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Leu-Semenescu, S.

Publications and source records attributed to Leu-Semenescu, S..

2 recordsLinked to original sources

Live access to the emotional dynamics of REM sleep dreams in lucid dreamers with narcolepsy

Sleep helps regulate emotions, but it is still unclear whether -and how- the emotions we experience in dreams contribute to this regulation. To uncover the potential function of dream emotions, we must first understand what they are and how they unfold in dreams. The emotional content of dreams has mostly been studied using post-sleep dream reports, which provide a biased and static snapshot of a complex and dynamic experience. In this study, we took a more direct approach, accessing dream emotions in real-time. We asked twenty-four lucid dreamers with narcolepsy to report the emotional valence of their dreams, - positive, negative or neutral-, while still asleep, using predefined facial codes during daytime naps monitored with polysomnography. Of the 126 naps recorded, 62 contained at least one emotional code during REM sleep, yielding 191 codes in total. The ratios of positive and negative codes were evenly balanced per nap. The 33 naps with at least two codes allowed us to track the dream emotional dynamics. Over half of these naps showed opposite emotional valences (positive and negative). By measuring the time elapsed between codes, we estimated the average duration of a given dreams emotional valence in REM sleep to be about one minute. Positive emotions emerged on average earlier than negative ones during lucid REM sleep. These findings confirm the highly emotional nature of dreams and, more importantly, highlight that emotions in REM sleep dreams are fluid and fast-changing. Such emotional dynamics during REM sleep dreams may help us to better understand the mechanisms of the emotional regulatory function of dreams.

neuroscience↗

EEG Signature of Idiopathic Hypersomnia: Insights from Sleep Microarchitecture and Hypnodensity Metrics

Background and ObjectivesPatients with idiopathic hypersomnia with long sleep time (IH) report daytime hypersomnolence despite prolonged sleep time and normal sleep macrostructure. As they often have non-restorative sleep, we investigated whether the structure of their sleep is abnormal. MethodsIn polysomnography recordings from 80 IH participants and 48 controls, we quantified hypnodensity metrics across the night (macro level), periodic and aperiodic spectral properties, infraslow fluctuations of sigma power within the night (meso level), slow waves, sleep spindles and their clustering (microstructure). Multivariate machine-learning models were used to classify IH vs. control sleep. ResultsHypnodensity metrics were comparable between IH and controls, apart from more mixed wake/N1 sleep epochs in IH, and greater divergence between consecutive epochs of the same stage during NREM sleep in IH. Sigma power was increased in N2 sleep in IH and sleep spindles were more frequent and clustered. Slow wave density was higher in IH. Higher mean spindle cluster size correlated with higher Epworth Sleepiness Scale scores. Multivariate machine learning models incorporating these features achieved a balanced accuracy of 74% in distinguishing IH from controls. DiscussionWhile spindles and slow waves are typically associated with good sleep quality, they are increased in IH patients. This could reflect greater need for sleep and increased difficulty waking up in IH, which is also characterized by more mixed wake/N1 stages.

neuroscience↗