No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
NREM sleep EEG activity and procedural memory: A comparison between young neurotypical and autistic adults without sleep complaints: NREM sleep EEG activity and procedural learning
Abstract
Lay summary:
Slow EEG waves recorded from the scalp during sleep are thought to facilitate learning and memory during daytime. We compared these EEG waves in young autistic adults to typically-developing young adults. We found less slow EEG waves in the ASD group and the pattern of relationship with memory differed between groups. This suggests atypicalities in the way sleep mechanisms are associated with learning and performance in a sensory-motor procedural memory task in ASD individuals.
... Furthermore, AI supports developmental progress tracking over the long term, adjusting strategies as needed to align with the individual's evolving needs. It also enhances communication for those with ASD by recognizing emotions through analysis of facial and vocal expressions and supporting real-time language processing, thus aiding in more effective communication [63][64][65] . ...
... This includes statistical and equivalence testing to ensure that synthetic data maintains fundamental statistical properties similar to patient data. Performance validation follows where AI models trained on synthetic data are tested against models trained on real-world data to ensure they retain efficacy and reliability when applied in genuine clinical situations [63][64][65] . ...
... This continual learning approach is essential to maintain the accuracy and relevance of AI tools. Implementing explainable AI (XAI) practices is also vital, allowing practitioners to understand and validate AI-driven recommendations based on transparent evidence and clinical knowledge [64][65][66] . ...
Integrating Artificial Intelligence (AI) into healthcare, specifically for managing Autism Spectrum Disorder (ASD), offers transformative potential to enhance diagnostic accuracy, personalize treatment, and improve patient outcomes. This review explores the application of various AI programs in ASD management, discussing their functionalities, ethical considerations, implementation challenges, and the need for comprehensive regulatory frameworks. Critical AI applications such as AI-driven diagnostic imaging, predictive analytics, assisted therapy robots, remote monitoring, treatment personalization, decision support systems, and therapeutic chatbots are examined. Each technology is analyzed for its ability to improve the quality of life for individuals with ASD by offering more personalized, efficient, and effective care and support. Ethical issues, particularly concerning data bias and privacy, are highlighted as significant challenges that need addressing to maximize AI's benefits while minimizing risks. Practical hurdles like integration with existing healthcare systems, the need for scalable solutions across diverse geographic and socio-economic contexts, and the high costs associated with AI development are also discussed. Furthermore, the review underscores the necessity for robust regulatory policies that ensure patient safety, protect data privacy, and maintain high ethical standards in AI deployment. The paper concludes that while AI presents substantial opportunities for advancing ASD management, achieving these benefits requires a concerted effort from technologists, clinicians, ethicists, and policymakers to develop AI tools that are not only innovative but also ethical, equitable, and universally beneficial.
... It is directly tied to our memory consolidation, physical growth/repair, etc (Born & Marshall, 2007;Saghir et al., 2018). Lack of sleep can lead to major disruptions in waking life, and can contribute to disorders like epilepsy, schizophrenia, etc. (Rochette et al., 2018). Despite dreaming being a nightly occurrence, this realm of sleep remains largely unexplored, and fundamental questions about the purpose and mechanisms of sleep are unanswered. ...
... Overall, physical growth/repair, and memory consolidation can be attributed to NREM sleep (Vahdat et al., 2017). Abnormal NREM sleep is associated with disorders such as schizophrenia (Siclari et al., 2017), epilepsy (Schmitt, 2015), Alzheimer's disease (Lucey et al., 2019), Parkinson's disease (Priano et al., 2019), and even autism spectrum disorders (Rochette et al., 2018). NREM sleep is an essential contributor to our overall health (Rochette et al., 2018). ...
... Abnormal NREM sleep is associated with disorders such as schizophrenia (Siclari et al., 2017), epilepsy (Schmitt, 2015), Alzheimer's disease (Lucey et al., 2019), Parkinson's disease (Priano et al., 2019), and even autism spectrum disorders (Rochette et al., 2018). NREM sleep is an essential contributor to our overall health (Rochette et al., 2018). REM sleep is characterized by the ponto-geniculo-occipital waves which coincide with the rapid eye movements (Siegel, 2001). ...
... Furthermore, it has been observed that during NREMS, delta waves with frequencies ranging from 1 to 4 Hz predominantly manifest in the frontal regions of the brain. Individuals with autism exhibit predominantly impaired functional integrity within the thalamocortical network in this specific region, which may account for the decreased delta Electroencephalogram (EEG) activity during NREMS (Rochette et al., 2018). ...
... In addition, the study unveiled distinct regional variations in SWA alterations among individuals with autism, along with interregional variances in the dynamic changes of sleep pressure across the cerebral cortex. Specifically, this is substantiated by diminished SWA activity in the occipital lobe leads in autistic children during the third stage of NREMS (Arazi et al., 2020), as well as in the leads of the parieto-occipital region in autistic adolescents (Rochette et al., 2018). These findings illustrate that, in neurotypical individuals, the occipital cortex, a prominent posterior brain region, exhibits heightened activity during REMS and is also rich in NREMS (Wang et al., 2022). ...
... Autistic individuals exhibit diminished delta EEG activity in the parieto-occipital region during NREMS, with a more pronounced decline observed from the frontal to posterior regions of the brain (Farmer et al., 2018). Additionally, theta EEG oscillation, characterized by low-amplitude signals, reveals a significant reduction in individuals with autism, particularly in the frontal, central, and specific temporal lobes (Rochette et al., 2018). The lower theta activity emerges as a potential predictor of heightened autism severity. ...
Autism Spectrum Disorder (ASD) often co-occurs with sleep disorders, which can exacerbate core symptoms such as social impairments and repetitive stereotyped behaviors. Sleep is crucial for brain function, as it enhances cerebrospinal fluid circulation, clears metabolic toxins, and consolidates memories. Despite the high prevalence of these sleep issues in individuals with autism, the specific brain networks involved remain poorly understood. This study reviews recent advancements in identifying the brain network mechanisms underlying sleep disorders in ASD. By focusing on the interplay between autism-related sleep disturbances and brain network functionality, we synthesize current findings to highlight key mechanisms involved. Our research provides a preliminary theoretical framework that advances understanding of how sleep disorders impact brain networks in autism, offering a valuable reference for future investigations in this area.
... Furthermore, AI supports developmental progress tracking over the long term, adjusting strategies as needed to align with the individual's evolving needs. It also enhances communication for those with ASD by recognizing emotions through analysis of facial and vocal expressions and supporting real-time language processing, thus aiding in more effective communication [63][64][65] . ...
... This includes statistical and equivalence testing to ensure that synthetic data maintains fundamental statistical properties similar to patient data. Performance validation follows where AI models trained on synthetic data are tested against models trained on real-world data to ensure they retain efficacy and reliability when applied in genuine clinical situations [63][64][65] . ...
... This continual learning approach is essential to maintain the accuracy and relevance of AI tools. Implementing explainable AI (XAI) practices is also vital, allowing practitioners to understand and validate AI-driven recommendations based on transparent evidence and clinical knowledge [64][65][66] . ...
Integrating Artificial Intelligence (AI) into healthcare, specifically for managing Autism Spectrum Disorder (ASD), offers transformative potential to enhance diagnostic accuracy, personalize treatment, and improve patient outcomes. This review explores the application of various AI programs in ASD management, discussing their functionalities, ethical considerations, implementation challenges, and the need for comprehensive regulatory frameworks. Critical AI applications such as AI-driven diagnostic imaging, predictive analytics, assisted therapy robots, remote monitoring, treatment personalization, decision support systems, and therapeutic chatbots are examined. Each technology is analyzed for its ability to improve the quality of life for individuals with ASD by offering more personalized, efficient, and effective care and support. Ethical issues, particularly concerning data bias and privacy, are highlighted as significant challenges that need addressing to maximize AI's benefits while minimizing risks. Practical hurdles like integration with existing healthcare systems, the need for scalable solutions across diverse geographic and socio-economic contexts, and the high costs associated with AI development are also discussed. Furthermore, the review underscores the necessity for robust regulatory policies that ensure patient safety, protect data privacy, and maintain high ethical standards in AI deployment. The paper concludes that while AI presents substantial opportunities for advancing ASD management, achieving these benefits requires a concerted effort from technologists, clinicians, ethicists, and policymakers to develop AI tools that are not only innovative but also ethical, equitable, and universally beneficial.
... Sleep studies have shown decreased slow-wave sleep together with atypical non-REM sleep EEG patterns in children and adults on the autism spectrum compared to neurotypical participants, including a decreased density and/or deviant topographical distribution of sleep spindles (12)(13)(14)(15)(16), K-complexes (15), and EEG slow-waves (17)(18)(19)(20)(21). Studies on REM sleep have not disclosed REM sleep architecture atypicalities in autism but QEEG showed significantly lower spectral power for beta frequency over parietal and occipital recording sites compared to neurotypical controls (22,23). ...
... Specifically in children on the autism spectrum, REM sleep theta spectral power was found to be lower compared to neurotypicals for the left parietal recording sites in one study (23) but not in another (24). In adults with ASD, QEEG analyses disclosed lower NREM sleep delta frequency power over parieto-occipital recording sites but not in the frontal areas as expected (19), suggesting an atypical recruitment of frontal areas. Taken together these results suggest an atypical developmental pattern of the thalamocortical network in children and adults on the autism spectrum. ...
... We expected that high-functioning ASD adults would show less beta activity on parietal and occipital electrodes compared to neurotypical adults. Based on previous results on EEG topography in adults with ASD (19,22), we hypothesized that REM sleep EEG spectral power of high-functioning ASD adults would be anteriorly biased, i.e., higher in anterior than posterior recording sites. Because children with ASD showed an inter-hemispheric left asymmetry in the temporal area (25), we parsimoniously expected the same in adults. ...
We tested the hypothesis of an atypical scalp distribution of electroencephalography (EEG) activity during Rapid Eye Movement (REM) sleep in young autistic adults. EEG spectral activity and ratios along the anteroposterior axis and across hemispheres were compared in 16 neurotypical (NT) young adults and 17 individuals with autism spectrum disorder (ASD). EEG spectral power was lower in the ASD group over the bilateral central and right parietal (beta activity) as well as bilateral occipital (beta, theta, and total activity) recording sites. The NT group displayed a significant posterior polarity of intra-hemispheric EEG activity while EEG activity was more evenly or anteriorly distributed in ASD participants. No significant inter-hemispheric EEG lateralization was found. Correlations between EEG distribution and ASD symptoms using the Autism Diagnostic Interview-Revised (ADI-R) showed that a higher posterior ratio was associated with a better ADI-R score on communication skills, whereas a higher anterior ratio was related to more restricted interests and repetitive behaviors. EEG activity thus appears to be atypically distributed over the scalp surface in young adults with autism during REM sleep within cerebral hemispheres, and this correlates with some ASD symptoms. These suggests the existence in autism of a common substrate between some of the symptoms of ASD and an atypical organization and/or functioning of the thalamo-cortical loop during REM sleep.
