On the Individuality of Sleep EEG Spectra

Lewandowski, Achim; Rosipal, Roman; Dorffner, Georg

doi:10.1027/0269-8803/a000092

Cited by 11 publications

(15 citation statements)

References 23 publications

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“…1). Analyses across nights confirmed several previous studies demonstrating the existence of robust spectral power fingerprints during sleep 34,61 . Indeed, these profiles have been shown to be the highly heritable 59 and remain stable throughout development 62 .…”

Section: Discussionsupporting

confidence: 84%

Individual differences in frequency and topography of slow and fast sleep spindles

Cox

Schapiro

Manoach

et al. 2017

Preprint

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Sleep spindles are transient oscillatory waveforms occurring during non--rapid eye movement (NREM) sleep and are implicated in plasticity and memory processes. In humans, spindles can be classified as either slow or fast, but large individual differences in spindle frequency as well as methodological difficulties have hindered progress towards understanding their function. Using two nights of high--density electroencephalography recordings from healthy individuals, we first characterize the individual variability of NREM spectra and demonstrate the difficulty of determining subject--specific spindle frequencies. We then introduce a novel spatial filtering approach that can reliably separate subject--specific spindle activity into slow and fast components that are stable across nights and across N2 and N3 sleep. We then proceed to provide detailed analyses of the topographical expression of individualized slow and fast spindle activity. Group--level analyses conform to known spatial properties of spindles, but also uncover novel differences between sleep stages and spindle classes. Moreover, subject--specific examinations reveal that individual topographies show considerable variability that is stable across nights. Finally, we demonstrate that topographical maps depend nontrivially on the spindle metric employed. In sum, our findings indicate that group--level approaches mask substantial individual variability of spindle dynamics, in both the spectral and spatial domains. We suggest that leveraging, rather than ignoring, such differences may prove useful to further our understanding of the physiology and functional role of sleep spindles.

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Section: Discussionsupporting

confidence: 84%

Individual differences in frequency and topography of slow and fast sleep spindles

Cox

Schapiro

Manoach

et al. 2017

Preprint

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“…In contrast, when analyzing night-to-night variability within an individual, some sleep variables are remarkably stable [5] . This stability was found in standard polysomnographic variables [1] , and especially in EEG spectral power values during different sleep stages [1] , [4] as well as in the frequency and topography of sleep spindles [2] . De Gennaro et al used the term “fingerprint” to describe this high intra-individual stability in sleep EEG parameters [3] .…”

Section: Introductionmentioning

confidence: 73%

“…Normal sleep shows remarkable variability among individuals. This variability applies to polysomnographically determined sleep variables like sleep duration and sleep efficiency [1] , sleep spindles [2] , EEG sigma activity [3] and total EEG spectral power [4] . In contrast, when analyzing night-to-night variability within an individual, some sleep variables are remarkably stable [5] .…”

Section: Introductionmentioning

confidence: 99%

The Relationship between Brain Morphology and Polysomnography in Healthy Good Sleepers

et al. 2014

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BackgroundNormal sleep continuity and architecture show remarkable inter-individual variability. Previous studies suggest that brain morphology may explain inter-individual differences in sleep variables.MethodThirty-eight healthy subjects spent two consecutive nights at the sleep laboratory with polysomnographic monitoring. Furthermore, high-resolution T1-weighted MRI datasets were acquired in all participants. EEG sleep recordings were analyzed using standard sleep staging criteria and power spectral analysis. Using the FreeSurfer software for automated segmentation, 174 variables were determined representing the volume and thickness of cortical segments and the volume of subcortical brain areas. Regression analyses were performed to examine the relationship with polysomnographic and spectral EEG power variables.ResultsThe analysis did not provide any support for the a-priori formulated hypotheses of an association between brain morphology and polysomnographic variables. Exploratory analyses revealed that the thickness of the left caudal anterior cingulate cortex was positively associated with EEG beta2 power (24–32 Hz) during REM sleep. The volume of the left postcentral gyrus was positively associated with periodic leg movements during sleep (PLMS).ConclusionsThe function of the anterior cingulate cortex as well as EEG beta power during REM sleep have been related to dreaming and sleep-related memory consolidation, which may explain the observed correlation. Increased volumes of the postcentral gyrus may be the result of increased sensory input associated with PLMS. However, due to the exploratory nature of the corresponding analyses, these results have to be replicated before drawing firm conclusions.

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“…We first describe in some detail individual differences of NREM spectra in general, and of sigma activity in particular. While previous reports have already described several aspects of this variability (De Gennaro et al, 2005 , 2008 ; Lewandowski et al, 2013 ; Ujma et al, 2015 ), we here revisit this issue qualitatively and quantitatively in order to make the motivation for a novel slow/fast spindle separation approach explicit. We examine spectral properties from both channel-averaged and single-channel (frontal and parietal) perspectives and demonstrate the difficulties of both employing fixed slow/fast spindle ranges and of utilizing channel-based data to define subject-specific frequency ranges.…”

Section: Resultsmentioning

confidence: 87%

Individual Differences in Frequency and Topography of Slow and Fast Sleep Spindles

Cox

Schapiro

Manoach

et al. 2017

Front. Hum. Neurosci.

176

205

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Sleep spindles are transient oscillatory waveforms that occur during non-rapid eye movement (NREM) sleep across widespread cortical areas. In humans, spindles can be classified as either slow or fast, but large individual differences in spindle frequency as well as methodological difficulties have hindered progress towards understanding their function. Using two nights of high-density electroencephalography recordings from 28 healthy individuals, we first characterize the individual variability of NREM spectra and demonstrate the difficulty of determining subject-specific spindle frequencies. We then introduce a novel spatial filtering approach that can reliably separate subject-specific spindle activity into slow and fast components that are stable across nights and across N2 and N3 sleep. We then proceed to provide detailed analyses of the topographical expression of individualized slow and fast spindle activity. Group-level analyses conform to known spatial properties of spindles, but also uncover novel differences between sleep stages and spindle classes. Moreover, subject-specific examinations reveal that individual topographies show considerable variability that is stable across nights. Finally, we demonstrate that topographical maps depend nontrivially on the spindle metric employed. In sum, our findings indicate that group-level approaches mask substantial individual variability of spindle dynamics, in both the spectral and spatial domains. We suggest that leveraging, rather than ignoring, such differences may prove useful to further our understanding of the physiology and functional role of sleep spindles.

show abstract

On the Individuality of Sleep EEG Spectra

Cited by 11 publications

References 23 publications

Individual differences in frequency and topography of slow and fast sleep spindles

Individual differences in frequency and topography of slow and fast sleep spindles

The Relationship between Brain Morphology and Polysomnography in Healthy Good Sleepers

Individual Differences in Frequency and Topography of Slow and Fast Sleep Spindles

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