Skip to main content
SpringerLink
Log in

Predicting Individual Differences from Brain Responses to Music: A Comparison of Functional Connectivity Measures

  • Conference paper
  • First Online:
Brain Informatics (BI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13974))

Included in the following conference series:

  • 520 Accesses

Abstract

Individual differences are known to modulate brain responses to music. Recent neuroscience research suggests that each individual has unique and fundamentally stable functional brain connections irrespective of task. Our study aims to identify individual differences such as gender and musical expertise from brain responses to music. Thirty-six participants’ functional Magnetic Resonance Imaging (fMRI) responses were measured while listening to three 8-min-long musical pieces representing different musical styles. They were analyzed using various temporal and spectral functional connectivity (FC) measures. These FC measures were compared to identify the one that captured the most variation associated with gender and musical expertise. Subsequently, the measures were used to classify distinct population groupings using a binary Support Vector Machine (SVM). Our results revealed that Coherence, a spectral-domain FC measure, captured the maximum variation for musical stimuli. However, in classifying individuals based on gender and musical expertise, a composite measure outperformed any single measure and had above-chance accuracy. This suggests that each FC measure captures different aspects of the relationship between brain regions and is influenced by the level of analysis (group or individual) and the task, such as similarity analysis or classification. Further ramifications of the findings are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Alluri, V., et al.: Musical expertise modulates functional connectivity of limbic regions during continuous music listening. Psychomusicology: Music Mind Brain 25(4), 443–454 (2015)

    Article  Google Scholar 

  2. Alluri, V., Toiviainen, P., Burunat, I., Kliuchko, M., Vuust, P., Brattico, E.: Connectivity patterns during music listening: evidence for action-based processing in musicians. Hum. Brain Mapp. 38(6), 2955–2970 (2017)

    Article  Google Scholar 

  3. Alluri, V., Toiviainen, P., Jääskeläinen, I.P., Glerean, E., Sams, M., Brattico, E.: Large-scale brain networks emerge from dynamic processing of musical timbre, key and rhythm. NeuroImage 59(4), 3677–3689 (2012)

    Article  Google Scholar 

  4. Angulo-Perkins, A., Aubé, W., Peretz, I., Barrios, F.A., Armony, J.L., Concha, L.: Music listening engages specific cortical regions within the temporal lobes: differences between musicians and non-musicians. Cortex 59, 126–137 (2014)

    Article  Google Scholar 

  5. Best, D.J., Roberts, D.E.: Algorithm AS 89: the upper tail probabilities of Spearman’s rho. Appl. Stat. 24(3), 377 (1975)

    Article  Google Scholar 

  6. Bonneville-Roussy, A., Rentfrow, P.J., Xu, M.K., Potter, J.: Music through the ages: trends in musical engagement and preferences from adolescence through middle adulthood. J. Pers. Soc. Psychol. 105(4), 703–717 (2013)

    Article  Google Scholar 

  7. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  8. Buck, J.R., Daniel, M.M., Singer, A.: Computer explorations in signals and systems using MATLAB. Prentice Hall (2002)

    Google Scholar 

  9. Burunat, I., Brattico, E., Puoliväli, T., Ristaniemi, T., Sams, M., Toiviainen, P.: Action in perception: prominent visuo-motor functional symmetry in musicians during music listening. PLOS ONE 10(9), e0138238 (2015)

    Article  Google Scholar 

  10. Damoiseaux, J.S., et al.: Consistent resting-state networks across healthy subjects. Proc. Natl. Acad. Sci. 103(37), 13848–13853 (2006)

    Article  Google Scholar 

  11. Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)

    Article  Google Scholar 

  12. Friedrich, S., Brunner, E., Pauly, M.: Permuting longitudinal data in spite of the dependencies. J. Multivar. Anal. 153, 255–265 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  13. Gaser, C., Schlaug, G.: Brain structures differ between musicians and non-musicians. J. Neurosci. 23(27), 9240–9245 (2003)

    Article  Google Scholar 

  14. Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3–42 (2006)

    Article  MATH  Google Scholar 

  15. Gibbons, J.D., Chakraborti, S.: Nonparametric statistical inference. In: Lovric, M. (eds.)International Encyclopedia of Statistical Science, pp. 977–979. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-04898-2_420

  16. Glerean, E., Salmi, J., Lahnakoski, J.M., Jääskeläinen, I.P., Sams, M.: Functional magnetic resonance imaging phase synchronization as a measure of dynamic functional connectivity. Brain Connectivity 2(2), 91–101 (2012)

    Article  Google Scholar 

  17. González, A.G., Rodrıguez, J., Sagartzazu, X., Schumacher, A., Isasa, I.: Multiple coherence method in time domain for the analysis of the transmission paths of noise and vibrations with non stationary signals. In: Proceedings of ISMA 2010 (2010)

    Google Scholar 

  18. Gratton, C., et al.: Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation. Neuron 98(2), 439–452 (2018)

    Article  Google Scholar 

  19. Greenberg, D.M., Baron-Cohen, S., Stillwell, D.J., Kosinski, M., Rentfrow, P.J.: Musical preferences are linked to cognitive styles. PLOS ONE 10(7), e0131151 (2015)

    Article  Google Scholar 

  20. Greicius, M.D., Krasnow, B., Reiss, A.L., Menon, V.: Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc. Natl. Acad. Sci. 100(1), 253–258 (2002)

    Article  Google Scholar 

  21. Grinsted, A., Moore, J.C., Jevrejeva, S.: Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Process. Geophys. 11(5/6), 561–566 (2004)