... Several studies provide preliminary evidence that features of NREM sleep are altered in neurodevelopmental disorders such as attention deficit hyperactivity disorder (ADHD; Arns & Kenemans, 2014;Ringli et al., 2013), and ASD (Farmer et al., 2018;Limoges, Mottron, Bolduc, & Berthiaume, 2005;Rochette, Soulières, Berthiaume, & Godbout, 2018;Tessier et al., 2015). These alterations suggest atypical development, and identifying these changes in conjunction with other NREM features may indicate transdiagnostic risk of a neurodevelopmental disorder as ASD. ...
... Research with adults and children with ASD has found fewer sleep spindles (Farmer et al., 2018;Tessier et al., 2015) and reduced delta activity (Rochette et al., 2018) suggesting anomalies in the thalamocortical network, the anatomical substrate of sleep spindles and delta activity (De Gennaro & Ferrara, 2003;Steriade, 2003). ...
... These electrophysiological differences are congruent with neuroanatomical features associated with ASD (Nair, Treiber, Shukla, Shih, & Müller, 2013). One hypothesis is that the differences in the thalamocortical system between ASD and healthy populations indicate atypical connectivity (Ferradal et al., 2018;Woodward, Giraldo-Chica, Rogers, & Cascio, 2017) and functioning of thalamocortical neural substrates in ASD (Daoust, Limoges, Bolduc, Mottron, & Godbout, 2004;Rochette et al., 2018), and result in atypical processing of cognitive, attentional, and sensorimotor pathways in children and adults with ASD (Nair et al., 2013). ...
Objective:
Autism spectrum disorder (ASD) is a pervasive neurodevelopmental disorder that emerges in the beginning years of life (12-48 months). Yet, an early diagnosis of ASD is challenging as it relies on the consistent presence of behavioral symptomatology, and thus, many children are diagnosed later in development, which prevents early interventions that could benefit cognitive and social outcomes. As a result, there is growing interest in detecting early brain markers of ASD, such as in the electroencephalogram (EEG) to elucidate divergence in early development. Here, we examine the EEG of nonrapid eye movement (NREM) sleep in the transition from infancy to toddlerhood, a period of rapid development and pronounced changes in early brain function. NREM features exhibit clear developmental trajectories, are related to social and cognitive development, and may be altered in neurodevelopmental disorders. Yet, spectral features of NREM sleep are poorly understood in infants/toddlers with or at high risk for ASD.
Methods:
The present pilot study is the first to examine NREM sleep in 13- to 30-month-olds with ASD in comparison with age-matched healthy controls (TD). EEG was recorded during a daytime nap with high-density array EEG.
Results:
We found topographically distinct decreased fast theta oscillations (5-7.25 Hz), decreased fast sigma (15-16 Hz), and increased beta oscillations (20-25 Hz) in ASD compared to TD.
Conclusion:
These findings suggest a possible functional role of NREM sleep during this important developmental period and provide support for NREM sleep to be a potential early marker for ASD.
... On the one hand, the similar morphological features of SW amplitude and slope reported here suggest that the synaptic strength Dissimilarities in EEG oscillations during NREM sleep between ASD and TD subjects is a common finding in adults and children with ASD, including short durations of SWS and low incidence of sleep spindles during stage 2 Limoges, Bolduc, Berthiaume, Mottron, & Godbout, 2013). We have also observed less K-complexes during stage 2 in school-aged children with ASD and low levels of delta EEG activity in adults with ASD (Rochette, Soulières, Berthiaume, & Godbout, 2018). In the case of sleep spindles, the location of divergent recording sites between ASD and TD groups was in the frontal area for children and in the central area in adults Limoges et al., 2013Limoges et al., , 2005, while delta EEG activity differences between ASD and TD adults occurred in the posterior recording areas (Rochette et al., 2018). ...
... We have also observed less K-complexes during stage 2 in school-aged children with ASD and low levels of delta EEG activity in adults with ASD (Rochette, Soulières, Berthiaume, & Godbout, 2018). In the case of sleep spindles, the location of divergent recording sites between ASD and TD groups was in the frontal area for children and in the central area in adults Limoges et al., 2013Limoges et al., , 2005, while delta EEG activity differences between ASD and TD adults occurred in the posterior recording areas (Rochette et al., 2018). These observations further support the notion that the topographical deviation of EEG activity in ASD constitutes an EEG trait of this neurodevelopmental disorder. ...
... Relationship patterns between EEG oscillations during NREM sleep (i.e. stage 2 sleep spindles, NREM sleep delta EEG activity, SWS) and daytime functioning are different in ASD and TD groups of individuals Limoges et al., 2013Limoges et al., , 2005Rochette et al., 2018). Studies should determine whether it also applies to the relationship between SW and daytime functioning, and correlate this with brain imaging measures of synaptic density and strength to further characterize the pathophysiology of ASD. ...
Autism is a developmental disorder with a neurobiological aetiology. Studies of the autistic brain identified atypical developmental trajectories that may lead to an impaired capacity to modulate electroencephalogram activity during sleep. We assessed the topography and characteristics of non‐rapid eye movement sleep electroencephalogram slow waves in 26 boys aged between 6 and 13 years old: 13 with an autism spectrum disorder and 13 typically developing. None of the participants was medicated, intellectually disabled, reported poor sleep, or suffered from medical co‐morbidities. Results are derived from a second consecutive night of polysomnography in a sleep laboratory. Slow waves (0.3–4.0 Hz; >75 µV) were automatically detected on artefact‐free sections of non‐rapid eye movement sleep along the anteroposterior axis in frontal, central, parietal and occipital derivations. Slow wave density (number per minute), amplitude (µV), slope (µV s⁻¹) and duration (s) were computed for the first four non‐rapid eye movement periods. Slow wave characteristics comparisons between groups, derivations and non‐rapid eye movement periods were assessed with three‐way mixed ANOVAs. Slow wave density, amplitude, slope and duration were higher in anterior compared with most posterior derivations in both groups. Children with autism spectrum disorder showed lower differences in slow waves between recording sites along the anteroposterior axis than typically developing children. These group differences in the topography of slow wave characteristics were stable across the night. We propose that slow waves during non‐rapid eye movement sleep could be an electrophysiological marker of the deviant cortical maturation in autism linked to an atypical functioning of thalamo‐cortical networks.
... For the typical development group, there was a decrease in beta power following the stimulus onset, whereas the ASD group demonstrated an elevation in beta power linked to the event. Atypical oscillatory beta band activity (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) led to unusual functional connections between the occipital regions (which handle initial stimulus analysis) and the infero-temporal areas, responsible for extracting object identity [21]. ...
... This topic was explored by Rochette et al., who demonstrated atypical thalamocortical activity in the parieto-occipital area in individuals with ASD. An abnormal connection between the frontal cortex and sensorimotor memory encoding in patients with ASD was noted in their findings [26]. The issue of EEG changes during non-rapid eye movement sleep, which was also addressed by Lehoux et al. study on slow waves in NREM sleep that were suggested as a potential electrophysiological indicator of altered cortical maturation in ASD, associated with atypical thalamo-cortical network functioning [27]. ...
Background/Objectives: Electroencephalography (EEG) has been widely used to differentiate individuals with autism spectrum disorder (ASD) and co-occurring conditions, particularly epilepsy. However, the relationship between EEG abnormalities and core features of ASD remains unclear. This study reviews the potential impact of EEG findings on the development, behavior, sleep, and seizure occurrence in ASD patients. Additionally, it evaluates whether routine EEG testing is warranted for all ASD patients, particularly in the absence of clinical seizures. Methods: A systematic review was conducted that covered literature published between 2014 and 2024. The review focused on EEG abnormalities, both epileptiform and non-epileptiform, in individuals with ASD. Studies were selected based on predefined inclusion criteria, emphasizing the prevalence, type, and clinical relevance of EEG findings. The analysis also included a critical assessment of whether EEG abnormalities correlate with specific ASD symptoms, such as cognitive impairment, speech delay, or behavioral issues. Results: EEG abnormalities were reported in 23–80% of ASD patients, indicating a broad range of findings. Despite their frequent occurrence, the evidence linking these abnormalities to specific clinical symptoms remains inconclusive. Some studies suggest an association between epileptiform patterns and more severe ASD traits, while others do not confirm this. Furthermore, the effectiveness of anticonvulsant treatment in children with EEG abnormalities and no seizures remains uncertain, with limited supporting data. Conclusions: Given the uncertain relationship between EEG findings and ASD symptoms, routine EEG testing for all children with ASD appears unnecessary. EEG should be considered primarily when epilepsy is clinically suspected.
... Sleep disturbances are predominant in patients suffering from neurodevelopmental disorders and often associated with worse disease outcomes. Decreased sleep duration, increased sleep fragmentation, and alterations in electroencephalographic activity have notably been reported in autism spectrum disorders (ASD) and schizophrenia [1][2][3][4][5]. Importantly, less total sleep time was correlated with worse social and communication skills in patients with ASD [2]. ...
... SW and slow-wave activity, which are implicated in cognitive functions including memory consolidation and extinction [28,29], were reported to be altered in autistic and epileptic patients [30][31][32]. Our previous findings show a large increase in SWS delta (1)(2)(3)(4) activity in Nlgn2 KO mice [24]. However, it remains to be defined whether this effect originates from an increased capacity to generate SW during SWS (increased density) or in more synchrony between individual neurons contributing to SW generation (higher amplitude and steeper slope). ...
Background
Sleep disturbances are a common comorbidity to most neurodevelopmental disorders and tend to worsen disease symptomatology. It is thus crucial to understand mechanisms underlying sleep disturbances to improve patients’ quality of life. Neuroligin-2 (NLGN2) is a synaptic adhesion protein regulating GABAergic transmission. It has been linked to autism spectrum disorders and schizophrenia in humans, and deregulations of its expression were shown to cause epileptic-like hypersynchronized cerebral activity in rodents. Importantly, the absence of Nlgn2 (knockout: KO) was previously shown to alter sleep-wake duration and quality in mice, notably increasing slow-wave sleep (SWS) delta activity (1–4 Hz) and altering its 24-h dynamics. This type of brain oscillation is involved in memory consolidation, and is also a marker of homeostatic sleep pressure. Sleep deprivation (SD) is notably known to impair cognition and the physiological response to sleep loss involves GABAergic transmission.
Methods
Using electrocorticographic (ECoG) recordings, we here first aimed to verify how individual slow wave (SW; 0.5-4 Hz) density and properties (e.g., amplitude, slope, frequency) contribute to the higher SWS delta activity and altered 24-h dynamics observed in Nlgn2 KO mice. We further investigated the response of these animals to SD. Finally, we tested whether sleep loss affects the gene expression of Nlgn2 and related GABAergic transcripts in the cerebral cortex of wild-type mice using RNA sequencing.
