Thomas Rusterholz

The Functional Val158Met Polymorphism of COMT Predicts Interindividual Differences in Brain α Oscillations in Young Men

Individual patterns of the electroencephalogram (EEG) in wakefulness and sleep are among the most heritable traits in humans, yet distinct genetic and neurochemical mechanisms underlying EEG phenotypes are largely unknown. A functional polymorphism in the gene encoding catechol-O-methyltransferase (COMT), an enzyme playing an important role in cortical dopamine metabolism, causes a common substitution of methionine (Met) for valine (Val) at codon 158 of COMT protein. Val allele homozygotes exhibit higher COMT activity and lower dopaminergic signaling in prefrontal cortex than Met/Met homozygotes. Evidence suggests that this polymorphism affects executive functions in healthy individuals. We hypothesized that it also modulates functional aspects of EEG in wakefulness and sleep. EEG recordings were conducted twice on separate occasions in 10 Val/Val and 12 Met/Met allele carriers (all men) in wakefulness, and in baseline and recovery sleep before and after 40 h prolonged waking. During sleep deprivation, subjects received placebo and modafinil in randomized, cross-over manner. We show that the Val158Met polymorphism predicts stable and frequency-specific, interindividual variation in brain α oscillations. α peak frequency in wakefulness was 1.4 Hz slower in Val/Val genotype than in Met/Met genotype. Moreover, Val/Val allele carriers exhibited less 11–13 Hz activity than Met/Met homozygotes in wakefulness, rapid-eye-movement (REM) sleep, and non-REM sleep. This difference was resistant against the effects of sleep deprivation and modafinil. The data demonstrate that mechanisms involving COMT contribute to interindividual differences in brain α oscillations, which are functionally related to executive performance such as counting tendency on a random number generation task in young adults.

Sleep EEG alterations: effects of different pulse‐modulated radio frequency electromagnetic fields

Previous studies have observed increases in electroencephalographic power during sleep in the spindle frequency range (approximately 11–15 Hz) after exposure to mobile phone‐like radio frequency electromagnetic fields (RF EMF). Results also suggest that pulse modulation of the signal is crucial to induce these effects. Nevertheless, it remains unclear which specific elements of the field are responsible for the observed changes. We investigated whether pulse‐modulation frequency components in the range of sleep spindles may be involved in mediating these effects. Thirty young healthy men were exposed, at weekly intervals, to three different conditions for 30 min directly prior to an 8‐h sleep period. Exposure consisted of a 900‐MHz RF EMF, pulse modulated at 14 Hz or 217 Hz, and a sham control condition. Both active conditions had a peak spatial specific absorption rate of 2 W kg−1. During exposure subjects performed three different cognitive tasks (measuring attention, reaction speed and working memory), which were presented in a fixed order. Electroencephalographic power in the spindle frequency range was increased during non‐rapid eye movement sleep (2nd episode) following the 14‐Hz pulse‐modulated condition. A similar but non‐significant increase was also observed following the 217‐Hz pulse‐modulated condition. Importantly, this exposure‐induced effect showed considerable individual variability. Regarding cognitive performance, no clear exposure‐related effects were seen. Consistent with previous findings, our results provide further evidence that pulse‐modulated RF EMF alter brain physiology, although the time‐course of the effect remains variable across studies. Additionally, we demonstrated that modulation frequency components within a physiological range may be sufficient to induce these effects.

Development of Brain EEG Connectivity across Early Childhood: Does Sleep Play a Role?

Sleep has beneficial effects on brain function and learning, which are reflected in plastic changes in the cortex. Early childhood is a time of rapid maturation in fundamental skills—e.g., language, cognitive control, working memory—that are predictive of future functioning. Little is currently known about the interactions between sleep and brain maturation during this developmental period. We propose coherent electroencephalogram (EEG) activity during sleep may provide unique insight into maturational processes of functional brain connectivity. Longitudinal sleep EEG assessments were performed in eight healthy subjects at ages 2, 3 and 5 years. Sleep EEG coherence increased across development in a region- and frequency-specific manner. Moreover, although connectivity primarily decreased intra-hemispherically across a night of sleep, an inter-hemispheric overnight increase occurred in the frequency range of slow waves (0.8–2 Hz), theta (4.8–7.8 Hz) and sleep spindles (10–14 Hz), with connectivity changes of up to 20% across a night of sleep. These findings indicate sleep EEG coherence reflects processes of brain maturation—i.e., programmed unfolding of neuronal networks—and moreover, sleep-related alterations of brain connectivity during the sensitive maturational window of early childhood.

Topographical aspects in the dynamics of sleep homeostasis in young men: individual patterns.

