Aberrant functional connectivity for diagnosis of major depressive disorder: A discriminant analysis

Authors

  • Longlong Cao MD,

    1. Mental Health Institute of The Second Xiangya Hospital, Hunan Province Technology Institute of Psychiatry, Key Laboratory of Psychiatry and Mental Health of Hunan Province, Central South University, Hunan, China
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  • Shuixia Guo PhD,

    1. Mathematics and Computer Science College, Hunan Normal University, Hunan, China
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  • Zhimin Xue MD, PhD,

    1. Mental Health Institute of The Second Xiangya Hospital, Hunan Province Technology Institute of Psychiatry, Key Laboratory of Psychiatry and Mental Health of Hunan Province, Central South University, Hunan, China
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  • Yong Hu MSc,

    1. Mathematics and Computer Science College, Hunan Normal University, Hunan, China
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  • Haihong Liu MD,

    1. Mental Health Center, Xiangya Hospital, Central South University, Hunan, China
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  • Tumbwene E. Mwansisya MSc,

    1. Mental Health Institute of The Second Xiangya Hospital, Hunan Province Technology Institute of Psychiatry, Key Laboratory of Psychiatry and Mental Health of Hunan Province, Central South University, Hunan, China
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  • Weidan Pu MD,

    1. Mental Health Institute of The Second Xiangya Hospital, Hunan Province Technology Institute of Psychiatry, Key Laboratory of Psychiatry and Mental Health of Hunan Province, Central South University, Hunan, China
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  • Bo Yang PhD,

    1. Mental Health Institute of The Second Xiangya Hospital, Hunan Province Technology Institute of Psychiatry, Key Laboratory of Psychiatry and Mental Health of Hunan Province, Central South University, Hunan, China
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  • Chang Liu MM,

    1. Mental Health Institute of The Second Xiangya Hospital, Hunan Province Technology Institute of Psychiatry, Key Laboratory of Psychiatry and Mental Health of Hunan Province, Central South University, Hunan, China
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  • Jianfeng Feng PhD,

    1. Centre for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai, China
    2. Department of Computer Science, University of Warwick, Coventry, UK
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  • Eric Y. H. Chen MD,

    1. Department of Psychiatry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
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  • Zhening Liu MD, PhD

    Corresponding author
    1. Mental Health Institute of The Second Xiangya Hospital, Hunan Province Technology Institute of Psychiatry, Key Laboratory of Psychiatry and Mental Health of Hunan Province, Central South University, Hunan, China
    • Correspondence: Zhening Liu, MD, PhD, Mental Health Institute of The Second Xiangya Hospital, Hunan Province Technology Institute of Psychiatry, Key Laboratory of Psychiatry and Mental Health of Hunan Province, Central South University, Changsha, Hunan 410011, China. Email: zningl@163.com

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Abstract

Aim

Aberrant brain functional connectivity patterns have been reported in major depressive disorder (MDD). It is unknown whether they can be used in discriminant analysis for diagnosis of MDD. In the present study we examined the efficiency of discriminant analysis of MDD by individualized computer-assisted diagnosis.

Methods

Based on resting-state functional magnetic resonance imaging data, a new approach was adopted to investigate functional connectivity changes in 39 MDD patients and 37 well-matched healthy controls. By using the proposed feature selection method, we identified significant altered functional connections in patients. They were subsequently applied to our analysis as discriminant features using a support vector machine classification method. Furthermore, the relative contribution of functional connectivity was estimated.

Results

After subset selection of high-dimension features, the support vector machine classifier reachedup to approximately 84% with leave-one-out training during the discrimination process. Through summarizing the classification contribution of functional connectivities, we obtained four obvious contribution modules: inferior orbitofrontal module, supramarginal gyrus module, inferior parietal lobule-posterior cingulated gyrus module and middle temporal gyrus-inferior temporal gyrus module.

Conclusion

The experimental results demonstrated that the proposed method is effective in discriminating MDD patients from healthy controls. Functional connectivities might be useful as new biomarkers to assist clinicians in computer auxiliary diagnosis of MDD.

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