Results
Our results show that Nlgn2 KO mice have both greater SW amplitude and density, and that SW density is the main property contributing to the altered 24-h dynamics. We also found the absence of Nlgn2 to accelerate paradoxical sleep recovery following SD, together with profound alterations in ECoG activity across vigilance states. Sleep loss, however, did not modify the 24-h distribution of the hypersynchronized ECoG events observed in these mice. Finally, RNA sequencing confirmed an overall decrease in cortical expression of Nlgn2 and related GABAergic transcripts following SD in wild-type mice.
Conclusions
This work brings further insight into potential mechanisms of sleep duration and quality deregulation in neurodevelopmental disorders, notably involving NLGN2 and GABAergic neurotransmission.
... Research concerning SWS has often shown inconsistent findings. Some studies have found that autistic children have a higher percentage of SWS (Buckley, Rodriguez, & Jennison, 2010), whereas others found less SWS compared to TD controls (Limoges et al., 2005;Rochette, Soulières, Berthiaume, & Godbout, 2018). Research on REM sleep in ASD has revealed similar inconsistencies. ...
... Overall, memory was worse in autistic children than their TD peers, requiring more trials to reach task criterion (Maski et al., 2015). However, Rochette et al. (2018) showed comparable procedural memory performance across sleep in ASD and TD, but that procedural memory is likely mediated by different brain areas in ASD. This finding emphasizes the importance of examining the possible differences in the topography of EEG effects. ...
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.
... Deep sleep is essential for proper cognitive function [35], stabilizing synaptic plasticity [36,37], and enabling learning and memory consolidation [38]. To our knowledge, only three studies to date have quantified the amplitude of SWA in ASD and all were performed with high-functioning adolescents and adults [39][40][41]. Two of these studies reported significantly weaker SWA in the ASD group [39,40], a difference that was particularly large during the first 2-3 hours of sleep. ...
... To our knowledge, only three studies to date have quantified the amplitude of SWA in ASD and all were performed with high-functioning adolescents and adults [39][40][41]. Two of these studies reported significantly weaker SWA in the ASD group [39,40], a difference that was particularly large during the first 2-3 hours of sleep. ...
Study Objectives
Sleep disturbances and insomnia are highly prevalent in children with Autism Spectrum Disorder (ASD). Sleep homeostasis, a fundamental mechanism of sleep regulation that generates pressure to sleep as a function of wakefulness, has not been studied in children with ASD so far, and its potential contribution to their sleep disturbances remains unknown. Here, we examined whether slow wave activity (SWA), a measure that is indicative of sleep pressure, differs in children with ASD.
Methods
In this case-control study, we compared overnight electroencephalogram (EEG) recordings that were performed during Polysomnography (PSG) evaluations of 29 children with ASD and 23 typically developing children.
Results
Children with ASD exhibited significantly weaker SWA power, shallower SWA slopes, and a decreased proportion of slow wave sleep in comparison to controls. This difference was largest during the first two hours following sleep onset and decreased gradually thereafter. Furthermore, SWA power of children with ASD was significantly, negatively correlated with the time of their sleep onset in the lab and at home, as reported by parents.
Conclusions
These results suggest that children with ASD may have a dysregulation of sleep homeostasis that is manifested in reduced sleep pressure. The extent of this dysregulation in individual children was apparent in the amplitude of their SWA power, which was indicative of the severity of their individual sleep disturbances. We, therefore, suggest that disrupted homeostatic sleep regulation may contribute to sleep disturbances in children with ASD.
... This fact could lead to different patterns of SWA distribution for different age ranges, since SWA during TD reflects changes in brain maturation [1]. Consistently, while Lehoux and collaborators [64] showed reduced differences in slow waves features along the anteroposterior axis compared to controls in children with AS, Rochette et al. [79] found an opposite pattern in an older sample (15e27 y), with a higher reduction in the delta activity along the anteroposterior axis in participants with AS/HFA. Moreover, they found a parietooccipital decrease in delta activity during Stage 2 and SWS in AS/HFA, but no differences between the two groups with regards to the frontal regions. ...
... Overall, starting from the reviewed data, we propose possible pathology-related processes that may affect SWA in AS/HFA and that should be directly investigated in future studies: 1) autismspecific alterations in the developmental trajectories, as suggested by the differential SWA pattern observed in different age ranges [64, 79,80]; 2) higher homeostatic sleep pressure, as hypothesised from L az ar et al. [80]; 3) alterations in sleep protective mechanisms, suggested by KC reductions [66]. ...
Neurodevelopmental disorders (NDDs) are often characterised by sleep problems, and recent evidence indicates alterations of the sleep electroencephalographic (EEG) oscillations in these patients. Sleep microstructure plays a crucial role in cognitive functioning and brain maturation. In this view, modifications in sleep EEG oscillations in NDDs could further impair the cognitive maturation process in these patients. We provide an overview of sleep microstructure alterations observed in three NDDs without intellectual disabilities (attention-deficit/hyperactivity disorder, high-functioning autism/Asperger syndrome and developmental dyslexia) and their relationships with the disorders' phenomenology. For each NDD, we discuss empirical evidence of altered EEG oscillations, and we consider their interaction with patients' cognitive and behavioural functioning, with the aim to elucidate their functional meaning. We highlight the limits of the present literature and propose possible future directions while underlining the clinical relevance of the research in this field. Beyond confirming the importance of sleep management in atypically developing children, the review findings suggest that sleep EEG oscillations in NDDs could become a target for specific clinical intervention.
... Deep sleep is essential for proper cognitive function 35 , stabilizing synaptic plasticity 36,37 , and enabling learning and memory consolidation 38 . To our knowledge, only three studies to date have quantified the amplitude of SWA in ASD and all were performed with high-functioning adolescents and adults [39][40][41] . Two All rights reserved. ...
... doi: bioRxiv preprint first posted online Jul. 22, 2019; 6 of these studies reported significantly weaker SWA in the ASD group 39,40 , a difference that was particularly large during the first 2-3 hours of sleep. ...
Importance: Sleep disturbances and insomnia are highly prevalent in children with Autism Spectrum Disorder (ASD). Sleep homeostasis, a fundamental mechanism of sleep regulation that generates pressure to sleep as a function of wakefulness, has not been studied in children with ASD. It is not clear whether abnormalities in sleep homeostasis contribute to sleep disturbances in children with ASD.
Objective: To determine whether slow wave activity (SWA), a potent measure of homeostatic sleep pressure, is impaired in children with ASD and associated with the severity of reported sleep disturbances.
Design: Case control comparison of overnight electroencephalogram (EEG) recordings that were performed during Polysomnography (PSG) evaluations between 2015 and 2018.
Setting: Children with autism were deeply phenotyped at the National Autism Research Center of Israel. PSG was performed at a neighboring regional sleep disorders unit that accepts children with primary care referrals.
Participants: Final analyses were performed with PSGs from 29 children with ASD, 8 females, mean age 4.6 years old (range 1.9 to 7.8), and 23 typically developing children, 8 females, mean age 5.3 years old (range 3.5 to 8.9).
Exposure: A single overnight PSG evaluation.
Main outcome measures: SWA power, SWA slope, and sleep architecture.
Results: Children with ASD exhibited significantly weaker SWA power, shallower SWA slopes, and a decreased proportion of slow wave sleep in comparison to controls. This difference was largest during the first two hours following sleep onset and decreased gradually during sleep. Furthermore, SWA power of children with ASD was significantly correlated with the time of sleep onset and with parental report of latency to sleep at home.
Conclusions and relevance: These results reveal that children with ASD exhibit reduced sleep pressure, which is a dysregulation of sleep homeostasis. The extent of dysregulation in individual children is apparent in the amplitude of their SWA power, which is indicative of the severity of their sleep disturbances. These findings motivate clinical trials with specific interventions that increase homeostatic sleep pressure.
... For example, young children with ASD (mean age 4.6 years) had reduced SWA activity in the occipital leads during N3 sleep vs. a clinically referred TD group [46]. Similarly, young adults with ASD (mean 22.8 years) showed less SWA activity during NREM sleep in parietal occipital leads vs. a TD group [47]. In terms of associations between SWA and behaviour, our NREM SWA findings are consistent with a recent abstract reporting negative associations fronto-occipital NREM SWA with externalizing behaviours in TD children ages 5-12 years [19]. ...
Objective: Insomnia and daytime behavioral problems are common issues in pediatric autism spectrum disorder (ASD), yet specific underlying relationships with NonRapid Eye Movement sleep (NREM) and Rapid Eye Movement (REM) sleep architecture are understudied. We hypothesize that REM sleep alterations (REM%, REM EEG power) are associated with more internalizing behaviors and NREM sleep deficits (N3%; slow wave activity (SWA) 0.5–3 Hz EEG power) are associated with increased externalizing behaviors in children with ASD vs. typical developing controls (TD). Methods: In an age- and gender-matched pediatric cohort of n = 23 ASD and n = 20 TD participants, we collected macro/micro sleep architecture with overnight home polysomnogram and daytime behavior scores with Child Behavior Checklist (CBCL) scores. Results: Controlling for non-verbal IQ and medication use, ASD and TD children have similar REM and NREM sleep architecture. Only ASD children show positive relationships between REM%, REM theta power and REM beta power with internalizing scores. Only TD participants showed an inverse relationship between NREM SWA and externalizing scores. Conclusion: REM sleep measures reflect concerning internalizing behaviours in ASD and could serve as a biomarker for mood disorders in this population. While improving deep sleep may help externalizing behaviours in TD, we do not find evidence of this relationship in ASD.
... Alternatively, reduced K-complex density in the central and prefrontal regions may indicate possible neurodegenerative processes 34,35 . Some other findings [36][37][38][39] suggest that changes in slow wave activity in ASD mirror the atypical cortical maturation detected in ...
... For example, it can be seen that during the activity of the Delta Band of brain signals, especially at sleep stage 2, suddenly decreases from the forehead to the posterior part. This topic indicates the unusual Thalamocortical function, which also shows an abnormal relationship between the forehead area and the procedural sensory-motor memory encoding [4]. Typically, these signals are recorded by a polysomnographic machine (PSG). ...
ASD: Autism Spectrum Disorder; PSG: Polysomnographic Machine; ADOS: Autism Diagnosis Programming; CBCL: Depression
Scale Epidemiologic Studies; CSHQ: Child Scale Health Cure; GARS: Grading Autism Range Scale; GARS: Gordon Diagnostic System; PBS: Child
Behavior Scale; PSG: Polysomnography; PSQ: Parental Sleep Questionnaire; RBS-R: Repeated Behavioral Scale – Modified; WISC: Intelligence
Scale for Children; WPPSI: Wexler Primary School Elemental Scale
... Sleep spindle abnormalities observed in ASD, and to a lesser extent DS, also suggest thalamocortical disruption. This assertion was corroborated recently by findings of reduced Delta EEG activity, which reflects disrupted thalamocortical function during NREM sleep, in adults with ASD (Rochette et al., 2018). ...