BackgroundSleep homeostasis refers to the increase of sleep pressure during waking and the decrease of sleep intensity during sleep. Electroencephalography (EEG) slow-wave activity (SWA; EEG power in the 0.75-4.5 Hz range) is a marker of non-rapid eye movement (NREM) sleep intensity and can be used to model sleep homeostasis (Process S). SWA shows a frontal predominance, and its increase after sleep deprivation is most pronounced in frontal areas. The question arises whether the dynamics of the homeostatic Process S also show regional specificity. Furthermore, the spatial distribution of SWA is characteristic for an individual and may reflect traits of functional anatomy. The aim of the current study was to quantify inter-individual variation in the parameters of Process S and investigate their spatial distribution. Polysomnographic recordings obtained with 27 EEG derivations of a baseline night of sleep and a recovery night of sleep after 40 h of sustained wakefulness were analyzed. Eight healthy young subjects participated in this study. Process S was modeled by a saturating exponential function during wakefulness and an exponential decline during sleep. Empirical mean SWA per NREM sleep episode at episode midpoint served for parameter estimation at each derivation. Time constants were restricted to a physiologically meaningful range.ResultsFor both, the buildup and decline of Process S, significant topographic differences were observed: The decline and buildup of Process S were slowest in fronto-central areas while the fastest dynamics were observed in parieto-occipital (decrease) and frontal (buildup) areas. Each individual showed distinct spatial patterns in the parameters of Process S and the parameters differed significantly between individuals.ConclusionsFor the first time, topographical aspects of the buildup of Process S were quantified. Our data provide an additional indication of regional differences in sleep homeostasis and support the notion of local aspects of sleep regulation.

The spectrum of the non-rapid eye movement sleep electroencephalogram following total sleep deprivation is trait-like

The sleep electroencephalogram (EEG) spectrum is unique to an individual and stable across multiple baseline recordings. The aim of this study was to examine whether the sleep EEG spectrum exhibits the same stable characteristics after acute total sleep deprivation. Polysomnography (PSG) was recorded in 20 healthy adults across consecutive sleep periods. Three nights of baseline sleep [12 h time in bed (TIB)] following 12 h of wakefulness were interleaved with three nights of recovery sleep (12 h TIB) following 36 h of sustained wakefulness. Spectral analysis of the non‐rapid eye movement (NREM) sleep EEG (C3LM derivation) was used to calculate power in 0.25 Hz frequency bins between 0.75 and 16.0 Hz. Intraclass correlation coefficients (ICCs) were calculated to assess stable individual differences for baseline and recovery night spectra separately and combined. ICCs were high across all frequencies for baseline and recovery and for baseline and recovery combined. These results show that the spectrum of the NREM sleep EEG is substantially different among individuals, highly stable within individuals and robust to an experimental challenge (i.e. sleep deprivation) known to have considerable impact on the NREM sleep EEG. These findings indicate that the NREM sleep EEG represents a trait.

Global field synchronization reveals rapid eye movement sleep as most synchronized brain state in the human EEG.

Sleep is characterized by a loss of consciousness, which has been attributed to a breakdown of functional connectivity between brain regions. Global field synchronization (GFS) can estimate functional connectivity of brain processes. GFS is a frequency-dependent measure of global synchronicity of multi-channel EEG data. Our aim was to explore and extend the hypothesis of disconnection during sleep by comparing GFS spectra of different vigilance states. The analysis was performed on eight healthy adult male subjects. EEG was recorded during a baseline night, a recovery night after 40 h of sustained wakefulness and at 3 h intervals during the 40 h of wakefulness. Compared to non-rapid eye movement (NREM) sleep, REM sleep showed larger GFS values in all frequencies except in the spindle and theta bands, where NREM sleep showed a peak in GFS. Sleep deprivation did not affect GFS spectra in REM and NREM sleep. Waking GFS values were lower compared with REM and NREM sleep except for the alpha band. Waking alpha GFS decreased following sleep deprivation in the eyes closed condition only. Our surprising finding of higher synchrony during REM sleep challenges the view of REM sleep as a desynchronized brain state and may provide insight into the function of REM sleep.

Relation of Heart Rate and its Variability during Sleep with Age, Physical Activity, and Body Composition in Young Children

Background: Recent studies have claimed a positive effect of physical activity and body composition on vagal tone. In pediatric populations, there is a pronounced decrease in heart rate with age. While this decrease is often interpreted as an age-related increase in vagal tone, there is some evidence that it may be related to a decrease in intrinsic heart rate. This factor has not been taken into account in most previous studies. The aim of the present study was to assess the association between physical activity and/or body composition and heart rate variability (HRV) independently of the decline in heart rate in young children. Methods: Anthropometric measurements were taken in 309 children aged 2–6 years. Ambulatory electrocardiograms were collected over 14–18 h comprising a full night and accelerometry over 7 days. HRV was determined of three different night segments: (1) over 5 min during deep sleep identified automatically based on HRV characteristics; (2) during a 20 min segment starting 15 min after sleep onset; (3) over a 4-h segment between midnight and 4 a.m. Linear models were computed for HRV parameters with anthropometric and physical activity variables adjusted for heart rate and other confounding variables (e.g., age for physical activity models). Results: We found a decline in heart rate with increasing physical activity and decreasing skinfold thickness. HRV parameters decreased with increasing age, height, and weight in HR-adjusted regression models. These relationships were only found in segments of deep sleep detected automatically based on HRV or manually 15 min after sleep onset, but not in the 4-h segment with random sleep phases. Conclusions: Contrary to most previous studies, we found no increase of standard HRV parameters with age, however, when adjusted for heart rate, there was a significant decrease of HRV parameters with increasing age. Without knowing intrinsic heart rate correct interpretation of HRV in growing children is impossible.

