finally, provider-level barriers such as limited knowledge about CBT-I among providers and siloing of CBT-I services within a single discipline (i.e. psychology) can potentially be addressed by growing multidisciplinary knowledge via increased education and training across disciplines, and public health promotion efforts. One recent study highlighted the pervasiveness of lack of provider knowledge about sleep; 82% of the surveyed medical practitioners (N = 88) reported they considered sleep disorders to be secondary problems, which may also reflect an unwillingness to refer for sleep disorders as a first priority [24]. Efforts are needed at each level of barriers to effectively increase access to CBT-I. The work of Cheng et al. [16] and other tests of dCBT-I demonstrate one avenue to reduce barriers to CBT-I across several levels by increasing accessibility to care (e.g. providing an avenue for access to CBT-I in areas not served by a CBT-I provider, eliminating wait times for CBT-I). The reduction of barriers is signaled by the increased inclusivity of the sample compared to typical CBT-I trials, suggesting the reach of dCBT-I may be greater than traditional in-person psychotherapy. Further research identifying characteristics of patients that will be successful with a self-guided, digital form of CBT-I, as well as those for whom dCBT-I can effectively prevent depression, is warranted. Given the recent pandemic and the rush to make virtual psychotherapy available, more work is also needed to understand how virtual insomnia treatment options, including dCBT-I, fit into the stepped-care landscape for insomnia during this crisis and beyond. Moreover, dCBT-I’s positive effects on depression prevention warrant an additional investigation into how cognitive behavioral interventions for sleep can be a gateway to psychotherapy to address other psychological conditions. Funding None declared. Conflict of interest statement. None declared. References 1. Morin CM , et al. Psychological and behavioral treatment of insomnia: update of the recent evidence (1998-2004) . Sleep. 2006 ; 29 ( 11 ): 1398 – 1414 . Google Scholar Crossref Search ADS PubMed WorldCat 2. Qaseem A , et al. ; Clinical Guidelines Committee of the American College of Physicians . Management of chronic insomnia disorder in adults: a clinical practice guideline from the American College of Physicians . 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Girard, Franck; von Siebenthal, Michelle; Davis, Fred P; Celio, Marco R

doi: 10.1093/sleep/zsaa085pmid: 32343818

Abstract Study Objectives: The brainstem contains several neuronal populations, heterogeneous in terms of neurotransmitter/neuropeptide content, which are important for controlling various aspects of the rapid eye movement (REM) phase of sleep. Among these populations are the Calbindin (Calb)-immunoreactive NPCalb neurons, located in the Nucleus papilio, within the dorsal paragigantocellular nucleus (DPGi), and recently shown to control eye movement during the REM phase of sleep. Methods: We performed in-depth data mining of the in situ hybridization data collected at the Allen Brain Atlas, in order to identify potentially interesting genes expressed in this brainstem nucleus. Our attention focused on genes encoding neuropeptides, including Cart (Cocaine and Amphetamine Regulated Transcripts) and Nesfatin 1. Results: While nesfatin 1 appeared ubiquitously expressed in this Calb-positive neuronal population, Cart was coexpressed in only a subset of these glutamatergic NPCalb neurons. Furthermore, an REM sleep deprivation and rebound assay performed with mice revealed that the Cart-positive neuronal population within the DPGi was activated during REM sleep (as measured by c-fos immunoreactivity), suggesting a role of this neuropeptide in regulating some aspects of REM sleep. Conclusions: The assembled information could afford functional clues to investigators, conducive to further experimental pursuits. Nucleus papilio, NPCalb, REM sleep, DPGi, calbindin, Cart, nesfatin Statement of Significance Several physiological and behavioral features are characteristics of the rapid eye movement (REM) phase of sleep, also called paradoxical sleep. These include muscle atonia, desynchronized EEG activity, vivid dreaming, and rapid eye movements. A small cluster of Calbindin-immunoreactive neurons (namely, the Nucleus papilio) has been recently identified in the brainstem and shown to be both necessary and sufficient for triggering eye movement during REM sleep. In the present study, we performed data mining of the in situ hybridization data collected at the Allen Brain Atlas, in order to identify genes expressed in these neurons. Our data show that the neuropeptide Cart (Cocaine and Amphetamine Regulated Transcript) is expressed in some of these Calbindin-immunoreactive neurons and that these Cart-neurons are activated during REM sleep. Introduction Several physiological and behavioral features are characteristics of the REM phase of sleep, also called paradoxical sleep. These include rapid eye movements, vivid dreaming, desynchronized electroencephalogram activity, and atonia of the postural muscles [1]. Among several brainstem structures that have been shown to be involved during REM sleep [2–6], the dorsal paragigantocellular nucleus (DPGi) appears as a very crucial area in sleep regulation, as it contains neurons of different nature, which apparently play particular roles in regulating some aspects of REM sleep. Indeed, GABAergic neurons of the DPGi were proposed to inhibit the noradrenergic wake-promoting neurons of the Locus ceruleus, the dorsal raphé nucleus, and the ventrolateral periaqueductal gray, thereby favoring the initiation of REM sleep [4, 7, 8]. Within the DPGi we recently identified the Nucleus papilio (NPCalb) as a bilateral, symmetric cluster of glutamatergic neurons expressing the calcium-binding protein Calbindin-D28k (Calb) [9]. Calb immunoreactivity in this nucleus is conserved in rodents (mouse and rat), monkey, and human. In mouse, it densely projects to the three contralateral eye-muscle nuclei (abducens, trochlear, and oculomotor), but also to several brain areas contributing to REM sleep control including the MCH-neurons of the lateral hypothalamus, the subcoeruleus nucleus (SubC), the pontine reticular formation (PnC), and the gigantocellular reticular nucleus (Gi). Noteworthy, activating or inactivating these neurons by means of optogenetics demonstrated both the necessity and sufficiency of the NPCalb for triggering eye movement during REM sleep [9]. The automated ALLENMINER search [10] of in situ hybridization (ISH) images in the Allen Brain Atlas (ABA) can be implemented to identify genes that are expressed in rather small agglomerations of cells, such as the PV1/Parvafox nucleus (parvalbumin-FoxB1 immunoreactive nucleus) in the lateral hypothalamus [11]. Using this protocol, potentially interesting information respecting very small neuronal populations can be elicited. By this means, ISH on adjacent sections has hitherto revealed most of the genes that were tested to be coexpressed with the mRNA for Pvalb [11]. We were therefore sanguine that a similar search would facilitate molecular and potentially functional characterization of the Calb-expressing neurons of the NPCalb. In addition, we screened the AGEA (Anatomic Gene expression Atlas) [12] at the ABA, focusing on genes expressed in the NPCalb/DPGi area. Methods Animals For analyzing the expression of several proteins potentially coexpressed in the NPCalb, 15 C57BL/6J mice (from our animal facility) of both sexes, aged 10–14 weeks, as well as 3 Wistar rats (Janvier, Lyon, France), were used. For the analysis of the neurotransmitter status of the Cart-expressing neurons, two mice of each genotype were used (Slc17a6::Cre and Slc32a1::Cre encoding, respectively, VGlut2 glutamate and VGat GABA transporters, both obtained from the Jackson Laboratory; Slc6a5-GFP::Cre mice encoding the Glyt2 glycine transporter, obtained from Dr Zeilhofer, Pharmacology, Zurich). Fifteen C57BL/6J female mice were included in the REM sleep deprivation and rebound assay. All animals were housed in our animal facilities and in accordance with the relevant Swiss laws. The Veterinary Commission for Animal Research of the Canton of Fribourg (Switzerland) approved this study. Animals were anesthetized with pentobarbital (100 mg/kg of body weight) and then perfused via the left ventricle, first with chilled (4°C) physiological (0.9%) saline and then with chilled (4°C) 4% paraformaldehyde. The brains were excised and post-fixed overnight at 4°C in 4% paraformaldehyde and subsequently immersed in 0.1 M Tris buffer (pH 7.3) containing 20% sucrose in preparation for cryo-sectioning. Immunohistochemistry The various brain specimens were cryo-sectioned into 30, or 40, µm coronal sections and collected directly in 0.1 M Tris buffer containing 0.02% sodium azide, within which they were maintained until the time of analysis. The sections were immunostained according to standard protocols. Free-floating sections were incubated for 1–3 days at 4°C with primary antibody mixture diluted in TBS containing 0.1% Triton X-100 and 10% calf serum. The primary antibodies used are described in Table 1. Depending on the experiment, secondary antibodies included Cy3- or Cy2-conjugated anti-rabbit/mouse, Alexa488-conjugated anti-rabbit/mouse, Cy3- or Cy2-conjugated Streptavidin (Jackson Immunoresearch, Suffolk, UK), biotinylated anti-rabbit/mouse (Vector Laboratories, Servion, Switzerland), all used at the dilution recommended by the suppliers. Table 1. Description of Primary Antibodies Used for Immunohistochemistry Antibody to . Host species . Antigen . Manufacturer . Catalog number . Dilution used . Calbindin D-28K Mouse Whole chicken protein from gut Swant, Marly, Swizerland CB300 1–2,000 Calbindin D-28K Rabbit Recombinant rat calbindin D-28K Swant, Marly, Swizerland CB38 1–2,000 Cart Rabbit Rat Cart aa 55–102 Phoenix Pharmaceuticals,Karlsruhe, Germany H-003-62 1–2,000 Nesfatin Rabbit Rat Nesfatin aa 1–82 Phoenix Pharmaceuticals, Karlsruhe, Germany H-003-22 1–1,000 c-fos Mouse Recombinant human c-fos aa 1–380 Abcam, Cambridge, UK ab208942 1–2,000 ChAT Rabbit Pig ChAT aa 150–250 Abcam, Cambridge, UK ab178850 1–1,000 Antibody to . Host species . Antigen . Manufacturer . Catalog number . Dilution used . Calbindin D-28K Mouse Whole chicken protein from gut Swant, Marly, Swizerland CB300 1–2,000 Calbindin D-28K Rabbit Recombinant rat calbindin D-28K Swant, Marly, Swizerland CB38 1–2,000 Cart Rabbit Rat Cart aa 55–102 Phoenix Pharmaceuticals,Karlsruhe, Germany H-003-62 1–2,000 Nesfatin Rabbit Rat Nesfatin aa 1–82 Phoenix Pharmaceuticals, Karlsruhe, Germany H-003-22 1–1,000 c-fos Mouse Recombinant human c-fos aa 1–380 Abcam, Cambridge, UK ab208942 1–2,000 ChAT Rabbit Pig ChAT aa 150–250 Abcam, Cambridge, UK ab178850 1–1,000 The antigen, host species, manufacturer, catalog number, and working dilution are given for all primary antibodies used in this study. Open in new tab Table 1. Description of Primary Antibodies Used for Immunohistochemistry Antibody to . Host species . Antigen . Manufacturer . Catalog number . Dilution used . Calbindin D-28K Mouse Whole chicken protein from gut Swant, Marly, Swizerland CB300 1–2,000 Calbindin D-28K Rabbit Recombinant rat calbindin D-28K Swant, Marly, Swizerland CB38 1–2,000 Cart Rabbit Rat Cart aa 55–102 Phoenix Pharmaceuticals,Karlsruhe, Germany H-003-62 1–2,000 Nesfatin Rabbit Rat Nesfatin aa 1–82 Phoenix Pharmaceuticals, Karlsruhe, Germany H-003-22 1–1,000 c-fos Mouse Recombinant human c-fos aa 1–380 Abcam, Cambridge, UK ab208942 1–2,000 ChAT Rabbit Pig ChAT aa 150–250 Abcam, Cambridge, UK ab178850 1–1,000 Antibody to . Host species . Antigen . Manufacturer . Catalog number . Dilution used . Calbindin D-28K Mouse Whole chicken protein from gut Swant, Marly, Swizerland CB300 1–2,000 Calbindin D-28K Rabbit Recombinant rat calbindin D-28K Swant, Marly, Swizerland CB38 1–2,000 Cart Rabbit Rat Cart aa 55–102 Phoenix Pharmaceuticals,Karlsruhe, Germany H-003-62 1–2,000 Nesfatin Rabbit Rat Nesfatin aa 1–82 Phoenix Pharmaceuticals, Karlsruhe, Germany H-003-22 1–1,000 c-fos Mouse Recombinant human c-fos aa 1–380 Abcam, Cambridge, UK ab208942 1–2,000 ChAT Rabbit Pig ChAT aa 150–250 Abcam, Cambridge, UK ab178850 1–1,000 The antigen, host species, manufacturer, catalog number, and working dilution are given for all primary antibodies used in this study. Open in new tab Stereotactic injections in mouse brains The experiment was conducted essentially as already described [9, 13]. Briefly, AAV2/1.CAG.Flex.Tomato.WPRE.bGH viral construct (Vector Core, University of Pennsylvania, USA) was stereotactically injected in the brain of either Slc17a6::Cre or Slc32a1::Cre mice. Injections were performed in the NPCalb, at the following Bregma coordinates: rostro-caudal: −6.36 mm, medio-lateral: −0.2 mm, and dorso-ventral: −4.35 mm. Two weeks after the stereotactic injections, the animals were anesthetized and perfused with 4% paraformaldehyde. The brains were excised and cryo-sectioned, and the specimens were analyzed immunohistochemically for Cart and Tomato expression. In these conditions, a specific and accurate Tomato expression is obtained in Cre-expressing neurons, as previously shown in Calb1::Cre mice [9]. REM sleep deprivation and rebound assay Mice were deprived of REM sleep by implementing a modified version of the flower-pot technique, which spared the animals of major stress [14, 15]. Three groups were established: in the first group (“REM sleep deprivation and rebound” = REMS-D + R), the animals (n = 5) were maintained together for 72 h on six small stone platforms (7 × 4 cm for rats, 3 × 3 for mice), placed in a water tank. The surface of the platform was 1 cm above the water level. During this 72 h period, the animals had free access to food and water. Owing to the loss of the muscular tone that characterizes the onset of REM sleep, the animals fell into the water and were thereby deprived of REM sleep. After 72 h, the animals were transferred to a conventional cage in a quiet room and were permitted for 3 h to undergo REM sleep (=rebound). In the second group (“REM sleep deprivation” = REMS-D), the animals (n = 5) were sacrificed immediately after the termination of the 72 h REM sleep deprivation period, without recovery. In the third group (“control” = C), the animals (n = 5) were maintained in their cages under standard conditions for 72 h prior to sacrifice. The animals were anesthetized and perfused with fixative as described above under the “Animals” section. The brains were then excised and cryo-sectioned. The sections were immunostained for Cart as well as for c-fos, a surrogate marker of neuronal activity [16] The number of double-stained c-fos/Cart was determined on alternating 30 µm coronal sections, on the following brain areas: dorsal motor nucleus of vagus (10N)/nucleus of the solitary tract (Sol), Nucleus prepositus (Pr), DPGi, gigantocellular reticular nucleus (Gi), medial longitudinal fasciculus (mlf), and lateral paragigantocellular nucleus (LPGi). Only cells with strong Cart immunoreactivity were taken into account. The percentage of Cart+Fos+ relative to the total Cart+ cells was statistically compared between the three different conditions using a one-tailed Student’s t-test, for each of the anatomical areas investigated. Data from n = 4 animals in each of the conditions were used. Image analysis The specimens were evaluated either in a Leica epifluorescence microscope, a Nikon Eclipse Ni fluorescence microscope, a Leica TCS SP5 confocal laser microscope, or a Hamamatsu Nanozoomer scanner. Postprocessing of the images and contrast adjustments therein were performed using the Adobe Photoshop and Nanozoomer slide-processing software. Informatics A search of the adult mouse ABA (https://portal.brain-map.org/) was undertaken to identify genes that might be coexpressed with Calb1 in the targeted NPCalb. First, we downloaded three-dimensional expression data measured by ISH from coronal sections of the adult mouse brain using ALLENMINER (v2.0) [10]. Since the Calb1 gene expression in the target nucleus was restricted to a small region, we only used data from the coronal ISH series (4,216 datasets available for 3,968 genes), which are more densely sampled than the sagittal ones. Next, we defined a region of interest (ROI), which bilaterally encompassed the Calb1-expressing cells and the local neighborhood as a cube with boundaries in ABA coordinate space of 11–12 mm rostro-caudal, 5.4–6.4 mm dorso-ventral, and 5–6.4 mm medio-lateral. Within this ROI, the patterns of gene expression were then ranked relative to that for Calb1 (ABA image series 71717640, https://mouse.brain-map.org/experiment/show/71717640 and 79556672, https://mouse.brain-map.org/experiment/show/79556672), as measured by the Pearson’s correlation of the expression energy reported in corresponding voxels in the region (ALLENMINER run mode—sim search). An additional search was performed using the AGEA facility available at the ABA, allowing users to screen for genes expressed in a selected ROI [12]. The area investigated, focused on the DPGi, corresponded to AGEA coordinates: 11.154/5.422/5.887. Results Data mining for genes expressed in the Nucleus papilio in the mouse brain The NPCalb was previously defined as a symmetric cluster of Calb-expressing neurons, lodged in the DPGi, which in the mouse brain spans a distance of ~0.6 mm, from Bregma levels −5.8 to −6.4 mm [9]. The ABA ISH data available for the Calb1 gene show that Calb1 mRNA expression fully recapitulates the protein expression (Figure 1) [9, 17]. Figure 1. Open in new tabDownload slide Expression of Calb1 mRNA (A and B) and protein (C) in the NPCalb. ISH images (A and B) were taken from the Allen Brain Atlas (Image credit: Allen Institute; http://mouse.brain-map.org/experiment/show/79556672). (B) It is a higher magnification of the image shown in (A), focusing only on one hemisphere. (C) It is a confocal image of a mouse brain coronal section immunostained for Calb protein, showing immunoreactivity in neuronal cell bodies within the DPGi and in neuritis in surrounding areas (Pr and Gi). DPGi, dorsal paragigantocellular nucleus; Gi, gigantocellular reticular nucleus; MVeMC, medial vestibular nucleus, magnocellular; MVePC, medial vestibular nucleus, parvicellular; NP, Nucleus papilio; Pr, prepositus nucleus; V4, fourth ventricle. For the ALLENMINER search, we drew only on data that were derived from coronally sectioned ISH series (4,216 datasets available for 3,968 genes—see the “Methods” section). Consequently, they relate to only a fraction of the murine genome. Additional data mining using AGEA yielded 107 pages each comprising 20 genes, with ISH data also corresponding to coronal sections. The genes that were selected by both automated searches were further screened by visual inspection of the imaged ISH sections that traversed the NPCalb area. After this second round of screening, we eliminated genes for which the signals were so low as to raise doubts respecting their specificity and those with ubiquitous expression in the medulla oblongata. The screens identified 141 genes, which are restrictedly expressed in discrete regions of the medulla oblongata, including the area comprising the NPCalb. Figures 2 and 3 illustrate examples of genes that manifest such restricted expression patterns, with a focus on the area corresponding to the NPCalb. The genes fall into several main categories (Figure 4; Tables 2–4): Table 2. Genes Expressed in the DPGi/NPCalb Region—Part 1 Gene . Complete name . Molecular activity . Biological process . Adarb1 Adenosine deaminase, RNA-specific, B1 Enzyme Nucleic acid processing Adcyap1 Adenylate cyclase activating polypeptide 1 (=PACAP) Neuropeptide Neurotransmission/synapse functioning (neuropeptide) [18, 19] Adk Adenosine kinase Enzyme Metabolism (adenine) [20] Ajap1 Adherens junction associated protein 1 (=Shrew1) Cell adhesion/ECM/axon guidance Apba1 Amyloid beta (A4) precursor protein binding, family A, member 1 (=X11/Mint1) Adaptator protein Neurotransmission/synapse functioning (neurotransmitter release) Arl10 ADP-ribosylation factor-like 10 GTPase activity Multiple Asic2 Acid-sensing (proton-gated) ion channel 2 Sodium channel Ion channel Baiap3 BAI1-associated protein 3 Neurotransmission/synapse functioning (SNARE-dependent exocytosis) Btbd11 BTB (POZ) domain containing 11 Cacna1g Calcium channel, voltage-dependent, T type, alpha 1G subunit (=Cav3.1) Calcium channel Ion channel [21–23] Cacna1h Calcium channel, voltage-dependent, T type, alpha 1H subunit (=Cav3.2) Calcium channel Ion channel [21–23] Cacna2d1 Calcium channel, voltage-dependent, alpha2/delta subunit 1 (=a2d1) Calcium channel Ion channel Cacna2d3 Calcium channel, voltage-dependent, alpha2/delta subunit 3 (=a2d3) Calcium channel Ion channel Cacng5 Calcium channel, voltage-dependent, gamma subunit 5 Calcium channel Ion channel Cadps2 Ca2+-dependent activator protein for secretion 2 Neurotransmission/synapse functioning (dendritic spine maintenance) Calb1 Calbindin 1 (=Calbindin D28k) EF-hand Ca binding; calcium sensor/buffer Calcium homeostasis [9] Calb2 Calbindin 2 (=Calretinin) EF-hand Ca binding; calcium sensor/buffer Calcium homeostasis CamkV CaM kinase-like vesicle-associated Enzyme Neurotransmission/synapse functioning Cartpt CART prepropeptide Neuropeptide Neurotransmission/synapse functioning (neuropeptide) [24–26] Cck Cholecystokinin Neuropeptide Neurotransmission/synapse functioning (neuropeptide) [27, 28] Cdh8 Cadherin 8 Protein binding Cell adhesion/ECM/axon guidance Cdh13 Cadherin 13 (=Tcad) Protein binding Cell adhesion/ECM/axon guidance Cd24a CD24a antigen Protein binding Cell adhesion/ECM/axon guidance Chrm2 Cholinergic receptor, muscarinic 2, cardiac (=AChR-M2) GPCR Neurotransmission/synapse functioning (cholinergic receptor) [29–31] Chrm3 Cholinergic receptor, muscarinic 3, cardiac (=AChR-M3) GPCR Neurotransmission/synapse functioning (cholinergic receptor) [29–31] Cnr1 Cannabinoid receptor 1 (brain) (=CB1) GPCR Neurotransmission/synapse functioning (cannabinoid receptor) [32, 33] Cntnap2 Contactin associated protein-like 2 (=Caspr2) Protein binding Cell adhesion/ECM/axon guidance [34] Coch Cochlin Protein binding Immunity Col6a1 Collagen, type VI, alpha 1 ECM structural component Cell adhesion/ECM/axon guidance Col27a1 Procollagen, type XXVII, alpha 1 ECM structural component Cell adhesion/ECM/axon guidance Cpne6 Copine VI Ca binding; Ca sensor Neurotransmission/synapse functioning Crh Corticotropin releasing hormone Neuropeptide Neurotransmission/synapse functioning (neuropeptide) [35] Crhr1 Corticotropin releasing hormone receptor 1 GPCR Neurotransmission/synapse functioning (neuropeptide receptor) Crtac1 Cartilage acidic protein 1 (=Lotus) Ca binding; protein binding Cell adhesion/ECM/axon guidance Ctxn1 Cortexin 1 Cux2 Cut-like homeobox 2 Transcription factor Nucleic acid processing Cyp26b1 Cytochrome P450, family 26, subfamily b, polypeptide 1 Enzyme Metabolism Deptor DEP domain containing MTOR-interacting protein (=Depdc6) Cell signaling Dkk3 Dickkopf WNT signaling pathway inhibitor 3 Secreted ligand Cell signaling Dpp10 Dipeptidylpeptidase 10 Enzyme Proteolysis Ecel1 Endothelin converting enzyme-like 1 Enzyme Multiple Esyt1 Extended synaptotagmin-like protein 1 Ca/lipid/protein binding Intracellular lipid dynamics Fbxw7 F-box and WD-40 domain protein 7 Protein binding Cell signaling Foxa1 Forkhead box A1 Transcription factor Nucleic acid processing Foxp1 Forkhead box P1 Transcription factor Nucleic acid processing Fxyd6 FXYD domain-containing ion transport regulator 6 Na-K ATPase regulator Ion channel regulation Fxyd7 FXYD domain-containing ion transport regulator 7 Na-K ATPase regulator Ion channel regulation Gene . Complete name . Molecular activity . Biological process . Adarb1 Adenosine deaminase, RNA-specific, B1 Enzyme Nucleic acid processing Adcyap1 Adenylate cyclase activating polypeptide 1 (=PACAP) Neuropeptide Neurotransmission/synapse functioning (neuropeptide) [18, 19] Adk Adenosine kinase Enzyme Metabolism (adenine) [20] Ajap1 Adherens junction associated protein 1 (=Shrew1) Cell adhesion/ECM/axon guidance Apba1 Amyloid beta (A4) precursor protein binding, family A, member 1 (=X11/Mint1) Adaptator protein Neurotransmission/synapse functioning (neurotransmitter release) Arl10 ADP-ribosylation factor-like 10 GTPase activity Multiple Asic2 Acid-sensing (proton-gated) ion channel 2 Sodium channel Ion channel Baiap3 BAI1-associated protein 3 Neurotransmission/synapse functioning (SNARE-dependent exocytosis) Btbd11 BTB (POZ) domain containing 11 Cacna1g Calcium channel, voltage-dependent, T type, alpha 1G subunit (=Cav3.1) Calcium channel Ion channel [21–23] Cacna1h Calcium channel, voltage-dependent, T type, alpha 1H subunit (=Cav3.2) Calcium channel Ion channel [21–23] Cacna2d1 Calcium channel, voltage-dependent, alpha2/delta subunit 1 (=a2d1) Calcium channel Ion channel Cacna2d3 Calcium channel, voltage-dependent, alpha2/delta subunit 3 (=a2d3) Calcium channel Ion channel Cacng5 Calcium channel, voltage-dependent, gamma subunit 5 Calcium channel Ion channel Cadps2 Ca2+-dependent activator protein for secretion 2 Neurotransmission/synapse functioning (dendritic spine maintenance) Calb1 Calbindin 1 (=Calbindin D28k) EF-hand Ca binding; calcium sensor/buffer Calcium homeostasis [9] Calb2 Calbindin 2 (=Calretinin) EF-hand Ca binding; calcium sensor/buffer Calcium homeostasis CamkV CaM kinase-like vesicle-associated Enzyme Neurotransmission/synapse functioning Cartpt CART prepropeptide Neuropeptide Neurotransmission/synapse functioning (neuropeptide) [24–26] Cck Cholecystokinin Neuropeptide Neurotransmission/synapse functioning (neuropeptide) [27, 28] Cdh8 Cadherin 8 Protein binding Cell adhesion/ECM/axon guidance Cdh13 Cadherin 13 (=Tcad) Protein binding Cell adhesion/ECM/axon guidance Cd24a CD24a antigen Protein binding Cell adhesion/ECM/axon guidance Chrm2 Cholinergic receptor, muscarinic 2, cardiac (=AChR-M2) GPCR Neurotransmission/synapse functioning (cholinergic receptor) [29–31] Chrm3 Cholinergic receptor, muscarinic 3, cardiac (=AChR-M3) GPCR Neurotransmission/synapse functioning (cholinergic receptor) [29–31] Cnr1 Cannabinoid receptor 1 (brain) (=CB1) GPCR Neurotransmission/synapse functioning (cannabinoid receptor) [32, 33] Cntnap2 Contactin associated protein-like 2 (=Caspr2) Protein binding Cell adhesion/ECM/axon guidance [34] Coch Cochlin Protein binding Immunity Col6a1 Collagen, type VI, alpha 1 ECM structural component Cell adhesion/ECM/axon guidance Col27a1 Procollagen, type XXVII, alpha 1 ECM structural component Cell adhesion/ECM/axon guidance Cpne6 Copine VI Ca binding; Ca sensor Neurotransmission/synapse functioning Crh Corticotropin releasing hormone Neuropeptide Neurotransmission/synapse functioning (neuropeptide) [35] Crhr1 Corticotropin releasing hormone receptor 1 GPCR Neurotransmission/synapse functioning (neuropeptide receptor) Crtac1 Cartilage acidic protein 1 (=Lotus) Ca binding; protein binding Cell adhesion/ECM/axon guidance Ctxn1 Cortexin 1 Cux2 Cut-like homeobox 2 Transcription factor Nucleic acid processing Cyp26b1 Cytochrome P450, family 26, subfamily b, polypeptide 1 Enzyme Metabolism Deptor DEP domain containing MTOR-interacting protein (=Depdc6) Cell signaling Dkk3 Dickkopf WNT signaling pathway inhibitor 3 Secreted ligand Cell signaling Dpp10 Dipeptidylpeptidase 10 Enzyme Proteolysis Ecel1 Endothelin converting enzyme-like 1 Enzyme Multiple Esyt1 Extended synaptotagmin-like protein 1 Ca/lipid/protein binding Intracellular lipid dynamics Fbxw7 F-box and WD-40 domain protein 7 Protein binding Cell signaling Foxa1 Forkhead box A1 Transcription factor Nucleic acid processing Foxp1 Forkhead box P1 Transcription factor Nucleic acid processing Fxyd6 FXYD domain-containing ion transport regulator 6 Na-K ATPase regulator Ion channel regulation Fxyd7 FXYD domain-containing ion transport regulator 7 Na-K ATPase regulator Ion channel regulation A list of the genes that are restrictedly expressed in discrete regions of the murine medulla oblongata, including the region embracing the Nucleus papilio, as revealed by ALLENMINER and AGEA searches of the ISH images in the ABA. The abbreviated as well as the full name of each gene are given, together with their known or putative molecular activity and functions (in ontologic terms). In gray: genes that have been experimentally implicated in the regulation of the sleep/wake cycle, with references indicated. Open in new tab Table 2. Genes Expressed in the DPGi/NPCalb Region—Part 1 Gene . Complete name . Molecular activity . Biological process . Adarb1 Adenosine deaminase, RNA-specific, B1 Enzyme Nucleic acid processing Adcyap1 Adenylate cyclase activating polypeptide 1 (=PACAP) Neuropeptide Neurotransmission/synapse functioning (neuropeptide) [18, 19] Adk Adenosine kinase Enzyme Metabolism (adenine) [20] Ajap1 Adherens junction associated protein 1 (=Shrew1) Cell adhesion/ECM/axon guidance Apba1 Amyloid beta (A4) precursor protein binding, family A, member 1 (=X11/Mint1) Adaptator protein Neurotransmission/synapse functioning (neurotransmitter release) Arl10 ADP-ribosylation factor-like 10 GTPase activity Multiple Asic2 Acid-sensing (proton-gated) ion channel 2 Sodium channel Ion channel Baiap3 BAI1-associated protein 3 Neurotransmission/synapse functioning (SNARE-dependent exocytosis) Btbd11 BTB (POZ) domain containing 11 Cacna1g Calcium channel, voltage-dependent, T type, alpha 1G subunit (=Cav3.1) Calcium channel Ion channel [21–23] Cacna1h Calcium channel, voltage-dependent, T type, alpha 1H subunit (=Cav3.2) Calcium channel Ion channel [21–23] Cacna2d1 Calcium channel, voltage-dependent, alpha2/delta subunit 1 (=a2d1) Calcium channel Ion channel Cacna2d3 Calcium channel, voltage-dependent, alpha2/delta subunit 3 (=a2d3) Calcium channel Ion channel Cacng5 Calcium channel, voltage-dependent, gamma subunit 5 Calcium channel Ion channel Cadps2 Ca2+-dependent activator protein for secretion 2 Neurotransmission/synapse functioning (dendritic spine maintenance) Calb1 Calbindin 1 (=Calbindin D28k) EF-hand Ca binding; calcium sensor/buffer Calcium homeostasis [9] Calb2 Calbindin 2 (=Calretinin) EF-hand Ca binding; calcium sensor/buffer Calcium homeostasis CamkV CaM kinase-like vesicle-associated Enzyme Neurotransmission/synapse functioning Cartpt CART prepropeptide Neuropeptide Neurotransmission/synapse functioning (neuropeptide) [24–26] Cck Cholecystokinin Neuropeptide Neurotransmission/synapse functioning (neuropeptide) [27, 28] Cdh8 Cadherin 8 Protein binding Cell adhesion/ECM/axon guidance Cdh13 Cadherin 13 (=Tcad) Protein binding Cell adhesion/ECM/axon guidance Cd24a CD24a antigen Protein binding Cell adhesion/ECM/axon guidance Chrm2 Cholinergic receptor, muscarinic 2, cardiac (=AChR-M2) GPCR Neurotransmission/synapse functioning (cholinergic receptor) [29–31] Chrm3 Cholinergic receptor, muscarinic 3, cardiac (=AChR-M3) GPCR Neurotransmission/synapse functioning (cholinergic receptor) [29–31] Cnr1 Cannabinoid receptor 1 (brain) (=CB1) GPCR Neurotransmission/synapse functioning (cannabinoid receptor) [32, 33] Cntnap2 Contactin associated protein-like 2 (=Caspr2) Protein binding Cell adhesion/ECM/axon guidance [34] Coch Cochlin Protein binding Immunity Col6a1 Collagen, type VI, alpha 1 ECM structural component Cell adhesion/ECM/axon guidance Col27a1 Procollagen, type XXVII, alpha 1 ECM structural component Cell adhesion/ECM/axon guidance Cpne6 Copine VI Ca binding; Ca sensor Neurotransmission/synapse functioning Crh Corticotropin releasing hormone Neuropeptide Neurotransmission/synapse functioning (neuropeptide) [35] Crhr1 Corticotropin releasing hormone receptor 1 GPCR Neurotransmission/synapse functioning (neuropeptide receptor) Crtac1 Cartilage acidic protein 1 (=Lotus) Ca binding; protein binding Cell adhesion/ECM/axon guidance Ctxn1 Cortexin 1 Cux2 Cut-like homeobox 2 Transcription factor Nucleic acid processing Cyp26b1 Cytochrome P450, family 26, subfamily b, polypeptide 1 Enzyme Metabolism Deptor DEP domain containing MTOR-interacting protein (=Depdc6) Cell signaling Dkk3 Dickkopf WNT signaling pathway inhibitor 3 Secreted ligand Cell signaling Dpp10 Dipeptidylpeptidase 10 Enzyme Proteolysis Ecel1 Endothelin converting enzyme-like 1 Enzyme Multiple Esyt1 Extended synaptotagmin-like protein 1 Ca/lipid/protein binding Intracellular lipid dynamics Fbxw7 F-box and WD-40 domain protein 7 Protein binding Cell signaling Foxa1 Forkhead box A1 Transcription factor Nucleic acid processing Foxp1 Forkhead box P1 Transcription factor Nucleic acid processing Fxyd6 FXYD domain-containing ion transport regulator 6 Na-K ATPase regulator Ion channel regulation Fxyd7 FXYD domain-containing ion