Using the Artificial Neural Network to discriminate between normal controls with different APOE e4 genotypes and probable AD cases in PIB-PET studies

The standardized uptake value ratio (SUVR) provides a semi-quantitative index of fibrillar amyloid deposition, a neuropathological feature of Alzheimer's disease (AD), in Pittsburgh Compound B (PIB) PET studies. As accurately identifying individual probable AD patients and subjects at increased risk for AD is of clinical use, we developed the classification model based on SUVR of several ADassociated brain regions to distinguish normal subjects with different APOE genotypes, the major AD genetic risk factor, and probable AD patients. After normalizing PIB PET scans to standard brain template coordinate space, SUVR was computed for 8 brain regions: frontal, posterior cingulate-precuneus, lateral temporal, lateral parietal, and basal ganglia, medial temporal, occipital, and a mean cortical region (consisting of frontal, posterior cingulate-precuneus and lateral temporal region). These regions were defined using anatomical automated labeling toolbox in SPM5. 70% of the subjects were randomly partitioned for training, with remaining 30% for testing. The Artificial Neural Network (ANN) is then applied to the SUVR data to classify the subjects into three groups: low risk (APOE non-carriers), high risk (APOE carriers) and certain (probable AD patients). The process of data partitioning and ANN training/testing was repeated 3 times. ANN was found to classify the subjects into these three groups with the average accuracy of 100% and 95.2% in training and testing respectively. ANN is a promising multivariate-based alternative to discriminate among normal subjects with different APOE genotypes and probable AD patients and is potentially useful in evaluating the change in risk profile of the individual subject.