Sleep problems are common among children with neurodevelopmental disorders (NDDs). We review sleep disturbance in three major NDDs: autism spectrum disorder, Down syndrome, and fetal alcohol spectrum disorder (FASD). We review associations with functional impairment, discuss how patterns of sleep disturbance inform understanding of etiology, and theorize about mechanisms of impairment. Sleep disturbance is a transdiagnostic feature of NDDs. Caregivers report high rates of sleep problems, including difficulty falling or staying asleep. Polysomnography data reveal differences in sleep architecture and increased rates of sleep disorders. Sleep disturbance is associated with functional impairment and stress among families. Further research is important for elucidating mechanisms of impairment and developing effective interventions. Despite significant sleep disturbance in FASD, limited research is available.
Background :
Autism spectrum disorder (ASD) is a complex-heterogeneous neurodevelopmental disorder manifesting as abnormalities in social communication and repetitive behaviors, generally observed from early childhood. These syndromic behaviors have neurophysiological basis which stems from altered activations of cortical structures in the pathways of functional neural networks and regulatory mechanisms. Frequency bands of Electroencephalography (EEG) have functional and topographical significance expressed through computed parameters like band power and asymmetry index. Previous studies have mapped these parameters to ASD symptoms, limited to select cortical locations, bands and restricted study conditions in either passive awake or selected sleep stage.
Methods:
Spontaneous EEG recorded from two clinically diagnosed groups of preschoolers, ASD and non-ASD in awake and 3 stages of Non-Rapid Eye Movement (NREM) sleep (N1–N3) was decomposed into 8 frequency bands spanning 0.5–24 Hz. Band powers were computed for 60 channels and hemispheric asymmetry index (AI) for 12 regions covering the entire scalp.
Results :
We found awake alpha with N1 slow and fast theta powers significantly lower for ASD. N1 fast beta power was higher in ASD. Sleep AI exhibited significant dominance with both groups displaying congruent orientation in N1 and contralateral in N2 and N3. ASD showed lower AI in N1 and N3 with higher AI in N2.
Conclusion:
Cyclical states of awake and sleep often tend to project their mental processes from one onto another making a use case for our pervasive approach. This pilot study highlights the need to include EEG spectral parameters into the heterogeneous relationship of awake/sleep states mentation, neuropsychology and ASD symptoms.
Integrating Artificial Intelligence (AI) into healthcare, specifically for managing autism spectrum disorder (ASD), offers transformative potential to enhance diagnostic accuracy, personalize treatment, and improve patient outcomes. This review explores the application of various AI programs in ASD management, discussing their functionalities, ethical considerations, implementation challenges, and the need for comprehensive regulatory frameworks. Critical AI applications such as AI-driven diagnostic imaging, predictive analytics, assisted therapy robots, remote monitoring, treatment personalization, decision support systems, and therapeutic chatbots are examined. Each technology is analyzed for its ability to improve the quality of life for individuals with ASD by offering more personalized, efficient, and effective care and support. Ethical issues, particularly concerning data bias and privacy, are highlighted as significant challenges that need addressing to maximize AI’s benefits while minimizing risks. Practical hurdles like integration with existing healthcare systems, the need for scalable solutions across diverse geographic and socio-economic contexts, and the high costs associated with AI development are also discussed. Furthermore, the review underscores the necessity for robust regulatory policies that ensure patient safety, protect data privacy and maintain high ethical standards in AI deployment. The paper concludes that while AI presents substantial opportunities for advancing ASD management, achieving these benefits requires a concerted effort from technologists, clinicians, ethicists, and policymakers to develop AI tools that are not only innovative but also ethical, equitable, and universally beneficial.
Purpose of Review
Sleep plays an important role in the long-term consolidation of memories across development. The current review examines sleep-dependent memory consolidation in five developmental disability diagnoses that experience significant sleep disturbances. A focus is placed on understanding how specific neural and mnemonic features of particular diagnoses contribute to distinct alterations to consolidation.
Recent Findings
Sleep and memory changes have been identified across diagnoses, but they differ in the extent to which sleep-related consolidation processes are specifically affected. In addition, Down syndrome may represent a unique case where sleep actually impairs memory consolidation, potentially related to profound disruptions to hippocampal-prefrontal connectivity.
Summary
Deficits in sleep-dependent memory consolidation are heterogeneous across developmental disabilities and are caused by a confluence of cognitive and neural factors. Sleep and memory research should target these specific population profiles. Future research should also examine sleep effects on forms of memory that may depend on alternate neural pathways, such as emotional memory.
Individuals with autism spectrum disorder (ASD) have been characterized by altered cerebral cortical structures; however, the field has yet to identify consistent markers and prior studies have included mostly adolescents and adults. While there are multiple cortical morphological measures, including cortical thickness, surface area, cortical volume, and cortical gyrification, few single studies have examined all these measures. The current study analyzed all of the four measures and focused on pre-adolescent children with ASD.
We employed the FreeSurfer pipeline to examine surface-based morphometry in 60 high-functioning boys with ASD (mean age = 8.35 years, range = 4–12 years) and 41 gender-, age-, and IQ-matched typically developing (TD) peers (mean age = 8.83 years), while testing for age-by-diagnosis interaction and between-group differences.
During childhood and in specific regions, ASD participants exhibited a lack of normative age-related cortical thinning and volumetric reduction and an abnormal age-related increase in gyrification. Regarding surface area, ASD and TD exhibited statistically comparable age-related development during childhood. Across childhood, ASD relative to TD participants tended to have higher mean levels of gyrification in specific regions. Within ASD, those with higher Social Responsiveness Scale total raw scores tended to have greater age-related increase in gyrification in specific regions during childhood.
ASD is characterized by cortical neuroanatomical abnormalities that are age-, measure-, statistical model-, and region-dependent. The current study is the first to examine the development of all four cortical measures in one of the largest pre-adolescent samples. Strikingly, Neurosynth-based quantitative reverse inference of the surviving clusters suggests that many of the regions identified above are related to social perception, language, self-referential, and action observation networks—those frequently found to be functionally altered in individuals with ASD. The comprehensive, multilevel analyses across a wide range of cortical measures help fill a knowledge gap and present a complex but rich picture of neuroanatomical developmental differences in children with ASD.
The natural history of brain growth in autism spectrum disorders remains unclear. Cross-sectional studies have identified regional abnormalities in brain volume and cortical thickness in autism, although substantial discrepancies have been reported. Preliminary longitudinal studies using two time points and small samples have identified specific regional differences in cortical thickness in the disorder. To clarify age-related trajectories of cortical development, we examined longitudinal changes in cortical thickness within a large mixed cross-sectional and longitudinal sample of autistic subjects and age- and gender-matched typically developing controls. Three hundred and forty-five magnetic resonance imaging scans were examined from 97 males with autism (mean age = 16.8 years; range 3-36 years) and 60 males with typical development (mean age = 18 years; range 4-39 years), with an average interscan interval of 2.6 years. FreeSurfer image analysis software was used to parcellate the cortex into 34 regions of interest per hemisphere and to calculate mean cortical thickness for each region. Longitudinal linear mixed effects models were used to further characterize these findings and identify regions with between-group differences in longitudinal age-related trajectories. Using mean age at time of first scan as a reference (15 years), differences were observed in bilateral inferior frontal gyrus, pars opercularis and pars triangularis, right caudal middle frontal and left rostral middle frontal regions, and left frontal pole. However, group differences in cortical thickness varied by developmental stage, and were influenced by IQ. Differences in age-related trajectories emerged in bilateral parietal and occipital regions (postcentral gyrus, cuneus, lingual gyrus, pericalcarine cortex), left frontal regions (pars opercularis, rostral middle frontal and frontal pole), left supramarginal gyrus, and right transverse temporal gyrus, superior parietal lobule, and paracentral, lateral orbitofrontal, and lateral occipital regions. We suggest that abnormal cortical development in autism spectrum disorders undergoes three distinct phases: accelerated expansion in early childhood, accelerated thinning in later childhood and adolescence, and decelerated thinning in early adulthood. Moreover, cortical thickness abnormalities in autism spectrum disorders are region-specific, vary with age, and may remain dynamic well into adulthood.
Sleep is universal, tightly regulated, and its loss impairs cognition. But why does the brain need to disconnect from the environment for hours every day? The synaptic homeostasis hypothesis (SHY) proposes that sleep is the price the brain pays for plasticity. During a waking episode, learning statistical regularities about the current environment requires strengthening connections throughout the brain. This increases cellular needs for energy and supplies, decreases signal-to-noise ratios, and saturates learning. During sleep, spontaneous activity renormalizes net synaptic strength and restores cellular homeostasis. Activity-dependent down-selection of synapses can also explain the benefits of sleep on memory acquisition, consolidation, and integration. This happens through the offline, comprehensive sampling of statistical regularities incorporated in neuronal circuits over a lifetime. This Perspective considers the rationale and evidence for SHY and points to open issues related to sleep and plasticity.
Psychophysiological tests for identifying the level of trait anxiety and polysomnology have been
used in this study. Gender differences in the organization of sleep phases during the first three cycles and the
spectral density of sleep EEGs for persons with high and low levels of trait anxiety have been studied.
Asperger's syndrome (AS) is a pervasive developmental disorder that may fall along the autistic spectrum. We compared the sleep of eight patients with AS with that of participants matched for age and gender. Patients with AS showed decreased sleep rime in the first two-thirds of the night, increased number of shifts into REM sleep from a waking epoch, and all but one patient showed signs of REM sleep disruption. EEG sleep spindles were significantly decreased while K complexes and REM sleep rapid eye movements were normal. Three patients with AS, but none of the comparison participants, showed a pathological index of periodic leg movements in sleep. These observations show that sleep disorders are associated with AS and suggest that defective sleep control systems may be associated with the clinical picture of AS. NeuroReport 11:127-130 (C) 2000 Lippincott Williams & Wilkins.
The Autism Diagnostic Observation Schedule—Generic (ADOS-G) is a semistructured, standardized assessment of social interaction, communication, play, and imaginative use of materials for individuals suspected of having autism spectrum disorders. The observational schedule consists of four 30-minute modules, each designed to be administered to different individuals according to their level of expressive language. Psychometric data are presented for 223 children and adults with Autistic Disorder (autism), Pervasive Developmental Disorder Not Otherwise Specified (PDDNOS) or nonspectrum diagnoses. Within each module, diagnostic groups were equivalent on expressive language level. Results indicate substantial interrater and test—retest reliability for individual items, excellent interrater reliability within domains and excellent internal consistency. Comparisons of means indicated consistent differentiation of autism and PDDNOS from nonspectrum individuals, with some, but less consistent, differentiation of autism from PDDNOS. A priori operationalization of DSM-IV/ICD-10 criteria, factor analyses, and ROC curves were used to generate diagnostic algorithms with thresholds set for autism and broader autism spectrum/PDD. Algorithm sensitivities and specificities for autism and PDDNOS relative to nonspectrum disorders were excellent, with moderate differentiation of autism from PDDNOS.
