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Biomedical subjects

P Achermann

Publications and source records attributed to P Achermann.

At least 55 records · Page 3Linked to original sources
PubMed1997

Spindle frequency activity in the sleep EEG: individual differences and topographic distribution.

The brain topography of EEG power spectra in the frequency range of sleep spindles was investigated in 34 sleep recordings from 20 healthy young men. Referential (F3-A2, C3-A2, P3-A2 and O1-A2) and bipolar derivations (F3-C3, C3-P3 and P3-O1) along the anteroposterior axis were used. Sleep spindles gave rise to a distinct peak in the EEG power spectrum. The distribution of the peak frequencies pooled over subjects and derivations showed a bimodal pattern with modes at 11.5 and 13.0 Hz, and a trough at 12.25 Hz. The large inter-subject variation in peak frequency (range: 1.25 Hz) contrasted with the small intra-subject variation between derivations, non-REM sleep episodes and different nights. In some individuals and/or some derivations, only a single spindle peak was present. The topographic distributions from referential and bipolar recordings showed differences. The power showed a declining trend over consecutive non-REM sleep episodes in the low range of spindle frequency activity and a rising trend in the high range. The functional and topographic heterogeneity of sleep spindles in conjunction with the intra-subject stability of their frequency are important characteristics for the analysis of sleep regulation on the basis of the EEG.

Adult↗
PubMed1997

Low-frequency (< 1 Hz) oscillations in the human sleep electroencephalogram.

Low-frequency (< 1 Hz) oscillations in intracellular recordings from cortical neurons were first reported in the anaesthetized cat and then also during natural sleep. The slow sequences of hyperpolarization and depolarization were reflected by slow oscillations in the electroencephalogram. The aim of the present study was to examine whether comparable low-frequency components are present in the human sleep electroencephalogram. All-night sleep recordings from eight healthy young men were subjected to spectral analysis in which the low-frequency attenuation of the amplifier was compensated. During sleep stages with a predominance of slow waves and in the first two episodes of non-rapid-eye-movement sleep, the mean power spectrum showed a peak at 0.7-0.8 Hz (range 0.55-0.95 Hz). The typical decline in delta activity from the first to the second non-rapid-eye-movement sleep episode was not present at frequencies below 2 Hz. To detect very low frequency components in the pattern of slow waves and sleep spindles, a new time series was computed from the mean voltage of successive 0.5 s epochs of the low-pass (< 4.5 Hz) or band-pass (12-15 Hz) filtered electroencephalogram. Spectral analysis revealed a periodicity of 20-30 s in the prevalence of slow waves and a periodicity of 4 s in the occurrence of activity in the spindle frequency range. The results demonstrate that distinct components below 1 Hz are also present in the human sleep electroencephalogram spectrum. The differences in the dynamics between the component with a mean peak value at 0.7-0.8 Hz and delta waves above 2 Hz is in accordance with results from animal experiments.

Adult↗
PubMed1997

Fronto-occipital EEG power gradients in human sleep.

The brain topography of power spectra along the antero-posterior (A-P) axis was studied in the all-night human sleep EEG. Spectra (0.25 - 25.0 Hz) were computed for an anterior (A; F3-C3), a middle (M; C3-P3) and a posterior (P; P3-O1) bipolar derivation, and the spectral gradients between two adjacent derivations were expressed by power ratios (A/M and M/P). At NREM-REM sleep transitions a power shift from A to M was present over almost the entire frequency range, while the direction of shifts between M and P differed between frequency bands. Within NREM sleep, frequency specific power gradients were present: In the low delta band power in both A (0.25 Hz bin) and P (0.25-1.0 Hz bins) was higher than in M. In the 4-9 Hz range the relation was A > M > P, and in the 15 - 25 Hz range power was largest in M. Power in the spindle frequency range was highest at 11.75 Hz in M, and at 13.5 - 13.75 Hz in A. Topographical differences were seen also in the temporal changes of power across and within NREM sleep episodes. Whereas NREM sleep power in the 2-Hz bin was higher in A than in M in the first episode, this difference vanished in the course of the night. This result points to a specific involvement of frontal parts of the cortex in sleep homeostasis. The regional differences in sleep EEG spectra indicate that sleep is not only a global phenomenon but also a local brain process with a different regional involvement of neuronal populations.

Adult↗
PubMed1996

Brain topography of the human sleep EEG: antero-posterior shifts of spectral power.

