Complexity of FMRI in Alzheimer's Disease

FMRI 在阿尔茨海默病中的复杂性

• 批准号: 10369601
• 负责人: KAY JANN
• 金额: $ 65.4万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10369601
• 关键词: Aging; Alzheimer' s Disease; Alzheimer' s disease care; Alzheimer’s disease biomarker; Amyloid; Amyloid beta-Protein; Architecture; Biological Markers; Brain; Clinical; Clinical Research; Cognitive; Computer software; Data; Development; Disease; Disease Marker; Disease Progression; Entropy; Frequencies; Functional Magnetic Resonance Imaging; Genotype; Goals; Impaired cognition; Inherited; Ions; Late Onset Alzheimer Disease; Machine Learning; Magnetic Resonance Imaging; Measures; Modeling; Neurocognitive; Neurofibrillary Tangles; Neuronal Injury; Neurons; Pathology; Pattern; Performance; Population; Positron-Emission Tomography; Process; Public Health; Radioactive Tracers; Reporting; Rest; Sample Size; Series; Signal Transduction; Staging; Statistical Data Interpretation; Techniques; Testing; Time; abeta accumulation; apolipoprotein E-4; autosomal dominant Alzheimer' s disease; base; clinical care; cloud based; cohort; connectome; functional MRI scan; imaging biomarker; imaging study; indexing; information processing; mild cognitive impairment; multimodality; mutation carrier; neuroimaging; neurophysiology; novel; pre-clinical; preventive intervention; public database; tau Proteins; tool

Project Summary/Abstract Considerable efforts have been spent in the past two decades to search for biomarkers for pre-symptomatic stages of Alzheimer's disease (AD). For neuroimaging, amyloid-PET imaging of amyloid beta (Aβ) accumulation in the brain is considered an early marker for the preclinical stage of AD, while tau-PET imaging correlates more closely with neuronal injury and cognitive decline. However, PET scans are expensive and involve radioactive tracers. Resting state fMRI (rs-fMRI) studies in AD have shown that the functional connectivity (FC) of resting brain networks is progressively diminished in subjects with mild cognitive impairment (MCI) and AD. However, FC analysis of rs-fMRI has limited capability to characterize the dynamic fluctuations of rs-fMRI signals that possess clinically meaningful information. Our group and others have recently explored the use of entropy measures as indices of the complexity and regularity of rs-fMRI time-series. Accumulating data showed decreasing entropy values associated with aging, APOE ɛ4 genotype, cognitive decline in autosomal dominant Alzheimer's disease (ADAD) and late-onset AD (LOAD). Our group began developing the Complexity Toolbox in 2013 as the first systematic and comprehensive software package dedicated to complexity analysis of neuroimaging (fMRI) data. In particular, a recent independent study using our toolbox to analyze the rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study reported progressive reductions of entropy from healthy controls, early MRI, to late MCI and AD groups, with significant associations between complexity measures of rs-fMRI and cognitive decline in MCI/AD subjects. Our preliminary data in ADAD and LOAD subjects further showed consistent negative correlations between rs-fMRI entropy and tau-PET signal. The goal of this project is to further develop our Complexity Toolbox and a cloud-based pipeline for comprehensive complexity analysis of (large scale) fMRI data. We will systematically evaluate the complexity of fMRI as a novel imaging marker of AD in both ADAD and LOAD populations, using 3 public databases of rs- fMRI and PET including Dominantly Inherited Alzheimer Network (DIAN), Connectome of ADAD, and the Alzheimer's Disease Neuroimaging Initiative (ADNI-3) with a total sample size >900. Finally, we will use advanced machine learning techniques to evaluate complexity of rs-fMRI as a predictor for transversion from healthy to MCI and to AD. We will generate a disease staging model based on multimodal AD biomarkers including PET, CSF and rs-fMRI measures. We hypothesize that the complexity of BOLD signals provides an index of the information processing capacity of regional neuron populations, and is therefore sensitive to tau- related neuronal injury and cognitive decline in the AD processes. The successful completion of this project will lead to a noninvasive, economical and alternative imaging biomarker of neuronal injury in MCI and AD with relevant tools ready to be deployed in clinical research and care of AD.

项目概要/摘要 在过去的二十年里，人们花费了大量的努力来寻找症状前的生物标志物。 阿尔茨海默病 (AD) 的各个阶段用于神经影像学、β 淀粉样蛋白 (Aβ) 积累的淀粉样蛋白 PET 成像。 大脑中的 tau 蛋白被认为是 AD 临床前阶段的早期标志物，而 tau-PET 成像与更多相关性 与神经元损伤和认知能力下降密切相关。然而，PET 扫描价格昂贵且涉及放射性。 AD 中的静息态功能磁共振成像 (rs-fMRI) 研究表明，静息状态下的功能连接 (FC) 然而，患有轻度认知障碍 (MCI) 和 AD 的受试者的大脑网络逐渐减弱。 rs-fMRI 的 FC 分析在表征 rs-fMRI 信号动态波动方面的能力有限， 我们的小组和其他人最近探索了熵的使用。 衡量 rs-fMRI 时间序列的复杂性和规律性的指标。 与衰老、APOE ɛ4 基因型、常染色体显性认知能力下降相关的熵值降低 阿尔茨海默病 (ADAD) 和迟发性 AD (LOAD) 我们的团队开始开发复杂性工具箱。 2013年，作为第一个专门用于复杂性分析的系统且全面的软件包 特别是最近的一项独立研究，使用我们的工具箱来分析 rs-fMRI。 来自阿尔茨海默病神经影像倡议 (ADNI) 研究的数据显示， 从健康对照、早期 MRI 到晚期 MCI 和 AD 组的熵，之间存在显着关联 我们在 ADAD 和 MCI/AD 受试者中的 rs-fMRI 复杂性测量和认知能力下降。 LOAD 受试者进一步表现出 rs-fMRI 熵和 tau-PET 信号之间一致的负相关性。 该项目的目标是进一步开发我们的复杂性工具箱和基于云的管道 （大规模）fMRI 数据的综合复杂性分析 我们将系统地评估复杂性。 fMRI 作为 ADAD 和 LOAD 人群中 AD 的新型成像标志物，使用 rs- 的 3 个公共数据库 fMRI 和 PET，包括显性遗传阿尔茨海默病网络 (DIAN)、ADAD 连接组和 最后，我们将使用总样本量> 900的阿尔茨海默病神经影像倡议（ADNI-3）。 先进的机器学习技术来评估 rs-fMRI 的复杂性作为转换的预测因子 我们将基于多模式 AD 生物标志物生成疾病分期模型。 包括 PET、CSF 和 rs-fMRI 测量，我们发现 BOLD 信号的复杂性提供了一种方法。 区域神经元群体信息处理能力的指数，因此对 tau 敏感 AD 过程中相关的神经损伤和认知能力下降。 导致 MCI 和 AD 神经元损伤的无创、经济和替代成像生物标志物 相关工具已准备好用于 AD 的临床研究和护理。