The purpose of the present study is to investigate relationships between novel neuroimaging markers based on DTI fiber tracking models, genetic polymorphisms of vascular disease and inflammation, and age-related cognitive decline in healthy individuals. Recent studies, including data from our lab, indicate that the integrity of the subcortical white matter as measured by diffusion tensor imaging (DTI) and subcortical hyperintensities on structural MRI are important correlates of age-related cognitive differences. Cortical brain volume changes also account for some variance in cognitive function among the elderly, however our data suggest that alterations in the subcortical white matter more strongly correlate with cognitive status. In addition, studies have demonstrated that white matter alterations in the elderly are heritable, implicating the role of genetic factors in the development of mild ischemic changes and associated inflammation. In this revised application we provide compelling new pilot data demonstrating the role of genetic polymorphisms for vascular injury on both imaging and cognitive indices. We also provide new data from our recent work demonstrating the sensitivity of our novel DTI metrics based on quantified fiber tracking models to explain variance in cognitive aging. These novel metrics allow us to examine the impact of changes in quantified fiber lengths on cognitive function. In the present study we will extend these preliminary studies and examine relationships between DTI and structural MRI, genetic polymorphisms associated with microvascular disease (angiotensinogen, paroxonase) and related CMS inflammation (C-reactive protein, IL- 6, TNF-a), and cognitive status in the healthy elderly. The study will capitalize on an existing database containing genetic and neurocognitive data on more than 1,000 individuals stratified in five groups from age 31-80 (n = 200/group). Neuroimaging data is currently available for 200 of these individuals and we will recruit an additional 120 individuals between the ages of 51-80 to supplement the older age spectrum and obtain novel fiber tracking maps from the DTI data. Newly recruited individuals will be followed longitudinally to examine the evolution and progression of white matter and cognitive abnormalities in the context of genetic risk factors. Group comparisons and latent variable modeling will be conducted to examine relationships between the neuroimaging indices, genetic polymorphisms, and differences in cognitive function in older healthy adults. The present study will be the first to integrate these approaches to examine a model of age-related cognitive decline implicating the subcortical white matter. The results will significantly inform our understanding of cognitive aging.
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