Sleep EEG slow-wave activity in medicated and unmedicated children and adolescents with attention-deficit/hyperactivity disorder

NREM Slow-Wave Activity in Adolescents Is Differentially Associated With ADHD Levels and Normalized by Pharmacological Treatment

BACKGROUND

A compelling hypothesis about attention-deficit/hyperactivity disorder (ADHD) etiopathogenesis is that the ADHD phenotype reflects a delay in cortical maturation. Slow-wave activity (SWA) of non-rapid eye movement (NREM) sleep electroencephalogram (EEG) is an electrophysiological index of sleep intensity reflecting cortical maturation. Available data on ADHD and SWA are conflicting, and developmental differences, or the effect of pharmacological treatment, are relatively unknown.

METHODS

We examined, in samples (Mage = 16.4, SD = 1.2), of ever-medicated adolescents at risk for ADHD (n = 18; 72% boys), medication-naïve adolescents at risk for ADHD (n = 15, 67% boys), and adolescents not at risk for ADHD (n = 31, 61% boys) matched for chronological age and controlling for non-ADHD pharmacotherapy, whether ADHD pharmacotherapy modulates the association between NREM SWA and ADHD risk in home sleep.

RESULTS

Findings indicated medication-naïve adolescents at risk for ADHD exhibited greater first sleep cycle and entire night NREM SWA than both ever-medicated adolescents at risk for ADHD and adolescents not at risk for ADHD and no difference between ever-medicated, at-risk adolescents, and not at-risk adolescents.

CONCLUSIONS

Results support atypical cortical maturation in medication-naïve adolescents at risk for ADHD that appears to be normalized by ADHD pharmacotherapy in ever-medicated adolescents at risk for ADHD. Greater NREM SWA may reflect a compensatory mechanism in middle-later adolescents at risk for ADHD that normalizes an earlier occurring developmental delay.

Mobile sleep EEG suggests delayed brain maturation in adolescents with ADHD: A focus on oscillatory spindle frequency

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder. Although data show ADHD is associated with sleep problems, approaches to analyze the association between ADHD and sleep electrophysiology are limited to a few methods with circumscribed foci.

AIMS

Sleep EEG was analyzed by a mixed-radix FFT routine and power spectrum parametrization in adolescents with ADHD and adolescents not at-risk for ADHD. Spectral components of sleep EEG were analyzed employing a novel, model-based approach of EEG power spectra.

METHODS AND PROCEDURES

The DREEM mobile polysomnography headband was used to record home sleep EEG from 19 medication-free adolescents with ADHD and 29 adolescents not at-risk for ADHD (overall: N = 56, age range 14-19 years) and groups were compared on characteristics of NREM sleep.

OUTCOMES AND RESULTS

Adolescents with ADHD exhibited lower frequency of spectral peaks indicating sleep spindle oscillations whereas adolescents not at-risk for ADHD showed lower spectral power in the slow sleep spindle and beta frequency ranges.

CONCLUSIONS AND IMPLICATIONS

The observed between-groups difference might indicate delayed brain maturity unraveled during sleep in ADHD.

Wake EEG oscillation dynamics reflect both sleep need and brain maturation across childhood and adolescence

An objective measure of brain maturation is highly insightful for monitoring both typical and atypical development. Slow wave activity, recorded in the sleep electroencephalogram (EEG), reliably indexes changes in brain plasticity with age, as well as deficits related to developmental disorders such as attention-deficit hyperactivity disorder (ADHD). Unfortunately, measuring sleep EEG is resource-intensive and burdensome for participants. We therefore aimed to determine whether wake EEG could likewise index developmental changes in brain plasticity. We analyzed high-density wake EEG collected from 163 participants 3-25 years old, before and after a night of sleep. We compared two measures of oscillatory EEG activity, amplitudes and density, as well as two measures of aperiodic activity, intercepts and slopes. Furthermore, we compared these measures in patients with ADHD (8-17 y.o., N=58) to neurotypical controls. We found that wake oscillation amplitudes behaved the same as sleep slow wave activity: amplitudes decreased with age, decreased after sleep, and this overnight decrease decreased with age. Oscillation densities were also substantially age-dependent, decreasing overnight in children and increasing overnight in adolescents and adults. While both aperiodic intercepts and slopes decreased linearly with age, intercepts decreased overnight, and slopes increased overnight. Overall, our results indicate that wake oscillation amplitudes track both development and sleep need, and overnight changes in oscillation density reflect some yet-unknown shift in neural activity around puberty. No wake measure showed significant effects of ADHD, thus indicating that wake EEG measures, while easier to record, are not as sensitive as those during sleep.

