Convolutional kernels with an element-wise weighting mechanism for identifying abnormal brain connectivity patterns

Abstract Deep learning based human brain network classification has gained increasing attention in recent years. However, current methods remain limited in exploring the topological structure information of a brain network. In this paper, we propose a kind of new convolutional kernels with an element-wise weighting mechanism (CKEW) to extract hierarchical topological features of brain networks, in which each weight is assigned to an element with a unique neuroscientific meaning. In addition, a novel classification framework based on CKEW is presented to diagnose brain diseases and explore the most important original features by a tracing feature analysis method efficiently. Experimental results on two autism spectrum disorder (ASD) datasets and an attention deficit hyperactivity disorder (ADHD) dataset with functional magnetic resonance imaging (fMRI) data demonstrate that our method can more accurately distinguish subject groups compared to several state-of-the-art methods in cerebral disease classification, and abnormal connectivity patterns and brain regions identified are more likely to become biomarkers associated with a cerebral disease.