Topographic Cortical Mapping of EEG Sleep Stages During Daytime Naps in Normal Subjects

Ms, Buchsbaum; Wb, Mendelson; Wc, Duncan; Coppola, Richard; Kelsoe, John R.; Jc, Gillin

doi:10.1093/sleep/5.3.245

Cited by 59 publications

(19 citation statements)

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“…When comparing typical EEG maps of each state with the results published by Buchsbaum et al [3] we have found differences. Buchsbaum et al [3] used a monopo lar ear lobe reference and observed enhanced slow wave spectral intensity during stages III and IV on the vertex, opposite the monopolar reference.…”

Section: Discussioncontrasting

confidence: 58%

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EEG Cartography of a Night of Sleep and Dreams

Etevenon¹,

Guillou²

1986

Neuropsychobiology

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A night of sleep has been recorded under the conditions of a sleep laboratory. The subject was a woman of 55 years, well-trained in dream recall. The subject was awakened three times at the end of sleep cycles. EEG was monitored for 7 h with a 16-channel polygraph (REEGA 16, Alvar) connected to two systems of EEG cartography: minicomputers (HP Fourier Analyser 5451 C and HP 1000) and a microinformatic system (Cartovar, Alvar). A second 8-channel polygraph (Mini-huit, Alvar) was used in parallel for polygraphy (EOG, EMG, respiration, acto-gram, EKG). Based on immediate visual inspection of EEG and polygraphic tracings, 500 EEG recordings of selected epochs (of 6, 30 or 60 s length) have been quantified, submitted on-line to spectral analysis (on Cartovar) and stored on floppy disks for further printing of EEG maps. The 16 EEG channels were placed over the scalp according to the 10/20 system and following Giannitrapani’s placement. We have chosen a common average electrode. For each of the 500 EEG epochs, four EEG maps were edited (raw EEG between 0 and 30 Hz, 0 and 7 Hz, 8 and 12 Hz, 13 and 30 Hz). Each of these 2,000 maps has been checked visually in comparison with the polygraphic recordings for visual rejection of artifacts or transitory states. The remaining EEG epochs and EEG maps, scored by 2 independent trained sleep scorers, were classified into stages I, II, III-IV, and REM, apart from control runs of active wakefulness with eyes open (EO) and quiet wakefulness with eyes closed (EC), which were undertaken on mini- and microsystems of EEG analysis. Later, the spectral values of chosen EEG maps were averaged on Hewlett-Packard minicomputer, for each of the six chosen states. Typical EEG maps of EO, EC, I, II, III-IV and REM were computed, together with averaged EEG maps for each state. Dynamic and statistical analyses have also been computed together with correlations between the four types of EEG maps (raw EEG, 0–7, 8–12, 13–30 Hz).

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Section: Discussioncontrasting

confidence: 58%

“…[12], In 1982. Buchsbaum et al [3] published the first study of EEG cartography of sleep stages, work- ing with 4 subjects recorded during the day. with monopolar ear lobe reference.…”

Section: Introductionmentioning

confidence: 99%

EEG Cartography of a Night of Sleep and Dreams

Etevenon¹,

Guillou²

1986

Neuropsychobiology

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“…Sleep studies of neocortical β-and γ-rhythms have been mostly focused on their macroscopic manifestation and grouping of different rhythms (23)(24)(25)(26). It has been shown that coherent γ-oscillations are present during REM in magnetoencephalography (MEG) recordings (10).…”

Section: Discussionmentioning

confidence: 99%

High-frequency oscillations in human and monkey neocortex during the wake–sleep cycle

Quyen

Muller

Teleńczuk

et al. 2016

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

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Beta (β)-and gamma (γ)-oscillations are present in different cortical areas and are thought to be inhibition-driven, but it is not known if these properties also apply to γ-oscillations in humans. Here, we analyze such oscillations in high-density microelectrode array recordings in human and monkey during the wake-sleep cycle. In these recordings, units were classified as excitatory and inhibitory cells. We find that γ-oscillations in human and β-oscillations in monkey are characterized by a strong implication of inhibitory neurons, both in terms of their firing rate and their phasic firing with the oscillation cycle. The β-and γ-waves systematically propagate across the array, with similar velocities, during both wake and sleep. However, only in slow-wave sleep (SWS) β-and γ-oscillations are associated with highly coherent and functional interactions across several millimeters of the neocortex. This interaction is specifically pronounced between inhibitory cells. These results suggest that inhibitory cells are dominantly involved in the genesis of β-and γ-oscillations, as well as in the organization of their large-scale coherence in the awake and sleeping brain. The highest oscillation coherence found during SWS suggests that fast oscillations implement a highly coherent reactivation of wake patterns that may support memory consolidation during SWS.excitation | inhibition | state-dependent firing | wave propagation | synchrony N eocortical beta (β)-and gamma (γ)-oscillations have been largely studied in the context of action/cognition during wakefulness (1-3). Although numerous studies have focused intensely on the information-processing role of β and γ during wakefulness, these rhythms do also occur during deep anesthesia and natural sleep (4-6). Additionally, although very-high-frequency allocortical oscillations (e.g., ripples at 80-200 Hz and fast ripples at 200-500 Hz in rodent/human hippocampus) have been thoroughly implicated in sleep-dependent memory consolidation (ref. 7 and reviewed in refs. 8 and 9), the occurrence and role of neocortical β-and γ-oscillations during sleep remain largely unexplored and related studies have been limited to macroscopic (whole-brain) scales (e.g., ref. 10). A recent study using microwires and intracranial electroencephalography (iEEG) recordings in the neocortex of epileptics has verified the strong presence of γ-oscillations during slow-wave sleep (SWS) (11). That study also showed a marked increase of spiking during γ, a suggestive indicator of association with cortical UP states. Here, we evaluate the presence of neocortical β-and γ-oscillations during the wake-sleep cycle at the level of local circuitry using dense 2D multielectrode recordings with identified excitatory and inhibitory cells (12, 13).Experimental and theoretical studies suggest that thalamocortical oscillations (including β and γ) are generated through a combination of intrinsic mechanisms involving the interplay between different ionic currents and extrinsic interaction of inhibitory and excitatory c...