What is sleep exactly? Global and local modulations of sleep oscillations all around the clock

Wakefulness, non-rapid eye-movement (NREM) and rapid eye-movement (REM) sleep differ from each other along three dimensions: behavioral, phenomenological, physiological. Although these dimensions often fluctuate in step, they can also dissociate. The current paradigm that views sleep as made of global NREM and REM states fail to account for these dissociations. This conundrum can be dissolved by stressing the existence and significance of the local regulation of sleep. We will review the evidence in animals and humans, healthy and pathological brains, showing different forms of local sleep and the consequences on behavior, cognition, and subjective experience. Altogether, we argue that the notion of local sleep provides a unified account for a host of phenomena: dreaming in REM and NREM sleep, NREM and REM parasomnias, intrasleep responsiveness, inattention and mind wandering in wakefulness. Yet, the physiological origins of local sleep or its putative functions remain unclear. Exploring further local sleep could provide a unique and novel perspective on how and why we sleep.

Lack of association between behavioral development and simplified topographical markers of the sleep EEG in infancy

The sleep EEG mirrors neuronal connectivity, especially during development when the brain undergoes substantial rewiring. As children grow, the slow-wave activity (SWA; 0.75-4.25 Hz) spatial distribution in their sleep EEG changes along a posterior-to-anterior gradient. Topographical SWA markers have been linked to critical neurobehavioral functions, such as motor skills, in school-aged children. However, the relationship between topographical markers in infancy and later behavioral outcomes is still unclear. This study aims to explore reliable indicators of neurodevelopment in infants by analyzing their sleep EEG patterns. Thirty-one 6-month-old infants (15 female) underwent high-density EEG recordings during nighttime sleep. We defined markers based on the topographical distribution of SWA and theta activity, including central/occipital and frontal/occipital ratios and an index derived from local EEG power variability. Linear models were applied to test whether markers relate to concurrent, later, or retrospective behavioral scores, assessed by the parent-reported Ages & Stages Questionnaire at ages 3, 6, 12, and 24 months. Results indicate that the topographical markers of the sleep EEG power in infants were not significantly linked to behavioral development at any age. Further research, such as longitudinal sleep EEG in newborns, is needed to better understand the relationship between these markers and behavioral development and assess their predictive value for individual differences.

Objectively measured sleep continuity in children and adolescents with ADHD: A systematic review and meta-analysis

Sleep disturbances are often linked to attention-deficit/hyperactivity disorder (ADHD). Consistent findings document that children and adolescents with ADHD report more sleep problems than their typically developing (TD) peers across subjective sleep domains. However, few differences between these groups were observed in objectively measured sleep parameters, such as polysomnography (PSG) and actigraphy. This study synthesized empirical studies to identify objectively measured sleep continuity differences between children and adolescents with ADHD and TD. We included observational research and baseline data from intervention studies between 5- to 18-year-old individuals with ADHD and their TD peers at five databases from inception and September 2022. This systematic review and meta-analysis of 45 articles, including 1622 children and adolescents with ADHD and 2013 TD, found that compared with TD, children and adolescents with ADHD have higher sleep latency and moderately decreased sleep efficiency measured by actigraphy. Polysomnography-measured differences between ADHD and TD were not significant. Medication status and comorbid psychiatric status significantly moderated the group differences in sleep efficiency between ADHD and TD. Also, the group differences in sleep latency between ADHD and TD were moderated by actigraphy recorded nights. These findings highlight the importance of reducing disparities in sleep parameters among children and adolescents with and without ADHD.

