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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 |
タイトル: | Functional connectivity-based classification of rapid eye movement sleep behavior disorder |
著者: | Matsushima, Toma Yoshinaga, Kenji Wakasugi, Noritaka Togo, Hiroki ![]() ![]() Hanakawa, Takashi ![]() ![]() ![]() Japan Parkinson's Progression Markers Initiative (J-PPMI) study group |
著者名の別形: | 吉永, 健二 東口, 大樹 花川, 隆 |
キーワード: | Resting-state functional magnetic resonance imaging Functional connectivity Rapid eye movement sleep behavior disorder Machine learning |
発行日: | Mar-2024 |
出版者: | Elsevier BV |
誌名: | Sleep Medicine |
巻: | 115 |
開始ページ: | 5 |
終了ページ: | 13 |
抄録: | 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. |
著作権等: | © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license. |
URI: | http://hdl.handle.net/2433/287294 |
DOI(出版社版): | 10.1016/j.sleep.2024.01.019 |
PubMed ID: | 38295625 |
出現コレクション: | 学術雑誌掲載論文等 |
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