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Hybrid High-order Brain Functional Networks for Schizophrenia-Aided Diagnosis

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Abstract

Background

Electroencephalogram technology provides a reference for the study of schizophrenia. Constructing brain functional networks using electroencephalogram technology is one of the important methods to analyze the human brain. Current methods to construct brain functional networks often ignore the deeper interactions between brain regions and the phenomenons that the connectivity patterns of brain change over time.

Methods

Therefore, for the aided diagnosis of schizophrenia, a hybrid high-order brain functional network model is proposed, the model characterizing more complex functional interactions of brain includes static low-order multilayer brain functional networks and dynamic high-order multilayer brain functional networks.

Results

The results show that the classification method based on the proposed model is effective and efficient, with an accuracy of 94.05%, a sensitivity of 95.56% and a specificity of 92.31%.

Conclusions

Experimental results on the schizophrenia dataset show that the proposed method has satisfied performances; the complementarity between low-order and high-order multilayer brain functional networks could better capture brain functional interactions. The findings which suggest the importance of improved relationships between brain regions and temporal features of connectivities in the brain bring new biologically inspired implications.

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Funding

The authors would like to thank the National Natural Science Foundation of China under Grant No. 62072089, the Fundamental Research Funds for the Central Universities of China under Grant Nos. N2104001, N2116016, N2019007, N2024005-2, N180101028, N180408019.

Author information

Authors and Affiliations

  1. School of Computer Science & Engineering, Northeastern University, Shenyang, China

    Junchang Xin, Keqi Zhou, Zhongyang Wang, Jinyi Chen & Xinlei Wang

  2. College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China

    Zhiqiong Wang & Qi Chen

  3. Key Laboratory of Big Data Management and Analytics (Liaoning Province), Northeastern University, Shenyang, China

    Junchang Xin & Zhongyang Wang

Authors
  1. Junchang Xin
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  2. Keqi Zhou
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  4. Zhiqiong Wang
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  5. Jinyi Chen
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  6. Xinlei Wang
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  7. Qi Chen
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Contributions

JC.X. and ZY.W. collected the background information. JC.X. and ZQ.W. analyzed and compared the current situation. JC.X. and KQ.Z. had the major responsibility for preparing the paper, KQ.Z. and JY.C. wrote part of the paper. XL.W. and Q.C. supervised the project. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Zhongyang Wang.

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Xin, J., Zhou, K., Wang, Z. et al. Hybrid High-order Brain Functional Networks for Schizophrenia-Aided Diagnosis. Cogn Comput 14, 1303–1315 (2022). https://doi.org/10.1007/s12559-022-10014-6

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  • DOI: https://doi.org/10.1007/s12559-022-10014-6

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