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Over the past 60 years, the impact of psychotropic drugs on dream recall and content has been scarcely explored. A review of the few existing experimental results on the topic leads us to the following conclusions. For antidepressant drugs, in the great majority, they reduce dream recall frequency (DRF), and the improvement of depressive symptoms is associated with an increase of positive emotion in dream content. For sedative psychotropic drugs, their improvement of sleep quality is associated with a reduction of DRF, but the effect on dream content is less clear. Few occurrences of nightmare frequency increase have been reported, with intake of molecules disturbing sleep or with the withdrawal of some psychotropic drugs. Importantly, the impact of psychotropic drugs on rapid eye movement (REM) sleep does not explain DRF modulations. The reduction of intra-sleep awakenings seems to be the parameter explaining best the modulation of DRF by psychotropic drugs. Indeed, molecules that improve sleep continuity by reducing intra-sleep awakenings also reduce the frequency of dream recall, which is coherent with the "arousal-retrieval model" stating that nighttime awakenings enable dreams to be encoded into long-term memory and therefore facilitate dream recall. DRF is nonetheless influenced by several other factors (e.g., interest in dreams, the method of awakening, and personality traits), which may explain a large part of the variability of results observed and cited in this article. [ABSTRACT FROM AUTHOR]

We introduce Sleep, a new Python open-source graphical user interface (GUI) dedicated to visualization, scoring and analyses of sleep data. Among its most prominent features are: (1) Dynamic display of polysomnographic data, spectrogram, hypnogram and topographic maps with several customizable parameters, (2) Implementation of several automatic detection of sleep features such as spindles, K-complexes, slow waves, and rapid eye movements (REM), (3) Implementation of practical signal processing tools such as re-referencing or filtering, and (4) Display of main descriptive statistics including publication-ready tables and figures. The software package supports loading and reading raw EEG data from standard file formats such as European Data Format, in addition to a range of commercial data formats. Most importantly, Sleep is built on top of the VisPy library, which provides GPU-based fast and high-level visualization. As a result, it is capable of efficiently handling and displaying large sleep datasets. Sleep is freely available and comes with sample datasets and an extensive documentation. Novel functionalities will continue to be added and open-science community efforts are expected to enhance the capacities of this module. [ABSTRACT FROM AUTHOR]

High dream recallers (HR) show a larger brain reactivity to auditory stimuli during wakefulness and sleep as compared to low dream recallers (LR) and also more intrasleep wakefulness (ISW), but no other modification of the sleep macrostructure. To further understand the possible causal link between brain responses, ISW and dream recall, we investigated the sleep microstructure of HR and LR, and tested whether the amplitude of auditory evoked potentials (AEPs) was predictive of arousing reactions during sleep. Participants (18 HR, 18 LR) were presented with sounds during a whole night of sleep in the lab and polysomnographic data were recorded. Sleep microstructure (arousals, rapid eye movements (REMs), muscle twitches (MTs), spindles, KCs) was assessed using visual, semi-automatic and automatic validated methods. AEPs to arousing (awakenings or arousals) and non-arousing stimuli were subsequently computed. No between-group difference in the microstructure of sleep was found. In N2 sleep, auditory arousing stimuli elicited a larger parieto-occipital positivity and an increased late frontal negativity as compared to non-arousing stimuli. As compared to LR, HR showed more arousing stimuli and more long awakenings, regardless of the sleep stage but did not show more numerous or longer arousals. These results suggest that the amplitude of the brain response to stimuli during sleep determine subsequent awakening and that awakening duration (and not arousal) is the critical parameter for dream recall. Notably, our results led us to propose that the minimum necessary duration of an awakening during sleep for a successful encoding of dreams into long-term memory is approximately 2 min. [ABSTRACT FROM AUTHOR]

