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Developmental trajectories of EEG sleep slow wave activity as a marker for motor skill development during adolescence: A pilot study
Abstract
Reliable markers for brain maturation are important to identify neural deviations that eventually predict the development of mental illnesses. Recent studies have proposed topographical EEG-derived slow wave activity (SWA) during NREM sleep as a mirror of cortical development. However, studies about the longitudinal stability as well as the relationship with behavioral skills are needed before SWA topography may be considered such a reliable marker. We examined six subjects longitudinally (over 5.1 years) using high-density EEG and a visuomotor learning task. All subjects showed a steady increase of SWA at a frontal electrode and a decrease in central electrodes. Despite these large changes in EEG power, SWA topography was relatively stable within each subject during development indicating individual trait-like characteristics. Moreover, the SWA changes in the central cluster were related to the development of specific visuomotor skills. Taken together with the previous work in this domain, our results suggest that EEG sleep SWA represents a marker for motor skill development and further supports the idea that SWA mirrors cortical development during childhood and adolescence.
... Furthermore, the peak absolute amount of slow wave activity moves along a posterior to anterior axis [10,11], and appears to predict myelin development [12]. Slow waves also show experience-dependent changes, tied to new learning in cognitive and motor tasks [10,[12][13][14][15]. Altogether, a somewhat consistent understanding is emerging of the characteristics of typically developing SWA, its EEG markers, and its functional role in cognition; yet an understanding of the potential developmental differences present in disordered populations with known brain abnormalities is lacking. ...
... To further explore slow oscillation characteristic changes across development, we separated our patients into the following age groups: child (ages 4-9), early adolescence (ages [10][11][12][13][14][15], and late adolescence (ages [16][17][18][19][20] creating groups of child, early adolescent, and late adolescent, respectively (see Figure 2A). Given the significant difference in slow oscillation density between N2 and N3 (see Figure 1C), we analyzed each stage separately. ...
... This age-related slow oscillation decline generally appears to mirror the progression of DMD. Given the accumulating evidence that slow oscillations and slow wave activity reflect neural maturation and are linked to motor development and cognition [10,[12][13][14], this careful characterization of slow oscillations in a medically complex population is the first fundamental step in pursuing optimal sleep-enhancing interventions. ...
Study objectives
From childhood through adolescence, brain rhythms during non-rapid eye movement (NREM) sleep show dramatic development that mirror underlying brain maturation. For example, the function and characteristics of slow oscillations (SOs, <1 Hz) in healthy children are linked to brain development, motor skill, and cognition. However, little is known of possible changes in pediatric populations with neurologic abnormalities.
Methods
We measured slow oscillations in 28 Duchenne and Becker Muscular Dystrophy male patients from age 4 to 20 years old during overnight in-lab clinical sleep studies. We compared our pediatric patients by age to evaluate the developmental changes of SOs from childhood to early and late adolescence.
Results
Consistent with the current neuro-and physically typical literature, we found greater slow oscillation density (count of SOs per minute of each sleep stage) in NREM N3 than N2, and significantly greater slow oscillation density in Frontal compared to Central and Occipital regions. However, separating patients into age-defined groups (Child, Early Adolescent and Late Adolescent) revealed a significant age effect, with a specific decline in the rate and amplitude of SOs.
Conclusions
We found that with age, pediatric patients with Duchenne Muscular Dystrophy show a significant decline in slow oscillation density. Given the role that slow oscillations play in memory formation and retention, it is critical to developmentally characterize these brain rhythms in medically complex populations. Our work converges with previous pediatric sleep literature that promotes the use of sleep electroencephalographic markers as prognostic tools and identifies potential targets to promote our patients’ quality of life.
... Cortical maturation is reflected in changes of the sleep electroencephalogram (EEG), specifically within nonrapid eye movement (NREM) sleep [2][3][4][5][6][7][8]. Dominant brain oscillations during NREM sleep, slow wave activity (SWA) and sleep spindles, have been associated with performance on measures of cognitive performance, "IQ" [9][10][11][12][13][14], learning efficiency [15,16], memory consolidation [17][18][19][20][21][22], and motor skill development [5,23,24]. Thus, demarcating normative features of sleep in young children represents an important foundation for understanding brain maturation and may ultimately serve as a marker of brain development. ...
... Thus, slow waves peak regionally over occipital regions in 2to 5-years-old children but show a clear maximum over frontal regions in adults. These findings were further verified in a longitudinal study [23]. The underlying shift from more posterior to frontal regions may reflect the development of more sophisticated cognitive processes that children develop with age [5]. ...
... We further found that the δ power topography displayed a clear occipital maximum for both age groups with power globally increasing with age and becoming more pronounced over frontal regions. SWA, which represents δ power band during NREM sleep, has been shown to regionally mature from toddlerhood to adulthood from posterior to anterior brain regions closely mirroring cortical maturation, specifically grey matter [4,23,63,64]. In children between 2 and 5 years, the most pronounced δ activity is over occipital regions, which is in line with our findings [4]. ...
Widespread change in behavior and the underlying brain network substrate is a hallmark of early development. Sleep plays a fundamental role in this process. Both slow waves and spindles are key features of non-rapid eye movement sleep (NREM) that exhibit pronounced developmental trajectories from infancy to adulthood. Yet, these prominent features of NREM sleep are poorly understood in infants and toddlers in the age range of 12 to 30 months. Moreover, it is unknown how network dynamics of NREM sleep are associated with outcomes of early development. Addressing this gap in our understanding of sleep during development will enable the subsequent study of pathological changes in neurodevelopmental disorders. The aim of the current study was to characterize the sleep topography with high-density electroencephalography (EEG) in this age group. We found that delta, theta, and beta oscillations and sleep spindles exhibited clear developmental changes. Low delta and high theta oscillations correlated with motor, language, and social skills, independent of age. These findings suggest an important role of network dynamics of NREM sleep in cortical maturation and the associated development of skills during this important developmental period.
... From childhood to late adolescence, the maximal peak of SWA shows a posterior-to-anterior shift [19]. Moreover, the cortical areas characterized by structural changes during TD show more SWA, and the regional distribution of SWA predicts the maturation of specific skills [74,76,77], highlighting the relation between SWA and anatomical/functional maturation. Finally, in line with the observation of differences in the behavioral and structural maturation between males and females during childhood [78e80], the topographic distribution of SWA during the first 60 minutes of NREM sleep is characterized by sex differences in children and adolescents: whereas females show higher SWA in cortical areas related to language, whereas males exhibit a SWA prevalence in the right prefrontal cortex, an area associated with some spatial abilities [81]. ...
... Generally, interest in the topographical modification of EEG oscillations during TD is growing [19,20,74,99], allowing a better understanding of the local EEG maturational changes and, in turn, of 13 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 their functional meaning in different age stages. Moreover, research about the concomitant maturation of sleep EEG features, brain structures [21,66,76], and cognitive functioning [74,76,77] appear to be promising. It should be noted that a role of sleep oscillations in the formation of synaptic connectivity during TD has been also proposed [192], and this issue needs to be systematically assessed in humans. ...
Sleep has a crucial role in brain functioning and cognition, and several sleep electroencephalography (EEG) hallmarks are associated with intellectual abilities, neural plasticity, and learning processes. Starting from this evidence, a growing interest has been raised regarding the involvement of the sleep EEG in brain maturation and cognitive functioning during typical development (TD). The aim of this review is to provide a general framework about the maturational changes and the functional role of the human sleep EEG during TD from birth to late adolescence (≤22 years). The reviewed findings show large developmental modifications in several sleep EEG hallmarks (slow wave activity, sleep spindles, theta activity, and cyclic alternating pattern) during TD, and many studies support the notion of an active role of sleep slow wave activity in supporting brain maturation. Moreover, we focus on the possible relation between sleep microstructure, intelligence, and several memory domains (declarative, emotional, procedural), showing that sleep EEG oscillations seem involved in intellectual abilities and learning processes during TD, although results are often conflicting and divergent from findings in adults. Starting from the present literature, we propose that larger methodological uniformity, greater attention to the topographical and maturational aspects of the sleep EEG oscillations and their mutual interactions, and a higher number of longitudinal studies will be essential to clarify the role of the sleep EEG in cognitive functioning during TD.
... Electroencephalographic (EEG) recording of neural activity has been used widely for sleep monitoring and diagnosis of sleep disorders [1] [2] [3] [4]. EEG activity in the α band (8)(9)(10)(11)(12) has long been associated with wakefulness with power attenuating and becoming sporadic during the Sleep Onset Process (SOP) [2] [5], then disappearing at the onset of sleep. ...
... The convergence of Algorithm (1) is determined by primal feasibility criteria Θ Update Θ (i) by solving (4). 4 Update Z ...
Understanding how different brain areas interact to generate complex behavior is a primary goal of neuroscience research. One approach, functional connectivity analysis, aims to characterize the connectivity patterns in brain networks. For example, resting state functional connectivity analysis infers statistical relationships between brain areas from fMRI data in the absence of an explicit task. In this paper, we address the problem of discriminative connectivity, i.e. determining the differences in network structure under different experimental conditions. We introduce a novel model called Sparse Multi-task Inverse Covariance Estimation (SMICE) which is capable of estimating a common connectivity network as well as discrim-inative networks across different tasks. We apply the method to EEG signals after solving the inverse problem of source localization, yielding networks defined on the cortical surface. We propose an efficient algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve SMICE. We apply our newly developed framework to find common and discriminative connectivity patterns for α-bursts during the Sleep Onset Process (SOP) and during Rapid Eye Movement (REM) sleep. Even though both stages exhibit a similar α-burst phenomenon, we show that the underlying networks are distinct.
