关键词: 空间动态, 功能磁共振成像, 支持向量机, 抑郁症

Abstract:

The existing classification model of mental diseases uses the static index of brain network as the characteristic while ignoring the spatial dynamic information of brain network, which will result in an inferior classification performance. To overcome this limitation and improve the performance of the classification model, a classification method based on the spatial dynamic of resting-state functional brain connections is proposed. The spatial dynamic characteristics of brain connections are extracted by analyzing the brain connections with high-dimensional templates. By selecting characteristics through the statistical analysis, a classification model based on resting-state functional brain connections can be constructed. The conducted experiments distinguish between depression patients and normal subjects and the results show that the classification accuracy of model utilized spatial dynamic characteristics （83.0%） is 5.2 percentage points higher than that with static index （77.8%）.

Key words: spatial dynamics, functional magnetic resonance imaging, support vector machine, depression