To investigate the brain topography of the human sleep EEG along the antero-posterior axis, spectra (0.25-25 Hz; 1 Hz bins) were computed from all-night EEG recordings (n = 20 subjects) obtained from an anterior F3-C3) and a posterior (P3-O1) derivation. State-dependent and frequency-dependent topographic differences were observed. In non-rapid eye movement (REM) sleep, power in the anterior derivation was higher than in the posterior derivation in the 2 Hz bin, and lower in the 4-10 Hz bins. In REM sleep, a posterior dominance was present in most bins below 18 Hz. The 2-6 Hz bins exhibited an antero-posterior shift of power over consecutive non-REM sleep episodes. Consistent shifts of power were also present within non-REM sleep episodes. The results suggest that anterior and posterior cortical regions may be differently involved in the sleep process.

Adult↗
PubMed1996

Effect of age on the sleep EEG: slow-wave activity and spindle frequency activity in young and middle-aged men.

The effect of age on sleep and the sleep EEG was investigated in middle-aged men (mean age: 62.0 years) and in young men (mean age: 22.4 years). Even though the older men reported a higher number of nocturnal awakenings, subjective sleep quality did not differ. Total sleep time, sleep efficiency, and slow wave sleep were lower in the middle-aged, while stage 1 and wakefulness after sleep onset were higher. The differences in wakefulness within nonREM-REM sleep cycles was most pronounced in the third and fourth cycle. In the older men, EEG power density in nonREM sleep was reduced in frequencies below 14.0 Hz, whereas in REM sleep age-related reductions were limited to he delta-theta (0.25-7.0 Hz) and low alpha (8.25-10.0 Hz) band. Slow-wave activity (SWA, power density in the 0.75-4.5 Hz range) decreased in the course of sleep in both age groups. The between-group difference in SWA diminished in the course of sleep, whereas the difference in activity in the frequency range of sleep spindles (12.25-14.0 Hz) increased. It is concluded that frequency and state specific changes occur as a function of age, and that sleep dependent decline in SWA and increase in sleep spindle activity are attenuated with age.

Adult↗
PubMed1996

Altered circadian activity rhythms and sleep in mice devoid of prion protein.

There is a wealth of data supporting a central role for the prion protein (PrP) in the neurodegenerative prion diseases of both humans and other species, yet the normal function of PrP, which is expressed at the cell surface of neurons and glial cells, is unknown. It has been speculated that neuropathology may be due to loss of normal function of PrP. Here we show that in mice devoid of PrP there is an alteration in both circadian activity rhythms and patterns. To our knowledge, this is the first null mutation that has been shown to affect sleep regulation and our results indicate that at least one of the inherited prion diseases, fatal familial insomnia, where there is a profound alteration in sleep and the daily rhythms of many hormones, may be related to the normal function of the prion protein.

Animals↗
PubMed1996

Simulations of circadian system and vigilance during space missions.

The effects of various space scenarios on the circadian system and alertness were investigated. The study of the circadian system under various conditions is important because core body temperature and many other physiological parameters (e.g., hormone levels) exhibit prominent circadian variation. The circadian system has a direct influence on the sleep-wake behavior and thus on performance. Alertness is an easily accessible subjective parameter which is strongly linked to cognitive performance. Simulation of alertness for different sleep-wake schedules may help to delineate crucial phases during missions and to plan counteractions. Although the level of alertness can be accounted for by the combination of circadian and homeostatic processes, additional factors such as motivation or excitement play a role.

Arousal↗
PubMed1996

Dynamics of the sleep EEG after an early evening nap: experimental data and simulations.

Increasing sleep pressure is associated with highly predictable changes in the dynamics of the sleep electroencephalogram (EEG). To investigate whether the effects of reduced sleep pressure also can be accounted for by homeostatic mechanisms, nighttime sleep following an evening nap was recorded in healthy young men. In comparison with the baseline night, sleep latency in the postnap night was prolonged, rapid eye movement sleep (REMS) latency was reduced, and EEG power density in non-REMS was decreased in the delta and theta band. The buildup of both EEG slow-wave activity (SWA; power density in the 0.75-to 4.5-Hz range) and spindle frequency activity (SFA; power density in the 12.25-to 15.0-Hz range) in non-REMS episodes was diminished (SWA: episodes 1-3; SFA: episode 1). The typical declining trend of SWA over consecutive non-REM sleep episodes was attenuated. The time course of SWA could be closely simulated with a homeostatic model of sleep regulation, although some discrepancies in level and buildup of SWA were apparent. We conclude that homeostatic mechanisms can largely account for the dynamics of the sleep EEG under conditions of reduced sleep pressure.

