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Classification of pure conduct disorder from healthy controls based on indices of brain networks during resting state

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Abstract

Conduct disorder (CD) is an important mental health problem in childhood and adolescence. There is presently a trend of revealing neural mechanisms using measures of brain networks. This study goes further by presenting a classification scheme to distinguish subjects with CD from typically developing healthy subjects based on measures of small-world networks. In this study, small-world networks were constructed, and feature data were generated for both the CD and healthy control (HC) groups. Two methods of feature selection, including the F-score and feature projection with singular value decomposition (SVD), were used to extract the feature data. Furthermore, and importantly, the classification performances were compared between the results from the two methods of feature selection. The selected feature data by SVD were employed to train three classifiers—least squares support vector machine (LS-SVM), naive Bayes and K-nearest neighbour (KNN)—for CD classification. Cross-validation results from 36 subjects showed that CD patients can be separated from HC with a sensitivity, specificity and overall accuracy of 88.89%, 100% and 94.44%, respectively, by using the LS-SVM classifier. These findings suggest that the combination of the LS-SVM classifier with SVD can achieve a higher degree of accuracy for CD diagnosis than the naive Bayes and KNN classifiers.

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Acknowledgements

We would like to thank the reviewers whose comments greatly improved the quality of the manuscript.

Funding

This research was supported by the National Natural Science Foundation of China (Grant No. 61876114) and the Sichuan Science and Technology Program (Grant No. 2018TJPT0008, Science & Technology Department of Sichuan Province, China).

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Authors and Affiliations

  1. College of Electrical Engineering, Sichuan University, Chengdu, 610065, China

    Jiang Zhang, Yuyan Liu & Ruisen Luo

  2. School of Information Science and Technology, Xichang University, Xichang, 615000, China

    Zhengcong Du

  3. The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Fengmei Lu

  4. Bioimaging Core, Faculty of Health Sciences, University of Macau, Macau, SAR, China

    Zhen Yuan

  5. Mental Health Institute, Second Xiangya Hospital, Hunan Province Technology Institute of Psychiatry, Key Laboratory of Psychiatry and Mental Health of Hunan Province, Central South University, Changsha, 410011, Hunan, China

    Jiansong Zhou

  6. Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Boston, MA, 02129, USA

    Shasha Li

  7. Harvard Medical School, Boston, MA, 02115, USA

    Shasha Li

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  1. Jiang Zhang
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  2. Yuyan Liu
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  3. Ruisen Luo
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  4. Zhengcong Du
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  5. Fengmei Lu
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  6. Zhen Yuan
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  7. Jiansong Zhou
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  8. Shasha Li
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Contributions

Jiang Zhang and Jiansong Zhou designed the experiments. Jiang Zhang, Fengmei Lu and Jiansong Zhou performed the experiments. Jiang Zhang, Zhengcong Du and Fengmei Lu analysed the data. Jiang Zhang, Yuyan Liu, Ruisen Luo, Zhen Yuan and Shasha Li wrote the manuscript. Yuyan Liu, Ruisen Luo, Zhengcong Du and Shasha Li provided support to this study.

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Correspondence to Ruisen Luo.

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Zhang, J., Liu, Y., Luo, R. et al. Classification of pure conduct disorder from healthy controls based on indices of brain networks during resting state. Med Biol Eng Comput 58, 2071–2082 (2020). https://doi.org/10.1007/s11517-020-02215-8

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  • DOI: https://doi.org/10.1007/s11517-020-02215-8

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