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“…Recently, regional aspects of sleep have gained increasing attention. Early reports that the location of the recording electrodes affects the pattern of the sleep EEG (Findji et al 1981;Buchsbaum et al 1982;Hori 1985) were reexamined by using contemporary methods of quantitative EEG analysis. Power spectra along the antero-posterior axis were shown to exhibit frequencyspecific and state-dependent gradients (Werth et al 1996(Werth et al , 1997) with a frontal predominance in the 2-Hz band during the initial part of sleep.…”

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confidence: 99%

Individual 'Fingerprints' in Human Sleep EEG Topography

Finelli

Achermann

Borbély

2001

Neuropsychopharmacology

172

129

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The sleep EEG of eight healthy young men was recorded from 27 derivations during a baseline night and a recovery night after 40 h of waking. Individual power maps of the nonREM sleep EEG were calculated for the delta, theta, alpha, sigma and beta range. The comparison of the normalized individual maps for baseline and recovery sleep revealed very similar individual patterns within each frequency band. This high correspondence was quantified and statistically confirmed by calculating the Manhattan distance between all pairs of maps within and between individuals. Although prolonged waking enhanced power inthe low-frequency range and reduced power in the high-frequency range Sleep is generally regarded as a global brain process. Recently, regional aspects of sleep have gained increasing attention. Early reports that the location of the recording electrodes affects the pattern of the sleep EEG (Findji et al. 1981;Buchsbaum et al. 1982;Hori 1985) were reexamined by using contemporary methods of quantitative EEG analysis. Power spectra along the antero-posterior axis were shown to exhibit frequencyspecific and state-dependent gradients (Werth et al. 1996(Werth et al. , 1997) with a frontal predominance in the 2-Hz band during the initial part of sleep. This hyperfrontality of low-frequency activity was accentuated by sleep deprivation (Cajochen et al. 1999). It may be associated with the reduction of regional cerebral blood flow which is known to occur during slow wave sleep (Finelli et al. 2000b; for a review see Maquet 2000) as well as in the course of prolonged waking (Thomas et al. 1998(Thomas et al. , 2000. These findings support the hypothesis that sleep has a local, use-dependent, facet and that cerebral structures that had been particularly active during waking may exhibit more intensive signs of sleep (Horne 1993;Krueger and Obál 1993;Kattler et al. 1994;Benington and Heller 1995;Borbély and Achermann 2000).Studies on regional differences in the sleep EEG are typically based on the statistical analysis of the records from several subjects. This approach emphasizes the changes common to all subjects, and tends to disregard individual differences. However, individual features may be important. This was recently demonstrated in a study where the relationship between the waking and sleep EEG was investigated (Finelli et al. 2000a). Examining the individual time course of theta power in the waking EEG in the course of a 40-h sleep deprivation period revealed a correlation with the change in delta power in the nonREM sleep EEG. The results suggested that a common regulatory process might control specific parts of the waking and sleep EEG. The aim of the present analysis was to investigate the individual topographic distribution of power in non-REM sleep before and after sleep deprivation, a manipulation known to cause massive changes in the sleep EEG. SUBJECTS AND METHODSEight right-handed healthy male subjects (mean age 23 y Ϯ 0.46 SEM, range 21-25 years) participated in the study. They were selected on the bas...

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Topographic Cortical Mapping of EEG Sleep Stages During Daytime Naps in Normal Subjects

Cited by 59 publications

References 0 publications

EEG Cartography of a Night of Sleep and Dreams

EEG Cartography of a Night of Sleep and Dreams

High-frequency oscillations in human and monkey neocortex during the wake–sleep cycle

Individual 'Fingerprints' in Human Sleep EEG Topography

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