It has recently been reported that selective REM sleep deprivation (REMD) in college students results in memory impairment of the application of a set of rules in a logic task, but not recall of a paired associate task. The present experiments were designed to examine the effects of Total Sleep Deprivation (TSD) and (REMD) following acquisition of a pure motor task, the pursuit rotor. In Experiment 1, subjects (N = 90) were exposed to TSD for one of several nights following training. Results showed that TSD on the same night as training resulted in poorer performance on retest one week later. In Experiment 2, subjects (N = 42) were exposed to various kinds of sleep deprivation on the night of task acquisition. One group was subjected to REMD. Other groups included a non-REM awakening control group (NREMA), a TSD group, a normally rested Control group and a group allowed the first 4 h of sleep in the night before being subjected to TSD (LH - TSD) for the rest of the night. Results showed the REMD and Control groups to have excellent memory for this task while the TSD and LH - TSD subjects had significantly poorer memory for the task. The NREMA group showed a slight, but not significant deficit. It was concluded that Stage 2 sleep, rather than REM sleep was the important stage of sleep for efficient memory processing of the pursuit rotor task.
Over the past two decades, research has accumulated compelling evidence that sleep supports the formation of long-term memory. The standard two-stage memory model that has been originally elaborated for declarative memory assumes that new memories are transiently encoded into a temporary store (represented by the hippocampus in the declarative memory system) before they are gradually transferred into a long-term store (mainly represented by the neocortex), or are forgotten. Based on this model, we propose that sleep, as an offline mode of brain processing, serves the 'active system consolidation' of memory, i.e. the process in which newly encoded memory representations become redistributed to other neuron networks serving as long-term store. System consolidation takes place during slow-wave sleep (SWS) rather than rapid eye movement (REM) sleep. The concept of active system consolidation during sleep implicates that (a) memories are reactivated during sleep to be consolidated, (b) the consolidation process during sleep is selective inasmuch as it does not enhance every memory, and (c) memories, when transferred to the long-term store undergo qualitative changes. Experimental evidence for these three central implications is provided: It has been shown that reactivation of memories during SWS plays a causal role for consolidation, that sleep and specifically SWS consolidates preferentially memories with relevance for future plans, and that sleep produces qualitative changes in memory representations such that the extraction of explicit and conscious knowledge from implicitly learned materials is facilitated.
To compare objective polysomnographic parameters between 3 cohorts: children with autism, typical development, and developmental delay without autism.
Overnight polysomnographic recordings were scored for sleep architecture according to American Academy of Sleep Medicine criteria by a board-certified sleep medicine specialist blind to diagnosis for studies collected between July 2006 and September 2009.
Subjects were evaluated in the pediatric ward in the Clinical Research Center of the National Institutes of Health.
First 60 consecutive children with autism, 15 with typical development, and 13 with developmental delay matched for nonverbal IQ to the autism group, ranging in age from 2 to 13 years, selected without regard to the presence or absence of sleep problem behavior.
Total sleep time, latencies to non-rapid eye movement (REM) and REM sleep, and percentages of total sleep time for stages 1 and 2 sleep, slow-wave sleep, and REM sleep.
There were no differences between the typical vs developmental delay groups. Comparison of children with autism vs typical children revealed shorter total sleep time (P = .004), greater slow-wave sleep percentage (P = .001), and much smaller REM sleep percentage (14.5% vs 22.6%; P < .001). Comparison of children with autism vs children with developmental delay revealed shorter total sleep time (P = .001), greater stage 1 sleep percentage (P < .001), greater slow-wave sleep percentage (P < .001), and much less REM sleep percentage (14.5% v 25%; P < .001).
A relative deficiency of REM sleep may indicate an abnormality in neural organization in young children with autism that is not directly associated with or related to inherent intellectual disability but may serve as a window into understanding core neurotransmitter abnormalities unique to this disorder.
Slow waves, a major electrophysiological characteristic of non-rapid eye movement sleep, undergo prominent changes across puberty. This study provides a detailed description of sleep slow waves of prepubertal children and mature adolescents to better understand the mechanisms underlying the decrease of activity in the slow-wave frequency range across puberty.
All-night sleep electroencephalographic recordings were performed for baseline and after sleep deprivation.
N/A.
Eight prepubertal children (Tanner 1/2, 11.9 +/- 0.8 years, 3 boys) and 6 mature adolescents (Tanner 4/5, 14.3 +/- 1.4 years, 3 boys).
Thirty-six hours of sleep deprivation.
Both during baseline and after sleep deprivation, a steeper slope of slow waves was observed in prepubertal children (351.0 +/- 49.5 microV/s), compared with mature adolescents (215.0 +/- 27.2 microV/s, P<0.05; mean of first 5 NREM sleep episodes from baseline), even accounting for overall amplitude differences.
Based on a recent thalamocortical computer model, these findings may indicate a greater synaptic strength of neurons involved in the generation of sleep slow waves in prepubertal children, compared with mature adolescents. Such increased synaptic strength may be due to greater density or greater efficacy of cortical synapses or both.
The consolidation of memories in a variety of learning processes benefits from post-training sleep, and recent work has suggested a role for sleep slow wave activity (SWA). Previous studies using a visuomotor learning task showed a local increase in sleep SWA in right parietal cortex, which was correlated with post-sleep performance enhancement. In these as in most similar studies, learning took place in the evening, shortly before sleep. Thus, it is currently unknown whether learning a task in the morning, followed by the usual daily activities, would also result in a local increase in sleep SWA during the night, and in a correlated enhancement in performance the next day. To answer this question, a group of subjects performed a visuomotor learning task in the morning and was retested the following morning. Whole night sleep was recorded with high-density EEG. We found an increase of SWA over the right posterior parietal areas that was most evident during the second sleep cycle. Performance improved significantly the following morning, and the improvement was positively correlated with the SWA increase in the second sleep cycle. These results suggest that training-induced changes in sleep SWA and post-sleep improvements do not depend upon the time interval between original training and sleep.
Radial cell minicolumns are basic cytoarchitectonic motifs of the mammalian neocortex. Recent studies reveal that autism is associated with a "minicolumnopathy" defined by decreased columnar width and both a diminished and disrupted peripheral neuropil compartment. This study further characterizes this cortical deficit by comparing minicolumnar widths across layers. Brains from seven autistic patients and an equal number of age-matched controls were celloidin embedded, serially sectioned at 200 microm and Nissl stained with gallocyanin. Photomicrograph mosaics of the cortex were analyzed with computerized imaging methods to determine minicolumnar width at nine separate neocortical areas: Brodmann Area's (BA) 3b, 4, 9, 10, 11, 17, 24, 43 and 44. Each area was assessed at supragranular, granular and infragranular levels. Autistic subjects had smaller minicolumns whose dimensions varied according to neocortical area. The greatest difference between autistic and control groups was observed in area 44. The interaction of diagnosis x cortical area x lamina (F(16,316) = 1.33; P = 0.175) was not significant. Diminished minicolumnar width across deep and superficial neocortical layers most probably reflects involvement of shared constituents among the different layers. In this article we discuss the possible role of double bouquet and pyramidal cells in the translaminar minicolumnar width narrowing observed in autistic subjects.
To investigate polysomnographic (PSG) sleep and NREM sleep characteristics, including sleep spindles and spectral activity involved in offline consolidation of a motor sequence learning task.
Counterbalanced within-subject design.
Three weekly visits to the sleep laboratory.
Fourteen healthy participants aged between 20 and 30 years (8 women).
Motor sequence learning (MSL) task or motor control (CTRL) task before sleep.
Subjects were trained on either the MSL or CTRL task in the evening and retested 12 hours later the following morning on the same task after a night of PSG sleep recording. Total number and duration of sleep spindles and spectral power between 0.5 and 24 Hz were quantified during NREM sleep. After performing the MSL task, subjects exhibited a large increase in number and duration of sleep spindles compared to after the CTRL task. Higher sigma (sigma; 13 Hz) and beta (beta; 18-20 Hz) spectral power during the post-training night's sleep were also observed after the MSL task.
These results provide evidence that sleep spindles are involved in the offline consolidation of a new sequence of finger movements known to be sleep dependent. Moreover, they expand on prior findings by showing that changes in NREM sleep following motor learning are specific to consolidation (and learning), and not to nonspecific motor activity. Finally, these data demonstrate, for the first time, higher fast rhythms (beta frequencies) during sleep after motor learning.
A slow (0.5-4 Hz) oscillation of thalamic neurons was recently described and attributed to the interplay of two intrinsic currents. In this study, we investigated the network modulation of this intrinsic thalamic oscillation within the frequency range of EEG sleep delta-waves. We performed intracellular and extracellular recordings of antidromically identified thalamocortical cells (n = 305) in sensory, motor, associational, and intralaminar nuclei of anesthetized cats. At the resting membrane potential, Vm (-60.3 +/- 0.4 mV, mean +/- SE), cortical stimulation induced spindle-like oscillations (7-14 Hz), whereas at Vm more negative than -65 mV the same stimuli triggered an oscillation within the EEG delta-frequency (0.5-4 Hz), consisting of low-threshold spikes (LTSs) followed by after hyperpolarizing potentials (AHPs). The LTS-AHP sequences outlasted cortical stimuli as a self-sustained rhythmicity at 1-2 Hz. Corticothalamic stimuli were able to transform subthreshold slow (0.5-4 Hz) oscillations, occurring spontaneously at Vm more negative than -65 mV, into rhythmic LTSs crowned by bursts of Na+ spikes that persisted for 10-20 sec after cessation of cortical volleys. Cortical volleys also revived a hyperpolarization-activated slow oscillation when it dampened after a few cycles. Auto- and crosscorrelograms of neuronal pairs revealed that unrelated cells became synchronized after a series of corticothalamic stimuli, with both neurons displaying rhythmic (1-2 Hz) bursts or spike trains. Since delta-thalamic oscillations, prevailing during late sleep stages, are triggered at more negative Vm than spindles characterizing the early sleep stage, we postulate a progressive hyperpolarization of thalamocortical neurons with the deepening of the behavioral state of EEG-synchronized sleep. In view of the evidence that cortical-elicited slow oscillations depend on synaptically induced hyperpolarization of thalamocortical cells, we propose that the potentiating influence of the corticothalamic input results from the engagement of two GABAergic thalamic cell classes, reticular and local-circuit neurons. The thalamocorticothalamic loop would transfer the spike bursts of thalamic oscillating cells to cortical targets, which in turn would reinforce the oscillation by direct pathways and/or indirect projections relayed by reticular and local-circuit thalamic cells. Stimulation of mesopontine cholinergic [peribrachial (PB) and laterodorsal tegmental (LDT)] nuclei in monoamine-depleted animals had an effect that was opposite to that exerted by corticothalamic volleys. PB/LDT stimulation reduced or suppressed the slow (1-4 Hz) oscillatory bursts of high-frequency spikes in thalamic cells.(ABSTRACT TRUNCATED AT 400 WORDS)
Developments in technologic and analytical procedures applied to the study of brain electrical activity have intensified interest in this modality as a means of examining brain function. The impact of these new developments on traditional methods of acquiring and analyzing electroencephalographic activity requires evaluation. Ultimately, the integration of the old with the new must result in an accepted standardized methodology to be used in these investigations. In this paper, basic procedures and recent developments involved in the recording and analysis of brain electrical activity are discussed and recommendations are made, with emphasis on psychophysiological applications of these procedures.
