Application of Pattern Recognition and Graph Theoretical Approaches to Analysis of Brain Network in Alzheimer's Disease

摘要

We used resting-state functional magnetic resonance imaging (fMRI) data to study functional brain network alteration in patients with Alzheimer's disease (AD). We combine graph theoretical approaches with advanced machine learning methods to automatically classify patients with AD from healthy subjects. Our method was applied on the resting-state fMRI data of twenty patients with AD and twenty age and gender matched healthy subjects. After preprocessing of data, signals from 90 brain regions, parcelated based on the automated anatomical labeling (AAL) atlas, were extracted and edges of the graph were calculated using the correlation between the signals of all pairs of the brain regions. Then a weighted undirected graph was constructed and graph measures were calculated. Extracted network-based features were fed to different feature selection algorithms to choose most significant features. Support vector machine (SVM) was used to explore the ability of graph measures in diagnosis of AD. Using the selected features, we were able to accurately classify patients with AD from healthy control ones with accuracy of 97.5%.