Adult↗
PubMed1995

Melatonin effect on daytime sleep in men: suppression of EEG low frequency activity and enhancement of spindle frequency activity.

The effect of melatonin (5 mg, p.o.) on electroencephalographic (EEG) activity during sleep was investigated in eight men in a placebo-controlled cross-over design. Melatonin was administered immediately prior to a 4-h daytime sleep episode (13-17 h) after a partial sleep deprivation. The non-REM sleep stages and REM sleep duration were not significantly affected. Melatonin enhanced EEG power density in non-REM sleep in the 13.75-14.0 Hz bin (i.e., within the frequency range of sleep spindles), and reduced activity in the 15.25-16.5 Hz band. In the first 2 h spectral values within the 2.25-5.0 Hz range were reduced. These changes in the EEG are to some extent similar to those induced by benzodiazepine hypnotics and to the contribution of the endogenous circadian pacemaker to the spectral composition of the sleep EEG when sleep occurs at night.

Adult↗
PubMed1995

Changes of sleep EEG slow-wave activity in response to sleep manipulations: to what extent are they related to changes in REM sleep latency?

Sleep interventions may have direct effects on slow-wave activity (SWA, i.e. power of the sleep EEG signal in the 0.75-4.5 Hz range) as well as indirect ones caused by changes in REM sleep (REMS) latency. The effects of changes in REMS latency on SWA were investigated by analysing simulations with a mathematical model. Mean SWA in the first non-REMS episode shows an initial increase and a later decline as a function of REMS latency. In the second non-REMS episode, mean SWA decreases with increasing REMS latency. These results of the simulations were validated with experimental data. In the evaluation of the effects of sleep interventions on SWA the effects of the timing of REMS have to be accounted for. The analysis of SWA over a sufficiently long constant amount of time spent in non-REMS proves to be relatively independent of REMS latency, which allows conclusions about the effects of sleep interventions on SWA per se.

Journal Article↗
PubMed1995

Time course of sleep inertia after nighttime and daytime sleep episodes.

Sleep inertia refers to the period of reduced vigilance following upon awakening from sleep. To investigate the time course of sleep inertia, self-ratings of alertness and reaction time in a memory task were repeatedly assessed after nighttime and daytime sleep episodes in healthy young men. Alertness gradually increased and reaction time gradually decreased within the first hour after awakening. Their time course could be described by exponential functions with time constants of 0.45 h and 0.3 h, respectively. The data demonstrate that sleep inertia is a robust, quantifiable process that can be incorporated in models of sleep and vigilance.

Adult↗
PubMed1994

Correlation dimension of the human sleep electroencephalogram: cyclic changes in the course of the night.

The complexity of the electroencephalogram (EEG) during human sleep can be estimated by calculating the correlation dimension. Due to the large number of calculations required by this approach, only selected short (4-164 s) segments of the sleep EEG have been analysed previously. By using a new type of personal supercomputer, we were able to calculate the correlation dimension of overlapping 1 min EEG segments for the entire sleep episode (480 min) of 11 subjects and thereby delineate the time course of the changes. The correlation dimension was high in episodes of rapid eye movement (REM) sleep, declined progressively within each non-REM sleep episode, and reached a low level at times when EEG slow waves (0.75-4.5 Hz) were dominant. However, whereas slow-wave activity showed its typical progressive decline from non-REM/REM sleep cycle 1 to 4, no such trend was present for the correlation dimension. By providing an estimate of the complexity of a signal and being independent of amplitude and frequency measures, the correlation dimension represents a novel approach to exploring the dynamics of sleep and the processes underlying its regulation.

Adult↗
PubMed1994

Simulation of daytime vigilance by the additive interaction of a homeostatic and a circadian process.

The two-process model of sleep regulation postulates that a homeostatic and a circadian process underlie sleep regulation. The timing of sleep and waking is accounted for by the interaction of these two processes. The assumptions of two separate processes or of a single process resulting from their additive interaction are mathematically equivalent but conceptually different. Based on an additive interaction, subjective alertness ratings in a forced desynchrony protocol and subjective sleepiness ratings in a photoperiod experiment were simulated. The correspondence between empirical and simulated data supports the basic assumption of the model.

Arousal↗
PubMed1994

All-night sleep EEG and artificial stochastic control signals have similar correlation dimensions.

