DE-NNs: Brain Network Analysis Algorithm Based on Dynamic Evidence Neural Networks

Dynamic Functional Connections (dFCs) can be regarded as a process of dynamic changes in multiple time windows to explore the changes in functional connections of the brain in different time periods. It has been widely used in resting state functional magnetic resonance imaging (rs-fMRI) analysis, providing a new perspective and strategy for the diagnosis of brain diseases. However, the common dynamic brain network analysis methods can not effectively use the potential correlation and timing between dynamic data, and ignore the uncertainty factors caused by the inconsistent data quality of each window. Therefore, this paper proposes a brain network analysis algorithm based on dynamic evidence neural networks (DE-NNs). This algorithm designs a multi-view evidence acquisition module of dynamic brain network, which treats each time window of dynamic brain network as a view. Three different convolution filters are used to extract the feature maps of each time window of the dynamic brain network, and the evidence of the dynamic level is fully obtained. A dynamic evidence fusion mechanism is designed to make full use of dynamic evidence. The dynamic trust function is constructed according to the time sequence of dFC data based on the evidence theory synthesis rules. The evidence generated by multiple windows is fused at the decision level of classification, the uncertainty information is fully considered, and the classification performance is significantly improved. Experiments were conducted on three schizophrenia datasets compared with existing advanced algorithms in order to verify the effectiveness of the proposed DE-NNs. The results showed that the accuracy and F1 scores of DE-NNs on the three brain disease diagnosis tasks were significantly improved.