Cueing emotional memories during slow wave sleep modulates next-day activity in the orbitofrontal cortex and the amygdala.

Emotional memories are preferentially consolidated during sleep, through the process of memory reactivation. Targeted memory reactivation (TMR) has been shown to boost memory consolidation during sleep, but its neural correlates remain unclear, particularly for emotional memories. Here, we aimed to examine how TMR of emotional material during slow wave sleep (SWS) impacts upon neural processing during a subsequent arousal rating task. Participants were trained on a spatial memory task including negative and neutral pictures paired with semantically matching sounds. The picture-sound pairs were rated for emotional arousal before and after the spatial memory task. Then, half of the sounds from each emotional category (negative and neutral) were cued during SWS. The next day, participants were retested on both the arousal rating and the spatial memory task inside an MRI scanner, followed by another retest session a week later. Memory consolidation and arousal processing did not differ between cued and non-cued items of either emotional category. We found increased responses to emotional stimuli in the amygdala and orbitofrontal cortex (OFC), and a cueing versus emotion interaction in the OFC, whereby cueing neutral stimuli led to an increase in OFC activity, while cueing negative stimuli led to decreased OFC activation. Interestingly, the effect of cueing on amygdala activation was modulated by time spent in REM sleep. We conclude that SWS TMR impacts OFC activity, while REM sleep plays a role in mediating the effect of such cueing on amygdala.

Optimising sounds for the driving of sleep oscillations by closed-loop auditory stimulation.

Recent studies have shown that slow oscillations (SOs) can be driven by rhythmic auditory stimulation, which deepens slow-wave sleep (SWS) and improves memory and the immune-supportive hormonal milieu related to this sleep stage. While different attempts have been made to optimise the driving of the SOs by changing the number of click stimulations, no study has yet investigated the impact of applying more than five clicks in a row. Likewise, the importance of the type of sounds in eliciting brain responses is presently unclear. In a study of 12 healthy young participants (10 females; aged 18-26 years), we applied an established closed-loop stimulation method, which delivered sequences of 10 pink noises, 10 pure sounds (B note of 247 Hz), 10 pronounced "a" vowels, 10 sham, 10 variable sounds, and 10 "oddball" sounds on the up phase of the endogenous SOs. By analysing area under the curve, amplitude, and event related potentials, we explored whether the nature of the sound had a differential effect on driving SOs. We showed that every stimulus in a 10-click sequence, induces a SO response. Interestingly, all three types of sounds that we tested triggered SOs. However, pink noise elicited a more pronounced response compared to the other sounds, which was explained by a broader topographical recruitment of brain areas. Our data further suggest that varying the sounds may partially counteract habituation.

[Sleep spindles-Function, detection and use as biomarker for diagnostics in psychiatry]. / Schlafspindeln ­ Funktion, Detektion und Nutzung als Biomarker für die psychiatrische Diagnostik.

BACKGROUND: The sleep spindle is a graphoelement of an electroencephalogram (EEG), which can be observed in light and deep sleep. Alterations in spindle activity have been described for a range of psychiatric disorders. Due to their relatively constant properties, sleep spindles may therefore be potential biomarkers in psychiatric diagnostics. METHOD: This article presents an overview of the state of the science on the characteristics and functions of the sleep spindle as well as known alterations of spindle activity in psychiatric disorders. Various methodological approaches and developments of spindle detection are explained with respect to their potential for application in psychiatric diagnostics. RESULTS AND CONCLUSION: Although alterations in spindle activity in psychiatric disorders are known and have been described in detail, their exact potential for psychiatric diagnostics has yet to be fully determined. In this respect, the acquisition of knowledge in research is currently constrained by manual and automated methods for spindle detection, which require high levels of resources and are error prone. Newer approaches to spindle detection based on deep-learning procedures could overcome the difficulties of previous detection methods, and thus open up new possibilities for the practical application of sleep spindles as biomarkers in the psychiatric practice.

Identification of memory reactivation during sleep by EEG classification.

Memory reactivation during sleep is critical for consolidation, but also extremely difficult to measure as it is subtle, distributed and temporally unpredictable. This article reports a novel method for detecting such reactivation in standard sleep recordings. During learning, participants produced a complex sequence of finger presses, with each finger cued by a distinct audio-visual stimulus. Auditory cues were then re-played during subsequent sleep to trigger neural reactivation through a method known as targeted memory reactivation (TMR). Next, we used electroencephalography data from the learning session to train a machine learning classifier, and then applied this classifier to sleep data to determine how successfully each tone had elicited memory reactivation. Neural reactivation was classified above chance in all participants when TMR was applied in SWS, and in 5 of the 14 participants to whom TMR was applied in N2. Classification success reduced across numerous repetitions of the tone cue, suggesting either a gradually reducing responsiveness to such cues or a plasticity-related change in the neural signature as a result of cueing. We believe this method will be valuable for future investigations of memory consolidation.

