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Lucid dreaming (LD) is a mental state in which we realize not being awake but are dreaming while asleep. It often involves vivid, perceptually intense dream images as well as peculiar kinesthetic sensations, such as flying, levitating, or out-of-body experiences. LD is in the crossspotlight of cognitive neuroscience and sleep research as a particular case to study consciousness, cognition, and the neural background of dream experiences. Here, we present a multicomponent framework for the study and understanding of neurocognitive mechanisms and phenomenological aspects of LD. We propose that LD is associated with prediction error signals arising during sleep and occurring at higher or lower levels of the processing hierarchy. Prediction errors are resolved by generating a superordinate self-model able to integrate ambiguous stimuli arriving from sensory periphery and higher-order cortical regions. While multisensory integration enables lucidity maintenance and contributes to peculiar kinesthetic experiences, attentional control facilitates multisensory integration by dynamically regulating the balance between the influence of top-down mental models and the precision weighting of bottom-up sensory inputs. Our novel framework aims to link neural correlates of LD with current concepts of sleep and arousal regulation and provide testable predictions on interindividual differences in LD as well as neurocognitive mechanisms inducing lucid dreams.

Novelty is central to the study of memory, but the wide range of experimental manipulations aimed to reveal its effects on learning produced inconsistent results. The novelty/encoding hypothesis suggests that novel information undergoes enhanced encoding and thus leads to benefits in memory, especially in recognition performance; however, recent studies cast doubts on this assumption. On the other hand, data from animal studies provided evidence on the robust effects of novelty manipulations on the neurophysiological correlates of memory processes. Conceptualizations and operationalizations of novelty are remarkably variable and were categorized into different subtypes, such as stimulus, context, associative or spatial novelty. Here, we summarize previous findings about the effects of novelty on memory and suggest that predictive coding theories provide a framework that could shed light on the differential influence of novelty manipulations on memory performance. In line with predictive coding theories, we emphasize the role of unexpectedness as a crucial property mediating the behavioral and neural effects of novelty manipulations.

Features of sleep were shown to reflect aging, typical sex differences and cognitive abilities of humans. However, these measures are characterized by redundancy and arbitrariness. Our present approach relies on the assumptions that the spontaneous human brain activity as reflected by the scalp-derived electroencephalogram (EEG) during non-rapid eye movement (NREM) sleep is characterized by arrhythmic, scale-free properties and is based on the power law scaling of the Fourier spectra with the additional consideration of the rhythmic, oscillatory waves at specific frequencies, including sleep spindles. Measures derived are the spectral intercept and slope, as well as the maximal spectral peak amplitude and frequency in the sleep spindle range, effectively reducing 191 spectral measures to 4, which were efficient in characterizing known age-effects, sex-differences and cognitive correlates of sleep EEG. Future clinical and basic studies are supposed to be significantly empowered by the efficient data reduction provided by our approach.

It has been argued that novel compared to familiar stimuli are preferentially encoded into memory. Nevertheless, treating novelty as a categorical variable in experimental research is considered simplistic. We highlight the dimensional aspect of novelty and propose an experimental design that manipulates novelty continuously. We created the Graded Novelty Encoding Task (GNET), in which the difference between stimuli (i.e. novelty) is parametrically manipulated, paving the way for quantitative models of novelty processing. We designed an algorithm which generates visual stimuli by placing colored shapes in a grid. During the familiarization phase of the task, we repeatedly presented five pictures to the participants. In a subsequent incidental learning phase, participants were asked to differentiate between the “familiars” and novel images that varied in the degree of difference to the familiarized pictures (i.e. novelty). Finally, participants completed a surprise recognition memory test, where the novel stimuli from the previous phase were interspersed with distractors with similar difference characteristics. We numerically expressed the differences between the stimuli to compute a dimensional indicator of novelty and assessed whether it predicted recognition memory performance. Based on previous studies showing the beneficial effect of novelty on memory formation, we hypothesized that the more novel a given picture was, the better subsequent recognition performance participants would demonstrate. Our hypothesis was confirmed: recognition performance was higher for more novel stimuli. The GNET captures the continuous nature of novelty, and it may be useful in future studies that examine the behavioral and neurocognitive aspects of novelty processing.

