Developing an Individualized Deep Connectome Framework for ADRD Analysis

作为最常见和第二常见的痴呆症，阿尔茨海默病和路易体痴呆(LBD)包括 路易体痴呆症和帕金森氏病痴呆症，占65%到85%的有路易体痴呆的人 AD/ADRD。AD和ADRD之间的误诊，例如AD与LBD之间的误诊，将导致无益、不完整或 甚至是有害的治疗和管理选择。AD的诊断和预测与正常对照的比较 随着年龄的增长，AD和LBD之间的区别非常具有挑战性，这是由于混合的病理和临床 症状。目前基于MRI的神经成像研究仅限于AD和LBD之间的分组分析 患者和对照，在处理显著的异质性方面存在重大挑战 AD/ADRD病理和临床症状，以及精确定位 不同个体的AD/ADRD大脑。在这个项目中，我们将显著推进和整合我们强大的 方法/工具，并将它们应用于多个AD/LBD数据集，以发现和识别个性化连接组- AD和LBD之间的规模差异，通过利用尖端的深度学习技术。具体来说， 我们将1)发现、定义和表示单个GyralNet，以表征大脑连接体的异质性 和AD/LBD相关的异常；2)学习皮质表面的转换，以 使用无监督球形网络将GyralNet从群体到个人对齐，以及3)开发一种新的 集成多种类型的连接组数据的基础设施，包括解剖、结构和功能 连接体，并将它们之间的深层关系刻画、表示和概括为一个单独的连接体 通过最大限度地提高AD和LBD之间的预测能力，实现了“签名”。

As the most and the second most common type of dementia, AD and Lewy body dementias (LBD), including dementia with Lewy bodies and Parkinson’s disease dementia, account for 65% to 85% individuals with AD/ADRD. Misdiagnosis between AD and ADRD, e.g., AD vs. LBD, will lead to non-beneficial, incomplete, or even harmful treatment and management options. Comparing to diagnosis and prediction of AD from normal aging, differentiation between AD and LBD is very challenging, due to both mixed pathologies and clinical symptoms. Current MRI-based neuroimaging studies are limited to group-wise analysis between AD and LBD patients and controls, and there are significant challenges in dealing with the remarkable heterogeneity in AD/ADRD pathologies and clinical symptoms, and in pinpointing specific and subtle abnormalities across different individual AD/ADRD brains. In this project, we will significantly advance and integrate our powerful methods/tools and apply them to multiple AD/LBD datasets to discover and identify individualized connectome- scale differences between AD and LBD, by leveraging the cutting-edge deep learning techniques. Specifically, we will 1) discover, define and represent individual GyralNets to characterize brain connectome heterogeneity and AD/LBD related abnormalities for individual AD/LBD patient; 2) learn a cortical surface transformation to align GyralNets from population to individuals using unsupervised spherical networks and 3) develop a new infrastructure to integrate multiple types of connectome data including anatomical, structural and functional connectome, and characterize, represent and summarize their deep relationship as a “individual connectome signature” by maximizing its prediction capability between AD and LBD.