Sleep in Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder

SLEEP IN AUTISM SPECTRUM DISORDER AND ATTENTION DEFICIT HYPERACTIVITY DISORDER: Kanwaljit Singh, Andrew W. Zimmerman Seminars in Pediatric Neurology Volume 22, Issue 2, June 2015, Pages 113-125 Sleep problems are common in autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). Sleep problems in these disorders may not only worsen daytime behaviors and core symptoms of ASD and ADHD but also contribute to parental stress levels. Therefore, the presence of sleep problems in ASD and ADHD requires prompt attention and management. This article is presented in 2 sections, one each for ASD and ADHD. First, a detailed literature review about the burden and prevalence of different types of sleep disorders is presented, followed by the pathophysiology and etiology of the sleep problems and evaluation and management of sleep disorders in ASD and ADHD.

The sleep and wake electroencephalogram over the lifespan

Both sleep and wake encephalograms (EEG) change over the lifespan. While prior studies have characterized age-related changes in the EEG, the datasets span a particular age group, or focused on sleep and wake macrostructure rather than the microstructure. Here, we present sex-stratified data from 3372 community-based or clinic-based otherwise neurologically and psychiatrically healthy participants ranging from 11 days to 80 years of age. We estimate age norms for key sleep and wake EEG parameters including absolute and relative powers in delta, theta, alpha, and sigma bands, as well as sleep spindle density, amplitude, duration, and frequency. To illustrate the potential use of the reference measures developed herein, we compare them to sleep EEG recordings from age-matched participants with Alzheimer's disease, severe sleep apnea, depression, osteoarthritis, and osteoporosis. Although the partially clinical nature of the datasets may bias the findings towards less normal and hence may underestimate pathology in practice, age-based EEG reference values enable objective screening of deviations from healthy aging among individuals with a variety of disorders that affect brain health.

Sleep Structure in Untreated Adults With ADHD: A Retrospective Study

OBJECTIVE

Polysomnographic findings in neurodevelopmental disorders have been reported, but previous studies have had several limitations. The purpose of this study was to characterize sleep structure in untreated adults diagnosed with ADHD, excluding ADHD-related sleep disorders as determined by polysomnography and multiple sleep latency testing.

METHODS

This study included 55 patients aged 18 years or older who visited the Kurume University Hospital Sleep Clinic between April 2015 and March 2020. The diagnosis of ADHD was determined by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (ADHD group, n = 28; non-ADHD, n = 27).

RESULTS

The ADHD group had significantly longer slow wave sleep (SWS) duration than the non-ADHD group (ADHD: 68.3 ± 31.0 minutes vs. non-ADHD: 43.4 ± 36.6 minutes; p = .0127).

CONCLUSIONS

The increased SWS volume observed in drug-naïve adult patients with ADHD may be related to the pathogenesis of this disorder.

Owner-rated hyperactivity/impulsivity is associated with sleep efficiency in family dogs: a non-invasive EEG study

Subjective sleep disturbances are reported by humans with attention-deficit/hyperactivity disorder (ADHD). However, no consistent objective findings related to sleep disturbances led to the removal of sleep problems from ADHD diagnostic criteria. Dogs have been used as a model for human ADHD with questionnaires validated for this purpose. Also, their sleep physiology can be measured by non-invasive methods similarly to humans. In the current study, we recorded spontaneous sleep EEG in family dogs during a laboratory session. We analyzed the association of sleep macrostructure and deep sleep (NREM) slow-wave activity (SWA) with a validated owner-rated ADHD questionnaire, assessing inattention (IA), hyperactivity/impulsivity (H/I) and total (T) scores. Higher H/I and T were associated with lower sleep efficiency and longer time awake after initial drowsiness and NREM. IA showed no associations with sleep variables. Further, no association was found between ADHD scores and SWA. Our results are in line with human studies in which poor sleep quality reported by ADHD subjects is associated with some objective EEG macrostructural parameters. This suggests that natural variation in dogs' H/I is useful to gain a deeper insight of ADHD neural mechanisms.

