Modeling Axonal Density and Inflammation-Associated Cellularity in Alzheimer’s Disease Using Hybrid Diffusion Imaging

使用混合扩散成像模拟阿尔茨海默病的轴突密度和炎症相关细胞结构

• 批准号: 9332250
• 负责人: Yu-Chien Wu
• 金额: $ 39.13万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-15 至 2021-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9332250
• 关键词: Affect; Alzheimer' s Disease; Alzheimer' s disease risk; Amyloid deposition; Apolipoprotein E; Area; Axon; Biological; Biological Markers; Brain; Cellularity; Cerebrospinal Fluid; Complex; Data; Development; Diagnostic; Diffuse; Diffusion; Diffusion Magnetic Resonance Imaging; Discrimination; Disease; Disease Progression; Early Diagnosis; Early Intervention; Elderly; Fiber; Fibrinogen; Future; Gaussian model; Goals; Human; Hybrids; Image; Individual; Inflammation; Inflammatory; Link; Magnetic Resonance; Magnetic Resonance Imaging; Measurement; Measures; Methods; Minor; Modeling; Monitor; Neurites; Neurodegenerative Disorders; Neuroglia; Pathologic; Patients; Phase; Prevalence; Prevention; Process; Property; Research; Risk; Risk Factors; Signal Transduction; Specificity; Symptoms; The Sun; Tissues; United States; Weight; aging population; base; cohort; density; extracellular; follow-up; gray matter; human imaging; imaging biomarker; in vivo; indexing; insight; interest; mild cognitive impairment; morphometry; novel; pre-clinical; public health relevance; screening; spectrograph; success; theories; tool; water diffusion; white matter

Project Abstract Alzheimer's disease (AD) affects as many as 5 million individuals over the age of 65 in the United States (US) and 35 million worldwide. Because of the aging population, the prevalence of AD will disproportionately increase in future years if no effective early interventions are developed. Converging evidence suggests that the pathophysiologic processes in the brains of AD patients begin decades before symptoms occur. The long preclinical phase of AD provides a valuable window for early intervention with disease-modifying therapy, if we are able to understand the underlying mechanisms of AD by identifying reliable biomarkers. Diffusion MRI (dMRI) probes microstructures of the human brain by measuring water diffusion properties at the cellular level in vivo and non-invasively, which is especially suitable for preclinical screening and monitoring disease progression for AD. Microstructural features with links to specific biologic targets, e.g., axons, glia, or extracellular substrates may provide direct insight into the pathophysiologic changes underlying neurodegenerative disorders. In theory, diffusion MRI provides significant advances for objectively detecting and characterizing the mechanisms of brain changes in AD. Current approaches using diffusion tensor imaging (DTI), however, have not achieved this potential. A very recent advance in the use of dMRI to image the human brain is the development of a method to reflect axonal density and volume fraction of glial cells (cellularity) among other microstructural features. These biologic specific diffusion metrics can be obtained by parametric analysis of the diffusion data via diffusion compartment modeling. We will use the hybrid diffusion imaging (HYDI) developed by the PI to acquire diffusion data with at least five diffusion-weighting b-value shells to sensitize diffusion compartments (e.g., axons, glia, and extracellular substrates) with different diffusivities. A novel feature of HYDI is its versatility for various diffusion model analyses and computational approaches. In the proposed research, we will use two diffusion modeling approaches: (1) neurite orientation dispersion and density imaging (NODDI) to extract the diffusion metric for axonal density, and (2) diffusion basis spectrum imaging (DBSI) to extract the cellularity of glial cells reflecting inflammatory processes. The goals of the proposed research are to determine the sensitivity (Aim 1), discrimination (Aim 2), and predictive power (Aim 3) of the diffusion metrics of axonal density and inflammation-associated cellularity cross-sectionally (Aims 1 and 2) and longitudinally (Aim 3) in a cohort of healthy control and preclinical (at-risk) older adults, and patients with early mild cognitive impairment (MCI), late MCI, and AD. The success of the proposed research will lead to the development of non-invasive differential diagnostic tools and reveal the micromechanisms of the pathophysiologic changes that occur in the early stages of AD.

项目摘要 在美国，阿尔茨海默病(AD)影响着多达500万65岁以上的人 全球有3500万人。由于人口老龄化，AD的患病率将不成比例 如果没有制定有效的早期干预措施，未来几年将会增加。不断有证据表明 AD患者大脑的病理生理过程在症状出现前几十年就开始了。《长河》 AD的临床前阶段为早期干预提供了一个宝贵的窗口，如果我们 能够通过识别可靠的生物标志物来了解AD的潜在机制。弥散磁共振 (DMRI)通过在细胞水平上测量水的扩散特性来探测人脑的微观结构 体内和非侵入性，特别适用于临床前筛查和疾病监测 AD的进展。具有链接到特定生物靶点的微结构特征，例如轴突、神经胶质或 细胞外基质可能提供对潜在的病理生理变化的直接洞察。 神经退行性疾病。从理论上讲，弥散磁共振成像为客观检测提供了重大进展 并对阿尔茨海默病的大脑变化机制进行了表征。当前使用扩散张量的方法 然而，成像(DTI)并没有实现这一潜力。 在使用dmri成像人脑方面的一个最新进展是开发了一种方法来反映 轴突密度和胶质细胞体积分数(细胞性)以及其他微结构特征。这些 通过扩散对扩散数据进行参数分析，可以获得生物特定的扩散度量 隔室模型。我们将使用PI开发的混合扩散成像(HYDI)来获取 具有至少五个扩散加权b值外壳以敏化扩散隔间的扩散数据(例如， 轴突、胶质细胞和细胞外基质)具有不同的扩散系数。HYDI的一个新特性是它的通用性 各种扩散模型分析和计算方法。在拟议的研究中，我们将使用两个 扩散建模方法：(1)轴突定向弥散和密度成像(NODI) 轴突密度的扩散度量，以及(2)用于提取细胞密度的扩散基谱成像(DBSI) 反映炎症过程的神经胶质细胞。拟议研究的目标是确定 轴突扩散指标的敏感度(目标1)、分辨能力(目标2)和预测力(目标3) 密度和炎症相关细胞的横截面(目标1和2)和纵向(目标3) 健康对照、临床前(高危)老年人和早期轻度认知障碍患者的队列 (MCI)、已故MCI和AD。拟议研究的成功将导致非侵入性技术的发展 不同的诊断工具，并揭示发生的病理生理变化的微观机制。 AD的早期阶段。