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ファイル | 記述 | サイズ | フォーマット | |
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j.sleep.2024.01.019.pdf | 1.73 MB | Adobe PDF | 見る/開く |
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DCフィールド | 値 | 言語 |
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dc.contributor.author | Matsushima, Toma | en |
dc.contributor.author | Yoshinaga, Kenji | en |
dc.contributor.author | Wakasugi, Noritaka | en |
dc.contributor.author | Togo, Hiroki | en |
dc.contributor.author | Hanakawa, Takashi | en |
dc.contributor.author | Japan Parkinson's Progression Markers Initiative (J-PPMI) study group | en |
dc.contributor.alternative | 吉永, 健二 | ja |
dc.contributor.alternative | 東口, 大樹 | ja |
dc.contributor.alternative | 花川, 隆 | ja |
dc.date.accessioned | 2024-03-11T05:10:10Z | - |
dc.date.available | 2024-03-11T05:10:10Z | - |
dc.date.issued | 2024-03 | - |
dc.identifier.uri | http://hdl.handle.net/2433/287294 | - |
dc.description.abstract | BACKGROUND: Isolated rapid eye movement sleep behavior disorder (iRBD) is a clinically important parasomnia syndrome preceding α-synucleinopathies, thereby prompting us to develop methods for evaluating latent brain states in iRBD. Resting-state functional magnetic resonance imaging combined with a machine learning-based classification technology may help us achieve this purpose. METHODS: We developed a machine learning-based classifier using functional connectivity to classify 55 patients with iRBD and 97 healthy elderly controls (HC). Selecting 55 HCs randomly from the HC dataset 100 times, we conducted a classification of iRBD and HC for each sampling, using functional connectivity. Random forest ranked the importance of functional connectivity, which was subsequently used for classification with logistic regression and a support vector machine. We also conducted correlation analysis of the selected functional connectivity with subclinical variations in motor and non-motor functions in the iRBDs. RESULTS: Mean classification performance using logistic regression was 0.649 for accuracy, 0.659 for precision, 0.662 for recall, 0.645 for f1 score, and 0.707 for the area under the receiver operating characteristic curve (p < 0.001 for all). The result was similar in the support vector machine. The classifier used functional connectivity information from nine connectivities across the motor and somatosensory areas, parietal cortex, temporal cortex, thalamus, and cerebellum. Inter-individual variations in functional connectivity were correlated with the subclinical motor and non-motor symptoms of iRBD patients. CONCLUSIONS: Machine learning-based classifiers using functional connectivity may be useful to evaluate latent brain states in iRBD. | en |
dc.language.iso | eng | - |
dc.publisher | Elsevier BV | en |
dc.rights | © 2024 The Authors. Published by Elsevier B.V. | en |
dc.rights | This is an open access article under the CC BY license. | en |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | Resting-state functional magnetic resonance imaging | en |
dc.subject | Functional connectivity | en |
dc.subject | Rapid eye movement sleep behavior disorder | en |
dc.subject | Machine learning | en |
dc.title | Functional connectivity-based classification of rapid eye movement sleep behavior disorder | en |
dc.type | journal article | - |
dc.type.niitype | Journal Article | - |
dc.identifier.jtitle | Sleep Medicine | en |
dc.identifier.volume | 115 | - |
dc.identifier.spage | 5 | - |
dc.identifier.epage | 13 | - |
dc.relation.doi | 10.1016/j.sleep.2024.01.019 | - |
dc.textversion | publisher | - |
dc.identifier.pmid | 38295625 | - |
dcterms.accessRights | open access | - |
datacite.awardNumber | 19H05726 | - |
datacite.awardNumber | 19H03536 | - |
datacite.awardNumber | 23H00414 | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/grant/KAKENHI-PLANNED-19H05726/ | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-19H03536/ | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-23H00414/ | - |
dc.identifier.pissn | 1389-9457 | - |
dc.identifier.eissn | 1878-5506 | - |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.awardTitle | 神経伝達物質の異常に伴う超適応を誘発する脳活動ダイナミクスの変容 | ja |
jpcoar.awardTitle | 学習による小脳構造マクロ可塑性の神経生物学的機構の解明研究 | ja |
jpcoar.awardTitle | ヒト組織・病理情報を表現する合成MRI技術開発に向けた基盤構築 | ja |
出現コレクション: | 学術雑誌掲載論文等 |
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