Developing an Individualized Deep Connectome Framework for ADRD Analysis

开发用于 ADRD 分析的个性化深度连接组框架

• 批准号: 10515550
• 负责人: Gang Li
• 金额: $ 168.66万
• 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: 2022-08-18 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10515550
• 关键词: Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer’s disease biomarker; Americas; Anatomy; Architecture; Base of the Brain; Biological Markers; Brain; Brain Mapping; Clinical; Clinical assessments; Communities; Computing Methodologies; Data; Data Set; Dementia; Dementia with Lewy Bodies; Development; Diagnosis; Diffusion Magnetic Resonance Imaging; Family; Free Will; Functional Magnetic Resonance Imaging; Goals; Healthcare Systems; Heterogeneity; Hour; Human; Individual; Infrastructure; Learning; Lewy Body Dementia; Magnetic Resonance Imaging; Maps; Measures; Methods; Modality; Multimodal Imaging; Parkinson' s Dementia; Pathology; Patients; Population; Process; Public Health; Research; Shapes; Structure; Surface; Symptoms; System; Techniques; Time; Training; Work; base; biobank; clinical predictors; computerized tools; connectome; connectome data; cost; deep learning; deep learning model; diagnostic criteria; flexibility; fluorodeoxyglucose positron emission tomography; human old age (65+); individual patient; individual variation; innovation; interest; mental state; neural network; neuroimaging; neuroimaging marker; normal aging; response; single photon emission computed tomography; tool

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.

作为最常见的痴呆症最常见的类型，AD和Lewy身体痴呆症（LBD），包括 Lewy身体和帕金森氏病痴呆症的痴呆症占65％至85％的患者 广告/adrd。 AD和ADRD之间的误诊，例如AD与LBD，将导致非脱皮，不完整或 甚至有害的治疗和管理选择。与正常的诊断和AD预测相比 衰老，AD和LBD之间的区分非常挑战，由于病理混合和临床 症状。当前基于MRI的神经影像学研究仅限于AD和LBD之间的小组分析 患者和对照，在处理显着的异质性方面面临重大挑战 AD/ADRD病理和临床症状，以及指出跨越特定和微妙的异常 不同的个人广告/大脑。在这个项目中，我们将大大促进并整合我们的强大 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Connectome- 通过利用尖端的深度学习技术，AD和LBD之间的比例差异。具体来说， 我们将1）发现，定义和表示单个旋转以表征大脑连接组异质性 单个AD/LBD患者的AD/LBD相关异常； 2）学习皮层表面转化 使用无监督的球形网络将gyralnets从人群与个体保持一致，3）开发一个新的 基础架构以整合多种类型的连接组数据，包括解剖，结构和功能 connectome且特征是代表并总结他们的深厚关系为“单个连接组 签名”通过最大化其AD和LBD之间的预测能力。