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Human brain network analysis based on machine learning has been paid much attention in the field of neuroimaging, where the application of convolutional neural network (CNN) is now becoming a new research hotspot. However, all present researches based on conventional CNN share weights on edges connected to the same node in a brain network, which ignores that each edge between any two nodes has a unique meaning and is not suitable for weight-sharing. In this paper, we propose a new convolutional neural network with element-wise filters (CNN-EW) for brain networks. More specifically, each element-wise filter gives a unique weight to each edge of brain network which may reflect the topological structure information more realistically. The experimental results on the autism brain imaging data exchange I (ABIDE I) dataset show that CNN-EW models can not only more accurately distinguish subject groups compared to some fashionable methods but also identify the abnormal brain regions associated with autism spectrum disorder (ASD). © 2018 IEEE.
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