... The topographical distribution of SWA shows local differences that are highly stable within but vary between individuals (Finelli et al., 2001;Lustenberger et al., 2017) and is therefore unique to each person (Markovic et al., 2018;Rusterholz and Achermann, 2011). Here, we investigated the association of the relative SWA topography with individual differences in prosocial preferences. ...
Prosocial behavior is crucial for the smooth functioning of the society. Yet, individuals differ vastly in the propensity to behave prosocially. Here, we try to explain these individual differences under normal sleep conditions without any experimental modulation of sleep. Using a portable high-density EEG, we measured the sleep data in 54 healthy adults (28 females) during a normal night's sleep at the participants' homes. To capture prosocial preferences, participants played an incentivized public goods game in which they faced real monetary consequences. The whole-brain analyses showed that a higher relative slow-wave activity (SWA, an indicator of sleep depth) in a cluster of electrodes over the right temporoparietal junction (TPJ) was associated with increased prosocial preferences. Source localization and current source density analyses further support these findings. Recent sleep deprivation studies imply that sleeping enough makes us more prosocial; the present findings suggest that it is not only sleep duration, but particularly sufficient sleep depth in the TPJ that is positively related to prosociality. Because the TPJ plays a central role in social cognitive functions, we speculate that sleep depth in the TPJ, as reflected by relative SWA, might serve as a dispositional indicator of social cognition ability, which is reflected in prosocial preferences. These findings contribute to the emerging framework explaining the link between sleep and prosocial behavior by shedding light on the underlying mechanisms.
... solidation[25] [52] [53] [54] [55][56], and motor skill development (e.g. in a study of 30 primary school children asked to complete finger sequence tapping tasks in a repeated-measures design, spanning 4 days, children performed better if they had less slow spindles, more fast spindles and faster slow waves)[41] [57] ...
... SO amplitude and frequency decreased from childhood to adolescence (Figure 2-figure supplement 1A,B). Both, SO and spindle features have been previously related to memory formation Huber et al., 2004;Lustenberger et al., 2017;Schabus et al., 2004;Schabus et al., 2006). However, neither spindle nor SO amplitude or peak frequency changes explained the behavioral differences ( Figure 2-figure supplement 1C,D). ...
Precise temporal coordination of slow oscillations (SO) and sleep spindles is a fundamental mechanism of sleep-dependent memory consolidation. SO and spindle morphology changes considerably throughout development. Critically, it remains unknown how the precise temporal coordination of these two sleep oscillations develops during brain maturation and whether their synchronization indexes the development of memory networks. Here, we use a longitudinal study design spanning from childhood to adolescence, where participants underwent polysomnography and performed a declarative word-pair learning task. Performance on the memory task was better during adolescence. After disentangling oscillatory components from 1/f activity, we found frequency shifts within SO and spindle frequency bands. Consequently, we devised an individualized cross-frequency coupling approach, which demonstrates that SO-spindle coupling strength increases during maturation. Critically, this increase indicated enhanced memory formation from childhood to adolescence. Our results provide evidence that improved coordination between SOs and spindles indexes the development of sleep-dependent memory networks.
... SO amplitude and frequency decreased from childhood to adolescence (Figure 2-figure supplement 1A,B). Both, SO and spindle features have been previously related to memory formation Huber et al., 2004;Lustenberger et al., 2017;Schabus et al., 2004;Schabus et al., 2006). However, neither spindle nor SO amplitude or peak frequency changes explained the behavioral differences ( Figure 2-figure supplement 1C,D). ...
Precise temporal coordination of slow oscillations (SO) and sleep spindles is a fundamental mechanism of sleep-dependent memory consolidation. SO and spindle morphology changes considerably throughout development. Critically, it remains unknown how the precise temporal coordination of these two sleep oscillations develops during brain maturation and whether their synchronization indexes the development of memory networks. Here, we use a longitudinal study design spanning from childhood to adolescence, where participants underwent polysomnography and performed a declarative word-pair learning task. Performance on the memory task was better during adolescence. After disentangling oscillatory components from 1/f activity, we found frequency shifts within SO and spindle frequency bands. Consequently, we devised an individualized cross-frequency coupling approach, which demonstrates that SO-spindle coupling strength increases during maturation. Critically, this increase indicated enhanced memory formation from childhood to adolescence. Our results provide evidence that improved coordination between SOs and spindles indexes the development of sleep-dependent memory networks.
... SO amplitude and frequency decreased from childhood to adolescence (Figure 2-figure supplement 1A,B). Both, SO and spindle features have been previously related to memory formation Huber et al., 2004;Lustenberger et al., 2017;Schabus et al., 2004;Schabus et al., 2006). However, neither spindle nor SO amplitude or peak frequency changes explained the behavioral differences ( Figure 2-figure supplement 1C,D). ...
Precise temporal coordination of slow oscillations (SO) and sleep spindles is a fundamental mechanism of sleep-dependent memory consolidation. SO and spindle morphology changes considerably throughout development. Critically, it remains unknown how the precise temporal coordination of these two sleep oscillations develops during brain maturation and whether their synchronization indexes the development of memory networks. Here, we use a longitudinal study design spanning from childhood to adolescence, where participants underwent polysomnography and performed a declarative word-pair learning task. Performance on the memory task was better during adolescence. After disentangling oscillatory components from 1/f activity, we found frequency shifts within SO and spindle frequency bands. Consequently, we devised an individualized cross-frequency coupling approach, which demonstrates that SO-spindle coupling strength increases during maturation. Critically, this increase indicated enhanced memory formation from childhood to adolescence. Our results provide evidence that improved coordination between SOs and spindles indexes the development of sleep-dependent memory networks.
... Brain oscillations investigated with electroencephalogram (EEG) within nonrapid eye movement (NREM) sleep, such as sleep spindles and slow waves, reflect anatomical and physiological features of brain circuits, and might therefore provide a window to detect alterations in brain development (Buchmann et al., 2010;. These NREM features have been associated not only with anatomical substrates but also with behavioral outcomes such as intelligence (Geiger, Huber, Kurth, & Ringli, 2011), visual perception (Bang, Khalilzadeh, Hämäläinen, Watanabe, & Sasaki, 2014), memory (Chatburn et al., 2013;Fogel, Fogel, Nader, Cote, & Smith, 2007), motor skills (Kurth et al., 2012;Lustenberger et al., 2017), language, social, and cognitive functioning in typical development (Page, Lustenberger, & Frohlich, 2018). We previously established evidence that NREM sleep spectral features undergo developmental changes, in a very early window from 12 to 30 months (Page et al., 2018), when children begin to show signs for developmental concerns. ...
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.
... SWA was averaged over the first hour of artifact-free NREM sleep because this yields a reliable estimate of deep sleep brain activity (Lustenberger et al., 2017). As in previous investigations addressing local sleep EEG topography (Ringli et al., 2013;Wilhelm et al., 2014), SWA was normalized by dividing the value of each electrode by the mean across all electrodes. ...
Objective/background:
Learning of a visuomotor adaptation task during wakefulness leads to a local increase in slow-wave activity (SWA, EEG power between 1 and 4.5 Hz) during subsequent deep sleep. Here, we examined this relationship between learning and SWA in children with attention-deficit/hyperactivity disorder (ADHD).
Patients/methods:
Participants were 15 children with ADHD (9.7-14.8 y, one female) and 15 age-matched healthy controls (9.6-15.7 y, three female). After the completion of a visuomotor adaptation task in the evening, participants underwent an all-night high-density (HD, 128 electrodes) sleep-EEG measurement.
Results:
Healthy control children showed the expected right-parietal increase in sleep SWA after visuomotor learning. Despite no difference in visuomotor learning, the local up-regulation during sleep was significantly reduced in ADHD patients compared to healthy controls.
Conclusions:
Our results indicate that the local, experience-dependent regulation of SWA is different in ADHD patients. Because the customarily observed heightened regulation in children was related to sensitive period maturation, ADHD patients may lack certain sensitive periods or show a developmental delay.
... Artifact-free sleep was included (skipping epochs with artifacts). We computed the maturational status of slow wave activity topography with the F/O-ratio of slow wave activity, as previously published (1-4.5 Hz; Fig. 1b) (Kurth et al., 2010;Lustenberger et al., 2017). ...
Epidemiological research reveals that insufficient sleep in children has negative cognitive and emotional consequences; however, the physiological underpinnings of these observations remain understudied. We tested the hypothesis that the topographical distribution of deep sleep slow wave activity during the childhood predicts brain white matter microstructure (myelin) 3.5 y later. Healthy children underwent sleep high-density EEG at baseline (n = 13; ages 2.4–8.0 y) and follow-up (n = 14; ages 5.5–12.2 y). At follow-up, myelin (myelin water fraction) and cortical morphology were also quantified. Our investigation revealed 3 main findings. (1) The Frontal/Occipital (F/O)-ratio at baseline strongly predicted whole brain myelin at follow-up. (2) At follow-up, the F/O-ratio was only minimally (negatively) linked to brain myelin. (3) Cortical morphology was not related to the F/O-ratio, neither at baseline nor at follow-up. Our results support the hypothesis that during child development EEG markers during sleep longitudinally predict brain myelin content. Data extend previous findings reporting a link between EEG markers of sleep need and cortical morphology, by supporting the hypothesis that sleep is a necessary component to underlying processes of brain, and specifically myelin, maturation. In line with the overarching theory that sleep contributes to neurodevelopmental processes, it remains to be investigated whether chronic sleep loss negatively affects white matter myelin microstructure growth during sensitive periods of development.
