Correlation Dimension of the Human Sleep Electroencephalogram: Cyclic Changes in the Course of the Night

Achermann, Peter; Hartmann, Rolf; Gunzinger, Anton; Guggenbühl, W.; Aa, Borbély

doi:10.1111/j.1460-9568.1994.tb00292.x

Cited by 49 publications

(23 citation statements)

References 23 publications

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“…who was blind to clinical data and positions of cortical recording sites. Sleep scoring was based on analysis of the cortical activity on 3-16 intracortical contacts per subject To characterize cerebral activity, we used a nonlinear time series analysis and considered the coefficient of DA (22), based on and derived from the dimensional complexity approach (SI Methods) (23,47). The nonlinear approach has been applied to EEG signals in several domains, mainly in sleep research where it has been validated against conventional spectral measures (references are given in SI Methods).…”

Section: Methodsmentioning

confidence: 99%

Thalamic deactivation at sleep onset precedes that of the cerebral cortex in humans

Magnin

Rey

Bastuji

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

197

147

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Thalamic and cortical activities are assumed to be time-locked throughout all vigilance states. Using simultaneous intracortical and intrathalamic recordings, we demonstrate here that the thalamic deactivation occurring at sleep onset most often precedes that of the cortex by several minutes, whereas reactivation of both structures during awakening is synchronized. Delays between thalamus and cortex deactivations can vary from one subject to another when a similar cortical region is considered. In addition, heterogeneity in activity levels throughout the cortical mantle is larger than previously thought during the descent into sleep. Thus, asynchronous thalamocortical deactivation while falling asleep probably explains the production of hypnagogic hallucinations by a still-activated cortex and the common self-overestimation of the time needed to fall asleep.A bundant electrophysiological and functional imaging data have revealed that sleep-related brain activity is not the result of a global deactivation of cerebral structures but rather is a multifocal process associated with local changes in brain activities (1-10). Examples of such functional heterogeneities are, among others, the fronto-occipital gradient in cortical activity during sleep (1, 2), the preponderant fronto-parietal localization of sleep spindles (3, 4), and interhemispheric imbalanced activity (5, 6). So far, very few studies have addressed the time course of these regional differences during transitions between vigilance states (11)(12)(13)(14), and most of these studies were based on scalp recordings performed during stable periods of wakefulness or sleep. Although reports that favor some asynchrony of sleep-onset activity between the different cortical areas are accumulating, there still is a firm belief that thalamic and cortical activities are tightly coupled, at both the cellular and integrative level, during wakefulness and sleep (15)(16)(17)(18). Recent intracranial data in humans, however, indicate that, during both paradoxical (rapid eye movement) sleep and sleep stage 2, thalamic and cortical activities may alternate periods of coupling and decoupling (19,20). In this context, the question is whether the dynamics of the neuronal deactivation that characterizes the transition from wakefulness to sleep is identical in thalamus and cortex, or, conversely, whether transient decoupling may occur at this transition time that would suggest different sleep-onset timing in these two structures. The opportunity to record thalamic and cortical activities simultaneously in epileptic patients chronically implanted with intracerebral electrodes allowed us to address this issue. In contrast to the generally accepted view that thalamic and cortical activities are tightly locked along the different vigilance states, we found that the thalamic activity most often decreased to sleep levels several minutes before the cortical activity started to abate. This finding suggests that the cortex remains neurophysiologically awake but decoupled from thalamic i...

