Cognitive Changes and Brain Connectivity in Age-Related Macular Degeneration

描述（由申请人提供）：本申请的目的是确定年龄相关性黄斑变性（AMD）患者的认知缺陷如何与大脑中功能连接和白色物质完整性的差异相关。 AMD几乎影响 75岁以上的美国人中有30%与痴呆症风险增加两倍有关。这个团队之前由NIH资助的研究发现了一个非常普遍的认知缺陷，即使在非痴呆的AMD患者中也是如此：语言流畅性差。然而，关于AMD和认知缺陷之间的联系的病因学存在基本的知识差距，并且这种差距阻碍了减少AMD认知障碍的策略的发展。我们的中心假设是，大脑连接在理解AMD和认知缺陷之间的联系方面起着关键作用（例如，语言流畅性）。具体来说，这种联系可能通过两种机制产生：1）黄斑疾病的视力变化可能对认知能力产生负面影响，或者2）共同的风险因素可能同时促进大脑和眼睛的损伤。这两种机制可能都在起作用，应该通过与AMD相关的认知缺陷相关的不同大脑连接模式来区分。这将是第一项结合联合收割机纵向神经认知测试和脑成像的研究，以更好地了解AMD大脑变化的程度和部位。 这项研究将包括120名AMD患者和120名年龄、性别和教育程度匹配的无AMD成年人。所有参与者将接受基线和2年的神经认知测试，一个子集将在每个时间点提供结构和“静息状态”功能磁共振图像（MRI）。目标1使用申请人开发和试验的神经认知电池来定义AMD相关的认知差异，而不使用需要视觉的任务。目的2和3使用从功能性MRI（fMRI）和扩散张量成像（DTI）导出的功能性脑连接和区域特异性白色物质完整性的测量。目标1。描述导致AMD语言流畅性缺陷的认知过程。我们将构建回归模型来测试AMD患者的言语流畅性表现在多大程度上反映了语义组织、处理速度、注意力转换和记忆方面的潜在认知缺陷。目标2.识别与语言流畅性相关的大脑连接差异，或其认知贡献者。我们将把认知测量 测量大脑内在功能和结构连接的能力。我们将检查与认知缺陷相关的“大脑信号”是否在患有和不患有AMD的老年人中不同。目标3。识别与AMD患者认知功能下降相关的认知特征和大脑特征。我们将确定某些认知模式或特定神经网络的差异，或两者兼而有之，预测AMD相关的认知衰退。我们的工作假设是基于我们的初步成像研究的结果，并有利于机制#1：AMD的言语流畅性缺陷反映了语义组织的问题，并与支持语言，语义和视觉的白色物质束（主要是腹侧）的差异有关。

DESCRIPTION (provided by applicant): The objective of this application is to determine how cognitive deficits in patients with age-related macular degeneration (AMD) are related to differences in functional connectivity and white matter integrity in the brain. AMD affects nearly 30% of Americans over age 75 and is associated with a two-fold increased risk of dementia. This team's previous NIH-funded work identified a strikingly prevalent cognitive deficit, even in non- demented AMD patients: poor verbal fluency. Yet there is a fundamental knowledge gap regarding the etiology of the link between AMD and cognitive deficits, and this gap impedes the development of strategies to reduce cognitive impairment in AMD. Our central hypothesis is that brain connectivity plays a critical role in understanding the link between AMD and cognitive deficits (e.g., verbal fluency). Specifically, the link may arise via two mechanisms: #1) vision changes from macular disease could have a negative effect on cognitive performance, or #2) a shared risk factor could promote damage in the brain and eye concurrently. These two mechanisms, which may both be at play, should be distinguished by different patterns of brain connectivity associated with AMD-related cognitive deficits. This will be the first study to combine longitudinal neurocognitive testing and brain imaging to better understand the extent and locus of brain changes in AMD. The study will include 120 people with AMD plus 120 age-, gender-, and education-matched adults without AMD. All participants will receive baseline and 2-year neurocognitive tests and a subset will provide structural and "resting state" functional magnetic resonance images (MRIs) at each time point. Aim 1 uses a neurocognitive battery developed and piloted by the applicants to define AMD-related differences in cognition without using tasks that require vision. Aims 2 & 3 use measures of functional brain connectivity and region- specific white matter integrity derived from functional MRI (fMRI) and diffusion tensor imaging (DTI). Aim 1. Characterize cognitive processes that contribute to verbal fluency deficit in AMD. We will construct regression models to test the extent to which verbal fluency performance in AMD reflects underlying cognitive deficits in semantic organization, processing speed, attention-switching, and memory. Aim 2. Identify differences in brain connectivity associated with verbal fluency, or its cognitive contributors. We will relate measures of cognitive ability to measures of intrinsic functional and structural connectivity in the brain. We will examie whether "brain signatures" associated with cognitive deficits differ among older adults with and without AMD. Aim 3. Identify cognitive profiles and brain signatures associated with cognitive decline in AMD patients. We will determine whether certain cognitive patterns or differences in specific neural networks, or both, predict AMD-related cognitive decline. Our working hypothesis is based on results of our pilot imaging study and favors mechanism #1: Verbal fluency deficit in AMD reflects problems in semantic organization and is related to differences in white matter tracts (primarily ventral) that support language, semantics, and vision.