Subjective and Electroencephalographic Sleep Parameters in Children and Adolescents with Autism Spectrum Disorder: A Systematic Review

Stress in autism (STREAM): A study protocol on the role of circadian activity, sleep quality and sensory reactivity

Mental health issues are markedly increased in individuals with autism, making it the number one research priority by stakeholders. There is a crucial need to use personalized approaches to understand the underpinnings of mental illness in autism and consequently, to address individual needs. Based on the risk factors identified in typical mental research, we propose the following themes central to mental health issues in autism: sleep difficulties and stress. Indeed, the prevalence of manifold circadian disruptions and sleep difficulties in autism, alongside stress related to sensory overload, forms an integral part of autistic symptomatology. This proof-of-concept study protocol outlines an innovative, individualised approach towards investigating the interrelationships between stress indices, sleep and circadian activation patterns, and sensory sensitivity in autism. Embracing an individualized methodology, we aim to collect 14 days of data per participant from 20 individuals with autism diagnoses and 20 without. Participants' sleep will be monitored using wearable EEG headbands and a sleep diary. Diurnal tracking of heart rate and electrodermal activity through wearables will serve as proxies of stress. Those objective data will be synchronized with subjective experience traces collected throughout the day using the Temporal Experience Tracing (TET) method. TET facilitates the quantification of relevant aspects of individual experience states, such as stress or sensory sensitivities, by providing a continuous multidimensional description of subjective experiences. Capturing the dynamics of subjective experiences phase-locked to neural and physiological proxies both between and within individuals, this approach has the potential to contribute to our understanding of critical issues in autism, including sleep problems, sensory reactivity and stress. The planned strives to provide a pathway towards developing a more nuanced and individualized approach to addressing mental health in autism.

Sleep quality relates to language impairment in children with autism spectrum disorder without intellectual disability

OBJECTIVES

This study aimed to identify sleep quality profiles of children with autism spectrum disorder (ASD), to compare these profiles with those of typically developing (TD) children, and to verify whether there are differences between them in terms of language skills.

METHODS

We evaluated the sleep quality and language skills of 47 children with ASD without intellectual disability (ID) and 32 children with TD. Using a hierarchical cluster analysis, we identified two sleep quality ASD profiles (poor and good). We then performed a series of MANCOVAs and ANOVAs to compare the sleep quality and language skills of the two ASD clusters and the TD group.

RESULTS

A main group effect (TD, "poor" cluster, and "good" cluster) was found in the total sleep quality and all its dimensions. Significant differences were revealed between the "good" and "poor" clusters in the total structural language score (F1,46 = 10.75, p < 0.001) and three of its subscales (speech: F1,46 = 9.19, p < 0.001; syntax, F1,46 = 8.61, p = 0.001; coherence: F1,46 = 11.36, p < 0.001); the total pragmatic language score (F1,46 = 7.00, p = 0.001) and three of its subscales (inappropriate initiation: F1,46 = 8.02, p = 0.001; use of context: F1,46 = 8.07, p = 0.001; nonverbal communication: F1,46 = 7.35, p = 0.001); and the social relations score (F1,46 = 9.97, p = 0.003).

CONCLUSIONS

Sleep quality in children with ASD (especially a subgroup) is worse than in children with TD. There is an association between sleep quality and language skills, both at the pragmatic and structural levels.

Sleep and Autism: Current Research, Clinical Assessment, and Treatment Strategies

Autism spectrum disorder is associated with a high rate of sleep problems, affecting over 80% of autistic individuals. Sleep problems have pervasive negative effects on health, behavior, mood, and cognition but are underrecognized in autistic children. Problems initiating and maintaining sleep-hallmarks of insomnia-are common. Sleep-disordered breathing and restless legs syndrome have also been described in autism at a higher prevalence than in community populations. The authors describe current research on sleep in autistic children and potential pathophysiologic mechanisms. They describe practical approaches to sleep assessment and synthesize approaches to addressing sleep problems in autistic children.

