生物标志物 生物标志物
Sokratis Charisis1, Sachintha Ransara Brandigampala2, Di Wang3
1Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Center at San Antonio, San Antonio, TX, USA.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 26, 2025
概括
机器学习模型可以检测阿尔茨海默病 (AD) 和老化大脑模式. 这些模型显示在不同人群中具有普遍性,但老年西班牙裔个人可能表现出不同的与衰老相关的大脑缩和认知关联.
科学领域:
- 神经成像是一种神经成像.
- 机器学习 机器学习
- 认知神经科学 认知神经科学
背景情况:
- 机器学习 (ML) 模型识别阿尔茨海默病 (AD) 和老化大脑缩模式.
- 现有的ML工具主要在白人群体中进行验证,这限制了它们在不同群体中的使用.
- 这项研究评估了ML指数在种族和种族不同的神经成像样本中的概括性.
研究的目的:
- 评估ML衍生的脑缩指数 (SPARE-AD和SPARE-BA) 在不同种族和民族群体的普遍性.
- 检查AD类和与衰老相关的大脑缩模式与多民族队列中的认知功能之间的关联.
主要方法:
- 利用了来自四项研究 (FHS,NACC,CHS,NOMAS) 的6700个MRI扫描的数据.
- 应用了强大的统计协调来消除扫描器效应,并得出SPARE-AD和SPARE-BA指数.
- 使用跨种族/种族群体的混合效应模型,研究了SPARE-AD与认知障碍以及SPARE-BA与全球认知之间的关联.
主要成果:
- SPARE-AD指数表明AD类缩的概率>50%,显著增加了认知障碍的几率 (OR=10.37),跨种族一致.
- SPARE-BA预测的大脑年龄增加1年与全球认知能力下降有关 (β=-0.086).
- 在西班牙裔个体中,SPARE-BA与认知的关联并不显著,并且在75岁以下的群体中更一致.
结论:
- 协调技术和多样化的培训数据使得ML指数能够扩展到各种种族和民族群体.
- 年长的西班牙裔个人可能会显示老化大脑模式和认知之间的明显关系.
- 神经病理学或其他因素的潜在差异可能是认知关联中观察到的变化的基础.
相关概念视频
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