Somnotate: A probabilistic sleep stage classifier for studying vigilance state transitions.

Autor: Brodersen PJN; Department of Pharmacology, University of Oxford; Mansfield Road, Oxford, United Kingdom., Alfonsa H; Department of Pharmacology, University of Oxford; Mansfield Road, Oxford, United Kingdom., Krone LB; Department of Physiology, Anatomy and Genetics, University of Oxford; Parks Road, United Kingdom., Blanco-Duque C; Department of Physiology, Anatomy and Genetics, University of Oxford; Parks Road, United Kingdom., Fisk AS; Nuffield Department of Clinical Neurosciences, University of Oxford; John Radcliffe Hospital, Oxford, United Kingdom., Flaherty SJ; Department of Physiology, Anatomy and Genetics, University of Oxford; Parks Road, United Kingdom., Guillaumin MCC; Nuffield Department of Clinical Neurosciences, University of Oxford; John Radcliffe Hospital, Oxford, United Kingdom.; Sleep and Circadian Neuroscience Institute, University of Oxford; Oxford, United Kingdom.; Institute for Neuroscience, Department of Health Sciences and Technology, ETH Zurich; Schwerzenbach, Switzerland., Huang YG; Department of Physiology, Anatomy and Genetics, University of Oxford; Parks Road, United Kingdom., Kahn MC; Department of Physiology, Anatomy and Genetics, University of Oxford; Parks Road, United Kingdom., McKillop LE; Department of Physiology, Anatomy and Genetics, University of Oxford; Parks Road, United Kingdom., Milinski L; Department of Physiology, Anatomy and Genetics, University of Oxford; Parks Road, United Kingdom., Taylor L; Nuffield Department of Clinical Neurosciences, University of Oxford; John Radcliffe Hospital, Oxford, United Kingdom., Thomas CW; Department of Physiology, Anatomy and Genetics, University of Oxford; Parks Road, United Kingdom., Yamagata T; Nuffield Department of Clinical Neurosciences, University of Oxford; John Radcliffe Hospital, Oxford, United Kingdom., Foster RG; Sleep and Circadian Neuroscience Institute, University of Oxford; Oxford, United Kingdom., Vyazovskiy VV; Department of Physiology, Anatomy and Genetics, University of Oxford; Parks Road, United Kingdom., Akerman CJ; Department of Pharmacology, University of Oxford; Mansfield Road, Oxford, United Kingdom.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2024 Jan 17; Vol. 20 (1), pp. e1011793. Date of Electronic Publication: 2024 Jan 17 (Print Publication: 2024).
DOI: 10.1371/journal.pcbi.1011793
Abstrakt: Electrophysiological recordings from freely behaving animals are a widespread and powerful mode of investigation in sleep research. These recordings generate large amounts of data that require sleep stage annotation (polysomnography), in which the data is parcellated according to three vigilance states: awake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep. Manual and current computational annotation methods ignore intermediate states because the classification features become ambiguous, even though intermediate states contain important information regarding vigilance state dynamics. To address this problem, we have developed "Somnotate"-a probabilistic classifier based on a combination of linear discriminant analysis (LDA) with a hidden Markov model (HMM). First we demonstrate that Somnotate sets new standards in polysomnography, exhibiting annotation accuracies that exceed human experts on mouse electrophysiological data, remarkable robustness to errors in the training data, compatibility with different recording configurations, and an ability to maintain high accuracy during experimental interventions. However, the key feature of Somnotate is that it quantifies and reports the certainty of its annotations. We leverage this feature to reveal that many intermediate vigilance states cluster around state transitions, whereas others correspond to failed attempts to transition. This enables us to show for the first time that the success rates of different types of transition are differentially affected by experimental manipulations and can explain previously observed sleep patterns. Somnotate is open-source and has the potential to both facilitate the study of sleep stage transitions and offer new insights into the mechanisms underlying sleep-wake dynamics.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Brodersen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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