A study on post-traumatic stress disorder classification based on multi-atlas multi-kernel graph convolutional network

• Published in: Sheng wu yi xue gong cheng xue za zhi Vol. 41; no. 6; p. 1110
• Main Authors: Zhou, Lijun, Zhu, Hongru, Liu, Yunfei, Mo, Xian, Yuan, Jun, Luo, Changyu, Zhang, Junran
• Format: Journal Article
• Language: Chinese
• Published: China 25.12.2024
• Subjects: Algorithms; Brain - diagnostic imaging; Brain - physiopathology; Humans; Magnetic Resonance Imaging; Neural Networks, Computer; Stress Disorders, Post-Traumatic - classification; Stress Disorders, Post-Traumatic - diagnosis; Stress Disorders, Post-Traumatic - diagnostic imaging; Functional connectivity; Multi-atlas; Multi-kernel graph convolution; Classification; Post-traumatic stress disorder
• ISSN: 1001-5515
• DOI: 10.7507/1001-5515.202407031

Post-traumatic stress disorder (PTSD) presents with complex and diverse clinical manifestations, making accurate and objective diagnosis challenging when relying solely on clinical assessments. Therefore, there is an urgent need to develop reliable and objective auxiliary diagnostic models to provide effective diagnosis for PTSD patients. Currently, the application of graph neural networks for representing PTSD is limited by the expressiveness of existing models, which does not yield optimal classification results. To address this, we proposed a multi-graph multi-kernel graph convolutional network (MK-GCN) model for classifying PTSD data. First, we constructed functional connectivity matrices at different scales for the same subjects using different atlases, followed by employing the k-nearest neighbors algorithm to build the graphs. Second, we introduced the MK-GCN methodology to enhance the feature extraction capability of brain structures at different scales for the same subjects. Finally, we classified th