[Article] A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD.
summary : They proposed novel approach to incorporate dynamic graph computation and 2-hop neighbor nodes feature aggregation into graph convolution for brain network modeling. They used convolutional pooling strategy to readout the graph, which jointly integrates graph convolutional and readout functions. They could visualize model weights which showed interpretable connectomic patterns facilitating the understanding of brain functional abnormalities.