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A clinical-translational review of sleep problems in neurodevelopmental disabilities
Journal of Neurodevelopmental Disorders volume 16, Article number: 41 (2024)
Abstract
Sleep disorders are very common across neurodevelopmental disorders and place a large burden on affected children, adolescents, and their families. Sleep disturbances seem to involve a complex interplay of genetic, neurobiological, and medical/environmental factors in neurodevelopmental disorders. In this review, we discuss animal models of sleep problems and characterize their presence in two single gene disorders, Rett Syndrome, and Angelman Syndrome and two more commonly occurring neurodevelopmental disorders, Down Syndrome, and autism spectrum disorders. We then discuss strategies for novel methods of assessment using wearable sensors more broadly for neurodevelopmental disorders in general, including the importance of analytical validation. An increased understanding of the mechanistic contributions and potential biomarkers of disordered sleep may offer quantifiable targets for interventions that improve overall quality of life for affected individuals and their families.
Background
Sleep disorders are extremely common in neurodevelopmental disabilities (NDD) and occur at a significantly higher rate as compared to typically developing children, adolescents, and adults [1]. Some studies show that between 50 and 95% of individuals with NDDs exhibit sleep problems [2], with sleep disturbances often emerging from a very young age and being prevalent throughout the lifespan [1]. Sleep problems are one of the major co-occurring conditions in many NDDs [2], place a large burden on children and families, and are associated with a range of cognitive and behavioral phenotypes [1, 3]. In fact, sleep problems are often included at least as a secondary outcome measure as part of clinical trials in a variety of single gene disorders and/or as a core feature to consider as part of Clinical Global Impression Severity Scales (CGI-S) [4, 5]. Numerous studies reveal that child sleep disturbances in NDDs adversely impact family quality of life [6,7,8]. Sleep problems in NDDs are not always related to behavioral sleep hygiene. Indeed, studies suggest mechanistic contributions to sleep disorders in NDDs, and investigations of sleep disturbances in animal models of these single gene disorders can offer potential clues as to etiology [9], as well as clinical translational approaches to treatment and mitigation. In this review article, we discuss the prevalence and etiology of sleep disturbances in two single gene disorders, Rett Syndrome, and Angelman Syndrome. We then expand this discussion to include more commonly occurring NDDs, Down Syndrome, and autism spectrum disorders and then discuss strategies for novel methods of assessment more broadly for NDDs in general. Increased understanding of the mechanistic contributions and potential biomarkers of disordered sleep in these NDDs may offer quantifiable targets for interventions that improve overall quality of life for affected individuals and their families. Specific sleep problems in these NDDs can include insomnia, sleep-disordered breathing, disruption to circadian rhythms, parasomnias, sleep-related movement disorder, and/or excessive daytime sleepiness. These sleep problems occur to varying degrees across the different disorders presented here and will be discussed in more detail within the sections for each disorder. Insomnia is defined as persistent difficulty with sleep initiation, duration, consolidation, or quality that occurs despite adequate opportunity and circumstances for sleep and results in daytime impairment [10]. Sleep-disordered breathing (SDB) includes a range of respiratory disorders such as snoring, sleep-induced hypoxemia, sleep-related hypoventilation, upper airway resistance syndrome, obstructive sleep apnea (OSA), and/or central sleep apnea [10]. Parasomnias include sleep terrors, sleepwalking, and confusional arousals and can occur during entry into sleep, during sleep, or during an arousal from sleep. Sleep-related movement disorders are movements that impact sleep onset or duration and can include bruxism, restless leg syndrome, and restless sleep disorder [11]. Disorders of hypersomnolence (narcolepsy) present with excessive daytime sleepiness. An overview of these sleep problems investigated in clinical studies as well as potential underlying etiology as investigated within specific genetic strains of animal models within these NDDs are presented within this review.
Sleep is regulated both by a homeostatic (Process S) and a circadian process (Process C) [12]. Together these two processes determine most aspects of sleep and related variables like sleepiness and alertness. Sleep homeostasis refers to the notion that when there is a loss of sleep, it elicits a compensatory increase/drive in the intensity and duration of sleep. The homeostatic mechanism regulates sleep intensity and the depth and maintenance of sleep, while the circadian clock regulates the timing of sleep. In each of the NDDs discussed in the review, studies from animal models suggest that the underlying etiology of sleep problems may include homeostatic and/or circadian processes. Circadian rhythms can be investigated by exposing mice to light/dark cycles, and sleep homeostasis is usually investigated by actively preventing the animal from engaging in sleep (despite the desire/drive to sleep). The “pacemaker” of the circadian system is located in neurons within the suprachiasmatic nucleus (SCN) of the hypothalamus, and examination of disruptions to homeostasis and circadian rhythms within neurons in the SCN has provided insights into potential mechanisms. The SCN controls the timing of the sleep-wake cycle and coordinates circadian changes in activity across the brain and body tissues.
Rett Syndrome
Rett Syndrome (RTT, MIM 312,750) is a severe neurodevelopmental disorder that primarily affects females. The prevalence is thought to be around 1:10,000 female births (total cases in US ~ 10,000). RTT is caused by mutations in the methyl CpG-binding protein 2 gene (MECP2) [13] and is associated with loss of function of MeCP2. The typical/classic form of RTT is characterized by regression, loss of purposeful hand skills and replacement with stereotyped hand movements/hand-washing motions, limited speech, dyspraxia, and abnormal muscle tone [14]. Included in the RTT phenotype are a variety of symptoms suggestive of autonomic dysregulation including breathing irregularities (e.g. hyperventilation, apnea, breath holding), heart rate variability, and temperature dysregulation [15,16,17]. Associated features include impaired sleep, breathing disturbances, bruxism, vasomotor disturbances, abnormal muscle tone, diminished response to pain, and scoliosis, among others [14, 17]. Sleep problems are highly prevalent in RTT [18,19,20] and are part of the supportive clinical criteria [14] with around 80% of the population being affected. Recent studies show that sleep difficulties, autonomic dysfunction, and milder clinical severity are associated with higher levels of anxiety in RTT [21, 22], suggesting adverse impacts of sleep problems on mental health as well as physical health. Disrupted sleep has a large burden on the health and well-being of both the child and caregivers affected by RTT, and clinical management is symptomatic, and does not appear to be associated with clear improvements [20].
Sleep disturbances in clinical research studies in RTT
Impairments in various aspects of sleep are quite common in RTT, with a recent study showing that 79–85% of caregivers reported their children with RTT experienced at least one sleep problem, including frequent nighttime waking, screaming spells and/or laughing at night (parasomnia), sleep-related movement (e.g., bruxism, restless legs), or daytime sleepiness [20]. Studies utilizing polysomnography (PSG) also show that individuals with RTT have increased sleep latency, increased wake after sleep onset (WASO), and reduced sleep efficiency [23, 24]. A case series of n = 13 participants with RTT also using PSG showed increased limb movements during sleep and also showed REM sleep is attenuated in RTT [25]. Breathing disturbances while awake have been described as alternating hyperpnea followed by apnea [26], which can be associated with cyanosis. Findings regarding SDB in RTT have been contradictory. Although earlier studies suggested that the breathing disturbances are normalized during sleep [26], more recent studies utilizing PSG show that both OSA and central sleep apnea (associated with hypoventilation) are common [24, 27] in RTT. A case series of n = 11 children with RTT [24] showed that 54.5% had OSA in both non rapid eye-movement (NREM) and rapid eye movement (REM) sleep which was unrelated to their clinical features. This study also revealed hypoxemia throughout nocturnal sleep in RTT [24]. Delta power on sleep electroencephalography (EEG) is considered a biomarker of sleep homeostasis since it is associated with sleep intensity and duration. Results from a sleep EEG study in RTT showed an increase in delta power during slow wave sleep (SWS) and decreased time spent in SWS in 2-9-year-old girls with RTT [28]. While delta power usually decreases over consecutive slow wave cycles during a night of sleep, this pattern was not noted in RTT [28], and is suggestive of chronice sleep deprivation [29]. In sum, sleep problems affect a majority of individuals with RTT, are associated with adverse outcomes in terms of mental and physical health, adversely impact caregiver quality of life, and the most recent studies suggest that sleep disruption, autonomic dysfunction, and anxiety are interconnected in RTT [30] and must be considered in tandem.
Animal models of sleep problems in RTT
The discovery of the X-linked MECP2 gene, which encodes the transcriptional regulator methyl-CpG-binding protein 2 (MeCP2), as a primary cause of RTT allowed for the creation of animal models to study underlying pathology and develop new treatments [13]. The first models of RTT were male mice with a total knockout of the MECP2 gene and complete disruption of the MeCP2 protein [31, 32] although there since have been other genetic models that model human point mutations, and a truncation mutation [33]. These mouse models recapitulate many of the features of Rett syndrome including stereotypic forelimb motions, uncoordinated gait, reduced spontaneous movement, and irregular breathing. These animal models serve to cross-validate findings from human clinical research studies and vice-versa. Sleep changes can be assessed in mice by investigating their activity in cages equipped with running wheels and by obtaining EEG recordings during the day/night cycle (assessing circadian rhythms and homeostasis). In one study, the investigators examined whether the circadian system was disrupted in Mecp2−/y mice and found a disruption to the circadian clock within the SCN such that mutant mice exhibited a decreased strength and precision of daily circadian rhythms and fragmented sleep [34]. Another study using a different genetic strain, the Mecp2tm1.1Bird mouse, found a significantly enhanced waking state and shorter duration of REM sleep, increased sleep fragmentation, and increased sleep inertia that recapitulate sleep problems described in RTT patients [35]. The possible mechanistic underpinnings of the sleep-wake cycle in RTT might be attributable to MeCP2 binding and transcriptionally activating the circadian clock genes, Per1 and Per2 [36], and that MeCP2 protein is highly expressed in the suprachiasmatic nucleus (SCN) [37]. More specifically, findings from the Mecp2−/y model showed that there was a reduction of neurons in the SCN expressing vasoactive intestinal peptide (VIP) as well as reduced spontaneous neural activity [34]. Circadian disruption was noted in the SCN and in peripheral organs, indicating a general disorganization of the circadian system. Taken together, findings from studies of animal models suggest a role for MeCP2 in the circadian timing system and provide a possible mechanistic explanation for the sleep/wake disturbances observed in RTT patients. It will be important, however, to determine the consistency of these findings across different genetic strains of mice. In sum, although studies in humans and animal models have contributed to a broader understanding of potential underlying mechanisms of sleep disturbances in RTT, further research is needed to investigate the molecular, neuronal, and non-neuronal pathways underlying sleep disorders.
