Effective graph kernels for evolving functional brain networks

• Main Authors: WANG, Xinlei, CHEN, Jinyi, DAI, Bing Tian, XIN, Junchang, GU, Yu, YU, Ge
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: disease diagnosis; evolving brain networks; global matching; graph kernels; local matching; Artificial Intelligence and Robotics; OS and Networks
• Online Access: https://ink.library.smu.edu.sg/sis_research/7802; https://ink.library.smu.edu.sg/context/sis_research/article/8805/viewcontent/EffectiveGraphKernels_av.pdf
• Institution: Singapore Management University

The graph kernel of the functional brain network is an effective method in the field of neuropsychiatric disease diagnosis like Alzheimer's Disease (AD). The traditional static brain networks cannot reflect dynamic changes of brain activities, but evolving brain networks, which are a series of brain networks over time, are able to seize such dynamic changes. As far as we know, the graph kernel method is effective for calculating the differences among networks. Therefore, it has a great potential to understand the dynamic changes of evolving brain networks, which are a series of chronological differences. However, if the conventional graph kernel methods which are built for static networks are applied directly to evolving networks, the evolving information will be lost and accurate diagnostic results will be far from reach. We propose an effective method, called Global Matching based Graph Kernels (GM-GK), which captures dynamic changes of evolving brain networks and significantly improves classification accuracy. At the same time, in order to reflect the natural properties of the brain activity of the evolving brain network neglected by the GM-GK method, we also propose a Local Matching based Graph Kernel (LM-GK), which allows the order of the evolving brain network to be locally fine-tuned. Finally, the experiments are conducted on real data sets and the results show that the proposed methods can significantly improve the neuropsychiatric disease diagnostic accuracy.