Functional hemispheric asymmetry was evidenced in many species during sleep. Dogs seem to show hemispheric asymmetry during wakefulness; however, their asymmetric neural activity during sleep was not yet explored. The present study investigated interhemispheric asymmetry in family dogs using non-invasive polysomnography. EEG recordings during 3-h-long afternoon naps were carried out (N = 19) on two occasions at the same location. Hemispheric asymmetry was assessed during NREM sleep, using bilateral EEG channels. To include periods with high homeostatic sleep pressure and to reduce the variance of the time spent in NREM sleep between dogs, the first two sleep cycles were analysed. Left hemispheric predominance of slow frequency range was detected in the first sleep cycle of sleep recording 1, compared to the baseline level of zero asymmetry as well as to the first sleep cycle of sleep recording 2. Regarding the strength of hemispheric asymmetry, we found greater absolute hemispheric asymmetry in the second sleep cycle of sleep recording 1 and 2 in the frequency ranges of alpha, sigma and beta, compared to the first sleep cycle. Differences between sleep recordings and consecutive sleep cycles might be indicative of adaptation-like processes, but do not closely resemble the results described in humans.

Rapid Eye Movement (REM) sleep is a peculiar neural state showing a combination of muscle atonia and intense cortical activity. REM sleep is usually considered as a unitary state in neuroscientific research; however, it is composed of two different microstates: phasic and tonic REM. These differ in awakening thresholds, sensory processing, and cortical oscillations. Nevertheless, studies examining cortical oscillations during REM microstates are scarce, and used low spatial sampling. Here, we analyzed the data of 18 healthy individuals assessed by high-density sleep EEG recordings. We systematically contrasted phasic and tonic REM periods in terms of topographical distribution, source localization, as well as local, global and long-range synchronization of frequency-specific cortical activity. Tonic periods showed relatively increased high alpha and beta power over frontocentral derivations. In addition, higher frequency components of beta power exhibited increased global synchronization during tonic compared to phasic states. In contrast, in phasic periods we found increased power and synchronization of low frequency oscillations coexisting with increased and synchronized gamma activity. Source localization revealed several multimodal, higher-order associative, as well as sensorimotor areas as potential sources of increased high alpha/beta power during tonic compared to phasic REM. Increased gamma power in phasic REM was attributed to medial prefrontal and right lateralized temporal areas associated with emotional processing. Our findings emphasize the heterogeneous nature of REM sleep, expressed in two microstates with remarkably different neural activity. Considering the microarchitecture of REM sleep may provide new insights into the mechanisms of REM sleep in health and disease.

•We use a cross-validated method to predict cognitive performance from sleep EEG features.•Up to 10% of cognition variance is accounted for by sleep EEG features.•The most associated features are: NREM theta/sigma, REM beta.•Other EEG features add comparatively little information.•Demographics and health only account for part of the association. Human cognitive performance is a key function whose biological foundations have been partially revealed by genetic and brain imaging studies. The sleep electroencephalogram (EEG) is tightly linked to structural and functional features of the central nervous system and serves as another promising biomarker. We used data from MrOS, a large cohort of older men and cross-validated regularized regression to link sleep EEG features to cognitive performance in cross-sectional analyses. In independent validation samples 2.5–10% of variance in cognitive performance can be accounted for by sleep EEG features, depending on the covariates used. Demographic characteristics account for more covariance between sleep EEG and cognition than health variables, and consequently reduce this association by a greater degree, but even with the strictest covariate sets a statistically significant association is present. Sigma power in NREM and beta power in REM sleep were associated with better cognitive performance, while theta power in REM sleep was associated with worse performance, with no substantial effect of coherence and other sleep EEG metrics. Our findings show that cognitive performance is associated with the sleep EEG (r = 0.283), with the strongest effect ascribed to spindle-frequency activity. This association becomes weaker after adjusting for demographic (r = 0.186) and health variables (r = 0.155), but its resilience to covariate inclusion suggest that it also partially reflects trait-like differences in cognitive ability.