Angelman syndrome
Angelman syndrome (AS) is a neurodevelopmental disorder affecting both females and males, with an estimated prevalence of around 1:15,000. AS is caused by the loss of function of the maternally expressed Ubiquitin-protein ligase E3A (UBE3A). Most cases (70%) of AS are caused by a deletion in the maternal copy of chromosome 15q11.2-q13. Other forms of AS are attributable to paternal uniparental disomy (3% of cases), an imprinting center defect (6% of cases), a mutation in the maternally inherited allele of UBE3A (11% of cases) [38]. The phenotype, present from birth, is characterized by absent or minimal spoken language, gait abnormalities, epilepsy, a happy/excitable personality, and abnormal movements [39]. Features of autism are also prominent within AS [40, 41]. Although phenotypic features vary depending upon the molecular subtype of AS [42], sleep problems are present in up to 80% of individuals with AS [43] and have not differed by molecular subtype [44]. Within the consensus guidelines for AS, abnormal sleep-wake cycles and diminished need for sleep are considered associated features [43]. Sleep problems are a major contributor to parental/caregiver stress in AS [45,46,47].
Sleep disturbances in clinical research studies in AS
Most clinical research studies of sleep problems in AS have involved parent/caregiver questionnaires, although some more recent studies have utilized PSG and actigraphy. Although sleep problems seem to be worse in younger children, being most prevalent between the ages of 2–9 years [48, 49], several studies suggest that a significant percentage of individuals with AS exhibit sleep problems that persist into adolescence and adulthood. Sleep problems are pervasive enough and adversely impact caregiver quality of life enough in AS to be included in a clinician-reported and a caregiver-reported outcome assessment scale that is being utilized in current clinical trials [50]. Seizures are very common in AS, and a high percentage of those with AS who have epilepsy (79% in one study) also have sleep problems, with the severity of seizures correlating with sleep disturbances [51]. Seizures disrupt sleep architecture, fragment sleep, and can decrease REM sleep [49]. Results from questionnaire studies show that between 35 and 60% of individuals with AS have difficulty initiating sleep and/or maintaining sleep [48, 52, 53]. Night wakings are very common in AS and may be accompanied by behaviors such as screaming [48]. Other salient findings from sleep questionnaire studies in AS include snoring, sleep terrors, sleepwalking, sleep fragmentation, nocturnal hyperkinesia, nocturnal laughing, and a reduced need for sleep [46, 48, 52, 53]. Some reports also suggest that individuals with AS only sleep 5–6 h/night and do not have daytime sleepiness even with fragmented and/or reduced night-time sleep [52]. PSG is very challenging in AS, and, as such, studies to date consist of smaller sample sizes (e.g., n = 10). Results from PSG studies suggest increased sleep latency due to difficulties with settling, decreased total sleep time, reduced sleep efficiency, and reduced percentage of slow wave sleep [54, 55]. In addition, these studies showed that the percentage and duration of REM sleep was significantly lower, and the percentage of slow wave sleep was significantly higher [54, 55]. Sleep EEG coherence is a measure of the brain’s connectivity during sleep, and it is assessed by examining interactions between adjacent brain regions (short-range) and between more distant regions (long-range). Gamma band coherence is usually lower during sleep, and increases suggest attentive wakefulness and tend to occur during REM sleep. In AS, a recent retrospective study of n = 28 children (ages 4–11 years) found increased long-range EEG coherence in the gamma band during sleep and decreased sleep spindle number and duration [56] which could have a role in neuronal plasticity as well as implications for memory and learning [57, 58]. To summarize, sleep problems are pervasive in AS and are often associated with epilepsy and increased behavioral challenges and adversely impact quality of life for affected individuals and their caregivers.
Animal models of sleep problems in AS
The most commonly used mouse model of AS recapitulates many phenotypic features observed in AS patients [38] including epilepsy, motor deficits, abnormal EEG, abnormal sleep patterns, increased anxiety, and repetitive behaviors. This model has been valuable for understanding disease processes in AS and in identifying appropriate drug targets [59]. Two studies in AS mice evaluated changes in the sleep-wake cycle [60, 61]. In one study [61], they found that circadian rhythms are intact, but abnormal sleep patterns arise from a deficit in accumulation of sleep drive (i.e. disruption to sleep homeostasis). In this study, they also determined that Ube3a protein was present in many neurons of the SCN, suggesting that it acts as a novel genetic regulator of sleep homeostasis. In contrast, in a different study Shi et al., 2015 showed alterations to circadian rhythms where they showed a longer circadian period that leads to delayed phase [60], which they hypothesize could account for the difficulty with settling to sleep (sleep onset latency) and shorter sleep duration in individuals with AS. These different findings suggest the importance of continuing to investigate how UBE3A affects sleep patterns in animal models, and differences in genetic backgrounds/strains could account for these differences. Although there are still gaps in understanding the mechanistic basis of sleep problems in AS, it is important to note that in deletion forms of AS, there is a disruption in the gene that encodes for the B3 subunit of the gamma-aminobutyric acid–mediated (GABA) receptor, and alterations to this receptor may cause inhibitory influences on thalamocortical interactions that could be responsible for the sleep problems in AS [49]. In homozygous Gabrb3-knockout mice (one of the models used to assist with studying AS), studies of reciprocal inhibitory connections demonstrated abolition of GABA-mediated inhibition in the thalamic reticular nucleus as well as an increase in oscillatory synchrony [62]. Excitingly, recent therapeutic advances in AS pre-clinical studies show that treatment with an antisense oligonucleotide (ASO) rescues abnormal EEG rhythms and sleep disturbances [63]. Since these compounds have started to advance to human clinical trials, it will be important to determine whether some of the sleep disturbances in AS are normalized in human clinical studies as well. In sum, sleep is significantly disrupted in AS but contributions to sleep disruption are likely multi-faceted and require further study to determine the underlying pathophysiology. Despite this, recent advances in therapeutic development suggest that normalization of at least some of the sleep disturbances could be possible with novel compounds.
Down syndrome
Down Syndrome (DS) is the most common genetic cause of NDD but is very complex genetically, with potentially more than 500 genes that could be overexpressed on chromosome 21. There are several significant medical co-occurring conditions in DS including: congenital heart disease, hypothyroidism, feeding problems and gastrointestinal issues [64], and obesity. Obesity in DS starts to become a concern between the ages of 4–5 years [65, 66], and this is the age when parents also begin to become increasingly concerned about challenging behaviors [67]. Individuals with obesity and DS are more likely to also have obstructive sleep apnea (OSA) [66].
Sleep disturbances in clinical research studies in DS
Studies that have utilized parental questionnaires and those that have conducted patient EEG recordings consistently show sleep architecture to be altered in children in DS with alterations consisting of reduced time spent in NREM sleep, increased sleep latency, and increased night wakings [68, 69]. A study utilizing actigraphy, sleep diaries, and PSG showed that children with DS were averaging 6.9 to 7.9 h of sleep, with poor sleep efficiency [70]. Studies have suggested 50 to 79% of children with Down syndrome have OSA compared to 1–4% of the rest of the population [71]. Sleep problems in DS can start from a very young age but are prevalent throughout the lifespan with studies also showing that 78 to 90% of adults with DS have OSA [72]. Increased prevalence of OSA in DS could be attributable to anatomic differences such as hypotonia, macroglossia, and midface hypoplasia [71]. In addition, many children with DS have enlarged adenoids and tonsils, which also cause obstruction within the already narrowed airway [73]. Importantly, while adenotonsillectomy (AT) is considered first-line therapy for the treatment of pediatric OSA, persistent OSA after AT occurs in many (58%) with DS [74, 75] requiring more intensive follow-up. OSA in DS is associated with a range of medical, cognitive, and behavioral outcomes such as lower verbal IQ [76], Attention-Deficit/Hyperactivity Disorder (ADHD) [77,78,79], and impaired autonomic/cardiovascular control [80]. A recent study showed that children with DS had elevated heart rate during N2 (NREM) and N3 (NREM) sleep as compared to typically developing children and did not exhibit the typical fall in heart rate from wake to sleep [80]. A study in middle-aged adults with DS shows that more disrupted sleep is associated with lower white matter integrity, increasing the risk of Alzheimer’s disease [81]. Individuals with DS frequently exhibit an abnormal PSG, with high sleep fragmentation, manifested by frequent awakenings and arousals, considerable limb movement [82], and frequent OSA [83]. Children with DS often require repeated PSG because of continued difficulties with sleep problems (e.g., even after AT) and/or a resurgence of sleep problems during adolescence [71] that coincides with puberty and the onset of obesity. In sum, sleep problems are extremely prevalent in DS, with most individuals having OSA, and this has a significant long-term impact on overall functioning, as well as the quality of life for individuals with DS as well as their families.
Animal models of sleep problems in DS
In contrast to RTT and AS, the development of animal models to study the underlying pathophysiology of DS and to develop and test potential therapies has been more challenging, in part because no model has captured all of the triplicated region. Animal models that have been developed for DS involve transgenic overexpression of single genes or larger human genomic segments and creation of mouse models carrying a partial triplication in regions of MMU16 syntenic to human chromosome 21 (segmental trisomy models or Ts). Among these models, to date, the Ts65Dn is mostly commonly used in research because it contains triplications of partially overlapping segments in the critical DS region and cover most of the region triplicated in humans [84]. Ts65Dn has also formed the basis of pre-clinical justification for clinical trials [85]. These mice recapitulate some of the phenotype in DS including intellectual disability (ID), hyperactivity, craniofacial malformations, and motor dysfunction [86]. The Tc1 (transchromosomic, Tc(Hsa21)1TybEmcf) line is a trans-species aneuploid line expressing a large portion of Hsa21 (83%, 269 genes) as a third copy. Most studies suggest no alterations in circadian rhythms for Ts65Dn animals. The mice do, however, exhibit sleep fragmentation and have OSA in REM sleep [87]. Tc1 mice exhibit moderate disruptions in rest/activity patterns and hyperactive episodes, with circadian rhythms also appearing similarly unaffected [88]. EEG signals have also been obtained in several mouse models of DS. Findings have differed, depending upon the mouse strain being studied. In one study using the Ts65Dn mouse, investigators found increased waking periods at the expense of non-REM sleep, increased power in theta waves during sleep, and a delayed sleep rebound after sleep deprivation [89]. In contrast, TC1 mice had limited sleep and EEG abnormalities, showing only a delayed sleep rebound after sleep deprivation and no difference in the power of theta oscillations [89]. Unlike RTT and AS, the potential molecular mechanisms underlying sleep problems in DS are less well-understood. Because of the challenges inherent with animal models of DS with regard to translational research, including that a clinical trial was halted in phase 2 because of failure to recapitulate pre-clinical findings from findings with the Ts65DS model, a newer mouse model of DS has been created, the TcMAC21 [90]. These mice have many of the same phenotypic features of DS that are evident in humans, including a distinct facial structure, a greater prevalence for congenital heart defects, a smaller-than-usual cerebellum and learning difficulties [90]. To date, sleep has yet to be studied in this newer model. It will therefore be important to continue to investigate sleep abnormalities in animal models of DS, to develop further treatment and interventions.