A failure of sleep-dependent consolidation of visuoperceptual procedural learning in young adults with ADHD

ADHD has been associated with cortico-striatal dysfunction that may lead to procedural memory abnormalities. Sleep plays a critical role in consolidating procedural memories, and sleep problems are an integral part of the psychopathology of ADHD. This raises the possibility that altered sleep processes characterizing those with ADHD could contribute to their skill-learning impairments. On this basis, the present study tested the hypothesis that young adults with ADHD have altered sleep-dependent procedural memory consolidation. Participants with ADHD and neurotypicals were trained on a visual discrimination task that has been shown to benefit from sleep. Half of the participants were tested after a 12-h break that included nocturnal sleep (sleep condition), whereas the other half were tested after a 12-h daytime break that did not include sleep (wakefulness condition) to assess the specific contribution of sleep to improvement in task performance. Despite having a similar degree of initial learning, participants with ADHD did not improve in the visual discrimination task following a sleep interval compared to neurotypicals, while they were on par with neurotypicals during the wakefulness condition. These findings represent the first demonstration of a failure in sleep-dependent consolidation of procedural learning in young adults with ADHD. Such a failure is likely to disrupt automatic control routines that are normally provided by the non-declarative memory system, thereby increasing the load on attentional resources of individuals with ADHD.

Sleep electroencephalogram evidence of delayed brain maturation in attention deficit hyperactivity disorder: a longitudinal study

Study Objectives

This study investigates whether longitudinally measured changes in adolescent brain electrophysiology corroborate the maturational lag associated with attention deficit hyperactivity disorder (ADHD) reported in magnetic resonance imaging (MRI) studies and cross-sectional sleep electroencephalogram (EEG) data.

Methods

Semiannually nine adolescents diagnosed with ADHD (combined presentation, DSM-V criteria, mean age 12.39 ± 0.61 years at first time-point, two females) and nine typically developing controls (12.08 ± 0.35 years, four females) underwent all-night laboratory polysomnography, yielding four recordings.

Results

Sleep macrostructure was similar between groups. A quadratic model of the age change in non-rapid eye movement (NREM) delta (1.07–4 Hz) power, with sex effects accounted for, found that delta power peaked 0.92 ± 0.37 years later in the ADHD group. A Gompertz function fit to the same data showed that the age of most rapid delta power decline occurred 0.93 ± 0.41 years later in the ADHD group (p = 0.037), but this group difference was not significant (p = 0.38) with sex effects accounted for. For very low frequency (0.29–1.07 Hz) EEG, the ADHD lag (1.07 ± 0.42 years later, p = 0.019) was significant for a Gompertz model with sex effects accounted for (p = 0.044). Theta (4–7.91 Hz) showed a trend (p = 0.064) toward higher power in the ADHD group. Analysis of the EEG decline across the night found that standardized delta and theta power in NREMP1 were significantly (p < 0.05 for both) lower in adolescents with ADHD.

Conclusions

This is the first longitudinal study to reveal electrophysiological evidence of a maturational lag associated with ADHD. In addition, our findings revealed basically unaltered sleep macrostructure but altered sleep homeostasis associated with ADHD.

Sleep homeostasis, seizures, and cognition in children with focal epilepsy

AIM

To investigate the link between sleep disruption and cognitive impairment in childhood epilepsy by studying the effect of epilepsy on sleep homeostasis, as reflected in slow-wave activity (SWA).

METHOD

We examined SWA from overnight EEG-polysomnography in 19 children with focal epilepsy (mean [SD] age 11 years 6 months [3 years], range 6 years 6 months-15 years 6 months; 6 females, 13 males) and 18 age- and sex-matched typically developing controls, correlating this with contemporaneous memory consolidation task scores, full-scale IQ, seizures, and focal interictal discharges.

RESULTS

Children with epilepsy did not differ significantly from controls in overnight SWA decline (p = 0.12) or gain in memory performance with sleep (p = 0.27). SWA was lower in patients compared to controls in the first hour of non-rapid eye movement sleep (p = 0.021), although not in those who remained seizure-free (p = 0.26). Full-scale IQ did not correlate with measures of SWA in patients or controls. There was no significant difference in SWA measures between focal and non-focal electrodes.

INTERPRETATION

Overnight SWA decline is conserved in children with focal epilepsy and may underpin the preservation of sleep-related memory consolidation in this patient group. Reduced early-night SWA may reflect impaired or immature sleep homeostasis in those with a higher seizure burden.

