Alzheimer's Disease Early Diagnosis Based on Resting-State Dynamic Functional Connectivity

Abstract

Functional magnetic resonance imaging (fMRI) technology has been widely used in the diagnosis of Alzheimer's disease, but there are some problems such as high data dimension and unclear characteristics. The nonlinear complex network of different brain regions based on the Lyapunov exponents and approximate entropy are extracted in this work. The open data set ADNI (Alzheimer's disease neuroimaging initiative) are used to test. The results show that in the other three different groups and patients with Alzheimer's disease, the accuracy of the classification results using SVM (support vector machine) classifier at the whole brain voxel level can reach more than 99%, which is better than the classification results using the correlation of the original time series. Our findings provide new insights into the complexity of brain structural networks in the process of Alzheimer's disease and other mental diseases.