Autism
Autism spectrum disorder (ASD) now affects approximately 1 in 36 children and is around 3.8 times as prevalent among boys as among girls [91]. A small percentage of individuals with ASD have a known genetic etiology, typically in the form of rare variants, while most cases are idiopathic. ASD is characterized by impairments in social communication, accompanied by restricted/repetitive interests and behaviors. ASD is very heterogeneous in terms of phenotypic presentation and functional abilities, especially compared to AS, RTT, and DS. It is important to note, however, that a proportion of individuals with RTT, AS, and DS also meet criteria for ASD, and there are potential overlapping pathways [40, 41, 66, 92, 93]. In addition, those with co-occurring diagnoses of autism often exhibit higher levels of impairment, and one recent study in DS showed that those individuals who had both DS and ASD had more negative effects on sleep architecture. Most of the animal models for ASD to date have therefore been developed using single gene disorders [94]. This approach has allowed for the potential study of shared phenotypes, inclusive of sleep problems and underlying cellular and molecular pathways [95], and the knowledge gleaned from single gene disorders has applicability to ASD.
Sleep disturbances in clinical research studies in ASD
Studies of parent-reported sleep problems of ASD reveal prevalence rates ranging between 40 and 80% [1, 96,97,98]. The most common problems that are reported include difficulties with initiating and maintaining sleep, frequent and prolonged night awakenings, early morning waking, and irregular-sleep wake schedules [99,100,101,102]. Reduced total sleep time, increased daytime sleepiness, and night-time laughing and/or talking are also common [97, 99, 100, 102]. Sleep problems for individuals with ASD can persist through adulthood [103], and one study found that adults with ASD had lower sleep efficiency as compared to neurotypical adults. Studies using PSG have shown increased nightwakings, reduced total sleep time, lower sleep efficiency, reductions in REM and non-REM sleep, and longer latency to fall asleep [99, 104]. An increased incidence of sleep problems in those with ASD has been associated with more behavioral difficulties (e.g., aggression, self-injury, anxiety, hyperactivity, irritability, and inattention) [105, 106], increased sensory sensitivity [107, 108], more pronounced repetitive behaviors, and more impairments in social communication [109]. As with the other NDDs discussed here, higher rates of sleep problems in ASD are associated with higher rates of caregiver burden and family stress [105, 110].
Etiology of sleep problems in ASD
The underlying etiology of sleep problems in ASD is multifaceted, with possible contributions of neurobiological factors, as well as medical and behavioral factors. Although ASD is genetically heterogeneous, there is emerging evidence that neurotransmitters that regulate sleep and wake cycles such as serotonin, and the hormone, melatonin could contribute to sleep problems in ASD in a subset of individuals. Melatonin is produced by the pineal gland and regulates sleep/wake cycles in humans. Some studies show decreased levels of melatonin in serum, saliva, and/or urine in children and adolescents with ASD [111, 112]. This reduction in melatonin is thought to be associated with a variation in a gene associated with the serotonin-melatonin synthesis pathway, the acetyl serotonin O-methyltransferase (ASMT) gene [111, 113]. ASMT converts serotonin into melatonin [114] and genetic variants within the promotor region are more frequent in individuals with ASD and are associated with a decrease in the number of ASMT transcripts [111, 115,116,117], although this impacts a very small subset of individuals. In addition, some studies suggest alterations of the serotonergic signaling system are involved in the pathophysiology of ASD, at least in some individuals [118,119,120]. One study showed that 40% of individuals with ASD had abnormally high levels of serotonin in blood and 51% had abnormally low levels of melatonin [113], with other work documenting normal overnight blood or evening salivary melatonin levels [103, 121]. These studies consisted of small sample sizes and may not be generalizable given the way samples were collected (in dim light or not). Additionally, studies have shown that supplementation with melatonin can be effective in treating sleep problems in some (53.7% in a recent randomized control trial) [122], but not all individuals with ASD, especially those with night wakings or short sleep duration [103, 123]. Prolonged release melatonin can be helpful for night wakings and short sleep duration, but requires swallowing a pill whole, without it being crushed (to maintain the prolonged release packaging). Given the inconsistency of responses to medications such as melatonin and the heterogeneity within ASD, behavioral strategies are typically recommended as first-line interventions and focus of treatment [100].
Medical/behavioral/environmental contributions to sleep problems in NDDs
Beyond the neurobiological contributions to sleep problems that have been described for each of the conditions reviewed here (RTT, AS, DS, ASD), there are also potential medical, behavioral, and/or environmental contributions to sleep difficulties. Gastrointestinal issues such as constipation and reflux are very common across all of these NDDs, and many individuals with NDDs are not able to verbally express their discomfort, which can exacerbate sleep problems in particular. For example, constipation can lead to abdominal cramping, which can interfere with a proper night’s sleep. Gastroesophageal reflux may be worsened by lying down at night and could be reflected in an increased latency to falling asleep/bedtime resistance. Sleep dysfunction is also very common in those with NDDs who have epilepsy [124]. Epilepsy disrupts sleep, and sleep disturbances can also lower the seizure threshold [8]. Sleep disruption is also a common side effect of many medications used to treat co-occurring conditions across all of these NDDs (anxiety, irritability, epilepsy, ADHD, etc.). Some medications have properties that contribute to difficulties with sleep onset and/or sleep maintenance. Selective serotonin reuptake inhibitors (SSRIs), for example, can disrupt sleep continuity, and/or decrease total sleep time, increase wake time, and increase stage N1 sleep [125, 126]. Some medications for epilepsy (clonazepam, felbamate, lamotrigine, oxcarbazepine, and phenobarbital) can worsen sleep [127]. Stimulants that are used to treat ADHD often have insomnia as a side-effect and, if given too late in the day, they can interfere with sleep-onset latency.
Novel methods of assessment of sleep problems
Polysomnography (PSG) is considered the gold standard in sleep research [8], but is resource and cost intensive. PSG is also limited as an outcome measure by the foreign and stressful environment of a sleep lab, the need for specialized personnel, and short interval of assessment. As such, current strategies for assessing sleep as an outcome in clinical trials of NDDs are often limited to caregiver completed questionnaires [8] and sleep diaries [128, 129]. While these strategies are widely used for studying sleep in a variety of neurodevelopmental disorders, they rely on caregiver reporting and thus are subject to multiple forms of bias including recall as well as observer bias. For example, the time of sleep onset may be mis-reported in a sleep diary if a child’s eye-closure and silence while still awake is misinterpreted as being asleep. Night-wakings [3] could be easily missed when relying on sleep questionnaires and/or diaries. In addition, self-reporting of sleep problems is not possible in many NDDs, given the severe communication impairment. There are weak correlations between parental reports of sleep and PSG in some NDDs [70], and a recent study of children with DS also showed that parents cannot accurately predict when their child has apnea [130]. Thus, it is important to validate other objective methods of assessing sleep that are less invasive and burdensome, and that are cost effective. In recent years, wearable sensors have become smaller, lighter, cheaper, and less obtrusive, and are being increasingly utilized in sleep research studies because of their sensitivity and ease of use, which have made them suitable for longitudinal monitoring of patients within their home environment.
Actigraphy devices are worn on the wrist and can estimate sleep parameters by assessing movement during sleep. Actigraphy, which does not capture physiological parameters, may not be sufficient to detect sleep problems and differentiate between sleep stages in lieu of PSG in some NDDs [70, 131]. Newer generation sensors that utilize photoplethysmography (PPG), which uses an infrared light to measure the volumetric variations of blood circulation, along with actigraphy tend to be more accurate with detecting wake times [132]. Some wearable devices also have the capacity to capture oxygen saturation (SpO2), and the addition of pulse oximetry (which captures SpO2) permits an assessment of hypoxemia and more closely approximates what is captured during gold-standard PSG. These tend to be finger-worn devices (vs. wrist-worn) and could have important implications/use cases for NDDs such as RTT and DS, where apneas and OSA are more common. Some wearable devices that include PPG and also capture electrodermal activity (EDA), skin temperature, and heart rate variability are being used to study stress/behavioral responses (e.g., anxiety) in NDDs such as ASD.
Given the concordance of sleep problems, autonomic dysfunction, and behavioral difficulties across many NDDs, actigraphy devices may be quite useful, especially given the challenges associated with self-report and reliance on caregiver reporting. Since sensory processing issues are prevalent in NDDs [133], and studies have shown that sensory sensitivities can make it challenging for children to be compliant with wearable sensors [134], it is important to directly test the feasibility of use of these wearable sensors in NDD populations. Positioning could have an impact on data quality (wrist vs. finger worn), and, in some cases for example, if a participant is likely to exhibit mouthing of objects, a finger-worn device might need to be placed after the participant is already settled in bed. “Nearable” devices passively monitor participants within their home environment, and therefore do not require a participant to wear anything. An ongoing study in RTT is using a “nearbale” device to monitor respiration, sleep quality, and sleep stages although it is not yet known how measurements compare to gold-standard PSG.
Actigraphy scoring algorithms that have been developed with typical populations (e.g., Cole-Kripke, Sadeh) [135,136,137] have limitations when applied to those with NDDs in that they can vary across devices and might underestimate sleep onset latency and overestimate sleep duration [134, 138]. To assist, recent advances in machine learning and artificial intelligence (AI) have allowed for the development of models using both movement and physiological data from wearable sensors to predict sleep problems, differentiate sleep stages, and even to detect OSA [139, 140−142]. Although most current work is based on typically developing adults and has yet to be applied to children with NDDs, we recently demonstrated the utility of a machine-learning approach in RTT that combined actigraphy with physiological parameters to create a model that aligned well with PSG [131]. There are also remote assessment devices for OSA. A recent study examined the feasibility and accuracy of Level 2 home sleep apnea testing (HSAT) in children, adolescents, and adults with DS and found that Level 2 HSAT was well-tolerated, preferred by parents/caregivers as compared to PSG, and had good accuracy for detecting moderate-severe OSA [143]. This has yet to be tested more broadly in other NDDs.