Sleep EEG in young people with 22q11.2 deletion syndrome: A cross-sectional study of slow-waves, spindles and correlations with memory and neurodevelopmental symptoms

Background

Young people living with 22q11.2 Deletion Syndrome (22q11.2DS) are at increased risk of schizophrenia, intellectual disability, attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). In common with these conditions, 22q11.2DS is also associated with sleep problems. We investigated whether abnormal sleep or sleep-dependent network activity in 22q11.2DS reflects convergent, early signatures of neural circuit disruption also evident in associated neurodevelopmental conditions.

Methods

In a cross-sectional design, we recorded high-density sleep EEG in young people (6-20 years) with 22q11.2DS (n=28) and their unaffected siblings (n=17), quantifying associations between sleep architecture, EEG oscillations (spindles and slow waves) and psychiatric symptoms. We also measured performance on a memory task before and after sleep.

Results

22q11.2DS was associated with significant alterations in sleep architecture, including a greater proportion of N3 sleep and lower proportions of N1 and REM sleep than in siblings. During sleep, deletion carriers showed broadband increases in EEG power with increased slow-wave and spindle amplitudes, increased spindle frequency and density, and stronger coupling between spindles and slow-waves. Spindle and slow-wave amplitudes correlated positively with overnight memory in controls, but negatively in 22q11.2DS. Mediation analyses indicated that genotype effects on anxiety, ADHD and ASD were partially mediated by sleep EEG measures.

Conclusions

This study provides a detailed description of sleep neurophysiology in 22q11.2DS, highlighting alterations in EEG signatures of sleep which have been previously linked to neurodevelopment, some of which were associated with psychiatric symptoms. Sleep EEG features may therefore reflect delayed or compromised neurodevelopmental processes in 22q11.2DS, which could inform our understanding of the neurobiology of this condition and be biomarkers for neuropsychiatric disorders.

Funding

This research was funded by a Lilly Innovation Fellowship Award (UB), the National Institute of Mental Health (NIMH 5UO1MH101724; MvdB), a Wellcome Trust Institutional Strategic Support Fund (ISSF) award (MvdB), the Waterloo Foundation (918-1234; MvdB), the Baily Thomas Charitable Fund (2315/1; MvdB), MRC grant Intellectual Disability and Mental Health: Assessing Genomic Impact on Neurodevelopment (IMAGINE) (MR/L011166/1; JH, MvdB and MO), MRC grant Intellectual Disability and Mental Health: Assessing Genomic Impact on Neurodevelopment 2 (IMAGINE-2) (MR/T033045/1; MvdB, JH and MO); Wellcome Trust Strategic Award 'Defining Endophenotypes From Integrated Neurosciences' Wellcome Trust (100202/Z/12/Z MO, JH). NAD was supported by a National Institute for Health Research Academic Clinical Fellowship in Mental Health and MWJ by a Wellcome Trust Senior Research Fellowship in Basic Biomedical Science (202810/Z/16/Z). CE and HAM were supported by Medical Research Council Doctoral Training Grants (C.B.E. 1644194, H.A.M MR/K501347/1). HMM and UB were employed by Eli Lilly & Co during the study; HMM is currently an employee of Boehringer Ingelheim Pharma GmbH & Co KG. The views and opinions expressed are those of the author(s), and not necessarily those of the NHS, the NIHR or the Department of Health funders.

Association of a novel EEG metric of sleep depth/intensity with attention-deficit/hyperactivity, learning, and internalizing disorders and their pharmacotherapy in adolescence

STUDY OBJECTIVES

Psychiatric/learning disorders are associated with sleep disturbances, including those arising from abnormal cortical activity. The odds ratio product (ORP) is a standardized electroencephalogram metric of sleep depth/intensity validated in adults, while ORP data in youth are lacking. We tested ORP as a measure of sleep depth/intensity in adolescents with and without psychiatric/learning disorders.

METHODS

Four hundred eighteen adolescents (median 16 years) underwent a 9-hour, in-lab polysomnography. Of them, 263 were typically developing (TD), 89 were unmedicated, and 66 were medicated for disorders including attention-deficit/hyperactivity (ADHD), learning (LD), and internalizing (ID). Central ORP during non-rapid eye movement (NREM) sleep was the primary outcome. Secondary/exploratory outcomes included central and frontal ORP during NREM stages, in the 9-seconds following arousals (ORP-9), in the first and second halves of the night, during REM sleep and wakefulness.

