Multimodal fusion for early prediction of Alzheimer’s disease: Request for supplemental funds for NIH R01: R01EB006841 “Multivariate methods for identifying multitask/multimodal brain imaging biomarke

摘要 阿尔茨海默病(AD)与大脑的关系已被广泛研究。AD的早期诊断为IS 具有挑战性的多种原因，包括临床和病理特征中存在的变异性 在受影响的个体中。在最近的工作中，我们发现大脑的敏感度和稳定性有了很大的改善-- 基于各种疾病的生物标记物，使用跨多个模型利用关节信息的方法 达蒂斯。在这项工作中，我们主要集中在三个有前景的方向上，包括第一，多功能的联合融合 采用并行多链接联合独立分量的具有脑结构的网络 分析。其次，我们将开发一种方法来利用生成性框架来捕获维度 使用基于变分自动编码器、高阶聚类和元映射的方法来改变AD 潜在空间变量和颜色之间的关系。最后，我们将把这项工作扩展到低维 通过将多个大型临床数据集与大型人群数据集相结合来预测AD风险 英国生物库数据和其他数据。拟议的目标有可能显著提高对 在未受影响的个体中预测AD的基于成像的方法。

Abstract The relationship of Alzheimer's disease (AD) to the brain has been widely studied. Early diagnosis of AD is challenging for multiple reasons, including the presence of variability in the clinical and pathological features within affected individuals. In recent work we have shown a large improvement in sensitivity and stability of brain- based biomarkers of various disorders using approaches that leverage the joint information across multiple mo- dalities. In this work we focus on three promising directions including first the joint fusion of multiple functional networks with brain structure by using a novel approach called parallel multilink joint independent component analysis. Secondly, we will develop an approach to leverage a generative framework for capturing dimensional changes in AD using an approach based on variational autoencoders, high order clustering, and meta-mapping between latent space variables and colors. Finally, we will extend this work to move towards a low dimensional prediction of AD risk by combining multiple large clinical datasets with large population datasets such as the UKbiobank data and others. The proposed aims have the potentially to significantly increase the sensitivity of imaging-based methods for predicting AD in unaffected individuals.