transport regulator 7 Na-K ATPase regulator Ion channel regulation Gene . Complete name . Molecular activity . Biological process . Adarb1 Adenosine deaminase, RNA-specific, B1 Enzyme Nucleic acid processing Adcyap1 Adenylate cyclase activating polypeptide 1 (=PACAP) Neuropeptide Neurotransmission/synapse functioning (neuropeptide) [18, 19] Adk Adenosine kinase Enzyme Metabolism (adenine) [20] Ajap1 Adherens junction associated protein 1 (=Shrew1) Cell adhesion/ECM/axon guidance Apba1 Amyloid beta (A4) precursor protein binding, family A, member 1 (=X11/Mint1) Adaptator protein Neurotransmission/synapse functioning (neurotransmitter release) Arl10 ADP-ribosylation factor-like 10 GTPase activity Multiple Asic2 Acid-sensing (proton-gated) ion channel 2 Sodium channel Ion channel Baiap3 BAI1-associated protein 3 Neurotransmission/synapse functioning (SNARE-dependent exocytosis) Btbd11 BTB (POZ) domain containing 11 Cacna1g Calcium channel, voltage-dependent, T type, alpha 1G subunit (=Cav3.1) Calcium channel Ion channel [21–23] Cacna1h Calcium channel, voltage-dependent, T type, alpha 1H subunit (=Cav3.2) Calcium channel Ion channel [21–23] Cacna2d1 Calcium channel, voltage-dependent, alpha2/delta subunit 1 (=a2d1) Calcium channel Ion channel Cacna2d3 Calcium channel, voltage-dependent, alpha2/delta subunit 3 (=a2d3) Calcium channel Ion channel Cacng5 Calcium channel, voltage-dependent, gamma subunit 5 Calcium channel Ion channel Cadps2 Ca2+-dependent activator protein for secretion 2 Neurotransmission/synapse functioning (dendritic spine maintenance) Calb1 Calbindin 1 (=Calbindin D28k) EF-hand Ca binding; calcium sensor/buffer Calcium homeostasis [9] Calb2 Calbindin 2 (=Calretinin) EF-hand Ca binding; calcium sensor/buffer Calcium homeostasis CamkV CaM kinase-like vesicle-associated Enzyme Neurotransmission/synapse functioning Cartpt CART prepropeptide Neuropeptide Neurotransmission/synapse functioning (neuropeptide) [24–26] Cck Cholecystokinin Neuropeptide Neurotransmission/synapse functioning (neuropeptide) [27, 28] Cdh8 Cadherin 8 Protein binding Cell adhesion/ECM/axon guidance Cdh13 Cadherin 13 (=Tcad) Protein binding Cell adhesion/ECM/axon guidance Cd24a CD24a antigen Protein binding Cell adhesion/ECM/axon guidance Chrm2 Cholinergic receptor, muscarinic 2, cardiac (=AChR-M2) GPCR Neurotransmission/synapse functioning (cholinergic receptor) [29–31] Chrm3 Cholinergic receptor, muscarinic 3, cardiac (=AChR-M3) GPCR Neurotransmission/synapse functioning (cholinergic receptor) [29–31] Cnr1 Cannabinoid receptor 1 (brain) (=CB1) GPCR Neurotransmission/synapse functioning (cannabinoid receptor) [32, 33] Cntnap2 Contactin associated protein-like 2 (=Caspr2) Protein binding Cell adhesion/ECM/axon guidance [34] Coch Cochlin Protein binding Immunity Col6a1 Collagen, type VI, alpha 1 ECM structural component Cell adhesion/ECM/axon guidance Col27a1 Procollagen, type XXVII, alpha 1 ECM structural component Cell adhesion/ECM/axon guidance Cpne6 Copine VI Ca binding; Ca sensor Neurotransmission/synapse functioning Crh Corticotropin releasing hormone Neuropeptide Neurotransmission/synapse functioning (neuropeptide) [35] Crhr1 Corticotropin releasing hormone receptor 1 GPCR Neurotransmission/synapse functioning (neuropeptide receptor) Crtac1 Cartilage acidic protein 1 (=Lotus) Ca binding; protein binding Cell adhesion/ECM/axon guidance Ctxn1 Cortexin 1 Cux2 Cut-like homeobox 2 Transcription factor Nucleic acid processing Cyp26b1 Cytochrome P450, family 26, subfamily b, polypeptide 1 Enzyme Metabolism Deptor DEP domain containing MTOR-interacting protein (=Depdc6) Cell signaling Dkk3 Dickkopf WNT signaling pathway inhibitor 3 Secreted ligand Cell signaling Dpp10 Dipeptidylpeptidase 10 Enzyme Proteolysis Ecel1 Endothelin converting enzyme-like 1 Enzyme Multiple Esyt1 Extended synaptotagmin-like protein 1 Ca/lipid/protein binding Intracellular lipid dynamics Fbxw7 F-box and WD-40 domain protein 7 Protein binding Cell signaling Foxa1 Forkhead box A1 Transcription factor Nucleic acid processing Foxp1 Forkhead box P1 Transcription factor Nucleic acid processing Fxyd6 FXYD domain-containing ion transport regulator 6 Na-K ATPase regulator Ion channel regulation Fxyd7 FXYD domain-containing ion transport regulator 7 Na-K ATPase regulator Ion channel regulation A list of the genes that are restrictedly expressed in discrete regions of the murine medulla oblongata, including the region embracing the Nucleus papilio, as revealed by ALLENMINER and AGEA searches of the ISH images in the ABA. The abbreviated as well as the full name of each gene are given, together with their known or putative molecular activity and functions (in ontologic terms). In gray: genes that have been experimentally implicated in the regulation of the sleep/wake cycle, with references indicated. Open in new tab Table 3. Genes Expressed in the DPGi/NPCalb Region—Part 2 (see the footnote of Table 2) Gene . Complete name . Molecular activity . Biological process . Gabra1 Gamma-aminobutyric acid (GABA) A receptor, subunit alpha 1 Transmitter gated ion channel activity Neurotransmission/synapse functioning (GABA receptor) Gad1 Glutamic acid decarboxylase 1 Enzyme Neurotransmission/synapse functioning (GABA synthesis) Gad2 Glutamic acid decarboxylase 2 Enzyme Neurotransmission/synapse functioning (GABA synthesis) Glra1 Glycine receptor, alpha 1 subunit Transmitter gated ion channel activity Neurotransmission/synapse functioning (glycine receptor) Glra4 Glycine receptor, alpha 4 subunit Transmitter gated ion channel activity Neurotransmission/synapse functioning (glycine receptor) Gpr125 G protein-coupled receptor 125 (=Adgra3) GPCR Cell signaling Gpr137 G protein-coupled receptor 137 GPCR Cell signaling Grid1 Glutamate receptor, ionotropic, delta 1 Transmitter gated ion channel activity Neurotransmission/synapse functioning (glutamate receptor) Grik1 Glutamate receptor, ionotropic, kainate 1 Transmitter gated ion channel activity Neurotransmission/synapse functioning (kainate/glutamate receptor) Grin3a Glutamate receptor ionotropic, NMDA3A Transmitter gated ion channel activity Neurotransmission/synapse functioning (NMDA/glutamate receptor) Grm8 Glutamate receptor, metabotropic 8 GPCR Neurotransmission/synapse functioning (glutamate receptor) Grsf1 G-rich RNA sequence binding factor 1 RNA binding Nucleic acid processing Gsta4 Glutathione S-transferase, alpha 4 Enzyme Metabolism (glutathione metabolism) Hap1 Huntingtin-associated protein 1 Protein binding Axonal transport Hcn1 Hyperpolarization-activated, cyclic nucleotide-gated K+ 1 Potassium channel Ion channel Htr2c 5-Hydroxytryptamine (serotonin) receptor 2C GPCR Neurotransmission/synapse functioning (serotonin receptor) [36] Igsf21 Immunoglobulin superfamily, member 21 Protein binding Cell adhesion/ECM/axon guidance Itm2c Integral membrane protein 2C Kcna1 Potassium voltage-gated channel, shaker-related subfamily, member 1 (=Kv1.1) Potassium channel Ion channel Kcnab1 Potassium voltage-gated channel, shaker-related subfamily, beta member 1 (=Kv1.3) Potassium channel Ion channel Kcnc2 Potassium voltage gated channel, Shaw-related subfamily, member 2 (=Kv3.2) Potassium channel Ion channel [37] Kcnc3 Potassium voltage gated channel, Shaw-related subfamily, member 3 (=Kv3.3) Potassium channel Ion channel [38] Kcng3 Potassium voltage-gated channel, subfamily G, member 3 (=Kv6.3) Potassium channel Ion channel Kcng4 Potassium voltage-gated channel, subfamily G, member 4 (=Kv6.4) Potassium channel Ion channel Kcnj3 Potassium inwardly rectifying channel, subfamily J, member 3 Potassium channel Ion channel Kcnip1 Kv channel-interacting protein 1 (=KCHIP1) K channel regulator (Ca binding) Ion channel regulation Kcnip4 Kv channel interacting protein 4 (=KCHIP4) K channel regulator (Ca binding) Ion channel regulation Lrrn1 Leucine rich repeat protein 1, neuronal Ly6h Lymphocyte antigen 6 complex, locus H Prototoxin Neurotransmission/synapse functioning (modulation of AchR activity) Megf11 Multiple EGF-like-domains 11 Cell adhesion/ECM/axon guidance Mesdc2 Mesoderm development candidate 2 Chaperone LDL Cell signaling Myo5b Myosin VB Multiple Cytoskeleton dynamics Ndst4 N-deacetylase/N-sulfotransferase (heparin glucosaminyl) 4 Enzyme Metabolism (glycosaminoglycan) Necab2 N-terminal EF-hand calcium binding protein 2 EF-hand Ca binding Necab3 N-terminal EF-hand calcium binding protein 3 EF-hand Ca binding Nell2 NEL-like 2 (neural EGF like 2) Ca/protein/ECM binding Cell adhesion/ECM/axon guidance Nnat Neuronatin Multiple Nos1 Nitric oxide synthase 1, neuronal Enzyme Neurotransmission/synapse functioning (NO synthesis) [39–41] Nos1ap Nitric oxide synthase 1 (neuronal) adaptor protein (=Capon) Protein binding Neurotransmission/synapse functioning (NO synthesis) Npnt Nephronectin Ca/protein/ECM binding Cell adhesion/ECM/axon guidance Nptx1 Neuronal pentraxin 1 (=NP1) Neurotransmission/synapse functioning Nrg1 Neuregulin 1 Protein binding Cell signaling Nrn1 Neuritin 1 Neurotransmission/synapse functioning Ntng1 Netrin G1 Protein binding Cell adhesion/ECM/axon guidance Nucb2 Nucleobindin 2 Ca2+/DNA binding/neuropeptide Neurotransmission/synapse functioning (neuropeptide) [42, 43] Nxph1 Neurexophilin 1 Neurexin ligand Neurotransmission/synapse functioning (neuropeptide-like) Nxph4 Neurexophilin 4 Neurexin ligand Neurotransmission/synapse functioning (neuropeptide-like) Gene . Complete name . Molecular activity . Biological process . Gabra1 Gamma-aminobutyric acid (GABA) A receptor, subunit alpha 1 Transmitter gated ion channel activity Neurotransmission/synapse functioning (GABA receptor) Gad1 Glutamic acid decarboxylase 1 Enzyme Neurotransmission/synapse functioning (GABA synthesis) Gad2 Glutamic acid decarboxylase 2 Enzyme Neurotransmission/synapse functioning (GABA synthesis) Glra1 Glycine receptor, alpha 1 subunit Transmitter gated ion channel activity Neurotransmission/synapse functioning (glycine receptor) Glra4 Glycine receptor, alpha 4 subunit Transmitter gated ion channel activity Neurotransmission/synapse functioning (glycine receptor) Gpr125 G protein-coupled receptor 125 (=Adgra3) GPCR Cell signaling Gpr137 G protein-coupled receptor 137 GPCR Cell signaling Grid1 Glutamate receptor, ionotropic, delta 1 Transmitter gated ion channel activity Neurotransmission/synapse functioning (glutamate receptor) Grik1 Glutamate receptor, ionotropic, kainate 1 Transmitter gated ion channel activity Neurotransmission/synapse functioning (kainate/glutamate receptor) Grin3a Glutamate receptor ionotropic, NMDA3A Transmitter gated ion channel activity Neurotransmission/synapse functioning (NMDA/glutamate receptor) Grm8 Glutamate receptor, metabotropic 8 GPCR Neurotransmission/synapse functioning (glutamate receptor) Grsf1 G-rich RNA sequence binding factor 1 RNA binding Nucleic acid processing Gsta4 Glutathione S-transferase, alpha 4 Enzyme Metabolism (glutathione metabolism) Hap1 Huntingtin-associated protein 1 Protein binding Axonal transport Hcn1 Hyperpolarization-activated, cyclic nucleotide-gated K+ 1 Potassium channel Ion channel Htr2c 5-Hydroxytryptamine (serotonin) receptor 2C GPCR Neurotransmission/synapse functioning (serotonin receptor) [36] Igsf21 Immunoglobulin superfamily, member 21 Protein binding Cell adhesion/ECM/axon guidance Itm2c Integral membrane protein 2C Kcna1 Potassium voltage-gated channel, shaker-related subfamily, member 1 (=Kv1.1) Potassium channel Ion channel Kcnab1 Potassium voltage-gated channel, shaker-related subfamily, beta member 1 (=Kv1.3) Potassium channel Ion channel Kcnc2 Potassium voltage gated channel, Shaw-related subfamily, member 2 (=Kv3.2) Potassium channel Ion channel [37] Kcnc3 Potassium voltage gated channel, Shaw-related subfamily, member 3 (=Kv3.3) Potassium channel Ion channel [38] Kcng3 Potassium voltage-gated channel, subfamily G, member 3 (=Kv6.3) Potassium channel Ion channel Kcng4 Potassium voltage-gated channel, subfamily G, member 4 (=Kv6.4) Potassium channel Ion channel Kcnj3 Potassium inwardly rectifying channel, subfamily J, member 3 Potassium channel Ion channel Kcnip1 Kv channel-interacting protein 1 (=KCHIP1) K channel regulator (Ca binding) Ion channel regulation Kcnip4 Kv channel interacting protein 4 (=KCHIP4) K channel regulator (Ca binding) Ion channel regulation Lrrn1 Leucine rich repeat protein 1, neuronal Ly6h Lymphocyte antigen 6 complex, locus H Prototoxin Neurotransmission/synapse functioning (modulation of AchR activity) Megf11 Multiple EGF-like-domains 11 Cell adhesion/ECM/axon guidance Mesdc2 Mesoderm development candidate 2 Chaperone LDL Cell signaling Myo5b Myosin VB Multiple Cytoskeleton dynamics Ndst4 N-deacetylase/N-sulfotransferase (heparin glucosaminyl) 4 Enzyme Metabolism (glycosaminoglycan) Necab2 N-terminal EF-hand calcium binding protein 2 EF-hand Ca binding Necab3 N-terminal EF-hand calcium binding protein 3 EF-hand Ca binding Nell2 NEL-like 2 (neural EGF like 2) Ca/protein/ECM binding Cell adhesion/ECM/axon guidance Nnat Neuronatin Multiple Nos1 Nitric oxide synthase 1, neuronal Enzyme Neurotransmission/synapse functioning (NO synthesis) [39–41] Nos1ap Nitric oxide synthase 1 (neuronal) adaptor protein (=Capon) Protein binding Neurotransmission/synapse functioning (NO synthesis) Npnt Nephronectin Ca/protein/ECM binding Cell adhesion/ECM/axon guidance Nptx1 Neuronal pentraxin 1 (=NP1) Neurotransmission/synapse functioning Nrg1 Neuregulin 1 Protein binding Cell signaling Nrn1 Neuritin 1 Neurotransmission/synapse functioning Ntng1 Netrin G1 Protein binding Cell adhesion/ECM/axon guidance Nucb2 Nucleobindin 2 Ca2+/DNA binding/neuropeptide Neurotransmission/synapse functioning (neuropeptide) [42, 43] Nxph1 Neurexophilin 1 Neurexin ligand Neurotransmission/synapse functioning (neuropeptide-like) Nxph4 Neurexophilin 4 Neurexin ligand Neurotransmission/synapse functioning (neuropeptide-like) Open in new tab Table 3. Genes Expressed in the DPGi/NPCalb Region—Part 2 (see the footnote of Table 2) Gene . Complete name . Molecular activity . Biological process . Gabra1 Gamma-aminobutyric acid (GABA) A receptor, subunit alpha 1 Transmitter gated ion channel activity Neurotransmission/synapse functioning (GABA receptor) Gad1 Glutamic acid decarboxylase 1 Enzyme Neurotransmission/synapse functioning (GABA synthesis) Gad2 Glutamic acid decarboxylase 2 Enzyme Neurotransmission/synapse functioning (GABA synthesis) Glra1 Glycine receptor, alpha 1 subunit Transmitter gated ion channel activity Neurotransmission/synapse functioning (glycine receptor) Glra4 Glycine receptor, alpha 4 subunit Transmitter gated ion channel activity Neurotransmission/synapse functioning (glycine receptor) Gpr125 G protein-coupled receptor 125 (=Adgra3) GPCR Cell signaling Gpr137 G protein-coupled receptor 137 GPCR Cell signaling Grid1 Glutamate receptor, ionotropic, delta 1 Transmitter gated ion channel activity Neurotransmission/synapse functioning (glutamate receptor) Grik1 Glutamate receptor, ionotropic, kainate 1 Transmitter gated ion channel activity Neurotransmission/synapse functioning (kainate/glutamate receptor) Grin3a Glutamate receptor ionotropic, NMDA3A Transmitter gated ion channel activity Neurotransmission/synapse functioning (NMDA/glutamate receptor) Grm8 Glutamate receptor, metabotropic 8 GPCR Neurotransmission/synapse functioning (glutamate receptor) Grsf1 G-rich RNA sequence binding factor 1 RNA binding Nucleic acid processing Gsta4 Glutathione S-transferase, alpha 4 Enzyme Metabolism (glutathione metabolism) Hap1 Huntingtin-associated protein 1 Protein binding Axonal transport Hcn1 Hyperpolarization-activated, cyclic nucleotide-gated K+ 1 Potassium channel Ion channel Htr2c 5-Hydroxytryptamine (serotonin) receptor 2C GPCR Neurotransmission/synapse functioning (serotonin receptor) [36] Igsf21 Immunoglobulin superfamily, member 21 Protein binding Cell adhesion/ECM/axon guidance Itm2c Integral membrane protein 2C Kcna1 Potassium voltage-gated channel, shaker-related subfamily, member 1 (=Kv1.1) Potassium channel Ion channel Kcnab1 Potassium voltage-gated channel, shaker-related subfamily, beta member 1 (=Kv1.3) Potassium channel Ion channel Kcnc2 Potassium voltage gated channel, Shaw-related subfamily, member 2 (=Kv3.2) Potassium channel Ion channel [37] Kcnc3 Potassium voltage gated channel, Shaw-related subfamily, member 3 (=Kv3.3) Potassium channel Ion channel [38] Kcng3 Potassium voltage-gated channel, subfamily G, member 3 (=Kv6.3) Potassium channel Ion channel Kcng4 Potassium voltage-gated channel, subfamily G, member 4 (=Kv6.4) Potassium channel Ion channel Kcnj3 Potassium inwardly rectifying channel, subfamily J, member 3 Potassium channel Ion channel Kcnip1 Kv channel-interacting protein 1 (=KCHIP1) K channel regulator (Ca binding) Ion channel regulation Kcnip4 Kv channel interacting protein 4 (=KCHIP4) K channel regulator (Ca binding) Ion channel regulation Lrrn1 Leucine rich repeat protein 1, neuronal Ly6h Lymphocyte antigen 6 complex, locus H Prototoxin Neurotransmission/synapse functioning (modulation of AchR activity) Megf11 Multiple EGF-like-domains 11 Cell adhesion/ECM/axon guidance Mesdc2 Mesoderm development candidate 2 Chaperone LDL Cell signaling Myo5b Myosin VB Multiple Cytoskeleton dynamics Ndst4 N-deacetylase/N-sulfotransferase (heparin glucosaminyl) 4 Enzyme Metabolism (glycosaminoglycan) Necab2 N-terminal EF-hand calcium binding protein 2 EF-hand Ca binding Necab3 N-terminal EF-hand calcium binding protein 3 EF-hand Ca binding Nell2 NEL-like 2 (neural EGF like 2) Ca/protein/ECM binding Cell adhesion/ECM/axon guidance Nnat Neuronatin Multiple Nos1 Nitric oxide synthase 1, neuronal Enzyme Neurotransmission/synapse functioning (NO synthesis) [39–41] Nos1ap Nitric oxide synthase 1 (neuronal) adaptor protein (=Capon) Protein binding Neurotransmission/synapse functioning (NO synthesis) Npnt Nephronectin Ca/protein/ECM binding Cell adhesion/ECM/axon guidance Nptx1 Neuronal pentraxin 1 (=NP1) Neurotransmission/synapse functioning Nrg1 Neuregulin 1 Protein binding Cell signaling Nrn1 Neuritin 1 Neurotransmission/synapse functioning Ntng1 Netrin G1 Protein binding Cell adhesion/ECM/axon guidance Nucb2 Nucleobindin 2 Ca2+/DNA binding/neuropeptide Neurotransmission/synapse functioning (neuropeptide) [42, 43] Nxph1 Neurexophilin 1 Neurexin ligand Neurotransmission/synapse functioning (neuropeptide-like) Nxph4 Neurexophilin 4 Neurexin ligand Neurotransmission/synapse functioning (neuropeptide-like) Gene . Complete name . Molecular activity . Biological process . Gabra1 Gamma-aminobutyric acid (GABA) A receptor, subunit alpha 1 Transmitter gated ion channel activity Neurotransmission/synapse functioning (GABA receptor) Gad1 Glutamic acid decarboxylase 1 Enzyme Neurotransmission/synapse functioning (GABA synthesis) Gad2 Glutamic acid decarboxylase 2 Enzyme Neurotransmission/synapse functioning (GABA synthesis) Glra1 Glycine receptor, alpha 1 subunit Transmitter gated ion channel activity Neurotransmission/synapse functioning (glycine receptor) Glra4 Glycine receptor, alpha 4 subunit Transmitter gated ion channel activity Neurotransmission/synapse functioning (glycine receptor) Gpr125 G protein-coupled receptor 125 (=Adgra3) GPCR Cell signaling Gpr137 G protein-coupled receptor 137 GPCR Cell signaling Grid1 Glutamate receptor, ionotropic, delta 1 Transmitter gated ion channel activity Neurotransmission/synapse functioning (glutamate receptor) Grik1 Glutamate receptor, ionotropic, kainate 1 Transmitter gated ion channel activity Neurotransmission/synapse functioning (kainate/glutamate receptor) Grin3a Glutamate receptor ionotropic, NMDA3A Transmitter gated ion channel activity Neurotransmission/synapse functioning (NMDA/glutamate receptor) Grm8 Glutamate receptor, metabotropic 8 GPCR Neurotransmission/synapse functioning (glutamate receptor) Grsf1 G-rich RNA sequence binding factor 1 RNA binding Nucleic acid processing Gsta4 Glutathione S-transferase, alpha 4 Enzyme Metabolism (glutathione metabolism) Hap1 Huntingtin-associated protein 1 Protein binding Axonal transport Hcn1 Hyperpolarization-activated, cyclic nucleotide-gated K+ 1 Potassium channel Ion channel Htr2c 5-Hydroxytryptamine (serotonin) receptor 2C GPCR Neurotransmission/synapse functioning (serotonin receptor) [36] Igsf21 Immunoglobulin superfamily, member 21 Protein binding Cell adhesion/ECM/axon guidance Itm2c Integral membrane protein 2C Kcna1 Potassium voltage-gated channel, shaker-related subfamily, member 1 (=Kv1.1) Potassium channel Ion channel Kcnab1 Potassium voltage-gated channel, shaker-related subfamily, beta member 1 (=Kv1.3) Potassium channel Ion channel Kcnc2 Potassium voltage gated channel, Shaw-related subfamily, member 2 (=Kv3.2) Potassium channel Ion channel [37] Kcnc3 Potassium voltage gated channel, Shaw-related subfamily, member 3 (=Kv3.3) Potassium channel Ion channel [38] Kcng3 Potassium voltage-gated channel, subfamily G, member 3 (=Kv6.3) Potassium channel Ion channel Kcng4 Potassium voltage-gated channel, subfamily G, member 4 (=Kv6.4) Potassium channel Ion channel Kcnj3 Potassium inwardly rectifying channel, subfamily J, member 3 Potassium channel Ion channel Kcnip1 Kv channel-interacting protein 1 (=KCHIP1) K channel regulator (Ca binding) Ion channel regulation Kcnip4 Kv channel interacting protein 4 (=KCHIP4) K channel regulator (Ca binding) Ion channel regulation Lrrn1 Leucine rich repeat protein 1, neuronal Ly6h Lymphocyte antigen 6 complex, locus H Prototoxin Neurotransmission/synapse functioning (modulation of AchR activity) Megf11 Multiple EGF-like-domains 11 Cell adhesion/ECM/axon guidance Mesdc2 Mesoderm development candidate 2 Chaperone LDL Cell signaling Myo5b Myosin VB Multiple Cytoskeleton dynamics Ndst4 N-deacetylase/N-sulfotransferase (heparin glucosaminyl) 4 Enzyme Metabolism (glycosaminoglycan) Necab2 N-terminal EF-hand calcium binding protein 2 EF-hand Ca binding Necab3 N-terminal EF-hand calcium binding protein 3 EF-hand Ca binding Nell2 NEL-like 2 (neural EGF like 2) Ca/protein/ECM binding Cell adhesion/ECM/axon guidance Nnat Neuronatin Multiple Nos1 Nitric oxide synthase 1, neuronal Enzyme Neurotransmission/synapse functioning (NO synthesis) [39–41] Nos1ap Nitric oxide synthase 1 (neuronal) adaptor protein (=Capon) Protein binding Neurotransmission/synapse functioning (NO synthesis) Npnt Nephronectin Ca/protein/ECM binding Cell adhesion/ECM/axon guidance Nptx1 Neuronal pentraxin 1 (=NP1) Neurotransmission/synapse functioning Nrg1 Neuregulin 1 Protein binding Cell signaling Nrn1 Neuritin 1 Neurotransmission/synapse functioning Ntng1 Netrin G1 Protein binding Cell adhesion/ECM/axon guidance Nucb2 Nucleobindin 2 Ca2+/DNA binding/neuropeptide Neurotransmission/synapse functioning (neuropeptide) [42, 43] Nxph1 Neurexophilin 1 Neurexin ligand Neurotransmission/synapse functioning (neuropeptide-like) Nxph4 Neurexophilin 4 Neurexin ligand Neurotransmission/synapse functioning (neuropeptide-like) Open in new tab Table 4. Genes Expressed in the DPGi/NPCalb Region—Part 3 (see the footnote of Table 2) Gene . Complete name . Molecular activity . Biological process . Pcp4 Purkinje cell protein 4 Ca/protein binding Multiple Pcp4l1 Purkinje cell protein 4-like 1 Ca/protein binding Multiple Penk Preproenkephalin Neuropeptide Neurotransmission/synapse functioning (neuropeptide) Pnoc Prepronociceptin Neuropeptide Neurotransmission/synapse functioning (neuropeptide) Psd Pleckstrin and Sec7 domain containing (=EFA6) GEF activity Axonal transport Ptpro Protein tyrosine phosphatase, receptor type, O Receptor/enzyme Neurotransmission/synapse functioning (promotes synapse formation) Pvalb Parvalbumin EF-hand Ca binding; calcium sensor/buffer Calcium homeostasis [44] Rec8 REC8 meiotic recombination protein Chromatin binding Nucleic acid processing Rgs4 Regulator of G-protein signaling 4 GTPase activator Cell signaling Rgs10 Regulator of G-protein signaling 10 GTPase activator Cell signaling Scn3b Sodium channel, voltage-gated, type III, beta Sodium channel Ion channel Scn4b Sodium channel, type IV, beta Sodium channel Ion channel Scrt1 Scratch family zinc finger 1 Transcription repressor Nucleic acid processing Sdk2 Sidekick homolog 2 (chicken) Cell adhesion/ECM/axon guidance Slc6a7 Solute carrier family 6 (neurotransmitter transporter, l-proline), member 7 Proline transporter Multiple Slc8a1 Solute carrier family 8 (sodium/calcium exchanger), member 1 (=Ncx1) Ca/Na antiporter Calcium homeostasis Slc17a6 Solute carrier family 6 (sodium-dependent inorganic phosphate cotransporter), member 6 Vesicular neurotransmitter transporter Neurotransmission/synapse functioning (=VGlut2, glutamate transporter) Slc17a7 Solute carrier family 6 (sodium-dependent inorganic phosphate cotransporter), member 7 Vesicular neurotransmitter transporter Neurotransmission/synapse functioning (=VGlut1, glutamate transporter) Slc32a1 Solute carrier family 32 (GABA vesicular transporter), member 1 Vesicular neurotransmitter transporter Neurotransmission/synapse functioning (=VGAT, GABA transporter) Slc36a1 Solute carrier family 36 (proton/amino acid symporter), member 1 Transmembrane transporter Multiple Sema3a Semaphorin 3A Protein/ECM binding Cell adhesion/ECM/axon guidance Sema6a Semaphorin 6a Protein/ECM binding Cell adhesion/ECM/axon guidance Sez6 Seizure related gene 6 (=BSRP-C) Neurotransmission/synapse functioning (shaping dendritic arborization) Sez6l Seizure related 6 homolog like Neurotransmission/synapse functioning (shaping dendritic arborization) Sh3bgrl2 SH3 domain binding glutamic acid-rich protein like 2 Slit1 Slit guidance ligand 1 Ca/protein/ECM binding Cell adhesion/ECM/axon guidance Slit2 Slit guidance ligand 2 Ca/protein/ECM binding Cell adhesion/ECM/axon guidance Snca Synuclein, alpha Protein binding Multiple (involved in synucleinopathies including PD; RBD) [45, 46] Sncg Synuclein, gamma Protein binding Multiple Sphkap SPHK1 interactor, AKAP domain containing (=SKIP) A kinase anchoring protein Spp1 Secreted phosphoprotein 1 (=OPN) Cytokine/ECM binding Cell adhesion/ECM/axon guidance Steap2 Six transmembrane epithelial antigen of prostate 2 Enzyme Multiple Sv2b Synaptic vesicle glycoprotein 2 b Transmembrane transporter Neurotransmission/synapse functioning (vesicular transport/exocytosis) Sv2c Synaptic vesicle glycoprotein 2c Transmembrane transporter Neurotransmission/synapse functioning (vesicular transport/exocytosis) Syt4 Synaptotagmin IV Ca/protein/lipid binding Neurotransmission/synapse functioning (vesicular transport/exocytosis) S100a10 S100 calcium-binding protein A10 (=calgizzarin) EF-hand Ca binding/protein binding Multiple S100b S100 protein, beta polypeptide, neural EF-hand Ca binding/protein binding Multiple (marker in sleep disturbance syndromes and PD) Tesc Tescalcin EF-hand Ca binding/protein binding Multiple Tmem65 Transmembrane protein 65 Tpbg Trophoblast glycoprotein Usp11 Ubiquitin-specific peptidase 11 Enzyme Proteolysis Vat1l Vesicle amine transport protein 1 homolog-like Whrn Whirlin Cytoskeleton dynamics Zfp385b Zinc finger protein 385B Transcription factor Nucleic acid processing Zfhx4 Zinc finger homeodomain 4 Transcription factor Nucleic acid processing Zfp365 Zinc finger protein 365 Transcription factor Nucleic acid processing Zkscan16 Zinc finger with KRAB and SCAN domains 16 Transcription factor Nucleic acid processing Gene . Complete name . Molecular activity . Biological process . Pcp4 Purkinje cell protein 4 Ca/protein binding Multiple Pcp4l1 Purkinje cell protein 4-like 1 Ca/protein binding Multiple Penk Preproenkephalin Neuropeptide Neurotransmission/synapse functioning (neuropeptide) Pnoc Prepronociceptin Neuropeptide Neurotransmission/synapse functioning (neuropeptide) Psd Pleckstrin and Sec7 domain containing (=EFA6) GEF activity Axonal transport Ptpro Protein tyrosine phosphatase, receptor type, O Receptor/enzyme Neurotransmission/synapse functioning (promotes synapse formation) Pvalb Parvalbumin EF-hand Ca binding; calcium sensor/buffer Calcium homeostasis [44] Rec8 REC8 meiotic recombination protein Chromatin binding Nucleic acid processing Rgs4 Regulator of G-protein signaling 4 GTPase activator Cell signaling Rgs10 Regulator of G-protein signaling 10 GTPase activator Cell signaling Scn3b Sodium channel, voltage-gated, type III, beta Sodium channel Ion channel Scn4b Sodium channel, type IV, beta Sodium channel Ion channel Scrt1 Scratch family zinc finger 1 Transcription repressor Nucleic acid processing Sdk2 Sidekick homolog 2 (chicken) Cell adhesion/ECM/axon guidance Slc6a7 Solute carrier family 6 (neurotransmitter transporter, l-proline), member 7 Proline transporter Multiple Slc8a1 Solute carrier family 8 (sodium/calcium exchanger), member 1 (=Ncx1) Ca/Na antiporter Calcium homeostasis Slc17a6 Solute carrier family 6 (sodium-dependent inorganic phosphate cotransporter), member 6 Vesicular neurotransmitter transporter Neurotransmission/synapse functioning (=VGlut2, glutamate transporter) Slc17a7 Solute carrier family 6 (sodium-dependent inorganic phosphate cotransporter), member 7 Vesicular neurotransmitter transporter Neurotransmission/synapse functioning (=VGlut1, glutamate transporter) Slc32a1 Solute carrier family 32 (GABA vesicular transporter), member 1 Vesicular neurotransmitter transporter Neurotransmission/synapse functioning (=VGAT, GABA transporter) Slc36a1 Solute carrier family 36 (proton/amino acid symporter), member 1 Transmembrane transporter Multiple Sema3a Semaphorin 3A Protein/ECM binding Cell adhesion/ECM/axon guidance Sema6a Semaphorin 6a Protein/ECM binding Cell adhesion/ECM/axon guidance Sez6 Seizure related gene 6 (=BSRP-C) Neurotransmission/synapse functioning (shaping dendritic arborization) Sez6l Seizure related 6 homolog like Neurotransmission/synapse functioning (shaping dendritic arborization) Sh3bgrl2 SH3 domain binding glutamic acid-rich protein like 2 Slit1 Slit guidance ligand 1 Ca/protein/ECM binding Cell adhesion/ECM/axon guidance Slit2 Slit guidance ligand 2 Ca/protein/ECM binding Cell adhesion/ECM/axon guidance Snca Synuclein, alpha Protein binding Multiple (involved in synucleinopathies including PD; RBD) [45, 46] Sncg Synuclein, gamma Protein binding Multiple Sphkap SPHK1 interactor, AKAP domain containing (=SKIP) A kinase anchoring protein Spp1 Secreted phosphoprotein 1 (=OPN) Cytokine/ECM binding Cell adhesion/ECM/axon guidance Steap2 Six transmembrane epithelial antigen of prostate 2 Enzyme Multiple Sv2b Synaptic vesicle glycoprotein 2 b Transmembrane transporter Neurotransmission/synapse functioning (vesicular transport/exocytosis) Sv2c Synaptic vesicle glycoprotein 2c Transmembrane transporter Neurotransmission/synapse functioning (vesicular transport/exocytosis) Syt4 Synaptotagmin IV Ca/protein/lipid binding Neurotransmission/synapse functioning (vesicular transport/exocytosis) S100a10 S100 calcium-binding protein A10 (=calgizzarin) EF-hand Ca binding/protein binding Multiple S100b S100 protein, beta polypeptide, neural EF-hand Ca binding/protein binding Multiple (marker in sleep disturbance syndromes and PD) Tesc Tescalcin EF-hand Ca binding/protein binding Multiple Tmem65 Transmembrane protein 65 Tpbg Trophoblast glycoprotein Usp11 Ubiquitin-specific peptidase 11 Enzyme Proteolysis Vat1l Vesicle amine transport protein 1 homolog-like Whrn Whirlin Cytoskeleton dynamics Zfp385b Zinc finger protein 385B Transcription factor Nucleic acid processing Zfhx4 Zinc finger homeodomain 4 Transcription factor Nucleic acid processing Zfp365 Zinc finger protein 365 Transcription factor Nucleic acid processing Zkscan16 Zinc finger with KRAB and SCAN domains 16 Transcription factor Nucleic acid processing Open in new tab Table 4. Genes Expressed in the DPGi/NPCalb Region—Part 3 (see the footnote of Table 2) Gene . Complete name . Molecular activity . Biological process . Pcp4 Purkinje cell protein 4 Ca/protein binding Multiple Pcp4l1 Purkinje cell protein 4-like 1 Ca/protein binding Multiple Penk Preproenkephalin Neuropeptide Neurotransmission/synapse functioning (neuropeptide) Pnoc Prepronociceptin Neuropeptide Neurotransmission/synapse functioning (neuropeptide) Psd Pleckstrin and Sec7 domain containing (=EFA6) GEF activity Axonal transport Ptpro Protein tyrosine phosphatase, receptor type, O Receptor/enzyme Neurotransmission/synapse functioning (promotes synapse formation) Pvalb Parvalbumin EF-hand Ca binding; calcium sensor/buffer Calcium homeostasis [44] Rec8 REC8 meiotic recombination protein Chromatin binding Nucleic acid processing Rgs4 Regulator of G-protein signaling 4 GTPase activator Cell signaling Rgs10 Regulator of G-protein signaling 10 GTPase activator Cell signaling Scn3b Sodium channel, voltage-gated, type III, beta Sodium channel Ion channel Scn4b Sodium channel, type IV, beta Sodium channel Ion channel Scrt1 Scratch family zinc finger 1 Transcription repressor Nucleic acid processing Sdk2 Sidekick homolog 2 (chicken) Cell adhesion/ECM/axon guidance Slc6a7 Solute carrier family 6 (neurotransmitter transporter, l-proline), member 7 Proline transporter Multiple Slc8a1 Solute carrier family 8 (sodium/calcium exchanger), member 1 (=Ncx1) Ca/Na antiporter Calcium homeostasis Slc17a6 Solute carrier family 6 (sodium-dependent inorganic phosphate cotransporter), member 6 Vesicular neurotransmitter transporter Neurotransmission/synapse functioning (=VGlut2, glutamate transporter) Slc17a7 Solute carrier family 6 (sodium-dependent inorganic phosphate cotransporter), member 7 Vesicular neurotransmitter transporter Neurotransmission/synapse functioning (=VGlut1, glutamate transporter) Slc32a1 Solute carrier family 32 (GABA vesicular transporter), member 1 Vesicular neurotransmitter transporter Neurotransmission/synapse functioning (=VGAT, GABA transporter) Slc36a1 Solute carrier family 36 (proton/amino acid symporter), member 1 Transmembrane transporter Multiple Sema3a Semaphorin 3A Protein/ECM binding Cell adhesion/ECM/axon guidance Sema6a Semaphorin 6a Protein/ECM binding Cell adhesion/ECM/axon guidance Sez6 Seizure related gene 6 (=BSRP-C) Neurotransmission/synapse functioning (shaping dendritic arborization) Sez6l Seizure related 6 homolog like Neurotransmission/synapse functioning (shaping dendritic arborization) Sh3bgrl2 SH3 domain binding glutamic acid-rich protein like 2 Slit1 Slit guidance ligand 1 Ca/protein/ECM binding Cell adhesion/ECM/axon guidance Slit2 Slit guidance ligand 2 Ca/protein/ECM binding Cell adhesion/ECM/axon guidance Snca Synuclein, alpha Protein binding Multiple (involved in synucleinopathies including PD; RBD) [45, 46] Sncg Synuclein, gamma Protein binding Multiple Sphkap SPHK1 interactor, AKAP domain containing (=SKIP) A kinase anchoring protein Spp1 Secreted phosphoprotein 1 (=OPN) Cytokine/ECM binding Cell adhesion/ECM/axon guidance Steap2 Six transmembrane epithelial antigen of prostate 2 Enzyme Multiple Sv2b Synaptic vesicle glycoprotein 2 b Transmembrane transporter Neurotransmission/synapse functioning (vesicular transport/exocytosis) Sv2c Synaptic vesicle glycoprotein 2c Transmembrane transporter Neurotransmission/synapse functioning (vesicular transport/exocytosis) Syt4 Synaptotagmin IV Ca/protein/lipid binding Neurotransmission/synapse functioning (vesicular transport/exocytosis) S100a10 S100 calcium-binding protein A10 (=calgizzarin) EF-hand Ca binding/protein binding Multiple S100b S100 protein, beta polypeptide, neural EF-hand Ca binding/protein binding Multiple (marker in sleep disturbance syndromes and PD) Tesc Tescalcin EF-hand Ca binding/protein binding Multiple Tmem65 Transmembrane protein 65 Tpbg Trophoblast glycoprotein Usp11 Ubiquitin-specific peptidase 11 Enzyme Proteolysis Vat1l Vesicle amine transport protein 1 homolog-like Whrn Whirlin Cytoskeleton dynamics Zfp385b Zinc finger protein 385B Transcription factor Nucleic acid processing Zfhx4 Zinc finger homeodomain 4 Transcription factor Nucleic acid processing Zfp365 Zinc finger protein 365 Transcription factor Nucleic acid processing Zkscan16 Zinc finger with KRAB and SCAN domains 16 Transcription factor Nucleic acid processing Gene . Complete name . Molecular activity . Biological process . Pcp4 Purkinje cell protein 4 Ca/protein binding Multiple Pcp4l1 Purkinje cell protein 4-like 1 Ca/protein binding Multiple Penk Preproenkephalin Neuropeptide Neurotransmission/synapse functioning (neuropeptide) Pnoc Prepronociceptin Neuropeptide Neurotransmission/synapse functioning (neuropeptide) Psd Pleckstrin and Sec7 domain containing (=EFA6) GEF activity Axonal transport Ptpro Protein tyrosine phosphatase, receptor type, O Receptor/enzyme Neurotransmission/synapse functioning (promotes synapse formation) Pvalb Parvalbumin EF-hand Ca binding; calcium sensor/buffer Calcium homeostasis [44] Rec8 REC8 meiotic recombination protein Chromatin binding Nucleic acid processing Rgs4 Regulator of G-protein signaling 4 GTPase activator Cell signaling Rgs10 Regulator of G-protein signaling 10 GTPase activator Cell signaling Scn3b Sodium channel, voltage-gated, type III, beta Sodium channel Ion channel Scn4b Sodium channel, type IV, beta Sodium channel Ion channel Scrt1 Scratch family zinc finger 1 Transcription repressor Nucleic acid processing Sdk2 Sidekick homolog 2 (chicken) Cell adhesion/ECM/axon guidance Slc6a7 Solute carrier family 6 (neurotransmitter transporter, l-proline), member 7 Proline transporter Multiple Slc8a1 Solute carrier family 8 (sodium/calcium exchanger), member 1 (=Ncx1) Ca/Na antiporter Calcium homeostasis Slc17a6 Solute carrier family 6 (sodium-dependent inorganic phosphate cotransporter), member 6 Vesicular neurotransmitter transporter Neurotransmission/synapse functioning (=VGlut2, glutamate transporter) Slc17a7 Solute carrier family 6 (sodium-dependent inorganic phosphate cotransporter), member 7 Vesicular neurotransmitter transporter Neurotransmission/synapse functioning (=VGlut1, glutamate transporter) Slc32a1 Solute carrier family 32 (GABA vesicular transporter), member 1 Vesicular neurotransmitter transporter Neurotransmission/synapse functioning (=VGAT, GABA transporter) Slc36a1 Solute carrier family 36 (proton/amino acid symporter), member 1 Transmembrane transporter Multiple Sema3a Semaphorin 3A Protein/ECM binding Cell adhesion/ECM/axon guidance Sema6a Semaphorin 6a Protein/ECM binding Cell adhesion/ECM/axon guidance Sez6 Seizure related gene 6 (=BSRP-C) Neurotransmission/synapse functioning (shaping dendritic arborization) Sez6l Seizure related 6 homolog like Neurotransmission/synapse functioning (shaping dendritic arborization) Sh3bgrl2 SH3 domain binding glutamic acid-rich protein like 2 Slit1 Slit guidance ligand 1 Ca/protein/ECM binding Cell adhesion/ECM/axon guidance Slit2 Slit guidance ligand 2 Ca/protein/ECM binding Cell adhesion/ECM/axon guidance Snca Synuclein, alpha Protein binding Multiple (involved in synucleinopathies including PD; RBD) [45, 46] Sncg Synuclein, gamma Protein binding Multiple Sphkap SPHK1 interactor, AKAP domain containing (=SKIP) A kinase anchoring protein Spp1 Secreted phosphoprotein 1 (=OPN) Cytokine/ECM binding Cell adhesion/ECM/axon guidance Steap2 Six transmembrane epithelial antigen of prostate 2 Enzyme Multiple Sv2b Synaptic vesicle glycoprotein 2 b Transmembrane transporter Neurotransmission/synapse functioning (vesicular transport/exocytosis) Sv2c Synaptic vesicle glycoprotein 2c Transmembrane transporter Neurotransmission/synapse functioning (vesicular transport/exocytosis) Syt4 Synaptotagmin IV Ca/protein/lipid binding Neurotransmission/synapse functioning (vesicular transport/exocytosis) S100a10 S100 calcium-binding protein A10 (=calgizzarin) EF-hand Ca binding/protein binding Multiple S100b S100 protein, beta polypeptide, neural EF-hand Ca binding/protein binding Multiple (marker in sleep disturbance syndromes and PD) Tesc Tescalcin EF-hand Ca binding/protein binding Multiple Tmem65 Transmembrane protein 65 Tpbg Trophoblast glycoprotein Usp11 Ubiquitin-specific peptidase 11 Enzyme Proteolysis Vat1l Vesicle amine transport protein 1 homolog-like Whrn Whirlin Cytoskeleton dynamics Zfp385b Zinc finger protein 385B Transcription factor Nucleic acid processing Zfhx4 Zinc finger homeodomain 4 Transcription factor Nucleic acid processing Zfp365 Zinc finger protein 365 Transcription factor Nucleic acid processing Zkscan16 Zinc finger with KRAB and SCAN domains 16 Transcription factor Nucleic acid processing Open in new tab Figure 2. Open in new tabDownload slide Genes showing expression in the area of the NPCalb—Part 1. The expression profile of the following genes is shown: (A) Calb1, (B) Cacna1g, (C) Cartpt, (D) Cck, (E) Cnr1, (F) Crh, (G) Crhr1, (H) Ecel1, (I) Fxyd7, (J) Grik1, (K) Grm8, (L) Htr2c, (M) Kcng3, (N) Kcnip3, and (O) Ly6h. For the complete name of the genes shown, as well as the function of the encoded proteins, see Tables 1–3. Red, respectively white, dashed lines delimit the NPCalb area. Black, respectively white, lines delimit the fourth ventricle. Data for the Calb1 gene are given in panel (A) as reference. For each gene are shown both the ISH image (left; obtained with a digoxygenin-based method) and the corresponding colored image (right; ranging from blue to red, respectively from low to high expression) (Image credit: Allen Institute; Calb1: http://mouse.brain-map.org/experiment/show/79556672; Cacna1g: http://mouse.brain-map.org/experiment/show/71587822; Cartpt: http://mouse.brain-map.org/experiment/show/72077479; Cck: http://mouse.brain-map.org/experiment/show/200; Cnr1: http://mouse.brain-map.org/experiment/show/79591675; Crh: http://mouse.brain-map.org/experiment/show/292; Crhr1: http://mouse.brain-map.org/experiment/show/297; Ecel1: http://mouse.brain-map.org/experiment/show/70231305; Fxyd7: http://mouse.brain-map.org/experiment/show/73592536; Grik1: http://mouse.brain-map.org/experiment/show/75749751; Grm8: http://mouse.brain-map.org/experiment/show/73771227; Htr2c: http://mouse.brain-map.org/experiment/show/73636098; Kcng3: http://mouse.brain-map.org/experiment/show/71717451; Kcnip3: http://mouse.brain-map.org/experiment/show/71587887; Ly6h: http://mouse.brain-map.org/experiment/show/71924388). Figure 3. Open in new tabDownload slide Genes showing expression in the area of the NPCalb—Part 2. The expression profile of the following genes is shown: (A) Necab2, (B) Nell2, (C) Nnat, (D) Nos1, (E) Nptx1, (F) Ntng1, (G) Nucb2, (H) Nxph1, (I) Pnoc, (J) Ptpro, (K) Rgs10, (L) Scn3b, (M) Sez6, (N) Slit1, and (O) Sncg. See the legend of Figure 2 for details (Image credit: Allen Institute; Necab2: http://mouse.brain-map.org/experiment/show/73788010; Nell2: http://mouse.brain-map.org/experiment/show/72103854; Nnat: http://mouse.brain-map.org/experiment/show/77887874; Nos1: http://mouse.brain-map.org/experiment/show/75147762; Nptx1: http://mouse.brain-map.org/experiment/show/73520998; Ntng1: http://mouse.brain-map.org/experiment/show/71924185; Nucb2: http://mouse.brain-map.org/experiment/show/75774683; Nxph1: http://mouse.brain-map.org/experiment/show/75084479; Pnoc: http://mouse.brain-map.org/experiment/show/75038402; Ptpro: http://mouse.brain-map.org/experiment/show/72340109; Rgs10: http://mouse.brain-map.org/experiment/show/74511849; Scn3b: http://mouse.brain-map.org/experiment/show/71064082; Sez6: http://mouse.brain-map.org/experiment/show/71063725; Slit1: http://mouse.brain-map.org/experiment/show/73788105; Sncg: http://mouse.brain-map.org/experiment/show/72081426). Figure 4. Open in new tabDownload slide Proportions of the several categories of genes expressed in the NPCalb area. See also Tables 1–3 for details on gene name and function. “Neurotransmission”: Neuropeptides: Adcyap1; Cartpt, Cck, Crh, Nucb2, Nxph1, Nxph4, Penk, Pnoc. Neurotransmitter/neuropeptide receptor: Chrm2, Chrm3, Cnr1, Crhr1, Gabra1, Glra1, Glra4, Grid1, Grik1, Grin3a, Grm8, Htr2c. Neurotransmitter synthesis/transport/release/exocytosis: Apba1, Baiap3, Gad1, Gad2, Nos1, Nos1ap; Slc17a6, Slc17a7, Slc32a1, Sv2b, Sv2c, Syt4. “Synapse functioning”: Cadps2, Nrn1, Ptpro, Sez6, Sez6l, Sv2b, Sv2c. “Ion channel”: For calcium: Cacna1g, Cacna1h, Cacna2d1, Cacna2d3, Cacng5. For potassium: Hcn1, Kcna1, Kcnb1, Kcnc2, Kcnc3, Kcng3, Kcng4, Kcnj3. For sodium: Asic2, Scn3b, Scn4b. Ion channel regulation: Fxyd6, Fxyd7, Kcnip1, Kcnip4. “Cell adhesion/Extracellular matrix/Axon guidance”: Ajap, Cdh8, Cdh13, Cd24a, Cntnap2, Col6a1, Col27a1, Crta1, Igsf21, Megf11, Nell2, Npnt, Ntng1, Sdk2, Sema3a, Sema6a, Slit1, Slit2, Spp1. “Nucleic acid processing”: Adarb1, Cux2, Foxa1, Foxp1, Grsf1, Rec8, Scrt1, Zfp365, Zfp385b, Zfhx4, Zkscan16. Some other less represented categories included metabolism, cell signaling, and calcium homeostasis. Identifying neurotransmitters and neuropeptides expressed in the Nucleus papilio We have started previously to investigate the neurotransmitter status of NPCalb neurons, by showing that a significant proportion (33.8%) of Calb-positive neurons were glutamatergic and that none were GABAergic [9]. Analyzing the ABA ISH data on sagittal sections revealed that the Slc6a5 gene, encoding the Glyt2 glycine vesicular transporter, and the Chat gene, encoding choline acetyltransferase, were also expressed in the region of the medulla oblongata. Immunostaining for Calb on coronal sections from GlyT2-GFP mouse brain revealed that NPCalb neurons were not glycinergic (Figure 5A–C′). Similarly, double immunostaining for Calb and Chat revealed the absence of Chat expression in NPCalb neurons (Figure 5D–F′). Figure 5. Open in new tabDownload slide NPCalb neurons are neither glycinergic nor cholinergic. (A–C′) Coronal sections from a GlyT2-GFP mouse brain, stained for Calb (red) and GFP (green). The dashed square in (C) marks the area shown at higher magnification in panels (A′–C′). (D–F′) Coronal sections from a C57Bl6 mouse brain, stained for Calb (red) and ChAT (green). The dashed square in (F) marks the area shown at higher magnification in panels (D′–F′). Bars represent 100 µm. Our search identified several genes encoding neuropeptides that are likely to be coexpressed with Calb1, namely, Cartpt (encoding Cart, cocaine-and-amphetamine-regulated transcript), Cck (encoding cholecystokinin), Crh (encoding corticotrophin-releasing hormone), Nucb2 (encoding nesfatin 1), Penk (encoding preproenkephalin), and Pnoc (encoding prepronociceptin) (see Figures 2 and 3 for the ABA ISH data). For the immunohistochemical revelation of coexpression, coronal sections through murine brains were double-stained for Calb and two of these neuropeptides. Nesfatin immunoreactivity was present in all Calb-immunoreactive neurons of the NPCalb (Figure 6A–C and A′–C′), whereas that for Cart (Figure 6D–F and D′–F′) was apparent in some, but not all. In addition, some cells lying close to the Calb-immunoreactive neurons were positive for each of the tested peptides, but not for Calb. Noteworthy, similar observations were made using sections from rat brains (not shown). We estimated that 27.2% ± 5.6% of the DPGi Calb+ cells were Cart+ (counting performed on each second sections in n = 8 mice) and that 65.9% ± 13.7% of the DPGi Cart+ cells were Calb+ (counting performed in n = 3 mice). Figure 6. Open in new tabDownload slide Cart and Nesfatin neuropeptide expression in NPCalb neurons. Coronal sections from a C57Bl6 mouse brain, stained for Calb and either Nesfatin (A–C) or Cart (D–F) in the NPCalb. All Calb-positive NPCalb-neurons coexpress Nesfatin, while coexpression with Cart is limited to few neurons (marked by arrows in D′–F′). Panels (A′–C′) and (D′–F′) are higher magnifications. All images were obtained with confocal microscope. Bars represent 100 µm. Cart-expressing neurons within the DPGi are glutamatergic With the aim of analyzing the neurotransmitter status of the Cart-expressing neurons in the NPCalb/DPGI area, glutamatergic, respectively GABAergic, neurons within the medulla oblongata were labeled by means of fluorescent adenovirus tracer injection in Slc17a6::Cre mouse brains (encoding the VGlut2 glutamate transporter) and Slc32a1::Cre (encoding the VGAT GABA transporter). In these conditions, glutamatergic, respectively GABAergic, cell bodies could be easily identified by their strong fluorescence. Co-staining with anti-Cart antibody revealed that all Cart-positive cells within the DPGi were VGlut2-positive and that none was VGat-positive, highlighting their glutamatergic nature (Figure 7). In addition, Cart-positive cells located in either the adjacent Nucleus prepositus or the gigantocellular reticular nucleus were also mostly glutamatergic, while only very few Cart-positive cells within the Nucleus prepositus appeared as GABAergic. Figure 7. Open in new tabDownload slide Cart-expressing neurons within the DPGi are glutamatergic. Immunostaining with Cart antibody reveals the glutamatergic nature of all Cart-expressing neurons in the DPGI. Shown are representative coronal sections through the medulla oblongata of a brain from (A–C′) Slc7a6::Cre (VGlut2-Cre) and (D–F′) Slc32a1::Cre (VGAT-Cre) mice, injected with Cre-dependent AAV-Tomato. In (C), respectively (F), a dashed square indicates the area presented at higher magnification in (A′–C′), respectively (D′–F′). White arrows in (A) and (D) point to Cart+ cells. Within the DPGi and the Gi, all Cart+ neurons are glutamatergic (yellow arrows in B), while none are GABAergic (white arrows in E). On the contrary, in the prepositus nucleus both glutamatergic and GABAergic Cart+ neurons are visible (yellow arrows pointing to Pr neurons in B and E). Bars represent 100 µm. Cart-expressing neurons within the DPGi are activated during REM sleep In an experimental paradigm consisting of REM sleep rebound following a 72 h REM sleep deprivation, we could demonstrate that neurons of the DPGi [47–49], and particularly the NPCalb neurons [9], were activated during REM sleep, as demonstrated by neuronal c-fos immunoreactivity. A similar test performed on mice revealed that Cart-positive neurons localized within the DPGi of the medulla oblongata were activated during REM sleep too. Only in the DPGi significant differences between the three different groups analyzed could be observed (REM sleep deprivation group; REM sleep deprivation + rebound group; Control group) (Figure 8). Indeed, in the REM sleep rebound group, we quantified 41.6% ± 6.9% of DPGi Cart-positive neurons displaying c-fos immunoreactivity and 31% ± 6.5% of DPGi c-fos+ cells being Cart+. Analyzing the other areas (including the Gi, the LPGi, the mlf, the 10N/Sol), no significant difference between the three groups was observed (Figure 8). This experiment suggests that Cart-expressing neurons in the DPGi are activated during the REM phase of sleep. Figure 8. Open in new tabDownload slide Analysis of c-fos immunoreactivity in Cart-expressing neurons in the medulla oblongata during REM sleep. (A–C) Representative coronal section from the brain of an animal of the group “REM sleep deprivation and rebound,” stained for Cart (red) and c-fos (green). The two arrows in panel C point to Cart+c-fos+ neurons. (D–F) Representative brain coronal sections from animals of each three groups used for the REM sleep deprivation and rebound assay (REMSD + R, REM sleep deprivation and rebound; REMSD, REM sleep deprivation; C, Control), immunostained for c-fos. (G) Percentage of c-fos immunoreactive cells within the Cart+ population, in different brain areas. Counting was performed on alternating coronal sections from three animals of each three groups (REMSD + R; REMSD; C). Statistical significance at p < 0.05 between the different groups was achieved only in the DPGi; group REMS-D+R versus group C: t = 10.78389 and p = 0.000019; group REMS-D+R versus group REMS-D: t = 10.78389 and p = 0.000019. DPGi, dorsal paragigantocellular nucleus; Gi, gigantocellular reticular nucleus; LPGi, lateral paragigantocellular nucleus; mlf, medial longitudinal fasciculus; MVe, medial vestibular nucleus; Pr, prepositus nucleus; 4V, fourth ventricle; 10N, dorsal motor nucleus of vagus/Sol: nucleus of the solitary tract. Discussion The Nucleus papilio is a recently described brainstem structure, characterized by Calb immunoreactivity, and by its involvement in triggering eye movement during the REM sleep period [9]. Through an extensive data mining of the ABA, we propose here a list of genes that are likely to be expressed in the NPCalb. Several of these genes encode proteins involved in the synthesis/transport of neurotransmitters, namely, Slc17a7 and Slc17a6 (encoding VGlut1- and VGlut2-glutamate transporter, respectively), Gad1/2 (encoding glutamic acid decarboxylase, which is responsible for the production of GABA), and Slc32a1 (encoding a GABA transporter). By injecting Cre-dependent AAV-Tomato virus in Slc17a6::Cre and Slc17a7::Cre (both specific for glutamatergic neurons) and Slc32a1::Cre mice (specific for GABAergic neurons), we were able to show the absence of Calb immunoreactivity in the GABAergic neurons of the DPGi, whereas a significant proportion of the Calb-positive neurons composing the NPCalb were of glutamatergic nature [9]. Analyzing the list of potential genes expressed in the NPCalb, several glutamate receptors and a single GABA receptor were found (Tables 2–4). Of special interest for us were the genes encoding neuropeptides. Nesfatin 1 is derived from a precursor, nucleobindin 2, which via posttranslational cleavage, yields either the neuropeptide nesfatin 1 or the DNA/Ca2+-binding proteins nesfatin 2 and 3. Nesfatin 1 has been identified as a satiety molecule in the hypothalamus [50]. Albeit so, its widespread extra-hypothalamic expression indicates that it might exert endocrine and autonomic effects on energy expenditure [51]. Cart peptides have been implicated in the regulation not only of food intake and body weight, but also of a variety of physiological processes, including drug reward/reinforcement and stress [52, 53], findings that are consistent with the complex pattern of Cart immunoreactivity in the rat brain [54]. Cart has been shown to coexist with Calb-D28k in granule cells of the dentate gyrus [55] and to be coexpressed with nesfatin in several hypothalamic and non-hypothalamic areas of the rat brain [51, 56, 57], including MCH-neurons. Data presented in the present study indicate that both peptides are coexpressed also in some Calb-immunoreactive neurons of the NPCalb. Several studies that have been recently conducted afford evidence for a role of nesfatin 1 in the regulation of REM sleep. Disruption of nesfatin signaling in the tuberal hypothalamic neurons, by the intra-cerebroventricular administration of either an antiserum against the neuropeptide or Nucb2 antisense, has been shown to suppress REM sleep [24]. Similarly, deprivation of REM sleep led to a downregulation of nesfatin 1 expression, which was reverted during REM rebound [25]. And finally, the activity of these hypothalamic nesfatin-positive neurons (which are also MCH positive), as monitored by c-fos immunostaining, was correlated with REM sleep [24, 25]. Clear evidence in favor of an involvement of Cart in the regulation of the sleep/wake regulation has not been forthcoming [26]. Indeed, although an intra-cerebroventricular injection of the Cart55-102-peptide promotes the wake phase in rats [18], both Cart-positive and Cart-negative MCH-immunoreactive neurons in the hypothalamus are activated during REM sleep [19, 20]. Our finding that a significant proportion of Cart-expressing neurons in the DPGi were c-fos-positive following REM sleep rebound is thus particularly interesting and suggests a possible involvement of Cart in regulating some aspects of REM sleep. The Calb-expressing neurons forming the NPCalb appear to form a heterogeneous population, involved partly in controlling eye movement during REM sleep [9], with a substantial number being excitatory glutamatergic neurons, and with the neuropeptides Cart and nociceptin being expressed only in a subset of these neurons while nesfatin being present in all of them (this study). In addition, the surrounding DPGi also contains another pool of inhibitory GABAergic neurons involved in the initiation of REM sleep, as well as glycinergic and cholinergic neurons [4, 7, 8]. Deciphering the specific neuronal connections made by these particular neuronal populations will be challenging toward a better understanding of the functions of this nucleus in regulating various aspects of REM sleep. Indeed, apart from their connections to the three eye motor nuclei, we observed strong efferent connections of the NPCalb neurons to several of the brain areas involved in the initiation and the maintenance of REM sleep (including the subcoeruleus nucleus and the pontine reticular nuclei) [9], suggesting additional roles for the NPCalb in regulating some aspects of REM sleep. Acknowledgments We thank Simone Eichenberger, Drs Viktoria Szabolcsi, and Diana Waldmeyer-Roccaro for their technical assistance. We thank Dr Zeilhofer for the kind gift of GlyT2-GFP mice. Funding This work was funded by the Canton of Fribourg (Switzerland) and the Swiss National Foundation (31003A-144036 and 320030-179565). Disclosure Statement Financial disclosure: none. 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This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact [email protected] © Sleep Research Society 2020. Published by Oxford University Press on behalf of the Sleep Research Society.

Bottary, Ryan; Simonelli, Guido; Cunningham, Tony J; Kensinger, Elizabeth A; Mantua, Janna

doi: 10.1093/sleep/zsaa131pmid: 32886777

Dear Editor, The Coronavirus Disease 2019 (COVID-19) pandemic has dramatically impacted the lifestyle of individuals across the globe. In many countries, nationally instituted shelter-in-place orders and closures of non-essential businesses have left many without employment and have forced others to work from home. These policy shifts have changed or removed the socially- and occupationally imposed demands that typically influence sleep schedules. Lifestyle and schedule changes that have stemmed from the COVID-19 pandemic have not gone unnoticed to those in the sleep research and sleep medicine communities. Several recently published and ongoing studies have aimed to track sleep and sleep-related behaviors during these unprecedented times. Initial reports suggest that sleep duration and quality changed during the early phases of the pandemic. For instance, multiple studies found increased sleep duration [1, 2] and time in bed [2, 3] in populations under varying degrees of lockdown (e.g. in the United States and in four European countries). However, unexpectedly, the same studies found a decrease in self-reported sleep quality [1, 3]. To explain these paradoxical findings, the authors proposed that increased stress from life changes (e.g. loss of employment, financial hardship, or new childcare demands) or increased psychological health issues (e.g. anxiety, loneliness) may have negatively impacted sleep quality despite increased total sleep time. In parallel, anecdotal evidence has surfaced that individuals are remembering more dreams and that dream content has become more vivid during the pandemic. Such reports have been covered by a number of major news outlets, including the New York Times, Time magazine, Smithsonian magazine, and National Geographic magazine. Many have turned to the dream simulation theory, which posits dreams provide a chance to “work out” future or current threatening or social scenarios [4], to explain why dream frequency and content are changing. However, we are not aware of any empirical work that has explored or confirmed this hypothesis. We posit that the three phenomena described above—longer sleep, more fragmented sleep, and frequent/vivid dream recall—are not coincidental. Instead, these sleep behaviors might all be caused by naturalistic “sleep extension,” which is defined as the act of extending one’s sleep duration beyond habitual levels [5]. Sleep extension has been used as a research tool for decades, and, more recently, has been implemented in both controlled and applied research settings as a means to “bank” sleep [6]. Studies on sleep extension have provided unique insight into the nature of reducing “sleep debt” and the limits of the sleep homeostat. That is, they hint at what might happen when an individual has unrestricted time for sleep over an extended period of time—something akin to what might be the case for some individuals during the COVID-19 pandemic. Experimental evidence has shown that although sleep duration tends to increase during the early stages of sleep extension (e.g. the first 7–10 days [5]), once sleep debt has been “satiated,” sleep duration tends to regress toward habitual levels. Importantly, if a continued attempt to extend sleep is made following sleep debt satiation, sleep continuity decreases and sleep becomes more fragmented [7, 8]. Dream recall is increased by more frequent awakenings, as demonstrated in a recent study in which self-reported high-frequency dreamers had more nighttime awakenings than low-frequency dreamers [9]. These findings are in support of the arousal-retrieval model of dream recall, which suggests individuals with higher dream recall may not necessarily dream more frequently. Rather, frequent awakenings simply provide more opportunities for memory encoding, rehearsal, and consolidation of dream content. Although no study has directly assessed the link between sleep extension, sleep fragmentation, and dream recall, at least one study has reported an increase in dream recall following experimental sleep extension, though the content of the dreams was not reported, as this was not the main objective of the study [10]. In conclusion, we hypothesize that increased dream recall during the COVID-19 pandemic may be attributable to increased sleep fragmentation caused by naturalistic sleep extension. However, we do not present this as a “unifying hypothesis” to explain all sleep changes and issues during the pandemic. Certainly, stress, anxiety, and social separation may play a role in impacting sleep. Further, we do not have hypotheses on whether sleep extension and consequent fragmentation influence the content of the dreams. The hypothesis proposed here is, therefore, not in opposition to those on changes in dream content or vividness (e.g. the simulation theory [4]). We look forward to future work on these topics and to gaining a greater understanding of how sleep and circadian processes are being impacted during this critical and unprecedented time. Understanding the relationship between pandemic-related schedule changes, psychosocial changes, and sleep changes will provide insight to guide sleep-related recommendations during future comparable scenarios, should any arise. Acknowledgments We would like to thank the brave first responders and military service members that have compromised their health and sleep during the COVID-19 pandemic. In particular, R.B. and T.J.C. would like to acknowledge our partners, Dr. Dorothy Liu, MD and Kate Cunningham, RN, respectively, who continue to amaze us with their commitment to the health and wellbeing of their patients. Material has been reviewed by the Walter Reed Army Institute of Research. There is no objection to its presentation and/or publication. The opinions or assertions contained herein are the private views of the authors and are not to be construed as official, or as reflecting true views of the Department of the Army or the Department of Defense. The investigators have adhered to the policies for the protection of human subjects as prescribed in AR 70–25. Funding None declared. Conflict of interest statement. None declared. References 1. Blume C , et al. Effects of the COVID-19 lockdown on human sleep and rest-activity rhythms . Curr Biol . 2020 ; 30 : R795 – R797 . Google Scholar Crossref Search ADS PubMed WorldCat 2. Wright KP , et al. Sleep in university students prior to and during COVID-19 stay-at-home orders . Curr Biol . 2020 ; 30 : R797 – R798 . Google Scholar Crossref Search ADS PubMed WorldCat 3. Cellini N , et al. Changes in sleep pattern, sense of time and digital media use during COVID-19 lockdown in Italy . J Sleep Res . 2020 : 1 – 5 . Google Scholar OpenURL Placeholder Text WorldCat 4. McNamara P The Neuroscience of Sleep and Dreams . Cambridge, England : Cambridge University Press ; 2019 . Google Scholar Crossref Search ADS Google Preview WorldCat COPAC 5. Mantua J , et al. Sleep extension reduces fatigue in healthy, normally-sleeping young adults . Sleep Sci. 2019 ; 12 ( 1 ): 21 – 27 . Google Scholar Crossref Search ADS PubMed WorldCat 6. Rupp TL , et al. Banking sleep: realization of benefits during subsequent sleep restriction and recovery . Sleep. 2009 ; 32 ( 3 ): 311 – 321 . Google Scholar Crossref Search ADS PubMed WorldCat 7. Roehrs T , et al. A two-week sleep extension in sleepy normals . Sleep. 1996 ; 19 ( 7 ): 576 – 582 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 8. Harrison Y , et al. Long-term extension to sleep–are we really chronically sleep deprived? Psychophysiology. 1996 ; 33 ( 1 ): 22 – 30 . Google Scholar Crossref Search ADS PubMed WorldCat 9. van Wyk M , et al. Increased awakenings from non-rapid eye movement sleep explains differences in dream recall frequency in healthy high and low recallers . Front Hum Neurosci . 2019 ; 13 : 370 . Google Scholar Crossref Search ADS PubMed WorldCat 10. Leproult R , et al. Beneficial impact of sleep extension on fasting insulin sensitivity in adults with habitual sleep restriction . Sleep. 2015 ; 38 ( 5 ): 707 – 715 . Google Scholar Crossref Search ADS PubMed WorldCat Published by Oxford University Press on behalf of Sleep Research Society (SRS) 2020. This work is written by (a) US Government employee(s) and is in the public domain in the US. Published by Oxford University Press on behalf of Sleep Research Society (SRS) 2020.

van den Munckhof, Bart; Gefferie, Silvano R; van Noort, Suus A M; van Teeseling, Heleen C; Schijvens, Mischa P; Smit, William; Teunissen, Nico W; Plate, Joost D J; Huiskamp, Geert Jan M; Leijten, Frans S S; Braun, Kees P J; Jansen, Floor E; Bölsterli, Bigna K

doi: 10.1093/sleep/zsaa088pmid: 32374855

Lecci,, Sandro;Cataldi,, Jacinthe;Betta,, Monica;Bernardi,, Giulio;Heinzer,, Raphaël;Siclari,, Francesca

doi: 10.1093/sleep/zsaa094pmid: 32409833

Abstract Feeling awake although sleep recordings indicate clear-cut sleep sometimes occurs in good sleepers and to an extreme degree in patients with so-called paradoxical insomnia. It is unknown what underlies sleep misperception, as standard polysomnographic (PSG) parameters are often normal in these cases. Here we asked whether regional changes in brain activity could account for the mismatch between objective and subjective total sleep times (TST). To set cutoffs and define the norm, we first evaluated sleep perception in a population-based sample, consisting of 2,092 individuals who underwent a full PSG at home and estimated TST the next day. We then compared participants with a low mismatch (normoestimators, n = 1,147, ±0.5 SD of mean) with those who severely underestimated (n = 52, <2.5th percentile) or overestimated TST (n = 53, >97.5th percentile). Compared with normoestimators, underestimators displayed higher electroencephalographic (EEG) activation (beta/delta power ratio) in both rapid eye movement (REM) and non-rapid eye movement (NREM) sleep, while overestimators showed lower EEG activation (significant in REM sleep). To spatially map these changes, we performed a second experiment, in which 24 healthy subjects and 10 insomnia patients underwent high-density sleep EEG recordings. Similarly to underestimators, patients displayed increased EEG activation during NREM sleep, which we localized to central-posterior brain areas. Our results indicate that a relative shift from low- to high-frequency spectral power in central-posterior brain regions, not readily apparent in conventional PSG parameters, is associated with underestimation of sleep duration. This challenges the concept of sleep misperception, and suggests that instead of misperceiving sleep, insomnia patients may correctly perceive subtle shifts toward wake-like brain activity. insomnia, sleep, subjective, EEG, misperception Statement of Significance A striking mismatch between subjectively perceived and objectively recorded sleep times sometimes occurs in good sleepers and to an extreme degree in patients with so-called paradoxical insomnia. Here we show that individuals who underestimate their total sleep time display a more “wake-like,” activated electroencephalography, while opposite changes are observed in rapid eye movement sleep in subjects who overestimate their sleep time. These findings challenge the concept of sleep misperception, and suggest that instead of misperceiving their sleep duration, insomnia patients may actually correctly perceive subtle shifts toward wake-like brain activity. Introduction Patients suffering from insomnia often underestimate their total sleep time (TST), and overestimate sleep latency as well wakefulness after sleep onset with respect to objective measures like standard polysomnography (PSG) or actigraphy [1–8]. When an extreme mismatch between subjective estimations and objective parameters occurs, the condition is typically referred to as “paradoxical insomnia (ParI)” or “sleep state misperception.” Although it is not uncommon to see such patients in clinical practice [9], ParI does not figure in the most recent version of the international classification of sleep disorders [10] as there is currently no universally accepted definition of this condition. The lack of definition has recently been pointed out as one of the major problems in the field of ParI research [11]. In fact, depending on the criteria that are used to define ParI, the prevalence of this disorder can vary four- to fivefold within the same sample [11], hampering comparability between study findings. Sleep macrostructure is often normal or near normal in patients with primary and ParI, although some studies have found a lower proportion of slow wave sleep compared with healthy controls [12, 13]. A series of studies also documented spectral power changes in the sleep of ParI patients [3, 14–17], but a clear physiopathological mechanism underlying this condition has not yet emerged [18]. Importantly, dissociations between objective and subjective sleep parameters are not restricted to patients with insomnia. It has been shown that even good sleepers consistently overestimate sleep latencies [11, 19] and conversely, appear to perceive a large part of intranight awakenings as sleep [20, 21]. These observations challenge the way we define and measure sleep, and suggest that standard PSG parameters are insufficient to capture changes in brain activity that account for subjective sleep perception. In the present study, we asked whether spectral power changes, which are not apparent in conventional PSG parameters, could explain why some subjects feel awake during parts of the night and therefore underestimate their sleep duration as defined by standard scoring criteria. To address the definition problem of ParI outlined above, and following recently issued recommendations [11], we set out to evaluate sleep perception in a large sample of the general population (n = 2,092), in which participants underwent a home-PSG and estimated TST the next day. The first aim was to define the norm and set cutoffs, separating subjects who accurately estimated their sleep duration, from those who severely underestimated or overestimated their sleep duration. The second aim was to evaluate spectral power electroencephalographic (EEG) changes that distinguished under- and overestimators from normoestimators in this population. Finally, to account for aspects strictly related to the home setting and to evaluate regional power changes associated with sleep perception in a clinical population of insomnia sufferers, we performed another study in the sleep laboratory, in which 10 patients with ParI, who were selected according to the same cutoffs as underestimators, underwent a supervised high-density EEG (hd-EEG) sleep recording in the laboratory. Methods Study 1: sleep perception in the HypnoLaus cohort Selection of participants The HypnoLaus sleep cohort [22] is a nested project of the CoLaus/PsyCoLaus cohort, a general population-based sample of the residents of Lausanne (Switzerland). Briefly, 2,162 consecutive participants of CoLaus/PsyCoLaus, aged 40–85 years, underwent an unattended overnight full PSG at home between September 2009 and June 2013. They also completed a series of questionnaires, of which the Horne and Ostberg morningness–eveningness questionnaire [23], the Epworth sleepiness scale [8], and the Pittsburgh Sleep Quality Index (PSQI) [24] were included in this study. Sleep recordings The PSG setup (Titanium, Embla Flaga, Reykjavik, Iceland) included EEG (F3, F4, C3, C4, O1, and O2 leads, 256 Hz sampling rate) electrooculography (EOG, right and left), electromyography (EMG, chin and anterior tibialis muscle), electrocardiography, airflow (nasal cannula), abdominal and thoracic respiratory inductance plethysmography, snoring, as well as body position and pulse oximetry (sampling rate: 32 Hz). Sleep scoring was performed according to the 2007 AASM criteria [25] in 30 s epochs. Hypopneas were rescored according to the 2012 AASM criteria [26]. The study was approved by the local ethical committee and written informed consent was obtained from all participants. Participants were allowed to follow their preferred bed and rise times. After the recording, they had to fill out a brief questionnaire in which they estimated how much time (in hours and minutes) they had slept and rated their sleep quality of the PSG night on a scale from 1 (excellent) to 4 (poor). A total of 2,092 out of 2,162 subjects provided estimates of TST and were included in the analyses. The recorders were programmed to start at least 1 hour before the scheduled bedtime and to stop at least 1 hour after the scheduled wake-up time. In addition, as part of the study procedure, all recordings were inspected for technical problems after acquisition. Recordings with technical problems were either repeated and replaced with the new recording, if the subject agreed, or eliminated from the study [22]. Definition of normoestimators, underestimators, and overestimators To quantify sleep perception, we computed a Sleep Perception Index (SPI), expressing the ratio between subjective TST (sTST) and objective TST (oTST) in percent ([sTST/oTST]*100). Subjects with an SPI in the lowest 2.5% of the population distribution (SPI < 58.82%, n = 52) were arbitrarily defined as underestimators (Table 1 and Figure 1), those with an SPI falling in the upper 2.5%, above the 97.5th percentile (SPI > 146.09%, n = 53) as overestimators (Table 2 and Figure 2). Subjects whose SPI fell within 0.5 SD of the mean (88.31 < SPI < 110.43%, n = 1,147) were called normoestimators (Figure 1, A and 2, A). Table 1. Demographic and PSG characteristics of underestimators and normoestimators . Normoestimators . Underestimators . P-value . n 1,147 52 Age (years) 57.25 ± 10.87 59.85 ± 9.89 0.09467 Gender 522 ♂/625 ♀ 28 ♂/24 ♀ >0.2 Obj. TST (min) 410.16 ± 62.03 401.72 ± 95.57 0.35114 Subj. TST (min) 407.32 ± 63.24 195.00 ± 74.56 <0.00001 SPI% 99.43 ± 6.05 46.62 ± 14.00 <0.00001 Sleep est. quality 2.59 ± 0.69 3.48 ± 0.54 <0.00001 Questionnaires  H-O Scale (#) 53.26 ± 4.25 (996) 52.61± 4.91 (41) 0.34074  Epworth (#) 6.39 ± 3.91 (1,061) 5.63 ± 3.89 (46) 0.19748  PSQI (#) 4.85 ± 3.01 (997) 7.68 ± 4.71 (41) <0.00001  DFA (#/30 min) 0.84 ± 1.00 (1,084) 1.59 ± 1.15 (46) <0.00001  FNA (#) 1.56 ± 1.13 (1,083) 1.98 ± 1.19 (47) 0.01247 Sleep  TRT (min) 486.41 ± 68.02 519.38 ± 83.28 0.00074  TST (min) 410.16 ± 62.03 401.72 ± 95.57 0.35114  Lat N1 (min) 14.87 ± 18.30 16.58 ± 20.11 0.51206  N1% 10.83 ± 6.25 14.65 ± 11.74 0.00005  N2% 46.36 ± 9.79 49.41 ± 11.25 0.02904  N3% 20.36 ± 8.28 15.69 ± 8.36 0.00007  REM% 22.45 ± 5.89 20.26 ± 7.08 0.00951  Lat REM (min) 90.71 ± 57.21 107.45 ± 86.14 0.04653  S. eff.% 87.31 ± 8.32 79.76 ± 15.16 <0.00001  WASO (min) 61.39 ± 45.95 101.48 ± 71.76 <0.00001  TAI (#/h) 20.20 ± 10.31 26.32 ± 11.35 0.00003  SS (#) 138.13 ± 46.65 156.19 ± 58.22 0.00706  AHI (#/h) 14.13 ± 15.18 19.13 ± 20.07 0.02228  PLMSi (#/h) 11.76 ± 20.03 14.91 ± 21.72 0.26885 . Normoestimators . Underestimators . P-value . n 1,147 52 Age (years) 57.25 ± 10.87 59.85 ± 9.89 0.09467 Gender 522 ♂/625 ♀ 28 ♂/24 ♀ >0.2 Obj. TST (min) 410.16 ± 62.03 401.72 ± 95.57 0.35114 Subj. TST (min) 407.32 ± 63.24 195.00 ± 74.56 <0.00001 SPI% 99.43 ± 6.05 46.62 ± 14.00 <0.00001 Sleep est. quality 2.59 ± 0.69 3.48 ± 0.54 <0.00001 Questionnaires  H-O Scale (#) 53.26 ± 4.25 (996) 52.61± 4.91 (41) 0.34074  Epworth (#) 6.39 ± 3.91 (1,061) 5.63 ± 3.89 (46) 0.19748  PSQI (#) 4.85 ± 3.01 (997) 7.68 ± 4.71 (41) <0.00001  DFA (#/30 min) 0.84 ± 1.00 (1,084) 1.59 ± 1.15 (46) <0.00001  FNA (#) 1.56 ± 1.13 (1,083) 1.98 ± 1.19 (47) 0.01247 Sleep  TRT (min) 486.41 ± 68.02 519.38 ± 83.28 0.00074  TST (min) 410.16 ± 62.03 401.72 ± 95.57 0.35114  Lat N1 (min) 14.87 ± 18.30 16.58 ± 20.11 0.51206  N1% 10.83 ± 6.25 14.65 ± 11.74 0.00005  N2% 46.36 ± 9.79 49.41 ± 11.25 0.02904  N3% 20.36 ± 8.28 15.69 ± 8.36 0.00007  REM% 22.45 ± 5.89 20.26 ± 7.08 0.00951  Lat REM (min) 90.71 ± 57.21 107.45 ± 86.14 0.04653  S. eff.% 87.31 ± 8.32 79.76 ± 15.16 <0.00001  WASO (min) 61.39 ± 45.95 101.48 ± 71.76 <0.00001  TAI (#/h) 20.20 ± 10.31 26.32 ± 11.35 0.00003  SS (#) 138.13 ± 46.65 156.19 ± 58.22 0.00706  AHI (#/h) 14.13 ± 15.18 19.13 ± 20.07 0.02228  PLMSi (#/h) 11.76 ± 20.03 14.91 ± 21.72 0.26885 Data are presented as mean ± SD. Numbers in parentheses indicate the number of subjects for which this data was available, if different from total number. TST, total sleep time; SPI, Sleep Perception Index; H-O, Horne–Ostberg; PSQI, Pittsburgh Sleep Quality Index; DFA, difficulties falling asleep in <30 min (scale from 0 [no difficulties in last month] to 3 [3–4 times per week]); FNA, frequency of nocturnal awakenings (scale from 0 [never woke up during the night in the last month] to 3 [3–4 times per week]); TRT, total recording time; Lat., latency; S. eff., sleep efficiency; WASO, wake after sleep onset; TAI, Total Arousal Index (number/h); SS, stage shifts; AHI, Apnea–Hypopnea Index; PLMSi, Periodic Limb Movements of Sleep Index. Alpha threshold set at 0.002 to account for multiple comparison (p-values lower than threshold appear in bold). Open in new tab Table 1. Demographic and PSG characteristics of underestimators and normoestimators . Normoestimators . Underestimators . P-value . n 1,147 52 Age (years) 57.25 ± 10.87 59.85 ± 9.89 0.09467 Gender 522 ♂/625 ♀ 28 ♂/24 ♀ >0.2 Obj. TST (min) 410.16 ± 62.03 401.72 ± 95.57 0.35114 Subj. TST (min) 407.32 ± 63.24 195.00 ± 74.56 <0.00001 SPI% 99.43 ± 6.05 46.62 ± 14.00 <0.00001 Sleep est. quality 2.59 ± 0.69 3.48 ± 0.54 <0.00001 Questionnaires  H-O Scale (#) 53.26 ± 4.25 (996) 52.61± 4.91 (41) 0.34074  Epworth (#) 6.39 ± 3.91 (1,061) 5.63 ± 3.89 (46) 0.19748  PSQI (#) 4.85 ± 3.01 (997) 7.68 ± 4.71 (41) <0.00001  DFA (#/30 min) 0.84 ± 1.00 (1,084) 1.59 ± 1.15 (46) <0.00001  FNA (#) 1.56 ± 1.13 (1,083) 1.98 ± 1.19 (47) 0.01247 Sleep  TRT (min) 486.41 ± 68.02 519.38 ± 83.28 0.00074  TST (min) 410.16 ± 62.03 401.72 ± 95.57 0.35114  Lat N1 (min) 14.87 ± 18.30 16.58 ± 20.11 0.51206  N1% 10.83 ± 6.25 14.65 ± 11.74 0.00005  N2% 46.36 ± 9.79 49.41 ± 11.25 0.02904  N3% 20.36 ± 8.28 15.69 ± 8.36 0.00007  REM% 22.45 ± 5.89 20.26 ± 7.08 0.00951  Lat REM (min) 90.71 ± 57.21 107.45 ± 86.14 0.04653  S. eff.% 87.31 ± 8.32 79.76 ± 15.16 <0.00001  WASO (min) 61.39 ± 45.95 101.48 ± 71.76 <0.00001  TAI (#/h) 20.20 ± 10.31 26.32 ± 11.35 0.00003  SS (#) 138.13 ± 46.65 156.19 ± 58.22 0.00706  AHI (#/h) 14.13 ± 15.18 19.13 ± 20.07 0.02228  PLMSi (#/h) 11.76 ± 20.03 14.91 ± 21.72 0.26885 . Normoestimators . Underestimators . P-value . n 1,147 52 Age (years) 57.25 ± 10.87 59.85 ± 9.89 0.09467 Gender 522 ♂/625 ♀ 28 ♂/24 ♀ >0.2 Obj. TST (min) 410.16 ± 62.03 401.72 ± 95.57 0.35114 Subj. TST (min) 407.32 ± 63.24 195.00 ± 74.56 <0.00001 SPI% 99.43 ± 6.05 46.62 ± 14.00 <0.00001 Sleep est. quality 2.59 ± 0.69 3.48 ± 0.54 <0.00001 Questionnaires  H-O Scale (#) 53.26 ± 4.25 (996) 52.61± 4.91 (41) 0.34074  Epworth (#) 6.39 ± 3.91 (1,061) 5.63 ± 3.89 (46) 0.19748  PSQI (#) 4.85 ± 3.01 (997) 7.68 ± 4.71 (41) <0.00001  DFA (#/30 min) 0.84 ± 1.00 (1,084) 1.59 ± 1.15 (46) <0.00001  FNA (#) 1.56 ± 1.13 (1,083) 1.98 ± 1.19 (47) 0.01247 Sleep  TRT (min) 486.41 ± 68.02 519.38 ± 83.28 0.00074  TST (min) 410.16 ± 62.03 401.72 ± 95.57 0.35114  Lat N1 (min) 14.87 ± 18.30 16.58 ± 20.11 0.51206  N1% 10.83 ± 6.25 14.65 ± 11.74 0.00005  N2% 46.36 ± 9.79 49.41 ± 11.25 0.02904  N3% 20.36 ± 8.28 15.69 ± 8.36 0.00007  REM% 22.45 ± 5.89 20.26 ± 7.08 0.00951  Lat REM (min) 90.71 ± 57.21 107.45 ± 86.14 0.04653  S. eff.% 87.31 ± 8.32 79.76 ± 15.16 <0.00001  WASO (min) 61.39 ± 45.95 101.48 ± 71.76 <0.00001  TAI (#/h) 20.20 ± 10.31 26.32 ± 11.35 0.00003  SS (#) 138.13 ± 46.65 156.19 ± 58.22 0.00706  AHI (#/h) 14.13 ± 15.18 19.13 ± 20.07 0.02228  PLMSi (#/h) 11.76 ± 20.03 14.91 ± 21.72 0.26885 Data are presented as mean ± SD. Numbers in parentheses indicate the number of subjects for which this data was available, if different from total number. TST, total sleep time; SPI, Sleep Perception Index; H-O, Horne–Ostberg; PSQI, Pittsburgh Sleep Quality Index; DFA, difficulties falling asleep in <30 min (scale from 0 [no difficulties in last month] to 3 [3–4 times per week]); FNA, frequency of nocturnal awakenings (scale from 0 [never woke up during the night in the last month] to 3 [3–4 times per week]); TRT, total recording time; Lat., latency; S. eff., sleep efficiency; WASO, wake after sleep onset; TAI, Total Arousal Index (number/h); SS, stage shifts; AHI, Apnea–Hypopnea Index; PLMSi, Periodic Limb Movements of Sleep Index. Alpha threshold set at 0.002 to account for multiple comparison (p-values lower than threshold appear in bold). Open in new tab Table 2. Demographic and PSG characteristics of overestimators and age-matched normoestimators . Normoestimators (age-matched) . Overestimators . P-value . n 536 53 Age (years) 64.67 ± 6.10 65.51 ± 10.75 0.38085 Gender 232 ♂/291 ♀ 42 ♂/11 ♀ <0.001 Obj. TST (min) 406.22 ± 63.10 257.96 ± 73.39 <0.00001 Subj. TST (min) 404.15 ± 65.22 445.94 ± 108.2 0.00004 SPI% 99.58 ± 6.18 178.2 ± 35.15 <0.00001 Sleep est. quality 2.56 ± 0.69 2.70 ± 0.76 0.17123 Questionnaires  H-O Scale (#) 53.45 ± 4.47 (459) 51.5 ± 3.95 (43) 0.00046  Epworth (#) 5.77 ± 3.71 (491) 5.78 ± 3.38 (46) 0.79850  PSQI (#) 4.87 ± 3.15 (453) 4.83 ± 2.67 (42) 0.79282  DFA (#/30 min) 0.88 ± 1.07 (505) 0.93 ± 1.04 (46) 0.63526  FNA (#) 1.52 ± 1.16 (503) 1.57 ± 1.28 (47) 0.93712 Sleep  TRT (min) 498.07 ± 69.32 459.27 ± 124.72 0.00041  TST (min) 406.22 ± 63.10 257.96 ± 73.39 <0.00001  Lat N1 (min) 16.34 ± 20.80 45.52 ± 53.98 <0.00001  N1% 11.73 ± 7.13 19.26 ± 12.78 <0.00001  N2% 47.72 ± 10.69 43.07 ± 14.34 0.00363  N3% 19.20 ± 8.79 19.05 ± 11.95 0.90349  REM % 21.33 ± 6.27 18.62 ± 8.66 0.00402  Lat REM (min) 99.77 ± 69.71 120.34 ± 96.88 0.05127  S. eff.% 84.71 ± 9.48 65.07 ± 17.62 <0.00001  WASO (min) 75.55 ± 51.73 158.18 ± 98.86 <0.00001  TAI (#/h) 22.47 ± 11.27 28.43 ± 17.35 0.00056  SS (#) 146.08 ± 51.74 117.87 ± 52.69 0.00017  AHI (#/h) 17.84 ± 16.67 23.86 ± 21.62 0.01529  PLMSi (#/h) 15.69 ± 22.48 22.92 ± 39.00 0.03998 . Normoestimators (age-matched) . Overestimators . P-value . n 536 53 Age (years) 64.67 ± 6.10 65.51 ± 10.75 0.38085 Gender 232 ♂/291 ♀ 42 ♂/11 ♀ <0.001 Obj. TST (min) 406.22 ± 63.10 257.96 ± 73.39 <0.00001 Subj. TST (min) 404.15 ± 65.22 445.94 ± 108.2 0.00004 SPI% 99.58 ± 6.18 178.2 ± 35.15 <0.00001 Sleep est. quality 2.56 ± 0.69 2.70 ± 0.76 0.17123 Questionnaires  H-O Scale (#) 53.45 ± 4.47 (459) 51.5 ± 3.95 (43) 0.00046  Epworth (#) 5.77 ± 3.71 (491) 5.78 ± 3.38 (46) 0.79850  PSQI (#) 4.87 ± 3.15 (453) 4.83 ± 2.67 (42) 0.79282  DFA (#/30 min) 0.88 ± 1.07 (505) 0.93 ± 1.04 (46) 0.63526  FNA (#) 1.52 ± 1.16 (503) 1.57 ± 1.28 (47) 0.93712 Sleep  TRT (min) 498.07 ± 69.32 459.27 ± 124.72 0.00041  TST (min) 406.22 ± 63.10 257.96 ± 73.39 <0.00001  Lat N1 (min) 16.34 ± 20.80 45.52 ± 53.98 <0.00001  N1% 11.73 ± 7.13 19.26 ± 12.78 <0.00001  N2% 47.72 ± 10.69 43.07 ± 14.34 0.00363  N3% 19.20 ± 8.79 19.05 ± 11.95 0.90349  REM % 21.33 ± 6.27 18.62 ± 8.66 0.00402  Lat REM (min) 99.77 ± 69.71 120.34 ± 96.88 0.05127  S. eff.% 84.71 ± 9.48 65.07 ± 17.62 <0.00001  WASO (min) 75.55 ± 51.73 158.18 ± 98.86 <0.00001  TAI (#/h) 22.47 ± 11.27 28.43 ± 17.35 0.00056  SS (#) 146.08 ± 51.74 117.87 ± 52.69 0.00017  AHI (#/h) 17.84 ± 16.67 23.86 ± 21.62 0.01529  PLMSi (#/h) 15.69 ± 22.48 22.92 ± 39.00 0.03998 Data are presented as mean ± SD. Numbers in parentheses indicate the number of subjects for which this data was available, if different from total number. TST, total sleep time; SPI, Sleep Perception Index; H-O, Horne–Ostberg; PSQI, Pittsburgh Sleep Quality Index; DFA, difficulties falling asleep in <30 min (scale from 0 [no difficulties in last month] to 3 [3–4 times per week]); FNA, frequency of nocturnal awakenings (scale from 0 [never woke up during the night in the last month] to 3 [3–4 times per week]); TRT, total recording time; Lat., latency; S. eff., sleep efficiency; WASO, wake after sleep onset; TAI, Total Arousal Index (number/h); SS, stage shifts; AHI, Apnea–Hypopnea Index; PLMSi, Periodic Limb Movements of Sleep Index. Alpha threshold set at 0.002 to account for multiple comparison (p-values lower than threshold appear in bold). Open in new tab Table 2. Demographic and PSG characteristics of overestimators and age-matched normoestimators . Normoestimators (age-matched) . Overestimators . P-value . n 536 53 Age (years) 64.67 ± 6.10 65.51 ± 10.75 0.38085 Gender 232 ♂/291 ♀ 42 ♂/11 ♀ <0.001 Obj. TST (min) 406.22 ± 63.10 257.96 ± 73.39 <0.00001 Subj. TST (min) 404.15 ± 65.22 445.94 ± 108.2 0.00004 SPI% 99.58 ± 6.18 178.2 ± 35.15 <0.00001 Sleep est. quality 2.56 ± 0.69 2.70 ± 0.76 0.17123 Questionnaires  H-O Scale (#) 53.45 ± 4.47 (459) 51.5 ± 3.95 (43) 0.00046  Epworth (#) 5.77 ± 3.71 (491) 5.78 ± 3.38 (46) 0.79850  PSQI (#) 4.87 ± 3.15 (453) 4.83 ± 2.67 (42) 0.79282  DFA (#/30 min) 0.88 ± 1.07 (505) 0.93 ± 1.04 (46) 0.63526  FNA (#) 1.52 ± 1.16 (503) 1.57 ± 1.28 (47) 0.93712 Sleep  TRT (min) 498.07 ± 69.32 459.27 ± 124.72 0.00041  TST (min) 406.22 ± 63.10 257.96 ± 73.39 <0.00001  Lat N1 (min) 16.34 ± 20.80 45.52 ± 53.98 <0.00001  N1% 11.73 ± 7.13 19.26 ± 12.78 <0.00001  N2% 47.72 ± 10.69 43.07 ± 14.34 0.00363  N3% 19.20 ± 8.79 19.05 ± 11.95 0.90349  REM % 21.33 ± 6.27 18.62 ± 8.66 0.00402  Lat REM (min) 99.77 ± 69.71 120.34 ± 96.88 0.05127  S. eff.% 84.71 ± 9.48 65.07 ± 17.62 <0.00001  WASO (min) 75.55 ± 51.73 158.18 ± 98.86 <0.00001  TAI (#/h) 22.47 ± 11.27 28.43 ± 17.35 0.00056  SS (#) 146.08 ± 51.74 117.87 ± 52.69 0.00017  AHI (#/h) 17.84 ± 16.67 23.86 ± 21.62 0.01529  PLMSi (#/h) 15.69 ± 22.48 22.92 ± 39.00 0.03998 . Normoestimators (age-matched) . Overestimators . P-value . n 536 53 Age (years) 64.67 ± 6.10 65.51 ± 10.75 0.38085 Gender 232 ♂/291 ♀ 42 ♂/11 ♀ <0.001 Obj. TST (min) 406.22 ± 63.10 257.96 ± 73.39 <0.00001 Subj. TST (min) 404.15 ± 65.22 445.94 ± 108.2 0.00004 SPI% 99.58 ± 6.18 178.2 ± 35.15 <0.00001 Sleep est. quality 2.56 ± 0.69 2.70 ± 0.76 0.17123 Questionnaires  H-O Scale (#) 53.45 ± 4.47 (459) 51.5 ± 3.95 (43) 0.00046  Epworth (#) 5.77 ± 3.71 (491) 5.78 ± 3.38 (46) 0.79850  PSQI (#) 4.87 ± 3.15 (453) 4.83 ± 2.67 (42) 0.79282  DFA (#/30 min) 0.88 ± 1.07 (505) 0.93 ± 1.04 (46) 0.63526  FNA (#) 1.52 ± 1.16 (503) 1.57 ± 1.28 (47) 0.93712 Sleep  TRT (min) 498.07 ± 69.32 459.27 ± 124.72 0.00041  TST (min) 406.22 ± 63.10 257.96 ± 73.39 <0.00001  Lat N1 (min) 16.34 ± 20.80 45.52 ± 53.98 <0.00001  N1% 11.73 ± 7.13 19.26 ± 12.78 <0.00001  N2% 47.72 ± 10.69 43.07 ± 14.34 0.00363  N3% 19.20 ± 8.79 19.05 ± 11.95 0.90349  REM % 21.33 ± 6.27 18.62 ± 8.66 0.00402  Lat REM (min) 99.77 ± 69.71 120.34 ± 96.88 0.05127  S. eff.% 84.71 ± 9.48 65.07 ± 17.62 <0.00001  WASO (min) 75.55 ± 51.73 158.18 ± 98.86 <0.00001  TAI (#/h) 22.47 ± 11.27 28.43 ± 17.35 0.00056  SS (#) 146.08 ± 51.74 117.87 ± 52.69 0.00017  AHI (#/h) 17.84 ± 16.67 23.86 ± 21.62 0.01529  PLMSi (#/h) 15.69 ± 22.48 22.92 ± 39.00 0.03998 Data are presented as mean ± SD. Numbers in parentheses indicate the number of subjects for which this data was available, if different from total number. TST, total sleep time; SPI, Sleep Perception Index; H-O, Horne–Ostberg; PSQI, Pittsburgh Sleep Quality Index; DFA, difficulties falling asleep in <30 min (scale from 0 [no difficulties in last month] to 3 [3–4 times per week]); FNA, frequency of nocturnal awakenings (scale from 0 [never woke up during the night in the last month] to 3 [3–4 times per week]); TRT, total recording time; Lat., latency; S. eff., sleep efficiency; WASO, wake after sleep onset; TAI, Total Arousal Index (number/h); SS, stage shifts; AHI, Apnea–Hypopnea Index; PLMSi, Periodic Limb Movements of Sleep Index. Alpha threshold set at 0.002 to account for multiple comparison (p-values lower than threshold appear in bold). Open in new tab Figure 1. Open in new tabDownload slide Subjective and objective sleep parameters in under- (n = 52) and normoestimators (n = 1,147). (A) Distribution of SPI among all subjects. Underestimators (lower 2.5%) are marked in red, normoestimators in blue. The red dashed line indicates the threshold used to identify underestimators (SPI<58.82%). (B) Correlation between sTST and oTST for both under- (red) and normoestimators (blue). Grey lines mark the fitted linear regression. (C) Topographic distribution of statistical differences in relative signal power between under- and normoestimators (T-values) for different frequency bands in NREM and REM sleep. Red colors indicate increased power in underestimators, blue-colors increased power in normoestimators. Channels used for analysis appear as black dots, those for which the difference was significant (uncorrected) as white dots. Figure 1. Open in new tabDownload slide Subjective and objective sleep parameters in under- (n = 52) and normoestimators (n = 1,147). (A) Distribution of SPI among all subjects. Underestimators (lower 2.5%) are marked in red, normoestimators in blue. The red dashed line indicates the threshold used to identify underestimators (SPI<58.82%). (B) Correlation between sTST and oTST for both under- (red) and normoestimators (blue). Grey lines mark the fitted linear regression. (C) Topographic distribution of statistical differences in relative signal power between under- and normoestimators (T-values) for different frequency bands in NREM and REM sleep. Red colors indicate increased power in underestimators, blue-colors increased power in normoestimators. Channels used for analysis appear as black dots, those for which the difference was significant (uncorrected) as white dots. Figure 2. Open in new tabDownload slide Subjective and objective sleep parameters in over- and normoestimators. (A) Distribution of SPI among all subjects. Overestimators (upper 2.5%, n = 53) are marked in green, normoestimators (n = 1,147) in blue. The green dashed line indicates the threshold used to identify overestimators (SPI > 146.09%). (B) Correlation between sTST and oTST for both over- (green, n = 53) and age-matched normoestimators (blue, n = 536). Grey lines mark the fitted linear regression. (C) Topographic distribution of statistical differences in relative signal power between over- and age-matched normoestimators (T-values) for different frequency bands in NREM and REM sleep. Red-colors indicate increased power in overestimators, blue-colors increased power in age-matched normoestimators. Channels used for analysis appear as black dots, those for which the difference was significant (uncorrected) as white dots. Figure 2. Open in new tabDownload slide Subjective and objective sleep parameters in over- and normoestimators. (A) Distribution of SPI among all subjects. Overestimators (upper 2.5%, n = 53) are marked in green, normoestimators (n = 1,147) in blue. The green dashed line indicates the threshold used to identify overestimators (SPI > 146.09%). (B) Correlation between sTST and oTST for both over- (green, n = 53) and age-matched normoestimators (blue, n = 536). Grey lines mark the fitted linear regression. (C) Topographic distribution of statistical differences in relative signal power between over- and age-matched normoestimators (T-values) for different frequency bands in NREM and REM sleep. Red-colors indicate increased power in overestimators, blue-colors increased power in age-matched normoestimators. Channels used for analysis appear as black dots, those for which the difference was significant (uncorrected) as white dots. Data preprocessing and analysis Movement artifacts were detected in 0.5 s overlapping (50%) windows of the chin EMG signal, and were defined as 10-fold increases in the total signal power computed on band-pass 30–95 Hz filtered data (45–55 Hz notch filter) with respect to the median power of a 5 min baseline around the 0.5 s-window. Similarly, generalized high-frequency EEG artifacts were defined as strong increases in high-frequency power affecting more than 50% of examined EEG electrodes. Specifically, for each channel, potential artifacts were detected in 0.5 s overlapping (50%) windows when the total EEG power computed on bandpass 55–95 Hz filtered data was greater than 12 times the median power of a 5 min baseline centered on the 0.5 s-window. The thresholds for these procedures were empirically defined through comparison with visual evaluation in a subsample of subjects. Additional criteria adapted from [27] were applied to identify EEG data epochs containing different types of artifacts. For these detection procedures, all EEG signals were initially bandpass filtered between 0.3 and 40 Hz and divided into non-overlapping 5-s epochs. In each EEG channel, any 5 s time window was marked if the standard deviation (SD) of the recorded values was <1 (flat) or >6,000 μV (artifactual), or if its SD was at least five times greater than the average SD of all the other EEG channels. Similarly, any 5-s time window was marked if the SD was <10 μV for at least 2 s. The threshold was lowered to 0.1 μV for at least 0.8 s in the case of mastoid channels. Artifacts caused by rapid eye movements (REMs) in REM sleep were reduced using adaptive filters as previously described [22]. Finally, portions in which the absolute amplitude of the EEG signal exceeded 500 μV were marked as artifacts. Channels containing artifacts for more than 50% of the recording time were excluded from the analysis. All artifactual portions were removed and replaced by ramps, then the signal was re-referenced to the average of the two mastoid channels, and band-pass filtered between 0.5 and 35 Hz with a finite impulse response. Across the entire analyzed sleep (sleep stages N2 + N3 + REM) we discarded an average of 5.54, 6.22, 6.12, and 5.35% of artifactual epochs for normoestimators, underestimators, overestimators, and age-matched normoestimators, respectively. The groups did not differ with respect to the proportion of excluded epochs F(3,1695) = 1.59, p = 0.19 (ANOVA). Power spectral densities (PSD) were calculated using the pwelch method on artifact-free consecutive, nonoverlapping 6 s epochs (Hamming windows, 8 segments, 50% overlap) and used to compute signal power in typical frequency bands, including low-delta (0.5–2 Hz), delta (1–4 Hz), theta (5–8 Hz), alpha (8–12 Hz), sigma (12–16 Hz), and beta (18–30 Hz). The resulting power values were averaged across epochs within each sleep stage that was considered (sleep stages N2 and N3 separately and combined, and REM) and normalized to the total signal power (0.5–30 Hz) in order to allow for between-subjects comparisons. We also computed an “EEG activation index,” defined as the logarithm of the ratio of beta (18–30 Hz) over delta (1–4 Hz) power. Statistical analysis Demographic data shown in Tables 1 and 2 were compared using unpaired two-tailed Student’s t-tests or Pearson’s chi-square tests, and family-wise error rate due to multiple comparisons was controlled using Bonferroni’s correction (corrected α = 0.002). Values in Tables 1 and 2 are expressed as mean ± SD. The average relative PSD for each frequency band of interest was calculated for each channel and compared between the different groups using unpaired two-tailed Student’s t-tests. Resulting T-values were topographically represented on a cartooned head. The threshold for statistical significance was set at α = 0.05. Since in this study few electrodes were used and the aim was to report global EEG changes (as opposed to regional changes in study 2) we did not correct for the number of channels testes, and results shown in Figures 1 and 2 represent uncorrected values, as indicated in the figure legends. For completeness, results after Bonferroni correction are reported in the text. Study 2: laboratory study Selection of participants Healthy subjects (HS, n = 24) were recruited through advertisement and word by mouth at the Lausanne University Hospital (Table 3). Inclusion criteria included regular bed and rise times, good subjective sleep quality (PSQI < 5) [24], and the absence of daytime sleepiness (ESS < 10) [28]. Subjects with extreme chronotypes (Horne and Ostberg morningness–eveningness questionnaire [23] scores >70 or <30), suffering from neurological, psychiatric or medical disorders affecting sleep, or taking regular medication other than birth control, and pregnant patients were not enrolled in the study. Table 3. Demographic and baseline sleep characteristics of HS and ParI patients . HS . ParI . P-value . n 24 10 Age (mean ± SD) 35.49 ± 9.00 40.85 ± 6.47 0.1305 Gender 8 ♂/16 ♀ 2 ♂/8 ♀ >0.2 Obj. TST (min) 376.59 ± 10.23 356.24 ± 16.29 0.335 Subj. TST (min) 364.71 ± 12.15 222.00 ± 34.70 1.00E−03 Sleep  TRT (min) 424.79 ± 5.46 415.93 ± 12.8 0.9247  TST (min) 376.59 ± 10.23 356.24 ± 16.29 0.335  Lat N1 (min) 7.21 ± 1.13 5.82 ± 1.30 0.6098  N1% 3.66 ± 0.51 4.33 ± 0.85 0.4053  N2% 52.23 ± 1.42 54.06 ± 1.31 0.2899  N3% 23.46 ± 1.00 17.32 ± 1.26 0.0014  REM% 20.23 ± 0.97 23.90 ± 1.34 0.0472  Lat REM (min) 103.92 ± 9.16 77.48 ± 11.16 0.0327  S. Eff % 89.25 ± 1.74 86.30 ± 3.14 0.3643  Wake% 10.02 ± 1.72 13.05 ± 3.05 0.4056  WASO (min) 40.92 ± 6.67 53.86 ± 12.96 0.3951  TAI (#/h) 15.85 ± 1.38 17.21 ± 2.22 0.6941  Awakenings (#) 19.75 ± 1.76 19.70 ± 2.53 0.9849 Subj. / Obj.  TST (SPI, %) 97.24 ± 2.44 61.60 ± 8.94 4.09E−04  Sleep latency (%) 489.61 ± 83.78 416.59 ± 103.86 0.7768  Awakenings (%) 18.84 ± 5.43 22.47 ± 3.39 0.0538  Wake time (%) 68.45 ± 12.27 362.0 ± 129.28 0.0033 . HS . ParI . P-value . n 24 10 Age (mean ± SD) 35.49 ± 9.00 40.85 ± 6.47 0.1305 Gender 8 ♂/16 ♀ 2 ♂/8 ♀ >0.2 Obj. TST (min) 376.59 ± 10.23 356.24 ± 16.29 0.335 Subj. TST (min) 364.71 ± 12.15 222.00 ± 34.70 1.00E−03 Sleep  TRT (min) 424.79 ± 5.46 415.93 ± 12.8 0.9247  TST (min) 376.59 ± 10.23 356.24 ± 16.29 0.335  Lat N1 (min) 7.21 ± 1.13 5.82 ± 1.30 0.6098  N1% 3.66 ± 0.51 4.33 ± 0.85 0.4053  N2% 52.23 ± 1.42 54.06 ± 1.31 0.2899  N3% 23.46 ± 1.00 17.32 ± 1.26 0.0014  REM% 20.23 ± 0.97 23.90 ± 1.34 0.0472  Lat REM (min) 103.92 ± 9.16 77.48 ± 11.16 0.0327  S. Eff % 89.25 ± 1.74 86.30 ± 3.14 0.3643  Wake% 10.02 ± 1.72 13.05 ± 3.05 0.4056  WASO (min) 40.92 ± 6.67 53.86 ± 12.96 0.3951  TAI (#/h) 15.85 ± 1.38 17.21 ± 2.22 0.6941  Awakenings (#) 19.75 ± 1.76 19.70 ± 2.53 0.9849 Subj. / Obj.  TST (SPI, %) 97.24 ± 2.44 61.60 ± 8.94 4.09E−04  Sleep latency (%) 489.61 ± 83.78 416.59 ± 103.86 0.7768  Awakenings (%) 18.84 ± 5.43 22.47 ± 3.39 0.0538  Wake time (%) 68.45 ± 12.27 362.0 ± 129.28 0.0033 Data are presented as mean ± SEM. Statistical comparisons were performed with Pearson’s chi-square tests and non-parametric Wilcoxon Rank Sum tests, alpha threshold was set at α = 0.0024 to account for multiple comparisons (p-values lower than threshold appear in bold). Open in new tab Table 3. Demographic and baseline sleep characteristics of HS and ParI patients . HS . ParI . P-value . n 24 10 Age (mean ± SD) 35.49 ± 9.00 40.85 ± 6.47 0.1305 Gender 8 ♂/16 ♀ 2 ♂/8 ♀ >0.2 Obj. TST (min) 376.59 ± 10.23 356.24 ± 16.29 0.335 Subj. TST (min) 364.71 ± 12.15 222.00 ± 34.70 1.00E−03 Sleep  TRT (min) 424.79 ± 5.46 415.93 ± 12.8 0.9247  TST (min) 376.59 ± 10.23 356.24 ± 16.29 0.335  Lat N1 (min) 7.21 ± 1.13 5.82 ± 1.30 0.6098  N1% 3.66 ± 0.51 4.33 ± 0.85 0.4053  N2% 52.23 ± 1.42 54.06 ± 1.31 0.2899  N3% 23.46 ± 1.00 17.32 ± 1.26 0.0014  REM% 20.23 ± 0.97 23.90 ± 1.34 0.0472  Lat REM (min) 103.92 ± 9.16 77.48 ± 11.16 0.0327  S. Eff % 89.25 ± 1.74 86.30 ± 3.14 0.3643  Wake% 10.02 ± 1.72 13.05 ± 3.05 0.4056  WASO (min) 40.92 ± 6.67 53.86 ± 12.96 0.3951  TAI (#/h) 15.85 ± 1.38 17.21 ± 2.22 0.6941  Awakenings (#) 19.75 ± 1.76 19.70 ± 2.53 0.9849 Subj. / Obj.  TST (SPI, %) 97.24 ± 2.44 61.60 ± 8.94 4.09E−04  Sleep latency (%) 489.61 ± 83.78 416.59 ± 103.86 0.7768  Awakenings (%) 18.84 ± 5.43 22.47 ± 3.39 0.0538  Wake time (%) 68.45 ± 12.27 362.0 ± 129.28 0.0033 . HS . ParI . P-value . n 24 10 Age (mean ± SD) 35.49 ± 9.00 40.85 ± 6.47 0.1305 Gender 8 ♂/16 ♀ 2 ♂/8 ♀ >0.2 Obj. TST (min) 376.59 ± 10.23 356.24 ± 16.29 0.335 Subj. TST (min) 364.71 ± 12.15 222.00 ± 34.70 1.00E−03 Sleep  TRT (min) 424.79 ± 5.46 415.93 ± 12.8 0.9247  TST (min) 376.59 ± 10.23 356.24 ± 16.29 0.335  Lat N1 (min) 7.21 ± 1.13 5.82 ± 1.30 0.6098  N1% 3.66 ± 0.51 4.33 ± 0.85 0.4053  N2% 52.23 ± 1.42 54.06 ± 1.31 0.2899  N3% 23.46 ± 1.00 17.32 ± 1.26 0.0014  REM% 20.23 ± 0.97 23.90 ± 1.34 0.0472  Lat REM (min) 103.92 ± 9.16 77.48 ± 11.16 0.0327  S. Eff % 89.25 ± 1.74 86.30 ± 3.14 0.3643  Wake% 10.02 ± 1.72 13.05 ± 3.05 0.4056  WASO (min) 40.92 ± 6.67 53.86 ± 12.96 0.3951  TAI (#/h) 15.85 ± 1.38 17.21 ± 2.22 0.6941  Awakenings (#) 19.75 ± 1.76 19.70 ± 2.53 0.9849 Subj. / Obj.  TST (SPI, %) 97.24 ± 2.44 61.60 ± 8.94 4.09E−04  Sleep latency (%) 489.61 ± 83.78 416.59 ± 103.86 0.7768  Awakenings (%) 18.84 ± 5.43 22.47 ± 3.39 0.0538  Wake time (%) 68.45 ± 12.27 362.0 ± 129.28 0.0033 Data are presented as mean ± SEM. Statistical comparisons were performed with Pearson’s chi-square tests and non-parametric Wilcoxon Rank Sum tests, alpha threshold was set at α = 0.0024 to account for multiple comparisons (p-values lower than threshold appear in bold). Open in new tab Insomnia patients (n = 10) were consecutively recruited among outpatients of the Center for Investigation and Research in Sleep at the Lausanne University Hospital between August 2016 and October 2019. We included only patients (1) fulfilling the current ICSD criteria for chronic insomnia [2, 10]) who underwent PSG for clinical reasons and (2) who underestimated TST measured by PSG (SPI <60%) despite normal sleep efficiency (>85%) and sleep latency <60 min (arbitrary cutoff). The SPI cutoff of 60% was based on the definition of underestimators of the general population (58.82%), after rounding to ease use in the clinical setting. Exclusion criteria comprised major psychiatric and neurological comorbidities, medication other than birth control, pregnancy, apnea–hypopnea index >15/h and periodic leg movement index in sleep >15/h. Written informed consent was obtained by all the participants and the study was approved by the local ethical committee (commission cantonale éthique de la recherche sur l’être humain du canton de Vaud). Experimental procedure All participants underwent a full-night undisturbed hd-EEG sleep recording in the sleep laboratory. Actigraphy was performed 5–7 days preceding the night and throughout the study to ensure regular sleep schedules and sufficient sleep. Subjects did not have access to watches or phones during the recordings and slept undisturbed during the recording night. In the morning they had to fill out a form in which they estimated how much time it had taken them to fall asleep and how much time they had slept in the course of the night. Sleep recordings EEG was acquired with a 256-channel system (Electrical Geodesics, Inc., Eugene, Oregon) with a sampling rate of 500 Hz. Four of the 256 electrodes located near the eyes were used to monitor eye movements, while electrodes overlying the masseter muscles and close to the chin were used to monitor muscle tone [29]. The EEG signal was band-pass filtered between 0.5 and 45 Hz off-line. Sleep scoring was performed over 30 s epochs according to standard criteria [25]. Data preprocessing and analysis Artifactual channels were visually identified and replaced by interpolation from nearby channels using spherical splines (NetStation, Electrical Geodesic Inc.). Ocular, muscular, and cardiac artifacts were eliminated with Independent Component Analysis using EEGLAB routines [30, 31]. Signal power was computed on the resampled average-referenced 128 Hz signal using a Morlet wavelet (length: 4 cycles, frequency resolution 1 Hz). For each subject and channel, power values for the same frequency bands described above (plus gamma, 30–45 Hz) were normalized to the total power (0.5–45 Hz) to allow for between-subject comparisons. Statistical analysis Between-group comparisons of demographic parameters (Table 3) were computed using Wilcoxon rank sum or Pearson’s chi-square tests with Bonferroni’s correction (α = 0.0024). Values in Table 3 are expressed as mean ± SEM. Topographic differences in signal power were computed using unpaired two-tailed Student’s t-tests for the 185 most internal channels. A probability- and cluster-based permutation correction was applied to account for multiple comparisons [32, 33]. Results Study 1: sleep perception in the HypnoLaus cohort Sleep perception in the general population The distribution of the SPI among the 2,092 participants of the HypnoLaus cohort (Figure 1, A) revealed a good concordance between sTST and oTST, with a mean SPI of 99.4% ± 22.1 (SD), corresponding to an average underestimation of TST of 0.6% (8.17 min). Underestimators versus normoestimators For both normoestimators and underestimators, there was a robust correlation between subjective and objective sleep times (R2 = 0.85, p < 0.001 and R2 = 0.71, p < 0.001, respectively, Figure 1, B), suggesting that the subjective perception of sleep time is not arbitrary or completely unfounded, but that it remains well in proportion with respect to oTST. Compared to normoestimators (Table 1), underestimators displayed similar oTST, but subjectively and significantly underestimated their TST. They also spent longer time in bed, which likely explains the lower sleep efficiency, the increased proportion of stage N1 and increased wake after sleep onset (WASO) in this group. Such a constellation of sleep parameters is typically observed in individuals with insomnia. Indeed, underestimators complained more frequently than normoestimators of insomnia symptoms, including lower sleep quality (Table 1) and difficulties falling asleep during the past month (PSQI). The concordance between ratings of the recording night and those relating to the past month in the PSQI suggests that the subjective perception of sleep quantity and quality in this sample is relatively stable over time, and that the recording night is representative of habitual sleep. This is important considering that there appear to be insomnia subtypes characterized by a high night-to-night variability in subjective sleep duration [34, 35]. Power analysis of sleep EEG revealed a lower relative power in the delta band over the right frontal electrode in both NREM sleep (N2 and N3 combined and N2) and REM sleep, and a higher relative power in the beta band in central regions in NREM sleep (N2 and N3 combined and N2). This resulted in a significantly increased EEG activation index (log[beta/delta]), with most consistent differences over central electrodes in both NREM sleep (N2 and N3 combined and N2) and REM sleep (Figure 1, C). After correction for multiple comparisons (Bonferroni), the increased activation index in underestimators was still significant in NREM sleep (N2 and N3 combined, and N2), but not in REM sleep. Overestimators versus normoestimators We then asked whether subjects who severely overestimated TST displayed opposite changes (Table 2, Figure 2, A). Overestimators were significantly older than normoestimators (65.51 ± 10.75 years vs. 57.25 ± 10.87; p < 0.001). Therefore, we compared overestimators to a subgroup of age-matched normoestimators. sTST and oTST showed a high correlation also in overestimators (R2 = 0.71, p < 0.001 for overestimators; R2 = 0.85, p < 0.001 for age-matched normoestimators, Figure 2, B). Compared to age-matched normoestimators, overestimators contained a higher proportion of male subjects, reported increased sTST despite significantly lower recording and TST, an increased sleep latency (to N1), a higher proportion of N1 sleep, lower sleep efficiency, as well as higher WASO, and more arousals and stage shifts. Power analysis revealed higher relative low-delta power (0.5–2 Hz) in all investigated sleep stages in overestimators compared with age-matched normoestimators (Figure 2, C). Alpha power was significantly decreased in N2 in a right occipital electrode and in REM sleep in a left frontal electrode. High-frequency power in the beta range was significantly decreased in REM sleep and significantly increased in NREM sleep (N2 and N3 combined). These changes resulted in an overall decreased EEG activation index in all investigated sleep stages (N2 and N3 separately and combined, as well as REM), which however reached significance only in REM sleep. The decreased activation index in REM sleep in overestimators was still significant after Bonferroni correction. Study 2: laboratory study Clinical and sleep characteristics In contrast to the previous comparison (underestimators vs. normoestimators), sleep efficiency, WASO, and number of arousals did not differ between the ParI patients and HS (with the exception of a lower proportion of N3 sleep; Table 3), possibly because total recording times were fixed by the experimental procedure (in contrast to the home-PSG study in which subjects were free to chose their sleep times), which may have resulted in a relative “condensation” of sleep in ParI patients. Similar to the previous comparison (underestimators vs. normoestimators), ParI underestimated their TST. They also overestimated their wake time. Both ParI and HS greatly overestimated sleep latency and underestimated the number of awakenings (Table 3). Full night power analysis Compared with HS, ParI patients displayed significantly lower power in the delta frequency band relative to total sleep power in NREM sleep (N2 and N3 combined, and N3), and higher beta (N2 and N3 combined) and gamma power (N3), resulting in an increased activation index in (N2 and N3 combined, and N3). These changes were significant in a centro-parietal region in NREM sleep (N2 and N3 combined) and were more spatially widespread in N3. No significant differences were seen in REM sleep (Figure 3), although patients displayed a general shift from lower low frequency (theta and alpha) to higher high frequency power (beta, gamma) compared to HS. Figure 3. Open in new tabDownload slide Topographic distribution of T-values for the comparison of relative spectral power between patients with ParI (n = 10) and HS (n = 24) during the baseline night. Red colors indicate increased power in ParI patients, blue colors increased power in HS. White dots indicate channels for which the comparison was statistically significant. Figure 3. Open in new tabDownload slide Topographic distribution of T-values for the comparison of relative spectral power between patients with ParI (n = 10) and HS (n = 24) during the baseline night. Red colors indicate increased power in ParI patients, blue colors increased power in HS. White dots indicate channels for which the comparison was statistically significant. Discussion In the present study, we aimed to determine what accounts for the subjective estimation of sleep duration. We were particularly interested in understanding whether spectral power EEG changes, which are not evident in conventional PSG recordings, could explain why some subjects feel awake during parts of the night and therefore underestimate their sleep duration as defined by standard scoring criteria. With the first study, we confirmed that the majority of individuals in the general population estimate their TST with high precision, within minutes of oTST [6, 36]. Indeed, the average SPI was 99.4%, corresponding to a mean underestimation of TST of only 0.6 % (8.17 min). Similarly precise estimates, in the order of minutes, have been reported for the Sleep Heart Health Study cohort (n = 2,113), in which TST was overestimated by 18 min on average [36], and in a subsample of 288 good sleepers of this study, who underestimated TST by 20.6 min [6]. The first study also allowed us to define the extremes of the distribution, consisting of subjects who severely under- or overestimated their TST. Underestimation of TST was associated with increased EEG activation over central electrodes in NREM and REM sleep, consisting in a relative shift from lower toward higher frequency power, and thus toward a more “wake-like” pattern. Overestimation of TST, on the other hand, was associated with opposite changes, consisting in increased low frequency power and an overall less “activated” EEG in REM sleep. The power-based findings of underestimators in NREM sleep are comparable to the changes that we observed in a clinical, non-medicated population of insomnia sufferers studied in the laboratory, suggesting that they can be extrapolated beyond the general population and study conditions. The results are also in line with previous EEG studies, which documented either relative increases in high frequency spectral power, comprising the alpha, sigma or beta frequency bands [37–39] and/or relative decreases in low-frequency power including the delta and theta band [16, 40] in NREM sleep and wakefulness [41] in insomnia patients, as well as more specifically in cases of sleep misperception [3, 13, 15, 42]. hd-EEG allowed us to map this EEG activation at the scalp level to posterior-central areas, likely comprising primary somatosensory cortices. EEG frequencies in the delta range reflect the presence of slow waves, which constitute a hallmark of NREM sleep. At the neuronal level, slow waves are associated with off-periods, which have been related to to the fading of consciousness in sleep [29, 43] possibly because they interfere with information integration, a theoretical prerequisite of consciousness [44]. High-frequencies, on the other hand, are generally considered to reflect neuronal firing. Beta power varies inversely with delta power in both REM and NREM sleep [45, 46], and correlates with measures of autonomic arousal [47]. A reduction of synchronized low-frequency activity and an increase in high frequencies has been termed EEG activation, and can be induced by stimulation of the reticular formation in sleeping or anesthetized animals [48]. Our results hence suggest that sleep underestimation is associated with increased activity of arousal-related systems, which could account for “feeling awake” and consequently for the underestimation of TST. Although ParI patients did not present a higher number of scorable arousals than HS (Table 3), it is possible that a frequent and long lasting “subthreshold” activation of arousal systems in ParI patients resulted in the EEG changes we observed. Such an explanation is supported by studies showing elevated Cyclic Alternating Pattern rates in light sleep, with increases in the activating A2 phase [49], as well as enhanced responsiveness to stimuli evidenced by event related potentials [50, 51] in ParI patients. Increased EEG activation in the combined N2/N3 analysis could potentially result from the lower proportion of N3 sleep in underestimators and ParI patients. However, we found increased EEG activation also within N2 and N3 in underestimators and ParI compared with their controls, respectively, Thus, the lower proportion of N3 cannot explain these within-stage differences. Assuming that increased EEG activation in underestimators and ParI patients reflects increased activity of arousal systems, than it could actually be the cause, and not the consequence, of the lower proportion of N3 in underestimators and insomnia patients, as increased firing of arousal systems may prevent subjects to enter or spend a long time in N3 sleep. Overestimators on the other hand displayed a lower EEG activation, which was mediated by an increase in very low frequencies (<2 Hz), despite a more fragmented sleep and even higher beta power in N2 sleep. As opposed to EEG changes observed in underestimators, in overestimators EEG activation was significantly lower only in REM sleep, a stage which accounts for good subjective sleep quality [52] (but also see [53]) and is associated with vivid dreaming. Our findings suggests that slow wave activity specifically in REM sleep may play a role in the overestimation of sleep duration. Although slow oscillations are the hallmarks of NREM sleep, recent work has shown that they can also occur locally in REM sleep [54, 55], in superficial layers of the primary sensory cortices [56], where they may account for the pronounced sensory disconnection that characterizes this state. One might thus speculate that overestimators feel more disconnected, especially during REM sleep, and that this disconnection, along with a marginally increased REM sleep duration, leads to an overestimation of the time spent asleep. This would be an opposite situation to the one of insomnia suffers, in whom hyperarousal in REM sleep is associated with thought-like as opposed to dream-like mentation that is characteristic of this sleep stage [57]. Likewise, one might further speculate that overestimators display a less activated EEG also in periods of PSG-defined wakefulness during the night, which are therefore perceived as “sleep.” This may explain why they overestimated their sleep despite a higher amount of wake after sleep onset with respect to normoestimators (Table 3). This aspect could not be investigated with the present dataset, as our automatic artifact rejection procedure was not suited to preprocess spontaneous wake EEG, which is often contaminated by major muscular and movement artifacts. This first study had a few limitations. Subjects, had in principle, access to watches and smartphones at home, which could have influenced the estimation of sleep duration. It also remained open whether the results we obtained in a sample of the general population held true in a clinical population of insomnia sufferers. Finally, the low-density EEG (6 electrodes) did not allow to draw accurate conclusions on the localization of changes. To clarify these aspects, we performed a second study, in which unmedicated insomnia patients who underestimated their TST by the same degree as underestimators in the HypnoLaus cohort, as well as healthy controls of similar age and gender, underwent a hd-EEG recordings in the sleep laboratory. Similarly to underestimators, ParI patients displayed increased EEG activation compared to healthy controls during the undisturbed night, although in this dataset, we found differences to be restricted to NREM sleep, and more specifically linked to stage N3. However, the overall similarity between the results of the two different studies suggests that ParI are comparable to underestimators in the general population and that differences in EEG activation, at least in NREM sleep, are unlikely to be related to age (ParI patients were on average 20 years younger than underestimators), medication (ParI patients were unmedicated), or the study setting (home vs. laboratory setting). The EEG activation in posterior-central areas likely comprises the primary somatosensory and parietal cortices, two anatomical regions in which noradrenaline is abundant in the human and monkey cortex [58–60], further corroborating a link between sleep perception and arousal systems. In this second study, we also assessed the subjective perception of sleep latency, which was greatly overestimated by both insomnia patients and controls to a similar degree. Overestimation of sleep latency has previously been reported to occur in insomnia patients and also good sleepers [13, 61, 62]. In line with our whole-night findings in insomnia patients, overestimation of sleep onset latency has been correlated with EEG markers of arousal, including EEG beta activity and K-complexes [3, 13]. Taken together, our results, which were largely consistent between two distinct datasets, show that underestimation of sleep duration is associated with a higher degree of EEG activation, which predominates in central posterior brain areas, and that overestimation of sleep is associated with opposite changes in REM sleep. These results challenge the concept of sleep perception, as they suggest that instead of misperceiving sleep, insomnia patients may correctly perceive subtle shifts from sleep- to wake-like brain activity, which are not reflected in standard PSG parameters. Limitations and future directions Although these results open the possibility that the overall degree of EEG activation could explain discrepancies between subjective and objective sleep times, they cannot, at this point, discriminate which frequency changes are determinant for sleep perception. This is because to perform comparisons between different groups and subjects, spectral power was normalized with respect to total power, with the consequence that changes in one frequency band are inevitably associated with secondary changes in other frequency bands. Therefore, it remains open whether differences in the high-frequencies drove differences in the low frequencies and vice versa. It should also be noted that this study is based on a sample of the population that severely under- or overestimated their sleep time. This was done on purpose, to avoid the “grey zone” between normoestimators and underestimators or overestimators. Whether these changes also hold true for subjects who under- or overestimate their sleep time a little less and where the cutoff lies that results in functional impairment remains open for now. In addition, the spectral power changes that we identified could result from factors besides sleep perception that differ between the groups, including secondary cognitive or psychological changes [63], or insomnia itself. Further, how changes associated with a retrospective estimation of sleep time over a whole night relate to the momentary perception of sleep, remains unclear. To clarify these aspects, future studies with serial awakenings [17] could evaluate how the momentaneous perception of sleep relates to EEG changes by performing within-subject contrasts in absolute power. Such a setup, in which EEG periods associated with feeling awake and asleep would be compared, would also be more suitable for source modeling, which could help localize EEG changes even more precisely. In fact, recent studies using techniques with high spatial resolution to record brain activity, including positron emission tomography [64], functional magnetic resonance [15], and hd-EEG [39] have found localized changes in brain activity in insomnia patients, opening the possibility that they may play a role in sleep perception. Acknowledgments The authors thank, in alphabetical order, David Albir, Françoise Cornette, Stéphanie Dutoit, Grégoire Gex, José Haba-Rubio, Eric Lainey, Gianpaolo Lecciso, Nicolas Petitpierre, and Tifenn Raffray for patient referral and help with data acquisition. Funding This work was supported by the Swiss National Science Foundation (Ambizione Grant PZ00P3_173955 to F.S.), the Fondation Divesa (F.S.), the Fondation Pierre Mercier pour la Science (F.S.), and by an EMBO short-term fellowship (ASTF 394–2015 to G.B.). Disclosure Statement Non-financial Disclosure: none. Financial Disclosure: none. References 1. Frankel BL , et al. 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Ambati,, Aditya;Ju,, Yo-El;Lin,, Ling;Olesen, Alexander, N;Koch,, Henriette;Hedou, Julien, Jacques;Leary, Eileen, B;Sempere, Vicente, Peris;Mignot,, Emmanuel;Taheri,, Shahrad

doi: 10.1093/sleep/zsaa086pmid: 32369590

Abstract Study Objectives Obstructive sleep apnea (OSA) is characterized by recurrent partial to complete upper airway obstructions during sleep, leading to repetitive arousals and oxygen desaturations. Although many OSA biomarkers have been reported individually, only a small subset have been validated through both cross-sectional and intervention studies. We sought to profile serum protein biomarkers in OSA in unbiased high throughput assay. Methods A highly multiplexed aptamer array (SomaScan) was used to profile 1300 proteins in serum samples from 713 individuals in the Stanford Sleep Cohort, a patient-based registry. Outcome measures derived from overnight polysomnography included Obstructive Apnea Hypopnea Index (OAHI), Central Apnea Index (CAI), 2% Oxygen Desaturation index, mean and minimum oxygen saturation indices during sleep. Additionally, a separate intervention-based cohort of 16 individuals was used to assess proteomic profiles pre- and post-intervention with positive airway pressure. Results OAHI was associated with 65 proteins, predominantly pathways of complement, coagulation, cytokine signaling, and hemostasis which were upregulated. CAI was associated with two proteins including Roundabout homolog 3 (ROBO3), a protein involved in bilateral synchronization of the pre-Bötzinger complex and cystatin F. Analysis of pre- and post intervention samples revealed IGFBP-3 protein to be increased while LEAP1 (Hepicidin) to be decreased with intervention. An OAHI machine learning classifier (OAHI >=15 vs OAHI<15) trained on SomaScan protein measures alone performed robustly, achieving 76% accuracy in a validation dataset. Conclusions Multiplex protein assays offer diagnostic potential and provide new insights into the biological basis of sleep disordered breathing. apnea, proteomics, polysomnography, serum, sleep-disordered breathing, biomarkers, oxygen saturation Statement of Significance Sleep apnea is a prevalent sleep disorder caused by recurrent collapse of upper airway leading to oxygen desaturation and arousals, with consequences for increased daytime sleepiness, impaired performance, and cardiovascular morbidity. Although, overnight polysomnography (PSG) is the gold standard in diagnosis of sleep apnea, it is costly, cumbersome, and limited in availability. Here we implemented blood serum-based proteomic assays in 713 individuals to find protein biomarkers of apnea correlating these measures with gold-standard PSG. Obstructive sleep apnea was associated with 65 proteins, predominantly modulating complement and coagulation pathways, while central apnea was associated with ROBO3 and cystatin F proteins. Our study identifies proteomic signatures and associated biological pathways in sleep apnea. Introduction Obstructive sleep apnea (OSA) is a common sleep disorder whose prevalence increases with obesity. It occurs more frequently in men, although the gender ratio equalizes following menopause in women [1]. OSA is characterized by recurrent complete or partial upper airway obstruction during sleep resulting in oxygen desaturation and repetitive arousals. In OSA, sleep fragmentation results in excessive daytime sleepiness [2–4] and recurrent hypoxemia resulting in a predisposal to cardiometabolic disorders and increased cardiovascular risk [2, 5–10]. The gold-standard diagnostic test for OSA is attended overnight polysomnography (PSG) in a sleep laboratory [11]. OSA severity is typically evaluated based on the average number of apneas and hypopneas per hour of sleep or the apnea–hypopnea index (AHI). This reliance on AHI is problematic because the number varies greatly depending on the hypopnea definition. The American Academy of Sleep Medicine (AASM) criteria define hypopnea as at least 10 s of reduced (30%) upper airway respiratory flow resulting in either a 3% oxygen desaturation or/and an electroencephalogram arousal [12], while the “Medicare” definition of hypopnea (generally used for older patients) is 10 s of reduced airflow with at least 4% oxygen desaturation. Based on AASM criterion, Peppard et al. [13] estimated that 26% of adults between 30 and 70 years have an AHI greater than 5 and 10% have an AHI greater than 15. Other recent studies have reported the prevalence of moderate-to-severe sleep apnea (AHI ≥15) at 23.4% and 49.7% in women and men, respectively [14]. Some studies emphasize the 4% desaturation criterion for scoring hypopneas to focus on the increased cardiovascular risk due to hypoxemia because this definition tends to exclude events leading to arousals. Other studies use AASM’s definition with a cut point of AHI at least 15, because cardiovascular risk is elevated at this level. The Medicare definition of hypopnea tends to miss patients with early disease where events are short and primarily associated with arousals (i.e. patients who are young, female, and lean) [2, 15]. Although the cardiovascular effects of OSA without hypoxemia are not established, these patients experience issues with daytime alertness [2]. Furthermore, in a recent population-based study, those with high arousal-based indices were found to transition to events with hypoxemia with aging and/or increasing weight [2], suggesting mild disease cannot be ignored. Alternatives to overnight PSG measures of AHI are Home Sleep Testing (HST), devices that are increasingly employed for OSA diagnosis. HST devices have limitations. They generally measure respiratory effort and oxygen saturation without electroencephalography, so cannot capture arousals and sleep fragmentation associated with OSA. AHI is generally underestimated because there is variation in both the number and type of sensors depending on the device. Additionally, sensors can shift during these unattended studies, resulting in unreliable measurements. In summary, the prevalence of OSA is high (particularly with the obesity pandemic) and accurately diagnosing OSA is challenging and costly. Qualified sleep laboratories are not universally available resulting in delayed diagnosis. HST has become more acceptable but misses arousals and sleep fragmentation. Therefore, there is a need to develop more efficient and cost-effective approaches to OSA diagnosis. An ideal biomarker should correlate with severity of disease and also indicate treatment response. The biomarker should help differentiate cases with recurrent hypoxemia versus with arousal/sleep fragmentation only and help differentiate between obstructive and central apnea. Identifying key biomarkers of OSA will also facilitate our understanding of its pathophysiology and complications. Although numerous efforts to profile biomarkers in OSA have been reported, none have been consistently reproduced nor do they meet the criteria for routine diagnostic use. Some studies have used high-throughput gene expression assays in moderate-to-severe OSA with reports of increased expression of endothelial junction, proapoptotic [16] and inflammatory gene signatures [17]. Studies have profiled microRNAs [18] and found that myocardial ischemia and heart failure associated microRNAs to be elevated in OSA [19]. Other studies used more conventional single- or low-throughput multiprotein measurements either by ELISA or Luminex to profile differences in OSA versus controls using plasma serum or CSF. Notably, reports showed associations with elevated Tau [20–23], amyloid beta [24, 25] in CSF, elevated blood IL-6 cytokine levels [22, 26–37], CRP [26, 32, 34], increased insulin [37], and elevated monocyte to high-density lipoprotein (HDL) cholesterol ratio [38]. Other groups have performed high-throughput Luminex-based proteomic assays with a focus on characterizing the cognitive impairment in OSA by profiling 254 serum proteins. These authors found a prominent insulin-related protein signature [39]. Notably, several others have utilized mass spectrometry. Characterization of the red blood cell proteome in OSA patients found associations with proteins involved in catalytic oxidoreductase and response to stress [40], while dysregulation of lipids was found by other groups [41]. Developing suitable biomarkers has been hampered by the inability to measure multiple biomarkers in the same patient cohort. Recent technological advances have enabled time-efficient, cost-effective measurement of multiple circulating biomarkers. In this study, we used the SomaScan array to profile 1,300 proteins to identify novel sleep apnea biomarkers and to develop multivariate constructs to predict sleep apnea phenotypes based on proteomic profiles. Methods The Stanford sleep cohort The Stanford sleep cohort includes 1,070 participants aged 18–91 years enrolled at the Stanford Sleep Clinic starting in 1999, from which a subset of 713 individuals were used as part of this current study (Table 1) [42, 43]. Approximately 8.5–10.0 mL of blood was drawn from each participant (typically fasting) the morning after the initial diagnostic overnight PSG using one glass red-top serum Vacutainer tube and allowed to clot for a minimum of 30 min, the serum was then aliquoted and stored at −80°C until assay. Laboratory PSG studies for cohort participants were scored using the alternate AASM hypopnea definition for AHI and standard criteria for the central apnea index (CAI) [12]. The lowest oxygen saturation values were also available. Approximately 49.2% of participants had an obstructive apnea hypopnea index (OAHI, with hypopneas defined with arousal or 3% desaturation) above or equal to 15/h. The first inclusion criteria were participants who had both PSG and SomaScan proteomics (n = 772 participants), from here participants who did not have continuous positive airway pressure (CPAP) treatment nor non-missing demographic variables, for example, age, gender, body mass index (BMI), and date of PSG were included in the analysis (n = 713). No prior sample size calculations were performed owing to the design of the study which was exploratory with an aim to generate unbiased hypotheses. Table 1. Summary of Variables Classified by Apnea Status in the Study Cohort (n = 713) Variables . Moderate/severe (n = 351) (Range or N) . Control/mild apnea (n = 362) (Range or N) . p . Statistic . 95% CI . PSG variables Sleep stage s1 % 14.34 (0–66.6) 9.62 (0–49.46) 1.3e−10 6.53 3.3 ± 6.13 Sleep stage s2 % 62.13 (19.52–91.94) 63.27 (14.92–98.2) 0.21180 −1.25 −2.94 ± 0.65 Sleep stage s3 % 4.61 (0–25.26) 6.73 (0–36.88) 1.4e−06 −4.87 −2.98 ± −1.27 Sleep stage s4 % 2.56 (0–28.85) 3.7 (0–44.63) 0.00994 −2.59 −2 ± −0.27 REM ratio % 16.37 (0–37.13) 16.67 (0–36.28) 0.55591 −0.59 −1.32 ± 0.71 Sleep efficiency % 77.77 (27.34–97.38) 78.16 (21.91–98.1) 0.68846 −0.4 −2.33 ± 1.54 2% Oxygen desaturation events 162.89 (5–651) 61.68 (0–459) <2e−16 15.56 88.43 ± 113.99 3% Oxygen desaturation events 76.9 (0–432) 21.13 (0–239) <2e−16 13.32 47.54 ± 64.01 Mean SaO2 % 95.39 (16.2–99.4) 96.3 (0–100.2) 0.03624 −2.1 −1.75 ± −0.06 Low SaO2 % 87.53 (32–98.3) 92.3 (76.7–98.9) <2e−16 −11.59 −5.58 ± −3.96 Baseline SaO2 % awake 97.4 (92.38–100) 97.94 (92.68–100.08) 9.2e−06 −4.47 −0.77 ± −0.3 Demographic variables Age (years) 48.87 (18.9–90.5) 42.49 (13–77.9) 2.0e−10 6.45 4.44 ± 8.32 BMI 28.44 (9.77–73.52) 25.89 (15.08–78.66) 1.3e−07 5.34 1.61 ± 3.49 Height (m) 1.74 (1.01–2.27) 1.72 (1.22–1.98) 0.02436 2.26 0 ± 0.03 Weight (kg) 85.79 (39.7–170.1) 76.61 (43.5–227.32) 4.0e−10 6.35 6.34 ± 12.02 Gender, male % 67.5% (237) 52.4% (184) 6.7e−06 0.5 0.36 ± 0.68 Systolic BP (mm/Hg) 129.22 (90–181) 124.6 (84–184) 0.00049 3.5 2.03 ± 7.2 Diastolic BP (mm/Hg) 80.67 (40–112) 77.89 (33–116) 0.00164 3.16 1.05 ± 4.5 Comorbidities Hypertension 42.7% (150) 30.5% (107) 0.00032 0.56 0.41 ± 0.78 Depression 15.1% (53) 19.7% (69) 0.16526 1.32 0.88 ± 2 Asthma 3.4% (12) 4.8% (17) 0.45045 1.39 0.62 ± 3.25 Thyroid disorders 5.7% (20) 2.6% (9) 0.03637 0.42 0.17 ± 0.99 Type 2 diabetes 1.4% (5) 1.4% (5) 1.00000 0.97 0.22 ± 4.25 Gastroesophageal reflux disease 6% (21) 4.3% (15) 0.30604 0.68 0.32 ± 1.41 Hypercholesterolemia 26.2% (92) 16.8% (59) 0.00132 0.55 0.37 ± 0.8 Blood variables Glucose (mg/dL) 91.32 (46–174) 88.34 (4–205) 0.01594 2.42 0.56 ± 5.4 Triglycerides (mg/dL) 141.35 (25–741) 121.85 (28–508) 0.00223 3.07 7.03 ± 31.97 Low density lipoproteins (mg/dL) 129.69 (52–251) 123.09 (19–331) 0.01370 2.47 1.36 ± 11.85 High density lipoproteins (mg/dL) 47.85 (6–106) 52.46 (23–100) 2.5e−05 −4.24 −6.74 ± −2.47 Total cholesterol (mg/dL) 197.47 (105–345) 193.01 (87–339) 0.13147 1.51 −1.34 ± 10.26 Very low density lipoproteins (mg/dL) 8.17 (2–66) 7.7 (2–32) 0.75662 0.31 −2.54 ± 3.49 Variables . Moderate/severe (n = 351) (Range or N) . Control/mild apnea (n = 362) (Range or N) . p . Statistic . 95% CI . PSG variables Sleep stage s1 % 14.34 (0–66.6) 9.62 (0–49.46) 1.3e−10 6.53 3.3 ± 6.13 Sleep stage s2 % 62.13 (19.52–91.94) 63.27 (14.92–98.2) 0.21180 −1.25 −2.94 ± 0.65 Sleep stage s3 % 4.61 (0–25.26) 6.73 (0–36.88) 1.4e−06 −4.87 −2.98 ± −1.27 Sleep stage s4 % 2.56 (0–28.85) 3.7 (0–44.63) 0.00994 −2.59 −2 ± −0.27 REM ratio % 16.37 (0–37.13) 16.67 (0–36.28) 0.55591 −0.59 −1.32 ± 0.71 Sleep efficiency % 77.77 (27.34–97.38) 78.16 (21.91–98.1) 0.68846 −0.4 −2.33 ± 1.54 2% Oxygen desaturation events 162.89 (5–651) 61.68 (0–459) <2e−16 15.56 88.43 ± 113.99 3% Oxygen desaturation events 76.9 (0–432) 21.13 (0–239) <2e−16 13.32 47.54 ± 64.01 Mean SaO2 % 95.39 (16.2–99.4) 96.3 (0–100.2) 0.03624 −2.1 −1.75 ± −0.06 Low SaO2 % 87.53 (32–98.3) 92.3 (76.7–98.9) <2e−16 −11.59 −5.58 ± −3.96 Baseline SaO2 % awake 97.4 (92.38–100) 97.94 (92.68–100.08) 9.2e−06 −4.47 −0.77 ± −0.3 Demographic variables Age (years) 48.87 (18.9–90.5) 42.49 (13–77.9) 2.0e−10 6.45 4.44 ± 8.32 BMI 28.44 (9.77–73.52) 25.89 (15.08–78.66) 1.3e−07 5.34 1.61 ± 3.49 Height (m) 1.74 (1.01–2.27) 1.72 (1.22–1.98) 0.02436 2.26 0 ± 0.03 Weight (kg) 85.79 (39.7–170.1) 76.61 (43.5–227.32) 4.0e−10 6.35 6.34 ± 12.02 Gender, male % 67.5% (237) 52.4% (184) 6.7e−06 0.5 0.36 ± 0.68 Systolic BP (mm/Hg) 129.22 (90–181) 124.6 (84–184) 0.00049 3.5 2.03 ± 7.2 Diastolic BP (mm/Hg) 80.67 (40–112) 77.89 (33–116) 0.00164 3.16 1.05 ± 4.5 Comorbidities Hypertension 42.7% (150) 30.5% (107) 0.00032 0.56 0.41 ± 0.78 Depression 15.1% (53) 19.7% (69) 0.16526 1.32 0.88 ± 2 Asthma 3.4% (12) 4.8% (17) 0.45045 1.39 0.62 ± 3.25 Thyroid disorders 5.7% (20) 2.6% (9) 0.03637 0.42 0.17 ± 0.99 Type 2 diabetes 1.4% (5) 1.4% (5) 1.00000 0.97 0.22 ± 4.25 Gastroesophageal reflux disease 6% (21) 4.3% (15) 0.30604 0.68 0.32 ± 1.41 Hypercholesterolemia 26.2% (92) 16.8% (59) 0.00132 0.55 0.37 ± 0.8 Blood variables Glucose (mg/dL) 91.32 (46–174) 88.34 (4–205) 0.01594 2.42 0.56 ± 5.4 Triglycerides (mg/dL) 141.35 (25–741) 121.85 (28–508) 0.00223 3.07 7.03 ± 31.97 Low density lipoproteins (mg/dL) 129.69 (52–251) 123.09 (19–331) 0.01370 2.47 1.36 ± 11.85 High density lipoproteins (mg/dL) 47.85 (6–106) 52.46 (23–100) 2.5e−05 −4.24 −6.74 ± −2.47 Total cholesterol (mg/dL) 197.47 (105–345) 193.01 (87–339) 0.13147 1.51 −1.34 ± 10.26 Very low density lipoproteins (mg/dL) 8.17 (2–66) 7.7 (2–32) 0.75662 0.31 −2.54 ± 3.49 Fisher’s exact test for categorical variables or student’s t-test was performed for continuous variables. 95% CI is the difference in means between the apnea and control. Open in new tab Table 1. Summary of Variables Classified by Apnea Status in the Study Cohort (n = 713) Variables . Moderate/severe (n = 351) (Range or N) . Control/mild apnea (n = 362) (Range or N) . p . Statistic . 95% CI . PSG variables Sleep stage s1 % 14.34 (0–66.6) 9.62 (0–49.46) 1.3e−10 6.53 3.3 ± 6.13 Sleep stage s2 % 62.13 (19.52–91.94) 63.27 (14.92–98.2) 0.21180 −1.25 −2.94 ± 0.65 Sleep stage s3 % 4.61 (0–25.26) 6.73 (0–36.88) 1.4e−06 −4.87 −2.98 ± −1.27 Sleep stage s4 % 2.56 (0–28.85) 3.7 (0–44.63) 0.00994 −2.59 −2 ± −0.27 REM ratio % 16.37 (0–37.13) 16.67 (0–36.28) 0.55591 −0.59 −1.32 ± 0.71 Sleep efficiency % 77.77 (27.34–97.38) 78.16 (21.91–98.1) 0.68846 −0.4 −2.33 ± 1.54 2% Oxygen desaturation events 162.89 (5–651) 61.68 (0–459) <2e−16 15.56 88.43 ± 113.99 3% Oxygen desaturation events 76.9 (0–432) 21.13 (0–239) <2e−16 13.32 47.54 ± 64.01 Mean SaO2 % 95.39 (16.2–99.4) 96.3 (0–100.2) 0.03624 −2.1 −1.75 ± −0.06 Low SaO2 % 87.53 (32–98.3) 92.3 (76.7–98.9) <2e−16 −11.59 −5.58 ± −3.96 Baseline SaO2 % awake 97.4 (92.38–100) 97.94 (92.68–100.08) 9.2e−06 −4.47 −0.77 ± −0.3 Demographic variables Age (years) 48.87 (18.9–90.5) 42.49 (13–77.9) 2.0e−10 6.45 4.44 ± 8.32 BMI 28.44 (9.77–73.52) 25.89 (15.08–78.66) 1.3e−07 5.34 1.61 ± 3.49 Height (m) 1.74 (1.01–2.27) 1.72 (1.22–1.98) 0.02436 2.26 0 ± 0.03 Weight (kg) 85.79 (39.7–170.1) 76.61 (43.5–227.32) 4.0e−10 6.35 6.34 ± 12.02 Gender, male % 67.5% (237) 52.4% (184) 6.7e−06 0.5 0.36 ± 0.68 Systolic BP (mm/Hg) 129.22 (90–181) 124.6 (84–184) 0.00049 3.5 2.03 ± 7.2 Diastolic BP (mm/Hg) 80.67 (40–112) 77.89 (33–116) 0.00164 3.16 1.05 ± 4.5 Comorbidities Hypertension 42.7% (150) 30.5% (107) 0.00032 0.56 0.41 ± 0.78 Depression 15.1% (53) 19.7% (69) 0.16526 1.32 0.88 ± 2 Asthma 3.4% (12) 4.8% (17) 0.45045 1.39 0.62 ± 3.25 Thyroid disorders 5.7% (20) 2.6% (9) 0.03637 0.42 0.17 ± 0.99 Type 2 diabetes 1.4% (5) 1.4% (5) 1.00000 0.97 0.22 ± 4.25 Gastroesophageal reflux disease 6% (21) 4.3% (15) 0.30604 0.68 0.32 ± 1.41 Hypercholesterolemia 26.2% (92) 16.8% (59) 0.00132 0.55 0.37 ± 0.8 Blood variables Glucose (mg/dL) 91.32 (46–174) 88.34 (4–205) 0.01594 2.42 0.56 ± 5.4 Triglycerides (mg/dL) 141.35 (25–741) 121.85 (28–508) 0.00223 3.07 7.03 ± 31.97 Low density lipoproteins (mg/dL) 129.69 (52–251) 123.09 (19–331) 0.01370 2.47 1.36 ± 11.85 High density lipoproteins (mg/dL) 47.85 (6–106) 52.46 (23–100) 2.5e−05 −4.24 −6.74 ± −2.47 Total cholesterol (mg/dL) 197.47 (105–345) 193.01 (87–339) 0.13147 1.51 −1.34 ± 10.26 Very low density lipoproteins (mg/dL) 8.17 (2–66) 7.7 (2–32) 0.75662 0.31 −2.54 ± 3.49 Variables . Moderate/severe (n = 351) (Range or N) . Control/mild apnea (n = 362) (Range or N) . p . Statistic . 95% CI . PSG variables Sleep stage s1 % 14.34 (0–66.6) 9.62 (0–49.46) 1.3e−10 6.53 3.3 ± 6.13 Sleep stage s2 % 62.13 (19.52–91.94) 63.27 (14.92–98.2) 0.21180 −1.25 −2.94 ± 0.65 Sleep stage s3 % 4.61 (0–25.26) 6.73 (0–36.88) 1.4e−06 −4.87 −2.98 ± −1.27 Sleep stage s4 % 2.56 (0–28.85) 3.7 (0–44.63) 0.00994 −2.59 −2 ± −0.27 REM ratio % 16.37 (0–37.13) 16.67 (0–36.28) 0.55591 −0.59 −1.32 ± 0.71 Sleep efficiency % 77.77 (27.34–97.38) 78.16 (21.91–98.1) 0.68846 −0.4 −2.33 ± 1.54 2% Oxygen desaturation events 162.89 (5–651) 61.68 (0–459) <2e−16 15.56 88.43 ± 113.99 3% Oxygen desaturation events 76.9 (0–432) 21.13 (0–239) <2e−16 13.32 47.54 ± 64.01 Mean SaO2 % 95.39 (16.2–99.4) 96.3 (0–100.2) 0.03624 −2.1 −1.75 ± −0.06 Low SaO2 % 87.53 (32–98.3) 92.3 (76.7–98.9) <2e−16 −11.59 −5.58 ± −3.96 Baseline SaO2 % awake 97.4 (92.38–100) 97.94 (92.68–100.08) 9.2e−06 −4.47 −0.77 ± −0.3 Demographic variables Age (years) 48.87 (18.9–90.5) 42.49 (13–77.9) 2.0e−10 6.45 4.44 ± 8.32 BMI 28.44 (9.77–73.52) 25.89 (15.08–78.66) 1.3e−07 5.34 1.61 ± 3.49 Height (m) 1.74 (1.01–2.27) 1.72 (1.22–1.98) 0.02436 2.26 0 ± 0.03 Weight (kg) 85.79 (39.7–170.1) 76.61 (43.5–227.32) 4.0e−10 6.35 6.34 ± 12.02 Gender, male % 67.5% (237) 52.4% (184) 6.7e−06 0.5 0.36 ± 0.68 Systolic BP (mm/Hg) 129.22 (90–181) 124.6 (84–184) 0.00049 3.5 2.03 ± 7.2 Diastolic BP (mm/Hg) 80.67 (40–112) 77.89 (33–116) 0.00164 3.16 1.05 ± 4.5 Comorbidities Hypertension 42.7% (150) 30.5% (107) 0.00032 0.56 0.41 ± 0.78 Depression 15.1% (53) 19.7% (69) 0.16526 1.32 0.88 ± 2 Asthma 3.4% (12) 4.8% (17) 0.45045 1.39 0.62 ± 3.25 Thyroid disorders 5.7% (20) 2.6% (9) 0.03637 0.42 0.17 ± 0.99 Type 2 diabetes 1.4% (5) 1.4% (5) 1.00000 0.97 0.22 ± 4.25 Gastroesophageal reflux disease 6% (21) 4.3% (15) 0.30604 0.68 0.32 ± 1.41 Hypercholesterolemia 26.2% (92) 16.8% (59) 0.00132 0.55 0.37 ± 0.8 Blood variables Glucose (mg/dL) 91.32 (46–174) 88.34 (4–205) 0.01594 2.42 0.56 ± 5.4 Triglycerides (mg/dL) 141.35 (25–741) 121.85 (28–508) 0.00223 3.07 7.03 ± 31.97 Low density lipoproteins (mg/dL) 129.69 (52–251) 123.09 (19–331) 0.01370 2.47 1.36 ± 11.85 High density lipoproteins (mg/dL) 47.85 (6–106) 52.46 (23–100) 2.5e−05 −4.24 −6.74 ± −2.47 Total cholesterol (mg/dL) 197.47 (105–345) 193.01 (87–339) 0.13147 1.51 −1.34 ± 10.26 Very low density lipoproteins (mg/dL) 8.17 (2–66) 7.7 (2–32) 0.75662 0.31 −2.54 ± 3.49 Fisher’s exact test for categorical variables or student’s t-test was performed for continuous variables. 95% CI is the difference in means between the apnea and control. Open in new tab OSA prevalence (moderate to severe ≥15 events/h) was 49.2% (n = 351/713) and average age in the moderate-to-severe apnea group was 48.8 years and predominantly male (67.5%; p = 6.7e–06) with significantly increased BMI (mean BMI 28.4 vs 25.8; p = 1.3e−07). The cohort was not followed up for any neurodegenerative disorders nor was there any data on comorbidities such as stroke, dementia, mild cognitive impairment, and congestive heart failure (likely low prevalence), but hypertension (42.7% vs 30.5%) and hypercholesteremia (26.2% vs 16.8%) was predominant in the moderate-to-severe apnea group (p < 0.001). Notably, blood HDL levels were significantly decreased in the moderate-to-severe apnea group (mean HDL 47.8 vs 52.46; p p = 2.5e–05), while other variables were unremarkable. Table 1 describes other clinical characteristics including summary PSG data stratified by moderate-to-severe apnea versus mild/control apnea. Positive airway pressure intervention cohort Plasma samples from 16 participants in a study at Washington University in Saint Louis, described in detail elsewhere [44], were used for protein measures. These 16 individuals had mild OSA (AHI of ≥5 and <15/h) or moderate‐to‐severe OSA (AHI ≥15/h) and gave a preintervention blood sample. The participants received positive airway pressure (PAP) treatment. Participants adherent to PAP, defined as usage at least 4 h on at least 70% of 30 preceding nights recorded by the PAP machine, provided a postintervention blood sample. The 16 individuals were randomly selected to assay proteomics from a bigger cohort described in detail elsewhere [44]. Protein measures The relative expression levels of 1,300 serum proteins were assayed with SomaScan, a highly multiplexed aptamer approach (see Supplementary Table S1 for a complete list of proteins assayed) as previously detailed elsewhere [45–48]. Several studies have performed evaluation of the SomaScan platform by characterizing protein quantitative trait loci (pQTL) and then comparing them to a Luminex-based platform [49, 50] with good concordance. SomaScan (SomaLogic Inc., Boulder CO) was designed to have extended dynamic range from fM to μM, with both extracellular and intracellular proteins (including soluble domains of membrane proteins) being included with predominantly proteins in the secretome being targeted. Serum (150 μL of each sample) was used for protein measurement assay. More detailed information on the procedure can be found at the manufacturer’s website (http://somalogic.com/wp-content/uploads/2017/06/SSM-002-Technical-White-Paper_010916_LSM1.pdf). Data quality control (QC) was performed by SomaScan (described in detail in http://somalogic.com/wp-content/uploads/2017/06/SSM-071-Rev-0-Technical-Note-SOMAscan-Data-Standardization.pdf) at both sample and protein levels. Sample-level QC involved using hybridization controls during hybridization to adjust for systematic variability, further median of the signal over all the protein dilution sets (0.005%, 1%, and 40%) was used to adjust for within-run variability. These hybridization and median scale factors were used to normalize data across samples in a run and acceptance criteria were for these values to be in the range of 0.5–1.8. Protein-level QC involved using same replicate calibrator serum sample, and median values from the calibrator sample signal are used to calculate a scale factor to correct for between-run variability. PSG data About 713 participants had PSG data available in European Data Format. The following parameters were extracted: baseline wake oxygen saturation and oxygen desaturation index (ODI) as described in the study of Koch et al. [2], detailed description of the subsample can be found in the studies of Andlauer et al. [51] and Moore et al. [52]. All indices were parsed from scored event files using custom python scripts. Parameters of interest chosen wake baseline oxygen saturation (SaO2), ODI2% and ODI3%, minimum oxygen saturation, AHI, OAHI, and CAI after performing cross-correlation with other PSG variables (data not shown). SomaScan data analysis SomaScan data were received in ADAT format and were parsed with custom scripts in python into a tidy format [53]. SomaLogic performed both inter- and intra-assay normalization as described previously [46]. The 1,300 proteins were measured in three dilutions (0.005%, 1%, and 40%) to capture the dynamic range (see Supplementary Table S1 for details). Principal component plots were computed to gauge underlying data structure and no deviations or outliers from expected structure were found by us or other researchers who used the platform [54]. Boxplots of RFU (relative fluorescence units) protein measures binned by individuals were further visually inspected, and there were no individuals with consistently high interquartile range greater than 75 relative to other individuals. Log-normalized RFU protein measures were analyzed for associations in a linear model with empirical Bayes moderation using the Limma library in R [55]. The model design included covariates such as Age, Gender, BMI, BMI2, Age × Gender × BMI, and years from blood draw to assay. In the intervention cohort, the SomaScan protein measures were analyzed in a paired approach, comparing preintervention to postintervention profiles within each individual. Significance analysis of microarrays [56] was used in paired mode to find proteins differentially expressed utilizing a permutation procedure to estimate null statistical distribution, local false discovery rate (FDR) was estimated as described by Storey et al. [57], and corresponding q values computed. Pathway analysis All 5% FDR significant protein sets were analyzed for biological pathway enrichment using the module Toppfun of the Toppgene suite [58]. The toppgene suite determines the similarity between candidate and known proteins/genes with the exception that this is modeled as network structure. This entails modeling proteins/genes as nodes in a graph while interactions between proteins/genes are modeled as edges or connections between the nodes. The goal of this network-based prioritization is to identify nodes (proteins/genes) that are relevant to biological processes or diseases. Candidate proteins/genes are scored based on their network distance to the known protein/genes. OAHI classifier Protein measures were used to train a lasso model with L1 regularization [59] to predict OAHI outcome (OAHI ≥15 or OAHI <15). The hyperparameters of the models were tuned via a 10-fold cross-validation approach: A 75% train–25% test split was adopted, SomaScan data on 549 individuals was used for model training, while the remaining data on 164 individuals was used for validation (total sample size 713 individuals). Three models were trained to classify OAHI. Model 1 incorporated demographic information (age, gender, and BMI) in addition to the protein measures, model 2 incorporated only protein measures, and model 3 incorporated only demographic information. All used same test-train split and same lasso-based approach. The R glmnet library [60] was used to train A L1 regularized lasso models [59] and class-balanced test-train splits and metrics were computed using caret library [61]. Receiver operating curves (ROCs) were constructed using R libraries plotROC and ggplot2 and statistical differences between the different model area under the curves (AUCs) were calculated using pROC package [62]. Results Differential expression of proteins associated with OAHI SomaScan protein measures were modeled as linear functions of OAHI adjusted for demographic variables (age, gender, and BMI) in 713 individuals. Sixty-five proteins (34 upregulated and 31 downregulated) were identified as differentially expressed at 5% FDR (Table 2). Among the top differentially expressed proteins (DEPs) (FDR p-value <0.005) were tPA (tissue-type plasminogen activator), laminin, aminoacylase-1, growth hormone receptor, and IL-18 Ra proteins which were upregulated, that is, positively correlated with OAHI index, while IGFBP-1 (insulin-like growth factor-binding protein 1), carbonic anhydrase I, and UNC5H4 (Netrin receptor UNC5D) were downregulated and negatively correlated with OAHI (see Supplementary Table S2 for full list of associations). Robust adjustment for BMI as described in Methods was performed as it is a major risk factor for elevated OAHI [1]. After adjustment, we did not find any evidence of residual effects of BMI on the differentially associated proteins as indicated by absence of BMI-associated proteins, for instance, leptin (data not shown). Table 2. Top differentially expressed proteins (n = 65) associated with Apnea index (OAHI) post 5% FDR adjustment. Protein Variables . t . p . LogFC . B (log odds) . Adjusted p . Laminin 4.851424 1.51E−06 0.003258 2.075003 0.000980002 tPA 4.893682 1.23E−06 0.003815 2.274444 0.000980002 IGFBP-1 −4.58588 5.35e−06 −0.00972 0.858886 0.002314424 Aminoacylase-1 4.22325 2.73e−05 0.006367 −0.69741 0.006852415 Carbonic anhydrase I −4.26599 2.26e−05 −0.01055 −0.5203 0.006852415 Growth hormone receptor 4.18514 3.21e−05 0.003941 −0.85391 0.006852415 IL-18 Ra 4.152204 3.7e−05 0.0019 −0.98808 0.006852415 LG3BP 4.079381 5.03e−05 0.004581 −1.28114 0.008160917 UNC5H4 −4.02498 6.32e−05 −0.00321 −1.49685 0.00910507 TFPI 3.97311 7.83e−05 0.002999 −1.69992 0.010152077 Factor H 3.89366 0.000108 0.001877 −2.0061 0.012500848 NRX1B −3.87705 0.000116 −0.00343 −2.06937 0.012500848 MBD4 −3.83584 0.000136 −0.00243 −2.22522 0.013609224 Coagulation Factor IXab 3.799048 0.000158 0.002919 −2.36299 0.014262141 DHH −3.75605 0.000187 −0.00364 −2.52238 0.014262141 Factor I 3.784641 0.000167 0.001665 −2.41659 0.014262141 PDE11 3.768198 0.000178 0.002108 −2.47753 0.014262141 Integrin a1b1 3.739266 0.0002 0.003899 −2.58415 0.014382903 Coagulation Factor IX 3.697546 0.000235 0.002423 −2.73649 0.015106209 Endothelin-converting enzyme 1 3.670881 0.00026 0.00183 −2.83299 0.015106209 GDF2 −3.67736 0.000254 −0.00423 −2.8096 0.015106209 LYVE1 −3.70172 0.000231 −0.00312 −2.72133 0.015106209 S100A4 −3.65204 0.00028 −0.00527 −2.90078 0.015106209 suPAR 3.662345 0.000269 0.001941 −2.86375 0.015106209 SAP 3.601961 0.000338 0.002407 −3.07932 0.017541381 CD70 3.55832 0.000398 0.003407 −3.23296 0.018453038 IFN-lambda 1 −3.57117 0.00038 −0.0023 −3.18792 0.018453038 MCP-4 −3.56662 0.000386 −0.00346 −3.20389 0.018453038 PLPP −3.52351 0.000453 −0.00455 −3.35423 0.020282104 Calcineurin 3.455373 0.000583 0.002789 −3.58825 0.023488989 Coagulation Factor Xa 3.444959 0.000605 0.002159 −3.62363 0.023488989 NACA −3.45357 0.000586 −0.00528 −3.5944 0.023488989 NOTC2 3.440176 0.000616 0.004877 −3.63984 0.023488989 Peroxiredoxin-6 −3.47393 0.000544 −0.00683 −3.52497 0.023488989 a-Synuclein −3.42896 0.000641 −0.00688 −3.67778 0.023765541 C5b, 6 Complex 3.404628 0.0007 0.001938 −3.75967 0.024456845 CD39 3.398232 0.000717 0.001537 −3.7811 0.024456845 IL-13 Ra1 3.412849 0.00068 0.001627 −3.73207 0.024456845 CK2-A1:B −3.3751 0.000779 −0.00558 −3.85828 0.025890244 IFN10 3.366182 0.000804 0.004 −3.88791 0.026060153 EPB41 −3.34035 0.000881 −0.00779 −3.97328 0.027206702 Myokinase, human −3.3412 0.000878 −0.00762 −3.97049 0.027206702 NADPH-P450 Oxidoreductase 3.325114 0.00093 0.004275 −4.02334 0.028045661 PAFAH beta subunit −3.3062 0.000994 −0.00353 −4.08515 0.029295837 Heparin cofactor II 3.288655 0.001057 0.002256 −4.14221 0.030462655 Protein Variables . t . p . LogFC . B (log odds) . Adjusted p . Laminin 4.851424 1.51E−06 0.003258 2.075003 0.000980002 tPA 4.893682 1.23E−06 0.003815 2.274444 0.000980002 IGFBP-1 −4.58588 5.35e−06 −0.00972 0.858886 0.002314424 Aminoacylase-1 4.22325 2.73e−05 0.006367 −0.69741 0.006852415 Carbonic anhydrase I −4.26599 2.26e−05 −0.01055 −0.5203 0.006852415 Growth hormone receptor 4.18514 3.21e−05 0.003941 −0.85391 0.006852415 IL-18 Ra 4.152204 3.7e−05 0.0019 −0.98808 0.006852415 LG3BP 4.079381 5.03e−05 0.004581 −1.28114 0.008160917 UNC5H4 −4.02498 6.32e−05 −0.00321 −1.49685 0.00910507 TFPI 3.97311 7.83e−05 0.002999 −1.69992 0.010152077 Factor H 3.89366 0.000108 0.001877 −2.0061 0.012500848 NRX1B −3.87705 0.000116 −0.00343 −2.06937 0.012500848 MBD4 −3.83584 0.000136 −0.00243 −2.22522 0.013609224 Coagulation Factor IXab 3.799048 0.000158 0.002919 −2.36299 0.014262141 DHH −3.75605 0.000187 −0.00364 −2.52238 0.014262141 Factor I 3.784641 0.000167 0.001665 −2.41659 0.014262141 PDE11 3.768198 0.000178 0.002108 −2.47753 0.014262141 Integrin a1b1 3.739266 0.0002 0.003899 −2.58415 0.014382903 Coagulation Factor IX 3.697546 0.000235 0.002423 −2.73649 0.015106209 Endothelin-converting enzyme 1 3.670881 0.00026 0.00183 −2.83299 0.015106209 GDF2 −3.67736 0.000254 −0.00423 −2.8096 0.015106209 LYVE1 −3.70172 0.000231 −0.00312 −2.72133 0.015106209 S100A4 −3.65204 0.00028 −0.00527 −2.90078 0.015106209 suPAR 3.662345 0.000269 0.001941 −2.86375 0.015106209 SAP 3.601961 0.000338 0.002407 −3.07932 0.017541381 CD70 3.55832 0.000398 0.003407 −3.23296 0.018453038 IFN-lambda 1 −3.57117 0.00038 −0.0023 −3.18792 0.018453038 MCP-4 −3.56662 0.000386 −0.00346 −3.20389 0.018453038 PLPP −3.52351 0.000453 −0.00455 −3.35423 0.020282104 Calcineurin 3.455373 0.000583 0.002789 −3.58825 0.023488989 Coagulation Factor Xa 3.444959 0.000605 0.002159 −3.62363 0.023488989 NACA −3.45357 0.000586 −0.00528 −3.5944 0.023488989 NOTC2 3.440176 0.000616 0.004877 −3.63984 0.023488989 Peroxiredoxin-6 −3.47393 0.000544 −0.00683 −3.52497 0.023488989 a-Synuclein −3.42896 0.000641 −0.00688 −3.67778 0.023765541 C5b, 6 Complex 3.404628 0.0007 0.001938 −3.75967 0.024456845 CD39 3.398232 0.000717 0.001537 −3.7811 0.024456845 IL-13 Ra1 3.412849 0.00068 0.001627 −3.73207 0.024456845 CK2-A1:B −3.3751 0.000779 −0.00558 −3.85828 0.025890244 IFN10 3.366182 0.000804 0.004 −3.88791 0.026060153 EPB41 −3.34035 0.000881 −0.00779 −3.97328 0.027206702 Myokinase, human −3.3412 0.000878 −0.00762 −3.97049 0.027206702 NADPH-P450 Oxidoreductase 3.325114 0.00093 0.004275 −4.02334 0.028045661 PAFAH beta subunit −3.3062 0.000994 −0.00353 −4.08515 0.029295837 Heparin cofactor II 3.288655 0.001057 0.002256 −4.14221 0.030462655 t is the empirical Bayes moderated t-statistic; B is th empirical Bayes log odds of differential expression. Open in new tab Table 2. Top differentially expressed proteins (n = 65) associated with Apnea index (OAHI) post 5% FDR adjustment. Protein Variables . t . p . LogFC . B (log odds) . Adjusted p . Laminin 4.851424 1.51E−06 0.003258 2.075003 0.000980002 tPA 4.893682 1.23E−06 0.003815 2.274444 0.000980002 IGFBP-1 −4.58588 5.35e−06 −0.00972 0.858886 0.002314424 Aminoacylase-1 4.22325 2.73e−05 0.006367 −0.69741 0.006852415 Carbonic anhydrase I −4.26599 2.26e−05 −0.01055 −0.5203 0.006852415 Growth hormone receptor 4.18514 3.21e−05 0.003941 −0.85391 0.006852415 IL-18 Ra 4.152204 3.7e−05 0.0019 −0.98808 0.006852415 LG3BP 4.079381 5.03e−05 0.004581 −1.28114 0.008160917 UNC5H4 −4.02498 6.32e−05 −0.00321 −1.49685 0.00910507 TFPI 3.97311 7.83e−05 0.002999 −1.69992 0.010152077 Factor H 3.89366 0.000108 0.001877 −2.0061 0.012500848 NRX1B −3.87705 0.000116 −0.00343 −2.06937 0.012500848 MBD4 −3.83584 0.000136 −0.00243 −2.22522 0.013609224 Coagulation Factor IXab 3.799048 0.000158 0.002919 −2.36299 0.014262141 DHH −3.75605 0.000187 −0.00364 −2.52238 0.014262141 Factor I 3.784641 0.000167 0.001665 −2.41659 0.014262141 PDE11 3.768198 0.000178 0.002108 −2.47753 0.014262141 Integrin a1b1 3.739266 0.0002 0.003899 −2.58415 0.014382903 Coagulation Factor IX 3.697546 0.000235 0.002423 −2.73649 0.015106209 Endothelin-converting enzyme 1 3.670881 0.00026 0.00183 −2.83299 0.015106209 GDF2 −3.67736 0.000254 −0.00423 −2.8096 0.015106209 LYVE1 −3.70172 0.000231 −0.00312 −2.72133 0.015106209 S100A4 −3.65204 0.00028 −0.00527 −2.90078 0.015106209 suPAR 3.662345 0.000269 0.001941 −2.86375 0.015106209 SAP 3.601961 0.000338 0.002407 −3.07932 0.017541381 CD70 3.55832 0.000398 0.003407 −3.23296 0.018453038 IFN-lambda 1 −3.57117 0.00038 −0.0023 −3.18792 0.018453038 MCP-4 −3.56662 0.000386 −0.00346 −3.20389 0.018453038 PLPP −3.52351 0.000453 −0.00455 −3.35423 0.020282104 Calcineurin 3.455373 0.000583 0.002789 −3.58825 0.023488989 Coagulation Factor Xa 3.444959 0.000605 0.002159 −3.62363 0.023488989 NACA −3.45357 0.000586 −0.00528 −3.5944 0.023488989 NOTC2 3.440176 0.000616 0.004877 −3.63984 0.023488989 Peroxiredoxin-6 −3.47393 0.000544 −0.00683 −3.52497 0.023488989 a-Synuclein −3.42896 0.000641 −0.00688 −3.67778 0.023765541 C5b, 6 Complex 3.404628 0.0007 0.001938 −3.75967 0.024456845 CD39 3.398232 0.000717 0.001537 −3.7811 0.024456845 IL-13 Ra1 3.412849 0.00068 0.001627 −3.73207 0.024456845 CK2-A1:B −3.3751 0.000779 −0.00558 −3.85828 0.025890244 IFN10 3.366182 0.000804 0.004 −3.88791 0.026060153 EPB41 −3.34035 0.000881 −0.00779 −3.97328 0.027206702 Myokinase, human −3.3412 0.000878 −0.00762 −3.97049 0.027206702 NADPH-P450 Oxidoreductase 3.325114 0.00093 0.004275 −4.02334 0.028045661 PAFAH beta subunit −3.3062 0.000994 −0.00353 −4.08515 0.029295837 Heparin cofactor II 3.288655 0.001057 0.002256 −4.14221 0.030462655 Protein Variables . t . p . LogFC . B (log odds) . Adjusted p . Laminin 4.851424 1.51E−06 0.003258 2.075003 0.000980002 tPA 4.893682 1.23E−06 0.003815 2.274444 0.000980002 IGFBP-1 −4.58588 5.35e−06 −0.00972 0.858886 0.002314424 Aminoacylase-1 4.22325 2.73e−05 0.006367 −0.69741 0.006852415 Carbonic anhydrase I −4.26599 2.26e−05 −0.01055 −0.5203 0.006852415 Growth hormone receptor 4.18514 3.21e−05 0.003941 −0.85391 0.006852415 IL-18 Ra 4.152204 3.7e−05 0.0019 −0.98808 0.006852415 LG3BP 4.079381 5.03e−05 0.004581 −1.28114 0.008160917 UNC5H4 −4.02498 6.32e−05 −0.00321 −1.49685 0.00910507 TFPI 3.97311 7.83e−05 0.002999 −1.69992 0.010152077 Factor H 3.89366 0.000108 0.001877 −2.0061 0.012500848 NRX1B −3.87705 0.000116 −0.00343 −2.06937 0.012500848 MBD4 −3.83584 0.000136 −0.00243 −2.22522 0.013609224 Coagulation Factor IXab 3.799048 0.000158 0.002919 −2.36299 0.014262141 DHH −3.75605 0.000187 −0.00364 −2.52238 0.014262141 Factor I 3.784641 0.000167 0.001665 −2.41659 0.014262141 PDE11 3.768198 0.000178 0.002108 −2.47753 0.014262141 Integrin a1b1 3.739266 0.0002 0.003899 −2.58415 0.014382903 Coagulation Factor IX 3.697546 0.000235 0.002423 −2.73649 0.015106209 Endothelin-converting enzyme 1 3.670881 0.00026 0.00183 −2.83299 0.015106209 GDF2 −3.67736 0.000254 −0.00423 −2.8096 0.015106209 LYVE1 −3.70172 0.000231 −0.00312 −2.72133 0.015106209 S100A4 −3.65204 0.00028 −0.00527 −2.90078 0.015106209 suPAR 3.662345 0.000269 0.001941 −2.86375 0.015106209 SAP 3.601961 0.000338 0.002407 −3.07932 0.017541381 CD70 3.55832 0.000398 0.003407 −3.23296 0.018453038 IFN-lambda 1 −3.57117 0.00038 −0.0023 −3.18792 0.018453038 MCP-4 −3.56662 0.000386 −0.00346 −3.20389 0.018453038 PLPP −3.52351 0.000453 −0.00455 −3.35423 0.020282104 Calcineurin 3.455373 0.000583 0.002789 −3.58825 0.023488989 Coagulation Factor Xa 3.444959 0.000605 0.002159 −3.62363 0.023488989 NACA −3.45357 0.000586 −0.00528 −3.5944 0.023488989 NOTC2 3.440176 0.000616 0.004877 −3.63984 0.023488989 Peroxiredoxin-6 −3.47393 0.000544 −0.00683 −3.52497 0.023488989 a-Synuclein −3.42896 0.000641 −0.00688 −3.67778 0.023765541 C5b, 6 Complex 3.404628 0.0007 0.001938 −3.75967 0.024456845 CD39 3.398232 0.000717 0.001537 −3.7811 0.024456845 IL-13 Ra1 3.412849 0.00068 0.001627 −3.73207 0.024456845 CK2-A1:B −3.3751 0.000779 −0.00558 −3.85828 0.025890244 IFN10 3.366182 0.000804 0.004 −3.88791 0.026060153 EPB41 −3.34035 0.000881 −0.00779 −3.97328 0.027206702 Myokinase, human −3.3412 0.000878 −0.00762 −3.97049 0.027206702 NADPH-P450 Oxidoreductase 3.325114 0.00093 0.004275 −4.02334 0.028045661 PAFAH beta subunit −3.3062 0.000994 −0.00353 −4.08515 0.029295837 Heparin cofactor II 3.288655 0.001057 0.002256 −4.14221 0.030462655 t is the empirical Bayes moderated t-statistic; B is th empirical Bayes log odds of differential expression. Open in new tab We further stratified our analysis based on the severity index of OAHI and performed several comparisons outlined below. (1) Proteomic profiles of individuals (n = 351) with moderate-to-severe apnea (OAHI ≥15) were compared to individuals (n = 362) with mild to no apnea (OAHI <15), we found nine proteins to be associated with moderate-to-severe apnea (OAHI ≥15) at 5% FDR (Supplementary Table S3). Interestingly, Factor I and calcineurin proteins were upregulated while SOST (sclerostin), NRX1B (neurexin-1b), GDF-2 (growth differentiation factor 2), lymphotactin, carbonic anhydrase I, CRTAM (cytotoxic and regulatory T cell molecule), and SuPAR (soluble urokinase-type plasminogen activator receptor) to be downregulated. (2) Proteomic profiles of individuals (n = 159) with severe apnea (OAHI ≥30) were compared to control (OAHI <5) individuals (n = 162), at 5% FDR threshold we found carbonic anhydrase I and Factor I proteins to be associated with moderate-to-severe apnea (Supplementary Table S4). We also ran association analyses and compared the proteomic profiles in individuals (n = 176) with moderate apnea (OAHI ≥15 and OAHI <30) to control individuals (OAHI <5, n = 162), at 5% FDR we did not find any proteins to be associated (Supplementary Table S5). Similarly, comparing proteomic profiles in individuals with mild apnea versus control individuals, no DEPs were observed at 5% FDR threshold (Supplementary Table S6). Pathway analysis and gene ontology (GO) biological process analyses done in Toppgene [58] webserver (FDR p-value <0.05) of DEPs in OAHI (modeled as a continuous variable) revealed overrepresentation of DEPs in several pathways (Figure 1)—Complement and coagulation cascades, L1CAM and Laminin interactions, extracellular matrix and extracellular matrix-associated proteins, Extrinsic Pathway of Fibrin Clot Formation and Hemostasis—in general agreement with previous reports of coagulation being associated with apnea events [63, 64]. GO biological processes analysis (FDR p-value <0.05) revealed the involvement of regulation of immune system process, positive regulation of signal transduction and cytokine-mediated signaling pathway, and others (see Supplementary Table S7 for full list). Of additional interest, we also exclusively used either upregulated and downregulated proteins associated with OAHI for overrepresentation in pathways or biological processes, at 5% FDR in agreement with overall analyses, upregulated pathways were complement and coagulation cascades and laminin interactions (Supplementary Table S8), while downregulated pathways at 5% FDR were protein kinase CK2 complex, positive regulation of signal transduction, and organophospate catabolic processes among others (Supplementary Table S9). Figure 1. Open in new tabDownload slide Pathway analysis and GO biological process analyses done in Toppgene webserver of 65 differentially expressed proteins (FDR p-value <0.05) in moderate-to-severe apnea. The y-axis is the negative logarithm 10 of the adjusted p-value of the overrepresented pathways, while the x-axis are the pathways and processes binned by categories. GO: Biological Process: the pathways and larger processes to which that gene product’s activity contributes. GO: Molecular Function: the molecular activities of individual gene products. GO: Cellular Component: where the gene products are active. The color gradient indicates the % shared overlap between the candidate apnea proteins and known pathway/processes-related proteins. GO, gene ontology; L1CAM, L1 cell adhesion molecule; MET, MET proto-oncogene alias hepatocyte growth factor receptor; PTK2, protein tyrosine kinase 2; ECM, extracellular matrix; CA1, the first region in the hippocampal circuit; CK2, casein kinase 2. Figure 1. Open in new tabDownload slide Pathway analysis and GO biological process analyses done in Toppgene webserver of 65 differentially expressed proteins (FDR p-value <0.05) in moderate-to-severe apnea. The y-axis is the negative logarithm 10 of the adjusted p-value of the overrepresented pathways, while the x-axis are the pathways and processes binned by categories. GO: Biological Process: the pathways and larger processes to which that gene product’s activity contributes. GO: Molecular Function: the molecular activities of individual gene products. GO: Cellular Component: where the gene products are active. The color gradient indicates the % shared overlap between the candidate apnea proteins and known pathway/processes-related proteins. GO, gene ontology; L1CAM, L1 cell adhesion molecule; MET, MET proto-oncogene alias hepatocyte growth factor receptor; PTK2, protein tyrosine kinase 2; ECM, extracellular matrix; CA1, the first region in the hippocampal circuit; CK2, casein kinase 2. ROBO3 protein and Cystatin-F are increased in central apneas CAI was fit as linear function of protein measures, this analysis revealed ROBO3 (Roundabout homolog 3) and CYTF (Cystatin-F) to be strongly upregulated in central apneas (FDR p-value <5e−4, Table 3 and Supplementary Table S10). Table 3. Differentially Expressed Proteins Associated With Central Apnea Index (CAI), 2% Oxygen Desaturation Events, Low Oxygen Saturation Levels (Low SaO2), and Mean Oxygen Saturation Levels (Mean SaO2) Protein variables . Variable . t . p . LogFC . B (log odds) . Adjusted p . CYTF Central apnea 4.965793 8.61e−07 0.071963 4.894893 0.000558 ROBO3 Central apnea 6.93464 9.29e−12 0.052575 15.97869 1.2E−08 Laminin 2% Oxygen desaturation events 4.258327 2.38e−05 0.004826 −0.03975 0.023049 UNC5H4 2% Oxygen desaturation events −4.11609 4.38e−05 −0.00554 −0.6197 0.023049 tPA 2% Oxygen desaturation events 4.069173 5.33e−05 0.005345 −0.80692 0.023049 UNC5H4 Low SaO2 4.941248 9.72e−07 1.222975 4.817064 0.001261 NovH Mean SaO2 −4.44758 1.01e−05 −2.50109 3.027134 0.013093 UNC5H4 Mean SaO2 4.267287 2.25e−05 3.88582 2.340392 0.014594 Protein variables . Variable . t . p . LogFC . B (log odds) . Adjusted p . CYTF Central apnea 4.965793 8.61e−07 0.071963 4.894893 0.000558 ROBO3 Central apnea 6.93464 9.29e−12 0.052575 15.97869 1.2E−08 Laminin 2% Oxygen desaturation events 4.258327 2.38e−05 0.004826 −0.03975 0.023049 UNC5H4 2% Oxygen desaturation events −4.11609 4.38e−05 −0.00554 −0.6197 0.023049 tPA 2% Oxygen desaturation events 4.069173 5.33e−05 0.005345 −0.80692 0.023049 UNC5H4 Low SaO2 4.941248 9.72e−07 1.222975 4.817064 0.001261 NovH Mean SaO2 −4.44758 1.01e−05 −2.50109 3.027134 0.013093 UNC5H4 Mean SaO2 4.267287 2.25e−05 3.88582 2.340392 0.014594 All associations were adjusted at 5% FDR. t is the empirical Bayes moderated t-statistic; B is the empirical Bayes log odds of differential expression. Open in new tab Table 3. Differentially Expressed Proteins Associated With Central Apnea Index (CAI), 2% Oxygen Desaturation Events, Low Oxygen Saturation Levels (Low SaO2), and Mean Oxygen Saturation Levels (Mean SaO2) Protein variables . Variable . t . p . LogFC . B (log odds) . Adjusted p . CYTF Central apnea 4.965793 8.61e−07 0.071963 4.894893 0.000558 ROBO3 Central apnea 6.93464 9.29e−12 0.052575 15.97869 1.2E−08 Laminin 2% Oxygen desaturation events 4.258327 2.38e−05 0.004826 −0.03975 0.023049 UNC5H4 2% Oxygen desaturation events −4.11609 4.38e−05 −0.00554 −0.6197 0.023049 tPA 2% Oxygen