A supervised method to assist the diagnosis and classification of the status of Alzheimer's disease using data from an fMRI experiment

The aim of this work is the development of a method to assist the diagnosis and classification of the status of Alzheimer's Disease (AD) using information that can be extracted from fMRI. The method consists of five stages: a) preprocessing of fMRI data to remove non-task related variability, b) modeling BOLD response depending on stimulus, c) feature extraction from fMRI data, d) feature selection and e) classification using the Random Forests (RF) algorithm. The proposed method is evaluated using data from 41 subjects (14 young adults, 14 non demented older adults and 13 demented older adults.