... Es wird angenommen, dass durch diese strukturelle und funktionelle Veränderung ein effizienteres und effektiveres kortikales Netzwerk entsteht. Beispielsweise konnte gezeigt werden, dass die Abnahme der langsamen Oszillationen in der Pubertät in Verbindung mit der Leistungssteigerung in einer visiomotorischen Aufgabe steht(Lustenberger et al., 2016). Das könnte auch den gefunden Anstieg der Gedächtnisleistung in der aktuellen Studie erklären.susanne.ring@sbg.ac.atAbb. ...
Sarkopenie, übersetzt die Fleischarmut (griech. sárka = Fleisch, penia = Mangel, Armut), meint den altersbedingten und normalen Verlust an Skelettmuskelmasse, sofern keine Erkrankung oder eine strenge Diät als Ursachen vorliegen. Bei einem über der Alters- und Geschlechtsnorm liegenden Verlust der Muskelmasse kommt es jedoch zu einem deutlichen Verlust der Funktionskapazität des Herzkreislauf- und Stoffwechselsystems, was sich im Alltag in einer Abnahme der Kraftfähigkeit und Reduzierung der Gehgeschwindigkeit äußern kann.
Aims: To compare the children's sleep electroencephalogram according to their intellectual profile.
Methods: Children were grouped according to their Wechsler Intelligence Scale for Children (WISC) scores (17 with normal intelligence quotient [IQ, NIQ] and 24 with high IQ [HIQ]). Comparisons of spectral power between groups and its relationship with WISC scores were assessed using analyses of variance and linear regression models, adjusted for age and sex.
Results: Children with HIQ had more rapid eye movement (REM) sleep, especially late at night, and more power in slow-frequency bands during REM sleep than those with NIQ. There were also positive associations between the processing speed index and the spectral power in β bands in NREM sleep, and with the spectral power in α, σ, β, and γ bands in REM sleep, with different associations between groups.
Conclusion: The enhanced power in slow bands during REM sleep in children with HIQ overlaps with that of typical REM sleep oscillations thought to be involved in emotional memory consolidation. The dissimilar relationships between spectral power and WISC scores in NIQ and HIQ groups may underlie functional differences in brain activity related to cognitive efficiency, questioning the direction of the relationship between sleep and cognitive functioning.
Working memory is frequently impaired in children with complex congenital heart disease (CHD), but little is known about the functional neuronal correlates. Sleep slow wave activity (SWA; 1–4.5 Hz EEG power) has previously been shown to reliably map neurofunctional networks of cognitive abilities in children with and without neurodevelopmental impairments. This study investigated whether functional networks of working memory abilities are altered in children with complex CHD using EEG recordings during sleep. Twenty-one children with complex CHD (aged 10.9 [SD: 0.3] years) and 17 typically-developing peers (10.5 [0.7] years) completed different working memory tasks and an overnight high-density sleep EEG recording (128 electrodes). The combined working memory score tended to be lower in children with complex CHD (CHD group: −0.44 [1.12], typically-developing group: 0.55 [1.24], d = 0.59, p = .06). The working memory score and sleep SWA of the first hour of deep sleep were correlated over similar brain regions in both groups: Strong positive associations were found over prefrontal and fronto-parietal brain regions – known to be part of the working memory network – and strong negative associations were found over central brain regions. Within these working memory networks, the associations between working memory abilities and sleep SWA (r between −.36 and .58, all p < .03) were not different between the two groups (no interactions, all p > .05). The current findings suggest that sleep SWA reliably maps working memory networks in children with complex CHD and that these functional networks are generally preserved in these patients.
Sleep plays a critical role in neural neurodevelopment. Hallmarks of sleep reflected in the electroencephalogram during nonrapid eye movement (NREM) sleep are associated with learning processes, cognitive ability, memory, and motor functioning. Research in adults is well-established; however, the role of NREM sleep in childhood is less clear. Growing evidence suggests the importance of two NREM sleep features: slow-wave activity and sleep spindles. These features may be critical for understanding maturational change and the functional role of sleep during development. Here, we review the literature on NREM sleep from infancy to preadolescence to provide insight into the network dynamics of the developing brain. The reviewed findings show distinct relations between topographical and maturational aspects of slow waves and sleep spindles; however, the direction and consistency of these relationships vary, and associations with cognitive ability remain unclear. Future research investigating the role of NREM sleep and development would benefit from longitudinal approaches, increased control for circadian and homeostatic influences, and in early childhood, studies recording daytime naps and overnight sleep to yield increased precision for detecting age-related change. Such evidence could help explicate the role of NREM sleep and provide putative physiological markers of neurodevelopment.
Background
Brain maturation is reflected in the sleep electroencephalogram (EEG) by a decline in non-rapid eye movement (NREM) slow wave activity (SWA) throughout adolescence and a related decrease in sleep depth. However, this trajectory and its sex and pubertal differences lack replication in population-based samples. We tested age-related changes in SWA (0.4-4 Hz) power and odds ratio product (ORP), a standardized measure of sleep depth.
Methods
We analyzed the sleep EEG of 572 subjects aged 6-21y (48% female, 26% racial/ethnic minority) and 332 subjects 5-12y followed-up at 12-22y. Multivariable-adjusted analyses tested age-related cross-sectional and longitudinal trajectories of SWA and ORP.
Results
SWA remained stable from age 6 to 10, decreased between ages 11 and 17, and plateaued from age 18 to 21 (p-cubic<0.001); females showed a longitudinal decline 23% greater than males by 13y, while males experienced a steeper slope after 14y and their longitudinal decline was 21% greater by 19y. More mature adolescents (75% female) experienced a greater longitudinal decline in SWA than less mature adolescents by 14y. ORP showed an age-related increasing trajectory (p-linear<0.001) with no sex or pubertal differences.
Conclusions
We provide population-level evidence for the maturational decline and sex and pubertal differences in SWA in the transition from childhood to adolescence, while introducing ORP as a novel metric in youth. Along with previous studies, the distinct trajectories observed suggest that age-related changes in SWA reflect brain maturation and local/synaptic processes during this developmental period, while those of ORP may reflect global/state control of NREM sleep depth.
Quantitative analyses of electroencephalogram (EEG) during sleep have made it possible to establish “slow-wave activity” (SWA) as a biological marker of sleep depth and sleep homeostasis. Homeostasis is a basic principle of sleep regulation: it indicates that a sleep deficit induces a compensatory increase in sleep intensity, while excessive sleep reduces sleep propensity. Sleep deprivation affects EEG activity in the subsequent recovery night, mainly by increasing SWA. This rebound is maximal over the frontal areas of the cortex. This and other quantitative EEG findings support the notion that every sleep phenomenon, from sleep onset to the awakening, is strictly local in nature.
Background:
After acquired brain injury (ABI), patients show various neurological impairments and outcome is difficult to predict. Identifying biomarkers of recovery could provide prognostic information about a patient's neural potential for recovery and improve our understanding of neural reorganization. In healthy subjects, sleep slow wave activity (SWA, EEG spectral power 1-4.5 Hz) has been linked to neuroplastic processes such as learning and brain maturation. Therefore, we suggest that SWA might be a suitable measure to investigate neural reorganization underlying memory recovery.
Objectives:
In the present study, we used SWA to investigate neural correlates of recovery of function in ten paediatric patients with ABI (age range 7-15 years).
Methods:
We recorded high-density EEG (128 electrodes) during sleep at the beginning and end of rehabilitation. We used sleep EEG data of 52 typically developing children to calculate age-normalized values for individual patients. In patients, we also assessed every-day life memory impairment at the beginning and end of rehabilitation.
Results:
In the course of rehabilitation, memory recovery was paralleled by longitudinal changes in SWA over posterior parietal brain areas. SWA over left prefrontal and occipital brain areas at the beginning of rehabilitation predicted memory recovery.
Conclusions:
We show that longitudinal sleep-EEG measurements are feasible in the clinical setting. While posterior parietal and prefrontal brain areas are known to belong to the memory "core network", occipital brain areas have never been related to memory. While we have to remain cautious in interpreting preliminary findings, we suggest that SWA is a promising measure to investigate neural reorganization.
Background:
Iron deficiency is the most prevalent micronutrient deficiency worldwide. There is evidence that iron deficiency produces alterations in the developing brain, eventually leading to long-lasting effects on various cognitive functions.
Methods:
Here, we investigated motor learning and its consolidation after sleep in adolescents who sustained iron deficiency anemia (IDA) in infancy, compared to healthy controls, in the context of a long-term follow-up Chilean research project. Fifty-three adolescents who formerly had iron deficiency anemia as infants and 40 control adolescents practiced a sequential motor finger tapping task, before and after a night of sleep. Performance was measured at the end of learning, 30 min later (boost effect), and the next morning.
