EARLY DETECTION OF ALZHEIMERS-DISEASE - A STATISTICAL APPROACH USING POSITRON EMISSION TOMOGRAPHIC DATA

Correlational analysis of regional cerebral glucose metabolism (rCMR(glc)) obtained by high-resolution positron emission tomography (PET) has demonstrated reduced neocortical rCMR(glc) interactions in mildly/moderately demented patients with probable Alzheimer's disease (AD). Thus, identification of individual differences in patterns of rCMR(glc) interactions may be important for the early detection of AD, particularly among individuals at greater risk for developing AD (e.g., those with a family history of AD). Recently, a statistical procedure, using multiple regression and discriminant analysis, was developed to assess individual differences in patterns of rCMR(glc) interdependencies. We applied this new statistical procedure to resting rCMR(glc) PET data from mildly/moderately demented patients with probable AD and age/sex-matched controls. The aims of the study were to identify a discriminant function that would (a) distinguish patients from controls and (b) identify an AD pattern in an individual at risk for AD with isolated memory impairment whose initial PET scan showed minor abnormalities, but whose second scan showed parietal hypometabolism, coincident with further cognitive decline. Two discriminant functions, reflecting interactions involving regions most involved in reduced correlations in probable AD, correctly classified 87% of the patients and controls, and successfully identified the first scan of the at-risk individual as AD (probability >0.70). The results suggest that this statistical approach may be useful for the early detection of AD.