生物标志物 生物标志物
Sebastian Moguilner1,2, Sandra Baez3,4, Hernan Hernandez5
1Massachusetts General Hospital, Boston, MA, USA.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 24, 2025
概括
大脑衰老和痴呆症的诊断受到来自同质人口的数据的限制. 这项研究揭示了社会经济和环境因素对大脑年龄差距产生重大影响,特别是在全球不同地区,提供可扩展的解决方案.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 全球健康 全球健康
背景情况:
- 目前的大脑衰老和痴呆症诊断依赖于来自全球北方的数据,限制了对不同人口的适用性.
- 在了解地理,社会经济和人口因素如何影响全球加速大脑衰老方面存在重大差距,特别是在服务不足的地区.
研究的目的:
- 开发和应用先进的计算方法来评估全球不同人口的大脑衰老.
- 研究暴露性因素对大脑年龄差距 (BAG) 的影响,并确定大脑衰老的差异.
主要方法:
- 开发了一个DenseNet分类器,用于在痴呆症患者和对照群中进行结构性MRI分析.
- 利用基于图形的深度学习架构,从15个国家的fMRI和EEG数据中分析大脑年龄差距 (BAG).
- 采用非线性通用添加模型 (GAMs) 在多式模式方法中将BAG与暴露因子联系起来.
主要成果:
- 在临床MRI数据上,DenseNet分类器表现强,识别了疾病特异性模式.
- 在拉丁美洲和加勒比海 (LAC) 人口中观察到大脑年龄明显较大,这与社会经济不平等,污染和健康差异有关.
- 一般化添加模型揭示了BAG与人类发展指数,经济平等和空气污染 (p < 0.0001) 等因素之间的显著关联.
结论:
- 研究结果为加速大脑衰老的差异提供了可扩展的解决方案,特别是在服务不足的地区.
- 使用GAM的多模式元模型提高了BAG估计的准确性.
- 个性化人工智能工具可以推动研究加速大脑衰老轨迹和可修改的风险因素.
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