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MMHGE: detecting mild cognitive impairment based on multi-atlas multi-view hybrid graph convolutional networks and ensemble learning

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Abstract

Currently, it is still a great challenge in clinical practice to accurately detect the early state of Alzheimer’s disease (AD), i.e., mild cognitive impairment (MCI) including early MCI (EMCI) and late MCI (LMCI). To address this challenge, we propose a new MCI detection framework based on multi-atlas multi-view hybrid graph convolutional networks and ensemble learning. We first construct nine different graphs based on three brain atlases and three morphological measurements using both imaging and non-imaging data of each subject. Then, in order to integrate the information of different graphs and obtain more discriminative feature representations for detecting MCI, we propose a hybrid graph convolutional network method. Finally, a new ensemble learning method is proposed to perform MCI detection tasks. An evaluation of our proposed framework has been conducted with 369 subjects with cognitively normal (CN), 779 subjects with MCI including 310 subjects with EMCI and 469 subjects with LMCI, and 301 subjects with AD on three classification tasks. Experimental results show that our proposed framework can get an accuracy of 90.8% and an AUC of 0.932 for MCI/CN classification, an accuracy of 88.6% and an AUC of 0.908 for MCI/AD classification, and an accuracy of 83.5% and an AUC of 0.851 for EMCI/LMCI classification, respectively. Compared with some state-of-the-art methods about MCI detection, our proposed framework can get better performance. Overall, our proposed framework is effective and promising for MCI detection in clinical practice.

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China under Grant No.61802442, No.61877059, the Natural Science Foundation of Hunan Province under Grant No.2019JJ50775, No.2018JJ2534, the 111 Project (No. B18059), and the Hunan Provincial Science and Technology Program (2018WK4001).

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Authors and Affiliations

  1. Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China

    Jin Liu, Dejiao Zeng, Rui Guo, Mingming Lu & Jianxin Wang

  2. Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, Canada

    Fang-Xiang Wu

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  1. Jin Liu
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Correspondence to Jin Liu or Jianxin Wang.

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Liu, J., Zeng, D., Guo, R. et al. MMHGE: detecting mild cognitive impairment based on multi-atlas multi-view hybrid graph convolutional networks and ensemble learning. Cluster Comput 24, 103–113 (2021). https://doi.org/10.1007/s10586-020-03199-8

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