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Higher-Order Brain Network Analysis for Auditory Disease

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Abstract

Auditory diseases such as deafness and tinnitus have been plaguing people for a long time. On the one hand, although cochlear implantation may serve as a cure for deafness to some degree, the mechanism of developmental neuroplasticity in the auditory and visual systems has not been well understood. On the other hand, there is still no cure for tinnitus, and investigating the cause and then developing the cure of tinnitus is particularly necessary. EEG signals provide us insights into these auditory diseases and have been widely studied for developing the cure of auditory diseases, in particular from the brain network perspective. However, most of the existing methods either simply utilize lower-order features of the brain network at the level of local connections within selected brain regions or fail to analyze the EEG signals from the brain region connectivity perspective. In this paper, based on the EEG signals, we develop a new higher-order brain network analysis method termed HBNmining (higher-order brain network mining) based on the weighted motifs and colored motifs for deepening the understanding of the auditory diseases. In particular, after constructing brain network from EEG signals, both the weighted motifs and the colored motifs are extracted, from which subject classification and brain region connectivity analysis can be conducted respectively. The results have confirmed the effectiveness of our method, which may be helpful for clinical treatment of auditory diseases.

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References

  1. Adami C, Qian J, Rupp M, Hintze A (2011) Information content of colored motifs in complex networks. Artif Life 17(4):375–390

    Article  Google Scholar 

  2. Ahirwal MK, Kumar A, Singh GK (2013) EEG/ERP adaptive noise canceller design with controlled search space (CSS) approach in cuckoo and other optimization algorithms. IEEE ACM Trans Comput Biol Bioinform 10(6):1491–1504

    Article  Google Scholar 

  3. Alvarenga KF, Amorim RB, Agostinho-Pesse RS, Costa OA, Nascimento LT, Bevilacqua MC (2012) Speech perception and cortical auditory evoked potentials in cochlear implant users with auditory neuropathy spectrum disorders. Int J Pediatr Otorhinolaryngol 76(9):1332–1338

    Article  Google Scholar 

  4. Benson AR, Gleich DF, Leskovec J (2016) Higher-order organization of complex networks. Science 353(6295):163–166

    Article  Google Scholar 

  5. Braitenberg V, Schüz A (2013) Cortex: statistics and geometry of neuronal connectivity. Springer, Berlin

    Google Scholar 

  6. Cao B, Zhan L, Kong X, Philip SY, Vizueta N, Altshuler LL, Leow AD (2015) Identification of discriminative subgraph patterns in fMRI brain networks in bipolar affective disorder. In: International conference on brain informatics and health. Springer, pp 105–114

  7. Chandaka S, Chatterjee A, Munshi S (2009) Cross-correlation aided support vector machine classifier for classification of EEG signals. Expert Syst Appl 36(2):1329–1336

    Article  Google Scholar 

  8. Cheng CY, Huang CY, Sun CT (2008) Mining bridge and brick motifs from complex biological networks for functionally and statistically significant discovery. IEEE Trans Syst Man Cybernet Part B (Cybernet) 38(1):17–24

    Article  Google Scholar 

  9. Choobdar S, Ribeiro P, Silva F (2012) Motif mining in weighted networks. In: 2012 IEEE 12th international conference on Data mining workshops (ICDMW). IEEE, pp 210–217

  10. Das MK, Dai HK (2007) A survey of DNA motif finding algorithms. BMC Bioinform 8(7):S21

    Article  Google Scholar 

  11. Davidson I, Gilpin S, Carmichael O, Walker P (2013) Network discovery via constrained tensor analysis of fMRI data. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 194–202

  12. Hu B, Li X, Sun S, Ratcliffe M (2016) Attention recognition in EEG-based affective learning research using CFS + KNN algorithm. IEEE ACM Trans Comput Biol Bioinform 15:38

    Article  Google Scholar 

  13. Huang S, Li J, Ye J, Fleisher A, Chen K, Wu T, Reiman E (2011a) Brain effective connectivity modeling for Alzheimer’s disease by sparse Gaussian Bayesian network. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 931–939

  14. Huang S, Li J, Ye J, Wu T, Chen K, Fleisher A, Reiman E (2011b) Identifying Alzheimer’s disease-related brain regions from multi-modality neuroimaging data using sparse composite linear discrimination analysis. In: Advances in neural information processing systems, pp 1431–1439

  15. Husain FT, Schmidt SA (2014) Using resting state functional connectivity to unravel networks of tinnitus. Hear Res 307:153–162

    Article  Google Scholar 

  16. Kashani ZRM, Ahrabian H, Elahi E, Nowzari-Dalini A, Ansari ES, Asadi S, Mohammadi S, Schreiber F, Masoudi-Nejad A (2009) Kavosh: a new algorithm for finding network motifs. BMC Bioinform 10(1):318

    Article  Google Scholar 

  17. Li PZ, Li JH, Wang CD (2016) A SVM-based EEG signal analysis: an auxiliary therapy for tinnitus. In: Proceedings of the advances in brain inspired cognitive systems. In: 8th international conference, BICS 2016, Beijing, China, November 28–30 2016. Springer, vol 8, pp 207–219

