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，已经被证明是阿尔茨海默病（AD）的一个重要危险因素。检查神经认知老化将帮助我们更好地描述整个生命周期中大脑的病理和非病理变化，并确定认知衰退的临床前标志物。它还将帮助我们确定认知过程和机制，可以改变延缓发病或完全逆转病理。随着60岁以上人口的迅速增长，这一领域的发现可能会对公共卫生产生巨大影响，并大大减轻家庭、政府和社会项目的负担。很明显，有必要对记忆和大脑进行有针对性的研究，以涵盖整个成人生命周期的衰老过程。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，以测试与年龄相关的记忆障碍的神经认知模型的几个关键预测。这项工作是基于计算模型以及行为学、电生理学和神经解剖学在啮齿动物衰老模型中的发现。这种方法是基于这样一个前提，即海马齿状回在情景记忆中起着至关重要的作用，因为它具有特殊的模式分离能力，或将相似的记忆彼此分离的能力。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区域的输入减少，导致系统进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降低相一致的行为障碍，并且CA3/DG活性的神经变化将与这种降低相一致。我们还预测，衰老将导致海马体内部以及海马体与周围皮质（如内嗅皮质）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ApoE4遗传易感性的个体差异将区分健康衰老和病理性衰老，这些差异将是预测随后衰退的关键。关键的是，这个丰富的数据集将有超出我们的问题和假设的用途。我们将提供和共享这个广泛数据集的所有组成部分，供其他研究人员使用强大的生物医学信息学研究网络（BIRN）基础设施进行研究。

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.