Document Type: Original Research

Authors

1 PhD, Department of Medical Physics and Biomedical engineering, School of Medicine, Shahid Beheshti University of Medical Sciences (SBMU), Tehran, Iran

2 PhD, Department of Medical Physics and Biomedical engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran

3 PhD, Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran

4 PhD, Department of Audiology, School of Rehabilitation, Shahid Beheshti University of Medical Sciences (SBMU), Tehran, Iran

5 MSc, Department of Medical Physics and Biomedical engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran

6 MSc, Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran

Abstract

Background: Tinnitus known as a central nervous system disorder is correlated with specific oscillatory activities within auditory and non-auditory brain areas. Several studies in the past few years have revealed that in the most tinnitus cases, the response pattern of neurons in auditory system is changed due to auditory deafferentation, which leads to variation and disruption of the brain networks.
Objective: In this paper, we introduce an approach to automatically distinguish tinnitus individuals from healthy controls based on whole-brain functional connectivity and network analysis.
Material and Methods: The functional connectivity analysis was applied to the resting state electroencephalographic (EEG) data of both groups using Weighted Phase Lag Index (WPLI) for various frequency bands in 2-44 Hz frequency range. In this case- control study, the classification was performed on graph theoretical measures using support vector machine (SVM) as a robust classification method.
Results: Experimental results showed promising classification performance with a high accuracy, sensitivity, and specificity in all frequency bands, specifically in the beta2 frequency band.
Conclusion: The current study provides substantial evidence that tinnitus network can be successfully detected by consistent measures of the brain networks based on EEG functional connectivity.