Interindividual differences in the dynamics of the homeostatic process are trait-like and distinct for sleep versus wakefulness.

The sleep homeostatic Process S reflects the build‐up of sleep pressure during waking and its dissipation during sleep. Process S is modelled as a saturating exponential function during waking and a decreasing exponential function during sleep. Slow wave activity is a physiological marker for non‐rapid eye movement (non‐REM) sleep intensity and serves as an index of Process S. There is considerable interindividual variability in the sleep homeostatic responses to sleep and sleep deprivation. The aim of this study was to investigate whether interindividual differences in Process S are trait‐like. Polysomnographic recordings of 8 nights (12‐h sleep opportunities, 22:00–10:00 hours) interspersed with three 36‐h periods of sustained wakefulness were performed in 11 healthy young adults. Empirical mean slow wave activity per non‐REM sleep episode at episode mid‐points were used for parameter estimation. Parameters of Process S were estimated using different combinations of consecutive sleep recordings, resulting in two to three sets of parameters per subject. Intraclass correlation coefficients were calculated to assess whether the parameters were stable across the study protocol and they showed trait‐like variability among individuals. We found that the group‐average time constants of the build‐up and dissipation of Process S were 19.2 and 2.7 h, respectively. Intraclass correlation coefficients ranged from 0.48 to 0.56, which reflects moderate trait variability. The time constants of the build‐up and dissipation varied independently among subjects, indicating two distinct traits. We conclude that interindividual differences in the parameters of the dynamics of the sleep homeostatic Process S are trait‐like.

Sleep physiology in toddlers: Effects of missing a nap on subsequent night sleep

The shift from a biphasic to a monophasic sleep schedule is a fundamental milestone in early childhood. This transition, however, may result in periods of acute sleep loss as children may nap on some but not all days. Although data indicating the behavioral consequences of nap deprivation in young children are accumulating, little is known about changes to sleep neurophysiology following daytime sleep loss. This study addresses this gap in knowledge by examining the effects of acute nap deprivation on subsequent nighttime sleep electroencephalographic (EEG) parameters in toddlers. Healthy children (n=25; 11 males; ages 30–36 months) followed a strict sleep schedule for ≥5 days before sleep EEG recordings performed on 2 non-consecutive days: one after 13 h of prior wakefulness and another at the same clock time but preceded by a daytime nap. Total slow-wave energy (SWE) was computed as cumulative slow-wave activity (SWA; EEG power in 0.75–4.5 Hz range) over time. Nap and subsequent night SWE were added and compared to SWE of the night after a missed nap. During the night following a missed nap, children fell asleep faster (11.9±8.7 min versus 37.3±22.1 min; d=1.6, p=0.01), slept longer (10.1±0.7 h versus 9.6±0.6 h; d=0.7, p<0.01) and exhibited greater SWA (133.3±37.5% versus 93.0±4.7%; d=0.9, p<0.01) compared to a night after a daytime nap. SWE for combined nap and subsequent night sleep did not significantly differ from the night following nap deprivation (12141.1±3872.9 μV²*h versus 11,588±3270.8 μV²*h; d=0.6, p=0.12). However, compared to a night following a missed nap, children experienced greater time in bed (13.0±0.8 h versus 10.9±0.5 h; d=3.1, p<0.01) and total sleep time (11.2±0.8 h versus 10.1±0.7 h; d=1.4, p<0.01). Shorter sleep latency, longer sleep duration, and increased SWA in the night following a missed nap indicate that toddlers experience a physiologically meaningful homeostatic challenge after prolonged wakefulness. Whether toddlers fully recover from missing a daytime nap in the subsequent night necessitates further examination of daytime functioning.

Three decades of continuous wrist‐activity recording: analysis of sleep duration

Motor activity recording by a wrist‐worn device is a common method to monitor the rest–activity cycle. The first author wore an actimeter continuously for more than three decades, starting in 1982 at the age of 43.5 years. Until November 2006 analysis was performed on a 15‐min time base, and subsequently on a 2‐min time base. The timing of night‐time sleep was determined from the cessation and re‐occurrence of daytime‐level activity. Sleep duration declined from an initial 6.8 to 6 h in 2004. The declining trend was reversed upon retirement, whereas the variance of sleep duration declined throughout the recording period. Before retirement, a dominant 7‐day rhythm of sleep duration as well as an annual periodicity was revealed by spectral analysis. These variations were attenuated or vanished during the years after retirement. We demonstrate the feasibility of continuous long‐term motor activity recordings to study age‐related variations of the rest–activity cycle. Here we show that the embeddedness in a professional environment imparts a temporal structure to sleep duration.