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Sleep. 2000 Dec 15;23(8):1025-40.

An automated system for recording and analysis of sleep in mice.

Author information

1
Center for Sleep and Respiratory Neurobiology, University of Pennsylvania, School of Medicine, Philadelphia 19104-4283, USA. veasey@mail.med.upenn.edu

Abstract

Significant differences in many aspects of sleep/wake activity among inbred strains of mice suggest genetic influences on the control of sleep. A number of genetic techniques, including transgenesis, random and targeted mutagenesis, and analysis of quantitative trait loci may be used to identify genetic loci. To take full advantage of these genetic approaches in mice, a comprehensive and robust description of behavioral states has been developed. An existing automated sleep scoring algorithm, designed for sleep analysis in rats, has been examined for acceptability in the analysis of baseline sleep structure and the response to sleep deprivation in mice. This algorithm was validated in three inbred strains (C57BL/6J, C3HeB/FeJ, 129X1/SvJ) and one hybrid line (C57BL/6J X C3HeB/FeJ). Overall accuracy rates for behavioral state detection (mean+/-SE) using this system in mice were: waking, 98.8%+/-0.4; NREM sleep, 97.1%+/-0.5; and REM sleep, 89.7%+/-1.4. Characterization of sleep has been extended to include measurements of sleep consolidation and fragmentation, REM sleep latency, and delta density decline with sleep. An experimental protocol is suggested for acquiring baseline sleep data for genetic studies. This sleep recording protocol, scoring, and analysis system is designed to facilitate the understanding of genetic basis of sleep structure.

PMID:
11145318
[Indexed for MEDLINE]

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