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.

项目摘要/摘要 在过去的二十年中，已经花费了大量的努力，以寻找生物标志物进行预症状 阿尔茨海默氏病（AD）的阶段。用于神经影像学，淀粉样蛋白β（Aβ）积累的淀粉样-PET成像 大脑被认为是AD临床前阶段的早期标记，而Tau-pet成像则更多 与神经元损伤和认知能力下降紧密相关。但是，PET扫描很昂贵，涉及放射性 示踪剂。 AD中的静止状态fMRI（RS-FMRI）研究表明，静止的功能连通性（FC） 在轻度认知障碍（MCI）和AD的受试者中，大脑网络逐渐减少。然而， RS-FMRI的FC分析能够表征RS-FMRI信号的动态波动的能力有限 拥有临床意义的信息。我们的小组和其他人最近探索了熵的使用 措施作为RS-FMRI时间序列的复杂性和规律性的指标。累积数据显示 降低与衰老相关的熵值，APOEɛ4基因型，常染色体显性率的认知下降 阿尔茨海默氏病（ADAD）和晚期AD（负载）。我们的小组开始开发复杂性工具箱 在2013年，是第一个系统和全面的软件包，致力于复杂性分析 神经影像学（fMRI）数据。特别是，最近使用我们的工具箱的独立研究来分析RS-FMRI 来自阿尔茨海默氏病神经影像学计划（ADNI）研究的数据报道了逐步减少 从健康对照，早期MRI到后期MCI和AD组的熵，与 MCI/AD受试者的RS-FMI和认知下降的复杂度度量。我们在Adad和 负载受试者进一步显示了RS-FMRI熵与Tau-Pet信号之间的一致负相关。 该项目的目的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 fMRI是ADAD和负载种群中AD的新成像标记，使用RS-的3个公共数据库 FMRI和PET包括主要继承的阿尔茨海默氏症网络（Dian），Adad的Connectome和The Connectome 总样本量> 900的阿尔茨海默氏病神经影像学倡议（ADNI-3）。最后，我们将使用 先进的机器学习技术，以评估RS-FMRI的复杂性作为横向的预测指标 对MCI和广告健康。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措施。我们假设大胆信号的复杂性提供了 区域神经元种群信息处理能力的索引，因此对tau-敏感 相关的神经元损伤和广告过程的认知下降。这个项目的成功完成将 导致MCI和AD中神经元损伤的无创，经济和替代成像生物标志物 相关工具准备部署在AD的临床研究和护理中。