Machine Learning and Large-scale Imaging analytics for dimensional representations of brain trajectories in aging and preclinical Alzheimer's Disease: The brain aging chart and the iSTAGING consortium

机器学习和大规模成像分析，用于衰老和临床前阿尔茨海默氏病大脑轨迹的维度表示：大脑衰老图表和 iSTAGING 联盟

• 批准号: 10530196
• 负责人: Christos Davatzikos
• 金额: $ 229.1万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10530196
• 关键词: Age; Aging; Alzheimer associated neurodegeneration; Alzheimer' s disease brain; Alzheimer' s disease related dementia; Biological Markers; Brain; Clinical; Clinical Data; Cognitive; Data; Data Set; Dementia; Development; Diabetes Mellitus; Dimensions; Early Diagnosis; Foundations; Funding; Goals; Grant; Heterogeneity; Hypertension; Image; Impaired cognition; Individual; Lead; Machine Learning; Magnetic Resonance Imaging; Maps; Measurement; Measures; Methods; Nerve Degeneration; Neurodegenerative Disorders; Obesity; Pathologic; Pathology; Pattern; Persons; Phase; Phenotype; Process; Resources; Scanning; Sleep disturbances; Smoking; Socioeconomic Status; Structure; System; Time; Underrepresented Populations; Work; aging brain; base; cardiovascular risk factor; cognitive performance; comorbidity; deep learning; effective therapy; frontier; imaging modality; in vivo; indexing; insight; learning strategy; machine learning method; neuroimaging; novel; personalized predictions; pre-clinical; predictive modeling; predictive panel; prodromal Alzheimer' s disease; statistics; tool; vascular cognitive impairment and dementia; β-amyloid burden

Abstract Brain aging is commonly accompanied by a number of neuropathologic processes, often co-occurring, that may lead to cognitive decline and dementia. Vascular contributions to cognitive impairment and dementia (VCID) are also extremely common and, due to associations with cardiovascular risk factors, may be mitigated with current therapies. It is clear that effective treatments for AD-related dementias (ADRD) will require early detection of pathologic brain change at prodromal and cognitively normal stages. Imaging methods offer the opportunity to study diverse brain changes present in aging and prodromal AD in ways that were previously impossible. Characterizing these multi-faceted aspects of brain structure, function and pathology not only provides insights into the underlying pathophysiological processes, but also novel predictive in vivo biomarkers. Various studies have shown that relatively early signs of neurodegenerative processes can be detected via AI-based pattern analysis and machine learning (PAML) methods, and that these tools can provide powerful predictive individualized panels of predictors. Our group has been on the frontier of developing PAML methods, and applying them to the new “Imaging-based coordinate SysTem for AGing and NeurodeGenerative diseases” (iSTAGING) consortium, a large-scale effort pursued in the current phase of our grant, which successfully brought together and harmonized over 51,000 MRIs and clinical data from 11 studies and ~34,000 individuals. We aim to capture the heterogeneity of brain change with aging and prodromal AD, by applying our heterogeneity analysis PAML deep learning (DL) methods, which help structuring imaging patterns associated with different brain aging trajectories. Our goal is to enrich the different dimensions of iSTAGING which will reflect various patterns of brain change, hence capturing the underlying heterogeneity in quantifiable and replicable metrics. Although we will include our previously derived measures of rsfMRI networks and of amyloid burden, in the proposed work we will focus on further dissecting neuroanatomical heterogeneity, i.e. on refining the `N' in the AT(N) framework to measure variability in AD neurodegeneration and the contributions of copathologies, and on using these intermediate neuroimaging phenotypes to predict cognitive decline and clinical progression. This will allow us to place each individual into the iSTAGING brain chart and map his/her trajectory, as well to determine predictive indices of brain change and cognitive decline. The current project builds on the foundational work of the previous funding phase, and expands this unique resource to include several studies focusing on longitudinal data, on groups of under-represented socio-economic status, as well as on various co-morbidities including hypertension, diabetes, obesity, smoking and sleep disturbances. The proposed work will also leverage recent developments in deep learning, and will offer advanced methods for harmonization, heterogeneity analysis, and predictive modeling.

摘要 脑老化通常伴随着许多神经病理过程，通常共同发生， 可能导致认知能力下降和痴呆。血管对认知障碍和痴呆的影响 （VCID）也非常常见，由于与心血管危险因素相关， 用目前的疗法缓解。很明显，AD相关痴呆（ADRD）的有效治疗将 需要在前驱期和认知正常阶段早期检测病理性脑部变化。成像 方法提供了机会，研究不同的大脑变化，目前在老化和前驱AD的方式 这在以前是不可能的。描述大脑结构、功能和 病理学不仅提供了对潜在的病理生理过程的见解， 预测性体内生物标志物。各种研究表明，神经退行性疾病的相对早期迹象 过程可以通过基于AI的模式分析和机器学习（PAML）方法进行检测，并且 这些工具可以提供强大的预测个体化预测器组。我们小组一直在 发展PAML方法的前沿，并将其应用于新的“基于成像的坐标系统” 老年化和神经退行性疾病”（iSTAGING）联盟，一个大规模的努力， 我们赠款的当前阶段，成功地汇集和协调了51，000多个MRI， 来自11项研究和约34，000人的临床数据。我们的目标是捕捉大脑的异质性 通过应用我们的异质性分析PAML深度学习（DL）， 这些方法有助于构建与不同大脑老化轨迹相关的成像模式。我们 目标是丰富iSTAGING的不同维度，这将反映大脑变化的各种模式， 从而在可量化和可复制的度量中捕获潜在的异质性。虽然我们会 包括我们以前推导的rsfMRI网络和淀粉样蛋白负荷的测量，在拟议的工作中， 我们将集中于进一步剖析神经解剖学的异质性，也就是细化AT（N）中的“N” 测量AD神经退行性变的变异性和共同病理学的贡献的框架，以及 使用这些中间神经影像表型来预测认知能力下降和临床进展。 这将使我们能够将每个人放入iSTAGING大脑图表中，并绘制出他/她的轨迹， 以确定大脑变化和认知能力下降的预测指标。目前的项目建立在 上一个融资阶段的基础工作，并扩大这一独特的资源，包括几个 研究重点是纵向数据、社会经济地位代表性不足的群体以及 各种合并症，包括高血压、糖尿病、肥胖、吸烟和睡眠障碍。的 拟议的工作还将利用深度学习的最新发展，并将提供先进的方法， 用于协调、异质性分析和预测建模。