Developing an Individualized Deep Connectome Framework for ADRD Analysis

作为最常见和第二常见的痴呆类型，AD和路易体痴呆（Lewy body dementias，LBD）包括 路易体痴呆和帕金森病痴呆，占65%至85%的个体， AD/ADRD。AD和ADRD之间的误诊，例如，AD与LBD，将导致非有益的，不完整的，或 甚至有害的治疗和管理选择。AD的诊断和预测与正常人的比较 随着年龄的增长，AD和LBD之间的区分是非常具有挑战性的，这是由于混合的病理和临床表现。 症状目前基于MRI的神经影像学研究仅限于AD和LBD之间的分组分析 患者和对照组，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，并在查明具体和微妙的异常， 不同个体的AD/ADRD大脑。在这个项目中，我们将大大推进和整合我们强大的 方法/工具，并将其应用于多个AD/LBD数据集，以发现和识别个性化的连接体- 通过利用尖端的深度学习技术，缩小AD和LBD之间的差异。具体地说， 我们将1）发现，定义和表示个体GyralNets以表征大脑连接体异质性 和个体AD/LBD患者的AD/LBD相关异常; 2）学习皮质表面变换， 使用无监督球形网络将GyralNets从群体调整到个体，3）开发新的 整合多种类型连接体数据的基础设施，包括解剖学、结构和功能 连接体，并表征、表示和总结它们作为“个体连接体”的深层关系 通过最大化其在AD和LBD之间的预测能力来实现“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.