Evidence map›Paper›PMID 40521226›Full record

ArticleSleep advances : a journal of the Sleep Research Society2025

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

Laura Rose, Alexander Neergaard Zahid, Louise Piilgaard, Christine Egebjerg, Frederikke Lynge Sørensen, Mie Andersen, Tessa Radovanovic, Anastasia Tsopanidou, Stefano Bastianini, Chiara Berteotti and 17 more

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In one paragraph

Article in Sleep advances : a journal of the Sleep Research Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

27 authors.

Laura RoseDepartment of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Alexander Neergaard ZahidDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark.ORCID https://orcid.org/0000-0001-6881-9766
Louise PiilgaardDepartment of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.ORCID https://orcid.org/0000-0003-4585-5996
Christine EgebjergDepartment of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.ORCID https://orcid.org/0009-0006-2035-6145
Frederikke Lynge SørensenDivision of Glial Disease and Therapeutics, Center for Translational Neuromedicine, University of Copenhagen, Copenhagen, Denmark.
Mie AndersenDivision of Glial Disease and Therapeutics, Center for Translational Neuromedicine, University of Copenhagen, Copenhagen, Denmark.
Tessa RadovanovicDivision of Glial Disease and Therapeutics, Center for Translational Neuromedicine, University of Copenhagen, Copenhagen, Denmark.
Anastasia TsopanidouDivision of Glial Disease and Therapeutics, Center for Translational Neuromedicine, University of Copenhagen, Copenhagen, Denmark.
Stefano BastianiniDepartment of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.
Chiara BerteottiDepartment of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.ORCID https://orcid.org/0000-0002-4143-9445
Viviana Lo MartireDepartment of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.
Micaela BorsaZentrum für Experimentelle Neurologie, Department of Neurology, University Hospital Bern, Bern, Switzerland.ORCID https://orcid.org/0000-0001-7634-7738
Ryan K TisdaleCenter for Neuroscience, Biosciences Division, SRI International, Menlo Park, CA, USA.
Yu SunCenter for Neuroscience, Biosciences Division, SRI International, Menlo Park, CA, USA.ORCID https://orcid.org/0000-0001-5002-1414
Maiken NedergaardDivision of Glial Disease and Therapeutics, Center for Translational Neuromedicine, University of Copenhagen, Copenhagen, Denmark.
Alessandro SilvaniDepartment of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.ORCID https://orcid.org/0000-0003-3992-3892
Giovanna ZoccoliDepartment of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.ORCID https://orcid.org/0000-0002-9670-9959
Antoine AdamantidisZentrum für Experimentelle Neurologie, Department of Neurology, University Hospital Bern, Bern, Switzerland.ORCID https://orcid.org/0000-0003-2531-5175
Thomas S KilduffCenter for Neuroscience, Biosciences Division, SRI International, Menlo Park, CA, USA.ORCID https://orcid.org/0000-0002-6823-0094
Noriaki SakaiDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.ORCID https://orcid.org/0000-0002-2918-6334
Seiji NishinoDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.ORCID https://orcid.org/0000-0003-3959-2886
Sébastien ArthaudCenter for Neuroscience Research in Lyon (CRNL), SLEEP Team, Université de Lyon, Lyon, France.
Christelle PeyronCenter for Neuroscience Research in Lyon (CRNL), SLEEP Team, Université de Lyon, Lyon, France.
Patrice FortCenter for Neuroscience Research in Lyon (CRNL), SLEEP Team, Université de Lyon, Lyon, France.ORCID https://orcid.org/0000-0003-1211-8631
Morten MørupDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark.
Emmanuel MignotCenter for Narcolepsy, Department of Psychiatry, Stanford University School of Medicine, Stanford, CA, USA.ORCID https://orcid.org/0000-0002-6928-5310
Birgitte Rahbek KornumDepartment of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.ORCID https://orcid.org/0000-0002-2515-9451

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Indexed as

automated phenotype detectionEEGEMGmouse modelnarcolepsysleep

Identifiers

PMID40521226
PMCPMC12163710

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

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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.