Research on Brain Network Classification Based on HubGLasso Attention Mechanism
Brain network classification is useful for early diagnosis of brain diseases and understanding the pathogenesis of brain diseases,which has important research and application values.Among them,convolutional neural network is widely used and can extract the topological features of brain network,which is a frontier hot spot in brain network classification.However,existing methods do not consider the important contribution of Hub nodes in brain networks to brain function,which may lead to inadequate feature extraction,limiting their classification performance.To this end,we propose a convolutional neural network model based on the HubGLasso attention mechanism for brain network classification tasks.It contains a new convolutional layer structure,which first removes redundant informa-tion from the brain network using the GLasso model,and then introduces Hub constraints and attention mechanisms that enable it to ex-tract important features associated with abnormal Hub structures and use them for brain disease diagnosis.The experiments showed that the proposed method achieved68.67%accuracy on a real autism dataset containing 1 112 subjects,which was significantly better than the existing methods and proved its application value.Further,the trained model can be characterized to obtain the information of brain re-gions and Hub node structures related to brain diseases,which provides a new perspective on the pathological mechanism of brain diseases.
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