Spindle event detection is a key component in analyzing human sleep. However, detection of these oscillatory patterns by experts is time consuming and costly. automated detection algorithms are cost efficient and reproducible but require robust datasets to be trained and validated. Using the MODa (Massive Online Data annotation) platform, we used crowdsourcing to produce a large opensource dataset of high quality, human-scored sleep spindles (5342 spindles, from 180 subjects). We evaluated the performance of three subtype scorers: "experts, researchers and non-experts", as well as 7 previously published spindle detection algorithms. Our findings show that only two algorithms had performance scores similar to human experts. Furthermore, the human scorers agreed on the average spindle characteristics (density, duration and amplitude), but there were significant age and sex differences (also observed in the set of detected spindles). This study demonstrates how the MODA platform can be used to generate a highly valid open source standardized dataset for researchers to train, validate and compare automated detectors of biological signals such as the EEG. 1 centre d'études avancées en médecine du sommeil, Montréal, canada.

Study Objectives: Nonrapid eye movement sleep boosts hippocampus-dependent, long-term memory formation more so than wake. Studies have pointed to several electrophysiological events that likely play a role in this process, including thalamocortical sleep spindles (12–15 Hz). However, interventional studies that directly probe the causal role of spindles in consolidation are scarce. Previous studies have used zolpidem, a GABA-A agonist, to increase sleep spindles during a daytime nap and promote hippocampal-dependent episodic memory. The current study investigated the effect of zolpidem on nighttime sleep and overnight improvement of episodic memories. Methods: We used a double-blind, placebo-controlled within-subject design to test the a priori hypothesis that zolpidem would lead to increased memory performance on a word-paired associates task by boosting spindle activity. We also explored the impact of zolpidem across a range of other spectral sleep features, including slow oscillations (0–1 Hz), delta (1–4 Hz), theta (4–8 Hz), sigma (12–15 Hz), as well as spindle–SO coupling. Results: We showed greater memory improvement after a night of sleep with zolpidem, compared to placebo, replicating a prior nap study. Additionally, zolpidem increased sigma power, decreased theta and delta power, and altered the phase angle of spindle–SO coupling, compared to placebo. Spindle density, theta power, and spindle–SO coupling were associated with next-day memory performance. Conclusions: These results are consistent with the hypothesis that sleep, specifically the timing and amount of sleep spindles, plays a causal role in the long-term formation of episodic memories. Furthermore, our results emphasize the role of nonrapid eye movement theta activity in human memory consolidation.

The pattern of sleep stages across a night (sleep architecture) is influenced by biological, behavioral, and clinical variables. However, traditional measures of sleep architecture such as stage proportions, fail to capture sleep dynamics. Here we quantify the impact of individual differences on the dynamics of sleep architecture and determine which factors or set of factors best predict the next sleep stage from current stage information. We investigated the influence of age, sex, body mass index, time of day, and sleep time on static (e.g. minutes in stage, sleep efficiency) and dynamic measures of sleep architecture (e.g. transition probabilities and stage duration distributions) using a large dataset of 3202 nights from a non-clinical population. Multi-level regressions show that sex effects duration of all Non-Rapid Eye Movement (NREM) stages, and age has a curvilinear relationship for Wake After Sleep Onset (WASO) and slow wave sleep (SWS) minutes. Bayesian network modeling reveals sleep architecture depends on time of day, total sleep time, age and sex, but not BMI. Older adults, and particularly males, have shorter bouts (more fragmentation) of Stage 2, SWS, and they transition less frequently to these stages. Additionally, we showed that the next sleep stage and its duration can be optimally predicted by the prior 2 stages and age. Our results demonstrate the potential benefit of big data and Bayesian network approaches in quantifying static and dynamic architecture of normal sleep.

Background Rapid eye movements (REMs) are a defining feature of REM sleep. The number of discrete REMs over time, or REM density, has been investigated as a marker of clinical psychopathology and memory consolidation. However, human detection of REMs is a time-consuming and subjective process. Therefore, reliable, automated REM detection software is a valuable research tool. New method We developed an automatic REM detection algorithm combining a novel set of extracted features and the ‘AdaBoost’ classification algorithm to detect the presence of REMs in Electrooculogram data collected from the right and left outer canthi (ROC/LOC). Algorithm performance measures of Recall (percentage of REMs detected) and Precision (percentage of REMs detected that are true REMs) were calculated and compared to the gold standard of human detection by three expert sleep scorers. REM detection by four non-experts were also investigated and compared to expert raters and the algorithm. Results The algorithm performance (78.1% Recall, 82.6% Precision) surpassed that of the average (expert & non-expert) single human detection performance (76% Recall, 83% Precision). Agreement between non-experts (Cronbach Alpha = 0.65) is markedly lower than experts (Cronbach Alpha = 0.80). Comparison with existing method(s) By following reported methods, we implemented all previously published LOC and ROC based detection algorithms on our dataset. Our algorithm performance exceeded all others. Conclusions The automatic detection algorithm presented is a viable and efficient method of REM detection as it reliably matches the performance of human scorers and outperforms all other known LOC- and ROC-based detection algorithms.

Real-world memories involve the integration of multiple events across time, yet the mechanisms underlying this integration is unknown. Recent rodent studies show that distinct memories encoded within a few hours, but not several days, share a common neural ensemble, and a common fate whereby later fear conditioning can transfer from one memory to the other. Here, we tested if distinct memories could be linked by temporal proximity in humans. 74 young adults encoded two memories (A and B) close (3-h) or far apart (7-day) in time. One day after encoding the second memory (B), Memory A was updated by pairing it with electric shock (i.e., fear conditioning). We tested whether the memory and fear associated with Memory B would be stronger in the 3-h, compared with the 7-day condition. Results were generally consistent with rodent studies, where we found heightened Memory B fear expression when the two memories were encoded close, but not far apart, in time. Furthermore, there was less forgetting of Memory B in the 3-h compared to 7-day condition. Our results suggest that temporally proximal memories may be linked, such that updating one experience updates the other.