Bayesian Physics-Based Modeling of Tau Propagation in Alzheimer's Disease.

Bayesian Physics-Based Modeling of Tau Propagation in Alzheimer's Disease.
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阿尔茨海默病患者Tau传播的贝叶斯物理模型。

DOI:
10.3389/fphys.2021.702975
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发表时间:
2021
影响因子:
4
通讯作者:
Alzheimer's Disease Neuroimaging Initiative (ADNI)
Alzheimer's Disease Neuroimaging Initiative (ADNI)
中科院分区:
医学2区
文献类型:
--
作者:
Schäfer A;Peirlinck M;Linka K;Kuhl E;Alzheimer's Disease Neuroimaging Initiative (ADNI)

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淀粉样蛋白-β和过度磷酸化的tau蛋白是已知的阿尔茨海默病神经病理学的驱动因素。尤其是Tau蛋白在患者大脑中的扩散遵循一种时空模式,这种模式高度刻板,与随后的神经退行性变相关。新的医学成像技术现在可以可视化tau在体内大脑中的分布,允许对这种生物标志物的动态有新的见解。本文针对阿尔茨海默病神经成像计划中76名受试者的纵向tau正电子发射断层扫描数据,个性化了具有全局扩散和局部产生条件的网络扩散模型。我们使用具有层次先验结构的贝叶斯推理来推断我们的模型参数在组和主题水平上的均值和可信区间。结果表明,淀粉样蛋白阳性组的蛋白质平均产率为0.019±0.27/yr,显著高于淀粉样蛋白阴性组的- 0.143±0.21/yr (p = 0.0075)。这些结果支持淀粉样蛋白病理驱动tau病理的假设。校准后的模型可以作为一种有价值的临床工具,用于确定随访扫描的最佳时间点,并预测疾病进展的时间表。
Amyloid-β and hyperphosphorylated tau protein are known drivers of neuropathology in Alzheimer's disease. Tau in particular spreads in the brains of patients following a spatiotemporal pattern that is highly sterotypical and correlated with subsequent neurodegeneration. Novel medical imaging techniques can now visualize the distribution of tau in the brain in vivo, allowing for new insights to the dynamics of this biomarker. Here we personalize a network diffusion model with global spreading and local production terms to longitudinal tau positron emission tomography data of 76 subjects from the Alzheimer's Disease Neuroimaging Initiative. We use Bayesian inference with a hierarchical prior structure to infer means and credible intervals for our model parameters on group and subject levels. Our results show that the group average protein production rate for amyloid positive subjects is significantly higher with 0.019±0.27/yr, than that for amyloid negative subjects with −0.143±0.21/yr (p = 0.0075). These results support the hypothesis that amyloid pathology drives tau pathology. The calibrated model could serve as a valuable clinical tool to identify optimal time points for follow-up scans and predict the timeline of disease progression.
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