Mining relations between neuropsychological data to characterize Alzheimer's disease

The quantitative characterization of Alzheimer's Disease (AD) in early stages allows timely detection and prediction of disease progression. These are important to disease intervention and monitoring before clinical diagnosis of dementia. We used cognitive, functional and behavioral data from 612 Alzheimer's Disease Neuroimaging Initiative (ADNI) individuals. First, we standardized, based on normative data, and dichotomized the selected variables. Grouping abnormal variables, we learned possible disease features from a sample of 225 cognitively impaired patients. Then, we quantify the manifestation for each disease feature and evaluated this quantitative characterization in the automated prediction of future progression from Mild Cognitive Impairment (MCI) to AD. Five groups of abnormal neuropsychological measures were established describing five possible disease features. The resulting quantitative characterization for AD at prodromal stages predicts disease progression within the next 36 months with an accuracy of 0.76.