High Performance Imaging for Assessment of Small Vessel Disease in Older Adults with Depression

项目概要/摘要： 抑郁症（该提案的目标疾病）每年影响3400万美国人，其中包括200万老年人。它 是老年人（本建议的目标人群）发病和死亡的主要原因。血管 抑郁假说（1998年提出）仍然是解释发病和 老年人抑郁症的持续存在。这个模型是基于这样的观察，即在老年人中，白色 高信号物质（小血管疾病的标志，SVD）与抑郁症的发作有关， 永久化在了解小血管疾病与老年抑郁症的关系方面取得了进展， 部分由于白色物质高信号缺乏特异性而受阻，这些高信号可以代表 水肿、神经胶质增生、局部缺血和炎症。传统的磁共振成像无法区分这些 这些成分，因此导致抑郁症的具体机制仍不清楚。 磁共振成像的出现使得WMH病变的特异性更高， 小血管疾病的其他组成部分。将7特斯拉（T）成像技术引入主流临床应用， 通过以下方式实现：1）具有专用（超过1.5/3 T）应用程序; 2）实现强大、安全 一致、同质的高质量图像。通过一个由哥伦比亚大学专家组成的联合会， 匹兹堡与FDA、明尼苏达大学和Quality Electrodynamics Inc.合作，我们 目的是提高我们对抑郁症的神经病理生理学、治疗和管理的理解 在老年人中。我们将通过开发强大的射频方法来实现这一目标， 以及产生具有上述必要属性的7 T图像的脉冲序列。这将是 与基线时的互补3 T MRI配对。我们将使用我们最近开发的（以及拟议的） 定制设计的7 T射频线圈系统和脉冲序列已经用于 疾病/患者研究。根据我们的初步结果，拟议的射频解决方案将提供 不同受试者/患者之间前所未有的同质性和一致性，因此信号极佳 信噪比和对比噪声比用于检测小血管疾病的成分。这项研究将 研究了两个队列：1）通过阿尔茨海默病研究中心招募的30名老年人 （ADRC）用于开发SVD的MRI到组织病理学统计模型，2）60名老年人， 抑郁症患者将在基线和2年后接受扫描。纵向7 T MRI和SVD模型 将用于帮助描述与老年人抑郁症相关的小血管变化。 综上所述，本研究开发了一种新兴的、及时的技术（高性能电磁场 MRI）研究临床相关的关键病理生理过程（脑小血管疾病）， 老年抑郁症（Late Life Depression）该项目将进一步推进这三个领域。

Project Summary/Abstract: Depression (target disease of this proposal) affects 34 million Americans including 2 million seniors per year. It is a leading cause of morbidity and mortality in older adults (target population of this proposal). The vascular depression hypothesis (proposed in 1998) remains the most salient theory explaining the onset and perpetuation of depression in older adults. This model is based on the observation that in older adults, white matter hyperintensities (a hallmark of small vessel disease, SVD) are associated with depression onset and perpetuation. Progress in understanding the relationship of small vessel disease with late-life depression has been stymied in part by lack of specificity of white matter hyperintensities which can represent components of edema, gliosis, ischemia, and inflammation. Traditional MR imaging is unable to distinguish between these components, and thus the specific mechanisms that contribute to depression remain unclear. The emergence of ultrahigh field MR imaging allows for greater specificity of the WMH lesions, and other components of small vessel disease. Bringing 7 tesla (T) imaging into mainstream clinical use will be accomplished through 1) having exclusive (over 1.5/3T) application(s) and 2) achieving robust, safe, consistent, and homogenous high-quality images. Through a consortium consisting of experts at University of Pittsburgh combined with collaboration at FDA, University of Minnesota, and Quality Electrodynamics Inc., our goal is to enhance our understanding of the neuropathophysiology, treatment, and management of depression in older adults. We will achieve this goal through the development of robust radiofrequency methodology, as well as pulse sequences that produce 7T images with the aforementioned necessary attributes. This will be paired with complementary 3T MRI at baseline. We will use our recently developed (as well as a proposed) custom designed 7T radiofrequency coil system and pulse sequences that are already being used in disease/patient studies. Based on our preliminary results, the proposed RF solution will provide unprecedented homogeneity and consistency among different subjects/patients, and therefore excellent signal to noise ratio and contrast to noise ratio for detection of components of small vessel disease. The study will examine two cohorts 1) a group of 30 older adults recruited through the Alzheimer's Disease Research Center (ADRC) for developing an MRI to histopathology statistical model of SVD and 2) 60 older adults with late-life depression who will undergo scanning at baseline and after 2 years. The longitudinal 7T MRI and SVD model will be used to help characterize the small vessel changes associated with depression in older adults. In summary, this study develops an emerging and timely technology (high-performance ultrahigh field MRI) to study a critical pathophysiological process (cerebral small vessel disease) in a clinically relevant population (Late-Life Depression). This project will further advance all of these three domains.