基于脑连接数据，精准定位阿尔茨海默病中淀粉样蛋白扩散路径。
SC-TauPath: A Structural Connectivity Attribution Framework for Mapping Tau Propagation Pathways in Alzheimer's Disease
- 融合网络扩散模型与梯度归因，量化每条脑连接对淀粉样蛋白预测的贡献。
- 在234名患者数据上实现高精度预测，路径图与经典分期解剖一致。
- 为神经退行性疾病机制研究提供可解释的可视化工具，适合临床与神经科学领域。
理解结构连接与阿尔茨海默病（AD）中tau蛋白传播的关系仍是关键挑战，现有计算模型或依赖强生物物理假设，或缺乏神经生物学可解释的路径图。本文提出SC-TauPath，一种基于结构连接（SC）的归因框架，从活体神经影像数据中映射tau传播路径。该方法结合网络扩散模型（NDM）增强的多层感知机与梯度×输入归因，对每条SC边进行贡献评分，并将其转化为多尺度路径图（主干边、高流量路径、枢纽脑区），结果与既定Braak分期解剖一致。在234名来自ADNI的数据集参与者中，结合DTI结构连接与18F-Flortaucipir PET数据，SC-TauPath实现了强交叉验证的tau预测性能，表明结构连接编码了区域tau分布的空间特异性信息。
原文摘要 · Abstract (English)
Understanding how structural connections are associated with tau propagation in Alzheimer's disease (AD) remains a central open question, yet existing computational models either rely heavily on biophysical assumptions or lack neurobiologically interpretable pathway maps. We present SC-TauPath, a structural connectivity (SC) attribution framework that maps tau propagation pathways from in vivo neuroimaging data. SC-TauPath combines a Network Diffusion Model (NDM)-augmented multilayer perceptron with gradient $\times$ input attribution to score each SC edge's contribution to tau prediction, then translates these attribution scores into multi-scale pathway maps (backbone edges, high-traffic routes, and hub ROIs), which validates established Braak staging anatomy. Applied to 234 ADNI participants with paired DTI SC and 18F-Flortaucipir PET, SC-TauPath achieves strong cross-validated tau prediction and yields attribution-based pathway maps consistent with established Braak staging anatomy, demonstrating that SC encode spatially specific information about regional tau distribution in AD.
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