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Human thalamic and cortical activities assessed by dimension of activation and spectral edge frequency during sleep wake cycles.

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TLDR
DA analysis proved reliable for quantification of cortical activity, in agreement with data issued from classical vigilance states scoring and spectral analysis.
Abstract
Study objectives: Using spectral edge frequency (SEF 95 ) and dimension of activation (DA), a new tool derived from the dimension of correlation, we assessed the activation of thalamus and cortex in the different vigilance states. Patients: Results were gathered from intracerebral recordings performed in 12 drug-resistant epileptic patients during video-stereoelectroencephalographic (SEEG) monitoring. Results: In the cortex, we observed a progressive decrease of DA from wake to sleep, with minimal DA values characterizing the deep slow wave sleep (dSWS) stage. During paradoxical sleep (PS), cortical level of activity returned to DA values similar to those obtained during wakefulness. In the thalamus, DA values during wakefulness were higher than the values observed during light SWS (lSWS), deep SWS (dSWS) and PS; there were no significant differences between the 3 sleep stages. Similar variations were observed with SEF 95 . Conclusion: DA analysis proved reliable for quantification of cortical activity, in agreement with data issued from classical vigilance states scoring and spectral analysis. At the thalamic level, only 2 levels of activity within a sleep wake cycle were observed, pointing to dissociated levels of activation between the thalamus and the neocortex during lSWS and PS.

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

TL;DR: Using simultaneous intracortical and intrathalamic recordings, it is demonstrated 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.
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Temporal Disorganization of Circadian Rhythmicity and Sleep-Wake Regulation in Mechanically Ventilated Patients Receiving Continuous Intravenous Sedation

TL;DR: The finding that most subjects exhibited preserved, but phase delayed, excretion of aMT6s suggests that the circadian pacemaker of such patients may be free-running, and the circadian rhythms of patients receiving mechanical ventilation and intravenous sedation exhibit pronounced temporal disorganization.
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Modeling Resting-State Functional Networks When the Cortex Falls Asleep: Local and Global Changes

TL;DR: Large-scale modeling of the human cortico-cortical anatomical connectivity is employed to evaluate changes in resting-state FC when the model "falls asleep" due to the progressive decrease in arousal-promoting neuromodulation, and it is shown that local slow waves are structured macroscopically in networks that resemble the resting- state networks.
Journal ArticleDOI

Heterogeneity of arousals in human sleep: A stereo-electroencephalographic study

TL;DR: The results suggest that the human cortex does not shift from sleep to wake in an abrupt binary way, and stereotyped arousals at the thalamic level seem to be associated with different patterns of cortical arousals due to various regulation factors.
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Filtering the reality: Functional dissociation of lateral and medial pain systems during sleep in humans

TL;DR: While the lateral operculo‐insular system subserving sensory analysis of somatic stimuli remained active during paradoxical‐REM sleep, mid‐anterior cingulate processes related to orienting and avoidance behavior were suppressed, explaining why nociceptive stimuli can be either neglected or incorporated into dreams without awakening the subject.
References
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Journal ArticleDOI

A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects.

TL;DR: Techniques of recording, scoring, and doubtful records are carefully considered, and Recommendations for abbreviations, types of pictorial representation, order of polygraphic tracings are suggested.
Journal ArticleDOI

Measuring the Strangeness of Strange Attractors

TL;DR: In this paper, the correlation exponent v is introduced as a characteristic measure of strange attractors which allows one to distinguish between deterministic chaos and random noise, and algorithms for extracting v from the time series of a single variable are proposed.
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