Keywords

  1. Eggermont JJ. Central tinnitus. Auris Nasus Larynx. 2003;30:7-12.
  2. Snow JB. Tinnitus: theory and management: PMPH-USA; 2004.
  3. Møller AR, Langguth B, De Ridder D, Kleinjung T. Textbook of tinnitus: Springer Science & Business Media; 2010.
  4. Meyerhoff W, Cooper J. Tinnitus. Otolaryngology. 1991;2:1169-79.
  5. Phoon WH, Lee HS, Chia SE. Tinnitus in noise-exposed workers. Occup Med (Lond). 1993;43:35-8. PubMed PMID: 8422445.
  6. Eggermont JJ, Roberts LE. The neuroscience of tinnitus. Trends Neurosci. 2004;27:676-82.
  7. Norena A, Micheyl C, Chery-Croze S, Collet L. Psychoacoustic characterization of the tinnitus spectrum: implications for the underlying mechanisms of tinnitus. Audiol Neurootol. 2002;7:358-69. doi: 10.1159/000066156. PubMed PMID: 12401967.
  8. Elgoyhen AB, Langguth B, De Ridder D, Vanneste S. Tinnitus: perspectives from human neuroimaging. Nat Rev Neurosci. 2015;16:632-42. doi: 10.1038/nrn4003. PubMed PMID: 26373470.
  9. Mohan A, De Ridder D, Vanneste S. Graph theoretical analysis of brain connectivity in phantom sound perception. Sci Rep. 2016;6:19683. doi: 10.1038/srep19683. PubMed PMID: 26830446; PubMed Central PMCID: PMC4735645.
  10. Vanneste S, De Ridder D. The auditory and non-auditory brain areas involved in tinnitus. An emergent property of multiple parallel overlapping subnetworks. Front Syst Neurosci. 2012;6:31. doi: 10.3389/fnsys.2012.00031. PubMed PMID: 22586375; PubMed Central PMCID: PMC3347475.
  11. 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;26:63-72. doi: 10.1523/JNEUROSCI.3874-05.2006. PubMed PMID: 16399673.
  12. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. 2009;10:186-98. doi: 10.1038/nrn2575. PubMed PMID: 19190637.
  13. Buzsaki G, Geisler C, Henze DA, Wang XJ. Interneuron Diversity series: Circuit complexity and axon wiring economy of cortical interneurons. Trends Neurosci. 2004;27:186-93. doi: 10.1016/j.tins.2004.02.007. PubMed PMID: 15046877.
  14. Stam CJ, Reijneveld JC. Graph theoretical analysis of complex networks in the brain. Nonlinear Biomed Phys. 2007;1:3. doi: 10.1186/1753-4631-1-3. PubMed PMID: 17908336; PubMed Central PMCID: PMC1976403.
  15. Sporns O, Honey CJ. Small worlds inside big brains. Proc Natl Acad Sci U S A. 2006;103:19219-20. doi: 10.1073/pnas.0609523103. PubMed PMID: 17159140; PubMed Central PMCID: PMC1748207.
  16. Watts DJ, Strogatz SH. Collective dynamics of ‘small-world’ networks. Nature. 1998;393:440-2. doi: 10.1038/30918. PubMed PMID: 9623998.
  17. Vertes PE, Alexander-Bloch AF, Gogtay N, Giedd JN, Rapoport JL, Bullmore ET. Simple models of human brain functional networks. Proc Natl Acad Sci U S A. 2012;109:5868-73. doi: 10.1073/pnas.1111738109. PubMed PMID: 22467830; PubMed Central PMCID: PMC3326510.
  18. Stam CJ. Modern network science of neurological disorders. Nat Rev Neurosci. 2014;15:683-95. doi: 10.1038/nrn3801. PubMed PMID: 25186238.
  19. Mohan A, De Ridder D, Vanneste S. Graph theoretical analysis of brain connectivity in phantom sound perception. Sci Rep. 2016;6:19683. doi: 10.1038/srep19683. PubMed PMID: 26830446; PubMed Central PMCID: PMC4735645.
  20. McCombe A, Baguley D, Coles R, McKenna L, McKinney C, Windle-Taylor P, et al. Guidelines for the grading of tinnitus severity: the results of a working group commissioned by the British Association of Otolaryngologists, Head and Neck Surgeons, 1999. Clin Otolaryngol Allied Sci. 2001;26:388-93. PubMed PMID: 11678946.
  21. Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004;134:9-21. doi: 10.1016/j.jneumeth.2003.10.009. PubMed PMID: 15102499.
  22. Oostenveld R, Fries P, Maris E, Schoffelen JM. FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci. 2011;2011:156869. doi: 10.1155/2011/156869. PubMed PMID: 21253357; PubMed Central PMCID: PMC3021840.
  23. Fraga Gonzalez G, Van der Molen MJW, Zaric G, Bonte M, Tijms J, Blomert L, et al. Graph analysis of EEG resting state functional networks in dyslexic readers. Clin Neurophysiol. 2016;127:3165-75. doi: 10.1016/j.clinph.2016.06.023. PubMed PMID: 27476025.
  24. Hardmeier M, Hatz F, Bousleiman H, Schindler C, Stam CJ, Fuhr P. Reproducibility of functional connectivity and graph measures based on the phase lag index (PLI) and weighted phase lag index (wPLI) derived from high resolution EEG. PLoS One. 2014;9:e108648. doi: 10.1371/journal.pone.0108648. PubMed PMID: 25286380; PubMed Central PMCID: PMC4186758.
  25. Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010;52:1059-69. doi: 10.1016/j.neuroimage.2009.10.003. PubMed PMID: 19819337.