Given the differences in aspects of sleep in the NDDs we discussed here as compared to typical populations as well as their sensory sensitivities, it is important to determine: (1) the utility of use of wearable sensors to assess both movement and physiological signals in NDDs, and (2) the degree to which these signals accurately predict sleep problems and OSA or other apnea episodes. It will be very important to take a stepwise approach for analytical validation [144] to determine the accuracy, prediction, and reliability of measurements in NDDs from wearable sensors before they can be used more broadly. Also, data sharing approaches (e.g., some companies allow open sourcing of data) across other intellectual and developmental disabilities research centers can further propel the use of wearable sensors and help determine the best use cases and parameters for analyses as well as clinical validation across NDDs. It will also be important to consider the cost per use for each wearable device and whether the device is reusable or disposable, especially since certain wearable and nearable devices are cost prohibitive for use in larger scaled studies such as clinical trials. Related to this, it is important to consider battery life for potential collection of sleep data over several days since up to seven days of data are recommended, for example, for determining total sleep time and sleep efficiency [145]. Finally, researchers should consider whether they will have access to raw data to develop novel algorithms, which is very important for the study of NDDs, as well as options for available data storage, and potential privacy concerns.
Summary and conclusions
Sleep is very commonly disrupted in intellectual and developmental disabilities and these disruptions not only place a large burden on caregivers, but also adversely impact the quality of life of individuals with NDDs themselves. The advantage of studying sleep in single gene disorders is that it has high clinical translational value given the common methodologies that can be utilized across human and animal model studies to bridge gaps in mechanistic understanding that can lead to improved treatments and interventions. These disease models allow for the understanding of neural circuits that contribute to sleep disruption. For more commonly occurring NDDs such as DS and ASD, there is a need for refinement of animal models to better characterize the underlying pathophysiology that contributes to sleep problems. In addition, however, within clinical research studies, defining and separating subsets of individuals within each NDD with similar functional levels, similar mutations/genetic subtypes, and/or similar co-occurring medical/behavioral conditions can also lead to enhanced insights for more targeted interventions and treatment. The use of PSG, sleep diaries, and standardized sleep questionnaires will continue to have high utility in clinical research studies and in clinical trials with NDDs, however, emerging studies are also showing that wearable sensors offer a way to objectively measure sleep in NDDs in the home/naturalistic environment that reduces caregiver burden. These methods can help develop a novel data collection format and determine when PSG is needed, determine what signal parameters are most essential resulting in the best model fit vs. PSG, and whether wearable sensors could be used in future clinical trials. The use of wearable and nearable sensors could also allow for larger scaled studies which could provide additional insights into sleep problems and targeted treatments for each of these NDDs since many current studies have more limited generalizability given the relatively small sample sizes. The use of wearble devices could also result in the need for fewer PSG’s, could reduce caregiver burden, and could assist in formulating clinical interventions and tracking improvement over time.
Data availability
Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.
References
Shelton AR, Malow B. Neurodevelopmental disorders commonly presenting with sleep disturbances. Neurotherapeutics. 2021;18(1):156–69. Epub 20210105. https://doi.org/10.1007/s13311-020-00982-8. PubMed PMID: 33403472; PMCID: PMC8116361.
Veatch OJ, Malow BA, Lee HS, Knight A, Barrish JO, Neul JL, Lane JB, Skinner SA, Kaufmann WE, Miller JL, Driscoll DJ, Bird LM, Butler MG, Dykens EM, Gold JA, Kimonis V, Bacino CA, Tan WH, Kothare SV, Peters SU, Percy AK, Glaze DG. Evaluating sleep disturbances in children with Rare Genetic Neurodevelopmental syndromes. Pediatr Neurol. 2021;123:30–7. https://doi.org/10.1016/j.pediatrneurol.2021.07.009. Epub 20210724.
Boban S, Leonard H, Wong K, Wilson A, Downs J. Sleep disturbances in Rett syndrome: impact and management including use of sleep hygiene practices. Am J Med Genet A. 2018;176(7):1569–77. https://doi.org/10.1002/ajmg.a.38829. Epub 2018/04/29.
Neul JL, Percy AK, Benke TA, Berry-Kravis EM, Glaze DG, Peters SU, Jones NE, Youakim JM. Design and outcome measures of LAVENDER, a phase 3 study of trofinetide for Rett syndrome. Contemp Clin Trials. 2022;114:106704. https://doi.org/10.1016/j.cct.2022.106704. Epub 20220208.
Kolevzon A, Ventola P, Keary CJ, Heimer G, Neul JL, Adera M, Jaeger J. Development of an adapted Clinical Global Impression scale for use in Angelman syndrome. J Neurodev Disord. 2021;13(1):3. Epub 2021/01/06. https://doi.org/10.1186/s11689-020-09349-8. PubMed PMID: 33397286; PMCID: PMC7784030
Trickett J, Heald M, Oliver C. Sleep in children with Angelman syndrome: parental concerns and priorities. Res Dev Disabil. 2017;69:105–15. https://doi.org/10.1016/j.ridd.2017.07.017. Epub 2017/08/28.
Goldman SE, Bichell TJ, Surdyka K, Malow BA. Sleep in children and adolescents with Angelman syndrome: association with parent sleep and stress2012;56(6):600–8. doi: https://doi.org/10.1111/j.1365-2788.2011.01499.x.
Robinson-Shelton A, Malow BA. Sleep Disturbances in Neurodevelopmental Disorders. Current psychiatry reports. 2016;18(1):6. Epub 2016/01/01. https://doi.org/10.1007/s11920-015-0638-1. PubMed PMID: 26719309.
Zhang X, Lin JS, Spruyt K. Sleep problems in Rett syndrome animal models: a systematic review. J Neurosci Res. 2021;99(2):529–44. https://doi.org/10.1002/jnr.24730. Epub 20200928.
Medicine AAoS. The International Classification of Sleep Disorders, Third Edition, Text Revision (ICSD-3-TR)2023.
Shelton AR. Sleep Disorders in Childhood. Continuum (Minneap Minn). 2023;29(4):1205-33. https://doi.org/10.1212/con.0000000000001285. PubMed PMID: 37590830.
Deboer T. Sleep homeostasis and the circadian clock: do the circadian pacemaker and the sleep homeostat influence each other’s functioning? Neurobiol Sleep Circadian Rhythms. 2018;5:68–77. https://doi.org/10.1016/j.nbscr.2018.02.003. Epub 20180301.
Amir RE, Van den Veyver IB, Wan M, Tran CQ, Francke U, Zoghbi HY. Rett syndrome is caused by mutations in X-linked MECP2, encoding methyl-CpG-binding protein 2. Nat Genet. 1999;23(2):185–8. https://doi.org/10.1038/13810. Epub 1999/10/03.
Neul JL, Kaufmann WE, Glaze DG, Christodoulou J, Clarke AJ, Bahi-Buisson N, Leonard H, Bailey ME, Schanen NC, Zappella M, Renieri A, Huppke P, Percy AK. Rett syndrome: revised diagnostic criteria and nomenclature. Ann Neurol. 2010;68(6):944–50. https://doi.org/10.1002/ana.22124. Epub 2010/12/15.
Ramirez JM, Ward CS, Neul JL. Breathing challenges in Rett syndrome: lessons learned from humans and animal models. Respir Physiol Neurobiol. 2013;189(2):280–7. https://doi.org/10.1016/j.resp.2013.06.022. Epub 2013/07/03.
Symons FJ, Byiers B, Hoch J, Dimian A, Barney C, Feyma T, Beisang A. Infrared thermal analysis and Individual Differences in skin temperature asymmetry in Rett Syndrome. Pediatr Neurol. 2015;53(2):169–72. 018. PubMed PMID: 26003587; PMCID: PMC4522200.
Fu C, Armstrong D, Marsh E, Lieberman D, Motil K, Witt R, Standridge S, Lane J, Dinkel T, Jones M, Hale K, Suter B, Glaze D, Neul J, Percy A, Benke T. Multisystem comorbidities in classic Rett syndrome: a scoping review. BMJ Paediatr Open. 2020;4(1):e000731. https://doi.org/10.1136/bmjpo-2020-000731. Epub 20200922.
Merbler AM, Byiers BJ, Garcia JJ, Feyma TJ, Symons FJ. The feasibility of using actigraphy to characterize sleep in Rett syndrome. J Neurodevelopmental Disorders. 2018;10(1):8. https://doi.org/10.1186/s11689-018-9227-z. Epub 2018/02/28.
Amaddeo A, De Sanctis L, Arroyo JO, Khirani S, Bahi-Buisson N, Fauroux B. Polysomnographic findings in Rett syndrome. European journal of paediatric neurology: EJPN : official journal of the European Paediatric Neurology Society. 2018. Epub 2018/09/29. https://doi.org/10.1016/j.ejpn.2018.09.003. PubMed PMID: 30262236.
Wong K, Leonard H, Jacoby P, Ellaway C, Downs J. The trajectories of sleep disturbances in Rett syndrome. J Sleep Res. 2015;24(2):223–33. https://doi.org/10.1111/jsr.12240. Epub 2014/09/16.
Kay C, Leonard H, Smith J, Wong K, Downs J. Genotype and sleep independently predict mental health in Rett syndrome: an observational study. J Med Genet. 2023;60(10):951–9. https://doi.org/10.1136/jmg-2022-108905. Epub 20230413.
Buchanan CB, Stallworth JL, Joy AE, Dixon RE, Scott AE, Beisang AA, Benke TA, Glaze DG, Haas RH, Heydemann PT, Jones MD, Lane JB, Lieberman DN, Marsh ED, Neul JL, Peters SU, Ryther RC, Skinner SA, Standridge SM, Kaufmann WE, Percy AK. Anxiety-like behavior and anxiolytic treatment in the Rett syndrome natural history study. J Neurodevelopmental Disorders. 2022;14(1):31. https://doi.org/10.1186/s11689-022-09432-2. Epub 20220514.
Amaddeo A, De Sanctis L, Arroyo JO, Khirani S, Bahi-Buisson N, Fauroux B. Polysomnographic findings in Rett syndrome. Eur J Paediatr Neurol. 2019;23(1):214–21. https://doi.org/10.1016/j.ejpn.2018.09.003. Epub 2018/09/29.
Zhang X, Smits M, Curfs L, Spruyt K. Sleep Respiratory Disturbances in Girls with Rett Syndrome. Int J Environ Res Public Health. 2022;19(20). Epub 20221012. https://doi.org/10.3390/ijerph192013082. PubMed PMID: 36293662; PMCID: PMC9602589.
Carotenuto M, Esposito M, D’Aniello A, Rippa CD, Precenzano F, Pascotto A, Bravaccio C, Elia M. Polysomnographic findings in Rett syndrome: a case-control study. Sleep Breath. 2013;17(1):93 – 8. Epub 20120307. https://doi.org/10.1007/s11325-012-0654-x. PubMed PMID: 22392651.
