关键词: 抑郁症, 不确定脑网络, 频繁子图挖掘, 判别性特征选择

Abstract: Resting-state functional brain networks have been widely studied in brain disease research. However, traditional functional connectivity networks mainly focus on the certain graph, and ignore uncertain information between brain regions. Therefore, the uncertain brain network is analyzed, which does not need to select threshold, and can more accurately construct model of the functional connection network. At the same time, this paper applies frequent subgraph mining to the uncertain graph, and proposes several new discriminative feature selection methods. The classification results show that the fMRI classification method based on uncertain brain network effectively improves the diagnostic accuracy of depression.

Key words: depression, uncertain brain network, frequent subgraph mining, discriminant feature selection