Graph Convolutional Network with Attention Mechanism for Discovering the Brain's Abnormal Activity of Attention Deficit Hyperactivity Disorder

At present, deep learning has been widely used in the research of brain structure, brain connectivity, brain diseases and other related fields. In particular, research on attention deficit hyperactivity disorder (ADHD) has been applied to assist in diagnosis, follow-up treatment, etc. However, there is a lack of explainable studies on abnormal functional connectivity in ADHD. In addition, the small amount of information available on ADHD lead to poor recognition accuracy and performance of deep learning. Therefore, we propose an explainable Graph convolutional networks (GCN) with attentional mechanisms to improve diagnostic accuracy and find abnormal neural markers of ADHD. We experiment with the method on fMRI clinical dataset of Connectomics in Neuroimaging Transfer Learning Challenge (CNI-TLC). The experimental results validate the reliability of the model, and find the abnormal regions and connections in ADHD patients. These abnormal regions and connections are mainly concentrated in cognitive and emotion-related regions such as frontal, parietal and temporal lobes.