Glaze DG, Frost JD Jr., Zoghbi HY, Percy AK. Rett’s syndrome: characterization of respiratory patterns and sleep. Ann Neurol. 1987;21(4):377–82. https://doi.org/10.1002/ana.410210410. PubMed PMID: 3579223.
Sarber KM, Howard JJM, Dye TJ, Pascoe JE, Simakajornboon N. Sleep-disordered breathing in Pediatric patients with Rett Syndrome. J Clin Sleep Med. 2019;15(10):1451–7. https://doi.org/10.5664/jcsm.7974. PubMed PMID: 31596210; PMCID: PMC6778339. Epub 2019/10/10.
Ammanuel S, Chan WC, Adler DA, Lakshamanan BM, Gupta SS, Ewen JB, Johnston MV, Marcus CL, Naidu S, Kadam SD. Heightened Delta Power during Slow-Wave-Sleep in patients with Rett Syndrome Associated with Poor Sleep Efficiency. PLoS ONE. 2015;10(10):e0138113. https://doi.org/10.1371/journal.pone.0138113. Epub 20151007.
Achermann P, Dijk DJ, Brunner DP, Borbély AA. A model of human sleep homeostasis based on EEG slow-wave activity: quantitative comparison of data and simulations. Brain Res Bull. 1993;31(1–2):97–113. https://doi.org/10.1016/0361-9230(93)90016-5. PubMed PMID: 8453498.
Singh J, Santosh P. Key issues in Rett syndrome: emotional, behavioural and autonomic dysregulation (EBAD) - a target for clinical trials. Orphanet J Rare Dis. 2018;13(1):128. https://doi.org/10.1186/s13023-018-0873-8. Epub 20180731.
Guy J, Hendrich B, Holmes M, Martin JE, Bird A. A mouse Mecp2-null mutation causes neurological symptoms that mimic Rett syndrome. Nat Genet. 2001;27(3):322–6. https://doi.org/10.1038/85899. PubMed PMID: 11242117.
Chen RZ, Akbarian S, Tudor M, Jaenisch R. Deficiency of methyl-CpG binding protein-2 in CNS neurons results in a rett-like phenotype in mice. Nat Genet. 2001;27(3):327–31. doi: 10.1038/85906. PubMed PMID: 11242118.
Katz DM, Berger-Sweeney JE, Eubanks JH, Justice MJ, Neul JL, Pozzo-Miller L, Blue ME, Christian D, Crawley JN, Giustetto M, Guy J, Howell CJ, Kron M, Nelson SB, Samaco RC, Schaevitz LR, St Hillaire-Clarke C, Young JL, Zoghbi HY, Mamounas LA. Preclinical research in Rett syndrome: setting the foundation for translational success. Dis Model Mech. 2012;5(6):733–45. https://doi.org/10.1242/dmm.011007. PubMed PMID: 23115203; PMCID: PMC3484856.
Li Q, Loh DH, Kudo T, Truong D, Derakhshesh M, Kaswan ZM, Ghiani CA, Tsoa R, Cheng Y, Sun YE, Colwell CS. Circadian rhythm disruption in a mouse model of Rett syndrome circadian disruption in RTT. Neurobiol Dis. 2015;77:155–64. https://doi.org/10.1016/j.nbd.2015.03.009. Epub 2015/03/18.
Johnston MV, Ammanuel S, O’Driscoll C, Wozniak A, Naidu S, Kadam SD. Twenty-four hour quantitative-EEG and in-vivo glutamate biosensor detects activity and circadian rhythm dependent biomarkers of pathogenesis in Mecp2 null mice. Front Syst Neurosci. 2014;8:118. https://doi.org/10.3389/fnsys.2014.00118. Epub 20140627.
Alvarez-Saavedra M, Sáez MA, Kang D, Zoghbi HY, Young JI. Cell-specific expression of wild-type MeCP2 in mouse models of Rett syndrome yields insight about pathogenesis. Hum Mol Genet. 2007;16(19):2315–25. https://doi.org/10.1093/hmg/ddm185. Epub 20070717.
Dragich JM, Kim YH, Arnold AP, Schanen NC. Differential distribution of the MeCP2 splice variants in the postnatal mouse brain. J Comp Neurol. 2007;501(4):526–42. .21264. PubMed PMID: 17278130.
Jiang Y, Lev-Lehman E, Bressler J, Tsai TF, Beaudet AL. Genetics of Angelman syndrome. Am J Hum Genet. 1999;65(1):1–6. 10.1086/302473. PubMed PMID: 10364509; PMCID: PMC1378067.
Williams CA. The behavioral phenotype of the Angelman syndrome. Am J Med Genet C Semin Med Genet. 2010;154c(4):432-7. https://doi.org/10.1002/ajmg.c.30278. PubMed PMID: 20981772.
Peters SU, Horowitz L, Barbieri-Welge R, Taylor JL, Hundley RJ. Longitudinal follow-up of autism spectrum features and sensory behaviors in Angelman syndrome by deletion class. J Child Psychol Psychiatry. 2012;53(2):152-9. Epub 2011/08/13. https://doi.org/10.1111/j.1469-7610.2011.02455.x. PubMed PMID: 21831244.
Peters SU, Beaudet AL, Madduri N, Bacino CA. Autism in Angelman syndrome: implications for autism research. Clin Genet. 2004;66(6):530–6. https://doi.org/10.1111/j.1399-0004.2004.00362. .x. PubMed PMID: 15521981.
Gentile JK, Tan WH, Horowitz LT, Bacino CA, Skinner SA, Barbieri-Welge R, Bauer-Carlin A, Beaudet AL, Bichell TJ, Lee HS, Sahoo T, Waisbren SE, Bird LM, Peters SU. A neurodevelopmental survey of Angelman syndrome with genotype-phenotype correlations. J Dev Behav Pediatr. 2010;31(7):592–601. Epub 2010/08/24. https://doi.org/10.1097/DBP.0b013e3181ee408e. PubMed PMID: 20729760; PMCID: PMC2997715.
Williams CA, Beaudet AL, Clayton-Smith J, Knoll JH, Kyllerman M, Laan LA, Magenis RE, Moncla A, Schinzel AA, Summers JA, Wagstaff J. Angelman syndrome 2005: updated consensus for diagnostic criteria. Am J Med Genet A. 2006;140(5):413–8. https://doi.org/10.1002/ajmg.a.31074. PubMed PMID: 16470747.
Tan WH, Bacino CA, Skinner SA, Anselm I, Barbieri-Welge R, Bauer-Carlin A, Beaudet AL, Bichell TJ, Gentile JK, Glaze DG, Horowitz LT, Kothare SV, Lee HS, Nespeca MP, Peters SU, Sahoo T, Sarco D, Waisbren SE, Bird LM. Angelman syndrome: mutations influence features in early childhood. Am J Med Genet A. 2011;155a(1):81–90. PubMed PMID: 21204213; PMCID: PMC3563320.
Miodrag N, Peters S. Parent stress across molecular subtypes of children with Angelman syndrome. J Intellect Disabil Res. 2015;59(9):816–26. https://doi.org/10.1111/jir.12195. Epub 20150401.
Goldman SE, Bichell TJ, Surdyka K, Malow BA. Sleep in children and adolescents with Angelman syndrome: association with parent sleep and stress. J Intellect Disabil Res. 2012;56(6):600–8. https://doi.org/10.1111/j.1365-2788.2011.01499.x. Epub 20111102.
Hagenaar DA, Bindels-de Heus K, Lubbers K, Ten Hoopen LW, Rietman AB, de Nijs PFA, Hillegers MHJ, Moll HA, de Wit MCY, Dieleman GC, Mous SE. Child characteristics associated with child quality of life and parenting stress in Angelman syndrome. J Intellect Disabil Res. 2023. https://doi.org/10.1111/jir.13106. Epub 20231127.
Didden R, Korzilius H, Smits MG, Curfs LM. Sleep problems in individuals with Angelman syndrome. Am J Ment Retard. 2004;109(4):275–84. https://doi.org/10.1352/0895-8017(2004)109%3C275:Spiiws%3E2. 0.Co;2. PubMed PMID: 15176919.
Pelc K, Cheron G, Boyd SG, Dan B. Are there distinctive sleep problems in Angelman syndrome? Sleep Med. 2008;9(4):434–41. https://doi.org/10.1016/j.sleep.2007.07.001. Epub 20070831.
Connor-Ahmad S, Tjeertes J, Chladek M, Newton L, Symonds T, Clinch S, Vincenzi B, McDougall F. Developing Angelman syndrome-specific clinician-reported and caregiver-reported measures to support holistic, patient-centered drug development. Orphanet J Rare Dis. 2023;18(1):156. https://doi.org/10.1186/s13023-023-02729-y. Epub 20230622.
Conant KD, Thibert RL, Thiele EA. Epilepsy and the sleep-wake patterns found in Angelman syndrome. Epilepsia. 2009;50(11):2497–500. https://doi.org/10.1111/j.1528-1167.2009.02109. Epub 20090512.
Bruni O, Ferri R, D’Agostino G, Miano S, Roccella M, Elia M. Sleep disturbances in Angelman syndrome: a questionnaire study. Brain Dev. 2004;26(4):233–40. https://doi.org/10.1016/s0387-7604(03)00160-8. PubMed PMID: 15130689.
Walz NC, Beebe D, Byars K. Sleep in individuals with Angelman syndrome: parent perceptions of patterns and problems. Am J Ment Retard. 2005;110(4):243–52. 10.1352/0895-8017(2005)110[243:Siiwas]2.0.Co;2. PubMed PMID: 15941362.
Miano S, Bruni O, Leuzzi V, Elia M, Verrillo E, Ferri R. Sleep polygraphy in Angelman syndrome. Clin Neurophysiol. 2004;115(4):938–45. PubMed PMID: 15003776.
Miano S, Bruni O, Elia M, Musumeci SA, Verrillo E, Ferri R. Sleep breathing and periodic leg movement pattern in Angelman Syndrome: a polysomnographic study. Clin Neurophysiol. 2005;116(11):2685–92. https://doi.org/10.1016/j.clinph.2005.08.005. Epub 20051006.
den Bakker H, Sidorov MS, Fan Z, Lee DJ, Bird LM, Chu CJ, Philpot BD. Abnormal coherence and sleep composition in children with Angelman syndrome: a retrospective EEG study. Mol Autism. 2018;9:32. https://doi.org/10.1186/s13229-018-0214-8. Epub 2018/05/03.