Introduction
Autism is a developmental disorder with a neurobiological etiology. Studies of the autistic brain point toward atypically organized brain networks which may lead to a lower capacity to synchronize the EEG during sleep. We compared the intrisic characteristics and topography of nonREM sleep EEG slow waves (SW) in autistic and neurotypical children.
Methods
The sleep of 13 autistic boys (mean age = 10.23, SEM= 0.57) and 13 neurotypical boys (mean age = 10.23, SEM = 0.57) was recorded in a laboratory for 2 consecutive nights. None of the participants were medicated, intellectually disabled, nor complained of poor sleep. SW (0.3–3.99Hz, >75µV) were detected for the whole night with an automatic algorithm on artefact free sections of nonREM sleep in frontal, central, parietal and occipital derivations. Three-way Anovas with one independent factor (2 groups) and 2 repeated measures (4 derivations X 4 nonREM periods) were performed to compare SW density (number per minute of NREM sleep), SW slope (velocity between SW negative and positive peaks), SW amplitude (µV), and SW duration (sec).
Results
Significant interactions between Groups and Derivations were found for SW density (p<0.01), slope (p<0.05), amplitude (p<0.05) and duration (p<0.01) showing lower topographical (inter-derivations) differences in autistic than in neurotypical children. No interaction between Groups and nonREM periods were found, indicating that these differences were stable across the night.
Conclusion
SW characteristics are more evenly distributed along the anteroposterior axis in autistic than in neurotypical children. These differences are not modulated by the dissipation of homeostatic sleep pressure across the night and probably reflect atypical cortical organization in autism.
Support (If Any)
Canadian Institutes of Health Research and Kids Brain Health Network Canada
Study Objectives
To determine whether the frequency spectrum of the sleep EEG is a physiologic correlate of 1) the degree to which individuals with persistent primary insomnia (PPI) underestimate their sleep time compared with the traditionally scored polysomnogram (PSG) and 2) the sleep complaints in PPI subjects who have relatively long traditionally scored PSG sleep times and relatively greater underestimation of sleep time.
Design
We compared EEG frequency spectra from REM and NREM sleep in PPI subjects subtyped as subjective insomnia sufferers (those with relatively long total sleep time and relative underestimation of sleep time compared with PSG), and objective insomnia sufferers (those with relatively short PSG total sleep time) with EEG frequency spectra in normals. We also studied the correlation between these indices and the degree of underestimation of sleep. Further, we determined the degree to which sleep EEG indexes related to sleep complaints.
Setting
Duke University Medical Center Sleep Laboratory.
Participants
Normal (N=20), subjective insomnia (N=12), and objective insomnia (N=18) subjects.
Interventions
N/A
Measurements and Results
Lower delta and greater alpha, sigma, and beta NREM EEG activity were found in the patients with subjective insomnia but not those with objective insomnia, compared with the normal subjects. These results were robust to changes in the subtyping criteria. No effects were found for REM spectral indexes. Less delta non- REM EEG activity predicted greater deviation between subjective and PSG estimates of sleep time across all subjects. For the subjective insomnia subjects, diminished low-frequency and elevated higher frequency non- REM EEG activity was associated with their sleep complaints.
Conclusions
NREM EEG frequency spectral indexes appear to be physiologic correlates of sleep complaints in patients with subjective insomnia and may reflect heightened arousal during sleep.
Autism spectrum (AS) is a neurodevelopmental condition associated with poor sleep, which impairs daytime functioning. Most studies of sleep in autism have been based on subjective measures, notably parental reports. A few studies have used objective, laboratory polysomnography (PSG) measures, but often include confounding factors such as intellectual disability, sleep problems, other psychiatric illnesses, and medication. To address these limitations, we examined the relationship between sleep and behavior in prototypical AS of typical level of intelligence and non-autistic children not complaining of sleep problems. We examined sleep variables with The Children' Sleep Habit Questionnaire (CSHQ) and a daily sleep agenda, both filled out by parents, and by PSG. These subjective and objective measures both revealed that sleep latency was longer in AS than in non-autistic children. Furthermore, AS children also showed less slow-wave sleep (SWS: stages 3 + 4), fewer sleep spindles and fewer K-complexes than non-autistic children. REM sleep, including eye movement density, was similar between the two groups. The proportion of light sleep, (stage 1 non-REM sleep) was negatively correlated with IQ (Wechsler and Raven matrices) in both groups of participants. A large amount of SWS predicted low levels of internalizing behavior in both groups and typical social functioning as determined by ADOS in AS children. These results indicate that autistic children not complaining of sleep problems may nonetheless be affected by poor sleep, which in turn influences their daytime functioning.
Introduction Autism is a pervasive developmental disorder with neurological origins [1], defined by a triad of symptoms in the social, communicative, and restricted interest and repetitive behaviors areas [2]. Asperger's syndrome is characterized by clinical manifestations that are very similar to autism except for the fact that the former do not suffer from a delay in the development of language. In the present chapter, autism and Asperger's syndrome will be grouped together under the term of autism spectrum disorders (ASD). The prevalence of ASD is estimated to be 13 per 10,000 with a male:female ratio of approximately 4.5:1 [3]. The first signs of ASD appear before 36 months of age and the syndrome lasts an entire life, with the clinical picture improving throughout lifespan particularly regarding the social and communicative areas [4, 5, but see 6]. There are currently two sets of arguments suggesting an atypical brain organization in autism: impaired transfer of information between brain regions, i.e., a connectivity disorder, and ectopic localization of brain regions associated with some cognitive functions, i.e., cortical reallocation. In terms of event-related electroencephalography (EEG), the former refers to a diminished synchrony of activation, relative size, or coherence of EEG signals among pairs of functional regions normally involved in a given task. The latter refers to the activation of a brain region different from that used by typical control individuals, for example during performance on a specific cognitive task; cross-modal plasticity is an example of this phenomenon.
The modular organization of nervous systems is a widely documented principle of design for both vertebrate and invertebrate brains of which the columnar organization of the neocortex is an example. The classical cytoarchitectural areas of the neocortex are composed of smaller units, local neural circuits repeated iteratively within each area. Modules may vary in cell type and number, in internal and external connectivity, and in mode of neuronal processing between different large entities; within any single large entity they have a basic similarity of internal design and operation. Modules are most commonly grouped into entities by sets of dominating external connections. This unifying factor is most obvious for the heterotypical sensory and motor areas of the neocortex. Columnar defining factors in homotypical areas are generated, in part, within the cortex itself. The set of all modules composing such an entity may be fractionated into different modular subsets by different extrinsic connections. Linkages between them and subsets in other large entities form distributed systems. The neighborhood relations between connected subsets of modules in different entities result in nested distributed systems that serve distributed functions. A cortical area defined in classical cytoarchitectural terms may belong to more than one and sometimes to several distributed systems. Columns in cytoarchitectural areas located at some distance from one another, but with some common properties, may be linked by long-range, intracortical connections.
The psychophysological tests for identification level of personal anxiety and polysomnology was used in present study. The gender differences in organization of sleep phases at first three cycles and spectral density of sleep EEG on persons with high and low levels of personal anxiety were studied.
Background: Pharmacological clinical trials in autism spectrum disorder(ASD) have yielded mixed results in efficacy. One challenge that may contribute to this situation is the heterogeneity in study participants, lack of biological targets associated with deficits in the core domains of ASD dysfunction, and the lack of objective biological measures of treatment response.
Objectives: The goal of this presentation is to discuss the promise of biomarkers in stratifying subjects to enrich for the specific deficit being tested in the trial, and to highlight outcome measures that may inform whether a treatment is working.
Methods: NIMH has created a program, called the “Fast Fail Trials” which is designed to develop and test ASD biomarkers in clinical trials of investigational compounds. Before initiating the first trial, we formed an advisory committee to establish compound selection criteria, identify compounds to test and then help inform the biomarker selection.
Results: There is a wide range of biomarkers that are being tested in ASD but much fewer being incorporated into trial designs. Examples of potential biomarkers include brain activity measures such as fMRI and EEG/MEG, peripheral measures that correlate with sympathetic nervous system activity, eye tracking, various cognitive assessments, actigraphy, sleep measures and peripheral blood measures. This presentation will provide an overview of different biomarkers being studied in ASD that could be used in stratifying subjects and/or assessing treatment response. The significant effort needed to incorporate biomarkers in ASD trials will be emphasized. To illustrate how this can be done, the presentation will provide 1-2 case examples on the application of biomarkers into an ASD trial, based on NIMH’s recent efforts in the Fast Fail Program
Conclusions:
Different methodologies are becoming available to test how biomarkers can be used to stratify or enrich for subjects with specific phenotypes (deficits in a core domain of function) in ASD clinical trials. However, many studies have used small numbers of subjects and broad inclusion criteria and therefore the measures have not been tested or validated for their ability to define subgroups with ASD that may benefit most from the intervention. Challenges remain as to the feasibility, specificity (developmental age, level of function, core symptom), reproducibility, sensitivity of measures to change over time, etc. Especially in early treatment trials, the ability to correlate a brain activity measure such as EEG or fMRI with cognitive outcomes may be more informative than reliance on behavioral measures or patient reported outcome measures. The timing is right to begin incorporating quantitative biological measures into trials to test novel hypotheses for therapeutic interventions.
Over more than a century of research has established the fact that sleep benefits the retention of memory. In this review we aim to comprehensively cover the field of "sleep and memory" research by providing a historical perspective on concepts and a discussion of more recent key findings. Whereas initial theories posed a passive role for sleep enhancing memories by protecting them from interfering stimuli, current theories highlight an active role for sleep in which memories undergo a process of system consolidation during sleep. Whereas older research concentrated on the role of rapid-eye-movement (REM) sleep, recent work has revealed the importance of slow-wave sleep (SWS) for memory consolidation and also enlightened some of the underlying electrophysiological, neurochemical, and genetic mechanisms, as well as developmental aspects in these processes. Specifically, newer findings characterize sleep as a brain state optimizing memory consolidation, in opposition to the waking brain being optimized for encoding of memories. Consolidation originates from reactivation of recently encoded neuronal memory representations, which occur during SWS and transform respective representations for integration into long-term memory. Ensuing REM sleep may stabilize transformed memories. While elaborated with respect to hippocampus-dependent memories, the concept of an active redistribution of memory representations from networks serving as temporary store into long-term stores might hold also for non-hippocampus-dependent memory, and even for nonneuronal, i.e., immunological memories, giving rise to the idea that the offline consolidation of memory during sleep represents a principle of long-term memory formation established in quite different physiological systems.
