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

通过个性化大脑锚节点绘制阿尔茨海默病的进展轨迹

• 批准号: 10346720
• 负责人: Gang Li
• 金额: $ 60.99万
• 依托单位: UNIVERSITY OF TEXAS ARLINGTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-15 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10346720
• 关键词: Affect; Aging; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease pathology; Anatomy; Atlases; Base of the Brain; Biology; Brain; Brain Mapping; Classification; Clinical; Computational Technique; Data; Data Set; Data Sources; Dementia; Development; Disease Progression; Early Diagnosis; Functional disorder; Heterogeneity; Hour; Human; Image; Impairment; Individual; Light; Magnetic Resonance Imaging; Maps; Measures; Methodology; Methods; Modeling; Monitor; Multimodal Imaging; Nature; Neurodegenerative Disorders; Pathway interactions; Patients; Pattern; Phase; Population; Process; Property; Series; Shapes; Structure; Surface; System; Testing; Time; Trees; Work; base; biobank; cohort; computerized tools; connectome; cost; deep learning; disease classification; flexibility; image registration; imaging biomarker; imaging study; improved; individual variation; inter-individual variation; large scale data; multimodality; neural network; neuroimaging; normal aging; personalized diagnostics; personalized predictions; pre-clinical; response; tool; trend

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.

项目概要 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 在不同的疾病进展阶段。尽管有大量研究调查了其临床效用 基于磁共振成像 (MRI) 的生物标志物用于表征从无症状到轻度的 AD 阶段 针对痴呆症的症状，对 AD 进行个性化精准预测和早期诊断仍然是 具有挑战性的。现有的成像生物标志物在代表不同生物标志物之间的显着异质性方面受到限制。 个体和不同临床阶段。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关系和个体差异 在正常衰老和 AD 进展期间。为此，该项目旨在：1）确定一组大脑锚点—— 基于分组一致模式和个性化解剖学和 正常衰老和 AD 进展过程中大量公开神经影像的连接特性 数据来源； 2）开发基于深度学习的高效个性化形状变换方法 通过灵活利用多模态个体特征，将群体锚节点映射到个体大脑；和 3) 使用锚节点衍生的大脑测量构建一个进展树来揭示和表示广泛的 AD 发展范围。因此，可以将各个主题投影到树结构，以有效且 方便地了解他们的临床状态并预测 AD 进展趋势。我们将测试我们的新框架 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。