Sleep spindles and K-complexes are among the most prominent micro-events observed in electroencephalographic (EEG) recordings during sleep. These EEG microstructures are thought to be hallmarks of sleep-related cognitive processes. Although tedious and time-consuming, their identification and quantification is important for sleep studies in both healthy subjects and patients with sleep disorders. Therefore, procedures for automatic detection of spindles and K-complexes could provide valuable assistance to researchers and clinicians in the field. Recently, we proposed a framework for joint spindle and K-complex detection (Lajnef et al., 2015a) based on a Tunable Q-factor Wavelet Transform (TQWT; Selesnick, 2011a) and morphological component analysis (MCA). Using a wide range of performance metrics, the present article provides critical validation and benchmarking of the proposed approach by applying it to open-access EEG data from the Montreal Archive of Sleep Studies (MASS; O'Reilly et al., 2014). Importantly, the obtained scores were compared to alternative methods that were previously tested on the same database. With respect to spindle detection, our method achieved higher performance than most of the alternative methods. This was corroborated with statistic tests that took into account both sensitivity and precision (i.e., Matthew's coefficient of correlation (MCC), F1, Cohen k). Our proposed method has been made available to the community via an open-source tool named Spinky (for spindle and K-complex detection). Thanks to a GUI implementation and access to Matlab and Python resources, Spinky is expected to contribute to an open-science approach that will enhance replicability and reliable comparisons of classifier performances for the detection of sleep EEG microstructure in both healthy and patient populations. [ABSTRACT FROM AUTHOR]

There have been reports and claims in the psychotherapeutic literature that the consideration of recent dreams can result in personal realizations and insight. There is theoretical support for these claims from work on rapid eye movement (REM) sleep having a function of the consolidation of emotional memories and the creative formation of connections between new and older memories. To investigate these claims, 11 participants (10 females, one male) reported and considered a recent home dream in a dream discussion group that following the "Appreciating dreams" method of Montague Ullman. The group ran 11 times, each participant attending and participating once. A further nine participants (seven females, two males) reported and considered a recent home dream in a group that followed the "Listening to the dreamer" method of Michael Schredl. The two studies each had a control condition where the participant also reported a recent event, the consideration of which followed the same technique as was followed for the dream report. Outcomes of the discussions were assessed by the participants on the Gains from Dream Interpretation (GDI) scale, and on its counterpart, the Gains from Event Interpretation scale. High ratings on the GDI experiential-insight subscale were reported for both methods, when applied to dreams, and for the Ullman method Exploration-Insight ratings for the dream condition were significantly higher than for the control event condition. In the Ullman method, self-assessment of personal insight due to consideration of dream content was also significantly higher than for the event consideration condition. The findings support the view that benefits can be obtained from the consideration of dream content, in terms of identifying the waking life sources of dream content, and because personal insight may also occur. To investigate the mechanisms for the findings, the studies should be repeated with REM and non-REM dream reports, hypothesizing greater insight from the former. [ABSTRACT FROM AUTHOR]

A novel framework for joint detection of sleep spindles and K-complex events, two hallmarks of sleep stage S2, is proposed. Sleep electroencephalography (EEG) signals are split into oscillatory (spindles) and transient (K-complex) components. This decomposition is conveniently achieved by applying morphological component analysis (MCA) to a sparse representation of EEG segments obtained by the recently introduced discrete tunable Q-factor wavelet transform (TQWT). Tuning the Q-factor provides a convenient and elegant tool to naturally decompose the signal into an oscillatory and a transient component. The actual detection step relies on thresholding (i) the transient component to reveal K-complexes and (ii) the time-frequency representation of the oscillatory component to identify sleep spindles. Optimal thresholds are derived from ROC-like curves (sensitivity vs. FDR) on training sets and the performance of the method is assessed on test data sets. We assessed the performance of our method using full-night sleep EEG data we collected from 14 participants. In comparison to visual scoring (Expert 1), the proposed method detected spindles with a sensitivity of 83.18% and false discovery rate (FDR) of 39%, while K-complexes were detected with a sensitivity of 81.57% and an FDR of 29.54%. Similar performances were obtained when using a second expert as benchmark. In addition, when the TQWT and MCA steps were excluded from the pipeline the detection sensitivities dropped down to 70% for spindles and to 76.97% for K-complexes, while the FDR rose up to 43.62 and 49.09%, respectively. Finally, we also evaluated the performance of the proposed method on a set of publicly available sleep EEG recordings. Overall, the results we obtained suggest that the TQWT-MCA method may be a valuable alternative to existing spindle and K-complex detection methods. Paths for improvements and further validations with large-scale standard open-access benchmarking data sets are discussed.