Neural Networks in Cognitive Aging

 描述(由申请人提供)： 阿尔茨海默病(AD)的特点是记忆和其他认知技能严重受损，使患者难以完成日常生活中的工具性活动(IADL)和独立生活。轻度认知障碍(MCI)描述的是老年人记忆力较差，但一般能够进行IADL的情况。执行功能(EF)指的是计划、推理、解决问题和多任务的能力，依赖于额顶网络。功能成像研究记录了MCI患者前额叶皮质活动的增加，以及记忆网络和额顶区之间的连接矛盾的增加。我们假设MCI的特征是额叶介导的网络重组，以补偿AD的病理。使用EF作为定义额顶网络的一种手段，本研究旨在研究MCI和AD前额网络的结构和功能连通性。在目标1中，我们将调查额顶网络中大脑活动的增加是否反映了该网络内结构连接的受损。在目标2中，我们将探索额叶网络功能连接性的增加和减少如何分别预测MCI和AD的IADL。我们将纳入55名MCI患者、55名AD患者和55名年龄匹配的老年对照组(EC)。所有参与者都将完成全面的临床评估，并在3T磁铁上进行MRI扫描。我们的第一个具体目标是检验这样一个假设，即在MCI中，额顶网络结构连接性的下降将与该网络中大脑活动的增加相关。这将支持这样的理论，即网络结构连通性的下降会导致功能活动的变化，以保护认知。为此，MCI和EC将完成EF和弥散张量成像(DTI)的认知功能磁共振任务。我们将使用DTI脑束成像来创建连接任务中涉及的额顶网络的白质束。我们将从这些束中提取平均分数各向异性值(FA)来询问它们的完整性。我们将把FA与fMRI脑活动联系起来，以了解结构连接性的变化如何影响网络的功能。我们的第二个目标将检验这一假设，即在MCI中，额叶介导的网络的功能连通性会增加，而在AD中，功能连通性会减少。此外，在MCI中，功能连接性增强将与较差的EF和IADL相关。这一发现将支持这样的理论，即在MCI中，功能连接的增加反映了额叶网络作为一种补偿机制的重组。为了验证这些假说，我们将首先确定在MCI和EC的EF fMRI任务中参与的额顶网络。接下来，MCI、EC和AD将完成静息状态fMRI(RsfMRI)。我们将比较不同组之间额叶网络的功能连接，并探索功能连接如何与临床症状相关联。这两个目标的结果都支持MCI是一个动态的理论，其特征是神经网络连通性的增减。额叶网络在大脑活动和功能连接方面看似自相矛盾的增加，可能是为了补偿记忆网络中积累的AD病理。当额顶网络的结构连接不再支持代偿性功能活动(目标1)，功能连接不再能抵消受损症状时，就可能发生AD诊断和IADL受损(目标2)。这项工作将为MCI中的大脑重组提供新的见解。它将支持将脑变化的非线性轨迹整合到AD疾病进展的神经成像模型中，导致疾病弹性生物标记物的开发，并开始表征专注于支持补偿的新的治疗靶点。

 DESCRIPTION (provided by applicant): Alzheimer's disease (AD) is characterized by profound impairment in memory and other cognitive skills that make it difficult for patients to complete instrumental activities of daily lving (IADL) and live independently. Mild Cognitive Impairment (MCI) describes a condition in which an older adult shows poor memory, but is generally able to perform IADLs. Executive functioning (EF) refers to the ability to plan, reason, solve problems, and multi-task, and is dependent upon fronto-parietal networks. Functional imaging studies have documented increases in prefrontal cortex activity in MCI, and paradoxical increases in the connectivity between memory networks and fronto-parietal regions. We hypothesize that MCI is characterized by reorganization of frontally-mediated networks to compensate for AD pathology. Using EF as a means of defining fronto-parietal networks, the work proposed here investigates the structural and functional connectivity of prefrontal networks in MCI and AD. In aim 1, we will investigate if increases in brain activity in fronto-parietal networks reflect impaired structural connectivity within the network. In aim 2 we will explore how increases and decreases to the functional connectivity of frontally based networks predict IADLs in MCI and AD, respectively. We will enroll 55 patients with MCI, 55 patients with AD, and 55 age matched elderly controls (EC). All participants will complete a comprehensive clinical assessment and undergo MRI scanning on a 3T magnet. Our first specific aim is to test the hypothesis that in MCI, declines to the structural connectivity of fronto- parietal networks will correlate with increases in brain activity in this network. This would support the theory that declines in network structural connectivity induce changes in functional activity to preserve cognition. Toward this aim, MCI and EC will complete cognitive fMRI tasks of EF and diffusion tensor imaging (DTI). We will use DTI tractography to create the white matter tracts that connect the fronto-parietal networks involved in the tasks. We will extract mean fractional anisotropy values (FA) from these tracts to interrogate their integrity. We will correlate FA with fMRI brain activity to understand how changes to structural connectivity impact the network's functioning. Our second aim will test the hypothesis that there will be increased functional connectivity in frontally mediated networks in MCI, but reduced functional connectivity in AD. Moreover, in MCI, increased functional connectivity will correlate with poorer EF and IADLs. This finding would support the theory that in MCI, increases in functional connectivity reflect reorganization of frontal networks as a compensatory mechanism. In order to test these hypotheses, we will first identify frontal-parietal networks engaged during EF fMRI tasks in MCI and EC. Next, MCI, EC, and AD will complete resting state fMRI (rsfMRI). We will compare the functional connectivity of frontally based networks between groups and explore how functional connectivity is associated with clinical symptoms. The results of both aims would support the theory that MCI is a dynamic state characterized by increases and decreases to the connectivity of neural networks. The seemingly paradoxical increases in brain activity and functional connectivity of frontal-based networks may be an attempt to compensate for accumulating AD pathology in memory networks. AD diagnosis and impaired IADLs may occur when the structural connectivity of fronto-parietal networks can no longer support compensatory functional activity (aim 1), and functional connections can no longer offset impairing symptoms (aim 2). This work will provide new insight into brain re-organization in MCI. It will support the integration of a nonlinear trajectory of brain changes int neuroimaging models of AD disease progression, lead to the development of disease resilience biomarkers, and begin to characterize new targets for treatment focused on supporting compensation.