  18. Liu J, Liang M, Chen Y, Wang Y, Cai Y, Chen S, Chen L, Li X, Qiu Z, Jiang J et al (2017) Visual cortex activation decrement following cochlear implantation in prelingual deafened children. Int J Pediatr Otorhinolaryngol 99:85–89

    Article  Google Scholar 

  19. Mangan S, Alon U (2003) Structure and function of the feed-forward loop network motif. Proc Nat Acad Sci 100(21):11,980–11,985

    Article  Google Scholar 

  20. Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U (2002) Network motifs: simple building blocks of complex networks. Science 298(5594):824–827

    Article  Google Scholar 

  21. Prat-Perez A, Dominguez-Sal D, Brunat JM, Larriba-Pey JL (2016) Put three and three together: triangle-driven community detection. ACM Trans Knowl Discov Data 10(3):22

    Article  Google Scholar 

  22. Ribeiro P, Silva F (2012) Querying subgraph sets with g-tries. In: Proceedings of the 2nd ACM SIGMOD workshop on databases and social networks. ACM, pp 25–30

  23. Ribeiro P, Silva F (2014a) Discovering colored network motifs. In: Complex networks V. Springer, pp 107–118

  24. Ribeiro P, Silva F (2014b) G-tries: a data structure for storing and finding subgraphs. Data Min Knowl Discov 28(2):337–377

    Article  MathSciNet  MATH  Google Scholar 

  25. Sharma A, Campbell J, Cardon G (2015) Developmental and cross-modal plasticity in deafness: evidence from the P1 and N1 event related potentials in cochlear implanted children. Int J Psychophysiol 95(2):135–144

    Article  Google Scholar 

  26. Shen-Orr SS, Milo R, Mangan S, Alon U (2002) Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet 31(1):64

    Article  Google Scholar 

  27. Sporns O, Kötter R (2004) Motifs in brain networks. PLoS Biol 2(11):e369

    Article  Google Scholar 

  28. Subasi A, Gursoy MI (2010) EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst Appl 37(12):8659–8666

    Article  Google Scholar 

  29. Vanneste S, De Ridder D (2015) Stress-related functional connectivity changes between auditory cortex and cingulate in tinnitus. Brain Connect 5(6):371–383

    Article  Google Scholar 

  30. Vanneste S, Plazier M, Van Der Loo E, Van de Heyning P, Congedo M, De Ridder D (2010) The neural correlates of tinnitus-related distress. NeuroImage 52(2):470–480

    Article  Google Scholar 

  31. Wang SJ, Cai YX, Sun ZR, Wang CD, Zheng YQ (2017) Tinnitus EEG classification based on multi-frequency bands. In: Proceedings of the 24th international conference on neural information processing, pp 788–797

  32. Wernicke S (2006) Efficient detection of network motifs. IEEE ACM Trans Comput Biol Bioinform 3(4):347–359

    Article  Google Scholar 

  33. Ye J, Chen K, Wu T, Li J, Zhao Z, Patel R, Bae M, Janardan R, Liu H, Alexander G, et al (2008) Heterogeneous data fusion for Alzheimer’s disease study. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 1025–1033

  34. Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, Roffman JL, Smoller JW, Zöllei L, Polimeni JR et al (2011) The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol 106(3):1125–1165

    Article  Google Scholar 

  35. Zalesky A, Fornito A, Bullmore ET (2010) Network-based statistic: identifying differences in brain networks. NeuroImage 53(4):1197–1207

    Article  Google Scholar 

  36. Zhang XD, Song J, Bork P, Zhao XM (2016) The exploration of network motifs as potential drug targets from post-translational regulatory networks. Sci Rep 6(20):558

    Google Scholar 

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Acknowledgements

This work was supported by NSFC (61502543, 81600808), Guangdong Natural Science Funds for Distinguished Young Scholar (2016A030306014), Tiptop Scientific and Technical Innovative Youth Talents of Guangdong special support program (2016TQ03X542) and Shenzhen Innovation Program (JCYJ20150401145529008).

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Authors and Affiliations

  1. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, People’s Republic of China

    Pei-Zhen Li & Chang-Dong Wang

  2. Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of China

    Yue-Xin Cai, Mao-Jin Liang & Yi-Qing Zheng

  3. Institute of Hearing and Speech-Language Science, Sun Yat-sen University, Guangzhou, People’s Republic of China

    Yue-Xin Cai, Mao-Jin Liang & Yi-Qing Zheng

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  1. Pei-Zhen Li
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  2. Yue-Xin Cai
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  4. Mao-Jin Liang
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Correspondence to Yue-Xin Cai.

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Li, PZ., Cai, YX., Wang, CD. et al. Higher-Order Brain Network Analysis for Auditory Disease. Neural Process Lett 49, 879–897 (2019). https://doi.org/10.1007/s11063-018-9815-7

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