Automated diagnosis of autism with artificial intelligence: State of the art

Autism spectrum disorder (ASD) represents a panel of conditions that begin during the developmental period and result in impairments of personal, social, academic, or occupational functioning. Early diagnosis is directly related to a better prognosis. Unfortunately, the diagnosis of ASD requires a long and exhausting subjective process. We aimed to review the state of the art for automated autism diagnosis and recognition in this research. In February 2022, we searched multiple databases and sources of gray literature for eligible studies. We used an adapted version of the QUADAS-2 tool to assess the risk of bias in the studies. A brief report of the methods and results of each study is presented. Data were synthesized for each modality separately using the Split Component Synthesis (SCS) method. We assessed heterogeneity using the I 2 statistics and evaluated publication bias using trim and fill tests combined with ln DOR. Confidence in cumulative evidence was assessed using the GRADE approach for diagnostic studies. We included 344 studies from 186,020 participants (51,129 are estimated to be unique) for nine different modalities in this review, from which 232 reported sufficient data for meta-analysis. The area under the curve was in the range of 0.71-0.90 for all the modalities. The studies on EEG data provided the best accuracy, with the area under the curve ranging between 0.85 and 0.93. We found that the literature is rife with bias and methodological/reporting flaws. Recommendations are provided for future research to provide better studies and fill in the current knowledge gaps.

Children with Autism Spectrum Disorder and Abnormalities of Clinical EEG: A Qualitative Review

Over the last decade, the comorbidity between Autism Spectrum Disorder (ASD) and epilepsy has been widely demonstrated, and many hypotheses regarding the common neurobiological bases of these disorders have been put forward. A variable, but significant, prevalence of abnormalities on electroencephalogram (EEG) has been documented in non-epileptic children with ASD; therefore, several scientific studies have recently tried to demonstrate the role of these abnormalities as a possible biomarker of altered neural connectivity in ASD individuals. This narrative review intends to summarize the main findings of the recent scientific literature regarding abnormalities detected with standard EEG in children/adolescents with idiopathic ASD. Research using three different databases (PubMed, Scopus and Google Scholar) was conducted, resulting in the selection of 10 original articles. Despite an important lack of studies on preschoolers and a deep heterogeneity in results, some authors speculated on a possible association between EEG abnormalities and ASD characteristics, in particular, the severity of symptoms. Although this correlation needs to be more strongly elucidated, these findings may encourage future studies aimed at demonstrating the role of electrical brain abnormalities as an early biomarker of neural circuit alterations in ASD, highlighting the potential diagnostic, prognostic and therapeutic value of EEG in this field.

The role of vitamin D deficiency in the development of paediatric diseases

Children's Vitamin D (VitD) fortification and supplementation are diminishing due to less outdoor exercise and insufficient VitD intake (low exogenous intake and endogenous malabsorption induced by gastrointestinal disease). Consequently, children in many developed countries suffer from VitD deficiency, which may contribute to many paediatric disorders. Our review briefly introduced the metabolic process of VitD, summarized the role of VitD in paediatric diseases such as autism, obesity, rickets and asthma. We sought to identify the link between VitD deficiency and these diseases.

The Complex Association between Sleep Quality, Psychological Wellbeing, and Neurodevelopmental Disorders in Childhood

During child development, the psychophysiological state is influenced by factors such as family routine, school experiences, stressful life events, or, in general, the environmental context in which the child grows up [...].

Critical periods and Autism Spectrum Disorders, a role for sleep

Brain development relies on both experience and genetically defined programs. Time windows where certain brain circuits are particularly receptive to external stimuli, resulting in heightened plasticity, are referred to as "critical periods". Sleep is thought to be essential for normal brain development. Importantly, studies have shown that sleep enhances critical period plasticity and promotes experience-dependent synaptic pruning in the developing mammalian brain. Therefore, normal plasticity during critical periods depends on sleep. Problems falling and staying asleep occur at a higher rate in Autism Spectrum Disorder (ASD) relative to typical development. In this review, we explore the potential link between sleep, critical period plasticity, and ASD. First, we review the importance of critical period plasticity in typical development and the role of sleep in this process. Next, we summarize the evidence linking ASD with deficits in synaptic plasticity in rodent models of high-confidence ASD gene candidates. We then show that the high-confidence rodent models of ASD that show sleep deficits also display plasticity deficits. Given how important sleep is for critical period plasticity, it is essential to understand the connections between synaptic plasticity, sleep, and brain development in ASD. However, studies investigating sleep or plasticity during critical periods in ASD mouse models are lacking. Therefore, we highlight an urgent need to consider developmental trajectory in studies of sleep and plasticity in neurodevelopmental disorders.

