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.

阿尔茨海默氏病（AD）和阿尔茨海默氏病有关的痴呆症（ADRD）在高度异质上 病理学与临床生物标志物上的混合签名，使早期的诊断挑战。过去 几十年来，已经收集了大量的多模式数据来确定这些数据之间的相互作用 关键病理。但是，此类队列的实用性已被异质性损害 从多个站点和扫描仪收集的数据，创建技术变异性，可以引入噪声和 偏见。这些非生物学来源没有全面的数据协调和聚合， 可以系统地偏向于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疾病病理标记及其演变。该项目旨在改善 早期检测AD和ADRD，以便将未来的调整疗法更有效地分配给 患者。为了实现这一目标，我们旨在协调阿尔茨海默氏病的跨国人群 神经影像倡议（ADNI），以改善AD和ADRD的诊断分类。中央 我们研究的假设是，通过协调多模式的美国ADNI（版本1、2、3和GO）和 日本ADNI数据集和从每种模式中建立最先进的预测模型 全面的合奏，我们可以确定早期广告诊断的新颖分类器和功能 与Adrd的区分。中央假设将通过追求三个具体目标来检验：1） 多模式ADNI数据的协调，2）开发潜水员的一组有效分类器， 统一的ADNI数据模式，3）将多模式预测因子整合到一个集合模型中 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。我们将通过采用创新的计算方法来追求这些目标 将传统的机器学习和非结构化的最新深度学习方法结合在一起 ADNI中的神经影像学和结构化临床数据。此外，我们将利用合奏学习 有效结合这些潜水员数据模式的模型以优化可靠的技术 AD，ADRD和患者的健康状况的分类器。该提案的结果将有很大的 影响更好地理解AD和ADRD发病机理的空间动力学和其他机制。 重要的是，该项目将创建用于多模式数据协调和预测性的公开资源 在未来的研究中，可用于探索进一步的AD，ADRD和其他神经系统疾病的建模。