Alzheimer's disease (AD) is the most common dementing illness of late life, and robs persons of vigorous activity and productivity in their later years. Future drug treatments may be capable of slowing the progression of the disease or even preventing the its clinical appearance, and when such treatments become available, the ability to detect the illness in its earliest stages will be needed to take full advantage of them. The overall goal of this project is to further develop and test the use of structural magnetic resonance scanning and computational neuroanatomy to distinguish subjects with both clinical and preclinical AD from elders without disease. This will be accomplished by conducting further studies to improve the discrimination of subjects with very mild DAT and nondemented controls and by initiating studies of the adult children of AD patients.
The specific aims of the project are to 1) improve the discrimination of subjects with very mild dementia of the Alzheimer type (DAT) and healthy age-matched controls via the combined analysis of the hippocampus, parahippocampal gyrus (including the entorhinal cortex), and cingulate gyrus, 2) determine whether neuromorphometric variables shown to discriminate between subjects with very mild dementia and healthy age-matched controls will predict the rate of cognitive decline in the healthy elder controls, at least some of whom will have preclinical forms of AD, and 3) attempt to discriminate the adult children of AD patients from age- and gender-matched controls, again using neuromorphometric variables shown to discriminate between the very mild DAT subject and healthy age-matched controls. To accomplish these aims, 60 subjects with very mild DAT (CDR 0.5) and 60 nondemented, age-matched subjects (CDR 0) will be scanned at baseline and two years later using a high-resolution, T1-weighted sequence. At baseline and yearly for the next four years, these subjects will be assessed using a battery of clinical and cognitive measures to determine the presence and severity of dementia symptoms and cognitive deficits. Adult children of AD patients and their controls will be similarly assessed, although the intervals between assessments will vary depending upon the age of the subjects. Neuroanatomical features, including volume, thickness, and 3D conformation (i.e., shape) will be quantified using computerized algorithms specifically designed for the evaluation of the selected brain structures.
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