Consciousness never fades during waking. However, when awakened from sleep, we sometimes recall dreams and sometimes recall no experiences. Traditionally, dreaming has been identified with rapid eye-movement (REM) sleep, characterized by wake-like, globally 'activated', high-frequency electroencephalographic activity. However, dreaming also occurs in non-REM (NREM) sleep, characterized by prominent low-frequency activity. This challenges our understanding of the neural correlates of conscious experiences in sleep. Using high-density electroencephalography, we contrasted the presence and absence of dreaming in NREM and REM sleep. In both NREM and REM sleep, reports of dream experience were associated with local decreases in low-frequency activity in posterior cortical regions. High-frequency activity in these regions correlated with specific dream contents. Monitoring this posterior 'hot zone' in real time predicted whether an individual reported dreaming or the absence of dream experiences during NREM sleep, suggesting that it may constitute a core correlate of conscious experiences in sleep.

Objectives:. To assess how the characteristics of slow waves and spindles change in the falling-asleep process.Design:. Participants undergoing overni

Previous work showed that two types of slow waves are temporally dissociated during the transition to sleep: widespread, large and steep slow waves predominate early in the falling asleep period (type I), while smaller, more circumscribed slow waves become more prevalent later (type II). Here, we studied the possible occurrence of these two types of slow waves in stable non-REM (NREM) sleep and explored potential differences in their regulation. A heuristic approach based on slow wave synchronization efficiency was developed and applied to high-density electroencephalographic (EEG) recordings collected during consolidated NREM sleep to identify the potential type I and type II slow waves. Slow waves with characteristics compatible with those previously described for type I and type II were identified in stable NREM sleep. Importantly, these slow waves underwent opposite changes across the night, with only type II slow waves displaying a clear homeostatic regulation. In addition, we showed that the occurrence of type I slow waves was often followed by larger type II slow waves, whereas the occurrence of type II slow waves was usually followed by smaller type I waves. Finally, type II slow waves were associated with a relative increase in spindle activity, while type I slow waves triggered periods of high-frequency activity. Our results provide evidence for the existence of two distinct slow wave synchronization processes that underlie two different types of slow waves. These slow waves may have different functional roles and mark partially distinct “micro-states” of the sleeping brain.

Dreaming—a particular form of consciousness that occurs during sleep—undergoes major changes in the course of the night. We aimed to outline state-dependent features of consciousness using a paradigm with multiple serial awakenings/questionings that allowed for within as well as between subject comparisons. Seven healthy participants who spent 44 experimental study nights in the laboratory were awakened by a computerized sound at 15–30 min intervals, regardless of sleep stage, and questioned for the presence or absence of sleep consciousness. Recall without content (“I was experiencing something but do not remember what”) was considered separately. Subjects had to indicate the content of the most recent conscious experience prior to the alarm sound and to estimate its duration and richness. We also assessed the degree of thinking and perceiving, self- and environment-relatedness and reflective consciousness of the experiences. Of the 778 questionings, 5% were performed during wakefulness, 2% in stage N1, 42% in N2, 33% in N3, and 17% in rapid eye movement (REM) sleep. Recall with content was reported in 34% of non-REM and in 77% of REM sleep awakenings. Sleep fragmentation inherent to the study design appeared to only minimally affect the recall of conscious experiences. Each stage displayed a unique combination of characteristic features of sleep consciousness. In conclusion, our serial awakening paradigm allowed us to collect a large and representative sample of conscious experiences across states of being. It represents a time-efficient method for the study of sleep consciousness that may prove particularly advantageous when combined with techniques such as functional MRI and high-density EEG.

Recent work has demonstrated that behavioral manipulations targeting specific cortical areas during prolonged wakefulness lead to a region-specific homeostatic increase in theta activity (5–9 Hz), suggesting that theta waves could represent transient neuronal OFF periods (local sleep). In awake rats, the occurrence of an OFF period in a brain area relevant for behavior results in performance errors. Here we investigated the potential relationship between local sleep events and negative behavioral outcomes in humans.Volunteers participated in two prolonged wakefulness experiments (24 h), each including 12 h of practice with either a driving simulation (DS) game or a battery of tasks based on executive functions (EFs). Multiple high-density EEG recordings were obtained during each experiment, both in quiet rest conditions and during execution of two behavioral tests, a response inhibition test and a motor test, aimed at assessing changes in impulse control and visuomotor performance, respectively. In addition, fMRI examinations obtained at 12 h intervals were used to investigate changes in inter-regional connectivity.The EF experiment was associated with a reduced efficiency in impulse control, whereas DS led to a relative impairment in visuomotor control. A specific spatial and temporal correlation was observed between EEG theta waves occurring in task-related areas and deterioration of behavioral performance. The fMRI connectivity analysis indicated that performance impairment might partially depend on a breakdown in connectivity determined by a “network overload.”Present results demonstrate the existence of an association between theta waves during wakefulness and performance errors and may contribute explaining behavioral impairments under conditions of sleep deprivation/restriction.