东南大学学报（英文版）2019，Vol.35Issue(2)：147-152,6.DOI:10.3969/j.issn.1003-7985.2019.02.001
基于稀疏功能连接及非负矩阵分解的阿尔兹海默症分类方法
Alzheimer's disease classification based on sparse functional connectivity and non-negative matrix factorization
摘要
Abstract
A novel framework is proposed to obtain physiologically meaningful features for Alzheimer's disease (AD) classification based on sparse functional connectivity and non-negative matrix factorization.Specifically,the non-negative adaptive sparse representation (NASR) method is applied to compute the sparse functional connectivity among brain regions based on functional magnetic resonance imaging (fMRI) data for feature extraction.Afterwards,the sparse non-negative matrix factorization (sNMF) method is adopted for dimensionality reduction to obtain low-dimensional features with straightforward physical meaning.The experimental results show that the proposed framework outperforms the competing frameworks in terms of classification accuracy,sensitivity and specificity.Furthermore,three sub-networks,including the default mode network,the basal ganglia-thalamus-limbic network and the temporal-insular network,are found to have notable differences between the AD patients and the healthy subjects.The proposed framework can effectively identify AD patients and has potentials for extending the understanding of the pathological changes of AD.关键词
阿尔兹海默症/稀疏表示/非负矩阵分解/功能连接Key words
Alzheimer's disease/sparse representation/non-negative matrix factorization/functional connectivity分类
医药卫生引用本文复制引用
李璇,陆雪松,王海贤..基于稀疏功能连接及非负矩阵分解的阿尔兹海默症分类方法[J].东南大学学报（英文版）,2019,35(2):147-152,6.基金项目
The Foundation of Hygiene and Health of Jiangsu Province (No.H2018042),the National Natural Science Foundation of China (No.61773114),the Key Research and Development Plan (Industry Foresight and Common Key Technology) of Jiangsu Province (No.BE2017007-3). （No.H2018042）