RESULTS

Unmedicated youth with ADHD/LD had greater central ORP than TD during stage 3 and in central and frontal regions during stage 2 and the second half of the sleep period, while ORP in youth with ADHD/LD on stimulants did not significantly differ from TD. Unmedicated youth with ID did not significantly differ from TD in ORP, while youth with ID on antidepressants had greater central and frontal ORP than TD during NREM and REM sleep, and higher ORP-9.

CONCLUSIONS

The greater ORP in unmedicated youth with ADHD/LD, and normalized levels in those on stimulants, suggests ORP is a useful metric of decreased NREM sleep depth/intensity in ADHD/LD. Antidepressants are associated with greater ORP/ORP-9, suggesting these medications induce cortical arousability.

Sleep Power Topography in Children with Attention Deficit Hyperactivity Disorder (ADHD)

OBJECTIVE

Recent years saw an increasing interest towards sleep microstructure abnormalities in attention-deficit/hyperactivity disorder (ADHD). However, the existing literature on sleep electroencephalographic (EEG) power in ADHD is still controversial, often based on single electrode recordings, and mainly focused on slow wave activity (SWA) during NREM sleep. This study aimed to systematically investigate sleep power topography in all traditional frequency bands, in all sleep stages and across sleep cycles using high-density EEG (HD-EEG).

METHOD

Thirty drug-naïve children with ADHD (10.5 ± 2.1 years, 21 male) and 23 typically developing (TD) control participants (mean age: 10.2 ± 1.6 years, 13 male) were included in the current analysis. Signal power topography was computed in classical frequency bands during sleep, contrasted between groups and sleep cycles, and correlated with measures of ADHD severity, cognitive functioning and estimated total sleep time.

RESULTS

Compared to TD subjects, patients with ADHD consistently displayed a widespread increase in low-frequency activity (between 3 and 10 Hz) during NREM sleep, but not during REM sleep and wake before sleep onset. Such a difference involved a wide centro-posterior cluster of channels in the upper SWA range, in Theta, and low-Alpha. Between-group difference was maximal in sleep stage N3 in the first sleep cycle, and positively correlated with average total sleep time.

CONCLUSIONS

These results support the concept that children with ADHD, compared to TD peers, have a higher sleep pressure and altered sleep homeostasis, which possibly interfere with (and delay) cortical maturation.

Evidence of a maturational disruption in non-rapid eye movement sleep slow wave activity in youth with attention-deficit/hyperactivity, learning and internalizing disorders

BACKGROUND

Sleep slow wave activity (SWA) peaks during childhood and declines in the transition to adolescence during typical development (TD). It remains unknown whether this trajectory differs in youth with neuropsychiatric disorders.

METHODS

We analyzed sleep EEGs of 664 subjects 6 to 21 y (449 TD, 123 unmedicated, 92 medicated) and 114 subjects 7-12 y (median 10.5 y) followed-up at 18-22 y (median 19 y). SWA (0.4-4 Hz) power was calculated during non-rapid eye movement sleep.

RESULTS

TD and unmedicated youth showed cubic central and frontal SWA trajectories from 6 to 21 y (p-cubic<0.05), with TD youth showing peaks in central SWA at 6.8 y and frontal at 8.2 y. Unmedicated attention-deficit/hyperactivity (ADHD) and/or learning disorders (LD) showed peak central SWA 2 y later (at 9.6 y, coinciding with peak frontal SWA) than TD, followed by a 67% steeper slope by 19 y. Frontal SWA peak and slope in unmedicated ADHD/LD, and that of central and frontal in internalizing disorders (ID), were similar to TD. Unmedicated ADHD/LD did not differ in the longitudinal SWA percent change by 18-22 y; unmedicated ID showed a lower longitudinal change in frontal SWA than TD. Medicated youth showed a linear decline in central and frontal SWA from 6 to 21 y (p-linear<0.05).