Abstract Sleep is a fundamental physiological state that facilitates neural recovery during periods of attenuated sensory processing. On the other hand, mammalian sleep is also characterized by the interplay between periods of increased sleep depth and environmental alertness. Whereas the heterogeneity of microstates during non-rapid-eye-movement (NREM) sleep was extensively studied in the last decades, transient microstates during rapid-eye-movement (REM) sleep received less attention. REM sleep features two distinct microstates: phasic and tonic. Previous studies indicate that sensory processing is largely diminished during phasic REM periods, whereas environmental alertness is partially reinstated when the brain switches into tonic REM sleep. Here, we investigated interoceptive processing as quantified by the heartbeat evoked potential (HEP) during REM microstates. We contrasted the HEPs of phasic and tonic REM periods using two separate databases that included the nighttime polysomnographic recordings of healthy young individuals (N = 20 and N = 19). We find a differential HEP modulation of a late HEP component (after 500 ms post-R-peak) between tonic and phasic REM. Moreover, the late tonic HEP component resembled the HEP found in resting wakefulness. Our results indicate that interoception with respect to cardiac signals is not uniform across REM microstates, and suggest that interoceptive processing is partially reinstated during tonic REM periods. The analyses of the HEP during REM sleep may shed new light on the organization and putative function of REM microstates.

Unfolding the overnight dynamics in human sleep features plays a pivotal role in understanding sleep regulation. Studies revealed the complex reorganization of the frequency composition of sleep electroencephalogram (EEG) during the course of sleep, however the scale-free and the oscillatory measures remained undistinguished and improperly characterized before. By focusing on the first four non-rapid eye movement (NREM) periods of night sleep records of 251 healthy human subjects (4–69 years), here we reveal the flattening of spectral slopes and decrease in several measures of the spectral intercepts during consecutive sleep cycles. Slopes and intercepts are significant predictors of slow wave activity (SWA), the gold standard measure of sleep intensity. The overnight increase in spectral peak sizes (amplitudes relative to scale-free spectra) in the broad sigma range is paralleled by a U-shaped time course of peak frequencies in frontopolar regions. Although, the set of spectral indices analyzed herein reproduce known age- and sex-effects, the interindividual variability in spectral slope steepness is lower as compared to the variability in SWA. Findings indicate that distinct scale-free and oscillatory measures of sleep EEG could provide composite measures of sleep dynamics with low redundancy, potentially affording new insights into sleep regulatory processes in future studies.

Rapid eye movement (REM) sleep is characterized by the alternation of two markedly different microstates, phasic and tonic REM. These periods differ in awakening and arousal thresholds, sensory processing, and spontaneous cortical oscillations. Previous studies indicate that although in phasic REM, cortical activity is independent of the external environment, attentional functions and sensory processing are partially maintained during tonic periods. Large-scale synchronization of oscillatory activity, especially in the α- and β-frequency ranges, can accurately distinguish different states of vigilance and cognitive processes of enhanced alertness and attention. Therefore, we examined long-range inter- and intrahemispheric as well as short-range electroencephalographic synchronization during phasic and tonic REM periods quantified by the weighted phase lag index. Based on the nocturnal polysomnographic data of 19 healthy adult participants, we showed that long-range inter- and intrahemispheric α and β synchrony was enhanced in tonic REM states in contrast to phasic ones, and resembled α and β synchronization of resting wakefulness. On the other hand, short-range synchronization within the γ-frequency range was higher in phasic compared with tonic periods. Increased short-range synchrony might reflect local and inwardly driven sensorimotor activity during phasic REM periods, whereas enhanced long-range synchrony might index frontoparietal activity that reinstates environmental alertness after phasic REM periods.