基于功能连接性对快速眼动睡眠行为障碍的分类 睡眠行为障碍
Toma Matsushima1, Kenji Yoshinaga2, Noritaka Wakasugi3
1Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, 187-8501, Japan; Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, Koganei, Tokyo, 184-8588, Japan.
Sleep medicine
|January 31, 2024
概括
机器学习有效地使用功能连接在孤立的快速眼动睡眠行为障碍 (iRBD) 中识别了大脑差异. 这种方法可能有助于检测神经退行性疾病的早期阶段,如帕金森病.
科学领域:
- 神经科学是一个神经科学.
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 孤立的快速眼动睡眠行为障碍 (iRBD) 是未来同核蛋白病变的关键指标.
- 开发评估iRBD潜在大脑状态的方法对于早期检测至关重要.
- 静态fMRI和机器学习为此评估提供了一个有希望的途径.
研究的目的:
- 开发和验证用于识别使用功能连接的iRBD的机器学习分类器.
- 探索iRBD患者的功能连接模式和临床症状之间的关系.
主要方法:
- 使用55名iRBD患者和97名健康对照 (HC) 的功能连接数据构建了一个机器学习分类器.
- 随机森林确定了重要的功能连接,然后通过后勤回归和SVM进行分类.
- 评估了分类性能,并分析了连接性和临床症状之间的相关性.
主要成果:
- 该分类器实现了显著的准确性 (0.649),精度 (0.659),回忆 (0.662) 和F1得分 (0.645),其AUC为0.707.
- 关键的功能连接涉及运动,体感,表层,,乳头和小脑区域.
- 功能连接的变化与iRBD患者的亚临床运动和非运动症状相关.
结论:
- 使用功能连接的机器学习分类器显示出在iRBD中评估潜在大脑状态的潜力.
- 这项技术可能有助于早期检测和管理同核蛋白病变.
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