Results:
Revealed slower learning in subjects with infant iron deficiency anemia than control subjects, followed by a proportionally similar performance boost at 30 min. Performance remained stable overnight in healthy controls but further improved in infant IDA adolescents, suggesting a beneficial effect of post-training sleep on the consolidation of incompletely learned motor skills. In particular, overnight gains in performance were observed in female, but not male infant iron deficiency anemic subjects, suggesting a gender effect.
Conclusions:
Our results indicate long-lasting motor learning deficits in infant IDA adolescents and provide support to the hypothesis that post-training sleep might, to some extent, compensate for hampered motor learning during wakefulness.
Jeden Tag verlieren wir durch den Schlaf im Durchschnitt acht Stunden unser Bewusstsein. Hierbei werden in einem 90 minütigen Zyklus zunächst die „Non-Rapid Eye Movement“ Phasen (NREM-1, 2, 3) mit zunehmender Schlaftiefe durchschritten. Beendet wird jeder Zyklus mit dem „Rapid Eye Movement Schlaf“ (REM), der sich durch rasche Augenbewegungen auszeichnet. Neben der erholenden Funktion des Schlafes (Siegel, 2005), ist Schlaf ein Zustand der durch die Weiterverarbeitung und Reaktivierung von neu gelernten Inhalten, die Gedächtniskonsolidierung begünstigt (Diekelmann und Born, 2010).
Executive function deficits are among the most frequent sequela of very preterm birth but the underlying neuronal mechanisms are not fully understood. We used high-density EEG recordings during sleep to assess alterations in the functional neuroanatomy of executive processes in adolescents born very preterm. The topographical distribution of sleep slow wave activity (SWA; 1-4.5 Hz EEG power) has previously been used to map cognitive abilities and is known to reflect the intensity of the prior use of the respective neuronal networks. We assessed 38 adolescents born before 32 weeks of gestation (age at assessment: 12.9 (SD: 1.7), range: 10.6-16.7 years) and 43 term-born peers (13.1 (2.0), 10.0-16.9). Executive function abilities were quantified with a composite score derived from a comprehensive task battery. All-night high-density EEG (128 electrodes) was recorded and SWA of the first hour of sleep was calculated. Abilities were significantly poorer in the very preterm compared to the term-group, particularly, if the tasks demands were high (P < .01). The score was positively correlated with sleep SWA in a cluster of 15 electrodes over frontal and negatively in a cluster of 14 electrodes over central brain regions after controlling for age at assessment and correcting for multiple comparisons. Within the frontal cluster, sleep SWA was higher in very preterm compared to term-born participants when controlling for executive function performance and age at assessment (P = .02). No difference in SWA between very preterm and term-born participants was found for the central cluster (P = .29). Our results demonstrate a local increase of sleep SWA over brain regions associated with executive processes in adolescents born very preterm compared to similarly performing term-born peers. Thus, sleep SWA maps the higher effort needed for executive function tasks in adolescents born very preterm.
High density EEG (hdEEG) during sleep combines the superior temporal resolution of EEG recordings with high spatial resolution. Thus, this method allows a topographical analysis of sleep EEG activity and thereby fosters the shift from a global view of sleep to a local one. HdEEG allowed to investigate sleep rhythms in terms of their characteristic behavior (e.g., the traveling of slow waves) and in terms of their relationship to cortical functioning (e.g., consciousness and cognitive abilities). Moreover, recent studies successfully demonstrated that hdEEG can be used to study brain functioning in neurological and neuro-developmental disorders, and to evaluate therapeutic approaches. This review highlights the potential, the problems, and future perspective of hdEEG in sleep research.
To examine developmental changes in the human sleep electroencephalogram (EEG) during late adolescence.
A 4-bed sleep laboratory.
Fourteen adolescents (5 boys) were studied at ages 15 or 16 (initial) and again at ages 17 to 19 (follow-up).
N/A MEASUREMENTS AND RESULTS: All-night polysomnography was recorded at each assessment and scored according to the criteria of Rechtschaffen and Kales. A 27% decline in duration of slow wave sleep, and a 22% increase of stage 2 sleep was observed from the initial to the follow-up session. All-night spectral analysis of 2 central and 2 occipital leads revealed a significant decline of NREM and REM sleep EEG power with increasing age across frequencies in both states. Time-frequency analysis revealed that the decline in power was consistent across the night for all bands except the delta band. The decreases in power were most pronounced over the left central (C3/A2) and right occipital (O2/A1) derivations.
Using longitudinal data, we show that the developmental changes to the sleeping EEG that begin in early adolescence continue into late adolescence. As with early adolescents, we observed hemispheric asymmetry in the decline of sleep EEG power. This decline was state and frequency nonspecific, suggesting that it may be due to the pruning of synapses known to occur during adolescence.
Waking and sleep data in adults show high heritability and trait-like characteristics in EEG spectra. This phenomenon has not been examined in children and adolescents where brain development influences the EEG. The present study examines whether a trait-like sleep EEG pattern is detectable across adolescent development. Two consecutive nights of standard sleep recordings were performed in 19 9-10-year-old children and 26 15-16-year-old teens, and were repeated 1.5-3 years later. EEG spectra averaged across the night for non-rapid eye movement and rapid eye movement sleep separately were classified using hierarchical cluster analysis, which showed that all 4 nights of a participant clustered together for a majority of participants. Intraclass correlation coefficients were also very high (>0.7) across nights separated by several years, indicating a trait-like feature of the sleep EEG. In summary, our results, using two measures of stability, indicate that a "trait-like" aspect can be detected in the sleep EEG across adolescent development despite considerable neurodevelopmental changes. This finding indicates that the brain oscillators responsible for generating the sleep EEG signal remain relatively stable across adolescent development.
Our ongoing longitudinal study has shown that NREM delta (1-4 Hz) and theta (4-8 Hz) power measured at C3 and C4 decrease by more than 60% between ages 11 and 17 years. Here, we investigate the age trajectories of delta and theta power at frontal, central, and occipital electrodes.
Baseline sleep EEG was recorded twice yearly for 6 years in 2 cohorts, spanning ages 9-18 years, with overlap at 12-15 years.
Sleep EEG was recorded in the subjects' homes with ambulatory recorders.
Sixty-seven subjects in 2 cohorts, one starting at age 9 (n = 30) and one at age 12 years (n = 37).
Sleep EEG recorded from Fz, Cz, C3, C4, and O1 was referred to mastoids. Visual scoring and artifact elimination was followed by FFT power analysis. Delta and theta EEG power declined steeply across this age range. The maturational trajectories of delta power showed a "back to front" pattern, with O1 delta power declining earliest and Fz delta power declining latest. Theta EEG power did not show this topographic difference in the timing of its decline. Delta, and to a lesser extent, theta power became frontally dominant in early adolescence.
We maintain our interpretation that the adolescent decline in EEG power reflects a widespread brain reorganization driven by synaptic pruning. The late decline in frontally recorded delta power indicates that plasticity is maintained in these circuits until a later age. Although delta and theta have similar homeostatic properties, they have different age and topographic patterns that imply different functional correlates.
Adolescence is marked by dramatic changes in sleep. Older adolescents go to bed later, have an increased preference for evening activities, and sleep less than younger adolescents. This behavior change is driven by external factors, notably increased pressures from academic, social, and extracurricular activities and by biological circadian factors. There are also substantial changes in sleep architecture across adolescence, with dramatic declines in slow wave sleep, and slow wave activity (delta, ~ 0.5-4.5 Hz). These changes are associated with underlying changes in brain structure and organization, with a decrease in synaptic density likely underlying the reduction in high amplitude slow waveforms. While changes in sleep across adolescence are a normal part of development, many adolescents are getting insufficient sleep and are consequently, less likely to perform well at school, more likely to develop mood-related disturbances, be obese, and are at greater risk for traffic accidents, alcohol and drug abuse.
Evidence that electroencephalography (EEG) slow-wave activity (SWA) (EEG spectral power in the 1-4.5 Hz band) during non-rapid eye movement sleep (NREM) reflects plastic changes is increasing (Tononi and Cirelli, 2006). Regional assessment of gray matter development from neuroimaging studies reveals a posteroanterior trajectory of cortical maturation in the first three decades of life (Shaw et al., 2008). Our aim was to test whether this regional cortical maturation is reflected in regional changes of sleep SWA. We evaluated all-night high-density EEG (128 channels) in 55 healthy human subjects (2.4-19.4 years) and assessed age-related changes in NREM sleep topography. As in adults, we observed frequency-specific topographical distributions of sleep EEG power in all subjects. However, from early childhood to late adolescence, the location on the scalp showing maximal SWA underwent a shift from posterior to anterior regions. This shift along the posteroanterior axis was only present in the SWA frequency range and remained stable across the night. Changes in the topography of SWA during sleep parallel neuroimaging study findings indicating cortical maturation starts early in posterior areas and spreads rostrally over the frontal cortex. Thus, SWA might reflect the underlying processes of cortical maturation. In the future, sleep SWA assessments may be used as a clinical tool to detect aberrations in cortical maturation.
