关键词: 多模态, 轻度认知障碍, 弥散张量成像, 静息态功能磁共振成像, 融合网络, 支持向量机, 分类

Abstract: Since the early Mild Cognitive Impairment (MCI) is very likely to be undiagnosed by the assessment of medical diagnostic cognitive scale, a multimodal network fusion method for the aided diagnosis and classification of MCI was proposed. The complex network analysis method based on graph theory has been widely used in the field of neuroimaging, but different effects of brain diseases on the network topology of the brain would be conducted by using imaging technologies based different modals. Firstly, the Diffusion Tensor Imaging (DTI) and resting-state functional Magnetic Resonance Imaging (rs-fMRI) data were used to construct the fusion network of brain function and structure connection. Then, the topological properties of the fusion network were analyzed by One-way ANalysis of VAriance (ANOVA), and the attributes with significant difference were selected as the classification features. Finally, the one way cross validation of Support Vector Machines (SVM) was used for the classification of healthy group and MCI group, and the accuracy was estimated. The experimental results show that, the classification result accuracy of the proposed method reaches 94.44%, which is significantly higher than that of single modal data method. Many brain regions, such as cingulate gyrus, superior temporal gyrus and parts of the frontal and parietal lobes, of the MCI patients diagnosed by the proposed method show significant differences, which is basically consistent with the existing research results.

Key words: multimodal, Mild Cognitive Impairment (MCI), Diffusion Tensor Imaging (DTI), resting-state functional Magnetic Resonance Imaging (rs-fMRI), fusion network, Support Vector Machine (SVM), classification