Currently, it remains one of the most challenging issues to distinguish brain functional networks of early mild cognitive impairment subjects (eMCIs) and normal control subjects (NCs). Unlike images, functional networks are non-Euclidean data and not easily classified by dilated convolutional neural network (DCNN). To address this problem, we developed a sparse structure deep network embedding (SSDNE) method which transforms brain functional networks into a double-channel image in eMCI classification. First, the eigenvector of each node in the functional network was obtained by SSDNE, and principal component analysis (PCA) was employed to sort the importance of eigenvectors. Next, two eigenvector groups with the highest contribution rate were extracted in turn and divided into several equal-length intervals along its direction respectively, and the numbers of nodes that fall into the intervals were counted to obtain the corresponding two-dimensional histograms. Then, the both histograms were stacked up and down into a double-channel image. Finally, a double-channel image was input into DCNN for feature learning to achieve final classification results. Experimental results show that, SSDNE performed better in maintaining the original structure of brain functional networks, and the transformed double-channel image achieved comparably identifying results on eMCI classification compared with other network embedding algorithms. This novel method solved the problem that brain functional networks cannot be directly applied to convolutional neural networks for feature extraction and classification. Meanwhile, it can provide a reference for the early auxiliary diagnosis of Alzheimer's disease (AD).

中文翻译：

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稀疏结构深度网络嵌入在早期轻度认知障碍分类中转化脑功能网络

目前，区分早期轻度认知障碍受试者 (eMCIs) 和正常对照受试者 (NCs) 的脑功能网络仍然是最具挑战性的问题之一。与图像不同，功能网络是非欧几里得数据，不容易被扩张卷积神经网络 (DCNN) 分类。为了解决这个问题，我们开发了一种稀疏结构深度网络嵌入（SSDNE）方法，该方法将脑功能网络转换为 eMCI 分类中的双通道图像。首先，通过SSDNE得到功能网络中每个节点的特征向量，并采用主成分分析（PCA）对特征向量的重要性进行排序。接下来依次提取贡献率最高的两个特征向量组，分别沿其方向分成若干个等长区间，统计落入区间的节点数，得到对应的二维直方图。然后，将两个直方图上下堆叠成双通道图像。最后，将一张双通道图像输入到 DCNN 中进行特征学习，以达到最终的分类结果。实验结果表明，SSDNE在保持脑功能网络原始结构方面表现更好，转换后的双通道图像在eMCI分类上取得了与其他网络嵌入算法相当的识别效果。这种新颖的方法解决了大脑功能网络不能直接应用于卷积神经网络进行特征提取和分类的问题。同时可为阿尔茨海默病的早期辅助诊断提供参考。