ArticleSleep2022
Optimal spindle detection parameters for predicting cognitive performance.
Article in Sleep, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Auditory stimulation during deep sleep enhances total slow-wave activity in a young cohort: A feasibility trial.Journal of sleep research · 2025Trial
- Article
- Deciphering Insomnia: Benchmarking Automated Sleep Staging Algorithms for Complex Sleep Disorders.Journal of sleep research · 2025Article
- Article
- A continuous multi-night sleep database for exploring sleep structure similarity.Scientific data · 2025Article
- Threshold Values of Sleep Spindles Features in Healthy Adults Using Scalp-EEG and Associations With Sleep Parameters.Annals of clinical and translational neurology · 2025Observational
- The role of sleep in Alzheimer's disease: a mini review.Frontiers in neuroscience · 2025Review
- Pulsatile corticoid therapy reduces interictal epileptic activity burden in children with genetic drug-resistant epilepsy.Epilepsia open · 2024Article
- Artificial Intelligence in Sleep Medicine: The Dawn of a New Era.Nature and science of sleep · 2024Article
- Memory ability and retention performance relate differentially to sleep depth and spindle type.iScience · 2023Article
- Article
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Authors and funding
15 authors.
Funding
Abstract
STUDY
objectivesAlterations in sleep spindles have been linked to cognitive impairment. This finding has contributed to a growing interest in identifying sleep-based biomarkers of cognition and neurodegeneration, including sleep spindles. However, flexibility surrounding spindle definitions and algorithm parameter settings present a methodological challenge. The aim of this study was to characterize how spindle detection parameter settings influence the association between spindle features and cognition and to identify parameters with the strongest association with cognition.
methodsAdult patients (n = 167, 49 ± 18 years) completed the NIH Toolbox Cognition Battery after undergoing overnight diagnostic polysomnography recordings for suspected sleep disorders. We explored 1000 combinations across seven parameters in Luna, an open-source spindle detector, and used four features of detected spindles (amplitude, density, duration, and peak frequency) to fit linear multiple regression models to predict cognitive scores.
resultsSpindle features (amplitude, density, duration, and mean frequency) were associated with the ability to predict raw fluid cognition scores (r = 0.503) and age-adjusted fluid cognition scores (r = 0.315) with the best spindle parameters. Fast spindle features generally showed better performance relative to slow spindle features. Spindle features weakly predicted total cognition and poorly predicted crystallized cognition regardless of parameter settings.
conclusionsOur exploration of spindle detection parameters identified optimal parameters for studies of fluid cognition and revealed the role of parameter interactions for both slow and fast spindles. Our findings support sleep spindles as a sleep-based biomarker of fluid cognition.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.