Anja Geiger

Mapping of Cortical Activity in the First Two Decades of Life: A High-Density Sleep Electroencephalogram Study

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.

EEG Sleep Slow-Wave Activity as a Mirror of 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.

Assessment of Chronotype in Four- to Eleven-Year-Old Children: Reliability and Validity of the Children's ChronoType Questionnaire (CCTQ)

Individual differences in circadian phase preference (“chronotype”) are linked to sleep schedule variability, psychosocial functioning, and specific properties of the circadian clock. While much is known about the development, distribution, and variability of chronotype in adolescents and adults, assessment in prepubertal children has been hindered by a lack of appropriate, reliable, and valid measures. This study presents a detailed description of the assessment of childrens chronotype by the Childrens ChronoType Questionnaire (CCTQ). The CCTQ is a parent-report, 27-item mixed-format questionnaire resulting in multiple measures of chronotype in 4- to 11-yr-old children: the midsleep point on free days (MSF), a morningness/eveningness scale (M/E) score, and a five-point chronotype (CT) score. The study provides validity data using actigraphy as well as test-retest reliability data for all three chronotype measures and sleep/wake parameters. Overall, the findings indicate moderate to strong agreement between the three measures, adequate associations between chronotype measures and sleep/wake parameters assessed by actigraphy, and excellent temporal stability (reliability).

Association between Sleep Duration and Intelligence Scores in Healthy Children.

We examined the association between sleep behavior and cognitive functioning in 60 healthy children between 7 and 11 years of age under nonexperimental conditions. Intellectual abilities were assessed by the Wechsler Intelligence Scale for Children (4th edition) and sleep variables by questionnaires, actigraphy, and sleep diaries. Correlation analysis revealed a negative association between sleep duration on weekends and measures of intelligence (full-scale IQ, r = -.29; fluid IQ, r = -.36). The regression coefficient for sleep duration on weekends was -6.11 (SE = 2.09), indicating an increase of 6.11 points on fluid IQ scores for each hour of shorter sleep duration. Attention measures did not correlate with cognitive or sleep variables. Daytime sleepiness as a potential moderator of the relationship between sleep duration and cognitive performance was not related to cognitive or sleep variables. We conclude that children with higher daytime cognitive efficiency (reflected by higher intelligence scores) show increased nighttime efficiency (reflected by shorter sleep duration). In the light of the neural efficiency hypothesis, the current results argue for an extension of the original theory-referring not only to daytime but also to nighttime behavior.

Mapping the electrophysiological marker of sleep depth reveals skill maturation in children and adolescents.

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.

Sleep Slow-Wave Activity Reveals Developmental Changes in Experience-Dependent Plasticity

Experience-dependent plasticity, the ability of the brain to constantly adapt to an ever-changing environment, has been suggested to be highest during childhood and to decline thereafter. However, empirical evidence for this is rather scarce. Slow-wave activity (SWA; EEG activity of 1–4.5 Hz) during deep sleep can be used as a marker of experience-dependent plasticity. For example, performing a visuomotor adaptation task in adults increased SWA during subsequent sleep over a locally restricted region of the right parietal cortex, which is known to be involved in visuomotor adaptation. Here, we investigated whether local experience-dependent changes in SWA vary as a function of brain maturation. Three age groups (children, adolescents, and adults) participated in a high-density EEG study with two conditions (baseline and adaptation) of a visuomotor learning task. Compared with the baseline condition, sleep SWA was increased after visuomotor adaptation in a cluster of eight electrodes over the right parietal cortex. The local boost in SWA was highest in children. Baseline SWA in the parietal cluster and right parietal gray matter volume, which both indicate region-specific maturation, were significantly correlated with the local increase in SWA. Our findings indicate that processes of brain maturation favor experience-dependent plasticity and determine how sensitive a specific brain region is for learning experiences. Moreover, our data confirm that SWA is a highly sensitive tool to map maturational differences in experience-dependent plasticity.

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

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.

Sleep, intelligence and cognition in a developmental context: differentiation between traits and state-dependent aspects

This article addresses associations between sleep, cognition and intelligence in a developmental context and clarifies the terminology. Research must differentiate between aspects related to general underlying traits and those aspects that are characterized by state-dependent fluctuations.

Sleep electroencephalography topography and children's intellectual ability.

The aim of the study was to investigate the relationship between regional aspects of the children’s sleep electroencephalogram (EEG) (high-density EEG recordings) and their intellectual ability. The spectral power in the &agr;, &sgr;, and &bgr; frequency ranges of 109 EEG derivations was correlated with the scores of full-scale intelligence quotient, fluid intelligence quotient, and working memory (14 participants, mean age: 10.5±1.0 years; six girls). The previously reported relationship (derivation C3/A2) between spectral band power and intellectual ability could further be refined, particular spatial patterns over central and parietal areas with positive correlations were found. Thus, neurobiological correlates of intelligence during sleep may exhibit brain region-specific patterns.