A novel CNN framework to extract multi-level modular features for the classification of brain networks

Brain disease diagnosis based on brain network classification has become a hot topic. Recently, classification methods based on convolutional neural networks (CNNs) have attracted much attention due to their ability to capture the basic topological structure of the brain network. However, they ignore abnormal structures within modules caused by brain disease, which limits the diagnostic accuracy. In this paper, we propose a novel brain network classification framework based on a CNN model capable of extracting modular features from brain networks at the node and whole-network levels. More specifically, we first develop a novel algorithm to obtain the modular structure of each node, which is then fed into a CNN model to extract the node-level modular features. Second, we minimize the harmonic modularity of the extracted node-level features to reveal the modular structure at the whole-brain network level. Finally, we employ a deep neural network to further extract high-level features for the classification of brain disease. The experimental results on a real-world autism spectrum disorder dataset show that our proposed method achieves the best accuracy of 68.55% and outperforms other common methods and demonstrates the discriminant power of the modular features at multiple levels. In addition, feature analysis based on the trained framework reveals the associations between modular structures and brain disease, which provides new insights into the pathological mechanism from the perspective of modular structures.