Mapping Trajectories of Alzheimer's Progression via Personalized Brain Anchor-nodes

项目摘要 阿尔茨海默病(AD)是一种异质性神经退行性疾病，不仅在病理生理学上，而且在 在不同的疾病发展阶段。尽管已有大量研究调查了阿司匹林的临床效用 基于磁共振成像的生物标志物在无症状到轻度AD分期中的应用 对痴呆症有症状，对AD进行个性化精确预测和早期诊断仍是 很有挑战性。现有的成像生物标记物在表示不同生物标记物的显著异质性方面受到限制 个体和处于不同临床阶段。这一挑战源于缺乏可靠的大脑里程碑 可以同时表征和表示稳健的群体对应关系和个体变异 在正常衰老和AD进展期间。作为回应，这个项目的目标是：1)确定一组大脑主播- 基于群体一致模式和个性化解剖结构的节点作为种群地标 在大量公开可用的神经成像中，正常衰老和AD进展期间的连接特性 2)开发了一种高效的基于深度学习的个性化形状转换方法 通过灵活地利用多通道个体特征，将群体锚节点映射到个体大脑；以及3) 使用锚节点派生的大脑测量构建一棵进程树，以揭示和表示广度 AD发展的光谱。因此，可以将各个主题投影到树结构，以有效地 方便地访问他们的临床状态并预测AD的发展趋势。我们将测试我们的新框架 关于四个大型独立老龄化/AD队列，包括HCP-Aging、UK Biobank、ADNI和最新阶段的 开放获取成像研究系列(OASIS-3)，并免费发布我们的计算工具和处理 向公众公布数据。

Project Summary Alzheimer’s disease (AD) is a heterogeneous neurodegenerative disorder, not only in pathophysiology, but also at different disease progression stages. Despite numerous studies that have investigated the clinical utility of magnetic resonance imaging (MRI) based biomarkers in characterizing AD stages from asymptomatic to mildly symptomatic to dementia, making a personalized precision prediction and early diagnosis of AD is still challenging. Existing imaging biomarkers are limited in representing significant heterogeneity across different individuals and at different clinical stages. This challenge originates from the lack of reliable brain landmarks that can simultaneously characterize and represent robust population correspondences and individual variation during normal aging and AD progression. In response, this project aims to: 1) Identify a set of brain anchor- nodes as population landmarks based on both group-wise consistent patterns and individualized anatomical and connectivity properties during normal aging and AD progression among massive, publicly available neuroimaging data sources; 2) Develop an efficient individualized shape transformation approach based on deep learning to map population anchor-nodes to individual brains by flexibly leveraging multimodal individual features; and 3) Construct a progression tree using anchor-nodes derived brain measures to unveil and represent the wide spectrum of AD development. Individual subjects can thus be projected to the tree structure to effectively and conveniently access their clinical status and predict the trend of AD progression. We will test our new frameworks on four large independent aging/AD cohorts including HCP-Aging, UK Biobank, ADNI and the latest stage of Open Access Series of Imaging Studies (OASIS-3), and freely release our computational tools and processed data to the public.