Sleep spindles in normal elderly: comparison with young adult patterns and relation to nocturnal awakening, cognitive function and brain atrophy

Guazzelli, Mario; Feinberg, Irwin; Aminoff, Michael J.; Fein, George; Floyd, Thomas C.; Maggini, Carlo

doi:10.1016/0013-4694(86)90140-9

Cited by 94 publications

(78 citation statements)

References 21 publications

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“…Most studies have compared only relatively young and old subjects [13][14][15]. Nicolas et al [16] compared six age groups from 10-19 to 60-69 years and found a progressive decrease in the number, density and duration of spindles.…”

Section: Discussionmentioning

confidence: 99%

A Study on Gender and Age Differences in Sleep Spindles

Huupponen

Himanen²,

Värri³

et al. 2002

Neuropsychobiology

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In the present work, gender differences in sleep spindle topography were examined in 40 subjects. Their median age was 32 years (range 22–49 years). Spindles were detected from 3,306,060 s of visually scored stage 2 sleep EEG by a previously validated automatic fuzzy detector at 1-second intervals. A total of 271,168 spindles were found from the six EEG channels analyzed. Females showed a significantly higher percentage of spindles in the left frontal channel than males (Fp1-A2; p = 0.026). To confirm that this difference was gender and not age related, the subjects were divided into two age groups. No significant differences in spindle activity of the frontal channels were found between the groups. However, the interindividual spindle variability seemed to be at least as large as that stemming from gender.

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Section: Discussionmentioning

confidence: 99%

A Study on Gender and Age Differences in Sleep Spindles

Huupponen

Himanen²,

Värri³

et al. 2002

Neuropsychobiology

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“…They are greatly reduced in developmental abnormalities in children (Ellingson and Peters 1980;Selvitellia et al 2009;Godbout et al 2000;Limoges et al 2005), in adult schizophrenia (Ferrarelli et al 2007;Ferrarelli et al 2010;Manoach et al 2010;Manoach et al 2014) and in patients with Parkinson's disease with dementia (Latreille et al 2015). Spindle density, duration and amplitude decrease with age but the rate of decline is not the same in all subjects (Nicolas et al 2001;Crowley et al 2002;Guazzelli et al 1986;Wei et al 1999;Principe and Smith 1982). There is a correlation between age-related decline in spindle activity and decline in cognitive functioning (Peters et al 2008;Mander et al 2014).…”

Section: Assessment Of Sleep Spindles and K Complexesmentioning

confidence: 99%

The case for using digital EEG analysis in clinical sleep medicine

Younes

2017

Sleep Science Practice

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Evaluation of sleep in clinical polysomnograms continues to rely almost exclusively on visual scoring that implements rules proposed by Rechtschaffen and Kales nearly 50 years ago. Apart from its cost and timeconsuming nature, visual scoring has limitations including: A) Sleep depth, which is a continuous variable, is treated as if it changes in a stepwise fashion from light (stage 1), to intermediate (stage 2) to deep (stage 3). B) Even with this limited scale, there is considerable inter-scorer variability, particularly in scoring stages 1 and 3 of non-REM sleep, thereby adding uncertainty to %time spent in these stages as a reliable metric for evaluating sleep depth. C) Limitation in scoring some of EEG features, including 1) arousal intensity, 2) extent of Alpha intrusion and 3) frequency, and characteristics of sleep spindles and K complexes. Digital analysis can solve these problems but producing a reliable system has been a challenge. In this review I begin with recent advances in digital scoring of sleep according to the Rechtschaffen and Kales rules and conclude that this technology has progressed enough to make it possible to obtain reliable, reproducible scoring, comparable in accuracy to scoring by highly experienced technologists, with minimal editing. This is followed by description of several new metrics that can be obtained if digital scoring systems were to be used routinely in clinical studies. The scientific evidence supporting the potential of these metrics to positively impact sleep medicine practice and the wide range of such metrics in patients studied in the sleep laboratory are highlighted.

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“…It is known that SS characteristics depend on factors such as age (Principe and Smith, 1982), time within sleep (Aeschbach et al, 1997;Himanen et al, 2002;Guazzelli et al, 1986), scalp location (Jobert et al, 1992), pathology and drug exposure, not to mention a normal intersubject variability (De Gennaro and Ferrara, 2003); however, systematic assessment of SS is still not a routine practice (Kubicki and Herrmann, 1996). Routine visual SS scoring is not feasible; there is evidence for superimposed rhythms obscuring visual SS discrimination (Campbell et al, 1980;Zygierewicz et al, 1999).…”

Section: Introductionmentioning

confidence: 98%