Artificial neural networks that use single-photon emission tomography to identify patients with probable Alzheimer's disease

Abstract

Single-photon emission tomographic (SPET) images using technetium-99m labelled hexamethyl-propylene amine oxime were obtained from 97 patients diagnosed as having Alzheimer's disease, as well as from a comparison group of 64 normal subjects. Multiple linear regression was used to predict subject type (Alzheimer's vs comparison) using scintillation counts from 14 different brain regions as predictors. These results were disappointing: the regression equation accounted for only 33.5% of the variance between subjects. However, the same data were also used to train parallel distributed processing (PDP) networks of different sizes to classify subjects. In general, the PDP networks accounted for substantially more (up to 95%) of the variance in the data, and in many instances were able to distinguish perfectly between the two subjects. These results suggest two conclusions. First, SPET images do provide sufficient information to distinguish patients with Alzheimer's disease from a normal comparison group. Second, to access this diagnostic information, it appears that one must take advantage of the ability of PDP networks to detect higher-order nonlinear relationships among the predictor variables.