As well as consolidating memory, sleep has been proposed to serve a second important function for memory, i.e. to free capacities for the learning of new information during succeeding wakefulness. The slow wave activity (SWA) that is a hallmark of slow wave sleep could be involved in both functions. Here, we aimed to demonstrate a causative role for SWA in enhancing the capacity for encoding of information during subsequent wakefulness, using transcranial slow oscillation stimulation (tSOS) oscillating at 0.75 Hz to induce SWA in healthy humans during an afternoon nap. Encoding following the nap was tested for hippocampus-dependent declarative materials (pictures, word pairs, and word lists) and procedural skills (finger sequence tapping). As compared with a sham stimulation control condition, tSOS during the nap enhanced SWA and significantly improved subsequent encoding on all three declarative tasks (picture recognition, cued recall of word pairs, and free recall of word lists), whereas procedural finger sequence tapping skill was not affected. Our results indicate that sleep SWA enhances the capacity for encoding of declarative materials, possibly by down-scaling hippocampal synaptic networks that were potentiated towards saturation during the preceding period of wakefulness.
Previous studies suggest that sleep-specific brain activity patterns such as sleep spindles and electroencephalographic slow-wave activity contribute to the consolidation of novel memories. The generation of both sleep spindles and slow-wave activity relies on synchronized oscillations in a thalamo-cortical network that might be implicated in synaptic strengthening (spindles) and downscaling (slow-wave activity) during sleep. This study further examined the association between electroencephalographic power during non-rapid eye movement sleep in the spindle (sigma, 12-16 Hz) and slow-wave frequency range (0.1-3.5 Hz) and overnight memory consolidation in 20 healthy subjects (10 men, 27.1 ± 4.6 years). We found that both electroencephalographic sigma power and slow-wave activity were positively correlated with the pre-post-sleep consolidation of declarative (word list) and procedural (mirror-tracing) memories. These results, although only correlative in nature, are consistent with the view that processes of synaptic strengthening (sleep spindles) and synaptic downscaling (slow-wave activity) might act in concert to promote synaptic plasticity and the consolidation of both declarative and procedural memories during sleep.
Total sleep deprivation in healthy subjects has a profound effect on the performance on tasks measuring sustained attention or vigilance. We here report how a selective disruption of deep sleep only, that is, selective slow-wave activity (SWA) reduction, affects the performance of healthy well-sleeping subjects on several tasks: a "simple" and a "complex" vigilance task, a declarative learning task, and an implicit learning task despite unchanged duration of sleep. We used automated electroencephalogram (EEG) dependent acoustic feedback aimed at selective interference with-and reduction of-SWA. In a within-subject repeated measures crossover design, performance on the tasks was assessed in 13 elderly adults without sleep complaints after either SWA-reduction or after normal sleep. The number of vigilance lapses increased as a result of SWA reduction, irrespective of the type of vigilance task. Recognition on the declarative memory task was also affected by SWA reduction, associated with a decreased activation of the right hippocampus on encoding (measured with fMRI) suggesting a weaker memory trace. SWA reduction, however, did not affect reaction time on either of the vigilance tasks or implicit memory task performance. These findings suggest a specific role of slow oscillations in the subsequent daytime ability to maintain sustained attention and to encode novel declarative information but not to maintain response speed or to build implicit memories. Of particular interest is that selective SWA reduction can mimic some of the effects of total sleep deprivation, while not affecting sleep duration.
Functional magnetic resonance imaging studies have had a profound impact on the delineation of the neurobiologic basis for autism. Advances in fMRI technology for investigating functional connectivity, resting state connectivity, and a default mode network have provided further detail about disturbances in brain organization and brain-behavior relationships in autism to be reviewed in this article.
Recent fMRI studies have provided evidence of enhanced activation and connectivity of posterior, or parietal-occipital, networks and enhanced reliance on visuospatial abilities for visual and verbal reasoning in high functioning individuals with autism. Evidence also indicates altered activation in frontostriatal networks for cognitive control, particularly involving anterior cingulate cortex, and altered connectivity in the resting state and the default mode network. The findings suggest that the specialization of many cortical networks of the human brain has failed to develop fully in high functioning individuals with autism.
This research provides a growing specification of to the neurobiologic basis for this complex syndrome and for the co-occurrence of the signs and symptoms as a syndrome. With this knowledge has come new neurobiologically based opportunities for intervention.
Sleep spindles and rapid eye movements have been found to increase following an intense period of learning on a combination of procedural memory tasks. It is not clear whether these changes are task specific, or the result of learning in general. The current study investigated changes in spindles, rapid eye movements, K-complexes and EEG spectral power following learning in good sleepers randomly assigned to one of four learning conditions: Pursuit Rotor (n=9), Mirror Tracing (n=9), Paired Associates (n=9), and non-learning controls (n=9). Following Pursuit Rotor learning, there was an increase in the duration of Stage 2 sleep, spindle density (number of spindles/min), average spindle duration, and an increase in low frequency sigma power (12-14Hz) at occipital regions during SWS and at frontal regions during Stage 2 sleep in the second half of the night. These findings are consistent with previous findings that Pursuit Rotor learning is consolidated during Stage 2 sleep, and provide additional data to suggest that spindles across all non-REM stages may be a mechanism for brain plasticity. Following Paired Associates learning, theta power increased significantly at central regions during REM sleep. This study provides the first evidence that REM sleep theta activity is involved in declarative memory consolidation. Together, these findings support the hypothesis that brain plasticity during sleep does not involve a unitary process; that is, different types of learning have unique sleep-related memory consolidation mechanisms that act in dissociable brain regions at different times throughout the night.
The purpose of the present investigation was to characterize and compare traditional sleep architecture and non-rapid eye movement (NREM) sleep microstructure in a well-defined cohort of children with regressive and non-regressive autism, and in typically developing children (TD). We hypothesized that children with regressive autism would demonstrate a greater degree of sleep disruption either at a macrostructural or microstructural level and a more problematic sleep as reported by parents. Twenty-two children with non-regressive autism, 18 with regressive autism without comorbid pathologies and 12 with TD, aged 5-10years, underwent standard overnight multi-channel polysomnographic evaluation. Parents completed a structured questionnaire (Childrens' Sleep Habits Questionnaire-CSHQ). The initial hypothesis, that regressed children have more disrupted sleep, was supported by our findings that they scored significantly higher on CSHQ, particularly on bedtime resistance, sleep onset delay, sleep duration and night wakings CSHQ subdomains than non-regressed peers, and both scored more than typically developing controls. Regressive subjects had significantly less efficient sleep, less total sleep time, prolonged sleep latency, prolonged REM latency and more time awake after sleep onset than non-regressive children and the TD group. Regressive children showed lower cyclic alternating pattern (CAP) rates and A1 index in light sleep than non-regressive and TD children. Our findings suggest that, even though no particular differences in sleep architecture were found between the two groups of children with autism, those who experienced regression showed more sleep disorders and a disruption of sleep either from a macro- or from a microstructural viewpoint.
Autistics often exhibit enhanced perceptual abilities when engaged in visual search, visual discrimination, and embedded figure detection. In similar fashion, while performing a range of perceptual or cognitive tasks, autistics display stronger physiological engagement of the visual system than do non-autistics. To account for these findings, the Enhanced Perceptual Functioning Model proposes that enhanced autistic performance in basic perceptual tasks results from stronger engagement of sensory processing mechanisms, a situation that may facilitate an atypically prominent role for perceptual mechanisms in supporting cognition. Using quantitative meta-analysis of published functional imaging studies from which Activation Likelihood Estimation maps were computed, we asked whether autism is associated with enhanced task-related activity for a broad range of visual tasks. To determine whether atypical engagement of visual processing is a general or domain-specific phenomenon, we examined three different visual processing domains: faces, objects, and words. Overall, we observed more activity in autistics compared to non-autistics in temporal, occipital, and parietal regions. In contrast, autistics exhibited less activity in frontal cortex. The spatial distribution of the observed differential between-group patterns varied across processing domains. Autism may be characterized by enhanced functional resource allocation in regions associated with visual processing and expertise. Atypical adult organizational patterns may reflect underlying differences in developmental neural plasticity that can result in aspects of the autistic phenotype, including enhanced visual skills, atypical face processing, and hyperlexia.
This study aimed to determine the distinct contribution of slow (11-13 Hz) and fast (13-15 Hz) spindles in the consolidation process of a motor sequence learning task (MSL). Young subjects (n = 12) were trained on both a finger MSL task and a control (CTRL) condition, which were administered one week apart in a counterbalanced order. Subjects were asked to practice the MSL or CTRL task in the evening (approximately 9:00 p.m.) and their performance was retested on the same task 12h later (approximately 9:00 a.m.). Polysomnographic (PSG) recordings were performed during the night following training on either task, and an automatic algorithm was used to detect fast and slow spindles and to quantify their characteristics (i.e., density, amplitude, and duration). Statistical analyses revealed higher fast (but not slow) spindle density after training on the MSL than after practice of the CTRL task. The increase in fast spindle density on the MSL task correlated positively with overnight performance gains on the MSL task and with difference in performance gain between the MSL and CTRL tasks. Together, these results suggest that fast sleep spindles help activate the cerebral network involved in overnight MSL consolidation, while slow spindles do not appear to play a role in this mnemonic process.
To investigate whether sleep macrostructure and EEG power spectral density and coherence during NREM sleep are different in Asperger syndrome (AS) compared to typically developing children and adolescents.
Standard all night EEG sleep parameters were obtained from 18 un-medicated subjects with AS and 14 controls (age range: 7.5-21.5years) after one adaptation night. Spectral, and phase coherence measures were computed for multiple frequency bands during NREM sleep.
Sleep latency and wake after sleep onset were increased in AS. Absolute power spectrum density (PSD) was significantly reduced in AS in the alpha, sigma, beta and gamma bands and in all 10 EEG derivations. Relative PSD showed a significant increase in delta and a decrease in the sigma band for frontal, and in beta for centro-temporal derivations. Intrahemispheric coherence measures were markedly lower in AS in the frontal areas, and the right hemisphere over all EEG channels. The most prominent reduction in intrahemispheric coherence was observed over the fronto-central areas in delta, theta, alpha and sigma EEG frequency bands.
EEG power spectra and coherence during NREM sleep, in particular in fronto-cortical derivations are different in AS compared to typically developing children and adolescents.
Quantitative analysis of the EEG during NREM sleep supports the hypothesis of frontal dysfunction in AS.
Sleep after learning often benefits memory consolidation, but the underlying mechanisms remain unclear. In previous studies, we found that learning a visuomotor task is followed by an increase in sleep slow wave activity (SWA, the electroencephalographic [EEG] power density between 0.5 and 4.5 Hz during non-rapid eye movement sleep) over the right parietal cortex. The SWA increase correlates with the postsleep improvement in visuomotor performance, suggesting that SWA may be causally responsible for the consolidation of visuomotor learning. Here, we tested this hypothesis by studying the effects of slow wave deprivation (SWD).
