Uncovering Heterogeneity in Alzheimer's Disease from Graphical Modeling of the Tau Spatiotemporal Topography.

Jiaxin Yue, Yonggang Shi
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Abstract

Growing evidence from post-mortem and in vivo studies have demonstrated the substantial variability of tau pathology spreading patterns in Alzheimer's disease(AD). Automated tools for characterizing the heterogeneity of tau pathology will enable a more accurate understanding of the disease and help the development of targeted treatment. In this paper, we propose a Reeb graph representation of tau pathology topography on cortical surfaces using tau PET imaging data. By comparing the spatial and temporal coherence of the Reeb graph representation across subjects, we can build a directed graph to represent the distribution of tau topography over a population, which naturally facilitates the discovery of spatiotemporal subtypes of tau pathology with graph-based clustering. In our experiments, we conducted extensive comparisons with state-of-the-art event-based model on synthetic and large-scale tau PET imaging data from ADNI3 and A4 studies. We demonstrated that our proposed method can more robustly achieve the subtyping of tau pathology with clear clinical significance and demonstrated superior generalization performance than event-based model.

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从 Tau 时空地形图的图形建模中揭示阿尔茨海默病的异质性
越来越多的尸检和活体研究证据表明,阿尔茨海默病(AD)中的tau病理学扩散模式具有很大的差异性。表征 Tau 病理学异质性的自动化工具将有助于更准确地了解这种疾病,并有助于开发有针对性的治疗方法。在本文中,我们利用 tau PET 成像数据,提出了皮质表面 tau 病理拓扑的 Reeb 图表示法。通过比较不同受试者的Reeb图表示的空间和时间一致性,我们可以建立一个有向图来表示人群中的tau拓扑分布,这自然有助于通过基于图的聚类发现tau病理的时空亚型。在实验中,我们对来自 ADNI3 和 A4 研究的合成和大规模 tau PET 成像数据与最先进的基于事件的模型进行了广泛的比较。结果表明,与基于事件的模型相比,我们提出的方法能更稳健地实现具有明确临床意义的 tau 病理学亚型,并表现出更优越的泛化性能。
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