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

项目摘要 阿尔茨海默病（Alzheimer's disease，AD）是一种异质性神经退行性疾病，不仅在病理生理上， 在不同的疾病进展阶段。尽管有许多研究调查了 基于磁共振成像（MRI）的生物标志物表征AD从无症状到轻度的阶段 有症状的痴呆，进行个性化的精确预测和早期诊断AD仍然是一个挑战。 挑战性现有的成像生物标志物在代表不同组织间的显著异质性方面是有限的。 个体和不同的临床阶段。这一挑战源于缺乏可靠的大脑标志， 可以同时表征和表示稳健的群体对应性和个体变异 在正常衰老和AD进展期间。作为回应，该项目旨在：1）识别一组大脑锚- 节点作为群体地标，基于群体一致的模式和个性化的解剖学和 在大规模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 数据源; 2）开发基于深度学习的高效个性化形状转换方法， 通过灵活地利用多模态个体特征将群体锚节点映射到个体大脑;以及3） 使用锚节点导出的大脑测量构建进展树，以揭示和表示广泛的 AD的发展。因此，可以将各个主题投影到树结构中，以有效地和 方便地了解他们的临床状态，并预测AD进展的趋势。我们将测试我们的新框架 四个大型独立的老龄化/AD队列，包括HCP老龄化，英国生物银行，ADNI和最新阶段的 开放获取系列成像研究（OASIS-3），并免费发布我们的计算工具和处理 向公众提供数据。

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.