The effect of zolpidem on memory consolidation over a night of sleep

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.

Massive online data annotation, crowdsourcing to generate high quality sleep spindle annotations from EEG data

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 open-source 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.