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

Abstract

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.

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Correspondence to Daoqiang Zhang or Guang-Ming Lu.

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This work was supported in part by the National Natural Science Foundation of China (Nos. 61,422,204, 61,473,149, 61,573,023, 81,230,032 and 31,322,020); the NUAA Fundamental Research Funds (No. NE2013105); the Fundamental Research Funds for the Central Universities (No. NP2017108).

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The authors declare that they have no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Zhang, D., Tu, L., Zhang, LJ. et al. Subnetwork mining on functional connectivity network for classification of minimal hepatic encephalopathy. Brain Imaging and Behavior 12, 901–911 (2018). https://doi.org/10.1007/s11682-017-9753-4

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Keywords

  • Minimal hepatic encephalopathy
  • Subnetwork mine
  • Discriminative subnetworks
  • Graph kernel