Graph kernel of brain networks considering functional similarity measures

• Published in: Computers in biology and medicine Vol. 171; p. 108148
• Main Authors: Wang, Xinlei, Xin, Junchang, Wang, Zhongyang, Qu, Luxuan, Li, Jiani, Wang, Zhiqiong
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.03.2024; Elsevier Limited
• Subjects: Algorithms; Brain; Brain - diagnostic imaging; Cerebral Cortex; Diagnosis; Functional similarity; Functionals; Graph kernel; Humans; Internal Medicine; Multi-attribute; Network analysis; Neurodegenerative disease; Neurodegenerative diseases; Neurodegenerative Diseases - diagnostic imaging; Nodes; Other; Similarity; Statistical tests; Structure-function relationships; Topology; Functional similarity; Neurodegenerative disease; Graph kernel; Multi-attribute
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108148

As a tool of brain network analysis, the graph kernel is often used to assist the diagnosis of neurodegenerative diseases. It is used to judge whether the subject is sick by measuring the similarity between brain networks. Most of the existing graph kernels calculate the similarity of brain networks based on structural similarity, which can better capture the topology of brain networks, but all ignore the functional information including the lobe, centers, left and right brain to which the brain region belongs and functions of brain regions in brain networks. The functional similarities can help more accurately locate the specific brain regions affected by diseases so that we can focus on measuring the similarity of brain networks. Therefore, a multi-attribute graph kernel for the brain network is proposed, which assigns multiple attributes to nodes in the brain network, and computes the graph kernel of the brain network according to Weisfeiler-Lehman color refinement algorithm. In addition, in order to capture the interaction between multiple brain regions, a multi-attribute hypergraph kernel is proposed, which takes into account the functional and structural similarities as well as the higher-order correlation between the nodes of the brain network. Finally, the experiments are conducted on real data sets and the experimental results show that the proposed methods can significantly improve the performance of neurodegenerative disease diagnosis. Besides, the statistical test shows that the proposed methods are significantly different from compared methods. •A multi-attribute brain network is constructed considering functional information.•A multi-attribute graph kernel considering functional similarity is proposed.•A multi-attribute hypergraph kernel considering high-order interaction is proposed.•Experiments prove the superior performance of the proposed methods.