Hanert A, Schönfeld R, Weber FD, Nowak A, Döhring J, Philippen S, Granert O, Burgalossi A, Born J, Berg D, Göder R, Häussermann P, Bartsch T. Reduced overnight memory consolidation and associated alterations in sleep spindles and slow oscillations in early Alzheimer’s disease. Neurobiol Dis. 2024;190:106378. https://doi.org/10.1016/j.nbd.2023.106378. Epub 20231215.
Manoach DS, Pan JQ, Purcell SM, Stickgold R. Reduced sleep spindles in Schizophrenia: a treatable endophenotype that links risk genes to impaired cognition? Biol Psychiatry. 2016;80(8):599–608. https://doi.org/10.1016/j.biopsych.2015.10.003. Epub 20151014.
Rotaru DC, Mientjes EJ, Elgersma Y. Angelman Syndrome: from mouse models to Therapy. Neuroscience. 2020;445:172–89. https://doi.org/10.1016/j.neuroscience.2020.02.017. Epub 20200221.
Shi SQ, Bichell TJ, Ihrie RA, Johnson CH. Ube3a imprinting impairs circadian robustness in Angelman syndrome models. Curr Biol. 2015;25(5):537–45. https://doi.org/10.1016/j.cub.2014.12.047. Epub 20150205.
Ehlen JC, Jones KA, Pinckney L, Gray CL, Burette S, Weinberg RJ, Evans JA, Brager AJ, Zylka MJ, Paul KN, Philpot BD, DeBruyne JP. Maternal Ube3a loss disrupts Sleep Homeostasis but leaves Circadian Rhythmicity largely intact. J Neurosci. 2015;35(40):13587–98. https://doi.org/10.1523/jneurosci.2194-15.2015. PubMed PMID: 26446213; PMCID: PMC4595617.
DeLorey TM, Handforth A, Anagnostaras SG, Homanics GE, Minassian BA, Asatourian A, Fanselow MS, Delgado-Escueta A, Ellison GD, Olsen RW. Mice lacking the beta3 subunit of the GABAA receptor have the epilepsy phenotype and many of the behavioral characteristics of Angelman syndrome. J Neurosci. 1998;18(20):8505-14. https://doi.org/10.1523/jneurosci.18-20-08505.1998. PubMed PMID: 9763493; PMCID: PMC6792844.
Lee D, Chen W, Kaku HN, Zhuo X, Chao ES, Soriano A, Kuncheria A, Flores S, Kim JH, Rivera A, Rigo F, Jafar-Nejad P, Beaudet AL, Caudill MS, Xue M. Antisense oligonucleotide therapy rescues disturbed brain rhythms and sleep in juvenile and adult mouse models of Angelman syndrome. Elife. 2023;12. Epub 20230103. doi: 10.7554/eLife.81892. PubMed PMID: 36594817; PMCID: PMC9904759.
Ravel A, Mircher C, Rebillat AS, Cieuta-Walti C, Megarbane A. Feeding problems and gastrointestinal diseases in Down syndrome. Arch Pediatr. 2020;27(1):53–60. PubMed PMID: 31784293.
Nordstrøm M, Retterstøl K, Hope S, Kolset SO. Nutritional challenges in children and adolescents with Down syndrome. Lancet Child Adolesc Health. 2020;4(6):455–64. https://doi.org/10.1016/s2352-4642(19)30400-6. Epub 2020/05/26.
Alexander M, Petri H, Ding Y, Wandel C, Khwaja O, Foskett N. Morbidity and medication in a large population of individuals with Down syndrome compared to the general population. Dev Med Child Neurol. 2016;58(3):246–54. https://doi.org/10.1111/dmcn.12868. Epub 2015/08/19.
Will EA, Gerlach-McDonald B, Fidler DJ, Daunhauer LA. Impact of maladaptive behavior on school function in Down syndrome. Res Dev Disabil. 2016;59:328–37. https://doi.org/10.1016/j.ridd.2016.08.018. Epub 2016/09/27.
Churchill SS, Kieckhefer GM, Landis CA, Ward TM. Sleep measurement and monitoring in children with Down syndrome: a review of the literature, 1960–2010. Sleep Med Rev. 2012;16(5):477–88. https://doi.org/10.1016/j.smrv.2011.10.003. Epub 20120310.
Hamaguchi H, Hashimoto T, Mori K, Tayama M. Sleep in the Down syndrome. Brain Dev. 1989;11(6):399–406. https://doi.org/10.1016/s0387-7604(89)80024-5. PubMed PMID: 2533465.
Esbensen AJ, Hoffman EK, Stansberry E, Shaffer R. Convergent validity of actigraphy with polysomnography and parent reports when measuring sleep in children with Down syndrome. J Intellect Disabil Res. 2018;62(4):281–91. https://doi.org/10.1111/jir.12464. Epub 2018/01/10.
Chawla JK, Burgess S, Heussler H. The impact of sleep problems on functional and cognitive outcomes in children with Down syndrome: a review of the literature. J Clin Sleep Med. 2020;16(10):1785–95. https://doi.org/10.5664/jcsm.8630. PubMed PMID: 32536364; PMCID: PMC7954012.
Giménez S, Videla L, Romero S, Benejam B, Clos S, Fernández S, Martínez M, Carmona-Iragui M, Antonijoan RM, Mayos M, Fortuna A, Peñacoba P, Plaza V, Osorio RS, Sharma RA, Bardés I, Rebillat AS, Lleó A, Blesa R, Videla S, Fortea J. Prevalence of Sleep disorders in adults with Down Syndrome: a comparative study of Self-Reported, Actigraphic, and polysomnographic findings. J Clin Sleep Med. 2018;14(10):1725–33. Epub 20181015. doi: 10.5664/jcsm.7382. PubMed PMID: 30353801; PMCID: PMC6175810.
Mitchell RB, Archer SM, Ishman SL, Rosenfeld RM, Coles S, Finestone SA, Friedman NR, Giordano T, Hildrew DM, Kim TW, Lloyd RM, Parikh SR, Shulman ST, Walner DL, Walsh SA, Nnacheta LC. Clinical practice Guideline: Tonsillectomy in Children (Update)-Executive Summary. Otolaryngol Head Neck Surg. 2019;160(2):187–205. doi: 10.1177/0194599818807917. PubMed PMID: 30921525.
Abdel-Aziz M, Azooz K, Naguib N, Reda R, Kamel A. The effect of adenotonsillectomy on obstructive sleep apnea in children with Down syndrome(). Acta Otolaryngol. 2017;137(9):981–5. https://doi.org/10.1080/00016489.2017.1312016. Epub 20170411.
Ishman SL, Maturo S, Schwartz S, McKenna M, Baldassari CM, Bergeron M, Chernobilsky B, Ehsan Z, Gagnon L, Liu YC, Smith DF, Stanley J, Zalzal H, Dhepyasuwan N. Expert Consensus Statement: management of Pediatric Persistent Obstructive Sleep Apnea after Adenotonsillectomy. Otolaryngol Head Neck Surg. 2023;168(2):115–30. https://doi.org/10.1002/ohn.159. PubMed PMID: 36757810; PMCID: PMC10105630.
Breslin J, Spanò G, Bootzin R, Anand P, Nadel L, Edgin J. Obstructive sleep apnea syndrome and cognition in Down syndrome. Dev Med Child Neurol. 2014;56(7):657–64. https://doi.org/10.1111/dmcn.12376. Epub 20140129.
Esbensen AJ, Hoffman EK. Impact of sleep on executive functioning in school-age children with Down syndrome. J Intellect Disabil Res. 2018;62(6):569–80. https://doi.org/10.1111/jir.12496. Epub 2018/04/27.
Esbensen AJ, Hoffman EK, Beebe DW, Byars KC, Epstein J. Links between sleep and daytime behaviour problems in children with Down syndrome. J Intellect Disabil Res. 2018;62(2):115–25. https://doi.org/10.1111/jir.12463. Epub 2017/12/29.
Joyce A, Elphick H, Farquhar M, Gringras P, Evans H, Bucks RS, Kreppner J, Kingshott R, Martin J, Reynolds J, Rush C, Gavlak J, Hill CM. Obstructive sleep Apnoea contributes to executive function impairment in Young Children with Down Syndrome. Behav Sleep Med. 2020;18(5):611–21. https://doi.org/10.1080/15402002.2019.1641501. Epub 20190716.
Horne RSC, Sakthiakumaran A, Bassam A, Thacker J, Walter LM, Davey MJ, Nixon GM. Children with Down syndrome and sleep disordered breathing have altered cardiovascular control. Pediatr Res. 2021;90(4):819–25. https://doi.org/10.1038/s41390-020-01285-6. Epub 20201123.
Fleming V, Piro-Gambetti B, Bazydlo A, Zammit M, Alexander AL, Christian BT, Handen B, Plante DT, Hartley SL. Sleep and white matter in adults with Down Syndrome. Brain Sci. 2021;11(10). https://doi.org/10.3390/brainsci11101322. Epub 20211005.
Rosen D, Berbert L, Weller E. High prevalence of periodic limb movements of sleep in children with Down syndrome. J Clin Sleep Med. 2020;16(3):347–52. https://doi.org/10.5664/jcsm.8202. Epub 20200114.
Heubi CH, Knollman P, Wiley S, Shott SR, Smith DF, Ishman SL, Meinzen-Derr J. Sleep Architecture in Children with Down Syndrome with and without obstructive sleep apnea. Otolaryngol Head Neck Surg. 2021;164(5):1108–15. https://doi.org/10.1177/0194599820960454. Epub 2020/10/07.
Xing Z, Li Y, Pao A, Bennett AS, Tycko B, Mobley WC, Yu YE. Mouse-based genetic modeling and analysis of Down syndrome. Br Med Bull. 2016;120(1):111–22. https://doi.org/10.1093/bmb/ldw040. Epub 20161027.
Akeson EC, Lambert JP, Narayanswami S, Gardiner K, Bechtel LJ, Davisson MT. Ts65Dn -- localization of the translocation breakpoint and trisomic gene content in a mouse model for Down syndrome. Cytogenet Cell Genet. 2001;93(3–4):270–6. 10.1159/000056997. PubMed PMID: 11528125.
Whitney KN, Wenger GR. Working memory in the aged Ts65Dn mouse, a model for Down syndrome. Behav Brain Res. 2012;232(1):202–9. https://doi.org/10.1016/j.bbr.2012.03.042. Epub 20120404.
Bartolucci ML, Berteotti C, Alvente S, Bastianini S, Guidi S, Lo Martire V, Matteoli G, Silvani A, Stagni F, Bosi M, Alessandri-Bonetti G, Bartesaghi R, Zoccoli G. Obstructive sleep apneas naturally occur in mice during REM sleep and are highly prevalent in a mouse model of Down syndrome. Neurobiol Dis. 2021;159:105508. https://doi.org/10.1016/j.nbd.2021.105508. Epub 20210910.