Deep (slow wave) sleep shows extensive maturational changes from childhood through adolescence, which is reflected in a decrease of sleep depth measured as the activity of electroencephalographic (EEG) slow waves. This decrease in sleep depth is paralleled by massive synaptic remodeling during adolescence as observed in anatomical studies, which supports the notion that adolescence represents a sensitive period for cortical maturation. To assess the relationship between slow-wave activity (SWA) and cortical maturation, we acquired sleep EEG and magnetic resonance imaging data in children and adolescents between 8 and 19 years. We observed a tight relationship between sleep SWA and a variety of indexes of cortical maturation derived from magnetic resonance (MR) images. Specifically, gray matter volumes in regions correlating positively with the activity of slow waves largely overlapped with brain areas exhibiting an age-dependent decrease in gray matter. The positive relationship between SWA and cortical gray matter was present also for power in other frequency ranges (theta, alpha, sigma, and beta) and other vigilance states (theta during rapid eye movement sleep). Our findings indicate a strong relationship between sleep EEG activity and cortical maturation. We propose that in particular, sleep SWA represents a good marker for structural changes in neuronal networks reflecting cortical maturation during adolescence.
The need to sleep grows with the duration of wakefulness and dissipates with time spent asleep, a process called sleep homeostasis. What are the consequences of staying awake on brain cells, and why is sleep needed? Surprisingly, we do not know whether the firing of cortical neurons is affected by how long an animal has been awake or asleep. Here, we found that after sustained wakefulness cortical neurons fire at higher frequencies in all behavioral states. During early NREM sleep after sustained wakefulness, periods of population activity (ON) are short, frequent, and associated with synchronous firing, while periods of neuronal silence are long and frequent. After sustained sleep, firing rates and synchrony decrease, while the duration of ON periods increases. Changes in firing patterns in NREM sleep correlate with changes in slow-wave activity, a marker of sleep homeostasis. Thus, the systematic increase of firing during wakefulness is counterbalanced by staying asleep.
The best characterized marker of sleep homeostasis is the amount of slow wave activity (SWA, 0.5-4 Hz) during NREM sleep. SWA increases as a function of previous waking time and declines during sleep, but the underlying mechanisms remain unclear. We have suggested that SWA homeostasis is linked to synaptic potentiation associated with learning during wakefulness. Indeed, studies in rodents and humans found that SWA increases after manipulations that presumably enhance synaptic strength, but the evidence remains indirect. Here we trained rats in skilled reaching, a task known to elicit long-term potentiation in the trained motor cortex, and immediately after learning measured SWA and cortical protein levels of c-fos and Arc, 2 activity-dependent genes involved in motor learning.
Intracortical local field potential recordings and training on reaching task.
Basic sleep research laboratory.
Long Evans adult male rats.
N/A.
SWA increased post-training in the trained cortex (the frontal cortex contralateral to the limb used to learn the task), with smaller or no increase in other cortical areas. This increase was reversible within 1 hour, specific to NREM sleep, and positively correlated with changes in performance during the prior training session, suggesting that it reflects plasticity and not just motor activity. Fos and Arc levels were higher in the trained relative to untrained motor cortex immediately after training, but this asymmetry was no longer present after 1 hour of sleep.
Learning to reach specifically affects gene expression in the trained motor cortex and, in the same area, increases sleep need as measured by a local change in SWA.
Variability in motor performance decreases with practice but is never entirely eliminated, due in part to inherent motor noise. The present study develops a method that quantifies how performers can shape their performance to minimize the effects of motor noise on the result of the movement. Adopting a statistical approach on sets of data, the method quantifies three components of variability (tolerance, noise, and covariation) as costs with respect to optimal performance. T-Cost quantifies how much the result could be improved if the location of the data were optimal, N-Cost compares actual results to results with optimal dispersion at the same location, and C-Cost represents how much improvement stands to be gained if the data covaried optimally. The TNC-Cost analysis is applied to examine the learning of a throwing task that participants practiced for 6 or 15 days. Using a virtual set-up, 15 participants threw a pendular projectile in a simulated concentric force field to hit a target. Two variables, angle and velocity at release, fully determined the projectile's trajectory and thereby the accuracy of the throw. The task is redundant and the successful solutions define a nonlinear manifold. Analysis of experimental results indicated that all three components were present and that all three decreased across practice. Changes in T-Cost were considerable at the beginning of practice; C-Cost and N-Cost diminished more slowly, with N-Cost remaining the highest. These results showed that performance variability can be reduced by three routes: by tuning tolerance, covariation and noise in execution. We speculate that by exploiting T-Cost and C-Cost, participants minimize the effects of inevitable intrinsic noise.
Converging evidence indicates that a profound reorganization of human brain function takes place during adolescence: the amount of deep sleep and the rate of brain metabolism fall sharply; the latency of certain event-related potentials declines; the capacity to recover function after brain injury diminishes; and adult problem-solving "power" appears. A reduction in cortical synaptic density has recently been observed and might account for all of these changes. Such synaptic "pruning" may be analogous to the programmed elimination of neural elements in very early development. A defect in this maturational process may underlie those cases of schizophrenia that emerge during adolescence.
A slow oscillation (< 1 Hz) has recently been described in intracellular recordings from the neocortex and thalamus (Steriade et al., 1993c-e). The aim of the present study was to determine the phase relations between cortical and thalamic neuronal activities during the slow EEG oscillation. Intracellular recordings were performed in anesthetized cats from neurons in motor and somatosensory cortical areas, the rostrolateral sector of the reticular (RE) thalamic nucleus, and thalamocortical (TC) cells from ventrolateral (VL) nucleus. The EEG was used as time reference for alignment of activities in different, simultaneously recorded neurons, including dual impalements of cortical cells as well as cortical and TC cells. The spontaneous EEG oscillation was characterized by slowly recurring (0.3-0.9 Hz) sequences of surface-positive (depth-negative) sharp deflections, often followed by oscillatory activity within the frequency range of sleep spindles (7-14 Hz) or at faster frequencies. Cortical and RE cells were similarly hyperpolarized during the depth-positive EEG waves and were depolarized during the depth-negative EEG deflections. In many instances, the cell depolarization was associated with oscillations at the spindle frequency or with tonic firing at rates related to the level of depolarization. TC neurons were hyperpolarized during the depth-positive EEG waves and displayed a series of IPSPs, at the spindle frequencies, during the depth-negative EEG waves. Depending on the membrane potential (Vm), TC cells could fire spike bursts at the onset of the EEG depth-negativity, or their firing could be delayed by subsequent IPSPs. The sequence of spontaneous EEG and cellular events described above also characterized the responses to cortical and thalamic stimulation. Simultaneous intracellular recordings of pairs of cortical cells or cortical and TC cells showed that spontaneous transitions from less synchronized to more synchronized EEG states were marked by a simultaneous hyperpolarization, coincident with an overt depth-positive EEG wave. We conclude that during low-frequency oscillatory states, characteristic of slow-wave sleep, neocortical and thalamic neurons display phase relations that are restricted to narrow time windows, and that synchronization results from a generalized inhibitory phenomenon. Moreover, EEG synchronization is reflected as active inhibition in TC neurons. That this pattern is also present in states of hypersynchronization, such as seizure activity, is shown in the following paper (Steriade and Contreras, 1994).
We describe a novel slow oscillation in intracellular recordings from cortical association areas 5 and 7, motor areas 4 and 6, and visual areas 17 and 18 of cats under various anesthetics. The recorded neurons (n = 254) were antidromically and orthodromically identified as corticothalamic or callosal elements receiving projections from appropriate thalamic nuclei as well as from homotopic foci in the contralateral cortex. Two major types of cells were recorded: regular-spiking (mainly slow-adapting, but also fast-adapting) neurons and intrinsically bursting cells. A group of slowly oscillating neurons (n = 21) were intracellularly stained and found to be pyramidal-shaped cells in layers III-VI, with luxuriant basal dendritic arbors. The slow rhythm appeared in 88% of recorded neurons. It consisted of slow depolarizing envelopes (lasting for 0.8-1.5 sec) with superimposed full action potentials or presumed dendritic spikes, followed by long-lasting hyperpolarizations. Such sequences recurred rhythmically at less than 1 Hz, with a prevailing oscillation between 0.3 and 0.4 Hz in 67% of urethane-anesthetized animals. While in most neurons (approximately 70%) the repetitive spikes superimposed on the slow depolarization were completely blocked by slight DC hyperpolarization, 30% of cells were found to display relatively small (3-12 mV), rapid, all-or-none potentials after obliteration of full action potentials. These fast spikes were suppressed in an all-or-none fashion at Vm more negative than -90 mV. The depolarizing envelope of the slow rhythm was reduced or suppressed at a Vm of -90 to -100 mV and its duration was greatly reduced by administration of the NMDA blocker ketamine. In keeping with this action, most (56%) neurons recorded in animals under ketamine and nitrous oxide or ketamine and xylazine anesthesia displayed the slow oscillation at higher frequencies (0.6-1 Hz) than under urethane anesthesia (0.3-0.4 Hz). In 18% of the oscillating cells, the slow rhythm mainly consisted of repetitive (15-30 Hz), relatively short-lasting (15-25 msec) IPSPs that could be revealed by bringing the Vm at more positive values than -70 mV. The long-lasting (approximately 1 sec) hyperpolarizing phase of the slow oscillation was best observed at the resting Vm and was reduced at about -100 mV. Simultaneous recording of another cell across the membrane demonstrated synchronous inhibitory periods in both neurons. Intracellular diffusion of Cl- or Cs+ reduced the amplitude and/or duration of cyclic long-lasting hyperpolaryzations.(ABSTRACT TRUNCATED AT 400 WORDS)
Psychophysical studies of reaching movements suggest that hand kinematics are learned from errors in extent and direction in an extrinsic coordinate system, whereas dynamics are learned from proprioceptive errors in an intrinsic coordinate system. We examined consolidation and interference to determine if these two forms of learning were independent. Learning and consolidation of two novel transformations, a rotated spatial reference frame and altered intersegmental dynamics, did not interfere with each other and consolidated in parallel. Thus separate kinematic and dynamic models were constructed simultaneously based on errors computed in different coordinate frames, and possibly, in different sensory modalities, using separate working-memory systems. These results suggest that computational approaches to motor learning should include two separate performance errors rather than one.
