基础科学和病原发生学
Robin Sandell1, Justin Torok2, Srikantan Nagaragan3
1University of California San Francisco, San Francisco, CA, USA.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 28, 2025
概括
一个新的混合模型使用基于事件的统计模型 (EBM) 和生物物理网络扩散模型 (NDM) 预测阿尔茨海默病的tau传播. 这种方法揭示了多样化的tau模式,挑战了传统的分阶段,并提供了个性化的治疗策略.
科学领域:
- 神经科学是一个神经科学.
- 生物物理学的生物物理.
- 计算生物学 计算生物学
背景情况:
- 阿尔茨海默病 (AD) 影响全球数百万人,其特点是蛋白积累.
- 在体内tau-PET成像显示了tau进展的显著个体变异性,与传统的布拉克分期不同.
- 现有的模型缺乏生物物理基础或需要纵向数据.
研究的目的:
- 开发一种混合建模方法,将基于事件的模型 (EBM) 和网络扩散模型 (NDM) 整合在一起,用于预测,个性化的tau传播建模.
- 通过将纵向数据见解与生物物理原理相结合,克服当前建模技术的局限性.
主要方法:
- 开发了一种混合模型,将EBM和NDM结合起来,用于tau传播预测.
- 在650名ADNI受试者身上应用了EBM来确定疾病的阶段,并生成纵向轨迹.
- 利用扩展NDM (eNDM) 来建模tau在脑网络上的扩散过程的传播,优化个体种子和动力参数.
主要成果:
- 与参数优化和之前的基准相比,个别种子优化产生了优越的模型匹配 (平均R=0.85).
- 模型预测与纵向的tau-PET数据有很强的相关性 (平均R=0.81).
- 型在发病时表现出最大的异质性,随着时间的推移趋同,并确定了两种不同的播种原型 (内腔主导和扩散的叶).
结论:
- 混合EBM-NDM方法可以从横截面数据中准确,个性化地预测tau传播.
- 这些发现挑战了经典的布拉克分期,表明多样化的tau启动途径随着时间的推移而趋同.
- 这一框架有可能为个性化阿尔茨海默氏症治疗策略和对其他神经退行性疾病的应用提供潜力.
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