CEREBRAL GLUCOSE METABOLISM IN DOWN SYNDROME

University Of California Irvine, Irvine CA

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Abstract

This project will use PET to determine the location and sequence of cerebral glucose metabolic changes as dementia develops in adults with Down Syndrome (DS). Based on the sequencing of brain lesions found in Alzheimer's Disease (AD) and a model proposed by Braak and Braak (1997), we hypothesize that: 1) as dementia begins in DS, the earliest brain changes will be glucose metabolic rate (GMR) decreases in basal neocortex or temporal- entorhinal cortical areas and 2) as dementia increases, hippocampal formation areas will show the greatest GMR decreases before frontal and occipital cortex decreases occur. We will examine 20 older non-demented DS patients already entered into the DS Core of the UCI Alzheimer's Disease Research Center and now at risk for dementia based on age (over 35 years old) and impairment scores. Each will receive a PET/FDG scan and neuropsychological assessment once a year for up to five years. A comparison group of mild probable AD patients (n=20) already entered into the Clinical Core also will be studied for five years as will a group of age and sex matched normal controls (n=15). All PET scans will be done with a simple cognitive activation (the Continuous Performance Test of attention) with groups matched on performance. PET and co-registered MRI data will be analyzed using regions-of-interest, SPM96, and other morphing and multivariate techniques. The analyses will include group comparisons of GMR over time in selected brain areas using accelerated growth curve techniques, determining functional relationships among brain areas selected a priori within groups using correlations, and person by person analyses to examine idiographic GMR patterns. We will also compare GMR in ApoE4- defined subgroups of the DS and AD subjects. This project will be unique in combining functional brain imaging using cognitive activation and a 5 year longitudinal design in DS and AD adults as dementia develops. These data will provide important information for the timing of treatment interventions and may aid in early detection of dementia and the classification of DS subtypes.