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Deep Learning‐Based Neuromelanin MRI Changes of Isolated REM Sleep Behavior Disorder
BACKGROUND: Isolated REM sleep behavior disorder (iRBD) is considered a prodromal stage of parkinsonism. Neurodegenerative changes in the substantia nigra pars compacta (SNc) in parkinsonism can be detected using neuromelanin‐sensitive MRI. OBJECTIVE: To investigate SNc neuromelanin changes in iRBD...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons, Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9302679/ https://www.ncbi.nlm.nih.gov/pubmed/35102604 http://dx.doi.org/10.1002/mds.28933 |
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author | Gaurav, Rahul Pyatigorskaya, Nadya Biondetti, Emma Valabrègue, Romain Yahia‐Cherif, Lydia Mangone, Graziella Leu‐Semenescu, Smaranda Corvol, Jean‐Christophe Vidailhet, Marie Arnulf, Isabelle Lehéricy, Stéphane |
author_facet | Gaurav, Rahul Pyatigorskaya, Nadya Biondetti, Emma Valabrègue, Romain Yahia‐Cherif, Lydia Mangone, Graziella Leu‐Semenescu, Smaranda Corvol, Jean‐Christophe Vidailhet, Marie Arnulf, Isabelle Lehéricy, Stéphane |
author_sort | Gaurav, Rahul |
collection | PubMed |
description | BACKGROUND: Isolated REM sleep behavior disorder (iRBD) is considered a prodromal stage of parkinsonism. Neurodegenerative changes in the substantia nigra pars compacta (SNc) in parkinsonism can be detected using neuromelanin‐sensitive MRI. OBJECTIVE: To investigate SNc neuromelanin changes in iRBD patients using fully automatic segmentation. METHODS: We included 47 iRBD patients, 134 early Parkinson's disease (PD) patients and 55 healthy volunteers (HVs) scanned at 3 Tesla. SNc regions‐of‐interest were delineated automatically using convolutional neural network. SNc volumes, volumes corrected by total intracranial volume, signal‐to‐noise ratio (SNR) and contrast‐to‐noise ratio were computed. One‐way general linear models (GLM) analysis of covariance (ANCOVA) was conducted while adjusting for age and sex. RESULTS: All SNc measurements differed significantly between the three groups (except SNR in iRBD). Changes in iRBD were intermediate between those in PD and HVs. CONCLUSIONS: Using fully automated SNc segmentation method and neuromelanin‐sensitive imaging, iRBD patients showed neurodegenerative changes in the SNc at a lower level than in PD patients. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society |
format | Online Article Text |
id | pubmed-9302679 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley & Sons, Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93026792022-07-22 Deep Learning‐Based Neuromelanin MRI Changes of Isolated REM Sleep Behavior Disorder Gaurav, Rahul Pyatigorskaya, Nadya Biondetti, Emma Valabrègue, Romain Yahia‐Cherif, Lydia Mangone, Graziella Leu‐Semenescu, Smaranda Corvol, Jean‐Christophe Vidailhet, Marie Arnulf, Isabelle Lehéricy, Stéphane Mov Disord Regular Issue Articles BACKGROUND: Isolated REM sleep behavior disorder (iRBD) is considered a prodromal stage of parkinsonism. Neurodegenerative changes in the substantia nigra pars compacta (SNc) in parkinsonism can be detected using neuromelanin‐sensitive MRI. OBJECTIVE: To investigate SNc neuromelanin changes in iRBD patients using fully automatic segmentation. METHODS: We included 47 iRBD patients, 134 early Parkinson's disease (PD) patients and 55 healthy volunteers (HVs) scanned at 3 Tesla. SNc regions‐of‐interest were delineated automatically using convolutional neural network. SNc volumes, volumes corrected by total intracranial volume, signal‐to‐noise ratio (SNR) and contrast‐to‐noise ratio were computed. One‐way general linear models (GLM) analysis of covariance (ANCOVA) was conducted while adjusting for age and sex. RESULTS: All SNc measurements differed significantly between the three groups (except SNR in iRBD). Changes in iRBD were intermediate between those in PD and HVs. CONCLUSIONS: Using fully automated SNc segmentation method and neuromelanin‐sensitive imaging, iRBD patients showed neurodegenerative changes in the SNc at a lower level than in PD patients. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society John Wiley & Sons, Inc. 2022-02-01 2022-05 /pmc/articles/PMC9302679/ /pubmed/35102604 http://dx.doi.org/10.1002/mds.28933 Text en © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Regular Issue Articles Gaurav, Rahul Pyatigorskaya, Nadya Biondetti, Emma Valabrègue, Romain Yahia‐Cherif, Lydia Mangone, Graziella Leu‐Semenescu, Smaranda Corvol, Jean‐Christophe Vidailhet, Marie Arnulf, Isabelle Lehéricy, Stéphane Deep Learning‐Based Neuromelanin MRI Changes of Isolated REM Sleep Behavior Disorder |
title | Deep Learning‐Based Neuromelanin MRI Changes of Isolated REM Sleep Behavior Disorder |
title_full | Deep Learning‐Based Neuromelanin MRI Changes of Isolated REM Sleep Behavior Disorder |
title_fullStr | Deep Learning‐Based Neuromelanin MRI Changes of Isolated REM Sleep Behavior Disorder |
title_full_unstemmed | Deep Learning‐Based Neuromelanin MRI Changes of Isolated REM Sleep Behavior Disorder |
title_short | Deep Learning‐Based Neuromelanin MRI Changes of Isolated REM Sleep Behavior Disorder |
title_sort | deep learning‐based neuromelanin mri changes of isolated rem sleep behavior disorder |
topic | Regular Issue Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9302679/ https://www.ncbi.nlm.nih.gov/pubmed/35102604 http://dx.doi.org/10.1002/mds.28933 |
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