17 research outputs found

    EEG Sleep Slow-Wave Activity as a Mirror of Cortical Maturation

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    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 adolescenc

    The sleep EEG topography in children and adolescents shows sex differences in language areas

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    The topographic distribution of slow wave activity (SWA, EEG power between 0.75 and 4.5 Hz) during non-rapid eye movement (NREM) sleep was proposed to mirror cortical maturation with a typical age-related pattern. Here, we examined whether sex differences occur in SWA topography of children and adolescents (22 age-matched subjects, 11 boys, mean age 13.4 years, range: 8.7-19.4, and 11 girls, mean age 13.4 years, range: 9.1-19.0 years). In females, SWA during the first 60 min of NREM sleep was higher over bilateral cortical areas that are related to language functions, while in males SWA was increased over the right prefrontal cortex, a region also involved in spatial abilities. We conclude that cortical areas governing functions in which one sex outperforms the other exhibit increased sleep SWA and, thus, may indicate maturation of sex-specific brain function and higher cortical plasticity during development

    SWA trajectory across age in sham and caffeine treated animals.

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    <p>(<b>A</b>) Sample ECoG traces of a sham and caffeine treated animal on P30 and P38, respectively. (<b>B</b>) Trajectory of sleep slow wave activity (ECoG power between 1 and 4 Hz, averaged over the first 3 hours after light onset) between postnatal day 25 (P25) and P45 for sham (n = 17) and caffeine (n = 11) treated animals. The grey shaded background illustrates the period of caffeine administration. A two-way repeated measures ANOVA with factor age (P25–P45) and condition (caffeine and sham) was significant for age and condition (p<0.05). Crosses indicate increased SWA in caffeine compared to sham treated animals (black, p<0.05, gray, p<0.08), unpaired Student's t-test).</p

    Vigilance states across age.

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    <p>Wakefulness and non rapid eye movement (NREM) sleep in sham (sh, n = 15) and caffeine (caf, n = 11) treated animals, expressed as a percentage of 12 hours recording time (rec. time) for the light and dark period before (P29), during (P31) and after (P38) caffeine treatment. A two-way repeated measures ANOVA with factor age (P29, P31 and P38) and condition (caffeine and sham) performed for NREM sleep and wakefulness during the light period was significant for age. The same analyses for NREM sleep and wakefulness during the dark period revealed an effect of age and an interaction between condition and age (all, p<0.05). The group comparison during caffeine application (P31) showed increased wakefulness and decreased NREM sleep during the dark period, respectively (#p<0.05, unpaired Student's t-test).</p

    Caffeine reduces the build up of slow wave energy (SWE) during early caffeine treatment.

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    <p>Filled circles represent accumulated SWE (see Materials and Methods for details) in 3 hour intervals during the period when caffeine was initiated on P30 until the end of the second day of treatment (P31). The period of caffeine administration is illustrated by the grey shaded background. The white and black bars at the top of each graph indicate the 12 hour light and the 12 hour dark period, respectively. Crosses indicate reduced SWE in caffeine (n = 11) compared to sham (n = 17) treated animals (p<0.05, unpaired t-test). Error bars indicate SEM.</p

    Structural changes across age.

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    <p>(<b>A</b>) Representative example image of a coronal section stained for vesicular acetylcholine transporter protein (VAChT), a specific marker for cholinergic presynaptic terminals. The dotted black box indicates the location of the randomly selected images for further analyses. One example, indicated by the solid black box is enlarged for postnatal day 30 (P30, right image). Below representative images for P42 after either sham or caffeine treatment are shown. (<b>B</b>) Reduction of the VAChT stained area, assumed to reflect cholinergic presynaptic terminals from P30 to P42 (n = 6 per group, *p<0.05, Mann-Whitney U-test). Caffeine treated animals show a diminished reduction of presynaptic cholinergic terminals at P42.</p

    Behavioral changes across age.

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    <p>(<b>A, B, C</b>) The amount of grooming, quiet waking and exploration expressed as a percentage of total behavior are shown for P28 and P42. Significant changes across age are illustrated by an asterisk (p<0.05, paired Student's t-test). (<b>D</b>) The increase in object exploration time from P28 to P42 was reduced in caffeine (n = 9) compared to sham (n = 8) treated animals (#p<0.05, Mann-Whitney U-test).</p

    Anatomical markers of sleep slow wave activity derived from structural magnetic resonance images

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    Sleep studies often observe differences in slow wave activity (SWA) during non-rapid eye movement sleep between subjects. This study investigates to what extent these absolute differences in SWA can be explained with differences in grey matter volume, white matter volume or the thickness of skull and outer liquor rooms. To do this, we selected the 10-min interval showing maximal SWA of 20 young adult subjects and correlated these values lobe-wise with grey matter, skull and liquor thickness and globally with white matter as well as segments of the corpus callosum. Whereas grey matter, skull thickness and liquor did not correlate significantly with maximal slow wave activity, there were significant correlations with the anterior parts of the corpus callosum and with one other white matter region. In contrast, electroencephalogram power of higher frequencies correlates positively with grey matter volumes and cortical surface area. We discuss the possible role of white matter tracts on the synchronization of slow waves across the cortex

    An infant sleep electroencephalographic marker of thalamocortical connectivity predicts behavioral outcome in late infancy

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    Infancy represents a critical period during which thalamocortical brain connections develop and mature. Deviations in the maturation of thalamocortical connectivity are linked to neurodevelopmental disorders. There is a lack of early biomarkers to detect and localize neuromaturational deviations, which can be overcome with mapping through high-density electroencephalography (hdEEG) assessed in sleep. Specifically, slow waves and spindles in non-rapid eye movement (NREM) sleep are generated by the thalamocortical system, and their characteristics, slow wave slope and spindle density, are closely related to neuroplasticity and learning. Spindles are often subdivided into slow (11.0-13.0 Hz) and fast (13.5-16.0 Hz) frequencies, for which not only different functions have been proposed, but for which also distinctive developmental trajectories have been reported across the first years of life. Recent studies further suggest that information processing during sleep underlying sleep-dependent learning is promoted by the temporal coupling of slow waves and spindles, yet slow wave-spindle coupling remains unexplored in infancy. Thus, we evaluated three potential biomarkers: 1) slow wave slope, 2) spindle density, and 3) the temporal coupling of slow waves with spindles. We use hdEEG to first examine the occurrence and spatial distribution of these three EEG features in healthy infants and second to evaluate a predictive relationship with later behavioral outcomes. We report four key findings: First, infants’ EEG features appear locally: slow wave slope is maximal in occipital and frontal areas, whereas slow and fast spindle density is most pronounced frontocentrally. Second, slow waves and spindles are temporally coupled in infancy, with maximal coupling strength in the occipital areas of the brain. Third, slow wave slope, fast spindle density, and slow wave-spindle coupling are not associated with concurrent behavioral status (6 months). Fourth, fast spindle density in central and frontocentral regions at age 6 months predicts overall developmental status at age 12 months, and motor skills at age 12 and 24 months. Neither slow wave slope nor slow wave-spindle coupling predict later behavioral development. We further identified spindle frequency as a determinant of slow and fast spindle density, which accordingly, also predicts motor skills at 24 months. Our results propose fast spindle density, or alternatively spindle frequency, as early EEG biomarker for identifying thalamocortical maturation, which can potentially be used for early diagnosis of neurodevelopmental disorders in infants. These findings are in support of a role of sleep spindles in sensorimotor microcircuitry development. A crucial next step will be to evaluate whether early therapeutic interventions may be effective to reverse deviations in identified individuals at risk.ISSN:1053-8119ISSN:1095-957
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