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性痴呆（ADRD）是高度异质性的， 临床生物标志物上具有混合特征的病理学，使得早期诊断具有挑战性。过去 几十年来，已经收集了大量的多模态数据，以确定这些数据之间的相互作用。 关键病理然而，这些队列的效用受到了研究对象的异质性的影响。 从多个站点和扫描仪收集的数据，造成技术可变性，可能会引入噪音， bias.如果没有全面的数据统一和汇总，这些非生物来源的变异性 可以系统地偏置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AD和ADRD疾病病理学标志物及其演变。该项目旨在改善 早期发现AD和ADRD，以便将来可以更有效地分配疾病改善治疗， 患者为了实现这一目标，我们的目标是协调阿尔茨海默病的跨国队列， 神经影像学倡议（ADNI），以改善AD和ADRD的诊断分类。中央 我们的研究假设是，通过协调多模态的美国ADNI（版本1，2，3和GO）和 日本ADNI数据集，并从每种模态中构建最先进的预测模型， 综合集成，我们可以确定新的分类和早期AD诊断的功能， 与ADRD的区别中心假设将通过追求三个具体目标来检验：1） 多模态ADNI数据的协调，2）开发一套有效的分类器， 协调ADNI数据模式，3）将多模态预测因子整合到一个集合模型中， AD/ADRD/健康对照分类，在国际ADNI队列中验证模型， 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 结合了联合收割机传统的机器学习和最近的深度学习方法， ADNI中的神经影像学和结构化临床数据。此外，我们将利用集成学习 有效地将从这些不同的数据模态构建的联合收割机模型组合起来的技术， AD、ADRD和患者健康状况的分类器。这项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的协调和预测创造公开可用的资源。 模型可用于在未来研究中进一步探索AD、ADRD和其他神经系统疾病。