High-resolution Brain Imaging of Medial Temporal Lobe in Neurocognitive Aging

描述(由申请人提供)：随着年龄的增长，认知能力下降，特别是在记忆领域，已被证明是阿尔茨海默病(AD)的一个重要风险因素。研究神经认知老化将有助于我们更好地描述大脑一生中的病理性和非病理性变化，并识别认知衰退的临床前标志。它还将帮助我们准确地确定认知过程和机制，这些过程和机制可以被改变来推迟发病或完全逆转病理。随着60岁以上人口的快速增长，这一领域的发现可能会对公共卫生产生巨大影响，并大幅减轻家庭以及政府和社会项目的负担。显然，有必要对记忆和大脑进行有针对性的研究，涵盖整个成年寿命中衰老的整个范围。这项提议的目标是收集一套全面的行为和高分辨率神经成像数据，以测试年龄相关记忆障碍的神经认知模型的几个关键预测。这项工作是基于从计算模型以及在啮齿动物衰老模型中的行为、电生理和神经解剖学发现汇聚的见解。该方法基于这样的前提，即海马齿状回由于其执行模式分离的特殊能力，或将相似的记忆彼此分离的能力，而关键地参与情景记忆。模式分离是许多记忆形式的关键计算组成部分，通常归因于海马体(例如，情景记忆、回忆等)。该模型假设，随着年龄的增长，第二层内嗅皮层神经元对齿状回和CA3区的输入退化，使系统执行模式分离的能力受损。我们建议使用行为实验和尖端神经成像技术(功能和结构MRI、DTI和PIB PET)的组合来测试该模型的预测。我们预测，衰老将导致与模式分离能力下降相一致的行为障碍，并且CA3/DG活动中的神经变化将与这种下降相一致。我们还预测，衰老将导致海马体内以及海马体与周围皮质(如内嗅皮层)之间的连通性发生变化。最后，我们预测，记忆表现、成像数据和载脂蛋白E4遗传易感性的个体差异将区分健康和病理性衰老，这些差异将是预测随后衰退的关键。关键是，这个丰富的数据集将具有超出我们的问题和假设的用途。我们将利用强大的生物医学信息学研究网络(BIRN)基础设施，为其他研究人员提供和共享这一广泛数据集的所有组成部分。 公共卫生相关性：预计到2050年，65岁以上的人口将增加到8670万(美国人口普查局，人口估计和预测，2004年)，老龄化和与老龄化相关的疾病，如阿尔茨海默病(AD)对医疗保健系统的影响将急剧上升，因为65岁以上的AD比率每五年翻一番。即使在AD之外，人们观察到的与衰老有关的主要抱怨和缺陷之一是学习和记忆功能下降，导致生活质量下降，给家庭和社会服务带来更大的负担。了解这些与年龄相关的缺陷背后的神经机制对于了解衰老对痴呆症的影响至关重要，并为改进对记忆正常和病理变化的治疗以及早期预防铺平道路。

DESCRIPTION (provided by applicant): Cognitive decline with aging, especially in the memory domain, has been documented as an important risk factor for Alzheimer's disease (AD). Examining neurocognitive aging will help us better characterize pathological and non-pathological changes in the brain throughout the lifespan and identify preclinical markers for cognitive decline. It will also help us pinpoint cognitive processes and mechanisms that can be altered to delay onset or reverse pathology altogether. With the population over 60 rising rapidly, discoveries in this domain will likely have dramatic impact on public health and substantially reduce the burden on families as well as government and social programs. There is a clear need for targeted investigations of memory and the brain that cover the entire spectrum of aging throughout the adult lifespan. The goal of this proposal is to collect a comprehensive set of behavioral and high-resolution neuroimaging data to test several key predictions of a neurocognitive model of age-related memory impairment. This work is based on converging insights from computational models as well as behavioral, electrophysiological, and neuroanatomical findings in rodent models of aging. The approach is based on the premise that the hippocampal dentate gyrus is critically involved in episodic memory by virtue of its exceptional capacity for performing pattern separation, or the ability to isolate similar memories from each other. Pattern separation is a key computational component of many forms of memory often attributed to the hippocampus (e.g., episodic memory, recollection, etc). The model posits that degraded input to the dentate gyrus and CA3 region from layer II entorhinal cortex neurons with aging leaves the system with an impaired ability to perform pattern separation. We propose to test predictions of this model using behavioral experiments and a combination of cutting-edge neuroimaging techniques (functional and structural MRI, DTI, and PIB PET). We predict that aging will result in behavioral impairments consistent with a reduction in pattern separation abilities, and that there will be neural changes in CA3/DG activity consistent with this reduction. We also predict that aging will result in changes in the connectivity within the hippocampus and between the hippocampus and surrounding cortices (e.g. entorhinal cortex). Finally, we predict that individual differences in memory performance, imaging data, and ApoE4 genetic susceptibility will differentiate healthy from pathological aging and that these differences will be key to predicting subsequent decline. Critically, this rich dataset will have uses beyond our questions and hypotheses. We will provide and share all components of this extensive dataset for other researchers to study using the robust Biomedical Informatics Research Network (BIRN) infrastructure. PUBLIC HEALTH RELEVANCE: The population over 65 is projected to increase to 86.7 million by 2050 (U.S. Census Bureau, Population Estimates and Projections, 2004) and the impact of aging and aging-related disorders e.g. Alzheimer's disease (AD) on the health care system will rise dramatically as the rate of AD doubles for every five year period beyond the age of 65. Even outside of AD, one of the primary complaints and deficits observed with aging is a decline in learning and memory function, leading to decreased quality of life and a greater burden on families and social services. Understanding the neural mechanisms that underlie these age-related deficits is crucial to understanding the effect of aging on dementia, and paving the way to improving treatments for both normal and pathological changes in memory and for early prevention.