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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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References

  1. Halldorsdottir T, Binder EB. Gene × environment interactions: from molecular mechanisms to behavior. Annu Rev Psychol. 2017;68:215–41. https://doi.org/10.1146/annurev-psych-010416-044053.

  2. Zou H, Yang J. Multiple functional connectivity networks fusion for schizophrenia diagnosis. Med Biol Eng Comput. 2020;58:1779–90.

    Article  Google Scholar 

  3. Phang CR, Ting CM, Samdin SB, Ombao H. Classification of EEG-based effective brain connectivity in schizophrenia using deep neural networks. In: 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE; 2019. p. 401–406. https://doi.org/10.1109/NER.2019.8717087.

  4. Zhu Q, Li H, Huang J, Xu X, Guan D, Zhang D. Hybrid functional brain network with first-order and second-order information for computer-aided diagnosis of schizophrenia. Front Neurosci. 2019;13:603.

    Article  Google Scholar 

  5. Cohen MX. Where does EEG come from and what does it mean? Trends Neurosci. 2017;40(4):208–18.

    Article  Google Scholar 

  6. Hasey GM, Kiang M. A review of recent literature employing electroencephalographic techniques to study the pathophysiology, phenomenology, and treatment response of schizophrenia. Curr Psychiatry Rep. 2013;15(9):388.

    Article  Google Scholar 

  7. Van Den Heuvel MP, Pol HEH. Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol. 2010;20(8):519–34.

    Article  Google Scholar 

  8. Khajehpour H, Mohagheghian F, Ekhtiari H, Makkiabadi B, Jafari AH, Eqlimi E, et al. Computer-aided classifying and characterizing of methamphetamine use disorder using resting-state EEG. Cogn Neurodyn. 2019;13(6):519–30.

    Article  Google Scholar 

  9. Hipp JF, Engel AK, Siegel M. Oscillatory synchronization in large-scale cortical networks predicts perception. Neuron. 2011;69(2):387–96.

    Article  Google Scholar 

  10. Chen X, Zhang H, Gao Y, Wee CY, Li G, Shen D, et al. High-order resting-state functional connectivity network for MCI classification. Hum Brain Mapp. 2016;37(9):3282–96.

    Article  Google Scholar 

  11. Zhang Y, Zhang H, Chen X, Lee SW, Shen D. Hybrid high-order functional connectivity networks using resting-state functional MRI for mild cognitive impairment diagnosis. Sci Rep. 2017;7(1):1–15.

    Article  Google Scholar 

  12. Harvy J, Thakor N, Bezerianos A, Li J. Between-frequency topographical and dynamic high-order functional connectivity for driving drowsiness assessment. IEEE Trans Neural Syst Rehabil Eng. 2019;27(3):358–67.

    Article  Google Scholar 

  13. Zhang H, Chen X, Shi F, Li G, Kim M, Giannakopoulos P, et al. Topographical information-based high-order functional connectivity and its application in abnormality detection for mild cognitive impairment. J Alzheimers Dis. 2016;54(3):1095–112.

    Article  Google Scholar 

  14. Achard S, Salvador R, Whitcher B, Suckling J, Bullmore E. A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J Neurosci. 2006;26(1):63–72.

    Article  Google Scholar 

  15. Tomasi D, Wang R, Wang GJ, Volkow ND. Functional connectivity and brain activation: a synergistic approach. Cereb Cortex. 2014;24(10):2619–29.

    Article  Google Scholar 

  16. Zheng Y, Chen X, Li D, Liu Y, Tan X, Liang Y, et al. Treatment-naive first episode depression classification based on high-order brain functional network. J Affect Disord. 2019;256:33–41. https://doi.org/10.1016/j.jad.2019.05.067.

    Article  Google Scholar 

  17. Guo H, Liu L, Chen J, Xu Y, Jie X. Alzheimer classification using a minimum spanning tree of high-order functional network on fMRI dataset. Front Neurosci. 2017;11:639. https://doi.org/10.3389/fnins.2017.00639.

    Article  Google Scholar 

  18. Zhang J, Wang N, Kuang H, Wang R. An improved method to calculate phase locking value based on Hilbert-Huang transform and its application. Neural Comput & Applic. 2014;24(1):125–32.

    Article  Google Scholar 

  19. Dimitriadis SI, Salis C, Tarnanas I, Linden DE. Topological filtering of dynamic functional brain networks unfolds informative chronnectomics: a novel data-driven thresholding scheme based on orthogonal minimal spanning trees (OMSTs). Front Neuroinform. 2017;11:28. https://doi.org/10.3389/fninf.2017.00028.

    Article  Google Scholar 

  20. Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010;52(3):1059–69.

    Article  Google Scholar 

  21. Yang J, Yang Jy, Zhang D, Lu Jf. Feature fusion: parallel strategy vs. serial strategy. Pattern Recogn. 2003;36(6):1369–1381.

  22. Wong TT. Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recogn. 2015;48(9):2839–46.

    Article  Google Scholar 

  23. Chang CC, Lin CJ. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST). 2011;2(3):1–27.

    Article  Google Scholar 

  24. Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Mach Learn. 2002;46(1):389–422.

    Article  Google Scholar 

  25. Van Wijk BC, Stam CJ, Daffertshofer A. Comparing brain networks of different size and connectivity density using graph theory. PloS One. 2010;5(10):e13701.

  26. Latora V, Marchiori M. Efficient behavior of small-world networks. Phys Rev Lett. 2001;87(19):198701.

  27. Brandes U, Borgatti SP, Freeman LC. Maintaining the duality of closeness and betweenness centrality. Soc Networks. 2016;44:153–9. https://doi.org/10.1016/j.socnet.2015.08.003.

    Article  Google Scholar 

  28. Honey CJ, Kötter R, Breakspear M, Sporns O. Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc Natl Acad Sci. 2007;104(24):10240–5.

    Article  Google Scholar 

  29. Pedroche F, Romance M, Criado R. A biplex approach to PageRank centrality: From classic to multiplex networks. Chaos: An Interdisciplinary Journal of Nonlinear Science. 2016;26(6):065301.

  30. Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, et al. Mapping the structural core of human cerebral cortex. PLoS Biol. 2008;6(7):1479–93.

    Article  Google Scholar 

  31. Kumar R, Indrayan A. Receiver operating characteristic (ROC) curve for medical researchers. Indian Pediatr. 2011;48(4):277–87.

    Article  Google Scholar 

  32. Metz CE. Basic principles of ROC analysis. In: Seminars in nuclear medicine. vol. 8. Elsevier; 1978. p. 283–298.

  33. Hajian-Tilaki K. Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian J Intern Med. 2013;4(2):627.

    Google Scholar 

  34. Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004;134(1):9–21.

    Article  Google Scholar 

  35. Smith SM, Miller KL, Salimi-Khorshidi G, Webster M, Beckmann CF, Nichols TE, et al. Network modelling methods for FMRI. Neuroimage. 2011;54(2):875–91.

    Article  Google Scholar 

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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.

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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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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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