State-Dependency of Neuronal Slow Dynamics During Sleep Observed in Cat Lateral Geniculate Nucleus
Kazuhiro Nakamura, Mitsuaki Yamamoto, Kazumi Takahashi, Mitsuyuki Nakao, Yoshinari Mizutani, Norihiro Katayama and Tohru Kodama

From the accumulated results, we hypothesize that neurons in the central processor systems of the brain generally exhibit a common state-dependency in slow dynamics of their spontaneous activities during sleep. In this paper, activities of relay cells in the cat's lateral geniculate nucleus (LGN) were studied to see if our hypothesis can be applied in this thalamic region. Data segments in polygraphically steady states were strictly extracted in order to sample the activities whose stationarity was guaranteed in a statistical sense. During slow wave sleep (SWS), the discharge pattern was characterized by short bursts. In contrast, the rather tonic discharge pattern was observed to prevail during rapid eye movement (REM) sleep. Spectral analyses showed white noise-like spectra in the low frequency range of 0.04-1.0 Hz during SWS, and 1/f noise-like spectra in the same frequency range during REM sleep. This state-dependency of the slow dynamics was consistently characterized by the other statistical parameters concerning the second-order moment as well. In contrast, the fast dynamics over 1.0 Hz tended to exhibit neuron-specific changes associated with the sleep state in terms of the Markovian dependency analysis. Consequently, our working hypothesis was not rejected for the LGN relay cells. The result here extends the possibility that the state-dependency of the slow dynamics we found is a general rule concerning single neuronal dynamics in widespread areas of the brain during sleep. The state-dependency of the slow dynamics of the LGN relay cells could be understood according to the proposed mechanism that a state-associated alteration in the global biasing input to a neural network during sleep induces the phenomenon with which we are concerned. The slow dynamics of neuronal activities might provide a novel framework defining SWS and REM sleep states instead of the polygraphic characteristics.

Topographical Distribution of Spindles: Variations Between and Within NREM Sleep Cycles
Luigi De Gennaro, Michele Ferrara and Mario Bertini

Spindle density, visually scored in the 12-15 Hz range over antero-posterior midline derivations, was assessed during a baseline night in ten normal subjects. Sleep spindles were found to be highly variable between subjects and more abundant during Stage 2. Topographical distribution of spindle density showed a centroparietal prevalence, stable between NREM sleep stages. Intra-night variations of spindle density exhibited a linear increase across consecutive NREM episodes, suggesting an inverse relation with the time course of slow wave sleep. Except for occipital leads reaching a maximum during the third NREM cycle and then decreasing, changes in spindle density across sleep cycles were similar over different derivations. Intra-cycle variations fit a fourth-order polynomial curve with a minimum in the middle part of each sleep episode (when most slow wave sleep is expressed); this intra-cycle trend also seems stable between derivations and consecutive sleep cycles. These results confirm and extend, to the level of macroscopic EEG, the reciprocal relationship between sigma and delta waves previously shown by spectral analysis of EEG frequencies and, at a neuronal level in the thalamocortical network, by changes of membrane potentials that oscillate in the frequency range of spindles or delta at different levels of hyperpolarization.ells. The result here extends the possibility that the state-dependency of the slow dynamics we found is a general rule concerning single neuronal dynamics in widespread areas of the brain during sleep. The state-dependency of the slow dynamics of the LGN relay cells could be understood according to the proposed mechanism that a state-associated alteration in the global biasing input to a neural network during sleep induces the phenomenon with which we are concerned. The slow dynamics of neuronal activities might provide a novel framework defining SWS and REM sleep states instead of the polygraphic characteristics.