  26. Vanneste S, Plazier M, Van Der Loo E, Van De Heyning P, De Ridder D. The difference between uni- and bilateral auditory phantom percept. Clin Neurophysiol. 2011;122:578-87. doi: 10.1016/j.clinph.2010.07.022. PubMed PMID: 20801079.
  27. Vanneste S, Van De Heyning P, De Ridder D. The neural network of phantom sound changes over time: a comparison between recent-onset and chronic tinnitus patients. Eur J Neurosci. 2011;34:718-31. doi: 10.1111/j.1460-9568.2011.07793.x. PubMed PMID: 21848924.
  28. Vinck M, Oostenveld R, Van Wingerden M, Battaglia F, Pennartz CM. An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. Neuroimage. 2011;55:1548-65. doi: 10.1016/j.neuroimage.2011.01.055. PubMed PMID: 21276857.
  29. Cohen MX. Analyzing neural time series data: theory and practice: MIT press; 2014.
  30. Ewald A, Aristei S, Nolte G, Abdel Rahman R. Brain Oscillations and Functional Connectivity during Overt Language Production. Front Psychol. 2012;3:166. doi: 10.3389/fpsyg.2012.00166. PubMed PMID: 22701106; PubMed Central PMCID: PMC3369188.
  31. Haufe S, Nikulin VV, Muller KR, Nolte G. A critical assessment of connectivity measures for EEG data: a simulation study. Neuroimage. 2013;64:120-33. doi: 10.1016/j.neuroimage.2012.09.036. PubMed PMID: 23006806.
  32. Vapnik V. The nature of statistical learning theory: Springer science & business media; 2013.
  33. Burges CJ. A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery. 1998;2:121-67.
  34. Alvar AA, Deevband MR, Ashtiyani M. Neutron spectrum unfolding using radial basis function neural networks. Appl Radiat Isot. 2017;129:35-41. doi: 10.1016/j.apradiso.2017.07.048. PubMed PMID: 28802156.
  35. Taherisadr M, Dehzangi O, Parsaei H. Single Channel EEG Artifact Identification Using Two-Dimensional Multi-Resolution Analysis. Sensors (Basel). 2017;17(12): 2895 doi: 10.3390/s17122895. PubMed PMID: 29236042; PubMed Central PMCID: PMC5750748.
  36. Latora V, Marchiori M. Efficient behavior of small-world networks. Phys Rev Lett. 2001;87:198701. doi: 10.1103/PhysRevLett.87.198701. PubMed PMID: 11690461.
  37. Humphries MD, Gurney K. Network ‘small-world-ness’: a quantitative method for determining canonical network equivalence. PLoS One. 2008;3:e0002051. doi: 10.1371/journal.pone.0002051. PubMed PMID: 18446219; PubMed Central PMCID: PMC2323569.
  38. Elgoyhen AB, Langguth B, Vanneste S, De Ridder D. Tinnitus: network pathophysiology-network pharmacology. Front Syst Neurosci. 2012;6:1. doi: 10.3389/fnsys.2012.00001. PubMed PMID: 22291622; PubMed Central PMCID: PMC3265967.
  39. Zobay O, Palmer AR, Hall DA, Sereda M, Adjamian P. Source space estimation of oscillatory power and brain connectivity in tinnitus. PLoS One. 2015;10:e0120123. doi: 10.1371/journal.pone.0120123. PubMed PMID: 25799178; PubMed Central PMCID: PMC4370720.
  40. Schlee W, Hartmann T, Langguth B, Weisz N. Abnormal resting-state cortical coupling in chronic tinnitus. BMC Neurosci. 2009;10:11. doi: 10.1186/1471-2202-10-11. PubMed PMID: 19228390; PubMed Central PMCID: PMC2649130.
  41. Schlee W, Lorenz I, Hartmann T, Müller N, Schulz H, Weisz N. A global brain model of tinnitus. Textbook of tinnitus. New York: Springer; 2011. p. 161-9.
  42. De Ridder D, Vanneste S, Weisz N, Londero A, Schlee W, Elgoyhen AB, et al. An integrative model of auditory phantom perception: tinnitus as a unified percept of interacting separable subnetworks. Neurosci Biobehav Rev. 2014;44:16-32. doi: 10.1016/j.neubiorev.2013.03.021. PubMed PMID: 23597755.
  43. Vanneste S, Plazier M, Der Loo E, De Heyning PV, Congedo M, De Ridder D. The neural correlates of tinnitus-related distress. Neuroimage. 2010;52:470-80. doi: 10.1016/j.neuroimage.2010.04.029. PubMed PMID: 20417285.
  44. Weisz N, Moratti S, Meinzer M, Dohrmann K, Elbert T. Tinnitus perception and distress is related to abnormal spontaneous brain activity as measured by magnetoencephalography. PLoS Med. 2005;2:e153. doi: 10.1371/journal.pmed.0020153. PubMed PMID: 15971936; PubMed Central PMCID: PMC1160568.
  45. Schlee W. Towards a Global Model of Tinnitus Perception: Multiple Evidence for a Long-Range Cortical Tinnitus Network 2009.
  46. Chen YC, Feng Y, Xu JJ, Mao CN, Xia W, Ren J, et al. Disrupted Brain Functional Network Architecture in Chronic Tinnitus Patients. Front Aging Neurosci. 2016;8:174. doi: 10.3389/fnagi.2016.00174. PubMed PMID: 27458377; PubMed Central PMCID: PMC4937025.
  47. Jastreboff PJ. Phantom auditory perception (tinnitus): mechanisms of generation and perception. Neurosci Res. 1990;8:221-54. PubMed PMID: 2175858.