在老年人中认知障碍的分类基于通过等级聚类分析通过大脑功能状态测量数据的认知障碍
Yangxiaoxue Liu1,2,3, Na Wang2, Xinling Su2
1Medical School of Chinese PLA, Beijing, China.
Frontiers in aging neuroscience
|January 1, 2024
概括
这项研究引入了一种新的大脑功能状态测量 (BFSM) 来分类认知障碍 (CI) 亚型. BFSM使认知能力下降的老年人能够进行个性化的早期康复.
科学领域:
- 神经科学是一个神经科学.
- 老年学是一门学科.
- 生物医学工程 生物医学工程
背景情况:
- 认知障碍 (CI) 是老年人群中普遍存在的一种退行性疾病.
- 目前的CI评估方法缺乏大脑功能状态分析,导致诊断延迟和早期干预机会有限.
研究的目的:
- 开发和验证用于分类认知障碍 (CI) 亚型的大脑功能状态测量 (BFSM).
- 为CI患者提供个性化的早期康复策略.
主要方法:
- 招募了149名社区老年人,使用蒙特利尔认知评估 (MoCA) 和迷你精神状态检查 (MMSE) 对CI进行查.
- 使用基于脑电图的BFSM评估大脑功能状态.
- 通过拉索和逻辑回归模型 (LRM) 选择CI相关的大脑功能状态指标.
- 将CI参与者分为不同的子类型,使用基于所选变量的层次聚类分析.
主要成果:
- 在经过MoCA选的参与者中,BFSM确定了五种CI亚型,其特点是大脑的不同功能状态 (例如,精神任务交换,感官值,精神疲劳).
- 在MMSE选的参与者中,BFSM确定了三种CI亚型,通过不同的指数来定义 (例如,内观强度,焦虑倾向,认知效率).
- 分类显示了良好的集团内部一致性和显著的集团内部变异性.
结论:
- 基于BFSM的分类有效地识别出临床和大脑功能相关的CI亚型.
- 这种方法为认知障碍的老年人提供了个性化的早期康复干预.
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