School of Computer Science and Engineering, Northeastern University, 110169, China.
School of Computer Science and Engineering, Northeastern University, 110169, China; Key Laboratory of Big Data Management and Analytics, Northeastern University, 110169, China.
Comput Biol Med. 2024 Mar;171:108148. doi: 10.1016/j.compbiomed.2024.108148. Epub 2024 Feb 14.
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.
作为脑网络分析的工具，图核常被用于辅助神经退行性疾病的诊断。它通过测量脑网络之间的相似性来判断对象是否患病。现有的大多数图核都是基于结构相似性来计算脑网络的相似性，这种方法可以更好地捕捉脑网络的拓扑结构，但都忽略了脑区所属的叶、中心、左右脑以及脑区的功能等功能信息。功能相似性有助于更准确地定位受疾病影响的特定脑区，以便我们集中测量脑网络的相似性。因此，提出了一种用于脑网络的多属性图核，为脑网络中的节点赋予多个属性，并根据 Weisfeiler-Lehman 颜色细化算法计算脑网络的图核。此外，为了捕捉多个脑区之间的相互作用，提出了一种多属性超图核，它同时考虑了脑网络节点的功能和结构相似性以及高阶相关性。最后，在真实数据集上进行了实验，实验结果表明，所提出的方法可以显著提高神经退行性疾病诊断的性能。此外，统计检验表明，所提出的方法与对比方法有显著差异。
