Sleep disturbances in highly stress reactive @entity19 : modeling endophenotypes of XXXX .

@entity1 @entity308 @entity19 @entity1316 @entity1636 @entity11755 @entity25
BACKGROUND: Neuronal mechanisms underlying @entity1316 such as @entity308 ( @entity308 ) are still poorly understood. By selectively breeding @entity19 for high (HR), intermediate (IR), or low (LR) reactivity of the @entity1636 , we recently established a new genetic animal model of extremes in stress reactivity (SR). Studies characterizing this SR @entity19 model on the behavioral, endocrine, and neurobiological levels revealed several similarities with key endophenotypes observed in @entity308 @entity1 . HR @entity19 were shown to have changes in rhythmicity and sleep measures such as @entity11755 ( @entity11755 ) and non-REM sleep (NREMS) as well as in slow wave activity, indicative of reduced sleep efficacy and increased @entity11755 . In the present study we were interested in how far a detailed spectral analysis of several electroencephalogram (EEG) parameters, including relevant frequency bands, could reveal further alterations of sleep architecture in this animal model. Eight adult males of each of the three breeding lines were equipped with epidural EEG and intramuscular electromyogram (EMG) electrodes. After recovery, EEG and EMG recordings were performed for two days. RESULTS: Differences in the amount of @entity11755 and wakefulness and in the number of transitions between vigilance states were found in HR @entity19 , when compared with IR and LR animals. Increased frequencies of transitions from NREMS to @entity11755 and from @entity11755 to wakefulness in HR animals were robust across the light-dark cycle. Detailed statistical analyses of spectral EEG parameters showed that especially during NREMS the power of the theta (6-9 Hz), alpha (10-15 Hz) and eta (16-22.75 Hz) bands was significantly different between the three breeding lines. Well defined distributions of significant power differences could be assigned to different times during the light and the dark phase. Especially during NREMS, group differences were robust and could be continuously monitored across the light-dark cycle. CONCLUSIONS: The HR @entity19 , i.e. those animals that have a genetic predisposition to @entity1636 in response to stressors, showed disturbed patterns in sleep architecture, similar to what is known from @entity308 @entity1 . Significant alterations in several frequency bands of the EEG, which also seem to at least partly mimic clinical observations, suggest the SR @entity19 lines as a promising animal model for basic research of mechanisms underlying @entity25 in @entity308 .