Heise I, Fisher SP, Banks GT, Wells S, Peirson SN, Foster RG, Nolan PM. Sleep-like behavior and 24-h rhythm disruption in the Tc1 mouse model of Down syndrome. Genes Brain Behav. 2015;14(2):209–16. https://doi.org/10.1111/gbb.12198. Epub 20150216.
Colas D, Valletta JS, Takimoto-Kimura R, Nishino S, Fujiki N, Mobley WC, Mignot E. Sleep and EEG features in genetic models of Down syndrome. Neurobiol Dis. 2008;30(1):1–7. https://doi.org/10.1016/j.nbd.2007.07.014. Epub 20070721.
Kazuki Y, Gao FJ, Li Y, Moyer AJ, Devenney B, Hiramatsu K, Miyagawa-Tomita S, Abe S, Kazuki K, Kajitani N, Uno N, Takehara S, Takiguchi M, Yamakawa M, Hasegawa A, Shimizu R, Matsukura S, Noda N, Ogonuki N, Inoue K, Matoba S, Ogura A, Florea LD, Savonenko A, Xiao M, Wu D, Batista DA, Yang J, Qiu Z, Singh N, Richtsmeier JT, Takeuchi T, Oshimura M, Reeves RH. A non-mosaic transchromosomic mouse model of down syndrome carrying the long arm of human chromosome 21. Elife. 2020;9. Epub 20200629. doi: 10.7554/eLife.56223. PubMed PMID: 32597754; PMCID: PMC7358007.
Maenner MJ, Warren Z, Williams AR, Amoakohene E, Bakian AV, Bilder DA, Durkin MS, Fitzgerald RT, Furnier SM, Hughes MM, Ladd-Acosta CM, McArthur D, Pas ET, Salinas A, Vehorn A, Williams S, Esler A, Grzybowski A, Hall-Lande J, Nguyen RHN, Pierce K, Zahorodny W, Hudson A, Hallas L, Mancilla KC, Patrick M, Shenouda J, Sidwell K, DiRienzo M, Gutierrez J, Spivey MH, Lopez M, Pettygrove S, Schwenk YD, Washington A, Shaw KA. Prevalence and characteristics of Autism Spectrum Disorder among children aged 8 years - Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2020. MMWR Surveill Summ. 2023;72(2):1–14. https://doi.org/10.15585/mmwr.ss7202a1. Epub 20230324.
Percy AK. Rett syndrome: exploring the autism link. Arch Neurol. 2011;68(8):985–9. https://doi.org/10.1001/archneurol.2011.149. PubMed PMID: 21825235; PMCID: PMC3674963.
Channell MM, Phillips BA, Loveall SJ, Conners FA, Bussanich PM, Klinger LG. Patterns of autism spectrum symptomatology in individuals with Down syndrome without comorbid autism spectrum disorder. J Neurodev Disord. 2015;7(1):5. https://doi.org/10.1186/1866-1955-7-5. Epub 20150117.
Silverman JL, Yang M, Lord C, Crawley JN. Behavioural phenotyping assays for mouse models of autism. Nat Rev Neurosci. 2010;11(7):490–502. https://doi.org/10.1038/nrn2851. PubMed PMID: 20559336; PMCID: PMC3087436.
Kazdoba TM, Leach PT, Crawley JN. Behavioral phenotypes of genetic mouse models of autism. Genes Brain Behav. 2016;15(1):7–26. Epub 20151022. doi: 10.1111/gbb.12256. PubMed PMID: 26403076; PMCID: PMC4775274.
Malow BA, Marzec ML, McGrew SG, Wang L, Henderson LM, Stone WL. Characterizing sleep in children with autism spectrum disorders: a multidimensional approach. Sleep. 2006;29(12):1563–71. https://doi.org/10.1093/sleep/29.12.1563. PubMed PMID: 17252887.
Richdale AL, Schreck KA. Sleep problems in autism spectrum disorders: prevalence, nature, & possible biopsychosocial aetiologies. Sleep Med Rev. 2009;13(6):403–11. https://doi.org/10.1016/j.smrv.2009.02.003. Epub 20090424.
Johnson KP, Zarrinnegar P, Clin. N Am. 2021;30(1):195–208. https://doi.org/10.1016/j.chc.2020.08.012. PubMed PMID: 33223062.
Smith AM, Johnson AH, Bashore L. Exploration of sleep disturbances in children and adolescents with and without autism in a paediatric sample referred for polysomnography. J Paediatr Child Health. 2023;59(8):948–54. https://doi.org/10.1111/jpc.16421. Epub 20230510.
Malow BA, Katz T, Reynolds AM, Shui A, Carno M, Connolly HV, Coury D, Bennett AE. Sleep Difficulties and Medications in Children With Autism Spectrum Disorders: A Registry Study. Pediatrics. 2016;137 Suppl 2:S98-s104. https://doi.org/10.1542/peds.2015-2851H. PubMed PMID: 26908483.
Elrod MG, Hood BS. Sleep differences among children with autism spectrum disorders and typically developing peers: a meta-analysis. J Dev Behav Pediatr. 2015;36(3):166–77. https://doi.org/10.1097/dbp.0000000000000140. PubMed PMID: 25741949.
Chen H, Yang T, Chen J, Chen L, Dai Y, Zhang J, Li L, Jia F, Wu L, Hao Y, Ke X, Yi M, Hong Q, Chen J, Fang S, Wang Y, Wang Q, Jin C, Li T. Sleep problems in children with autism spectrum disorder: a multicenter survey. BMC Psychiatry. 2021;21(1):406. https://doi.org/10.1186/s12888-021-03405-w. Epub 20210816.
Goldman SE, Alder ML, Burgess HJ, Corbett BA, Hundley R, Wofford D, Fawkes DB, Wang L, Laudenslager ML, Malow BA. Characterizing sleep in adolescents and adults with Autism Spectrum disorders. J Autism Dev Disord. 2017;47(6):1682–95. https://doi.org/10.1007/s10803-017-3089-1. PubMed PMID: 28286917; PMCID: PMC5433911.
Yavuz-Kodat E, Reynaud E, Geoffray MM, Limousin N, Franco P, Bourgin P, Schroder CM. Validity of Actigraphy compared to Polysomnography for Sleep Assessment in Children with Autism Spectrum Disorder. Front Psychiatry. 2019;10:551. https://doi.org/10.3389/fpsyt.2019.00551. Epub 2019/08/21.
Goldman SE, Richdale AL, Clemons T, Malow BA. Parental sleep concerns in autism spectrum disorders: variations from childhood to adolescence. J Autism Dev Disord. 2012;42(4):531–8. https://doi.org/10.1007/s10803-011-1270-5. PubMed PMID: 21538171.
Lindor E, Sivaratnam C, May T, Stefanac N, Howells K, Rinehart N. Problem Behavior in Autism Spectrum Disorder: considering Core Symptom Severity and Accompanying Sleep Disturbance. Front Psychiatry. 2019;10:487. https://doi.org/10.3389/fpsyt.2019.00487. Epub 20190712.
Molcho-Haimovich A, Tikotzky L, Meiri G, Ilan M, Michaelovski A, Schtaierman H, Golan HM, Sadaka Y, Menashe I, Dinstein I. Sleep disturbances are associated with irritability in ASD children with sensory sensitivities. J Neurodevelopmental Disorders. 2023;15(1):21. https://doi.org/10.1186/s11689-023-09491-z. Epub 20230721.
Tzischinsky O, Meiri G, Manelis L, Bar-Sinai A, Flusser H, Michaelovski A, Zivan O, Ilan M, Faroy M, Menashe I, Dinstein I. Sleep disturbances are associated with specific sensory sensitivities in children with autism. Mol Autism. 2018;9:22. https://doi.org/10.1186/s13229-018-0206-8. Epub 20180327.
Hundley RJ, Shui A, Malow BA. Relationship between subtypes of restricted and repetitive behaviors and Sleep Disturbance in Autism Spectrum Disorder. J Autism Dev Disord. 2016;46(11):3448–57. https://doi.org/10.1007/s10803-016-2884-4. PubMed PMID: 27511195.
Galli J, Loi E, Visconti LM, Mattei P, Eusebi A, Calza S, Fazzi E. Sleep disturbances in children affected by Autism Spectrum Disorder. Front Psychiatry. 2022;13:736696. https://doi.org/10.3389/fpsyt.2022.736696. Epub 20220217.
Melke J, Goubran Botros H, Chaste P, Betancur C, Nygren G, Anckarsäter H, Rastam M, Ståhlberg O, Gillberg IC, Delorme R, Chabane N, Mouren-Simeoni MC, Fauchereau F, Durand CM, Chevalier F, Drouot X, Collet C, Launay JM, Leboyer M, Gillberg C, Bourgeron T. Abnormal melatonin synthesis in autism spectrum disorders. Mol Psychiatry. 2008;13(1):90–8. https://doi.org/10.1038/sj.mp.4002016. Epub 20070515.
Leu RM, Beyderman L, Botzolakis EJ, Surdyka K, Wang L, Malow BA. Relation of melatonin to sleep architecture in children with autism. J Autism Dev Disord. 2011;41(4):427–33. https://doi.org/10.1007/s10803-010-1072-1. PubMed PMID: 20683768; PMCID: PMC3746009.
Pagan C, Delorme R, Callebert J, Goubran-Botros H, Amsellem F, Drouot X, Boudebesse C, Le Dudal K, Ngo-Nguyen N, Laouamri H, Gillberg C, Leboyer M, Bourgeron T, Launay JM. The serotonin-N-acetylserotonin-melatonin pathway as a biomarker for autism spectrum disorders. Transl Psychiatry. 2014;4(11):e479. https://doi.org/10.1038/tp.2014.120. Epub 20141111.
Lee BH, Hille B, Koh DS. Serotonin modulates melatonin synthesis as an autocrine neurotransmitter in the pineal gland. Proc Natl Acad Sci U S A. 2021;118(43). https://doi.org/10.1073/pnas.2113852118. PubMed PMID: 34675083; PMCID: PMC8639368.
Jonsson L, Ljunggren E, Bremer A, Pedersen C, Landén M, Thuresson K, Giacobini M, Melke J. Mutation screening of melatonin-related genes in patients with autism spectrum disorders. BMC Med Genomics. 2010;3:10. Epub 20100408. https://doi.org/10.1186/1755-8794-3-10. PubMed PMID: 20377855; PMCID: PMC3020629.
