Subnetwork mining on functional connectivity network for classification of minimal hepatic encephalopathy

• Published in: Brain imaging and behavior Vol. 12; no. 3; pp. 901 - 911
• Main Authors: Zhang, Daoqiang, Tu, Liyang, Zhang, Long-Jiang, Jie, Biao, Lu, Guang-Ming
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-06-2018; Springer Nature B.V
• Subjects: Algorithms; Biomedical and Life Sciences; Biomedicine; Brain; Brain - diagnostic imaging; Brain - physiopathology; Brain diseases; Cirrhosis; Classification; Connectome - methods; Data Mining - methods; Diagnosis; Female; Hepatic encephalopathy; Hepatic Encephalopathy - classification; Hepatic Encephalopathy - diagnostic imaging; Hepatic Encephalopathy - physiopathology; Humans; Identification methods; Liver cirrhosis; Magnetic Resonance Imaging - methods; Male; Middle Aged; Neural networks; Neural Pathways - diagnostic imaging; Neural Pathways - physiopathology; Neuropsychology; Neuroradiology; Neurosciences; Original Research; Patients; Psychiatry; ROC Curve; Minimal hepatic encephalopathy; Subnetwork mine; Discriminative subnetworks; Graph kernel
• ISSN: 1931-7557; 1931-7565
• DOI: 10.1007/s11682-017-9753-4

Hepatic encephalopathy (HE), as a complication of cirrhosis, is a serious brain disease, which may lead to death. Accurate diagnosis of HE and its intermediate stage, i.e., minimal HE (MHE), is very important for possibly early diagnosis and treatment. Brain connectivity network, as a simple representation of brain interaction, has been widely used for the brain disease (e.g., HE and MHE) analysis. However, those studies mainly focus on finding disease-related abnormal connectivity between brain regions, although a large number of studies have indicated that some brain diseases are usually related to local structure of brain connectivity network (i.e., subnetwork), rather than solely on some single brain regions or connectivities. Also, mining such disease-related subnetwork is a challenging task because of the complexity of brain network. To address this problem, we proposed a novel frequent-subnetwork-based method to mine disease-related subnetworks for MHE classification. Specifically, we first mine frequent subnetworks from both groups, i.e., MHE patients and non-HE (NHE) patients, respectively. Then we used the graph-kernel based method to select the most discriminative subnetworks for subsequent classification. We evaluate our proposed method on a MHE dataset with 77 cirrhosis patients, including 38 MHE patients and 39 NHE patients. The results demonstrate that our proposed method can not only obtain the improved classification performance in comparison with state-of-the-art network-based methods, but also identify disease-related subnetworks which can help us better understand the pathology of the brain diseases.