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Sleep talking (ST) has been rarely studied as an isolated phenomenon. Late investigations over the psycholinguistic features of vocal production in ST pointed to coherence with wake language formal features. Therefore, we investigated the EEG correlates of Verbal ST as the overt manifestation of sleep-related language processing, with the hypothesis of shared electrophysiological correlates with wake language production.From a sample of 155 highly frequent STs, we recorded 13 participants (age range 19-30y, mean age 24.6 ± 3.3; 7F) via vPSG for at least 2-consecutive nights, and a total of 28 nights. We first investigated the sleep macrostructure of STs compared to 13 age and gender matched subjects. We then compared the EEG signal before 21 Verbal STs vs. 21 Non-verbal STs (moaning, laughing, crying, ...) in 6 STs reporting both vocalization types in Stage 2 NREM sleep.The 2x2 mixed ANOVA Group x Night interaction showed no statistically significant effect for macrostructural variables, but significant main effects for Group with lower REM (%), TST, TBT SEI and greater NREM (%) for STs compared to controls.EEG statistical comparisons (paired-samples Student's t-Test) showed a decrement in power spectra for Verbal STs vs. Non-verbal STs within the theta and alpha EEG bands, strongly lateralized to the left hemisphere and localized on centro-parietal-occipitals channels. A single left parietal channel (P7) held significance after Bonferroni correction.Our results suggest shared neural mechanisms between Verbal ST and language processing during wakefulness and a possible functional overlapping with linguistic planning in wakefulness.

Introduction Non-rapid eye movement (NREM) parasomnias are defined as abnormal nocturnal behaviors that typically arise from the NREM sleep stage 3 during the first sleep cycle. The polysomnographic studies showed an increase in sleep fragmentation and an atypical slow wave activity (SWA) in participants with NREM parasomnias compared to healthy controls. To date, the pathophysiology of NREM parasomnias is still poorly understood. The recent investigation of the EEG patterns immediately before parasomnia events could shed light on the motor activations’ processes. This systematic review aims to summarize empirical evidence about these studies and provide an overview of the methodological issues. Methods A systematic literature search was carried out in PubMed, Web of Science, and Scopus, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The documents obtained were evaluated using the Newcastle–Ottawa Scale (NOS). Results Nine studies were included in the qualitative synthesis. The major evidence revealed an increased slow frequency EEG activity immediately before the motor activations in frontal and central areas and increased beta activity in the anterior cingulate cortices. Discussion The investigation of EEG patterns before parasomniac episodes could provide new insight into the study of NREM parasomnia pathophysiology. The high- and low-frequency EEG increase before the episodes could represent a predictive electrophysiological pattern of the motor activations’ onset. Overall, identifying specific sleep markers before parasomnias might also help differentiate between NREM parasomnias and other motor sleep disorders. Different methodological protocols should be integrated for overcoming the lack of consistent empirical findings. Thus, future studies should focus on the topographical examination of canonical EEG frequency bands to better understand spatial and time dynamics before the episodes and identify the networks underlying the onset of activations.

Background: Several studies highlighted that sleepiness affects driving abilities. In particular, road traffic injuries due to excessive daytime sleepiness are about 10–20%. Considering that aging is related to substantial sleep changes and the number of older adults with driving license is increasing, the current review aims to summarize recent studies on this issue. Further, we intend to provide insights for future research. Methods: From the 717 records screened, ten articles were selected and systematically reviewed. Results: Among the selected articles, (a) five studies investigated sleepiness only by self-reported standardized measures; (b) two studies assessed sleepiness also using a behavioral task; (c) three studies obtained objective measures by electroencephalographic recordings. Conclusions: The available literature on the topic reports several limitations. Overall, many findings converge in evidencing that older drivers are less vulnerable to sleep loss and sleepiness-related driving impairments than young adults. These discrepancies in sleepiness vulnerability between age groups may be ascribed to differences in subjects’ lifestyles. Moreover, it has been hypothesized that older adults self-regulate their driving and avoid specific dangerous situations. We believe that an easy protocol to objectively evaluate the vigilance level in elderly and young adults is required, and further studies are needed.

Purpose COVID-19 pandemic waves have strongly influenced individuals’ behaviors and mental health. Here, we analyzed longitudinal data collected in the Spring 2020 and 2021 from a large Italian sample with the aim of assessing changes in dream features between the first and third wave. Specifically, we evaluated the modifications of pandemic dream activity as a function of the general distress variations over time. Also, we detected the best explanatory variables of nightmare frequency and distress. Materials and Methods Participants previously involved in the web survey during the first wave of the pandemic were asked to complete a new online survey on sleep and dream features available in Spring 2021 (N=728). Subjects decreasing their level of psychological general distress in the third (T3) vs the first (T1) pandemic wave were defined as “Improved” (N=330). In contrast, participants with an unchanged or increased level of general distress were defined as “Not Improved” (N=398). Results Statistical comparisons revealed that dream recall frequency, nightmare frequency, lucid dream frequency, and emotional intensity show a reduction in T3 than T1. Moreover, the Improved group is characterized by lower nightmare rate and nightmare distress than Not Improved people. Our findings confirmed that beyond the trait-like variables (ie, age, sex), specific sleep-related measures are related to nightmare features. In particular, poor sleep hygiene was one of the best determinants of nightmare distress among Not Improved subjects. Conclusion Our findings revealed that people experienced an adaptation to the pandemic during the third wave. We also strengthen the notion that nightmares and their variations over time are strongly related to human well-being, suggesting that specific trait-like and sleep-related factors could modulate the relationship between mental health and nightmare features.