Probability estimation of narcolepsy type 1 in DTA mice using unlabeled EEG and EMG data

The manual evaluation of mouse sleep studies is labor-intensive and time-consuming. Although several approaches for automatic sleep stage classification have been proposed, no automatic pipeline for detecting a specific mouse phenotype has yet been developed. Here, we present a fully automated pipeline for estimating the probability of Narcolepsy Type 1 (NT1) in the hypocretin-tTA;TetO-Diphteria toxin A (DTA) mouse model using unlabeled electroencephalographic (EEG) and electromyographic (EMG) data. The pipeline is divided into three modules: (1) automatic sleep stage classification, (2) feature extraction, and (3) phenotype classification. We trained two automatic sleep stage classifiers, Usleep<sub>EEG</sub> and Usleep<sub>EMG</sub>, using data from 83 wild-type (WT) mice. We next computed features such as EEG spectral power bands, EMG root mean square, and bout metrics from 11 WT and 19 DTA mice. The features were used to train an L1-penalized logistic regression classifier in a Leave-One-Subject-Out approach, achieving an accuracy of 97%. Finally, we validated the pipeline in a held-out dataset of EEG/EMG recordings at four different timepoints during disease development in seven DTA mice, finding that the pipeline captured disease progression in all mice. While our pipeline generalizes well to data from other laboratories, it is sensitive to artifacts, which should be considered in its application. With this study, we present a pipeline that facilitates a fast assessment of NT1 probability in the DTA model and thus can accelerate large-scale evaluations of NT1 treatments.

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Publication Details

Journal
SLEEP Advances
Published
2025-04-01
DOI
https://doi.org/10.1093/sleepadvances/zpaf025
Primary Topic
Sleep and Wakefulness Research
Type
article
Field-Weighted Citation Impact
0.00

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article

Probability estimation of narcolepsy type 1 in DTA mice using unlabeled EEG and EMG data

Chiara Berteotti, Giovanna Zoccoli, Alessandro Silvani, Christelle Peyron et al.
SLEEP Advances
Sleep and Wakefulness Research
article

Probability estimation of narcolepsy type 1 in DTA mice using unlabeled EEG and EMG data

Chiara Berteotti, Giovanna Zoccoli, Alessandro Silvani, Christelle Peyron, Birgitte Rahbek Kornum, Yu Sun, Mie Andersen, Micaela Borsa, Antoine Adamantidis, Sébastien Arthaud, Patrice Fort, Viviana Lo Martire, Ryan K. Tisdale, Tessa Radovanovic, Frederik Sørensen, Laura Ellen Rose, Noriaki Sakai, Morten Mørup, Stefano Bastianini, Christine Egebjerg, Emmanuel Mignot, Anastasia Tsopanidou, Thomas S. Kilduff, Louise Piilgaard, Maiken Nedergaard, Alexander Neergaard Zahid, Seiji Nishino
article en

Abstract

The manual evaluation of mouse sleep studies is labor-intensive and time-consuming. Although several approaches for automatic sleep stage classification have been proposed, no automatic pipeline for detecting a specific mouse phenotype has yet been developed. Here, we present a fully automated pipeline for estimating the probability of Narcolepsy Type 1 (NT1) in the hypocretin-tTA;TetO-Diphteria toxin A (DTA) mouse model using unlabeled electroencephalographic (EEG) and electromyographic (EMG) data. The pipeline is divided into three modules: (1) automatic sleep stage classification, (2) feature extraction, and (3) phenotype classification. We trained two automatic sleep stage classifiers, Usleep<sub>EEG</sub> and Usleep<sub>EMG</sub>, using data from 83 wild-type (WT) mice. We next computed features such as EEG spectral power bands, EMG root mean square, and bout metrics from 11 WT and 19 DTA mice. The features were used to train an L1-penalized logistic regression classifier in a Leave-One-Subject-Out approach, achieving an accuracy of 97%. Finally, we validated the pipeline in a held-out dataset of EEG/EMG recordings at four different timepoints during disease development in seven DTA mice, finding that the pipeline captured disease progression in all mice. While our pipeline generalizes well to data from other laboratories, it is sensitive to artifacts, which should be considered in its application. With this study, we present a pipeline that facilitates a fast assessment of NT1 probability in the DTA model and thus can accelerate large-scale evaluations of NT1 treatments.

SLEEP AdvancesVol. 6(2)
Université Claude Bernard Lyon 1 (FR), University of Bern (CH), University of Copenhagen (DK), SRI International (US), University Hospital of Bern (CH), Centre de Recherche en Neurosciences de Lyon (FR), University of Bologna (IT), Technical University of Denmark (DK), Stanford University (US)
Lundbeckfonden, H. Lundbeck A/S
Decent work and economic growth
Openalex Percentile: Top 91%
Sleep and Wakefulness Research
DOIOpen Access PDFOpenAlex
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