Behavioral studies indicate that the ability to acquire long-term memories is severely impaired during sleep. It is unclear, however, why the highly synchronous discharge of neurons during sleep should not be followed by the induction of enduring plastic changes. Here we show that the expression of phosphorylated CRE-binding protein, Arc, and BDNF, three genes whose induction is often associated with synaptic plasticity, is high during waking and low during sleep. We also show that the induction of these genes during waking depends on the activity of the noradrenergic system, which is high in waking and low in sleep. These molecular results complement behavioral evidence and provide a mechanism for the impairment of long-term memory acquisition during sleep.
Neurodevelopmental models for the pathology of schizophrenia propose both polygenetic and environmental risks, as well as early (pre/perinatal) and late (usually adolescent) developmental brain abnormalities. With the use of brain mapping algorithms, we detected striking anatomical profiles of accelerated gray matter loss in very early-onset schizophrenia; surprisingly, deficits moved in a dynamic pattern, enveloping increasing amounts of cortex throughout adolescence. Early-onset patients were rescanned prospectively with MRI, at 2-year intervals at three time points, to uncover the dynamics and timing of disease progression during adolescence. The earliest deficits were found in parietal brain regions, supporting visuospatial and associative thinking, where adult deficits are known to be mediated by environmental (nongenetic) factors. Over 5 years, these deficits progressed anteriorly into temporal lobes, engulfing sensorimotor and dorsolateral prefrontal cortices, and frontal eye fields. These emerging patterns correlated with psychotic symptom severity and mirrored the neuromotor, auditory, visual search, and frontal executive impairments in the disease. In temporal regions, gray matter loss was completely absent early in the disease but became pervasive later. Only the latest changes included dorsolateral prefrontal cortex and superior temporal gyri, deficit regions found consistently in adult studies. These emerging dynamic patterns were (i) controlled for medication and IQ effects, (ii) replicated in independent groups of males and females, and (iii) charted in individuals and groups. The resulting mapping strategy reveals a shifting pattern of tissue loss in schizophrenia. Aspects of the anatomy and dynamics of disease are uncovered, in a changing profile that implicates genetic and nongenetic patterns of deficits.
Study Objective: Slow-wave activity (SWA; 0.5-4.0Hz) during non-rapid eye movement (NREM) sleep is a reliable indicator of sleep need, as it increases with the duration of prior wakefulness and decreases during sleep. However, which biologic process occurring during wakefulness is responsible for the increase of sleep SWA remains unknown. The aim of the study was to determine whether neuronal plasticity underlies the link between waking activities and the SWA response. Design: We manipulated, in rats, the amount of exploratory activity while maintaining the total duration of waking constant. We then measured the extent to which exploration increases cortical expression of plasticity-related genes (BDNF, Arc, Homer, NGFI-A), and the SWA response once the animals were allowed to sleep. Setting: Basic neurophysiology and molecular laboratory. Participants: Male Wistar Kyoto rats (250-300g; 2-3 month old). Interventions: None. Results: We found that, within the same animal, the amount of exploratory behavior during wakefulness could predict the extent to which BDNF was induced, as well as the extent of the homeostatic SWA response during subsequent sleep. Conclusions: This study suggests a direct link between the synaptic plasticity triggered by waking activities and the homeostatic sleep response and identifies BDNF as a major mediator of this link at the molecular level.
focuses on late developmental events [those occurring during the second trimester of gestation and continuing into the postnatal period]—synaptogenesis and synapse elimination—in human cerebral cortex [in fetuses–70 yr olds], and stresses functional correlates where these can be determined (PsycINFO Database Record (c) 2012 APA, all rights reserved)
The neurons of the human cerebral cortex are arranged in a highly folded sheet, with the majority of the cortical surface area buried in folds. Cortical maps are typically arranged with a topography oriented parallel to the cortical surface. Despite this unambiguous sheetlike geometry, the most commonly used coordinate systems for localizing cortical features are based on 3-D stereotaxic coordinates rather than on position relative to the 2-D cortical sheet. In order to address the need for a more natural surface-based coordinate system for the cortex, we have developed a means for generating an average folding pattern across a large number of individual subjects as a function on the unit sphere and of nonrigidly aligning each individual with the average. This establishes a spherical surface-based coordinate system that is adapted to the folding pattern of each individual subject, allowing for much higher localization accuracy of structural and functional features of the human brain.
Humans have an individual profile of the electroencephalographic power spectra at the 8 to 16Hz frequency during non–rapid eye movement sleep that is stable over time and resistant to experimental perturbations. We tested the hypothesis that this electroencephalographic “fingerprint” is genetically determined, by recording 40 monozygotic and dizygotic twins during baseline and recovery sleep after prolonged wakefulness. We show a largely greater similarity within monozygotic than dizygotic pairs, resulting in a heritability estimate of 96%, not influenced by sleep need and intensity. If replicated, these results will establish the electroencephalographic profile during sleep as one of the most heritable traits of humans. Ann Neurol 2008
Electroencephalographically (EEG) recorded slow wave activity (SWA, 1-4.5Hz), reflecting the depth of sleep, is suggested to play a crucial role in synaptic plasticity. Mapping of SWA by means of high-density EEG reveals that cortical regions showing signs of maturational changes (structural and behavioral) during childhood and adolescence exhibit more SWA. Moreover, the maturation of specific skills is predicted by the topographical distribution of SWA. Thus, SWA topography may serve as a promising neuroimaging tool with prognostic potential. Finally, our data suggest that deep sleep SWA in humans is involved in cortical development that optimizes performance.
To examine the variations in regional cerebral blood flow during execution and learning of reaching movements, we employed a family of kinematically and dynamically controlled motor tasks in which cognitive, mnemonic and executive features of performance were differentiated and characterized quantitatively. During 15O-labeled water positron emission tomography (PET) scans, twelve right-handed subjects moved their dominant hand on a digitizing tablet from a central location to equidistant targets displayed with a cursor on a computer screen in synchrony with a tone. In the preceding week, all subjects practiced three motor tasks: 1) movements to a predictable sequence of targets; 2) learning of new visuomotor transformations in which screen cursor motion was rotated by 30°–60°; 3) learning new target sequences by trial and error, by using previously acquired routines in a task placing heavy load on spatial working memory. The control condition was observing screen and audio displays. Subtraction images were analyzed with Statistical Parametric Mapping to identify significant brain activation foci. Execution of predictable sequences was characterized by a modest decrease in movement time and spatial error. The underlying pattern of activation involved primary motor and sensory areas, cerebellum, basal ganglia. Adaptation to a rotated reference frame, a form of procedural learning, was associated with decrease in the imposed directional bias. This task was associated with activation in the right posterior parietal cortex. New sequences were learned explicitly. Significant activation was found in dorsolateral prefrontal and anterior cingulate cortices. In this study, we have introduced a series of flexible motor tasks with similar kinematic characteristics and different spatial attributes. These tasks can be used to assess specific aspects of motor learning with imaging in health and disease.
Community epidemiological data on the prevalence and correlates of adolescent mental disorders are needed for policy planning purposes. Only limited data of this sort are available.
To present estimates of 12-month and 30-day prevalence, persistence (12-month prevalence among lifetime cases and 30-day prevalence among 12-month cases), and sociodemographic correlates of commonly occurring DSM-IV disorders among adolescents in the National Comorbidity Survey Replication Adolescent Supplement.
The National Comorbidity Survey Replication Adolescent Supplement is a US national survey of DSM-IV anxiety, mood, behavior, and substance disorders among US adolescents based on face-to-face interviews in the homes of respondents with supplemental parent questionnaires.
Dual-frame household and school samples of US adolescents.
A total of 10,148 adolescents aged 13 to 17 years (interviews) and 1 parent of each adolescent (questionnaires).
The DSM-IV disorders assessed with the World Health Organization Composite International Diagnostic Interview and validated with blinded clinical interviews based on the Schedule for Affective Disorders and Schizophrenia for School-Age Children. Good concordance (area under the receiver operating characteristic curve ≥0.80) was found between Composite International Diagnostic Interview and Schedule for Affective Disorders and Schizophrenia for School-Age Children diagnoses.
The prevalence estimates of any DSM-IV disorder are 40.3% at 12 months (79.5% of lifetime cases) and 23.4% at 30 days (57.9% of 12-month cases). Anxiety disorders are the most common class of disorders, followed by behavior, mood, and substance disorders. Although relative disorder prevalence is quite stable over time, 30-day to 12-month prevalence ratios are higher for anxiety and behavior disorders than mood or substance disorders, suggesting that the former are more chronic than the latter. The 30-day to 12-month prevalence ratios are generally lower than the 12-month to lifetime ratios, suggesting that disorder persistence is due more to episode recurrence than to chronicity. Sociodemographic correlates are largely consistent with previous studies.
