Although the EEG slow wave of sleep is typically considered to be a hallmark of nonrapid eye movement (NREM) sleep, recent work in mice has shown that slow waves can also occur in REM sleep. Here, we investigated the presence and cortical distribution of negative delta (1-4 Hz) waves in human REM sleep by analyzing high-density EEG sleep recordings obtained in 28 healthy subjects. We identified two clusters of delta waves with distinctive properties: (1) a frontal-central cluster characterized by ϳ2.5-3.0 Hz, relatively large, notched delta waves (so-called "sawtooth waves") that tended to occur in bursts, were associated with increased gamma activity and rapid eye movements (EMs), and upon source modeling displayed an occipital-temporal and a frontal-central component and (2) a medialoccipital cluster characterized by more isolated, slower (Ͻ2 Hz), and smaller waves that were not associated with rapid EMs, displayed a negative correlation with gamma activity, and were also found in NREM sleep. Therefore, delta waves are an integral part of REM sleep in humans and the two identified subtypes (sawtooth and medial-occipital slow waves) may reflect distinct generation mechanisms and functional roles. Sawtooth waves, which are exclusive to REM sleep, share many characteristics with ponto-geniculo-occipital waves described in animals and may represent the human equivalent or a closely related event, whereas medial-occipital slow waves appear similar to NREM sleep slow waves.The EEG slow wave is typically considered a hallmark of nonrapid eye movement (NREM) sleep, but recent work in mice has shown that it can also occur in REM sleep. By analyzing high-density EEG recordings collected in healthy adult individuals, we show that REM sleep is characterized by prominent delta waves also in humans. In particular, we identified two distinctive clusters of delta waves with different properties: a frontal-central cluster characterized by faster, activating "sawtooth waves" that share many characteristics with ponto-geniculo-occipital waves described in animals and a medial-occipital cluster containing slow waves that are more similar to NREM sleep slow waves. These findings indicate that REM sleep is a spatially and temporally heterogeneous state and may contribute to explaining its known functional and phenomenological properties.

Feeling awake although sleep recordings indicate clear-cut sleep sometimes occurs in good sleepers and to an extreme degree in patients with so-called paradoxical insomnia. It is unknown what underlies sleep misperception, as standard polysomnographic (PSG) parameters are often normal in these cases. Here we asked whether regional changes in brain activity could account for the mismatch between objective and subjective total sleep times (TST). To set cutoffs and define the norm, we first evaluated sleep perception in a population-based sample, consisting of 2,092 individuals who underwent a full PSG at home and estimated TST the next day. We then compared participants with a low mismatch (normoestimators, n = 1,147, ±0.5 SD of mean) with those who severely underestimated (n = 52, <2.5th percentile) or overestimated TST (n = 53, >97.5th percentile). Compared with normoestimators, underestimators displayed higher electroencephalographic (EEG) activation (beta/delta power ratio) in both rapid eye movement (REM) and non-rapid eye movement (NREM) sleep, while overestimators showed lower EEG activation (significant in REM sleep). To spatially map these changes, we performed a second experiment, in which 24 healthy subjects and 10 insomnia patients underwent high-density sleep EEG recordings. Similarly to underestimators, patients displayed increased EEG activation during NREM sleep, which we localized to central-posterior brain areas. Our results indicate that a relative shift from low- to high-frequency spectral power in central-posterior brain regions, not readily apparent in conventional PSG parameters, is associated with underestimation of sleep duration. This challenges the concept of sleep misperception, and suggests that instead of misperceiving sleep, insomnia patients may correctly perceive subtle shifts toward wake-like brain activity.

The weighted Phase Lag Index (wPLI) and the weighted Symbolic Mutual Information (wSMI) represent two robust and widely used methods for MEG/EEG functional connectivity estimation. Interestingly, both methods have been shown to detect relative alterations of brain functional connectivity in conditions associated with changes in the level of consciousness, such as following severe brain injury or under anaesthesia. Despite these promising findings, it was unclear whether wPLI and wSMI may account for distinct or similar types of functional interactions. Using simulated high-density (hd-)EEG data, we demonstrate that, while wPLI has high sensitivity for couplings presenting a mixture of linear and nonlinear interdependencies, only wSMI can detect purely nonlinear interaction dynamics. Moreover, we evaluated the potential impact of these differences on real experimental data by computing wPLI and wSMI connectivity in hd-EEG recordings of 12 healthy adults during wakefulness and deep (N3-)sleep, characterised by different levels of consciousness. In line with the simulation-based findings, this analysis revealed that both methods have different sensitivity for changes in brain connectivity across the two vigilance states. Our results indicate that the conjoint use of wPLI and wSMI may represent a powerful tool to study the functional bases of consciousness in physiological and pathological conditions.

Humans use emotions to decipher complex cascades of internal events. However, which mechanisms link descriptions of affective states to brain activity is unclear, with evidence supporting either local or distributed processing. A biologically favorable alternative is provided by the notion of gradient, which postulates the isomorphism between functional representations of stimulus features and cortical distance. Here, we use fMRI activity evoked by an emotionally charged movie and continuous ratings of the perceived emotion intensity to reveal the topographic organization of affective states. Results show that three orthogonal and spatially overlapping gradients encode the polarity, complexity and intensity of emotional experiences in right temporo-parietal territories. The spatial arrangement of these gradients allows the brain to map a variety of affective states within a single patch of cortex. As this organization resembles how sensory regions represent psychophysical properties (e.g., retinotopy), we propose emotionotopy as a principle of emotion coding.

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