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.

Creativity is hypothesized to arise from a mental state which balances spontaneous thought and cognitive control, corresponding to functional connectivity between the brain's Default Mode (DMN) and Executive Control (ECN) Networks. Here, we conduct a large-scale, multi-center examination of this hypothesis. Employing a meta-analytic network neuroscience approach, we analyze resting-state fMRI and creative task performance across 10 independent samples from Austria, Canada, China, Japan, and the United States (N = 2433)-constituting the largest and most ethnically diverse creativity neuroscience study to date. Using time-resolved network analysis, we investigate the relationship between creativity (i.e., divergent thinking ability) and dynamic switching between DMN and ECN. We find that creativity, but not general intelligence, can be reliably predicted by the number of DMN-ECN switches. Importantly, we identify an inverted-U relationship between creativity and the degree of balance between DMN-ECN switching, suggesting that optimal creative performance requires balanced brain network dynamics. Furthermore, an independent task-fMRI validation study (N = 31) demonstrates higher DMN-ECN switching during creative idea generation (compared to a control condition) and replicates the inverted-U relationship. Therefore, we provide robust evidence across multi-center datasets that creativity is tied to the capacity to dynamically switch between brain networks supporting spontaneous and controlled cognition.

Transfer stigma refers to a type of stigma associated with students' transfer status and/or their community college background. It plays a significant role in post-transfer adjustment and may negatively impact post-transfer outcomes such as retention and obtaining a baccalaureate degree. This study focused on quantitatively measuring transfer stigma among transfer students attending a four-year institution. Drawing from previous studies, we developed a 13-item transfer stigma measure and included it in a transfer student survey. The survey data was collected from 450 current transfer students at a public, flagship four-year university in Louisiana. Through an exploratory factor analysis and a confirmatory factor analysis, we revealed a four-factor structure of transfer stigma measures: internalized self-stigma, perceptions about community colleges, lack of support, and perceived judgment. Subsequent statistical analyses examined group differences in the degree of transfer stigma across various transfer student subgroups defined by transfer type, gender, race/ethnicity, age, and major.