CONCLUSIONS

ADHD/LD youth show a maturational delay and potential topographical disruption in SWA during childhood and steeper decline throughout adolescence, suggesting faster synaptic pruning. Youth with ID experience less changes in frontal SWA by late adolescence. Psychotropic medications may impact the maturational trajectory of SWA, but not the magnitude of developmental decline by late adolescence.

Local sleep: A new concept in brain plasticity

Traditionally, sleep and wakefulness have been considered as two global, mutually exclusive states. However, this view has been challenged by the discovery that sleep and wakefulness are actually locally regulated and that islands of these two states may often coexist in the same individual. Importantly, such a local regulation seems to be the key for many essential functions of sleep, including the maintenance of cognitive efficiency and the consolidation of new skills and memories. Indeed, local changes in sleep-related oscillations occur in brain areas that are used and involved in learning during wakefulness. In turn, these changes directly modulate experience-dependent brain adaptations and the consolidation of newly acquired memories. In line with these observations, alterations in the regional balance between wake- and sleep-like activity have been shown to accompany many pathologic conditions, including psychiatric and neurologic disorders. In the last decade, experimental research has started to shed light on the mechanisms involved in the local regulation of sleep and wakefulness. The results of this research have opened new avenues of investigation regarding the function of sleep and have revealed novel potential targets for the treatment of several pathologic conditions.

Sleep Stage Classification Based on Multi-Centers: Comparison Between Different Ages, Mental Health Conditions and Acquisition Devices

PURPOSE

To investigate the general sleep stage classification performance of deep learning networks, three datasets, across different age groups, mental health conditions, and acquisition devices, comprising adults (SHHS) and children without mental health conditions (CCSHS), and subjects with mental health conditions (XJ), were included in this study.

METHODS

A long short-term memory (LSTM) network was used to evaluate the effect of different ages, mental health conditions, and acquisition devices on the sleep stage classification performance and the general performance.

RESULTS

Results showed that the age and different mental health conditions may affect the sleep stage classification performance of the network. The same acquisition device using different parameters may not have an obvious effect on the classification performance. When using a single dataset and two datasets for training, the network performed better only on the training dataset. When training was conducted with three datasets, the network performed well for all datasets with a Cohen's Kappa of 0.8192 and 0.8472 for the SHHS and CCSHS, respectively, but performed relatively worse (0.6491) for the XJ, which indicated the complexity effect of different mental health conditions on the sleep stage classification task. Moreover, the performance of the network trained using three datasets was similar for each dataset to that of the network trained using a single dataset and tested on the same dataset.

CONCLUSION

These results suggested that when more datasets across different age groups, mental health conditions, and acquisition devices (ie, more datasets with different feature distributions for each sleep stage) are used for training, the general performance of a deep learning network will be superior for sleep stage classification tasks with varied conditions.

Sleep EEG microstructure in children and adolescents with attention deficit hyperactivity disorder: a systematic review and meta-analysis

Attention deficit hyperactivity disorder (ADHD) is commonly associated with sleep problems, possibly due to shared pathophysiology. Microstructural sleep electroencephalographic (EEG) alterations may likely represent markers of disordered cortical maturation in ADHD, although literature data are still conflicting, deserving further assessment. After having systematically reviewed the literature, we included 11 studies from 598 abstracts, and assessed 23 parameters of cyclic alternating pattern (CAP), four parameters of sleep EEG power and one parameter of sleep graphoelements through 29 meta-analyses and, when possible, univariate meta-regressions. Slow wave activity (SWA) in ADHD was significantly higher in early childhood and lower in late childhood/adolescence compared to controls, with an inversion point at 10 years. Total CAP rate and CAP A1 index in non-rapid eye movement (NREM) stage 2 sleep, and CAP A1 rate in NREM sleep were significantly lower in ADHD patients than controls. SWA and CAP A1 changes are therefore possible markers of altered cortical maturation in ADHD, consistently with the neuropsychological deficits characterizing the disorder, likely fostering earlier detection of at-risk/milder conditions, and more tailored therapeutic interventions.

Sleep's role in memory consolidation: What can we learn from atypical development?

