Cognitive Changes and Brain Connectivity in Age-Related Macular Degeneration

描述(申请人提供)：本申请的目的是确定老年性黄斑变性(AMD)患者的认知障碍与大脑功能连接和白质完整性的差异之间的关系。AMD影响近20年 30%的75岁以上的美国人患痴呆症的风险增加了一倍。该团队之前由美国国立卫生研究院资助的研究发现，即使在非痴呆性AMD患者中，认知缺陷也非常普遍：语言流畅性差。然而，关于AMD和认知缺陷之间的联系的病因存在一个基本的知识鸿沟，这种鸿沟阻碍了减少AMD认知损害的策略的发展。我们的中心假设是，大脑连通性在理解AMD和认知缺陷(例如，语言流利性)之间的联系方面发挥着关键作用。具体地说，这种联系可能通过两种机制产生：1)黄斑疾病导致的视力变化可能会对认知能力产生负面影响，或者2)共同的风险因素可能会同时促进大脑和眼睛的损害。这两种机制可能都在发挥作用，应该通过与AMD相关的认知缺陷相关的大脑连接的不同模式来区分。这将是首次将纵向神经认知测试和脑成像相结合的研究，以更好地了解AMD大脑变化的程度和位置。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未患AMD的成年人。所有参与者都将接受基线和为期两年的神经认知测试，其中一个子集将在每个时间点提供结构和“静息状态”功能磁共振成像(MRI)。AIM 1使用由申请者开发和试验的神经认知电池来定义与AMD相关的认知差异，而不使用需要视力的任务。AIMS 2和3使用功能磁共振成像(FMRI)和扩散张量成像(DTI)得出的脑功能连通性和区域特定脑白质完整性的测量方法。目的1.描述导致AMD患者语言流利性缺陷的认知过程。我们将构建回归模型来测试AMD的言语流利性表现在多大程度上反映了语义组织、加工速度、注意力转换和记忆方面的潜在认知缺陷。目标2.找出与语言流利性或其认知因素有关的大脑连通性的差异。我们将相关的认知测量 测量大脑内在功能和结构连通性的能力。我们将研究患有和不患有AMD的老年人之间与认知缺陷相关的“脑信号”是否有所不同。目的3.确定与AMD患者认知功能减退相关的认知特征和脑信号。我们将确定是否某些认知模式或特定神经网络中的差异，或两者兼而有之，预测与AMD相关的认知能力下降。我们的工作假设是基于我们的初步成像研究的结果，并支持机制1：AMD的语言流畅性缺陷反映了语义组织方面的问题，并与支持语言、语义和视觉的白质束(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.