Closed-loop auditory stimulation of sleep slow oscillations: Basic principles and best practices.

Sleep is essential for our physical and mental well-being. During sleep, despite the paucity of overt behavior, our brain remains active and exhibits a wide range of coupled brain oscillations. In particular slow oscillations are characteristic for sleep, however whether they are directly involved in the functions of sleep, or are mere epiphenomena, is not yet fully understood. To disentangle the causality of these relationships, experiments utilizing techniques to detect and manipulate sleep oscillations in real-time are essential. In this review, we first overview the theoretical principles of closed-loop auditory stimulation (CLAS) as a method to study the role of slow oscillations in the functions of sleep. We then describe technical guidelines and best practices to perform CLAS and analyze results from such experiments. We further provide an overview of how CLAS has been used to investigate the causal role of slow oscillations in various sleep functions. We close by discussing important caveats, open questions, and potential topics for future research.

Advanced sleep spindle identification with neural networks.

Sleep spindles are neurophysiological phenomena that appear to be linked to memory formation and other functions of the central nervous system, and that can be observed in electroencephalographic recordings (EEG) during sleep. Manually identified spindle annotations in EEG recordings suffer from substantial intra- and inter-rater variability, even if raters have been highly trained, which reduces the reliability of spindle measures as a research and diagnostic tool. The Massive Online Data Annotation (MODA) project has recently addressed this problem by forming a consensus from multiple such rating experts, thus providing a corpus of spindle annotations of enhanced quality. Based on this dataset, we present a U-Net-type deep neural network model to automatically detect sleep spindles. Our model's performance exceeds that of the state-of-the-art detector and of most experts in the MODA dataset. We observed improved detection accuracy in subjects of all ages, including older individuals whose spindles are particularly challenging to detect reliably. Our results underline the potential of automated methods to do repetitive cumbersome tasks with super-human performance.

The clinical effects of sleep restriction therapy for insomnia: A meta-analysis of randomised controlled trials.

Sleep restriction therapy (SRT) is an established treatment for insomnia that has been used in clinical practise for over 30 y. It is commonly delivered as part of multicomponent cognitive-behavioural therapy (CBT-I) but has also been linked to beneficial effects as a standalone intervention. In order to quantify the efficacy of SRT we performed a comprehensive meta-analysis of randomised controlled trials (RCTs) comparing SRT to minimally active or non-active control groups. Primary outcomes were self-reported insomnia severity and sleep diary metrics at post-treatment. Weighted effect sizes were calculated with Hedges' g and risk of bias was assessed by two independent raters with the Cochrane tool. Our search yielded eight RCTs meeting inclusion/exclusion criteria. Random effects models revealed large treatment effects in favour of SRT versus control for insomnia severity measured with the insomnia severity index (g = -0.93; 95% CI = -1.15, -0.71), sleep efficiency (g = 0.91; 95% CI = 0.52, 1.31), sleep onset latency (g = -0.62; 95% CI = -0.84, -0.40), and wake-time after sleep onset (g = -0.83; 95% CI = -1.11, -0.55). No effects were found for total sleep time (g = 0.02; 95% CI = -0.29, 0.34). Results should be interpreted in the context of the small number of comparisons (≤6 per outcome), high risk of bias (6 out of 8 studies met criteria for high risk), and heterogeneity in study design and SRT administration. Only a small number of studies provided outcomes at follow-up (n ≤ 3), hindering assessment of long-term effects. Sleep restriction therapy effectively improves insomnia severity and sleep continuity in the short term; more studies are needed to assess if effects are sustained at long-term follow-up (>3 m). Post-treatment effect sizes appear as large as multicomponent CBT-I. To reduce risk of bias, future studies should consider testing the effects of SRT against control groups that are matched for non-specific treatment effects. Large-scale pragmatic trials are also needed to test if SRT is effective in clinical practise and to quantify effects on daytime functioning and quality of life.

Sleep and daytime problems during the COVID-19 pandemic and effects of coronavirus infection, confinement and financial suffering: a multinational survey using a harmonised questionnaire.

