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Sleep and memory studies often focus on overnight rather than long-term memory changes, traditionally associating overnight memory change (OMC) with sleep architecture and sleep patterns such as spindles. In addition, (para-)sympathetic innervation has been associated with OMC after a daytime nap using heart rate variability (HRV). In this study we investigated overnight and long-term performance changes for procedural memory and evaluated associations with sleep architecture, spindle activity (SpA) and HRV measures (R-R interval [RRI], standard deviation of R-R intervals [SDNN], as well as spectral power for low [LF] and high frequencies [HF]). All participants (N = 20, M age  = 23.40 ± 2.78 years) were trained on a mirror-tracing task and completed a control (normal vision) and learning (mirrored vision) condition. Performance was evaluated after training (R1), after a full-night sleep (R2) and 7 days thereafter (R3). Overnight changes (R2-R1) indicated significantly higher accuracy after sleep, whereas a significant long-term (R3-R2) improvement was only observed for tracing speed. Sleep architecture measures were not associated with OMC after correcting for multiple comparisons. However, individual SpA change from the control to the learning night indicated that only "SpA enhancers" exhibited overnight improvements for accuracy and long-term improvements for speed. HRV analyses revealed that lower SDNN and LF power was associated with better OMC for the procedural speed measure. Altogether, this study indicates that overnight improvement for procedural memory is specific for spindle enhancers, and is associated with HRV during sleep following procedural learning., (© 2019 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society.)

Background: Seltorexant is a potent and selective antagonist of the orexin-2 receptor that is being developed for the treatment of insomnia and major depressive disorder., Aims: The primary objective was to investigate the effect of seltorexant on sleep efficiency after single and multiple dose administration in subjects with insomnia disorder without psychiatric comorbidity. Secondary objectives included evaluation of total sleep time, latency to persistent sleep, and wake after sleep onset. Subjects received 40 mg of seltorexant for five days during Period 1 and placebo during Period 2 or vice versa in this randomized, two-way crossover study. Objective sleep parameters were evaluated by polysomnography over 8 h on Day 1/2 (single dose) and on Day 5/6 (multiple doses). Subjective sleep parameters were assessed by questionnaires., Results: Twenty-seven subjects completed the study. The mean changes in sleep efficiency (% (SD)) of seltorexant from placebo at Day 1/2 were 5.8 (9.2), and 7.9 (9.8) at Day 5/6 ( p < 0.001 at both time points); in total sleep time (min (SD)) 27.7 (44.3) and 37.9 (47.1), respectively; in latency to persistent sleep (min (SD)) -18.8 (21.3) and -29.9 (27.7), respectively; and in wake after sleep onset (min (SD)) -11.1 (36.4) and -11.3 (46.5). The most common adverse events were headache and somnolence., Conclusions: Sleep efficiency was increased with seltorexant treatment compared with placebo. Treatment with seltorexant resulted in a prolonged total sleep time, shorter latency to persistent sleep and wake after sleep onset. There were no unexpected safety findings.

The goal of our study was to investigate different aspects of sleep, namely the sleep-wake cycle and sleep stages, in the vegetative state/unresponsive wakefulness syndrome (VS/UWS), and minimally conscious state (MCS). A 24-h polysomnography was performed in 20 patients who were in a UWS (n=10) or in a MCS (n=10) because of brain injury. The data were first tested for the presence of a sleep-wake cycle, and the observed sleep patterns were compared with standard scoring criteria. Sleep spindles, slow wave sleep, and rapid eye movement sleep were quantified and their clinical value was investigated. According to our results, an electrophysiological sleep-wake cycle was identified in five MCS and three VS/UWS patients. Sleep stages did not always match the standard scoring criteria, which therefore needed to be adapted. Sleep spindles were present more in patients who clinically improved within 6 months. Slow wave sleep was present in eight MCS and three VS/UWS patients but never in the ischemic etiology. Rapid eye movement sleep, and therefore dreaming that is a form of consciousness, was present in all MCS and three VS/UWS patients. In conclusion, the presence of alternating periods of eyes-open/eyes-closed cycles does not necessarily imply preserved electrophysiological sleep architecture in the UWS and MCS, contrary to previous definition. The investigation of sleep is a little studied yet simple and informative way to evaluate the integrity of residual brain function in patients with disorders of consciousness with possible clinical diagnostic and prognostic implications.

Aging and Alzheimer's disease (AD) are both characterized by memory impairments and sleep changes. We investigated the potential link between these disturbances, focusing on sleep spindles, involved in memory consolidation. Two episodic memory tasks were given to young and old healthy participants, as well as to AD patients. Postlearning sleep was recorded. Sleep spindles were globally reduced in aging and AD. AD patients also exhibited a further decrease in fast spindles. Besides, mean intensity of fast spindles was positively correlated, in AD patients, with immediate recall performance. Our results are the first report of a specific decrease in fast spindles in AD, associated with learning abilities. They also give further hints for a functional differentiation between slow and fast spindles.