Impact of sleep disorders on behavioral issues in preschoolers with autism spectrum disorder

Background

Sleep disorders are one of the most common problems in children with Autism Spectrum Disorder (ASD). However, they often tend to be underdiagnosed and incorrectly treated in clinical practice. This study aims to identify sleep disorders in preschool children with ASD and to explore their relationship with the core symptoms of autism, the child's developmental and cognitive level as well as the psychiatric comorbidities.

Methods

We recruited 163 preschool children with a diagnosis of ASD. The Children's Sleep Habits Questionnaire (CSHQ) assessed sleep conditions. Multiple standardized tests were used to evaluate intellectual abilities, the presence of repetitive behaviors (through the Repetitive Behavior Scale-Revised), as well as the emotional-behavioral problems and the psychiatric comorbidities (through the Child Behavior Checklist -CBCL 11/2-5).

Results

The results showed that poor disorders had consistently higher scores in all areas assessed by the CSHQ and on the CBCL across all domains. The correlational analysis showed that severe sleep disorders were associated with higher scores in internalizing, externalizing, and total problems at the CBCL syndromic scales, and in all DSM-oriented CBCL subscales. Moreover, we found that the association between sleep disorders and restricted and repetitive behaviors (RRBs) is explained by the anxiety-related symptoms.

Conclusion

Based on these findings, the study recommends that screening for sleep problems followed by early intervention should constitute a routine part of clinical practice for children with ASD.

Identification of atypical sleep microarchitecture biomarkers in children with autism spectrum disorder

Importance

Sleep disorders are one of the most frequent comorbidities in children with autism spectrum disorder (ASD). However, the link between neurodevelopmental effects in ASD children with their underlying sleep microarchitecture is not well understood. An improved understanding of etiology of sleep difficulties and identification of sleep-associated biomarkers for children with ASD can improve the accuracy of clinical diagnosis.

Objectives

To investigate whether machine learning models can identify biomarkers for children with ASD based on sleep EEG recordings.

Design setting and participants

Sleep polysomnogram data were obtained from the Nationwide Children' Health (NCH) Sleep DataBank. Children (ages: 8-16 yrs) with 149 autism and 197 age-matched controls without neurodevelopmental diagnosis were selected for analysis. An additional independent age-matched control group (n = 79) selected from the Childhood Adenotonsillectomy Trial (CHAT) was also used to validate the models. Furthermore, an independent smaller NCH cohort of younger infants and toddlers (age: 0.5-3 yr.; 38 autism and 75 controls) was used for additional validation.

Main outcomes and measures

We computed periodic and non-periodic characteristics from sleep EEG recordings: sleep stages, spectral power, sleep spindle characteristics, and aperiodic signals. Machine learning models including the Logistic Regression (LR) classifier, Support Vector Machine (SVM), and Random Forest (RF) model were trained using these features. We determined the autism class based on the prediction score of the classifier. The area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, and specificity were used to evaluate the model performance.

Results

In the NCH study, RF outperformed two other models with a 10-fold cross-validated median AUC of 0.95 (interquartile range [IQR], [0.93, 0.98]). The LR and SVM models performed comparably across multiple metrics, with median AUC 0.80 [0.78, 0.85] and 0.83 [0.79, 0.87], respectively. In the CHAT study, three tested models have comparable AUC results: LR: 0.83 [0.76, 0.92], SVM: 0.87 [0.75, 1.00], and RF: 0.85 [0.75, 1.00]. Sleep spindle density, amplitude, spindle-slow oscillation (SSO) coupling, aperiodic signal's spectral slope and intercept, as well as the percentage of REM sleep were found to be key discriminative features in the predictive models.

Conclusion and relevance

Our results suggest that integration of EEG feature engineering and machine learning can identify sleep-based biomarkers for ASD children and produce good generalization in independent validation datasets. Microstructural EEG alterations may help reveal underlying pathophysiological mechanisms of autism that alter sleep quality and behaviors. Machine learning analysis may reveal new insight into the etiology and treatment of sleep difficulties in autism.