    Article  Google Scholar 

  22. Imfeld, A., Oechslin, M.S., Meyer, M., Loenneker, T., Jancke, L.: White matter plasticity in the corticospinal tract of musicians: a diffusion tensor imaging study. NeuroImage 46(3), 600–607 (2009)

    Article  Google Scholar 

  23. Ingalhalikar, M., et al.: Sex differences in the structural connectome of the human brain. Proc. Natl. Acad. Sci. 111(2), 823–828 (2013)

    Article  Google Scholar 

  24. Laurienti, P.J., Joyce, K.E., Telesford, Q.K., Burdette, J.H., Hayasaka, S.: Universal fractal scaling of self-organized networks. Phys. A: Stat. Mech. Appl. 390(20), 3608–3613 (2011)

    Article  Google Scholar 

  25. Mohanty, R., Sethares, W.A., Nair, V.A., Prabhakaran, V.: Rethinking measures of functional connectivity via feature extraction. Sci. Rep. 10(1), 1298 (2020)

    Article  Google Scholar 

  26. Niranjan, D., Toiviainen, P., Brattico, E., Alluri, V.: Dynamic functional connectivity in the musical brain. In: Liang, P., Goel, V., Shan, C. (eds.) BI 2019. LNCS, vol. 11976, pp. 82–91. Springer, Cham (2019)

    Chapter  Google Scholar 

  27. North, A.C.: Individual differences in musical taste. Am. J. Psychol. 123(2), 199–208 (2010)

    Article  Google Scholar 

  28. North, A.C., Hargreaves, D.J., O’Neill, S.A.: The importance of music to adolescents. Br. J. Educ. Psychol. 70(2), 255–272 (2000)

    Article  Google Scholar 

  29. Paliwal, K., Agarwal, A., Sinha, S.S.: A modification over Sakoe and Chiba’s dynamic time warping algorithm for isolated word recognition. Sig. Process. 4(4), 329–333 (1982)

    Article  Google Scholar 

  30. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  31. Rentfrow, P.J., Gosling, S.D.: The do re MI’s of everyday life: the structure and personality correlates of music preferences. J. Pers. Soc. Psychol. 84(6), 1236–1256 (2003)

    Article  Google Scholar 

  32. Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. Int. J. Comput. Vision 40, 99–121 (2004)

    Article  MATH  Google Scholar 

  33. Saari, P., Burunat, I., Brattico, E., Toiviainen, P.: Decoding musical training from dynamic processing of musical features in the brain. Sci. Rep. 8(1), 708 (2018)

    Article  Google Scholar 

  34. Salvador, R., Suckling, J., Coleman, M.R., Pickard, J.D., Menon, D., Bullmore, E.: Neurophysiological architecture of functional magnetic resonance images of human brain. Cereb. Cortex 15(9), 1332–1342 (2005)

    Article  Google Scholar 

  35. Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379–423 (1948)

    Article  MathSciNet  MATH  Google Scholar 

  36. Shirer, W.R., Ryali, S., Rykhlevskaia, E., Menon, V., Greicius, M.D.: Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cereb. Cortex 22(1), 158–165 (2011)

    Article  Google Scholar 

  37. Simpson, S.L., Bowman, F.D., Laurienti, P.J.: Analyzing complex functional brain networks: fusing statistics and network science to understand the brain. Stat. Surv. 7, 1 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  38. Sørensen, T., Sørensen, T., Biering-Sørensen, T., Sørensen, T., Sorensen, J.T.: A method of establishing group of equal amplitude in plant sociobiology based on similarity of species content and its application to analyses of the vegetation on Danish commons (1948)

    Google Scholar 

  39. Telesford, Q.K., Simpson, S.L., Burdette, J.H., Hayasaka, S., Laurienti, P.J.: The brain as a complex system: using network science as a tool for understanding the brain. Brain Connectivity 1(4), 295–308 (2011)

    Article  Google Scholar 

  40. Toiviainen, P., Burunat, I., Brattico, E., Vuust, P., Alluri, V.: The chronnectome of musical beat. NeuroImage 216, 116191 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. International Institute of Information Technology, Hyderabad, Hyderabad, India

    Arihant Jain & Vinoo Alluri

  2. Finnish Centre for Interdisciplinary Music Research, Department of Music, Art, and Culture Studies, University of Jyvaskyla, Jyväskylä, Finland

    Petri Toiviainen

Authors
  1. Arihant Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Petri Toiviainen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vinoo Alluri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arihant Jain .

Editor information

Editors and Affiliations

  1. Stevens Institute of Technology, Hoboken, NJ, USA

    Feng Liu

  2. Lehigh University, Bethlehem, PA, USA

    Yu Zhang

  3. Maebashi Institute of Technology, Maebashi, Japan

    Hongzhi Kuai

  4. Boston University, Boston, MA, USA

    Emily P. Stephen

  5. Stevens Institute of Technology, Hoboken, NJ, USA

    Hongjun Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jain, A., Toiviainen, P., Alluri, V. (2023). Predicting Individual Differences from Brain Responses to Music: A Comparison of Functional Connectivity Measures. In: Liu, F., Zhang, Y., Kuai, H., Stephen, E.P., Wang, H. (eds) Brain Informatics. BI 2023. Lecture Notes in Computer Science(), vol 13974. Springer, Cham. https://doi.org/10.1007/978-3-031-43075-6_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-43075-6_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43074-9

  • Online ISBN: 978-3-031-43075-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation