Abstract
Objective:
Children with Down Syndrome (DS) may experience changes in sleep architecture (i.e., different sleep stages) that then affect waketime functioning, including learning, mood, and disruptive behavior. For designing and testing interventions, it is important to document any differences in sleep architecture in children with DS with and without co-occuring diagnoses, including neuropsychiatric diagnoses and obstructive sleep apnea (OSA).
Methods:
A retrospective cohort study was performed at Massachusetts General Hospital for children and adolescents with DS who underwent polysomnography (PSG) between August 2016 and July 2022. Patient data collected from the electronic medical record included diagnoses, age at PSG, and PSG report. Statistical analysis included unpaired T tests to test hypotheses about differences in sleep architecture within age groups, and differences between children with DS and a co-occuring diagnosis. One way ANOVA was used to determine statistical significance of OSA severity within patients with DS.
Results:
When compared by age group, those with DS had negative changes in sleep architecture (e,g., less sleep and more wake) when compared to normative data. Within this cohort, having a co-occuring diagnosis of autism resulted in further, negative effects on sleep architecture. 89% of those with DS had diagnosed OSA but only those with severe OSA experienced negative effects on sleep architecture.
Conclusion:
Age is an important covariate when studying the sleep of children with DS and neurotypical children. Studies are needed to test whether minimizing the observed differences in sleep architecture will translate to improved learning, mood, and behavioral outcomes, and how treating OSA affects sleep architecture.
Keywords: Down Syndrome, Sleep, Sleep Architecture, Autism, Obstructive Sleep Apnea
Introduction
Sleep is a major focus of care for children and adolescents with Down Syndrome (DS). Sleep disturbances, such as early awakenings, shortened sleep time, and symptoms of sleep apnea, can be extremely stressful for both patient and family. Sleep disturbances also affect daytime function including attention, learning, mood and behavior problems. Children with DS are at higher risk for multiple comorbidities that negatively affect sleep, including obstructive sleep apnea (OSA) and autism (Bull et al., 2022; Gringras et al., 2014; Mylonas et al., 2022). The American Academy of Pediatrics health supervision guidelines for DS recommends screening for symptoms of sleep disturbance beginning in the child’s first year of life and at every subsequent health visit (Bull et al., 2022). Independent of parent reported symptoms, it is recommended that children with DS have a formal sleep study by age 4 (Bull et al., 2022). To date, there has been a large focus on treating OSA in children with and without DS, with a goal of improving daytime sleepiness and neurocognitive function. Little focus, however, has been given to documenting and potentially improving underlying sleep architecture (i.e., amount of time spent in different stages of sleep [N1, N2, N3, REM)]) in this population (Marcus et al., 2013; Yu et al., 2022).
There are limited data suggesting that children with DS have altered sleep architecture compared to neurotypical peers (Heubi et al., 2021). In children, sleep patterns and architecture can vary dramatically throughout development so evaluating by age group is important (Kurdziel et al., 2013). In neurotypical children, the amount of N1 sleep is relatively stable throughout development (Scholle et al., 2011). In contrast, there is an age-dependent decrease in the amount of rapid eye movement (REM) and N3 sleep from age 1 to 18, and N2 and REM sleep latency increases with age (Scholle et al., 2011), until adolescence, when sleep architecture is similar to that of adults (Scholle et al., 2011).
Sleep architecture is important in memory and learning. Specific sleep stages have been found to be critical for consolidation of various forms of memory (Stickgold & Walker, 2007). Consolidation of motor skills has been connected to both N2 sleep and slow wave sleep (SWS), which is most often seen in N3 sleep. SWS and REM have been associated with consolidation of memory for visual tasks (Stickgold & Walker, 2007). Sleep spindles, which are the defining EEG oscillation of N2 sleep, have been associated with stabilization and consolidation of memory (Kurdziel et al., 2013; Mylonas et al., 2022).
We therefore aimed to document any differences in sleep architecture throughout childhood and adolescence in children with DS when compared to age-matched normative data and the effect of co-occuring diagnoses, including neuropsychiatric diagnoses and obstructive sleep apnea, on sleep architecture in those with DS.
Methods
Editorial Policies and Ethical Considerations
This study was approved by the Mass General Brigham Institutional Review Board (2022P002174) with a waiver of patient/parental consent.
Patient population
A retrospective cohort study was performed of 182 children with DS between the ages of 1 and 21 years who underwent diagnostic polysomnography (PSG) between August 2016 and July 2022. Subject data were idenitified using a central patient data registry at Massachusetts General Hospital (MGH). All PSGs were performed and scored in the pediatric sleep laboratory at MGH; PSG’s performed elsewhere were excluded. PSG performed for titration of oxygen, positive pressure, or hypoglossal nerve stimulator were excluded. Patient data collected from the electronic medical record (EMR) included diagnoses, demographics, age at PSG, and PSG report. Co-occuring diagnoses were identified by ICD code in the EMR. Age normative data from neurotypical children were extracted from published reports (Scholle et al., 2011).
Clinical Definitions
PSG recording and scoring was done using standard criteria established by the American Academy of Sleep Medicine (AASM) (Berry et al. 2020). Obstructive Apnea Hypopnea Index (OAHI) was used to establish the diagnosis and severity of OSA. For children <18 years old pediatric scoring criteria were utilitized; mild OSA was defined as an OAHI of >1 to <5 events/hr, moderate OSA was defined as an OAHI of 5 to <10 events/hr, and severe OSA was defined as an OAHI of ≥ 10 events/hr (Berry et al. 2020). For children 18 −21 years, adult scoring criteria was utilized; mild OSA was defined as an OAHI of 5 to <15 events/hr, moderate OSA was defined as an OAHI of 15 to <30 events/hr, and severe OSA was defined as an OAHI of ≥ 30 events/hr (Berry et al. 2020). Definitions of the components of the PSG are established by the AASM (Berry et al. 2020). Total sleep time (TST) is the total time spent asleep during the PSG. Wakefulness after sleep onset (WASO) is the total number of minutes that a patient is awake after having initially fallen asleep. Arousal index is the number of arousals per hour during sleep. Sleep efficiency is TST divided by the total time spent in bed during PSG. Sleep latency is the amount of time it takes to fall asleep after turning off the lights. REM latency is the amount of time between sleep onset and the first REM stage. Sleep stages (N1, N2, N3, REM) are reported as a percentage of TST.
Co-occuring diagnoses evaluated were anxiety, ADHD, autism, and behavioral problems. Behavioral problems coded in the EMR were documented as behavior change, hyperactivity, behavior concern, aggression, impulsivity, irritability, anger, and behavior regression.
Statistical analysis
Unpaired T tests were used to test hypotheses about differences in sleep structure and architecture within age groups for children with DS and age normative data. Unpaired T-tests were used to test hypotheses about differences between groups between children with DS and a co-occuring diagnosis. One way ANOVA was used to determine statistical significance (adjusted p-value) of OSA severity within patients with DS using the absence of OSA as the reference category. All analyses were completed by using GraphPad Prism Version 9.5.1 and GraphPad QuickCalc (Dotmatics, Boston, MA).
Results
Sleep Architecture by Age Group
Sleep metrics were compared by age group between those with DS and age-matched normative data (Figure 1a). TST did not differ significantly from age 1–4 years but was significantly less in those with DS in every age group thereafter. WASO and Arousal Index were significantly increased across all age groups in those with DS. Sleep latency did not differ significantly in any age group between those with DS and age matched neurotypical peers. Sleep efficiency decreased in older patient groups in those with DS. Stage N1 sleep had varying significance across age groups in those with DS, most often resulting in less time spent in N1 sleep. Stage N2 sleep was increased in those with DS throughout every age group. Stage N3 sleep trended downwards in older age groups in those with DS with varying significance. REM sleep trended downwards in older age groups, with statistical significance achieved around age 10 in those with DS (Figures 1a and 1b). REM latency increased with age in those with DS.
Figure 1a: Sleep Metrics and Architecture by Age Group.
Sleep metrics were compared by age group in children with DS to age-matched normative data (Scholle et al., 2011). NS signifies no stastical significance. * signifies p <0.05, ** signifies p <0.005, *** signifies p <0.0005.
Figure 1b: Down Syndrome Sleep Stages by Age Group.
Box plots of percentage of total sleep time spent in each sleep stage by age group in children with DS. X is representative of the mean within each age group.
Sleep Architecture and Co-Occuring Neuropsychiatric Diagnoses
Sleep metrics were compared between those with DS and those with DS and a co-occuring neuropsychiatric diagnosis (Table 2). TST was decreased in those with a co-occuring diagnosis of anxiety. WASO, arousal index, and sleep latency were not affected by the presence of a co-occuring diagnosis. Sleep efficiency was decreased in those with a co-occuring diagnosis of anxiety. Stage N1 sleep was decreased in those with a co-occuring diagnosis of autism (Table 2, Figure 2). Stage N2 sleep was increased in those with a co-occuring diagnosis of autism (Table 2, Figure 2). Stage N3 sleep was not affected by the presence of a co-occuring diagnosis. REM sleep was decreased in those with a co-occuring diagnosis of autism or a behavior problem (Table 2, Figure 2). REM latency was not affected by the presence of a co-occuring diagnosis, though tended to be longer in those with autism or behavior problems.
Table 2:
Sleep Metrics and Architecture of Children with DS vs DS with Co-Occuring Diagnoses
| Diagnosis (n/%) | Total Sleep Time | WASO | Arousal index | Sleep latency | Sleep efficiency | Stage N1 | Stage N2 | Stage N3 | REM | REM latency |
|---|---|---|---|---|---|---|---|---|---|---|
| Anxiety (15/8%) | ⬇ | ns | ns | ns | ⬇ | ns | ns | ns | ns | ns |
| ADHD (19/10%) | ns | ns | ns | ns | ns | ns | ns | ns | ns | ns |
| Autism (8/4%) | ns | ns | ns | ns | ns | ⬇ | ⬆ | ns | ⬇ | ns |
| Behavior Problem (9/5%) | ns | ns | ns | ns | ns | ns | ns | ns | ⬇ | ns |
Sleep metrics were compared in children with DS and children with DS and a co-occuring diagnosis (anxiety, ADHD, Autism, or behavior problem). Arrows represent directionality and statistical significance level. An arrow indicates p<0.05.
Figure 2: Sleep Stages in Children with Down Syndrome and Autism.
Box plots of percentage of total sleep time spent in each sleep stage for children with DS and children with DS and a co-occuring diagnosis of autism. X is representative of the mean within each group. * signifies p <0.05.
Sleep Architecture and Obstructive Sleep Apnea
Sleep metrics were compared within the cohort of those with DS to determine the significance of OSA on sleep architecture (Figures 3a and 3b). Severity of OSA was compared to those without OSA. TST, WASO, sleep efficiency, sleep latency, REM sleep, and REM latency were not affected by the presence of OSA. Arousal index, N1 sleep, and N2 sleep were only significantly increased by the presence of severe OSA. Stage N3 sleep was only significantly decreased by the presence of severe OSA.
Figure 3a: Sleep Stages by OSA Severity.
Box plots of percentage of total sleep time spent in each sleep stage of children with DS by their degree of OSA. X is representative of the mean within each group. * signifies p <0.05.
Figure 3b: Sleep Metrics and Architecture by OSA Severity.
As in Figure 3A for different sleep metrics. * signifies p <0.05, ** signifies p <0.005, *** signifies p <0.0005.
Discussion
We found changes in sleep architecture in children with DS compared with age-matched controls and in children with DS if there are co-occuring diagnoses. Sleep architecture is clinically important as specific sleep stages impact memory formation and learning. Consolidation of motor skills has been linked to N2 sleep and SWS/N3 sleep (Stickgold & Walker, 2007). An important part of N2 sleep are sleep spindles, which are generated in the brain by the thalamic reticular nucleus (TRN)(Mylonas et al., 2022). Some evidence suggests that dysregulation of thalamocortical interactions, mediated by the TRN, can contribute to the development of autism and those with autism often have significant sleep issues (Manoach et al., 2020). Sleep spindles can be correlated with IQ as well as learning ability and the sleep dependent consolidation of procedural and declarative memory (Manoach et al., 2020). Sleep dependent changes in information recall have been associated with sleep spindle density, though not with the duration of N2 sleep (Kurdziel et al., 2013). SWS and REM sleep have been associated with consolidation of memory for visual tasks (Stickgold & Walker, 2007).
Clinically, sleep related issues are commonly reported in the DS population and should be screened for at every health supervision visit starting at 6 months of age (Bull et al., 2022). Our data aligns with what is seen in clinical practice: children with DS had more difficulty falling back to sleep once they awoke from sleep (WASO), a higher arousal index, and decreased TST. Children with DS and a co-occuring diagnosis of autism had further changes in sleep architecture. While OSA is a significant focus of care in the DS population, previous studies have reported on clinical outcomes, but not on the sleep architecture. We found that only severe OSA had a statistically significant effect on sleep metrics and architecture.
Sleep Architecture by Age Group
While some data suggested children with DS have altered sleep architecture when compared to those without DS, this is not often evaluated by age group (Heubi et al., 2021; Mylonas et al., 2022), which is why we chose an age-based analysis. As neurotypical children age from 1 to 18 years, N3 sleep and REM sleep decrease in an age dependent fashion, while N2 sleep and REM sleep latency increase with age (Scholle et al., 2011). Sleep spindles, which are an important part of N2 sleep, develop throughout the first year of life and are structurally different than those of an older child or adolescent (Berry et al. 2020; Gruber & Wise, 2016). Sleep spindles have been shown to serve as a “gating mechanism” to protect sleep from being interrupted by external stimuli thereby allowing optimal time for information processing in sleep (Gruber & Wise, 2016). Sleep spindle density and duration may possibly affect the quality of sleep and memory consolidation. In neurotypical children, higher spindle density predicted better overnight memory consolidation, but the opposite was seen in children with autism (Mylonas et al., 2022). The significant increase in N2 sleep in children with DS as compared to age matched normative data across all age groups that we saw in our data is intriguing and should be investigated. Future work should also quantify sleep spindles in the DS population, and their relationship to other sleep and memory/learning outcomes.
Our study found that with increasing age, in children with DS the REM sleep latency increases more significantly than age-matched peer data and there is a decrease in sleep efficiency. Interestingly, our data did not show that children with DS had more difficulty falling asleep (i.e., longer sleep latency) than age matched data, which is a common clinical complaint.
Sleep Architecture and Co-Occuring Neuropsychiatric Diagnoses
We examined children with DS and a co-occuring neuropsychiatric diagnosis. Sleep problems are commonly reported in children and adolescents with psychiatric disorders (McMakin & Alfano, 2015). PSG data do not often show any sleep architecture differences in those with versus without anxiety (without DS), though some studies suggest an increased sleep latency (McMakin & Alfano, 2015). Our data aligns with this, in that those with DS and anxiety had a decrease in TST and sleep efficiency, though no appreciable changes were seen in time spent in each sleep stage. In children with ADHD without DS), as many as 70% have reported sleep difficulties, including increased sleep latency, nighttime awakenings, difficulty awakening in the morning, sleep disordered breathing, and daytime sleepiness (Gruber & Wise, 2016). There is conflicting evidence as to whether sleep spindles are altered in those with ADHD, though there have been differences in memory consolidation seen in children with ADHD versus those without (Gruber & Wise, 2016). Our data showed no differences in any sleep metrics or sleep architecture in those with DS versus those with DS and ADHD.
Sleep difficulties in children with autism are very common, and chronic, often resulting in additional learning and behavior problems (Gringras et al., 2014). Common sleep complaints include increased sleep latency, restless sleep, frequent nighttime awakenings, and decreased TST (Gruber & Wise, 2016). Children with autism have been noted to have increased sleep spindle duration in N2 sleep and higher spindle density (Mylonas et al., 2022). In children with autism, their memory performance benefits from N2 sleep but spindle density has been correlated with worse memory consolidation which suggests that the relationship between sleep spindles and memory may be different in these children (Mylonas et al., 2022). We found that having DS and a co-occuring diagnosis of autism results in altered sleep architecture with less time spent in N1 and REM sleep and more time spent in N2 sleep (Figure 2, Table 2). This supports current data that children with autism have altered sleep architecture.
Sleep Architecture and OSA
OSA is a significant issue in those with DS with a prevalence of 59–79% (Bull et al., 2022). Our study had a slightly higher prevalence than previous reports: 89% of our study population had OSA (Table 1a). Long-term sequelae of untreated OSA have been well studied, and include growth failure, impaired neurocognitive development, and cardiac complications (Heubi et al., 2021). In both neurotypical children and those with DS, treatment or improvement in OSA severity have been shown to improve school function and behavior, daytime sleepiness, and quality of life scores (Marcus et al., 2013; Yu et al., 2022). In neurotypical children, treatment of OSA had a modest effect on sleep architecture with a decrease in arousal index and percentage of time spent in light sleep (N1 sleep) though it did not change the percentage of time spent in N3 or REM sleep (N2 sleep was not reported) (Marcus et al., 2013). In children with DS whose OSA was treated with a hypoglossal nerve stimulator, improvement in their OSA did not change their sleep architecture one-year post-implantation (Yu et al., 2022). Our data showed that only severe OSA had a modest effect on sleep architecture (Figure 3b), with more time spent in N1 and N2 sleep, and less time spent in N3 sleep (Figure 3a). REM sleep was not affected by the presence or severity of OSA (Figure 3a). This suggests several hypotheses: (i) OSA may permanently alter sleep architecture or (ii) longer longitudinal follow up is required to see any changes. Given the importance of sleep architecture and its effects on memory and learning, this should be a focus of future research to optimize the timing of follow-up PSG after treatment of OSA.
Table 1a:
Demographics of study cohort
| Obstructive Sleep Apnea | n (%) |
|---|---|
| None | 20 (11) |
| Mild | 80 (44) |
| Moderate | 31 (17) |
| Severe | 51 (28) |
Limitations
This study has several limitations. We chose to limit data collection to that from the pediatric sleep laboratory at MGH to have better interscorer reliability and documentation. Any adolescent patients who had PSGs performed in the adult sleep laboratory were excluded. While we rely heavily on PSG data to evaluate sleep, this is only a snapshot in time of one night of sleep and may not be an accurate reflection of every night’s sleep. In addition, the population of children studied at the MGH pediatric sleep laboratory may not reflect the overall population of children with DS in terms of race or co-occurring diagnoses.
Racial disparities in health care are important to recognize, especially in those with DS, as literature shows poorer health outcomes for nonwhite patients (Seither et al., 2023). Age normative data used for PSG scoring was from Caucasian German children (Scholle et al., 2011). More research needs to be dedicated to understanding the influence of race on sleep architecture. Our study did not specifically evaluate this due to the small numbers of children within each age group. Similarly, evaluation of the effect of OSA on sleep architecture was performed by severity of OSA and not stratified by age.
Future, larger, multicenter studies are required to address these limitations.
Conclusion
Children and adolescents with DS have altered sleep architecture compared to age-matched neurotypical peers. Additionally, those with DS and a co-occuring diagnosis of autism have further alterations in sleep architecture when compared to those with only DS. While treating OSA has been shown to improve clinical outcomes in both neurotypical children and those with DS, our data suggests only severe OSA has any effect on underlying sleep architecture in those with DS (Marcus et al., 2013; Yu et al., 2022). Specific stages of sleep, as well as the structure of those sleep stages (i.e., sleep spindles), are implicated in memory and learning. More studies are needed to understand how and if treating OSA changes sleep architecture over time as well as evaluating whether improving sleep architecture affects multiple important outcomes (e.g., memory, learning, mood, behavioral problems).
Table 1:
Demographics of study cohort
| Age (yrs) |
|||||||||
|---|---|---|---|---|---|---|---|---|---|
| Sex | 1–1.9 | 2–3.9 | 4–5.9 | 6–9.9 | 10–12.9 | 13–13.9 | 14–15.9 | 16–21 | Total (%) |
|
|
|||||||||
| Male | 6 | 21 | 18 | 18 | 13 | 2 | 14 | 4 | 96 (53) |
| Female | 5 | 15 | 18 | 20 | 9 | 5 | 6 | 8 | 86 (47) |
| Race | |||||||||
| White | 9 | 25 | 15 | 28 | 14 | 6 | 17 | 10 | 124 (68) |
| Black | 1 | 4 | 4 | 2 | 3 | 0 | 1 | 1 | 16 (9) |
| Asian | 0 | 1 | 5 | 4 | 0 | 0 | 0 | 0 | 10 (5) |
| Other/ Unknown |
1 | 6 | 12 | 4 | 5 | 1 | 2 | 1 | 32 (18) |
| Additional Diagnosis | |||||||||
| Anxiety | 0 | 0 | 0 | 4 | 3 | 1 | 3 | 4 | 15 (8) |
| ADHD | 0 | 0 | 2 | 10 | 4 | 0 | 1 | 2 | 19 (10) |
| Autism | 1 | 0 | 0 | 1 | 4 | 0 | 2 | 0 | 8 (4) |
| Behavior Problem |
0 | 0 | 1 | 4 | 4 | 0 | 0 | 0 | 9 (5) |
Behavior problem was coded as behavior change, hyperactivity, behavior concern, aggression, impulsivity, irritability, anger, and regression.
Acknowledgments
We are grateful to Dr. Brian Skotko, Director of the Down Syndrome Program at Massachusetts General Hospital, Emma Campbell Endowed Chair on Down Syndrome, for his guidance and mentorship in developing this project.
Funding/Support:
Dr. Klerman received funding from the NIH (R01-NS099055, U01-NS114001, U54-AG062322, R21-DA052861, R01-NS114526–02S1, R01-HD107064, R01-HL166205), the DOD (W81XWH201076) and the Leducq Foundation for Cardiovascular Research. The other authors received no additional funding.
Abbreviations:
- DS
Down Syndrome
- NREM
Non-REM
- N1
NREM Sleep Stage 1
- N2
NREM Sleep stage 2
- N3
NREM Sleep Stage 3
- OAHI
Obstructive Apnea Hypopnea Index
- OSA
Obstructive Sleep Apnea
- PSG
polysomnography
- REM
Rapid Eye Movement
- Sleep, SWS
Slow Wave Sleep
Footnotes
Disclosures (includes financial disclosures): Dr. Klerman serves as a consultant for the American Academy of Sleep Medicine Foundation, Circadian Therapeutics, National Sleep Foundation, Sleep Research Society Foundation, Yale University Press; received travel support from the European Biological Rhythms Society, EPFL Pavilion; serves on the Scientific Advisory Board (unpaid) of Chronsulting; and her partner is founder, director, and chief scientific officer of Chronsulting.
Ethics Approval and Patient Consent Statement: This study was approved by the Mass General Brigham Institutional Review Board (2022P002174) with a waiver of patient/parental consent.
Permission to Reproduce Material from Other Sources: No materials were used from other sources.
Clinical Trial Registration: This was not a clinical trial
Conflict of Interest
The other authors have no conflicts of interest to disclose.
Data Availability:
The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
- Berry RB, Quan SF, Abreu AR, et al. : for the American Academy of Sleep Medicine (2020). The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications. Version 2.6. [Google Scholar]
- Bull MJ, Trotter T, Santoro SL, Christensen C, Grout RW, COUNCIL ON GENETICS, Burke LW, Berry SA, Geleske TA, Holm I, Hopkin RJ, Introne WJ, Lyons MJ, Monteil DC, Scheuerle A, Stoler JM, Vergano SA, Chen E, Hamid R, … Spire P. (2022). Health Supervision for Children and Adolescents With Down Syndrome. Pediatrics, 149(5). [DOI] [PubMed] [Google Scholar]
- Gringras P, Green D, Wright B, Rush C, Sparrowhawk M, Pratt K, Allgar V, Hooke N, Moore D, Zaiwalla Z, & Wiggs L. (2014). Weighted blankets and sleep in autistic children--a randomized controlled trial. Pediatrics, 134(2), 298–306. [DOI] [PubMed] [Google Scholar]
- Gruber R, & Wise MS. (2016). Sleep Spindle Characteristics in Children with Neurodevelopmental Disorders and Their Relation to Cognition. Neural Plasticity, 2016, 4724792. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Heubi CH, Knollman P, Wiley S, Shott SR, Smith DF, Ishman SL, & Meinzen-Derr J. (2021). Sleep Architecture in Children With Down Syndrome With and Without Obstructive Sleep Apnea. Otolaryngology--Head and Neck Surgery: Official Journal of American Academy of Otolaryngology-Head and Neck Surgery, 164(5), 1108–1115. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kurdziel L, Duclos K, & Spencer RMC. (2013). Sleep spindles in midday naps enhance learning in preschool children. Proceedings of the National Academy of Sciences of the United States of America, 110(43), 17267–17272. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Manoach DS, Mylonas D, & Baxter B. (2020). Targeting sleep oscillations to improve memory in schizophrenia. Schizophrenia Research, 221, 63–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Marcus CL, Moore RH, Rosen CL, Giordani B, Garetz SL, Taylor HG, Mitchell RB, Amin R, Katz ES, Arens R, Paruthi S, Muzumdar H, Gozal D, Thomas NH, Ware J, Beebe D, Snyder K, Elden L, Sprecher RC, … Childhood Adenotonsillectomy Trial (CHAT). (2013). A randomized trial of adenotonsillectomy for childhood sleep apnea. The New England Journal of Medicine, 368(25), 2366–2376. [DOI] [PMC free article] [PubMed] [Google Scholar]
- McMakin DL, & Alfano CA. (2015). Sleep and anxiety in late childhood and early adolescence. Current Opinion in Psychiatry, 28(6), 483–489. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mylonas D, Machado S, Larson O, Patel R, Cox R, Vangel M, Maski K, Stickgold R, & Manoach DS. (2022). Dyscoordination of non-rapid eye movement sleep oscillations in autism spectrum disorder. Sleep, 45(3). [DOI] [PubMed] [Google Scholar]
- Scholle S, Beyer U, Bernhard M, Eichholz S, Erler T, Graness P, Goldmann-Schnalke B, Heisch K, Kirchhoff F, Klementz K, Koch G, Kramer A, Schmidtlein C, Schneider B, Walther B, Wiater A, & Scholle HC. (2011). Normative values of polysomnographic parameters in childhood and adolescence: quantitative sleep parameters. Sleep Medicine, 12(6), 542–549. [DOI] [PubMed] [Google Scholar]
- Seither K, Helm BM, Heubi C, Swarr D, & Suhrie KR. (2023). Sleep Apnea in Children With Down Syndrome. Pediatrics, 151(3). [DOI] [PubMed] [Google Scholar]
- Stickgold R, & Walker MP. (2007). Sleep-dependent memory consolidation and reconsolidation. Sleep Medicine, 8(4), 331–343. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yu PK, Stenerson M, Ishman SL, Shott SR, Raol N, Soose RJ, Tobey A, Baldassari C, Dedhia RC, Pulsifer MB, Grieco JA, Abbeduto LJ, Kinane TB, Keamy DG Jr, Skotko BG, & Hartnick CJ. (2022). Evaluation of Upper Airway Stimulation for Adolescents With Down Syndrome and Obstructive Sleep Apnea. JAMA Otolaryngology-- Head & Neck Surgery, 148(6), 522–528. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.