desaturation events 4.069173 5.33e−05 0.005345 −0.80692 0.023049 UNC5H4 Low SaO2 4.941248 9.72e−07 1.222975 4.817064 0.001261 NovH Mean SaO2 −4.44758 1.01e−05 −2.50109 3.027134 0.013093 UNC5H4 Mean SaO2 4.267287 2.25e−05 3.88582 2.340392 0.014594 Protein variables . Variable . t . p . LogFC . B (log odds) . Adjusted p . CYTF Central apnea 4.965793 8.61e−07 0.071963 4.894893 0.000558 ROBO3 Central apnea 6.93464 9.29e−12 0.052575 15.97869 1.2E−08 Laminin 2% Oxygen desaturation events 4.258327 2.38e−05 0.004826 −0.03975 0.023049 UNC5H4 2% Oxygen desaturation events −4.11609 4.38e−05 −0.00554 −0.6197 0.023049 tPA 2% Oxygen desaturation events 4.069173 5.33e−05 0.005345 −0.80692 0.023049 UNC5H4 Low SaO2 4.941248 9.72e−07 1.222975 4.817064 0.001261 NovH Mean SaO2 −4.44758 1.01e−05 −2.50109 3.027134 0.013093 UNC5H4 Mean SaO2 4.267287 2.25e−05 3.88582 2.340392 0.014594 All associations were adjusted at 5% FDR. t is the empirical Bayes moderated t-statistic; B is the empirical Bayes log odds of differential expression. Open in new tab Proteins associated with low oxygen saturation indices ODI2% was fit as a function of protein measures adjusted for demographic variables, and three proteins were found to be differentially expressed (FDR p-value <0.05). Laminin and tPA were upregulated while UNC5H4 was downregulated (Table 3 and Supplementary Table S11). ODI3% was not associated with any proteins at 5% FDR (data not shown). Mean SaO2 levels during sleep was significantly associated (FDR p-value <0.05) with downregulated NovH (Protein NOV homolog) and upregulated UNC5H4 (Table 3 and Supplementary Table S12). Minimum SaO2 level during sleep was associated only with increased expression of UNC5H4 and trend to decreased tPA protein expression (Table 3 and Supplementary Table S13). Proteins associated with PAP intervention Paired analyses of pre- and postintervention samples from the intervention cohort who were treated with PAP showed that IGFBP-3 and BMP-1 (bone morphogenetic protein 1) were increased, while LEAP-1 (hepicidin) was decreased, postintervention (5% FDR, see Supplementary Table S14 for list of proteins). Moderate-to-severe apnea can be predicted by SomaScan protein panel Since we found a relatively large number of proteins (n = 65) differentially expressed and significantly associated with OAHI when modeled as continuous variable and additionally among all the stratified analyses (see above) the largest number of proteins was associated with moderate-to-severe apnea versus mild/control apnea, we capitalized on these associations to train a machine learning classifier on SomaScan protein measures in a test set (n = 549) to predict moderate-to-severe apnea (OAHI ≥15 was treated as case while OAHI <15 was treated as control or mild apnea) in an validation set (n = 164). Model 1 incorporating demographic variables and protein measures achieved 77.5% accuracy in classifying OAHI, model 2 (only SomaScan measures) achieved 76.1% accuracy, and model 3 (only demographic variables) achieved a lower accuracy of 68.3% (see Figure 2 for ROC and AUCs and Table 4 for classifier metrics and Supplementary Table S15 for confusion matrix). The sensitivity for detecting OAHI at least 15 status was 73.8% for model 1, 72.1% for model 2, and 65.1% for model 3. The specificity for identifying control status was 81.2% for model 1, 80.8% for model 2, and 71.6% for model 3. The corresponding negative predictive values (NPVs) were 74.7%, 72.4%, and 66.7% for models 1, 2, and 3, respectively. Figure 2. Open in new tabDownload slide The receiver operating curve (ROC) of an OAHI classifier to classify severe-to-moderate apnea (≥15/h). The training set included 549 individuals and untouched test set included 164 individuals. Three models were trained: model 1 incorporating demographic variables and SomaScan protein measures, model 2 trained only SomaScan protein measures, and model 3 that was trained only demographic variables (age, gender, and BMI). The y-axis represents the true positive fraction while the x-axis represents the false positive fraction. *Model 3 AUC was statistically significant when compared to model 1 and model 2 AUC (p < 0.05). Figure 2. Open in new tabDownload slide The receiver operating curve (ROC) of an OAHI classifier to classify severe-to-moderate apnea (≥15/h). The training set included 549 individuals and untouched test set included 164 individuals. Three models were trained: model 1 incorporating demographic variables and SomaScan protein measures, model 2 trained only SomaScan protein measures, and model 3 that was trained only demographic variables (age, gender, and BMI). The y-axis represents the true positive fraction while the x-axis represents the false positive fraction. *Model 3 AUC was statistically significant when compared to model 1 and model 2 AUC (p < 0.05). Table 4. Performance Metrics of a Machine Learning OAHI Classifiers (OAHI ≥15), the Model Was Trained on 549 Individuals and Validated in 164 Individuals (total individuals = 713) Model name . F1 score . Accuracy . Sensitivity . Specificity . PPV . NPV . AUC . Model 1 includes SomaScan proteins, age, gender, and BMI 0.77 0.775 0.738 0.812 0.805 0.747 0.84 Model 2 includes only SomaScan proteins 0.761 0.764 0.721 0.808 0.805 0.724 0.815 Model 3 includes only age, gender, and BMI 0.675 0.683 0.651 0.716 0.701 0.667 0.72 Model name . F1 score . Accuracy . Sensitivity . Specificity . PPV . NPV . AUC . Model 1 includes SomaScan proteins, age, gender, and BMI 0.77 0.775 0.738 0.812 0.805 0.747 0.84 Model 2 includes only SomaScan proteins 0.761 0.764 0.721 0.808 0.805 0.724 0.815 Model 3 includes only age, gender, and BMI 0.675 0.683 0.651 0.716 0.701 0.667 0.72 PPV, positive predictive value; NPV, negative predictive value; AUC, area under the curve. Open in new tab Table 4. Performance Metrics of a Machine Learning OAHI Classifiers (OAHI ≥15), the Model Was Trained on 549 Individuals and Validated in 164 Individuals (total individuals = 713) Model name . F1 score . Accuracy . Sensitivity . Specificity . PPV . NPV . AUC . Model 1 includes SomaScan proteins, age, gender, and BMI 0.77 0.775 0.738 0.812 0.805 0.747 0.84 Model 2 includes only SomaScan proteins 0.761 0.764 0.721 0.808 0.805 0.724 0.815 Model 3 includes only age, gender, and BMI 0.675 0.683 0.651 0.716 0.701 0.667 0.72 Model name . F1 score . Accuracy . Sensitivity . Specificity . PPV . NPV . AUC . Model 1 includes SomaScan proteins, age, gender, and BMI 0.77 0.775 0.738 0.812 0.805 0.747 0.84 Model 2 includes only SomaScan proteins 0.761 0.764 0.721 0.808 0.805 0.724 0.815 Model 3 includes only age, gender, and BMI 0.675 0.683 0.651 0.716 0.701 0.667 0.72 PPV, positive predictive value; NPV, negative predictive value; AUC, area under the curve. Open in new tab Proteins associated with age of serum samples The serum samples assayed in this study were collected and processed with a mean delay of 11.6 years (blood draw to assay). Although above results were carefully adjusted for sample age, we observed 535 proteins to have significant (FDR p-value <0.05) differential expression (464 proteins downregulated and 71 upregulated) when age of samples was fit as a function of the protein matrix. The full list of proteins associated with age of samples is given in Supplementary Table S16. Discussion To our knowledge, this is the first study to look at highly multiplexed protein biomarkers (1,300) at once in association with sleep apnea and oxygen desaturation indices utilizing an aptamer-based approach (SomaScan assay) in the Stanford sleep cohort of 713 individuals, a patient-based registry. The overall prevalence of OAHI in our cohort was 49.2% predominantly biased in men (67.5 %), while the overall CAI prevalence was relatively lower at 1.3%. We analyzed differential expression patterns in cross-sectional Stanford sleep cohort of 713 individuals as a function of OAHI that revealed 65 proteins to be dysregulated. On the other hand, CAI was associated with only two proteins including ROBO3, a protein involved in bilateral synchronization of the pre-Bötzinger complex and cystatin F. Further analysis of pre- and post-CPAP intervention in longitudinal cohort of 16 individuals was less revealing with three proteins that were differentially associated with active CPAP treatment: IGFBP3 and BMP-1 increased while LEAP1 (hepicidin) decreased with intervention. We found significant changes with OAHI in 65 proteins, notably, tPA, laminin, growth hormone receptor, IL-18 Ra, aminoacylase-1 (involved in oxidative stress), and LG3BP (adhesion molecule) were increased with apnea while IGFBP-1, carbonic anhydrase I, and UNC5H4 were inversely associated with moderate-to-severe apnea and observed to be decreased. Pathway analysis (FDR p-value <0.05) of DEPs in OAHI revealed overrepresentation of DEPs in complement and coagulation cascades, L1CAM and Laminin interactions, extracellular matrix and extracellular matrix-associated proteins, and extrinsic pathway of fibrin clot formation and hemostasis. These changes show that OAHI is associated with disturbances of multiple pathways, growth factors, cell adhesion factors, enzymes, notably with previous reports of increased coagulation including tPA being associated with apnea events [65, 66], which may play a role in the increased risk of stroke in these patients [67]. tPA is a serine protease found on endothelial cells that line blood vessels and functions to catalyze the conversion of plasminogen to plasmin. Plasminogen is the pro-enzyme of plasmin and is implicated in fibrin degradation in blood vessels [68]. tPA while being circadian dependent [69] has been linked to host of disorders. For instance, tPA increase has been associated with acute myocardial infarction [70, 71] and coronary artery disease [70] and while deficiencies in tpA production have been linked to deep vein thrombosis [72]. Interestingly, previous studies have found an accumulation of fibrin in patients with sleep apnea [73], while other investigators have found increased plasminogen activator inhibitor-1 protein levels in patients with sleep apnea [74]. In agreement our data indicate increased tPA levels in patients with high OAHI indices. Interestingly, growth hormone receptor and IGFBP-1 proteins were associated with moderate-to-severe apnea in our study, these associations could be attributed to disruptions in growth hormone pathway in which both IGFBP-1 and IGFBP-3 (associated with CPAP treatment in our study) are important conduits [75–77]. It is established that growth hormone is secreted predominantly during slow-wave sleep [78, 79], while OSA is associated with disruptions in slow-wave sleep [80–82]. Furthermore, decreased IGFBP-1 levels have been recognized as important predictors to development of glucose tolerance/diabetes [83–85] and increased cardiovascular risk. Our findings are consistent with these observations and establish dysregulation of insulin and growth hormone pathway associated proteins in apnea. We should however note that the incidence of diabetes was low (<5%) in our cohort, although this could be attributed to missing data. Other data on IGFBP-1 are sparse in the context of apnea, with one study suggesting no association with apnea nor CPAP treatment [86]. Additional studies are warranted to understand the role of these proteins in apnea. Taken together, recurrent episodes of hypoxia and sleep disruption dispose to a state of hypercoagulability and fibrin dysregulation, which in turn may promote the incidence of coronary events in patients with sleep apnea. The strong positive association of OAHI with IL18RA (IL18 receptor alpha, called IL18R1, 2q12) is particularly interesting considering the interleukin 18 receptor IL18RAP associated protein (IL18 beta chain) is one of two genome-wide significant findings reported for minimum oxyhemoglobin saturation (rs78136548) during sleep [87], a variable correlated with OAHI. IL18R1 and IL18RAP are located close to each other and rs78136548 and linked markers are an eQTL for both transcripts in various tissues, and for the nearby gene SLC9A4, a proton gene pump [87]. As the rs78136548 region linked with minimum oxygen desaturation in the Cade et al. study [87] regulates both subunits of the ILR18, it is plausible that the ILR18 is a causal pathway for sleep apnea. Problematically, however, ILR18RAP was also measured in this study and did not vary with OSA (data not shown). Notably, low oxygen saturation in this study was not significantly associated with IL18RA. Finally, Suhre et al. [50], using the same technology as this study, found significant cis pQTL but not trans QTL for these loci in blood. Additional studies of the IL18 pathway in OSA are thus warranted to follow-up on these findings. Proteomic signatures associated with CPAP treatment were much less pronounced, we found a cluster of three proteins that were associated with CPAP treatment. Among the three proteins, notably IGFBP-3 protein level was positively correlated with CPAP treatment, this finding is in line with other investigators who also found increased IGFBP-3 protein levels post-CPAP adherence using an ELISA assay [88]. BMP-1 protein was found to be increased in response to CPAP treatment, incidentally it is also associated with moderate-to-severe apnea in our cross-sectional cohort (Table 2), while it is recognized as a major player in tissue remodeling and repair [89], its role in relation to apnea remains to be elucidated. Strikingly, LEAP-1 (hepicidin) was decreased in response to CPAP treatment in the current study, hepicidin is recognized as a liver-derived protein that regulates iron homeostasis and also plays a key role as an antimicrobial agent preferentially inhibiting growth of fungal pathogens. This finding is in agreement with other reports of increased hepcidin in severe apnea [90], although we could not find any association with moderate-to-severe apnea in our cross-sectional cohort. Central apneas were associated with two proteins, ROBO3 and CYTF. ROBO3 is a critical protein for proper neural migration and axon guidance. At least 19 different mutations in the ROBO3 gene have been identified in people with horizontal gaze palsy with progressive scoliosis. This gene is required for hindbrain axon midline crossing [91]. Interestingly, ROBO3 has been shown to be a critical component of pre-Bötzinger complex (pre-BötC) responsible for pace inspiration. Inactivation of ROBO3 results in left–right de-synchronization of the pre-BötC oscillator, with asymmetric independence of left–right breathing activities and diaphragm contractions in Robo3 null mice, although rhythm generation is unaffected [92]. Although the role of ROBO3 post-development is unknown, it may be that increased ROBO3 levels in central apnea is a compensatory mechanism in response to repeated central apnea events that aim at strengthening the central pacemaker. Previous studies of ROBO3 have identified eQTLs for expression in various tissues, but not in blood using the aptamer technology [50]. Further investigations of the role of ROBO3 in the regulation of central sleep apnea are warranted. CYTF on the other hand is a cysteine protease inhibitor and is selectively enriched in immune cell subsets [93], and CYTF has been reported to have a strong affinity to cathepsin L that is implicated in normal lysomal mediated protein turnover. Interestingly, CYTF has been found to promote neovascularization after ischemia via cathepsin L [94]. It is unclear what role increased CYTF plays in central apneas but it is possible that CYTF promotes tissue repair, further studies are needed to understand this mechanism. Central apnea incidence was 1.9% in our cohort, this is a limitation that larger cohorts will need to address. Finding a robust blood biomarker for sleep apnea could be very useful in clinical practice. In this work, we used a regularized lasso model to classify moderate-to-severe apnea (OAHI ≥15/h) on 549 participants with validation in 164 additional participants. We found that training a machine learning classifier using only protein measures could achieve 76.1% accuracy (72.1% sensitivity) in classification of moderate-to-severe apnea (OAHI ≥15/h), while a classifier also incorporating demographic variables (age, gender, and BMI) performed with higher accuracy of 77.5%. In comparison, the STOP-Bang questionnaire [95] had higher sensitivity at 94% to detect moderate-to-severe OSA (AHI ≥15/h), but a specificity of 34% and an NPV of 75%. The Berlin questionnaire had 54% sensitivity, 97% specificity to classify moderate-to-severe OSA (AHI ≥15/h) [96]. Our classifier (model 2 using only protein measures) had 80.8% specificity with an NPV of 72.4%. This suggests that although there is modest and comparatively similar performance of the protein measures in classifying moderate-to-severe OSA (AHI ≥15/h) with questionnaire-based methods, larger studies are warranted. While the aptamer-based approach holds considerable promise in exploratory analysis of sleep disorders and can generate hypotheses that can be studied in greater detail, we note several limitations to our study. First, our serum samples were old, with mean blood draw to assay time being 11.6 years. Sample storage time (the samples were kept at −80ºC) correlated with many measures, and while we controlled for sample age in our analyses, ideally this type of analysis should be conducted on recent samples. Second, all samples were derived at a single sleep center. Replicating findings with samples from other cohorts would be needed to show generalization. Third, the protein expression patterns from the cross-sectional cohort (Stanford sleep cohort) may not be directly comparable to the longitudinal CPAP interventional cohort, this can be attributed to the fact that while Stanford sleep cohort was assayed using serum samples the CPAP cohort used plasma samples. Fourth, we did not have data on comorbidities such as stroke, dementia, mild cognitive impairment, and congestive heart failure in our cohort, these conditions could present as altered protein expression profiles. Finally, the study used an earlier SomaScan panel of proteins that included only 1,300 proteins, while a more recent panel includes 5,500 proteins. To summarize, this large proteomic analysis of sleep-disordered breathing identified differential protein expression patterns associated with obstructive respiratory events, oxygen desaturations, central apneas, and PAP treatment for OSA. Multiplex protein assays offer diagnostic potential and provide new insights into the biological basis of sleep-disordered breathing. Acknowledgments We are thankful to the individuals who consented to be studied as part of the Stanford sleep cohort, further we thank Jing Zhang at the Stanford Center for sleep science and medicine for sample biobanking. We thank the anonymous reviewers whose inputs have improved the manuscript. Funding The Stanford Sleep cohort was supported by unrestricted funds of Stanford Center for Sleep Sciences and Medicine and NIH 5R01HL071515. Longitudinal CPAP cohort study was supported by National Institutes of Health awards K23-NS089922, UL1RR024992 Sub-Award KL2-TR000450, and the Washington University Institute of Clinical and Translational Sciences grant UL1TR000448 from the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official view of the National Institutes of Health. SomaScan assays for the entire cohort were supported by Biomedical Research Program at Weill Cornell Medicine Qatar funded by Qatar Foundation. Disclosure Statement Nonfinancial disclosures: All authors report no disclosures, we certify that the submission is not under review at any other publication. The corresponding author, EM, takes full responsibility for the data, the analyses and interpretation, and the conduct of the research. 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Filon, Mikolaj, J;Wallace,, Eli;Wright,, Samantha;Douglas, Dylan, J;Steinberg, Lauren, I;Verkuilen, Carissa, L;Westmark, Pamela, R;Maganti, Rama, K;Westmark, Cara, J

doi: 10.1093/sleep/zsaa087pmid: 32369586

Abstract Study Objectives Accumulating evidence suggests a strong association between sleep, amyloid-beta (Aβ) deposition, and Alzheimer’s disease (AD). We sought to determine if (1) deficits in rest-activity rhythms and sleep are significant phenotypes in J20 AD mice, (2) metabotropic glutamate receptor 5 inhibitors (mGluR5) could rescue deficits in rest-activity rhythms and sleep, and (3) Aβ levels are responsive to treatment with mGluR5 inhibitors. Methods Diurnal rest-activity levels were measured by actigraphy and sleep-wake patterns by electroencephalography, while animals were chronically treated with mGluR5 inhibitors. Behavioral tests were performed, and Aβ levels measured in brain lysates. Results J20 mice exhibited a 4.5-h delay in the acrophase of activity levels compared to wild-type littermates and spent less time in rapid eye movement (REM) sleep during the second half of the light period. J20 mice also exhibited decreased non-rapid eye movement (NREM) delta power but increased NREM sigma power. The mGluR5 inhibitor CTEP rescued the REM sleep deficit and improved NREM delta and sigma power but did not correct rest-activity rhythms. No statistically significant differences were observed in Aβ levels, rotarod performance, or the passive avoidance task following chronic mGluR5 inhibitor treatment. Conclusions J20 mice have disruptions in rest-activity rhythms and reduced homeostatic sleep pressure (reduced NREM delta power). NREM delta power was increased following treatment with a mGluR5 inhibitor. Drug bioavailability was poor. Further work is necessary to determine if mGluR5 is a viable target for treating sleep phenotypes in AD. actigraphy, Alzheimer’s disease, CTEP, EEG, fenobam, J20 mice, mGluR5, sleep Statement of Significance Sleep disruption is emerging as an important risk factor as well as the phenotype of neurological diseases including Alzheimer’s disease. This study is novel in determining alterations in the rest-activity rhythm and sleep-wake pattern of J20 Alzheimer’s disease mice and wild-type littermates. Specifically, there is a delay in acrophase with prolonged hyperactivity during the dark cycle and reduced sleep pressure that was improved by treatment with mGluR5 inhibitor. Critical remaining knowledge gaps and future directions include testing the effects of Alzheimer’s disease drugs on the rescue of sleep and rest-activity patterns in other Alzheimer’s disease models. These studies are relevant to human Alzheimer’s disease as monitoring sleep phenotypes may predict disease risk, and therapies that normalize sleep patterns may slow progression. Introduction Alzheimer’s disease (AD) is the sixth most common cause of death in the United States, afflicting approximately 5.4 million Americans, and presents a tremendous emotional and financial hardship on patients and caregivers. AD is a progressive form of dementia characterized histologically by amyloid-beta (Aβ) plaques, neurofibrillary tangles, and neuronal cell death. In a small percentage of cases, AD is directly associated with specific genetic mutations in amyloid-beta protein precursor (AβPP) (chromosome 21), presenilin 1 (chromosome 14), or presenilin 2 (chromosome 1); however, in the vast majority of cases, the cause of the disease is unknown. Patients experience memory loss, impaired judgment, cognitive dysfunction, the inability to perform everyday tasks, and behavioral problems. There are currently no cures for AD, which provides a strong impetus to discover novel therapeutic strategies for treatment and improved outcome measures to bridge preclinical and clinical research. Deterioration of rest-activity cycles is a progressive phenotype in patients with AD in whom reported sleep disturbances include increased nocturnal awakenings, decreased duration of rapid eye movement (REM) sleep, and diminished slow-wave sleep [1–5]. There is now evidence that rest-activity rhythm fragmentation and sleep disturbances may precede the onset of AD and drive disease pathology [6, 7]. Restlessness, agitation, irritability, and/or confusion worsen in the late afternoon and evening and last into the night with less pronounced symptoms earlier in the day. Thus, we asked if an AD mouse model exhibited altered diurnal rest-activity patterns as determined by actigraphy. We assessed electroencephalogram (EEG)-based sleep-wake patterns to examine correlations between actigraphy and EEG readouts. And, we determined whether any aberration in AD mice could be rescued by modulation of metabotropic glutamate receptor 5 (mGluR5) signaling. Two classes of drugs, cholinesterase inhibitors (donepezil, rivastigmine, and galantamine) and NMDA receptor antagonists (memantine), are currently approved by the FDA to treat cognitive symptoms of AD. These drugs act on healthy neurons to compensate for lost acetylcholine activity or modulate NMDA receptor activity, respectively. They improve the cognitive ability for a year or less but do not reduce Aβ or neurofibrillary tangle accumulation and subsequent disease progression. Aβ immunotherapy has proven to be very effective in reducing soluble Aβ, amyloid plaque, and soluble tau as well as associated cognitive decline; however, there are questions about safety and it is only experimental at this point [8–11]. An alternative, viable therapeutic target for the treatment of AD may be mGluR5 inhibitors. There is a strong rationale for studying mGluR5 inhibitors in AD models. mGluR5 is a glutamate-activated, G-protein-coupled receptor widely expressed in the central nervous system and clinically investigated as a drug target for a range of indications including depression, Parkinson’s disease, and fragile X syndrome (FXS). Amyloid protein precursor (APP) synthesis is regulated through a mGluR5-dependent signaling pathway [12, 13]. The knockout of mGluR5 in APPSWE/PS1ΔE9 AD mice reduces spatial learning deficits, Aβ oligomer formation, and Aβ plaque number [14]. Treatment with mGluR5 inhibitors reduces APP and Aβ levels and improves memory and cognitive function in mouse models of AD [15–17]. Herein, we test the effects of mGluR5 inhibition on rest-activity rhythms, sleep, locomotor ability, learning and memory, and Aβ levels in J20 mice. The J20 mouse model is an established rodent model for the study of AD that expresses the human amyloid protein precursor (hAPP) gene containing both the Swedish and Indiana familial mutations. J20 mice exhibit greatly exacerbated Aβ production and cognitive deficits [18]. The inclusion of flanking sequences in the transgenic construct is expected to affect posttranscriptional regulation of the APP gene and more closely mimic normal temporal and spatial expression of APP and metabolites [19]. Herein, we show that J20 mice exhibited a pronounced 4.5-h shift in acrophase (peak activity levels) during the dark phase of the diurnal cycle and reduced sleep homeostatic pressure as measured by non-rapid eye movement (NREM) delta power. Treatment with mGluR5 inhibitors did not change rest-activity rhythms but CTEP (2-chloro-4-((2,5-dimethyl-1-(4-(trifluoromethoxy)phenyl)-1H-imidazol-4-yl)ethynyl)pyridine) improved NREM delta power with the caveat that therapeutic levels of CTEP were not reached. Methods Mouse husbandry The J20 (B6.Cg-Tg[PDGFB-APPSwInd]20Lms/2Mmjax) mouse model of AD expresses a mutant version of hAPP carrying both the Swedish (K670N/M671L) and the Indiana (V717F) mutations directed by the human PDGFB promoter. Hemizygous male J20 mice were purchased from Jackson Laboratories (catalog #006293) and mated with C57BL/6J female mice (Jackson Laboratories, catalog #000664) to generate J20 and wild-type (WT) littermates. Mice were group-housed in microisolator cages on a 06:00 am–06:00 pm light cycle with ad libitum access to food (Teklad 2019) and water. Mouse ages and treatments for specific experiments are defined in the figure legends. The bedding (Shepherd’s Cob + Plus, ¼ inch cob) contained nesting material as the only source of environmental enrichment. Drug dosing and testing were conducted in multiple seasons. All animal husbandry and euthanasia procedures were performed in accordance with the National Institutes of Health and an approved University of Wisconsin–Madison IACUC animal care protocol. J20 genotypes were determined by PCR analysis of DNA extracted from tail biopsies with HotStarTaq polymerase (Qiagen, catalog #203205) and Jackson Laboratories’ primer sequences oIMR2044 (transgene forward; 5′-GGT GAG TTT GTA AGT GAT GCC-3′) and oIMR2045 (transgene reverse; 5′-TCT TCT TCT TCC ACC TCA GC-3′) targeted at the APPSW/IND transgene (360 base pair [bp] PCR product) and oIMR8744 (internal positive control forward; 5′-CAA ATG TTG CTT GTC TGG TG-3′) and oIMR8745 (internal positive control reverse; 5′-GTC AGT CGA GTG CAC AGT TT-3′), which produce an internal positive control PCR product of 200 bp. J20 mice exhibited a premature mortality phenotype (Supplementary Figure S1), which is consistent with prior studies in Tg2576 [20]. Drug preparation and chronic dosing Method 1, adult mice. The drugs fenobam (gift from FRAXA Research Foundation), 37.5 mg, and CTEP (2-chloro-4-((2,5-dimethyl-1-(4-(trifluoromethoxy)phenyl)-1H-imidazol-4-yl)ethynyl)pyridine) (MedChem Express, catalog #HY-15445), 5 mg, were transferred into an IKA Ball-Mill Tube BMT-20-S containing 10 stainless steel balls with 7.5 mL 0.9% NaCl, 0.3% Tween-80. Drug and vehicle were mixed on low velocity for 1 min and high-velocity setting #9 for 5 min. Additional vehicle (7.5 mL) was added and the drug was mixed on high-velocity setting #9 for 5 min. Fenobam stock was 2.5 mg/mL. CTEP was further diluted with 9.75 mL vehicle resulting in final drug concentrations of 0.2 mg/mL CTEP. Vehicle and drugs were frozen in single-use aliquots at −20°C. Mice were dosed once daily with fenobam and once every other day with CTEP at 400 µL per 40 g body weight by oral gavage with 22g 1.4-inch feeding needles with ball ends (Kent Scientific, catalog #FNC-22-1.5). Final drug concentrations were 24 mg/kg fenobam and 2 mg/kg CTEP. Mice were typically dosed midway through the light cycle (11:00 am–01:00 pm). Method 2, aged mice. CTEP (10 mg) was dissolved in 200 µL DMSO and aliquots at 50 mg/mL were frozen at −20°C. On the day of use, 40 µL of CTEP was mixed with vehicle (1 wt% Hypromellose [HPMC], Sigma catalog #H3785 and 1 wt% Tween-80) in an IKA Ball-Mill Tube BMT-20-S containing 10 stainless steel balls as described above to a final concentration of 0.2 mg/mL CTEP. Mice were dosed at 2 mg/kg by oral gavage. Neuroassessment Mice underwent an abbreviated Irwin murine neurobehavioral screen, including weekly weight measurements, at the beginning and end of the drug dosing regimen (Supplementary Tables S1 and S2) [21–23]. Actigraphy Rest-activity rhythms were assessed under standard lighting conditions in home-made Plexiglas chambers containing passive infrared sensors mounted on the underside of the lids [24, 25]. The dimensions of the transparent cylindrical Plexiglas chambers were 6-inch diameter × 10-inch height. Mice were individually housed during actigraphy with access to food and water. Each gross movement of the animal was recorded as an activity count with VitalView acquisition software (Minimitter Inc., Bend, OR). Activity counts were binned in 60-s epochs and scored on an activity scale (0–50) over a 3- to 9-day period. Data were analyzed with ACTIVIEW Biological Rhythm Analysis software (Mini Mitter Company, Inc.). A chi-square periodogram method was used to determine the diurnal rest-activity period. EEG sleep analysis Mice (age 11–12 months old) were recorded in sleep-wake patterns using electroencephalographic monitoring. For EEG electrode implantation surgery (day 1), anesthesia was induced with 5% isoflurane and maintained at 1%–2% in oxygen flowing at 0.5–1 L/min. Three stainless steel epidural screws were placed as electrodes with two screws over the frontal (Bregma +1.5 mm and +1 mm laterally) and parietal cortex (Bregma −3 mm and −1 mm laterally) and one occipital reference (lambda −1 mm at midline). Two stainless steel wire electrodes were placed in the nuchal muscles for electromyography (EMG) recording. The EEG and EMG electrodes were connected to a head cap and secured with dental acrylic. Standard analgesia was administered per local IACUC recommendations. Mice were allowed to recover from the surgery (days 2 and 3, singly housed) prior to transfer to the individual, tethered EEG acquisition chambers and dosing with CTEP (days 4, 6, 8, and 10). EEG recordings and analyses have been previously described [25, 26]. Recordings were acquired days 8–12 on an XLTEK machine (Natus, Madison, WI) with a 512 Hz sampling rate, and the three full days of recordings (days 9–11) were used for the analysis (n = 3–4 mice/treatment cohort). EEG recordings were manually scored in 4-s epochs for REM, NREM, and awake vigilance states with Sirenia Sleep software v.2.0.4 by scorers blinded with respect to treatment group. Waking epochs were identified as those with high EMG amplitude (Supplementary Figure S2A). Epochs with relatively quiescent EMG were scored as sleep. Specific sleep states were differentiated based on predominant EEG power such that NREM was associated with high amplitude delta (1–4 Hz, Supplementary Figure S2B) and REM was associated with low amplitude theta (5–7 Hz, Supplementary Figure S2C) activity. NREM EEG power among the different treatment groups was analyzed. Power-spectral density was computed for each 4-s segment of 60 Hz notch-filtered EEG using a Fast-Fourier transform, from which power was calculated as integrals of frequencies in delta (0.5–4 Hz), theta (5–9 Hz), sigma (10–14 Hz), and gamma (25–100 Hz) frequency bands. To account for inter-animal variability, EEG power values were normalized to the summed power of delta, theta, sigma, and gamma bands. Records were divided into 2-h bins and the EEG power of NREM epochs within each bin was grouped. Rotarod The mice were acclimated to the test room for at least 20 min prior to testing on a Rotarod Treadmill (Med Associates Inc., Vermont). The rotarod was set to a speed setting of 9, which accelerates from 4.0 to 40 rpm over 5 min. Mice were placed on the rotarod and the latency time to when the mouse fell off was recorded. If a mouse made two complete turns hanging onto the grip bar without actively walking/running, the mouse was counted as falling off of the beam. If more than 300 s elapsed, the mouse was removed from the beam. Experiments entailed four trials on day 1 and 2 trials on day 2. Passive avoidance Mice were acclimated to the experimental room for at least 20 min prior to testing in a footshock passive avoidance paradigm using an aversive stimulator/scrambler (Med Associates Inc.). A bench-top lamp was turned on behind the center of a light/dark shuttle box and aimed toward the back-left corner away from the dark side of the shuttle box. The power supply on the shock grid was set at 0.6 mA. On the training day, a mouse was placed in the light side of the shuttle box toward the back corner away from the opening to the dark side of the shuttle box. The trap door in the shuttle box was open. After