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 病理学在临床生物标记物上的签名参差不齐，这使得早期诊断具有挑战性。在过去的时间里 几十年来，人们收集了大量的多模式数据，以确定这些模式之间的相互作用 关键的病理改变。然而，这类队列的效用已受到 从多个站点和扫描仪收集的数据，造成技术上的可变性，可能会带来噪音和 偏见。如果没有全面的数据协调和汇总，这些非生物来源的可变性 可以系统地对生物标记物开发中数据驱动的努力的结果产生偏见。我们的长期目标是 确定特定的AD和ADRD疾病的病理标记物及其演变过程。这个项目的目的是改善 及早发现AD和ADRD，以便将来的疾病修改治疗可以更有效地分配到 病人。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 神经影像倡议(ADNI)，以提高AD和ADRD的诊断分类。中环 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，来自集成到 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。核心假设将通过追求三个具体目标来检验：1) 协调多模式ADNI数据，2)开发一套有效的分类器， 协调的ADNI数据模式，3)将多模式预报器整合到一个集合模型中 AD/ADRD/健康控制分类，模型在国际ADNI队列中的验证，以及分享 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 它结合了传统的机器学习和更新的深度学习方法，用于非结构化 ADNI的神经影像和结构化临床数据。此外，我们将利用整体学习 有效地组合从这些不同数据模式构建的模型以优化健壮性的技术 AD、ADRD和患者健康状况的分类器。这项提议的结果将产生重大的 有助于更好地理解AD和ADRD发病的空间动力学等机制。 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 在未来的研究中可以用来进一步探索AD、ADRD和其他神经疾病的建模。