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（英文）

Alzheimer’s disease classification based on sparse functional connectivity and non-negative matrix factorization

为了获得更具神经生理学意义的分类特征,提出了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.该方法基于功能核磁共振信号,采用非负自适应稀疏表示法计算脑区间的稀疏功能连接,以提取分类特征.然后,采用稀疏非负矩阵分解法进行特征降维,以获得具有明确物理意义的低维特征.实验结果表明该方法的分类准确率、灵敏度和特异度均优于其他对比方法.此外,发现了默认模式网络、基底神经节丘脑边缘结构网络及颞叶脑岛网络在阿尔兹海默症病人和健康被试间具有明显差异.此方法能够有效地对阿尔兹海默症进行识别,同时具有加深对该病症功能病变理解的潜力.

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.