Sleep disturbances in Norwegian children with fetal alcohol spectrum disorders (FASD) with and without a diagnosis of attention-deficit hyperactivity disorder or epilepsy

BACKGROUND

Fetal alcohol spectrum disorder (FASD) describes a combination of developmental, cognitive, and behavioral disabilities in children with prenatal exposure to alcohol. The literature suggests that there are higher rates of sleep disturbances in these children. Few studies have investigated sleep disturbances in relation to common comorbidities of FASD. We examined the prevalence of disturbed sleep and the relationship between parent-reported sleep problems in different FASD subgroups and comorbidities like epilepsy or attention-deficit hyperactivity disorder (ADHD) and impact on clinical functioning.

METHODS

In this prospective cross-sectional survey, caregivers of 53 children with FASD completed the Sleep Disturbance Scale for Children (SDSC). Information about comorbidities was collected, and EEG and assessment of IQ, daily-life executive and adaptive functioning were performed. Group comparisons and ANCOVA interaction models were used to test the associations between different sleep disturbances and clinical factors that could interfere with sleep.

RESULTS

An abnormal sleep score on the SDSC was very common, affecting 79% of children (n = 42) with equal prevalence in all FASD subgroups. Difficulty falling asleep was the most common sleep problem, followed by difficulty staying asleep and waking early. The incidence of epilepsy was 9.4%, with an abnormal EEG seen in 24.5%, and a diagnosis of ADHD in 47.2% of children. The distribution of these conditions was equal in all FASD subgroups. Children with signs of sleep disturbance had poorer working memory, executive function, and adaptive functioning. Children with ADHD had a greater prevalence of sleep disturbance than those without ADHD (OR 1.36; 95% CI 1.03 to 1.79).

CONCLUSION

Problems with sleep are very common in FASD children and seem independent of FASD subgroup and the presence of epilepsy or a pathological EEG finding, while those with ADHD had more sleep problems. The study underscores the importance of screening for sleep disturbances in all children with FASD as these problems may be treatable.

Sleep problems in children with autism spectrum disorder in Hong Kong: a cross-sectional study

Background

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a growing prevalence of sleep problems associated with significant behavioral problems and more severe autism clinical presentation. Little is known about the relationships between autism traits and sleep problems in Hong Kong. Therefore, this study aimed to examine whether children with autism have increased sleep problems than non-autistic children in Hong Kong. The secondary objective was to examine the factors associated with sleep problems in an autism clinical sample.

Methods

This cross-sectional study recruited 135 children with autism and 102 with the same age range of non-autistic children, aged between 6 and 12 years. Both groups were screened and compared on their sleep behaviors using the Children's Sleep Habits Questionnaire (CSHQ).

Results

Children with autism had significantly more sleep problems than non-autistic children [t _(226.73) = 6.20, p < 0.001]. Bed -sharing [beta = 0.25, t ₍₁₆₅₎ = 2.75, p = 0.07] and maternal age at birth [beta = 0.15, t ₍₁₆₅₎ = 2.05, p = 0.043] were significant factors associated with CSHQ score on the top of autism traits. Stepwise linear regression modeling identified that only separation anxiety disorder (beta = 4.83, t = 2.40, p = 0.019) best-predicted CSHQ.

Conclusion

In summary, autistic children suffered from significantly more sleep problems and co-occurring separation anxiety disorder brings greater sleep problems as compared to non-autistic children. Clinicians should be more aware of sleep problems to provide more effective treatments to children with autism.

