STUDY OBJECTIVES: This study aimed to compare YASA's automated sleep staging to manual staging in the context of a multi-night experimental sleep restriction protocol. METHODS: Seventy-five adults (58% female; 57% nonwhite) participated in up to seven nights of laboratory-based polysomnography measurements. The study involved one adaptation night, followed by three nights of normal sleep (9 h time in bed) and three nights of sleep restriction (5.5 h time in bed). Condition order was counterbalanced. Manual sleep staging was performed by a registered polysomnographic technician. Sleep data were exported and processed using Python 3.12, MNE 1.8.0, and YASA 0.6.5 for automated sleep staging. RESULTS: Across 483 valid sleep nights, there was 82.9% overall agreement between YASA scoring and manual scoring. Stage-specific agreement was 40.3% (N1), 85.1% (N2), 86.9% (N3), and 78.7% (REM). Agreement was higher during normal sleep nights than sleep restriction nights, particularly for wake, N1, and REM sleep classifications. CONCLUSIONS: YASA-based staging exhibited good overall agreement with manual scoring during normal sleep nights. However, caution is needed when interpreting N1 estimates, as well as in interpreting data during short sleep nights. CURRENT KNOWLEDGE/STUDY RATIONALE: Sleep stage scoring is typically conducted manually by at least one experienced technician, but this process is laborious and subject to biases. This study investigated whether a machine-learning-based open tool-YASA-could automatically stage polysomnography data with acceptable accuracy. STUDY IMPACT: Across 430,813 epochs, YASA showed acceptable accuracy in sleep staging, making it an efficient tool for the sleep community. However, caution is still needed for some applications, such as interpreting YASA's N1 estimates as well as data from short sleep nights.

Abstract Introduction It has been established that sleep after learning something (“post-learning sleep”) actively consolidates what was learned. Yet, the underlying cellular and molecular mechanisms are undergoing active investigation. The fear conditioning (FC) memory paradigm, in which an animal learns to associate an environmental cue to an aversive foot shock, has been widely used for memory investigation. While amygdala and hippocampus are two critical sites for processing and storing the FC memory, how the amygdala consolidate this memory during sleep, from both cellular and molecular perspectives, has been little understood, compared to the hippocampus. Moreover, one potential molecular substrate of memory, DNA methylation, has been studied in the hippocampus for its role in learning and memory. Yet it is little known for its memory role in the BLA. Methods Here, we used fiber photometry, miniscope calcium imaging, EEG recording, optogenetic manipulation, and whole genome bisulfite sequencing to study the neural mechanisms underlying memory consolidation in the basolateral amygdala (BLA) during sleep. Results (1) We found that the BLA neurons display increased activity during REM sleep, compared to that in non-REM (NREM) sleep. (2) Using closed-loop optogenetic strategy, our preliminary results showed that silencing BLA activity during REM sleep leads to memory impairment in contextual fear conditioning task. (3) We compared DNA methylation of BLA neurons at a whole genome level between mice with and without sleep after FC learning and identified differentially methylated regions between them. Conclusion Together, our data suggests that the BLA activity during REM sleep is required for consolidation of fear memory, and DNA methylation might mediate this process. Support (if any)