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

Correlational analysis of regional cerebral glucose metabolism (rCMR(g lc)) obtained by high-resolution positron emission tomography (PET) ha s demonstrated reduced neocortical rCMR(glc) interactions in mildly/mo derately demented patients with probable Alzheimer's disease (AD). Thu s, identification of individual differences in patterns of rCMR(glc) i nteractions may be important for the early detection of AD, particular ly among individuals at greater risk for developing AD (e.g., those wi th a family history of AD). Recently, a statistical procedure, using m ultiple 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 f rom mildly/moderately demented patients with probable AD and age/sex-m atched controls. The aims of the study were to identify a discriminant function that would (a) distinguish patients from controls and (b) id entify an AD pattern in an individual at risk for AD with isolated mem ory impairment whose initial PET scan showed minor abnormalities, but whose second scan showed parietal hypometabolism, coincident with furt her cognitive decline. Two discriminant functions, reflecting interact ions involving regions most involved in reduced correlations in probab le AD, correctly classified 87% of the patients and controls, and succ essfully identified the first scan of the at-risk individual as AD (pr obability >0.70). The results suggest that this statistical approach m ay be useful for the early detection of AD.