We reported earlier that overnight change in explicit memory is positively related to the change in sleep spindle activity (between a control and a learning night). However, it remained unclear whether this effect was restricted to good memory performers and whether a general association of sleep spindles and a "sleep-related learning trait" may not account for this effect. Here we now present a secondary and more detailed analysis of our randomized multicenter study. Subjects were studied over a 4-week study period (including actigraphy and daily sleep diaries), including three overnight stays in the sleep laboratory. In the course of the study, subjects completed test-batteries of memory (Wechsler-Memory-Scale-revised; WMS) and other cognitive abilities (Raven's Advanced-Progressive-Matrices; APM) and were asked to study 160 word pairs in the evening before being tested by cued-recall. Afterwards, subjects went to bed in the laboratory with full polysomnographic montages. Additionally, subjects participated on another occasion in a non-learning control (perceptual priming) task that was counterbalanced either before or after the learning condition. Slow as well as fast spindle activities were analyzed at frontopolar and central topographies. Although it was found that spindle activity is generally (in learning as well as control nights) elevated in highly gifted subjects, spindle analyses revealed that spindle increase (control to learning night) is specifically related to explicit memory improvement overnight, independent of individual learning traits. Together these findings suggest that the spindle increase after learning is related to elaborate encoding before sleep, whereas an individual's general learning ability is well reflected in interindividual (and trait-like) differences of absolute sleep spindle activity.

Conventionally, polysomnographic recordings are classified according to the rules published in 1968 by Rechtschaffen and Kales (R&K). The present paper describes an automatic classification system embedded in an e-health solution that has been developed and validated in a large database of healthy controls and sleep disturbed patients. The Somnolyzer 24x7 adheres to the decision rules for visual scoring as closely as possible and includes a structured quality control procedure by a human expert. The final system consists of a raw data quality check, a feature extraction algorithm (density and intensity of sleep/wake-related patterns such as sleep spindles, delta waves, slow and rapid eye movements), a feature matrix plausibility check, a classifier designed as an expert system and a rule-based smoothing procedure for the start and the end of stages REM and 2. The validation based on 286 recordings in both normal healthy subjects aged 20 to 95 years and patients suffering from organic or nonorganic sleep disorders demonstrated an overall epoch-by-epoch agreement of 80% (Cohen's Kappa: 0.72) between the Somnolyzer 24x7 and the human expert scoring, as compared with an inter-rater reliability of 77% (Cohen's Kappa: 0.68) between two human experts. Two Somnolyzer 24x7 analyses (including a structured quality control by two human experts) revealed an inter-rater reliability close to 1 (Cohen's Kappa: 0.991). Moreover, correlation analysis in R&K derived target variables revealed similar -- in 36 out of 38 variables even higher -- relationships between Somnolyzer 24x7 and expert evaluations as compared to the concordance between two human experts. Thus, the validation study proved the high reliability and validity of the Somnolyzer 24x7 both, on the epoch-by-epoch and on the target variable level. These results demonstrate the applicability of the Somnolyzer 24x7 evaluation in clinical routine and sleep studies.

To date, the only standard for the classification of sleep-EEG recordings that has found worldwide acceptance are the rules published in 1968 by Rechtschaffen and Kales. Even though several attempts have been made to automate the classification process, so far no method has been published that has proven its validity in a study including a sufficiently large number of controls and patients of all adult age ranges. The present paper describes the development and optimization of an automatic classification system that is based on one central EEG channel, two EOG channels and one chin EMG channel. It adheres to the decision rules for visual scoring as closely as possible and includes a structured quality control procedure by a human expert. The final system (Somnolyzer 24 x 7) consists of a raw data quality check, a feature extraction algorithm (density and intensity of sleep/wake-related patterns such as sleep spindles, delta waves, SEMs and REMs), a feature matrix plausibility check, a classifier designed as an expert system, a rule-based smoothing procedure for the start and the end of stages REM, and finally a statistical comparison to age- and sex-matched normal healthy controls (Siesta Spot Report). The expert system considers different prior probabilities of stage changes depending on the preceding sleep stage, the occurrence of a movement arousal and the position of the epoch within the NREM/REM sleep cycles. Moreover, results obtained with and without using the chin EMG signal are combined. The Siesta polysomnographic database (590 recordings in both normal healthy subjects aged 20-95 years and patients suffering from organic or nonorganic sleep disorders) was split into two halves, which were randomly assigned to a training and a validation set, respectively. The final validation revealed an overall epoch-by-epoch agreement of 80% (Cohen's kappa: 0.72) between the Somnolyzer 24 x 7 and the human expert scoring, as compared with an inter-rater reliability of 77% (Cohen's kappa: 0.68) between two human experts scoring the same dataset. Two Somnolyzer 24 x 7 analyses (including a structured quality control by two human experts) revealed an inter-rater reliability close to 1 (Cohen's kappa: 0.991), which confirmed that the variability induced by the quality control procedure, whereby approximately 1% of the epochs (in 9.5% of the recordings) are changed, can definitely be neglected. Thus, the validation study proved the high reliability and validity of the Somnolyzer 24 x 7 and demonstrated its applicability in clinical routine and sleep studies.