Alzheimer's Disease (AD) begins several years before the symptoms develop. It starts with Mild Cognitive Impairment (MCI) which can be separated into Early MCI and Late MCI (EMCI and LMCI). Functional connectivity analysis and classification are done among the different stages of illness with Functional Magnetic Resonance Imaging (fMRI). In this study, in addition to the four stages including healthy, EMCI, LMCI, and AD, the patients have been tracked for a year. Indeed, the classification has been done among 7 groups to analyze the functional connectivity changes in one year in different stages. After generating the functional connectivity graphs for eliminating the weak links, three different sparsification methods were used. In addition to simple thresholding, spectral sparsification based on effective resistance and sparse autoencoder were performed in order to analyze the effect of sparsification routine on classification results. Also, instead of extracting common features, the correlation matrices were reshaped to a correlation vector and used as a feature vector to enter the classifier. Since the correlation matrix is symmetric, in another analysis half of the feature vector was used, moreover, the Genetic Algorithm (GA) also utilized for feature vector dimension reduction. The non-linear SVM classifier with a polynomial kernel applied. The results showed that the autoencoder sparsification method had the greatest discrimination power with the accuracy of 98.35% for classification when the feature vector was the full correlation matrix.

中文翻译：

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使用 fMRI 时间序列对阿尔茨海默病不同阶段的患者进行多类分类

阿尔茨海默病 (AD) 在症状出现前几年就开始了。它从轻度认知障碍 (MCI) 开始，可分为早期 MCI 和晚期 MCI（EMCI 和 LMCI）。使用功能磁共振成像 (fMRI) 在疾病的不同阶段进行功能连接分析和分类。本研究除健康、EMCI、LMCI、AD四个阶段外，对患者进行了一年的追踪。事实上，已经在 7 组中进行了分类，以分析一年内不同阶段的功能连接变化。在生成功能连接图以消除弱链接后，使用了三种不同的稀疏化方法。除了简单的阈值，为了分析稀疏例程对分类结果的影响，进行了基于有效电阻和稀疏自动编码器的频谱稀疏化。此外，不是提取共同特征，而是将相关矩阵重新整形为相关向量，并用作特征向量进入分类器。由于相关矩阵是对称的，在另一个分析中使用了一半的特征向量，此外，遗传算法（GA）也用于特征向量的降维。应用多项式核的非线性 SVM 分类器。结果表明，当特征向量为全相关矩阵时，自编码器稀疏化方法的分类准确率最高，为98.35%。