OBJECTIVES: Sleep is important for human health and well-being. No previous study has assessed whether the COVID-19 pandemic impacts sleep and daytime function across the globe. METHODS: This large-scale international survey used a harmonised questionnaire. Fourteen countries participated during the period of May-August 2020. Sleep and daytime problems (poor sleep quality, sleep onset and maintenance problems, nightmares, hypnotic use, fatigue and excessive sleepiness) occurring 'before' and 'during' the pandemic were investigated. In total, 25 484 people participated and 22 151 (86.9%) responded to the key parameters and were included. Effects of COVID-19, confinement and financial suffering were considered. In the fully adjusted logistic regression models, results (weighted and stratified by country) were adjusted for gender, age, marital status, educational level, ethnicity, presence of sleep problems before COVID-19 and severity of the COVID-19 pandemic in each country at the time of the survey. RESULTS: The responders were mostly women (64%) with a mean age 41.8 (SD 15.9) years (median 39, range 18-95). Altogether, 3.0% reported having had COVID-19; 42.2% reported having been in confinement; and 55.9% had suffered financially. All sleep and daytime problems worsened during the pandemic by about 10% or more. Also, some participants reported improvements in sleep and daytime function. For example, sleep quality worsened in about 20% of subjects and improved in about 5%. COVID-19 was particularly associated with poor sleep quality, early morning awakening and daytime sleepiness. Confinement was associated with poor sleep quality, problems falling asleep and decreased use of hypnotics. Financial suffering was associated with all sleep and daytime problems, including nightmares and fatigue, even in the fully adjusted logistic regression models. CONCLUSIONS: Sleep problems, fatigue and excessive sleepiness increased significantly worldwide during the first phase of the COVID-19 pandemic. Problems were associated with confinement and especially with financial suffering.

Susceptibility to auditory closed-loop stimulation of sleep slow oscillations changes with age.

STUDY OBJECTIVES: Cortical slow oscillations (SOs) and thalamocortical sleep spindles hallmark slow wave sleep and facilitate memory consolidation, both of which are reduced with age. Experiments utilizing auditory closed-loop stimulation to enhance these oscillations showed great potential in young and older subjects. However, the magnitude of responses has yet to be compared between these age groups. We examined the possibility of enhancing SOs and performance on different memory tasks in a healthy middle-aged population using this stimulation and contrast effects to younger adults. METHODS: In a within-subject design, 17 subjects (55.7 ± 1.0 years) received auditory stimulation in synchrony with SO up-states, which was compared to a no-stimulation sham condition. Overnight memory consolidation was assessed for declarative word-pairs and procedural finger-tapping skill. Post-sleep encoding capabilities were tested with a picture recognition task. Electrophysiological effects of stimulation were compared to a previous younger cohort (n = 11, 24.2 ± 0.9 years). RESULTS: Overnight retention and post-sleep encoding performance of the older cohort revealed no beneficial effect of stimulation, which contrasts with the enhancing effect the same stimulation protocol had in our younger cohort. Auditory stimulation prolonged endogenous SO trains and induced sleep spindles phase-locked to SO up-states in the older population. However, responses were markedly reduced compared to younger subjects. Additionally, the temporal dynamics of stimulation effects on SOs and spindles differed between age groups. CONCLUSIONS: Our findings suggest that the susceptibility to auditory stimulation during sleep drastically changes with age and reveal the difficulties of translating a functional protocol from younger to older populations.

Examining the optimal timing for closed-loop auditory stimulation of slow-wave sleep in young and older adults.

STUDY OBJECTIVES: Closed-loop auditory stimulation (CLAS) is a method for enhancing slow oscillations (SOs) through the presentation of auditory clicks during sleep. CLAS boosts SOs amplitude and sleep spindle power, but the optimal timing for click delivery remains unclear. Here, we determine the optimal time to present auditory clicks to maximize the enhancement of SO amplitude and spindle likelihood. METHODS: We examined the main factors predicting SO amplitude and sleep spindles in a dataset of 21 young and 17 older subjects. The participants received CLAS during slow-wave-sleep in two experimental conditions: sham and auditory stimulation. Post-stimulus SOs and spindles were evaluated according to the click phase on the SOs and compared between and within conditions. RESULTS: We revealed that auditory clicks applied anywhere on the positive portion of the SO increased SO amplitudes and spindle likelihood, although the interval of opportunity was shorter in the older group. For both groups, analyses showed that the optimal timing for click delivery is close to the SO peak phase. Click phase on the SO wave was the main factor determining the impact of auditory stimulation on spindle likelihood for young subjects, whereas for older participants, the temporal lag since the last spindle was a better predictor of spindle likelihood. CONCLUSIONS: Our data suggest that CLAS can more effectively boost SOs during specific phase windows, and these differ between young and older participants. It is possible that this is due to the fluctuation of sensory inputs modulated by the thalamocortical networks during the SO.