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Section: Methodsmentioning

confidence: 99%

Thalamic deactivation at sleep onset precedes that of the cerebral cortex in humans

Magnin

Rey

Bastuji

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

197

147

View full text Add to dashboard Cite

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“…The pioneering studies used methods such as correlation dimension (D2) [48][49][50], Hurst exponent (H) [51] and detrended fluctuations analysis (DFA) [52][53][54]. Complexity of brain activity was quantified by a relative increase or decrease where D2 and H measures decreased from wakefulness to NREM sleep and increased during REM sleep.…”

Section: Discussionmentioning

confidence: 99%

Characterisation of the Effects of Sleep Deprivation on the Electroencephalogram Using Permutation Lempel–Ziv Complexity, a Non-Linear Analysis Tool

Tosun

Abásolo

Stenson

et al. 2017

Entropy

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Specific patterns of brain activity during sleep and waking are recorded in the electroencephalogram (EEG). Time-frequency analysis methods have been widely used to analyse the EEG and identified characteristic oscillations for each vigilance state (VS), i.e., wakefulness, rapid-eye movement (REM) and non-rapid-eye movement (NREM) sleep. However, other aspects such as change of patterns associated with brain dynamics may not be captured unless a non-linear-based analysis method is used. In this pilot study, Permutation Lempel-Ziv complexity (PLZC), a novel symbolic dynamics analysis method, was used to characterise the changes in the EEG in sleep and wakefulness during baseline and recovery from sleep deprivation (SD). The results obtained with PLZC were contrasted with a related non-linear method, Lempel-Ziv complexity (LZC). Both measure the emergence of new patterns. However, LZC is dependent on the absolute amplitude of the EEG, while PLZC is only dependent on the relative amplitude due to symbolisation procedure and thus, more resistant to noise. We showed that PLZC discriminates activated brain states associated with wakefulness and REM sleep, which both displayed higher complexity, compared to NREM sleep. Additionally, significantly lower PLZC values were measured in NREM sleep during the recovery period following SD compared to baseline, suggesting a reduced emergence of new activity patterns in the EEG. These findings were validated using PLZC on surrogate data. By contrast, LZC was merely reflecting changes in the spectral composition of the EEG. Overall, this study implies that PLZC is a robust non-linear complexity measure, which is not dependent on amplitude variations in the signal, and which may be useful to further assess EEG alterations induced by environmental or pharmacological manipulations.

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“…Therefore, further efforts shall be made to examine which of the specific characteristics in the EEG allows the recurrence method to extract information about improved sleep patterns better than the other approaches. As an example, from previous non-linear approaches on EEG sleep recordings, it is known that the correlation dimension successfully distinguishes slow-wave episodes of sleep from other stages [28]. The correlation dimension is high for REM sleep and low for slow-wave sleep.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Different Methods for the Evaluation of Treatment Effects from the Sleep EEG of Patients with Major Depression

et al. 2008

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In healthy subjects, sleep has a typical structure of three to five cyclic transitions between different sleep states. In major depression, this regular pattern is often destroyed but can be reestablished during successful treatment. The differences between healthy and abnormal sleep are generally assessed in a time-consuming process, which consists of determining the nightly variations of the sleep states (the hypnogram) based on visual inspection of the electroencephalogram (EEG), electrooculogram, and electromyogram. In this study, three different methods of sleep EEG analysis (spectrum, outlier, and recurrence analysis) have been examined with regard to their ability to extract information about treatment effects in patients with major depression. Our data suggest that improved sleep patterns during treatment with antidepressant medication can be identified with an appropriate analysis of the EEG. By comparing different methods, we have found that many treatment effects identified by spectrum analysis can be reproduced by the much simpler technique of outlier analysis. Finally, the cyclic structure of sleep and its modification by antidepressant treatment is best illustrated by a non-linear approach, the so-called recurrence method.

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Correlation Dimension of the Human Sleep Electroencephalogram: Cyclic Changes in the Course of the Night

Cited by 49 publications

References 23 publications

Thalamic deactivation at sleep onset precedes that of the cerebral cortex in humans

Thalamic deactivation at sleep onset precedes that of the cerebral cortex in humans

Characterisation of the Effects of Sleep Deprivation on the Electroencephalogram Using Permutation Lempel–Ziv Complexity, a Non-Linear Analysis Tool

Comparison of Different Methods for the Evaluation of Treatment Effects from the Sleep EEG of Patients with Major Depression

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