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

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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.