Sleep is known to contribute to motor memory consolidation. Recent studies have provided evidence that a night of sleep plays a similar functional role following motor imagery (MI), while the simple passage of time does not result in performance gains. Here, we examined the benefits of a daytime nap on motor memory consolidation after MI practice. Participants were trained by MI on an explicitly known sequence of finger movements at 11:00. Half of the participants were then subjected (at 14:00) to either a short nap (10 min of stage 2 sleep) or a long nap (60-90 min, including slow wave sleep and rapid eye movement sleep). We also collected data from both quiet and active rest control groups. All participants remained in the lab until being retested at 16:00. The data revealed that a daytime nap after imagery practice improved motor performance and, therefore, facilitated motor memory consolidation, as compared with spending a similar time interval in the wake state. Interestingly, the results revealed that both short and long naps resulted in similar delayed performance gains. The data might also suggest that the presence of slow wave and rapid eye movement sleep does not provide additional benefits for the sleep-dependent motor skill consolidation following MI practice.

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Assessment of hypnotic susceptibility is usually obtained through the application of psychological instruments. A satisfying classification obtained through quantitative measures is still missing, although it would be very useful for both diagnostic and clinical purposes. Aiming at investigating the relationship between the cortical brain activity and the hypnotic susceptibility level, we propose the combined use of two methodologies - Recurrence Quantification Analysis and Detrended Fluctuation Analysis - both inherited from nonlinear dynamics. Indicators obtained through the application of these techniques to EEG signals of individuals in their ordinary state of consciousness allowed us to obtain a clear discrimination between subjects with high and low susceptibility to hypnosis. Finally a neural network approach was used to perform classification analysis.