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

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

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DOI
10.48620/89525
Publisher DOI
10.1093/sleepadvances/zpaf025
PubMed ID
40521226
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, UsleepEEG and UsleepEMG, 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.
Date Issued
2025-04
Publication Type
Article
Subject(s)
600 Technology > 610 Medicine & health
Subjects
EEG
•
EMG
•
automated phenotype detection
•
mouse model
•
narcolepsy
•
sleep
Language(s)
en
Author(s)
Rose, Laura
Zahid, Alexander Neergaard
Piilgaard, Louise
Egebjerg, Christine
Sørensen, Frederikke Lynge
Andersen, Mie
Radovanovic, Tessa
Tsopanidou, Anastasia
Bastianini, Stefano
Berteotti, Chiara
Lo Martire, Viviana
Borsa, Micaela  
Clinic of Neurology  
Tisdale, Ryan K
Sun, Yu  
Nedergaard, Maiken
Silvani, Alessandro
Zoccoli, Giovanna
Adamantidis, Antoine  
Clinic of Neurology  
Department for BioMedical Research, Forschungsgruppe Neurologie  
Kilduff, Thomas S
Sakai, Noriaki
Nishino, Seiji
Arthaud, Sébastien
Peyron, Christelle
Fort, Patrice
Mørup, Morten
Mignot, Emmanuel
Kornum, Birgitte Rahbek
Additional Credits
Department for BioMedical Research, Forschungsgruppe Neurologie  
Clinic of Neurology  
Journal
SLEEP Advances
Publisher
Oxford University Press
ISSN
2632-5012
Access(Rights)
open.access
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