Research conducted over the last century has suggested a role for sleep in the processes guiding healthy cognition and development, including memory consolidation. Children with intellectual and developmental disabilities (IDDs) tend to have higher rates of sleep disturbances, which could relate to behavior issues, developmental delays, and learning difficulties. While several studies examine whether sleep exacerbates daytime difficulties and attention deficits in children with IDDs, this chapter focuses on the current state of knowledge regarding sleep and memory consolidation in typically developing (TD) groups and those at risk for learning difficulties. In particular, this chapter summarizes the current literature on sleep-dependent learning across developmental disabilities, including Down syndrome, Williams syndrome, Autism Spectrum Disorder, and Learning Disabilities (Attention-Deficit/Hyperactivity Disorder and Dyslexia). We also highlight the gaps in the current literature and identify challenges in studying sleep-dependent memory in children with different IDDs. This burgeoning new field highlights the importance of considering the role of sleep in memory retention across long delays when evaluating children's memory processes. Further, an understanding of typical and atypical development can mutually inform recent theories of sleep's role in memory.

Longitudinal assessment of NREM sleep EEG in typically developing and medication-free ADHD adolescents: first year results

OBJECTIVE

Clinical observation and structural MRI studies suggest that delayed brain maturation is a major cause of attention deficit hyperactivity disorder (ADHD). Sleep electroencephalogram (EEG) which exhibits major changes across adolescence provides an opportunity to investigate brain electrophysiology evidence for maturational delay. We present data from an ongoing longitudinal study of sleep EEG in medication-free ADHD and typically developing adolescents to investigate brain electrophysiological evidence for this maturational delay.

METHODS

Nine adolescents diagnosed with ADHD (combined presentation, DSM-V criteria, mean age 12.39 ± 0.61 years, 2 females), and nine typically developing controls (12.08 ± 0.35 years, 4 females) were recruited. Subjects underwent an adaptation night and all night polysomnography twice yearly at the Laboratory.

RESULTS

Basic sleep structure did not differ between the ADHD and control groups. In addition, we found no group differences on delta power (p = 0.77), but found a possible trend toward higher theta power (p = 0.057) for the ADHD group. The decline of standardized delta power across the 4 non-rapid eye movement (NREM) periods differed by group (p < 0.05) with the percent delta power in the first NREM period being lower in the ADHD group.

CONCLUSIONS

Our data support the preponderant evidence that basic sleep structure is unaltered with ADHD. Our data do suggest altered sleep homeostatic recuperative processes in ADHD. The theta findings from the first two recordings are suggestive of a maturational delay associated with ADHD, but follow-up data-points are needed.

What Is the Link Between Attention-Deficit/Hyperactivity Disorder and Sleep Disturbance? A Multimodal Examination of Longitudinal Relationships and Brain Structure Using Large-Scale Population-Based Cohorts

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) comorbid with sleep disturbances can produce profound disruption in daily life and negatively impact quality of life of both the child and the family. However, the temporal relationship between ADHD and sleep impairment is unclear, as are underlying common brain mechanisms.

METHODS

This study used data from the Quebec Longitudinal Study of Child Development (n = 1601, 52% female) and the Adolescent Brain Cognitive Development Study (n = 3515, 48% female). Longitudinal relationships between symptoms were examined using cross-lagged panel models. Gray matter volume neural correlates were identified using linear regression. The transcriptomic signature of the identified brain-ADHD-sleep relationship was characterized by gene enrichment analysis. Confounding factors, such as stimulant drugs for ADHD and socioeconomic status, were controlled for.

RESULTS

ADHD symptoms contributed to sleep disturbances at one or more subsequent time points in both cohorts. Lower gray matter volumes in the middle frontal gyrus and inferior frontal gyrus, amygdala, striatum, and insula were associated with both ADHD symptoms and sleep disturbances. ADHD symptoms significantly mediated the link between these structural brain abnormalities and sleep dysregulation, and genes were differentially expressed in the implicated brain regions, including those involved in neurotransmission and circadian entrainment.

CONCLUSIONS

This study indicates that ADHD symptoms and sleep disturbances have common neural correlates, including structural changes of the ventral attention system and frontostriatal circuitry. Leveraging data from large datasets, these results offer new mechanistic insights into this clinically important relationship between ADHD and sleep impairment, with potential implications for neurobiological models and future therapeutic directions.