Coherent Pattern in Multi-Layer Brain Networks: Application to Epilepsy Identification

• Published in: IEEE journal of biomedical and health informatics Vol. 24; no. 9; pp. 2609 - 2620
• Main Authors: Huang, Jiashuang, Zhu, Qi, Wang, Mingliang, Zhou, Luping, Zhang, Zhiqiang, Zhang, Daoqiang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Attorneys; Brain; Brain diseases; Brain modeling; Coherence; Coherent pattern; Epilepsy; Feature extraction; Functional connectivity; Functional magnetic resonance imaging; Graph theory; Mathematical analysis; Matrix decomposition; Matrix methods; Multi-layer network; Multilayers; Network analysis; Neural networks; Structural connectivity; Support vector machines
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2019.2962519

Currently, how to conjointly fuse structural connectivity (SC) and functional connectivity (FC) for identifying brain diseases is a hot topic in the area of brain network analysis. Most of the existing works combine two types of connectivity in decision level, thus ignoring the underlying relationship between SC and FC. To solve this problem, in this paper, we model the brain network as the multi-layer network formed by the SC and FC, and then propose a coherent pattern to represent structural information of the multi-layer network for the brain disease identification. The proposed coherent pattern consists of a paired-subgraph extracted from the FC and SC within the same node-set. Compared with the previous methods, this coherent pattern not only describes the connectivity information of both SC and FC by subgraphs at each layer, but also reflects their intrinsic relationship by the co-occurrence pattern of the paired-subgraph. Based on this coherent pattern, we further develop a framework for identifying brain diseases. Specifically, we first construct multi-layer networks by using SC and FC for each subject and then mine coherent patterns that frequently appear in each group. Next, we select the discriminative coherent pattern from these frequent coherent patterns according to their frequency of occurrence. Finally, we construct a feature matrix for each subject based on the binary indicator vector and then use the support vector machine (SVM) as its classifier. Experimental results on real epilepsy datasets demonstrate that our method outperforms several state-of-the-art approaches in the tasks of brain disease classification.