Among US adolescents, DSM-IV disorders are highly prevalent and persistent. Persistence is higher for adolescents than among adults and appears to be due more to recurrence than chronicity of child-adolescent onset disorders.
Sleep slow waves are the major electrophysiological features of non-rapid eye movement (NREM) sleep. Although there is growing understanding of where slow waves originate and how they are generated during sleep, the function of slow waves is still largely unclear. A recently proposed hypothesis relates slow waves to the homeostatic regulation of synaptic plasticity. While several studies confirm a correlation between experimentally triggered synaptic changes and slow-wave activity (SWA), little is known about its association to synaptic changes occurring during cortical maturation. Interestingly, slow waves undergo remarkable changes during development that parallel the time course of cortical maturation. In a recent cross-sectional study including children and adolescents, the topographical distribution of SWA was analyzed with high-density electroencephalography. The results showed age-dependent differences in SWA topography: SWA was highest over posterior regions during early childhood and then shifted over central derivations to the frontal cortex in late adolescence. This trajectory of SWA topography matches the course of cortical gray maturation. In this chapter, the major changes in slow waves during development are highlighted and linked to cortical maturation and behavior. Interestingly, synaptic density and slow-wave amplitude increase during childhood are highest shortly before puberty, decline thereafter during adolescence, reaching overall stable levels during adulthood. The question arises whether SWA is merely reflecting cortical changes or if it plays an active role in brain maturation. We thereby propose a model, by which sleep slow waves may contribute to cortical maturation. We hypothesize that while there is a balance between synaptic strengthening and synaptic downscaling in adults, the balance of strengthening/formation and weakening/elimination is tilted during development.
The contribution of slow brain oscillations including delta, theta, alpha, and sigma frequencies (0.5-16 Hz) to the sleep electroencephalography (EEG) is finely regulated by circadian and homeostatic influences, and reflects functional aspects of wakefulness and sleep. Accumulating evidence demonstrates that individual sleep EEG patterns in non-rapid-eye-movement (NREM) sleep and rapid-eye-movement (REM) sleep are heritable traits. More specifically, multiple recordings in the same individuals, as well as studies in monozygotic and dizygotic twins suggest that a very high percentage of the robust interindividual variation and the high intraindividual stability of sleep EEG profiles can be explained by genetic factors (> 90% in distinct frequency bands). Still little is known about which genes contribute to different sleep EEG phenotypes in healthy humans. The genetic variations that have been identified to date include functional polymorphisms of the clock gene PER3 and of genes contributing to signal transduction pathways involving adenosine (ADA, ADORA2A), brain-derived neurotrophic factor (BDNF), dopamine (COMT), and prion protein (PRNP). Some of these polymorphisms profoundly modulate sleep EEG profiles; their effects are reviewed here. It is concluded that the search for genetic contributions to slow sleep EEG oscillations constitutes a promising avenue to identify molecular mechanisms underlying sleep-wake regulation in humans.
Proceeding from the assumptions that specific frontal regions control discrete functions and that very basic cognitive processes can be systematically manipulated to reveal those functions, recent reports have demonstrated consistent anatomical/functional relationships: dorsomedial for energization, left dorsolateral for task setting, and right dorsolateral for monitoring. There is no central executive. There are, instead, numerous domain general processes discretely distributed across several frontal regions that act in concert to accomplish control. Beyond these functions, there are two additional "frontal" anatomical/functional relationships: ventral-medial/orbital for emotional and behavioral regulation, and frontopolar for integrative-even meta-cognitive-functions.
Magnetic resonance imaging (MRI) allows unprecedented access to the anatomy and physiology of the developing brain without the use of ionizing radiation. Over the past two decades, thousands of brain MRI scans from healthy youth and those with neuropsychiatric illness have been acquired and analyzed with respect to diagnosis, sex, genetics, and/or psychological variables such as IQ. Initial reports comparing size differences of various brain components averaged across large age spans have given rise to longitudinal studies examining trajectories of development over time and evaluations of neural circuitry as opposed to structures in isolation. Although MRI is still not of routine diagnostic utility for evaluation of pediatric neuropsychiatric disorders, patterns of typical versus atypical development have emerged that may elucidate pathologic mechanisms and suggest targets for intervention. In this review we summarize general contributions of structural MRI to our understanding of neurodevelopment in health and illness.
WE THANK DRS. TAROKH AND CARSKADON FOR THEIR GENEROUS ACKNOWLEDGMENT OF OUR WORK IN THEIR RECENT PAPER.1 HOWEVER, WE do wish to clarify a point of disagreement regarding the age at which the delta EEG power begins its adolescent decline. Tarokh and Carskadon report a significant decline in NREM delta power in 14 subjects between their first recording at 9 or 10 years of age and their second at age 11 to 13 years. They state that this decline contradicts our finding that NREM delta power does not change between ages 9 and 11 years.2 They conclude that we “missed” a decline that occurs over this age range, perhaps because our subjects were studied at home on their habitual sleep schedules and were becoming increasingly sleep deprived. A homeostatic response to such deprivation might have “masked” declining delta power.
In fact, Tarokh and Carskadon's data are entirely consistent with our published age curves.3 Their data cannot contradict our finding that delta power does not change between 9 and 11 years because they did not study this age range. They compared subjects studied at mean age 10.1 years vs. 12.3 years. The decrease in delta power found in their second recording agrees with our published finding demonstrating that delta power starts to decline by age 12 years.
To evaluate this issue further, we examined delta power in our 30 subjects over 5 semiannual recordings between mean age 10.4 and 12.4 years, closely approximating the mean ages (10.1 and 12.3) of Tarokh and Carskadon's 14 Ss at their first and second recordings. Over this age range, delta power indeed shows the significant (F1,108 = 11.4, P = 0.001) decline predicted by our age curves. We next examined delta power in these same subjects over 4 semiannual recordings between mean age 9.3 and 10.9 years. Confirming our previous report, delta power showed no decline (F1,84 = 1.06, P = 0.31). Finally, Tarokh and Carskadon's speculation that our subjects were becoming increasingly sleep deprived between 9 and 11 years is not supported by our data. Total sleep times did not change (F1,83 = 1.35, P = 0.25) in the four semiannual recordings between ages 9.3 and 10.9 years, the same age range when delta power remained stable.
We emphasize these points because our discovery that the rapid delta power decline of adolescence starts between ages 11 and 12 years3 is one of the major findings of our longitudinal study. We hypothesized that the onset of this decline signals the onset of brain adolescence. It is now recognized that the human brain undergoes a profound reorganization during adolescence characterized by massive, parallel declines in cerebral metabolic rate, synaptic density and delta wave amplitude.4 Our longitudinal data show further that this rapid decline slows sharply between 16.5 and 17 years of age.3 We believe that this deceleration signals the termination of brain adolescence, although the delta decline continues at a very slow rate well into adulthood. We have hypothesized that the brain reorganization of adolescence underlies the emergence of adult cognitive power and that errors in this normal maturational process can cause mental illness, notably schizophrenia.5 It should be of particular interest to the readers of this journal that this profoundly important aspect of brain development can be followed noninvasively and inexpensively with sleep EEG recordings.
Recent studies with brain magnetic resonance imaging (MRI) have scanned large numbers of children and adolescents repeatedly over time, as their brains develop, tracking volumetric changes in gray and white matter in remarkable detail. Focusing on gray matter changes specifically, here we explain how earlier studies using lobar volumes of specific anatomical regions showed how different lobes of the brain matured at different rates. With the advent of more sophisticated brain mapping methods, it became possible to chart the dynamic trajectory of cortical maturation using detailed 3D and 4D (dynamic) models, showing spreading waves of changes evolving through the cortex. This led to a variety of time-lapse films revealing characteristic deviations from normal development in schizophrenia, bipolar illness, and even in siblings at genetic risk for these disorders. We describe how these methods have helped clarify how cortical development relates to cognitive performance, functional recovery or decline in illness, and ongoing myelination processes. These time-lapse maps have also been used to study effects of genotype and medication on cortical maturation, presenting a powerful framework to study factors that influence the developing brain.
To study the development of motor speed and associated movements in participants aged 5 to 18 years for age, sex, and laterality.
Ten motor tasks of the Zurich Neuromotor Assessment (repetitive and alternating movements of hands and feet, repetitive and sequential finger movements, the pegboard, static and dynamic balance, diadochokinesis) were administered to 593 right-handed participants (286 males, 307 females).
A strong improvement with age was observed in motor speed from age 5 to 10, followed by a levelling-off between 12 and 18 years. Simple tasks and the pegboard matured early and complex tasks later. Simple tasks showed no associated movements beyond early childhood; in complex tasks associated movements persisted until early adulthood. The two sexes differed only marginally in speed, but markedly in associated movements. A significant laterality (p<0.001) in speed was found for all tasks except for static balance; the pegboard was most lateralized, and sequential finger movements least. Associated movements were lateralized only for a few complex tasks. We also noted a substantial interindividual variability.
Motor speed and associated movements improve strongly in childhood, weakly in adolescence, and are both of developmental relevance. Because they correlate weakly, they provide complementary information.
