An automated system for recording and analysis of sleep in mice

Significant differences in many aspects of sleep/wake activity among inbred strains of mice suggest genetic influences on the control of sleep. A numb er of genetic techniques, including transgenesis, random and targeted mutag enesis, and analysis of quantitative trait loci may be used to identify gen etic loci. To take full advantage of these genetic approaches in mice, a co mprehensive 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 s leep structure and the response to sleep deprivation in mice. This algorith m was validated in three inbred strains (C57BL/6J, C3HeB/FeJ, 129X1/SvJ) an d one hybrid line (C57BL/6J X C3HeB/FeJ). Overall accuracy rates for behavi oral 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. Characteriz ation of sleep has been extended to include measurements of sleep consolida tion and fragmentation, REM sleep latency, and delta density decline with s leep. An experimental protocol is suggested for acquiring baseline sleep da ta for genetic studies. This sleep recording protocol, scoring, and analysi s system is designed to facilitate the understanding of genetic basis of sl eep structure.