Shank3 influences mammalian sleep development

Sleep problems are prevalent in autism spectrum disorder (ASD), can be observed before diagnosis, and are associated with increased restricted and repetitive behaviors. Therefore, sleep abnormalities may be a core feature of the disorder, but the developmental trajectory remains unknown. Animal models provide a unique opportunity to understand sleep ontogenesis in ASD. Previously we showed that adult mice with a truncation in the high-confidence ASD gene Shank3 (Shank3^∆C ) recapitulate the clinical sleep phenotype. In this study we used longitudinal electro-encephalographic (EEG) recordings to define, for the first time, changes in sleep from weaning to young adulthood in an ASD mouse model. We show that Shank3^∆C male mice sleep less overall throughout their lifespan, have increased rapid eye movement (REM) sleep early in life despite significantly reduced non-rapid eye movement (NREM) sleep, and have abnormal responses to increased sleep pressure that emerge during a specific developmental period. We demonstrate that the ability to fall asleep quickly in response to sleep loss develops normally between 24 and 30 days in mice. However, mutants are unable to reduce sleep latency after periods of prolonged waking and maintain the same response to sleep loss regardless of age. This phenomenon seems independent of homeostatic NREM sleep slow-wave dynamics. Overall, our study recapitulates both preclinical models and clinical studies showing that reduced sleep is consistently associated with ASD and suggests that problems falling asleep may reflect abnormal development of sleep and arousal mechanisms.

A Placebo-Controlled Trial of Cannabinoid Treatment for Disruptive Behavior in Children and Adolescents with Autism Spectrum Disorder: Effects on Sleep Parameters as Measured by the CSHQ

Autism spectrum disorder (ASD) is often associated with debilitating sleep disturbances. While anecdotal evidence suggests the positive effect of cannabinoids, randomized studies are lacking. Here, we report the effects of cannabinoid treatment on the sleep of 150 children and adolescents with ASD, as part of a double-blind, placebo-controlled study that assessed the impact of cannabinoid treatment on behavior (NCT02956226). Participants were randomly assigned to one of the following three treatments: (1) whole-plant cannabis extract, containing cannabidiol (CBD) and Δ9-Tetrahydrocannabinol (THC) in a 20:1 ratio, (2) purified CBD and THC extract in the same ratio, and (3) an oral placebo. After 12 weeks of treatment (Period 1) and a 4-week washout period, participants crossed over to a predetermined, second 12-week treatment (Period 2). Sleep disturbances were assessed using the Children’s Sleep-Habit Questionnaire (CSHQ). We found that the CBD-rich cannabinoid treatment was not superior to the placebo treatment in all aspects of sleep measured by the CSHQ, including bedtime resistance, sleep-onset delay, and sleep duration. Notably, regardless of the treatment (cannabinoids or placebo), improvements in the CSHQ total score were associated with improvements in the autistic core symptoms, as indicated by the Social Responsiveness Scale total scores (Period 1: r = 0.266, p = 0.008; Period 2: r = 0.309, p = 0.004). While this study failed to demonstrate that sleep improvements were higher with cannabinoids than they were with the placebo treatment, further studies are required.

Analysis of sleep problem in children aged 1-3 years with autism spectrum disorder in Zhejiang province, China

BACKGROUND

High prevalence of sleep problems have been founded in children with Autism Spectrum Disorder (ASD), with rates ranging from 50 to 80%. We aimed to study the sleep status and the occurrence of sleep problems in children with autism spectrum disorder (ASD) aged 1-3 years, and to provide reference for guiding early comprehensive intervention for ASD children from the perspective of sleep.

METHODS

From January 1 to December 31, 2021, 74 ASD children who met the diagnostic criteria of "Diagnostic and Statistical Manual of Mental Disorders-5 (DSM-V)" served as case group while 84 typically-developing children of the same sex and age served as control group. An original Children's Sleep Habit Questionnaire was adopted to compare the sleep status of children in the two groups and to conduct statistical analysis on related factors.

RESULTS

The incidence of sleep problems in the case group (78.4%) was significantly higher than that in the control group (34.5%) (P < 0.001). Compared with the children in the control group, children in the case group had later bedtime (P < 0.05) and less sleep duration (P < 0.05), and required longer time to fall asleep (P < 0.001) The incidence of sleep problems in children who could fall asleep autonomously in the case group was significantly lower than that in children who needed parental help (P < 0.05). In the case group, the longer the screen exposure time, the higher the incidence of sleep problems (P < 0.05).

CONCLUSIONS

The incidence of sleep problems in ASD children aged 1-3 years is also high, mainly manifested in late bedtime, difficulty falling asleep, frequent night awakenings and less sleep duration. Both sleep patterns and screen exposure can impact their sleep. In the early comprehensive intervention of ASD children, it is necessary to pay full heed to their sleep status and take timely intervention measures in order to improve the quality of life for the ASD children and their families.