关键词: 脑网络, 支持向量机, 轻度认知障碍, 图核

Abstract: Most of the present brain network classification methods are to construct a brain network by collecting the signals, and then it is classified according to one brain network feature attribute between one or more brain regions. So other attributes of the brain network in the classification are ignored, but these attributes likely have a greater impact on the experimental results. In order to solve these problems, this paper proposes a brain network classification method based on multi-form eigenvectors and new graph kernels. The classification method consists of four steps：firstly, the experimental original data is preprocessed to complete the brain network construction; secondly, according to different thresholds, various brain network attribute values are extracted in the brain network; thirdly, the support vector machine is used to train all data, according to the quality of the training results, the optimal threshold parameters are selected for each network attribute value, the feature combination is carried out. Finally, the eigenvectors trained by support vector machine. Through the analysis of experimental data and comparison with existing methods, the effectiveness of this method for brain network classification in mild cognitive impairment dataset is verified.

Key words: brain networks, support vector machine, Mild Cognitive Impairment（MCI）, graph kernels