Joint Estimate Diffusion Imaging (JEDI) for improved Tissue Characterization and Neural Connectivity in Aging and Alzheimer's Disease

阿尔茨海默氏病（AD）和相关痴呆（ADRD）的特征在于进行性结构性痴呆， 脑组织的变化，导致认知和功能能力的衰弱性丧失，并具有深刻的 社会和经济影响。虽然标志性AD病理学（例如β淀粉样蛋白沉积和 神经纤维缠结）在细胞水平上非常明显，目前还没有成功的非- 侵入性脑成像技术来报告这些微结构变化。 扩散磁共振成像（dMRI）是一种广泛使用的非侵入性临床成像方法 具有这种潜力，因为它对复杂的大脑灰质（GM）内的水的微妙运动敏感， 白色物质（WM）组织结构。原则上，dMRI既可以报告局部组织结构，也可以报告长组织结构。 范围神经束的连接，以确定病理和确定AD对功能的影响， 大脑网络不幸的是，标准dMRI方法的临床实用性受到以下因素的严重影响： 它缺乏对低于图像分辨率的微观组织变化的特异性。 然而，最近，我们开发了一种新的捕获和分析方法，称为联合估计 扩散成像（JEDI）对GM和GM/WM边界区域的显微结构特征高度敏感， 并且还提供了来自WM的改进的连通性映射。JEDI可在临床扫描仪上轻松实现， 我们最近将其纳入了第一项研究，研究对象从认知正常（CN）到轻度 认知障碍（MCI）早期AD，以评估其检测这些群体变化的能力。 扩展JEDI在AD中的临床效用的两个关键步骤是：1）表征 JEDI数据和特定组织的显微结构特征之间的关系，以开发定量的临床 2）为微观结构敏感性和有限的患者开发有效的采集方案 扫描时间这是本提案的重点，将涉及三个方面的工作：1）数值计算机 在现实组织模型中模拟JEDI实验，这将使我们能够有效地优化 最大特异性和最短时间的采集协议; 2）通过体内试验验证这些优化 雪貂正常衰老过程的评估和尸检中的离体放射病理学分析 来自不同阶段AD患者的人体组织; 3）将这些优化纳入JEDI 在我们的临床扫描仪上采集和分析人体协议，并具体应用于检查 前驱微结构组织的变化跨越老化-MCI-AD连续体。 通过实现一种可靠、经验证和临床可行的方法， 脑组织在衰老-MCI-AD连续体中的变化，JEDI将显著增强我们的能力， 了解AD最早的神经退行性特征，并对其原因提供新的见解， 可能的治疗目标和可能的治疗方法。

Alzheimer's Disease (AD) and related dementias (ADRD) are characterized by progressive structural changes of brain tissue that results in a debilitating loss of cognitive and functional abilities and has profound social and economic implications. While hallmark AD pathology (e.g. beta amyloid depositions and neurofibrillary tangles) are remarkably pronounced at the cellular level, there are currently no successful non- invasive brain imaging techniques to report these microstructural changes. Diffusion magnetic resonance imaging (dMRI) is a widely available non-invasive clinical imaging method with this potential, as it is sensitive to the subtle motion of water within the complex brain gray matter (GM) and white matter (WM) tissue architecture. In principle, dMRI can report both the local tissue structure and the long range connectivity of neural tracts in order to identify pathology and determine the effects of AD on functional brain networks. Unfortunately, the clinical utility of the standard dMRI methodology is severely compromised by its lack of specificity to microstructural tissue changes below the image resolution. Recently, however, we have developed a novel acquisition and analysis method called Joint Estimation Diffusion Imaging (JEDI) that is highly sensitive to microstructural features of GM and GM/WM border regions, and also provides improved connectivity maps from WM. JEDI is easily implemented on a clinical scanner and we have recently incorporated it into a first study on subjects ranging from cognitively normal (CN) to Mild Cognitive Impairment (MCI) to early AD in order to assess its ability to detect changes in these groups. Two critical steps in extending the clinical utility of JEDI in AD are: 1) To characterize the relationship between the JEDI data and specific tissue microstructural features in order to develop quantitative clinical metrics and 2) To develop efficient acquisition protocols for both microstructural sensitivity and limited patient scan time. That is the focus of this proposal, which will involve three lines of work: 1) Numerical computer simulations of the JEDI experiment in realistic tissue models that will allow us to efficiently optimize the acquisition protocol for maximum specificity and minimal time; 2) Validate these optimizations through in-vivo evaluation of normal aging processes in the ferret and in ex-vivo radiologic-pathologic analysis in post-mortem human tissue from patients with different stages of AD; 3) Incorporate these optimizations into the JEDI acquisition and analysis of human protocols on our clinical scanners with specific application to examining the prodromal microstructure tissue changes across the aging-MCI-AD continuum. By enabling a reliable, validated and clinically viable method for the quantitative characterization of subtle brain tissue changes across the aging-MCI-AD continuum, JEDI will significantly enhance our ability to understand the earliest neurodegenerative features of AD and provide new insights into its causes, consequences, and possible treatment targets.