Large-scale harmonization and integration of multi-modal ADNI data for the early detection of Alzheimer's disease and related dementias

大规模协调和整合多模式 ADNI 数据，以早期发现阿尔茨海默病和相关痴呆症

• 批准号: 10659223
• 负责人: Jeiran Choupan
• 金额: $ 79.4万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10659223
• 关键词: Address; Alzheimer disease detection; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease diagnostic; Alzheimer' s disease model; Alzheimer' s disease pathology; Alzheimer' s disease related dementia; American; Amyloid; Automobile Driving; Biological; Biological Markers; Blood; Blood Vessels; Brain Pathology; Categories; Classification; Clinical; Clinical Data; Computer software; Data; Data Aggregation; Data Set; Development; Diagnosis; Diagnostic; Disease; Early Diagnosis; Ethnic Population; Funding; Future; Genotype; Goals; Health Status; Hemorrhage; Heterogeneity; Image; Individual; International; Japanese; Label; Learning; Letters; Long-Term Effects; Machine Learning; Magnetic Resonance Imaging; Methods; Modality; Modeling; Nerve Degeneration; Noise; Participant; Pathogenesis; Pathology; Patients; Performance; Positron-Emission Tomography; Process; Resources; Scanning; Shapes; Site; Software Tools; Source; Structure; Syndrome; Systematic Bias; Techniques; Testing; Texture; Time; United States National Institutes of Health; Validation; White Matter Hyperintensity; Work; apolipoprotein E-4; biomarker development; biomarker identification; clinical biomarkers; clinical phenotype; cohort; combat; data harmonization; data portal; data sharing; deep learning; diverse data; improved; innovation; insight; interest; learning strategy; model building; multimodal data; multimodality; nervous system disorder; neuroimaging; nonalzheimer dementia; novel; pre-clinical; predictive modeling; structured data; tau Proteins; tool

Alzheimer’s disease (AD) and Alzheimer’s Disease Related Dementia (ADRD) are highly heterogeneous in pathology with mixed signatures on clinical biomarkers, making the early diagnosis challenging. Over the past few decades, large cohorts of multi-modal data have been collected to identify the interactions between these key pathologies. However, the utility of such cohorts has been compromised by the heterogeneity of the data collected from multiple sites and scanners, creating technical variability that can introduce noise and bias. Without comprehensive data harmonization and aggregation, these non-biological sources of variability can systematically bias the results of data-driven efforts in biomarker development. Our long-term goal is to identify specific AD and ADRD disease pathology markers and how they evolve. This project aims to improve the early detection of AD and ADRD so that future disease-modifying therapy can be allocated more efficiently to patients. To achieve this objective, we aim to harmonize trans-national cohorts of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to improve the diagnostic classification of AD and ADRD. The central hypothesis of our study is that by harmonizing the multi-modal American ADNI (versions 1, 2, 3, and GO) and Japanese ADNI datasets and building state of the art predictive models from each modality integrated into comprehensive ensembles, we can identify novel classifiers and features for early AD diagnosis and differentiation from ADRD. The central hypothesis will be tested by pursuing three specific aims: 1) Harmonization of multi-modal ADNI data, 2) Development of a suite of effective classifiers from diverse, harmonized ADNI data modalities, 3) Integration of multi-modal predictors into an ensemble model for AD/ADRD/healthy control classification, validation of the model in international ADNI cohorts, and sharing of the data and software products. We will pursue these aims by applying innovative computational approaches that combine traditional machine learning and more recent deep learning methods for unstructured neuroimaging and structured clinical data in ADNI. Moreover, we will leverage ensemble learning techniques to effectively combine models built from these diverse data modalities to optimize for robust classifiers of AD, ADRD, and the health status of patients. The results from this proposal will have a significant impact on better understanding the spatial dynamics and other mechanisms of AD and ADRD pathogenesis. Importantly, this project will create publicly available resources for multi-modal data harmonization and predictive modeling that can be used to explore further AD, ADRD, and other neurological disorders in future studies.

阿尔茨海默病（AD）和阿尔茨海默病相关痴呆（ADRD）在疾病方面具有高度异质性。 病理学在临床生物标志物上具有混合特征，使得早期诊断具有挑战性。过去的事 几十年来，人们收集了大量的多模式数据来识别这些数据之间的相互作用 关键病理。然而，此类群体的效用因群体的异质性而受到损害。 从多个站点和扫描仪收集的数据，造成技术可变性，可能引入噪音和 偏见。如果没有全面的数据协调和汇总，这些非生物来源的变异性 可以系统地偏向生物标志物开发中数据驱动的努力的结果。我们的长期目标是 确定特定的 AD 和 ADRD 疾病病理标志物及其演变方式。该项目旨在改善 及早发现 AD 和 ADRD，以便未来的疾病缓解治疗能够更有效地分配给 患者。为了实现这一目标，我们的目标是协调跨国阿尔茨海默病队列 神经影像倡议 (ADNI) 旨在改进 AD 和 ADRD 的诊断分类。中央 我们研究的假设是，通过协调多模式美国 ADNI（版本 1、2、3 和 GO）和 日本 ADNI 数据集和构建最先进的预测模型，将每种模态集成到 综合集成，我们可以识别新的分类器和特征用于早期 AD 诊断和 与 ADRD 的区别。将通过追求三个具体目标来检验中心假设：1） 协调多模态 ADNI 数据，2) 开发一套来自不同、 统一的 ADNI 数据模态，3) 将多模态预测因子集成到集成模型中 AD/ADRD/健康对照分类、国际 ADNI 队列中模型的验证以及共享 数据和软件产品。我们将通过应用创新的计算方法来实现这些目标 结合了传统机器学习和最新的非结构化深度学习方法 ADNI 中的神经影像和结构化临床数据。此外，我们将利用集成学习 有效组合从这些不同数据模态构建的模型的技术，以优化鲁棒性 AD、ADRD 和患者健康状况的分类器。该提案的结果将产生重大影响 对更好地理解 AD 和 ADRD 发病机制的空间动力学和其他机制的影响。 重要的是，该项目将为多模式数据协调和预测创建公开可用的资源 模型可用于在未来的研究中进一步探索 AD、ADRD 和其他神经系统疾病。