洞察认知衰老的异质性:对两个数据驱动的集群算法进行比较分析
Truc Nguyen1, Yu-Ling Chang2,3
1Taiwan International Graduate Program in Interdisciplinary Neuroscience, National Taiwan University and Academia Sinica, Taipei, Taiwan.
概括
两种数据驱动的算法在老年人中发现了类似的认知亚型,这些老年人具有正常的认知能力和轻微的认知障碍. 这些发现支持使用多种方法可靠地分层认知衰老模式.
科学领域:
- 神经科学是一个神经科学.
- 老年学是一门学科.
- 数据科学数据科学数据科学
背景情况:
- 认知衰老在认知,脑部成像和生物标志物方面呈现出多样化的特征.
- 很少有研究在同一数据集上比较多个聚类算法,用于认知衰老研究.
研究的目的:
- 用数据驱动的方法分析正常认知和轻度认知障碍的老年人的神经心理表现.
- 为了比较非负矩阵因子化 (NMF) 和基于模型的集群 (MBC) 在识别认知亚型的性能.
主要方法:
- 311名没有痴呆症的老年人接受了17项认知测试和记忆投诉问卷.
- 非负矩阵因子化 (NMF) 和基于模型的集群 (MBC) 被应用到数据中.
- 用人口统计,临床和大脑形态学数据分析了集群特征.
主要成果:
- 无论是NMF还是MBC,都确定了具有良好数据匹配的两组和三组集群解决方案.
- 两个集群解决方案区分认知完整 (CI) 和认知次优 (CS) 组.
- 三个集群解决方案进一步将CI个体分为记忆熟练和非记忆熟练的子组.
- 认知异质性模式和与人口统计/神经成像数据的关联在NMF和MBC之间是可比的.
- 通过在各个算法中识别一致和不匹配的集群成员身份,改善了表型均性.
结论:
- 两种不同的数据驱动算法在正常认知和轻度认知障碍中产生了可比的认知异质性模式.
- 在未来的认知衰老亚型研究中,建议对不同方法进行分层的复制.
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