Pagan C, Botros HG, Poirier K, Dumaine A, Jamain S, Moreno S, de Brouwer A, Van Esch H, Delorme R, Launay JM, Tzschach A, Kalscheuer V, Lacombe D, Briault S, Laumonnier F, Raynaud M, van Bon BW, Willemsen MH, Leboyer M, Chelly J, Bourgeron T. Mutation screening of ASMT, the last enzyme of the melatonin pathway, in a large sample of patients with intellectual disability. BMC Med Genet. 2011;12:17. https://doi.org/10.1186/1471-2350-12-17. Epub 20110120.
Benabou M, Rolland T, Leblond CS, Millot GA, Huguet G, Delorme R, Leboyer M, Pagan C, Callebert J, Maronde E, Bourgeron T. Heritability of the melatonin synthesis variability in autism spectrum disorders. Sci Rep. 2017;7(1):17746. https://doi.org/10.1038/s41598-017-18016-3. Epub 20171218.
Yang CJ, Tan HP, Du YJ. The developmental disruptions of serotonin signaling may involved in autism during early brain development. Neuroscience. 2014;267:1–10. https://doi.org/10.1016/j.neuroscience.2014.02.021. Epub 20140227.
Toma C, Rossi M, Sousa I, Blasi F, Bacchelli E, Alen R, Vanhala R, Monaco AP, Järvelä I, Maestrini E. Is ASMT a susceptibility gene for autism spectrum disorders? A replication study in European populations. Mol Psychiatry. 2007;12(11):977–9. https://doi.org/10.1038/sj.mp.4002069. PubMed PMID: 17957233.
Siemann JK, Muller CL, Forsberg CG, Blakely RD, Veenstra-VanderWeele J, Wallace MT. An autism-associated serotonin transporter variant disrupts multisensory processing. Transl Psychiatry. 2017;7(3):e1067. https://doi.org/10.1038/tp.2017.17. Epub 20170321.
Goldman SE, Adkins KW, Calcutt MW, Carter MD, Goodpaster RL, Wang L, Shi Y, Burgess HJ, Hachey DL, Malow BA. Melatonin in children with autism spectrum disorders: endogenous and pharmacokinetic profiles in relation to sleep. J Autism Dev Disord. 2014;44(10):2525–35. https://doi.org/10.1007/s10803-014-2123-9. PubMed PMID: 24752680; PMCID: PMC4372534.
Schroder CM, Malow BA, Maras A, Melmed RD, Findling RL, Breddy J, Nir T, Shahmoon S, Zisapel N, Gringras P. Pediatric prolonged-release melatonin for Sleep in Children with Autism Spectrum Disorder: Impact on Child Behavior and Caregiver’s quality of life. J Autism Dev Disord. 2019;49(8):3218–30. https://doi.org/10.1007/s10803-019-04046-5. PubMed PMID: 31079275; PMCID: PMC6647439.
Maras A, Schroder CM, Malow BA, Findling RL, Breddy J, Nir T, Shahmoon S, Zisapel N, Gringras P. Long-term efficacy and safety of Pediatric prolonged-release melatonin for Insomnia in Children with Autism Spectrum Disorder. J Child Adolesc Psychopharmacol. 2018;28(10):699–710. https://doi.org/10.1089/cap.2018.0020. Epub 20181011.
Roliz AH, Kothare S. The Interaction Between Sleep and Epilepsy. Curr Neurol Neurosci Rep. 2022;22(9):551 – 63. Epub 20220708. https://doi.org/10.1007/s11910-022-01219-1. PubMed PMID: 35802300.
Aarts N, Zuurbier LA, Noordam R, Hofman A, Tiemeier H, Stricker BH, Visser LE. Use of selective serotonin reuptake inhibitors and Sleep Quality: a Population-based study. J Clin Sleep Med. 2016;12(7):989–95. https://doi.org/10.5664/jcsm.5932. Epub 20160715.
Luiselli JK, Magee C, Sperry JM, Parker S. Descriptive assessment of sleep patterns among community-living adults with mental retardation. Ment Retard. 2005;43(6):416–20. doi: 10.1352/0047-6765(2005)43[416:Daospa]2.0.Co;2. PubMed PMID: 16266209.
Liguori C, Toledo M, Kothare S. Effects of anti-seizure medications on sleep architecture and daytime sleepiness in patients with epilepsy: a literature review. Sleep Med Rev. 2021;60:101559. https://doi.org/10.1016/j.smrv.2021.101559. Epub 20211008.
Moore M, Evans V, Hanvey G, Johnson C. Assessment of Sleep in Children with Autism Spectrum Disorder. Children (Basel). 2017;4(8). Epub 2017/08/09. https://doi.org/10.3390/children4080072. PubMed PMID: 28786962; PMCID: PMC5575594.
Bélanger M-È, Bernier A, Paquet J, Simard V, Carrier J. Validating actigraphy as a measure of Sleep for Preschool Children. J Clin Sleep Medicine: JCSM : Official Publication Am Acad Sleep Med. 2013;9(7):701–6. https://doi.org/10.5664/jcsm.2844. PubMed PMID: PMC3671336.
Friedman NR, Ruiz AG, Gao D, Ingram DG. Accuracy of parental perception of Nighttime Breathing in Children with Down Syndrome. Otolaryngol Head Neck Surg. 2018;158(2):364–7. Epub 20170905. doi: 10.1177/0194599817726286. PubMed PMID: 28871845.
Migovich M, Ullal A, Fu C, Peters SU. Sarkar, N. Feasibility of wearable devices for sleep classification in children with Rett Syndrome: a pilot study. Digit Health. 2023.
Eylon G, Tikotzky L, Dinstein I. Performance evaluation of Fitbit Charge 3 and actigraphy vs. polysomnography: sensitivity, specificity, and reliability across participants and nights. Sleep Health. 2023;9(4):407–16. https://doi.org/10.1016/j.sleh.2023.04.001. Epub 20230601.
Will EA, Daunhauer LA, Fidler DJ, Raitano Lee N, Rosenberg CR, Hepburn SL. Sensory Processing and Maladaptive Behavior: profiles within the Down Syndrome phenotype. Phys Occup Ther Pediatr. 2019;39(5):461–76. PubMed PMID: 31070074; PMCID: PMC8011957.
Alder ML, Johnson CR, Zauszniewski JA, Malow BA, Burant CJ, Scahill L. Feasibility of actigraphy for evaluating sleep and daytime physical activity in children with Autism Spectrum Disorder. J Autism Dev Disord. 2022. https://doi.org/10.1007/s10803-022-05661-5. Epub 20220713.
Liu F, Schrack J, Wanigatunga SK, Rabinowitz JA, He L, Wanigatunga AA, Zipunnikov V, Simonsick EM, Ferrucci L, Spira AP. Comparison of sleep parameters from wrist-worn ActiGraph and actiwatch devices. Sleep. 2024;47(2). https://doi.org/10.1093/sleep/zsad155. PubMed PMID: 37257489; PMCID: PMC10851854.
van Kooten J, Jacobse STW, Heymans MW, de Vries R, Kaspers GJL, van Litsenburg RRL. A meta-analysis of accelerometer sleep outcomes in healthy children based on the Sadeh algorithm: the influence of child and device characteristics. Sleep. 2021;44(4). https://doi.org/10.1093/sleep/zsaa231. PubMed PMID: 33161428.
Quante M, Kaplan ER, Cailler M, Rueschman M, Wang R, Weng J, Taveras EM, Redline S. Actigraphy-based sleep estimation in adolescents and adults: a comparison with polysomnography using two scoring algorithms. Nat Sci Sleep. 2018;10:13–20. https://doi.org/10.2147/nss.S151085. Epub 20180118.
Alder ML, Ye F, Run F, Bagai K, Fawkes DB, Peterson BT, Malow BA. Application of a novel actigraphy algorithm to detect movement and sleep/wake patterns in children with autism spectrum disorder. Sleep Med. 2020;71:28–34. https://doi.org/10.1016/j.sleep.2020.02.020. Epub 20200306.
Tran NT, Tran HN, Mai AT. A wearable device for at-home obstructive sleep apnea assessment: state-of-the-art and research challenges. Front Neurol. 2023;14:1123227. https://doi.org/10.3389/fneur.2023.1123227. Epub 20230207.
Santaji S, Desai V. Analysis of EEG Signal to Classify Sleep stages using machine learning. Sleep Vigilance. 2020;4(2):145–52. https://doi.org/10.1007/s41782-020-00101-9.
Haghayegh S, Khoshnevis S, Smolensky MH, Diller KR, Castriotta RJ. Accuracy of Wristband Fitbit models in assessing sleep: systematic review and Meta-analysis. J Med Internet Res. 2019;21(11):e16273. Epub 20191128. doi: 10.2196/16273. PubMed PMID: 31778122; PMCID: PMC6908975.
Mikkelsen KB, Ebajemito JK, Bonmati-Carrion MA, Santhi N, Revell VL, Atzori G, Della Monica C, Debener S, Dijk DJ, Sterr A, de Vos M. Machine-learning-derived sleep-wake staging from around-the-ear electroencephalogram outperforms manual scoring and actigraphy. J Sleep Res. 2019;28(2):e12786. https://doi.org/10.1111/jsr.12786. Epub 20181113.
Cielo CM, Kelly A, Xanthopoulos M, Pipan M, Arputhan A, Walega R, Ward M, Falvo J, Roman Y, Xiao R, Tapia IE. Feasibility and performance of home sleep apnea testing in youth with Down syndrome. J Clin Sleep Med. 2023;19(9):1605–13. https://doi.org/10.5664/jcsm.10610. PubMed PMID: 37185265; PMCID: PMC10476042.
Goldsack JC, Coravos A, Bakker JP, Bent B, Dowling AV, Fitzer-Attas C, Godfrey A, Godino JG, Gujar N, Izmailova E, Manta C, Peterson B, Vandendriessche B, Wood WA, Wang KW, Dunn J. Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs). NPJ Digit Med. 2020;3:55. https://doi.org/10.1038/s41746-020-0260-4. Epub 20200414.
Aili K, Åström-Paulsson S, Stoetzer U, Svartengren M, Hillert L. Reliability of actigraphy and subjective sleep measurements in adults: the design of Sleep assessments. J Clin Sleep Med. 2017;13(1):39–47. https://doi.org/10.5664/jcsm.6384. Epub 20170115.
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Peters, S.U., Shelton, A.R., Malow, B.A. et al. A clinical-translational review of sleep problems in neurodevelopmental disabilities. J Neurodevelop Disord 16, 41 (2024). https://doi.org/10.1186/s11689-024-09559-4
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DOI: https://doi.org/10.1186/s11689-024-09559-4