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Converging data that attribute a central role to sleep in memory consolidation have increased the interest to understand the characteristics of the hippocampal sleep and their relations with the processing of new information. Neural synchronization between different brain regions is thought to be implicated in long-term memory consolidation by facilitating neural communication and by promoting neural plasticity. However, the majority of studies have focused their interest on intra-hippocampal, rhinal-hippocampal or cortico-hippocampal synchronization, while inter-hemispheric synchronization has been so far neglected. To clarify the features of spontaneous human hippocampal activity and to investigate inter-hemispheric hippocampal synchronization across vigilance states, pre-sleep wakefulness and nighttime sleep were recorded from right and left homologous hippocampal loci using stereo-EEG techniques. Hence, quantitative and inter-hemispheric coherence analyses of hippocampal activity across sleep and waking states were carried out. The results showed the presence of delta activity in human hippocampal spontaneous EEG also during wakefulness. The activity in the delta range exhibited a peculiar bimodal distribution, namely a low frequency non-oscillatory activity (up to 2Hz) synchronized between hemispheres mainly during wake and REM sleep, and a faster oscillatory rhythm (2-4Hz). The latter was less synchronized between the hippocampi and seemed reminiscent of animal RSA (rhythmic slow activity). Notably, the low-delta activity showed high inter-hemispheric hippocampal coherence during REM sleep and, to a lesser extent, during wakefulness, paralleled by a (unexpected) decrease of coherence during NREM sleep. Therefore, low-delta hippocampal state-dependent synchronization starkly contrasts with neocortical behavior in the same frequency range. Further studies might shed light on the role of these low frequency rhythms in the encoding processes during wakefulness and in the consolidation processes during subsequent sleep.

STUDY OBJECTIVE: We present a mathematical model of sleep-EEG structure applied to the analysis of sleep patterns in narcoleptics by combining the 2-process model of sleep regulation and the reciprocal interaction model of REM regulation suggested by McCarley and Hobson. The aim was the individuation of parameters characterizing narcoleptic sleep in comparison to controls. DESIGN: Polysomnographic data were drawn from a previous study about sleep in narcolepsy. The mathematical model was fitted to quantitative EEG data by an optimization procedure. SETTING: Polysomnographic data were recorded in single and sound attenuated hospital rooms, for one night following an adaptation night. PARTICIPANTS: 9 narcoleptic subjects (7 males, 2 females, mean age 39.6 +/- 4.3 years) and 9 age- and sex- matched controls. MEASUREMENTS: Slow Wave Activity (SWA) time series were evaluated by spectral analysis. The sleep model was fitted to SWA profile for each recording and to the averaged SWA profile for each group. Bartlett and Kolmogorov-Smirnov test were used to evaluate the goodness of fit and the accuracy of model predictions. RESULTS: In both controls and narcoleptics the optimization procedure produced a good fit of SWA raw data. The only significant difference between the groups were the RemOn/RemOff coupling parameters, reflecting an enhanced strength of the REM oscillator in narcoleptics. CONCLUSIONS: The mathematical model of sleep provides a substantial description of empirical patterns for both controls and narcoleptics. The variation of values in the parameters describing the strength of RemOn /RemOff interaction is the major feature characterizing narcoleptics; it can explain sleep onset REM periods (SOREMPs) and variations of REM-NREM sleep cycle duration.

There is growing evidence that cyclic alternating pattern (CAP) and arousals are woven into the basic mechanisms of sleep regulation. In the present study, the overnight sleep cycles (SC) of 20 normal subjects were analyzed according to their stage composition, CAP rate, phase A subtypes and arousals. Individual SC were then divided into 10 normalized temporal epochs. CAP parameters and arousals were measured in each epoch and averaged in relation to the SC order. Subtypes A2 and A3 of CAP in non-rapid eye movement (NREM) sleep, and arousals, both in REM and NREM sleep when not coincident with a A2 or A3 phases, were lumped together as fast electroencephalographic (EEG) activities (FA). Subtypes A1 of CAP, characterized by slow EEG activities (SA), were analyzed separately. The time distribution of SA and FA was compared to the mathematical model of normal sleep structure including functions representing the homeostatic process S, the circadian process C, the ultradian process generating NREM/REM cycles and the slow wave activity (SWA) resulting from the interaction between homeostatic and ultradian processes. The relationship between SA and FA and the sleep-model components was evaluated by multiple regression analysis in which SA and FA were considered as dependent variables while the covariates were the process S, process C, SWA, REM-on and REM-off activities and their squared values. Regression was highly significant (P < 0.0001) for both SA and FA. SA were prevalent in the first three SC, and exhibited single or multiple peaks immediately before and in the final part of deep sleep (stages 3 + 4). The peaks of FA were delayed and prevailed during the pre-REM periods of light sleep (stages 1 + 2) and during REM sleep. SA showed an exponential decline across the successive SC, according to the homeostatic process. In contrast, the distribution of FA was not influenced by the order of SC, with periodic peaks of FA occurring before the onset of REM sleep, in accordance with the REM-on switch. The dynamics of CAP and arousals during sleep can be viewed as an intermediate level between cellular activities and macroscale EEG phenomena as they reflect the decay of the homeostatic process and the interaction between REM-off and REM-on mechanisms while are slightly influenced by circadian rhythm.