Evidence map›Paper›PMID 38775861›Full record

ArticleNeurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology2024

Automatic quantification of REM sleep without atonia reliably identifies patients with REM sleep behavior disorder: a possible screening tool?

Raffaele Mancini, Pietro Mattioli, Francesco Famà, Laura Giorgetti, Francesco Calizzano, Miki Nikolic, Rune Frandsen, Poul Jennum, Silvia Morbelli, Matteo Pardini and 1 more

Abstract read
In one paragraph

Article in Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Research priorities to improve classification of sleep-wake disorders.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2026
    Review
  2. Article
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

11 authors.

Raffaele ManciniDepartment of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy.ORCID http://orcid.org/0009-0002-7676-8297
Pietro MattioliDepartment of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy. mattiolipietro092@gmail.com.
Francesco FamàDepartment of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy.
Laura GiorgettiDepartment of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy.
Francesco CalizzanoDepartment of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy.
Miki NikolicDanish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet, Copenhagen, Denmark.
Rune FrandsenDanish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet, Copenhagen, Denmark.
Poul JennumDanish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet, Copenhagen, Denmark.
Silvia MorbelliNuclear Medicine Unit, AOU Città della Salute e della Scienza di Torino, Turin, Italy.
Matteo PardiniDepartment of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy.
Dario ArnaldiDepartment of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy.

Funding

Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), project MNESYS (PE0000006) DN. 1553 11.10.2022
6 · The paper itself

Abstract

backgroundREM Sleep Behavior Disorder (RBD) is characterized by absence of physiological muscle atonia during REM sleep (REM sleep without atonia, RWA). Nigro-striatal dopaminergic impairment is a feature of Parkinson disease (PD) and can be identified in prodromal stages as well, such as idiopathic RBD (iRBD). Aims of this study are to explore the efficacy of an automatic RWA quantification in identifying RBD patients and the correlation between RWA and nigro-striatal dopaminergic function.

methodsForty-five iRBD, 46 PD with RBD, 24 PD without RBD patients and 11 healthy controls were enrolled in the Genoa Center (group A) and 25 patients with iRBD (group B) were enrolled in the Danish Center. Group A underwent brain [123I]FP-CIT-SPECT and group B underwent brain [18F]PE2I-PET as measures of nigro-striatal dopaminergic function. Chin muscle activity was recorded in all subjects and analyzed by applying a published automatic algorithm. Correlations between RWA and nigro-striatal dopaminergic function were explored.

resultsThe automatic quantification of RWA significantly differentiated RBD from non-RBD subjects (AUC = 0.86), although with lower accuracy compared with conventional visual scoring (AUC = 0.99). No significant correlation was found between RWA and nigro-striatal dopaminergic function.

conclusionThe automatic quantification of RWA is a reliable tool to identify subjects with RBD and may be used as a first-line screening tool, but without correlations with nigro-striatal dopaminergic functioning.

Indexed as

Parkinson DiseaseREM Sleep Behavior DisorderTomography, Emission-Computed, Single-PhotonAgedBrainFemaleHumansMaleMiddle AgedPolysomnographyPositron-Emission TomographySleep, REMTropanes2-carbomethoxy-8-(3-fluoropropyl)-3-(4-iodophenyl)tropaneTropanesDAT-SPECTPE2IPETRBDRWA

Identifiers

PMID38775861
PMCPMC11422455

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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.