EEG signals have been considered to be generated either by stochastic processes or by non-linear deterministic systems exhibiting chaotic behavior. To address this problem, the correlation dimension of the EEG was computed and compared to the correlation dimension of an artificial signal with identical power spectrum. By using a new type of personal super computer we were able for the first time to calculate the correlation dimension for the sleep episode of an entire night as well as for the corresponding artificial signal. The correlation dimension was high in episodes of rapid eye movement (REM) sleep, declined progressively within each non-REM sleep episode, and reached a low level at times when EEG slow waves (0.75-4.5 Hz) were dominant. The correlation dimension of the artificial signal and the EEG changed largely in parallel, although on average the values of the artificial signal were 7.3% higher. These results do not support the hypothesis that the sleep EEG is generated by a chaotic attractor.

Adult↗
PubMed1993

A model of human sleep homeostasis based on EEG slow-wave activity: quantitative comparison of data and simulations.

EEG slow-wave activity (SWA; spectral power in the 0.75-4.5 Hz band) is a function of the duration of prior waking and, thereby, an indicator of sleep homeostasis. We present a model that accounts for both the declining trend of SWA during sleep and for its variation within the successive nonrapid eye movement (non-REM) sleep episodes. The values of the model parameters were estimated by an optimization procedure in which empirical SWA of baseline nights (16 subjects, 26 nights) served as a reference. A sensitivity analysis revealed the model to be quite robust to small changes (+/- 5%) of the parameter values. The estimated parameter values were used to simulate data sets from three different experimental protocols (sleep in the evening or sleep in the morning after prolonged waking, or extended sleep initiated at the habitual bedtime; n = 8 or 9). The timing of the REM trigger parameter was derived from the empirical data. A close fit was obtained between the simulated and empirical SWA data, and even the occasional late SWA peaks during extended sleep could be reproduced. Minor discrepancies suggest indirect or direct circadian influences on SWA. The simulations demonstrate that the concept of sleep homeostasis as proposed in the two-process model of sleep regulation can be refined to account in quantitative terms for empirical data and to predict the changes induced by the prolongation of waking or sleep.

Adult↗
PubMed1993

Period-amplitude analysis and power spectral analysis: a comparison based on all-night sleep EEG recordings.

Both period-amplitude analysis (PAA) and power spectral analysis (PSA) were performed on all-night human sleep EEG recordings obtained from 11 subjects. The comparison of the two methods was based on the PAA variables time in band (a wave incidence measure) and rectified amplitude, and on the PSA variables spectral power density and spectral amplitude (the square root of power). The mean time course of these variables was determined for the first 4 nonREM-REM sleep cycles. Spectral power density and spectral amplitude in the delta range were high in nonREM sleep and low in REM sleep, and showed a declining trend over consecutive nonREM sleep episodes. In the frequency range below 2 Hz, rectified amplitude was highly correlated with both time in band and spectral amplitude, and there was no evidence for a dissociation between wave amplitude and wave incidence measures. However, in frequencies above 2 Hz, the modulation of time in band was a mirror image of that below 2 Hz. This result does not reflect a property of the data, but is inherent to the methodology applied. The reversal point of modulation was merely shifted when the high-pass filter settings were changed. It is concluded that band-pass filtering is necessary prior to PAA even for the analysis of the lowest frequency range, and that the indiscriminate use of PAA may give rise to spurious results.

Journal Article↗
PubMed1992

Concepts and models of sleep regulation: an overview.

Various mathematical models have been proposed to account for circadian, ultradian and homeostatic aspects of sleep regulation. Most circadian models assume that multiple oscillators underlie the differences in period and entrainment properties of the sleep/wake cycle and other rhythms (e.g. body temperature). Interactions of the oscillators have been postulated to account for multimodal sleep/wake patterns. The ultradian models simulate the cyclic alternation of nonREM sleep and REM sleep by assuming a reciprocal interaction of two cell groups. The homeostatic models propose that a sleep/wake dependent process (Process S) underlies the rise in sleep pressure during waking and its decay during sleep. The time course of this process has been derived from EEG slow-wave activity, an indicator of nonREM sleep intensity. The predictions of homeostatic models have been most extensively tested in experiments. The interaction of Process S with a single circadian process can account for multimodal sleep/wake patterns, internal desynchronization and the time course of daytime sleepiness. Close links have emerged between the processes postulated by the various models and specific brain mechanisms. Due to its recent quantitative elaboration and experimental validation, the modelling approach has become one of the potent research strategies in sleep science.

Journal Article↗