Xiang Yizhen, Wang Jianxin, Tan Guanxin, Wu Fang-Xiang, Liu Jin
School of Computer Science and Engineering, Central South University, Changsha, China.
Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, China.
Front Bioeng Biotechnol. 2020 Jan 15;7:479. doi: 10.3389/fbioe.2019.00479. eCollection 2019.
Schizophrenia (SZ) is a functional mental disorder that seriously affects the social life of patients. Therefore, accurate diagnosis of SZ has raised extensive attention of researchers. At present, study of brain network based on resting-state functional magnetic resonance imaging (rs-fMRI) has provided promising results for SZ identification by studying functional network alteration. However, previous studies based on brain network analysis are not very effective for SZ identification. Therefore, we propose an improved SZ identification method using multi-view graph measures of functional brain networks. Firstly, we construct an individual functional connectivity network based on Brainnetome atlas for each subject. Then, multi-view graph measures are calculated by the brain network analysis method as feature representations. Next, in order to consider the relationships between measures within the same brain region in feature selection, multi-view measures are grouped according to the corresponding regions and Sparse Group Lasso is applied to identify discriminative features based on this feature grouping structure. Finally, a support vector machine (SVM) classifier is employed to perform SZ identification task. To evaluate our proposed method, computational experiments are conducted on 145 subjects (71 schizophrenic patients and 74 healthy controls) using a leave-one-out cross-validation (LOOCV) scheme. The results show that our proposed method can obtain an accuracy of 93.10% for SZ identification. By comparison, our method is more effective for SZ identification than some existing methods.
精神分裂症（SZ）是一种严重影响患者社会生活的功能性精神障碍。因此，SZ的准确诊断引起了研究人员的广泛关注。目前，基于静息态功能磁共振成像（rs-fMRI）的脑网络研究通过研究功能网络改变为SZ识别提供了有前景的结果。然而，以往基于脑网络分析的研究对SZ识别效果不是很好。因此，我们提出一种使用功能性脑网络多视图图测度的改进SZ识别方法。首先，我们基于脑图谱为每个受试者构建个体功能连接网络。然后，通过脑网络分析方法计算多视图图测度作为特征表示。接下来，为了在特征选择中考虑同一脑区测量值之间的关系，将多视图测度按相应区域分组，并基于此特征分组结构应用稀疏组套索来识别判别性特征。最后，采用支持向量机（SVM）分类器执行SZ识别任务。为了评估我们提出的方法，使用留一法交叉验证（LOOCV）方案对145名受试者（71名精神分裂症患者和74名健康对照）进行了计算实验。结果表明，我们提出的方法在SZ识别中可获得93.10%的准确率。相比之下，我们的方法在SZ识别方面比一些现有方法更有效。
