关键词: 功能磁共振影像, 超网络, 多特征, 子图特征, 抑郁症

Abstract: Focused on the issue that local brain region properties is extracted as features on hyper-network, ignoring the global topology information, which affects the evaluation of network topology and reduces the performance of the classifier. Machine learning classification method combining multiple features of brain function hyper-network is proposed. Firstly, hyper-networks are constructed on major depression disorder dataset. Secondly, brain region features and subgraph features are combined as features. Finally, multi-kernel SVM is adopted to classify. To certify the proposed method, 28 normal control subjects and 38 major depression disorder patients are selected for experiment. The experimental results show that the proposed method achieves satisfactory accuracy, with an average of 91.60%. The abnormal brain regions include left Lingual gyrus, left Caudate nucleus, left Thalamus and so on important brain regions of major depression disorder. Machine learning classification method combining multiple features of brain function hyper-network can effectively classify normal control subjects and major depression disorder patients.

Key words: functional Magnetic Resonance Imaging（fMRI）, hyper-network, multiple feature, subgraph feature, major depression disorder