Brain network classification considering directed propagation mechanisms of dynamic graphs

The classification of functional brain networks plays an important role in the diagnosis of neurodegenerative diseases, brain decoding and other fields. Functional brain networks can effectively reflect the functional connection relationships between brain regions or neurons and accurately represent brain activities. Therefore, a large number of problems related to the classification of functional brain networks have been studied. However, the traditional functional brain network merely measures the static correlation between brain regions or neurons in a simple way, and does not reflect the causal transmission effect between brain regions. This directionality is crucial for the regulatory relationship between brain regions. Furthermore, since the brain is constantly in a state of dynamic change, the dynamics of functional connectivity also plays a very important role in the classification of functional brain networks. Therefore, we propose a classification framework named Dynamic Directed Propagation Networks (DDPN) for functional brain networks considering the dynamic directed propagation mechanism. This method effectively captures the dynamics and directionality of the dynamic directed brain network and further improves the classification accuracy of the functional brain network. To verify the effectiveness of the proposed method, we conduct experiments on real datasets. The experiments show that the proposed method improved by 3.1-4.1% compared with state-of-the art methods in two datasets.