After learning the task, subjects went to sleep, and acoustic stimuli were timed either to suppress slow waves (SWD) or to interfere as little as possible with spontaneous slow waves (control acoustic stimulation, CAS).
Sound-attenuated research room.
Healthy subjects (mean age 24.6 +/- 1.0 years; n = 9 for EEG analysis, n = 12 for behavior analysis; 3 women).
Sleep time and efficiency were not affected, whereas SWA and the number of slow waves decreased in SWD relative to CAS. Relative to the night before, visuomotor performance significantly improved in the CAS condition (+5.93% +/- 0.88%) but not in the SWD condition (-0.77% +/- 1.16%), and the direct CAS vs SWD comparison showed a significant difference (P = 0.0007, n = 12, paired t test). Changes in visuomotor performance after SWD were correlated with SWA changes over right parietal cortex but not with the number of arousals identified using clinically established criteria, nor with any sign of "EEG lightening" identified using a novel automatic method based on event-related spectral perturbation analysis.
These results support a causal role for sleep slow waves in sleep-dependent improvement of visuomotor performance.
Studies on homeostatic aspects of sleep regulation have been focussed upon non-rapid eye movement (NREM) sleep, and direct comparisons with regional changes in rapid eye movement (REM) sleep are sparse. To this end, evaluation of electroencephalogram (EEG) changes in recovery sleep after extended waking is the classical approach for increasing homeostatic need. Here, we studied a large sample of 40 healthy subjects, considering a full-scalp EEG topography during baseline (BSL) and recovery sleep following 40 h of wakefulness (REC). In NREM sleep, the statistical maps of REC versus BSL differences revealed significant fronto-central increases of power from 0.5 to 11 Hz and decreases from 13 to 15 Hz. In REM sleep, REC versus BSL differences pointed to significant fronto-central increases in the 0.5-7 Hz and decreases in the 8-11 Hz bands. Moreover, the 12-15 Hz band showed a fronto-parietal increase and that at 22-24 Hz exhibited a fronto-central decrease. Hence, the 1-7 Hz range showed significant increases in both NREM sleep and REM sleep, with similar topography. The parallel change of NREM sleep and REM sleep EEG power is related, as confirmed by a correlational analysis, indicating that the increase in frequency of 2-7 Hz possibly subtends a state-aspecific homeostatic response. On the contrary, sleep deprivation has opposite effects on alpha and sigma activity in both states. In particular, this analysis points to the presence of state-specific homeostatic mechanisms for NREM sleep, limited to <2 Hz frequencies. In conclusion, REM sleep and NREM sleep seem to share some homeostatic mechanisms in response to sleep deprivation, as indicated mainly by the similar direction and topography of changes in low-frequency activity.
Autism spectrum disorder is a complex neurodevelopmental variant thought to affect 1 in 166 [Fombonne (2003): J Autism Dev Disord 33:365-382]. Individuals with autism demonstrate atypical social interaction, communication, and repetitive behaviors, but can also present enhanced abilities, particularly in auditory and visual perception and nonverbal reasoning. Structural brain differences have been reported in autism, in terms of increased total brain volume (particularly in young children with autism), and regional gray/white matter differences in both adults and children with autism, but the reports are inconsistent [Amaral et al. (2008): Trends Neurosci 31:137-145]. These inconsistencies may be due to differences in diagnostic/inclusion criteria, and age and Intelligence Quotient of participants. Here, for the first time, we used two complementary magnetic resonance imaging techniques, cortical thickness analyses, and voxel-based morphometry (VBM), to investigate the neuroanatomical differences between a homogenous group of young adults with autism of average intelligence but delayed or atypical language development (often referred to as "high-functioning autism"), relative to a closely matched group of typically developing controls. The cortical thickness and VBM techniques both revealed regional structural brain differences (mostly in terms of gray matter increases) in brain areas implicated in social cognition, communication, and repetitive behaviors, and thus in each of the core atypical features of autism. Gray matter increases were also found in auditory and visual primary and associative perceptual areas. We interpret these results as the first structural brain correlates of atypical auditory and visual perception in autism, in support of the enhanced perceptual functioning model [Mottron et al. (2006): J Autism Dev Disord 36:27-43].
The present study examined whether slow and/or fast sleep spindles are related to visuomotor learning, by examining the densities of current sleep spindle activities.
Participants completed a visuomotor task before and after sleep on the learning night. This task was not performed on the non-learning night. Standard polysomnographic recordings were made. After the amplitudes of slow and fast spindles were calculated, sLORETA was used to localize the source of slow and fast spindles and to investigate the relationship between spindle activity and motor learning.
Fast spindle amplitude was significantly larger on the learning than on the non-learning nights, particularly at the left frontal area. sLORETA revealed that fast spindle activities in the left frontal and left parietal areas were enhanced when a new visuomotor skill was learned. There were no significant learning-dependent changes in slow spindle activity.
Fast spindle activity increases in cortical areas that are involved in learning a new visuomotor skill. The thalamocortical network that underlies the generation of fast spindles may contribute to the synaptic plasticity that occurs during sleep.
Activity of fast sleep spindles is a possible biomarker of memory deficits.
Motor deficits are commonly reported in autism, with one of the most consistent findings being impaired execution of skilled movements and gestures. Given the developmental nature of autism, it is possible that deficits in motor/procedural learning contribute to impaired acquisition of motor skills. Thus, careful examination of mechanisms underlying learning and memory may be critical to understanding the neural basis of autism. A previous study reported impaired motor learning in children with high-functioning autism (HFA); however, it is unclear whether the observed deficits in motor learning are due, in part, to impaired motor execution and whether these deficits are specific to autism. In order to examine these questions, 153 children (52 with HFA, 39 with attention-deficit/hyperactivity disorder (ADHD) and 62 typically developing (TD) children) participated in two independent experiments using a Rotary Pursuit task, with change in performance across blocks as a measure of learning. For both tasks, children with HFA demonstrated significantly less change in performance than did TD children, even when differences in motor execution were minimized. Differences in learning were not seen between ADHD and TD groups on either experiment. Analyses of the pattern of findings revealed that compared with both ADHD and TD children, children with HFA showed a similar degree of improvement in performance; however, they showed significantly less decrement in performance when presented with an alternate ("interference") pattern. The findings suggest that mechanisms underlying acquisition of novel movement patterns may differ in children with autism. These findings may help explain impaired skill development in children with autism and help to guide approaches for helping children learn novel motor, social and communicative skills.
It is now recognized that extensive maturational changes take place in the human brain during adolescence, and that the trajectories of these changes are best studied longitudinally. We report the first longitudinal study of the adolescent decline in non-rapid eye movement (NREM) delta (1-4 Hz) and theta (4-8 Hz) EEG. Delta and theta are the homeostatic frequencies of human sleep. We recorded sleep EEG in 9- and 12-year-old cohorts twice yearly over a 5-year period. Delta power density (PD) was unchanged between age 9 and 11 years and then fell precipitously, decreasing by 66% between age 11 and 16.5 years (P < .000001). The decline in theta PD began significantly earlier than that in delta PD and also was very steep (by 60%) between age 11 and 16.5 years (P < .000001). These data suggest that age 11-16.5 years is a critically important maturational period for the brain processes that underlie homeostatic NREM EEG, a finding not suggested in previous cross-sectional data. We hypothesize that these EEG changes reflect synaptic pruning. Comparing our data with published longitudinal declines in MRI-estimated cortical thickness suggests the theta age curve parallels the earlier maturational thinning in 3-layer cortex, whereas the delta curve tracks the later changes in 5-layer cortex. This comparison also reveals that adolescent declines in NREM delta and theta are substantially larger than decreases in cortical thickness (>60% vs. <20%). The magnitude, interindividual difference, and tight link to age of these EEG changes indicate that they provide excellent noninvasive tools for investigating neurobehavioral correlates of adolescent brain maturation.
The development of a new standardized investigator-based interview for use in the differential diagnosis of pervasive developmental disorders is described, together with a diagnostic algorithm (using ICD-10 criteria) based on its use. Good interrater reliability for algorithm items was shown between four raters, two in Canada and two in the UK, who rated 32 videotaped interviews. The items also significantly discriminated between 16 autistic and 16 nonautistic mentally handicapped subjects. The algorithm based on ICD-10 identified all 16 autistic individuals and none of the 16 nonautistic subjects.
The modular organization of nervous systems is a widely documented principle of design for both vertebrate and invertebrate brains of which the columnar organization of the neocortex is an example. The classical cytoarchitectural areas of the neocortex are composed of smaller units, local neural circuits repeated iteratively within each area. Modules may vary in cell type and number, in internal and external connectivity, and in mode of neuronal processing between different large entities; within any single large entity they have a basic similarity of internal design and operation. Modules are most commonly grouped into entities by sets of dominating external connections. This unifying factor is most obvious for the heterotypical sensory and motor areas of the neocortex. Columnar defining factors in homotypical areas are generated, in part, within the cortex itself. The set of all modules composing such an entity may be fractionated into different modular subsets by different extrinsic connections. Linkages between them and subsets in other large entities form distributed systems. The neighborhood relations between connected subsets of modules in different entities result in nested distributed systems that serve distributed functions. A cortical area defined in classical cytoarchitectural terms may belong to more than one and sometimes to several distributed systems. Columns in cytoarchitectural areas located at some distance from one another, but with some common properties, may be linked by long-range, intracortical connections.
To investigate the brain topography of the human sleep EEG along the antero-posterior axis during the wakefulness-sleep transition, by means of both a single Hz analysis and a grouped-frequency analysis of EEG changes.
EEG power values were calculated across a 1-28 Hz frequency range in a 1 Hz resolution during the wakefulness-sleep transition of 7 normal subjects. Topographical changes were assessed from C3-A2, C4-A1, Fpz-A1, Fz-A1, Cz-A1, Pz-A1, Oz-A1 recordings, after averaging individual time series, aligned with respect to the onset of stage 2.
The single Hz analysis showed that before sleep onset (SO), the <7 Hz slow frequencies were more prominent at the more anterior scalp locations; this anterior prominence was counterbalanced by a reciprocal prevalence across the >8 Hz frequencies of EEG activity from the occipital areas; while the >13 Hz fast frequencies were not characterized by significant antero-posterior differences. After SO, more EEG power was found in the range of slow frequencies at the centro-frontal scalp locations and a second peak of EEG activity was also revealed within the range of the sigma frequency, higher at the centro-parietal scalp locations. No consistent topographical changes were observed within the range of faster EEG frequencies. Grouped-frequency analysis confirmed these results, also pointing to different changes in the alpha frequency as a function of the SO point.
The results suggest that: (a) the alpha rhythm spreads anteriorly as the transition progresses; (b) several anterior areas first synchronize EEG activity; (c) the functional meaning of the EEG bands during the SO period should be partially revised with regard at least to alpha rhythm; (d) SO coincides with the start of stage 2.