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.
Density of synaptic profiles in layer 3 of middle frontal gyrus was quantitated in 21 normal human brains ranging from newborn to age 90 years. Synaptic profiles could be reliably demonstrated by the phosphotungstic acid method (Bloom and Aghajanian) in tissue fixed up to 36 h postmortem. Synaptic density was constant throughout adult life (ages 16--72 years) with a mean of 11.05 X 10(8) synapses/cu.mm +/- 0.41 S.E.M. There was a slight decline in synaptic density in brains of the aged (ages 74--90 years) with a mean of 9.56 X 10(8) synapses/cu.mm +/- 0.28 S.E.M. in 4 samples (P less than 0.05). Synaptic density in neonatal brains was already high--in the range seen in adults. However, synaptic morphology differed; immature profiles had an irregular presynaptic dense band instead of the separate presynaptic projections seen in mature synapses. Synaptic density increased during infancy, reaching a maximum at age 1--2 years which was about 50% above the adult mean. The decline in synaptic density observed between ages 2--16 years was accompanied by a slight decrease in neuronal density. Human cerebral cortex is one of a number of neuronal systems in which loss of neurons and synapses appears to occur as a late developmental event.
The purpose of this study was to assess the reliability and validity of a new self-rating scale to measure children's pubertal status without pictorial representations or interviews. The scale is an adaptation of an interview-based puberty-rating scale by Petersen, and included scores for each of five items rating physical development, an overall maturation measure, and a categorical maturation score designed to be similar to Tanner staging categories. Each measure was obtained from independent ratings by students and parents, and a 3-point categorical scale was also obtained from teachers. Subjects included 698 5th- and 6th-grade students (323 boys and 375 girls) from 61 schools and their parents and teachers. Fifth-grade students rated themselves and were rated by parents as less mature than 6th graders; 6th-grade girls were consistently rated more mature than boys of the same age. Significant correlations were found between parents and students for all of the measures for 6th-graders and 5th-grade girls and several measures for 5th-grade boys. This new scale is a useful tool for assessing pubertal status in settings that require noninvasive measures.
Cyclic changes in hormones, body temperature, and metabolic rate characterize the menstrual cycle. To investigate whether these changes are associated with changes in sleep and the sleep electroencephalogram (EEG), a total of 138 sleep episodes from 9 women with no premenstrual syndrome symptoms were recorded every second night throughout one ovulatory menstrual cycle and analyzed in relation to menstrual phase. Ovulation and menstrual cycle stage were confirmed by measurements of temperature, urinary LH, and midluteal plasma levels of estrogen and progesterone. No significant variation across the menstrual cycle was observed for subjective ratings of sleep quality and mood as well as for objective measures of total sleep time, sleep efficiency, sleep latency, rapid eye movement sleep latency, and slow wave sleep. In nonrapid eye movement sleep, EEG power density in the 14.25-15.0 hertz band, which corresponds to the upper frequency range of the sleep spindles, exhibited a large variation across the menstrual cycle, with a maximum in the luteal phase. The data show that in healthy young women, sleep spindle frequency activity varies in parallel with core body temperature, whereas homeostatic sleep regulatory mechanisms, as indexed by the time course of EEG slow wave activity are not substantially affected by the menstrual cycle.
Several transcription factors are expressed at higher levels in the waking than in the sleeping brain. In experiments with
rats, the locus coeruleus, a noradrenergic nucleus with diffuse projections, was found to regulate such expression. In brain
regions depleted of noradrenergic innervation, amounts of c-Fos and nerve growth factor-induced A after waking were as low
as after sleep. Phosphorylation of cyclic adenosine monophosphate response element-binding protein was also reduced. In contrast,
electroencephalographic activity was unchanged. The reduced activity of locus coeruleus neurons may explain why the induction
of certain transcription factors, with potential effects on plasticity and learning, does not occur during sleep.
Recent studies have disclosed several oscillations occurring during resting sleep within the frequency range of the classical delta band (0.5-4 Hz). There are at least 3 oscillations with distinct mechanisms and sites of origin: a slow (<1 Hz) cortically-generated oscillation, a clock-like thalamic oscillation (1-4 Hz), and a cortical oscillation (1-4 Hz). The present paper reviews data on these oscillations and the possible mechanisms which coalesce them into the polymorphic waves of slow wave sleep. Data stem from intracellular (over 500 single cell and 50 double impalements) and field potentials recorded from the cortex and thalamus of cats (120 animals) under ketamine and xylazine anesthesia. Other experiments were based on whole night EEG recordings from humans (5 subjects). The frequency of the slow oscillation both in anesthetized animals and in naturally sleeping humans ranged between 0.1 and 1 Hz (89% of the cases being between 0.5 and 0.9 Hz). The slow (<1 Hz) oscillation is reflected in the EEG as rhythmic sequences of surface-negative waves (associated with hyperpolarizations of deeply-lying neurons) and surface-positive K-complexes (representing excitation in large pools of cortical neurons). Through its long-range synchronization, the slow oscillation has the ability to trigger and to group thalamically-generated spindles and two delta (1-4 Hz) oscillations. Finally, it is argued that the analysis of the electroencephalogram should transcend the spectral analyses, by taking into account the shape of the waves and, when possible, the basic mechanisms that generate those waves.
Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the cortical surface. In order to study such cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the cortical surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the cortical surface are described in a companion paper. These procedures allow for the routine use of cortical surface-based analysis and visualization methods in functional brain imaging.
According to the two-process model of sleep regulation, the timing and structure of sleep are determined by the interaction of a homeostatic and a circadian process. The original qualitative model was elaborated to quantitative versions that included the ultradian dynamics of sleep in relation to the non-REM-REM sleep cycle. The time course of EEG slow-wave activity, the major marker of non-REM sleep homeostasis, as well as daytime alertness were simulated successfully for a considerable number of experimental protocols. They include sleep after partial sleep deprivation and daytime napping, sleep in habitual short and long sleepers, and alertness in a forced desynchrony protocol or during an extended photoperiod. Simulations revealed that internal desynchronization can be obtained for different shapes of the thresholds. New developments include the analysis of the waking EEG to delineate homeostatic and circadian processes, studies of REM sleep homeostasis, and recent evidence for local, use-dependent sleep processes. Moreover, nonlinear interactions between homeostatic and circadian processes were identified. In the past two decades, models have contributed considerably to conceptualizing and analyzing the major processes underlying sleep regulation, and they are likely to play an important role in future advances in the field.
The aim of the study was to investigate whether the electromagnetic field (EMF) emitted by digital radiotelephone handsets affects brain physiology. Healthy, young male subjects were exposed for 30 min to EMF (900 MHz; spatial peak specific absorption rate 1 W/kg) during the waking period preceding sleep. Compared with the control condition with sham exposure, spectral power of the EEG in non-rapid eye movement sleep was increased. The maximum rise occurred in the 9.75-11.25 Hz and 12.5-13.25 Hz band during the initial part of sleep. These changes correspond to those obtained in a previous study where EMF was intermittently applied during sleep. Unilateral exposure induced no hemispheric asymmetry of EEG power. The present results demonstrate that exposure during waking modifies the EEG during subsequent sleep. Thus the changes of brain function induced by pulsed high-frequency EMF outlast the exposure period.
The sleep EEG of healthy young men was recorded during baseline and recovery sleep after 40 h of waking. To analyse the EEG topography, power spectra were computed from 27 derivations. Mean power maps of the nonREM sleep EEG were calculated for 1-Hz bins between 1.0 and 24.75 Hz. Cluster analysis revealed a topographic segregation into distinct frequency bands which were similar for baseline and recovery sleep, and corresponded closely to the traditional frequency bands. Hallmarks of the power maps were the frontal predominance in the delta and alpha band, the occipital predominance in the theta band, and the sharply delineated vertex maximum in the sigma band. The effect of sleep deprivation on EEG topography was determined by calculating the recovery/baseline ratio of the power spectra. Prolonged waking induced an increase in power in the low-frequency range (1-10.75 Hz) which was largest over the frontal region, and a decrease in power in the sigma band (13-15.75 Hz) which was most pronounced over the vertex. The topographic pattern of the recovery/baseline power ratio was similar to the power ratio between the first and second half of the baseline night. These results indicate that changes in sleep propensity are reflected by specific regional differences in EEG power. The predominant increase of low-frequency power in frontal areas may be due to a high 'recovery need' of the frontal heteromodal association areas of the cortex.
The sleep EEG of eight healthy young men was recorded from 27 derivations during a baseline night and a recovery night after 40 h of waking. Individual power maps of the nonREM sleep EEG were calculated for the delta, theta, alpha, sigma and beta range. The comparison of the normalized individual maps for baseline and recovery sleep revealed very similar individual patterns within each frequency band. This high correspondence was quantified and statistically confirmed by calculating the Manhattan distance between all pairs of maps within and between individuals. Although prolonged waking enhanced power in the low-frequency range (0.75-10.5 Hz) and reduced power in the high-frequency range (13.25-25 Hz), only minor effects on the individual topography were observed. Nevertheless, statistical analysis revealed frequency-specific regional effects of sleep deprivation. The results demonstrate that the pattern of the EEG power distribution in nonREM sleep is characteristic for an individual and may reflect individual traits of functional anatomy.