the mouse crossed over to the dark side, the trap door was closed and the latency time for the mouse to move from the light to the dark side was recorded. The mouse was allowed to equilibrate in the dark side for 5 s before receiving a 2-s 0.6 mA footshock. After 15 s, the mouse was removed from the shuttle box and returned to its home cage. The apparatus was cleaned with 70% EtOH between animals. At test times (6, 24, and 48 h after training), the mouse was placed in the light side of the shuttle box facing the left rear corner away from the opening to the dark side with the trap door open. After the mouse crossed to the dark side, the trap door was closed and the latency time for the mouse to move from the light to the dark side was recorded. If the mouse did not move to the dark side within 300 s, it was gently guided to the dark side and the trap door was closed. The mouse was allowed to equilibrate to the dark side for 5 s before return to the home cage. Mice only received one shock on the training day. Testing at 24 and 48 h measured extinction. Tissue collection Mice were treated with isoflurane for 1 min and blood collected from the abdominal artery with a 21G × ¾ inch × 12 inch vacutainer blood collection set (Becton Dickinson, catalog #367296). The blood was immediately mixed with sodium heparin (20 µL of 10 mg/mL; Sigma #H3393). Brain tissues (hippocampus, cerebellum, and right and left cortices) were dissected and quickly frozen on dry ice. Tissue was collected to confirm genotype analyses. The heparinized blood was spun for 10 min at 5,000 rpm at room temperature and the upper plasma layer was transferred to a 1.5 mL Eppendorf tube, frozen on dry ice, and stored at −80°C. Pharmacokinetics Plasma and brain left cortices were shipped to Tandem Labs (Durham, NC) for the detection of CTEP levels by mass spectrometry analysis. Study samples were analyzed using standards prepared in sodium heparin mouse plasma. The method calibration range was 0.500–10,000 ng/mL using two different transitions for CTEP. The C13 peak for CTEP was used for the top 5 calibration points and the C12 peak for CTEP was used for the bottom 5 points of the curve. CTEP and fenobam stock solutions for the calibration curve and internal standard, respectively, were prepared at 1 mg/mL in 50:50 water:acetonitrile. Brain lysates and Aβ ELISA Diethylamine (DEA) protein extraction buffer (20 µL DEA [0.2% final; Fisher catalog #A11716], 0.5 mL 1M NaCl [50 mM final], 2 mL 10× protease inhibitor cocktail [RPI catalog #P50600] in a 10 mL final volume) was chilled in ice. Tissue to be homogenized (right cortex of the brain) was transferred to a Dounce glass-glass homogenizer with 5 volumes ice-cold DEA protein extraction buffer (1 mL per 200 mg tissue) and homogenized with 35 strokes. Lysates were spun at 20,000g for 30 min at 4°C. The cleared supernatant was removed and neutralized with 1/10 volume 0.5M Tris, pH 6.8. Aliquots were quick-frozen at −80°C and protein concentrations quantitated by the BCA Assay (Pierce, catalog #23235) per the manufacturer’s instructions. Aβ 1–40 and Aβ 1–42 levels were quantitated with Wako Human/Rat Aβ40 (catalog #294–64701) and Wako Human/Rat Aβ 1–42 high sensitivity (catalog #292–64501) ELISA kits per the manufacturer instructions. Plasma samples were diluted fourfold with standard diluent buffer containing a protease inhibitor cocktail to disrupt interactions between Aβ with masking proteins. Brain samples were diluted 1:50 (WT) and 1:500 (J20) with standard diluent. Antibody-coated plates were incubated with standards and samples overnight at 4°C. Statistical methods Statistical significance for peak acrophase comparing two genotypes by three seasons was determined by two-way ANOVA using GraphPad Prism v8.3.0 for Mac OS X (GraphPad Software, San Diego, CA) (Table 1). Statistical significance for peak acrophase and ELISAs comparing two genotypes by two treatments was determined by two-way ANOVA using GraphPad Prism v8.3.0 for Mac OS X (GraphPad Software, San Diego, CA) followed by two-sided t-tests using Excel v16.21 software (Supplementary Table S4). Statistical analyses for EEG-based experiments utilized Matlab software (The MathWorks, Inc., Natick, MA) utilizing ANOVAN and multcompare with custom parsing scripts (Supplementary Tables S6–S9). EEG power analysis compared normalized power of delta, theta, sigma, and gamma frequency bands of NREM sleep bouts as determined by manual scoring. As with sleep scoring, all frequency bands were calculated from 4-s epochs of 60 Hz notch-filtered EEG and grouped within 2-h segments across the light cycle. Sleep efficiency was assessed as percent-time in each vigilance state (i.e. wake, NREM, and REM) in four 6-h bins. Both were evaluated using a mixed-model N-way ANOVA using group (i.e. WT-Vehicle, J20-CTEP, etc.) as a fixed-effect variable and time as a random-effect variable. Multiple comparisons were completed with Tukey–Kramer post hoc tests. Each group included in ANOVA analysis was tested for skewness as computed with the skew function in Matlab and satisfied normality with values less than |2|. Statistical analyses were conducted with an α value of 0.05. Cohort sizes varied from n = 6–20 for the actigraphy experiments to n = 3–4 for the EEG and are specified in the figure legends. Means and SEM or 95% confidence intervals are graphed. Table 1. Mouse Cohorts for Actigraphy Experiments Experiment . WT . . . J20 . . . t-test (p) . . N . Age (days) . Peak acrophase (min) . N . Age (days) . Peak acrophase (min) . . A: fall 15 231 ± 8 836 ± 82 13 232 ± 8 1,081 ± 143 <0.000006 B: winter 20 248 ± 13 941 ± 142 19 244 ± 13 1,041 ± 121 <0.01 C: spring 12 260 ± 5 859 ± 117 8 259 ± 7 1,096 ± 104 <0.0003 Experiment . WT . . . J20 . . . t-test (p) . . N . Age (days) . Peak acrophase (min) . N . Age (days) . Peak acrophase (min) . . A: fall 15 231 ± 8 836 ± 82 13 232 ± 8 1,081 ± 143 <0.000006 B: winter 20 248 ± 13 941 ± 142 19 244 ± 13 1,041 ± 121 <0.01 C: spring 12 260 ± 5 859 ± 117 8 259 ± 7 1,096 ± 104 <0.0003 Three cohorts of wild-type (WT) and J20 littermate mice were tested by actigraphy during various seasons (set A: fall, set B: winter, set C: spring). A minimum of eight mice were tested per cohort. Average age of the mice was 8 months old (presented in days ± SD). Average peak acrophase is the peak activity time in minutes from Zeitgeber time zero ± SD. Two-way ANOVA based on season and genotype: interaction F(2,81) = 3.56, p = 0.033; season F(2,81) = 0.57, p = 0.57; genotype, F(1,81) = 49.8, p < 0.0001. One-way ANOVA for WT cohorts as a function of season F(2,44) = 3.73, p = 0.032. One-way ANOVA for J20 cohorts as a function of season: F(2,37) = 0.68, p = 0.51. Post hoc t-tests for WT: fall versus winter p = 0.015, fall versus spring p = 0.55, winter versus spring p = 0.10. Open in new tab Table 1. Mouse Cohorts for Actigraphy Experiments Experiment . WT . . . J20 . . . t-test (p) . . N . Age (days) . Peak acrophase (min) . N . Age (days) . Peak acrophase (min) . . A: fall 15 231 ± 8 836 ± 82 13 232 ± 8 1,081 ± 143 <0.000006 B: winter 20 248 ± 13 941 ± 142 19 244 ± 13 1,041 ± 121 <0.01 C: spring 12 260 ± 5 859 ± 117 8 259 ± 7 1,096 ± 104 <0.0003 Experiment . WT . . . J20 . . . t-test (p) . . N . Age (days) . Peak acrophase (min) . N . Age (days) . Peak acrophase (min) . . A: fall 15 231 ± 8 836 ± 82 13 232 ± 8 1,081 ± 143 <0.000006 B: winter 20 248 ± 13 941 ± 142 19 244 ± 13 1,041 ± 121 <0.01 C: spring 12 260 ± 5 859 ± 117 8 259 ± 7 1,096 ± 104 <0.0003 Three cohorts of wild-type (WT) and J20 littermate mice were tested by actigraphy during various seasons (set A: fall, set B: winter, set C: spring). A minimum of eight mice were tested per cohort. Average age of the mice was 8 months old (presented in days ± SD). Average peak acrophase is the peak activity time in minutes from Zeitgeber time zero ± SD. Two-way ANOVA based on season and genotype: interaction F(2,81) = 3.56, p = 0.033; season F(2,81) = 0.57, p = 0.57; genotype, F(1,81) = 49.8, p < 0.0001. One-way ANOVA for WT cohorts as a function of season F(2,44) = 3.73, p = 0.032. One-way ANOVA for J20 cohorts as a function of season: F(2,37) = 0.68, p = 0.51. Post hoc t-tests for WT: fall versus winter p = 0.015, fall versus spring p = 0.55, winter versus spring p = 0.10. Open in new tab Results Rest-activity rhythms are disrupted in J20 mice Three independent experiments were performed on cohorts of WT and J20 littermate mice at 8 months of age to assess the effect of genotype on rest-activity patterns (Figure 1, Table 1). The experiments were conducted in different seasons (fall, winter, and spring). There was a highly reproducible delay in peak acrophase during the dark cycle in the J20 mice irrespective of the season. Specifically, WT mice exhibited peak activity between 07:00 pm and 08:00 pm, and J20 exhibited peak activity between 11:00 pm and 12:00 am resulting in an approximately 4.5-h delay in acrophase in J20 mice. The findings were consistent in three independent sets of data that compared testing during the fall, winter, and spring, albeit the differences were the most pronounced during the spring followed by the fall and winter. On periodograms, the diurnal period was similar between WT (24.3 ± 0.8; n = 8) and J20 (24.5 ± 0.9; n = 11) mice (p = 0.63). Genotype did not have a significant effect on total daytime activity levels or habituation to the novel actigraphy chambers (Supplementary Figure S3 and Supplementary Table S3). Figure 1. Open in new tabDownload slide J20 mice exhibit delayed acrophase during the dark cycle. Activity counts on day 1 in the actigraphy chambers were assessed in three separate cohorts of wild-type (WT) (blue) and J20 (orange) 8-month-old mice (A = fall, B = winter, C = spring). Total activity counts (binned in 1 min increments) were averaged for cohorts and plotted on the y-axis versus a 24-h time period (in minutes) on the x-axis. Time zero is “Lights On.” (A) Cohort 1 consists of WT (n = 15) and J20 (n = 13). (B) Cohort 2 consists of WT (n = 20) and J20 (n = 19). (C) Cohort 3 consists of WT (n = 12) and J20 (n = 8). Figure 1. Open in new tabDownload slide J20 mice exhibit delayed acrophase during the dark cycle. Activity counts on day 1 in the actigraphy chambers were assessed in three separate cohorts of wild-type (WT) (blue) and J20 (orange) 8-month-old mice (A = fall, B = winter, C = spring). Total activity counts (binned in 1 min increments) were averaged for cohorts and plotted on the y-axis versus a 24-h time period (in minutes) on the x-axis. Time zero is “Lights On.” (A) Cohort 1 consists of WT (n = 15) and J20 (n = 13). (B) Cohort 2 consists of WT (n = 20) and J20 (n = 19). (C) Cohort 3 consists of WT (n = 12) and J20 (n = 8). mGluR5 Inhibitors do not restore rest-activity rhythms in J20 mice We then tested whether the mGluR5 inhibitors fenobam and CTEP could restore typical rest-activity rhythms. We also performed a behavioral battery along with the actigraphy (Supplementary Figure S4). Mice underwent a pretreatment evaluation of general fitness and grip strength as previously described [21] as well as weekly assessments throughout dosing (Supplementary Table S1 [fenobam] and Supplementary Table S2 [CTEP]). The mGluR5 inhibitors were administered by oral gavage either daily (fenobam) or every other day (CTEP). Neither WT nor J20 mice exhibited alterations in general fitness resulting from treatment. No differences were seen in rest-activity patterns with fenobam (Figure 2). Peak acrophase in the WT and J20 cohorts was similar to the data for the untreated mice (Table 1; Supplementary Table S4). Likewise, CTEP did not alter peak acrophase in WT mice (Figure 3; Supplementary Table S4); however, the stress of chronic injections likely muted the difference between WT and J20 mice (Figure 3, C), which was restored with CTEP (Figure 3, D). We also tested CTEP in aged WT and J20 mice (16- to 19-month-old mice). Neither genotype nor CTEP statistically altered peak acrophase in aged J20 mice (Supplementary Figure S5 and Supplementary Table S4). The average total daily activity counts were not statistically different between WT and J20 mice irrespective of treatment, albeit there were trends for increased activity counts in the J20 mice (Supplementary Table S5). It should be noted that studies in the J20 mice represent the survivors as there was a premature mortality phenotype in the animals (Supplementary Figure S1). Overall, chronic dosing with fenobam and CTEP did not rescue altered rest-activity rhythms in J20 mice. Figure 2. Open in new tabDownload slide Diurnal activity levels in wild-type (WT) and J20 mice in response to fenobam. Activity counts were assessed in 8-month-old WT and J20 mice after chronic treatment with vehicle or fenobam. Total activity counts (binned in 1 min increments) were averaged over 3–4 days of readings for cohorts and plotted on the y-axis versus a 24-h time period (in minutes). Time zero is “Lights On.” Cohorts consist of WT mice treated with vehicle (n = 7), J20 treated with vehicle (n = 7), WT treated with fenobam (n = 8), and J20 treated with fenobam (n = 6). (A) vehicle-treated WT (blue) versus J20 (orange). (B) WT mice treated with vehicle (blue) versus fenobam (red). (C) J20 mice treated with vehicle (orange) versus fenobam (green). Mice for these cohorts were tested in fall and winter. Figure 2. Open in new tabDownload slide Diurnal activity levels in wild-type (WT) and J20 mice in response to fenobam. Activity counts were assessed in 8-month-old WT and J20 mice after chronic treatment with vehicle or fenobam. Total activity counts (binned in 1 min increments) were averaged over 3–4 days of readings for cohorts and plotted on the y-axis versus a 24-h time period (in minutes). Time zero is “Lights On.” Cohorts consist of WT mice treated with vehicle (n = 7), J20 treated with vehicle (n = 7), WT treated with fenobam (n = 8), and J20 treated with fenobam (n = 6). (A) vehicle-treated WT (blue) versus J20 (orange). (B) WT mice treated with vehicle (blue) versus fenobam (red). (C) J20 mice treated with vehicle (orange) versus fenobam (green). Mice for these cohorts were tested in fall and winter. Figure 3. Open in new tabDownload slide Diurnal activity levels in wild-type (WT) and J20 mice in response to CTEP (2-chloro-4-((2,5-dimethyl-1-(4-(trifluoromethoxy)phenyl)-1H-imidazol-4-yl)ethynyl)pyridine). Activity counts were assessed in 9-month-old WT and J20 mice after chronic treatment with vehicle or CTEP. Total activity counts (binned in 1 min increments) were averaged over 2–4 days of readings for cohorts and plotted on the y-axis versus a 24-h time period (in minutes). Time zero is “Lights On.” Cohorts consist of WT mice treated with vehicle (n = 14, blue), J20 treated with vehicle (n = 9, orange), WT treated with CTEP (n = 12, red), and J20 treated with CTEP (n = 12, green). (A) WT mice treated with vehicle (blue) versus CTEP (red). (B) J20 mice treated with vehicle (orange) versus CTEP (green). (C) Vehicle-treated WT (blue) versus J20 (orange). (D) CTEP-treated WT (red) versus J20 (green). Mice for these cohorts were tested in winter and spring. Figure 3. Open in new tabDownload slide Diurnal activity levels in wild-type (WT) and J20 mice in response to CTEP (2-chloro-4-((2,5-dimethyl-1-(4-(trifluoromethoxy)phenyl)-1H-imidazol-4-yl)ethynyl)pyridine). Activity counts were assessed in 9-month-old WT and J20 mice after chronic treatment with vehicle or CTEP. Total activity counts (binned in 1 min increments) were averaged over 2–4 days of readings for cohorts and plotted on the y-axis versus a 24-h time period (in minutes). Time zero is “Lights On.” Cohorts consist of WT mice treated with vehicle (n = 14, blue), J20 treated with vehicle (n = 9, orange), WT treated with CTEP (n = 12, red), and J20 treated with CTEP (n = 12, green). (A) WT mice treated with vehicle (blue) versus CTEP (red). (B) J20 mice treated with vehicle (orange) versus CTEP (green). (C) Vehicle-treated WT (blue) versus J20 (orange). (D) CTEP-treated WT (red) versus J20 (green). Mice for these cohorts were tested in winter and spring. Analysis of sleep-wake patterns To quantify the effects of CTEP administration on sleep patterns, EEG was assessed in WT and J20 mice (Figure 4; Supplementary Figure S6). First, 6-h binned mixed-effect ANOVA analyses showed a main effect of time in percent time spent for each vigilance state (Supplementary Table S6), suggesting significant oscillation across the light–dark cycle. There was also a main effect of treatment/genotype group in percent awake time (Supplementary Table S6). Multiple comparisons of all interactions (time × group) revealed a significant reduction in an estimated marginal mean of time spent in REM sleep by vehicle-treated J20 (5.3% ± 0.5%) relative to vehicle-treated WT (9.0% ± 0.6%) mice during the second half of the light period, a difference not seen when J20 were treated with mGluR5 inhibitor (Figure 4; Supplementary Table S7). Figure 4. Open in new tabDownload slide Manually scored sleep based on EEG recordings from wild-type (WT) and J20 mice with and without CTEP. Data were separated into four 6-h bins, starting at Zeitgeber time zero, lights on. The lighting condition is annotated by the bar below the graphs: open: lights on and closed: lights off. Percent time in (A) waking, and (B) NREM and (C) REM sleep are presented as marginal means ± 95% confidence interval (95% CI) for each treatment group: WT treated with vehicle (n = 3 mice for 3 days, blue), WT treated with CTEP (2-chloro-4-((2,5-dimethyl-1-(4-(trifluoromethoxy)phenyl)-1H-imidazol-4-yl)ethynyl)pyridine) (n = 4 mice for 3 days, red), J20 treated with vehicle (n = 4 mice for 3 days, orange), and J20 treated with CTEP (n = 3 mice for 3 days, green). Nonoverlapping 95% CI bars indicate a significant difference (p < 0.05). Figure 4. Open in new tabDownload slide Manually scored sleep based on EEG recordings from wild-type (WT) and J20 mice with and without CTEP. Data were separated into four 6-h bins, starting at Zeitgeber time zero, lights on. The lighting condition is annotated by the bar below the graphs: open: lights on and closed: lights off. Percent time in (A) waking, and (B) NREM and (C) REM sleep are presented as marginal means ± 95% confidence interval (95% CI) for each treatment group: WT treated with vehicle (n = 3 mice for 3 days, blue), WT treated with CTEP (2-chloro-4-((2,5-dimethyl-1-(4-(trifluoromethoxy)phenyl)-1H-imidazol-4-yl)ethynyl)pyridine) (n = 4 mice for 3 days, red), J20 treated with vehicle (n = 4 mice for 3 days, orange), and J20 treated with CTEP (n = 3 mice for 3 days, green). Nonoverlapping 95% CI bars indicate a significant difference (p < 0.05). We then analyzed NREM EEG power among the treatment groups. The oscillation magnitude of EEG delta power in vehicle-treated J20 mice was significantly reduced compared to WT animals treated with vehicle, and there was a delayed dark phase rise, which was similar to the delay in acrophase determined by actigraphy (Figure 5; Supplementary Tables S8 and S9). Of note, treatment with CTEP in J20 mice resulted in a significant increase in delta power, though not to that of WT animals. CTEP appeared to have the opposite effect in WT mice where the overall and oscillation of NREM delta power were reduced compared to vehicle-treated WT animals. Conversely, vehicle-treated J20 mice exhibited consistently increased NREM EEG power in theta and sigma frequency bands (Figure 5, B and C), which was moderately reduced in CTEP-treated J20 animals and increased in CTEP-treated WT animals. Gamma power of NREM sleep showed less consistent differences between vehicle-treated WT and J20 animals (Figure 5, B and C; Supplementary Tables S8 and S9). WT animals showed significantly greater oscillation of NREM gamma power across the day (Figure 5, D; Supplementary Tables S8 and S9). Treatment with CTEP reduced overall NREM gamma power for both genotypes, though the effect was more pronounced in WT animals. Figure 5. Open in new tabDownload slide Power spectra of NREM sleep. Electroencephalographic power spectra of 4-s NREM sleep epochs were determined with a fast-Fourier transform. Resulting power of delta (1–4 Hz), theta (5–9 Hz), sigma (10–15 Hz), and gamma (25–100 Hz) frequencies was isolated and normalized to the summed power of all frequency bands. Each 24-h recording was divided into 2-h segments, and manually scored NREM epochs within each bin were grouped. Here, normalized (A) delta, (B) theta, (C) sigma, and (D) gamma powers are presented as mean ± 95% confidence interval (CI) for each treatment group: wild type (WT) treated with vehicle (n = 3 mice for 3 days, blue); J20 treated with vehicle (n = 4 mice for 3 days, orange); WT treated with CTEP (2-chloro-4-((2,5-dimethyl-1-(4-(trifluoromethoxy)phenyl)-1H-imidazol-4-yl)ethynyl)pyridine) (n = 4 mice for 3 days, red); and J20 treated with CTEP (n = 3 mice for 3 days, green). Nonoverlapping CI bars indicate a significant difference (p < 0.05). Lighting conditions are shown below the graph. Mixed-model ANOVA statistics are provided in Supplementary Table S7. Figure 5. Open in new tabDownload slide Power spectra of NREM sleep. Electroencephalographic power spectra of 4-s NREM sleep epochs were determined with a fast-Fourier transform. Resulting power of delta (1–4 Hz), theta (5–9 Hz), sigma (10–15 Hz), and gamma (25–100 Hz) frequencies was isolated and normalized to the summed power of all frequency bands. Each 24-h recording was divided into 2-h segments, and manually scored NREM epochs within each bin were grouped. Here, normalized (A) delta, (B) theta, (C) sigma, and (D) gamma powers are presented as mean ± 95% confidence interval (CI) for each treatment group: wild type (WT) treated with vehicle (n = 3 mice for 3 days, blue); J20 treated with vehicle (n = 4 mice for 3 days, orange); WT treated with CTEP (2-chloro-4-((2,5-dimethyl-1-(4-(trifluoromethoxy)phenyl)-1H-imidazol-4-yl)ethynyl)pyridine) (n = 4 mice for 3 days, red); and J20 treated with CTEP (n = 3 mice for 3 days, green). Nonoverlapping CI bars indicate a significant difference (p < 0.05). Lighting conditions are shown below the graph. Mixed-model ANOVA statistics are provided in Supplementary Table S7. Chronic mGluR5 inhibition does not significantly reduce Aβ levels There was decreased (32%) plasma Aβ 1–40 in J20 mice in response to fenobam that was not statistically significant by two-way ANOVA and no other differences in Aβ 1–40 or Aβ 1–42 levels observed in plasma or brain for either strain treated with fenobam or CTEP (Supplementary Figure S7). Chronic dosing with fenobam or CTEP did not affect mouse performance in passive avoidance or rotarod testing (Supplementary Figures S8 and S9). Of note, we achieved low bioavailability of CTEP in the mice using established oral gavage dosing protocols. Based on published studies, we expected the dosing regimen to result in a minimal (trough level) drug exposure of 98 ± 14 ng/mL in plasma and 215 ± 28 ng/g in the brain [21–27]; however, we achieved at least a 50-fold lower dose in both blood and brain (Supplementary Figure S10). Poor bioavailability could be due to a variety of factors (Supplementary Text S1). Discussion Disruption in rest-activity rhythm and sleep may serve as a disease biomarker in AD [7]. When we examined rest-activity and sleep phenotypes in J20 and WT littermates under diurnal conditions, we found (1) rest-activity rhythms are disrupted in J20 mice as evidenced by altered peak acrophase; and (2) sleep regulation is disrupted in J20 mice as evidenced by reduced NREM EEG delta power, which was partially rescued with CTEP. Our analysis showed that targeting mGluR5 rescued only the sleep phenotype in part but not the rest-activity rhythm or Aβ 40/Aβ 42 levels. Low levels of CTEP bioavailability limit our ability to firmly conclude if CTEP has effects on sleep-activity rhythms or Aβ levels. Since we did not record sleep EEG with fenobam, we cannot comment on its effects on sleep. J20 mice There are numerous mouse models available for the study of AD with many exhibiting altered sleep-wake states and diurnal rest-activity rhythms, albeit, there are variations in outcomes among the models [28–33]. Our study is the first, to our knowledge, to directly compare diurnal rest-activity levels to sleep phenotypes in J20 mice. J20 mice are transgenic for the hAPP gene with the Swedish 670/671KM-NL and Indiana 717V-F double mutations under regulation by the PDGFβ chain promoter [34]. The transgenic construct contains 70 bases of 5′-UTR, the cDNA, and the 3′-UTR up to the Sph1 site (base 3119 of APP695). The inclusion of flanking sequences in the transgenic construct is expected to affect posttranscriptional regulation of the APP gene and temporal and spatial expression of APP and metabolites. J20 mice are devoid of 3D6-immunoreactive Aβ deposits at 2–4 months of age, but amyloid deposition can be observed in 50% of J20 mice by 5–7 months of age and in 100% of mice by 8–10 months (human equivalent, 42–50 years old) [34, 35]. J20 mice exhibit an altered rest-activity rhythm characterized by a 4-h shift in the acrophase of peak activity, delays in activity onset and activity offset, and increased total activity during the dark cycle. Activity onset is a measure of the time of day in which the animals begin their most active period, and activity offset is a measure of when this active period ends. Since rodents are nocturnal, activity onset should begin at or around the start of the dark phase [36]. Our findings are consistent with clinical studies showing later acrophase in patients with AD [37–39]. J20 mice are hyperactive in the open field and exhibit reduced anxiety [40, 41]. However, we did not find a significant increase in total activity or decreased habituation to the novel actigraphy environment. The EEG-based analysis showed minimal differences in time spent in NREM and REM sleep, but a profound decrease in NREM EEG-delta power in J20 mice. Specifically, with regard to REM, vehicle-treated J20 mice have a lower marginal mean (a weighted estimate of population means) of percent time spent in REM sleep from Zeitgeber time (ZT) 6–12 h compared to vehicle-treated WT animals, which is rescued by CTEP in J20 mice. According to the two-process model of sleep introduced by Borbély, sleep is regulated by both circadian and homeostatic mechanisms [42]. The latter has been described as the pressure for sleep that grows during periods of wakefulness and is expunged by NREM sleep. Delta power is a commonly employed correlate of homeostatic sleep pressure, normally decreasing across the light period when mice are mainly resting and increasing with activity across the active dark period [43, 44]. This relationship between activity and EEG delta power of NREM sleep is evidenced by substantial increases in delta (1–4 Hz) EEG power following brief (4 h) total sleep deprivation in mice [43]. Our findings agree with previous murine AD studies demonstrating a decrease in delta band EEG power, while the activity of higher frequencies is increased [45]. This apparent shift may be due to the large decreases in J20 NREM delta power, which has the largest influence on the power normalization, but it may also be indicative of hyperexcitability of neurons contributing to activity outside the typical on-off periods underlying high-amplitude, slow activity recorded at cortical surfaces during NREM sleep [46]. Importantly, the increase in delta power occurs later in the subjective day, and it can be assumed that it increases in proportion to the delayed waking duration and associated intensity of activity in J20 mice. It appears that CTEP treatment improves NREM delta power (sleep pressure), although it reduces oscillatory amplitude in both WT and J20. Overall, the cyclic decay and accrual of delta power across the 24-h period fit reasonably well with actigraphy, suggesting its viability as a substitute diagnostic tool for AD in place of invasive EEG-based methods. Taken together with delayed acrophase in locomotor activity observed by actigraphy during the dark phase, the phase-shifted NREM delta power may indicate a perturbed function of the central pacemaker, affecting typical consolidation of sleep to subjectively appropriate times of the day. mGluR5 inhibition All of the currently approved drugs for the treatment of AD act on healthy neurons to compensate for lost acetylcholine activity in the case of cholinesterase inhibitors or to modulate NMDA receptor activity in the case of memantine. They improve the cognitive ability for a year or less but do not reduce Aβ accumulation or subsequent disease progression. The therapeutic potential of targeting mGluR5 in AD has been reviewed [47]. App mRNA is a synaptic target for regulation by FMRP and mGluR5. Activation of mGluR5 signaling induces the release of the translational repressor FMRP from App mRNA and the subsequent synthesis of AβPP [12]. Excessive AβPP production favors amyloidogenic processing and the production of Aβ. Aβ disrupts human NREM slow waves and related hippocampus-dependent memory consolidation [48]. We have observed that treatment with the mGluR5 inhibitor CTEP partly rescued the sleep phenotype, which has not been previously reported to our knowledge. There is evidence that mGluR5 may have a modulatory role in the molecular machinery of sleep homeostasis [49]. Thus, mGluR5 inhibitors may affect sleep-wake patterns but further study into the mechanism is required. Whether this translates to improved cognition remains to be determined. Fenobam and CTEP are potent and highly selective noncompetitive inhibitors of mGluR5 [27, 50, 51]. CTEP has a 30- to 100-fold higher in vivo potency compared to MPEP and fenobam and is 1,000-fold more selective for mGluR5 when compared to 103 molecular targets including all known mGluRs [27]. Thus, if fenobam and/or CTEP are proven effective in reducing Aβ accumulation and the cognitive decline associated with AD, mGluR5 inhibitors could provide an alternative, orally administered treatment for AD, which lack the problems associated with antibody-based therapies. The dose of fenobam used herein (24 mg/kg/day) was calculated based on published rodent and human pharmacokinetic data. Phase I dose-escalation trials showed safety and a lack of cognitive dysfunction in humans receiving up to 8–9 mg/kg/day fenobam for 3 weeks [52]. Thus, the dose is threefold higher than that safely tested in humans, but far less than that safely tested in rats [53]. Chronic dosing for 10 weeks at this dose resulted in no adverse side effects on weight gain or home cage behavior [15]. As there are no reports of toxicity with the drug, we proposed to err on the side of overdosing to ascertain fenobam effects on learning and memory and biomarker expression. CTEP is the first reported mGluR5 inhibitor with both a long half-life of approximately 18 h and high oral bioavailability, allowing chronic treatment with continuous receptor blockade with one dose every 48 h in adult animals [27]. Chronic treatment (2 mg/kg every 48 h) inhibits mGluR5 with a receptor occupancy of 81% and rescues cognitive deficits in Fmr1KO mice [21]. For this study, we dosed by oral gavage as published pharmacokinetic data by this method are available in other rodent models [21, 27]. Treatment with mGluR5 inhibitors, fenobam or CTEP, did not rescue altered rest-activity profiles, affect mouse performance in rotarod or passive avoidance testing, or decrease Aβ levels, although there were modest improvements in NREM delta power in CTEP-treated J20 mice. Oddly, oral gavage with vehicle shifts peak acrophase in WT mice. Specifically, in Figure 3, B with oral gavage every 48 h, average peak acrophase occurs at least 1 h later in the WT mice, thus attenuating differences observed between WT and J20 in the absence of restraint/oral gavage (Figure 1). Increased activity in J20 is observed at the end of the dark phase. This is a finding that we could not explain. The actigraphy and behavioral analyses involved chronic dosing with CTEP over 30 days whereas the EEG involved treatment for 1 week. Drug tolerance with mGluR5 inhibitors has been raised as an issue in failed FXS clinical trials [54]. Consistent with chronic dosing studies of fenobam as a feed supplement in AD mice (Tg2576 and R1.40HET), and with chronic dosing of CTEP by oral gavage in FXS mice (Fmr1KO) [15, 21], we observed normal weight gain, motor activity, grooming, and home-cage behavior with no adverse side effects. In contrast, genetic reduction of mGluR5 or chronic oral administration of CTEP rescues spatial learning deficits in APPSWE/PS1dE9 mice [14, 17], and BMS-984923 mGluR5 inhibitor treatment rescues memory deficits and synaptic depletion in APPSWE/PS1dE9 mice [55]. We did not find genotype or drug-dependent effects on learning and memory by passive avoidance in J20 mice. Prior chronic dosing studies with fenobam in AD model mice reduced Aβ levels [15]; however, those mice were dosed with a feed supplement and here we had poor bioavailability by oral gavage. Study limitations Limitations of the study include poor bioavailability and possibly drug tolerance of the mGluR5 inhibitors, the use of one AD mouse model, mice are nocturnal, and the oral gavage procedure shifts peak acrophase in WT mice. To begin to address these issues, future studies can include the administration of drugs as feed supplements, testing the effects of drugs that modulate Aβ production such as β-secretase inhibitors, and determination of diurnal activity and sleep patterns in additional AD mouse models. Conclusions Sleep disturbances and behavioral symptoms are the main reasons to institutionalize patients with AD [56, 57]. We observed disruptions in rest-activity rhythms and sleep in J20 mice, and sleep was partially rescued with one mGluR5 inhibitor. Chronic treatment with mGluR5 inhibitors did not rescue rest-activity rhythms or Aβ levels in J20 mice, but altered dosing administration methods or alternative drugs may be effective and deserve further investigation. Actigraphy was a reasonable surrogate for EEG with the noted limitations. Overall, targeting sleep may be an avenue to delay the development and/or progression of AD. Funding This research was supported by National Institute on Aging grant AG044714, the University of Wisconsin–Madison Alzheimer’s Disease Research Center (ADRC) grant P50-AG033514, the Clinical and Translational Science Award (CTSA) program through the National Center for Advancing Translational Sciences (NCATS) grant UL1TR002373, the Department of Defense (DOD) grant W81XWH-16-1-0082, and FRAXA Research Foundation. Acknowledgments We thank Dr Andrzej Dekundy (Merz Pharmaceuticals GmbH) and Dr Lothar Lindemann (F. Hoffmann-La Roche) for advice on CTEP inhibitor solubility. Disclosure Statement Financial Disclosure: None. Nonfinancial Disclosure: None. Prior deposit of manuscript in a Preprint database: BioRxiv. Ethical Statement All mouse procedures were performed in accordance with the NIH guidelines and an approved University of Wisconsin–Madison animal care protocol administered through their Institutional Animal Care and Use Committee. Conflict of interest statement. 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