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Neural Correlation of Brain Activities and Gaming Using Functional Near-Infrared Spectroscopy and Iowa Gambling Task

  • Sagar Kora Venu
  • Roozbeh Sadeghian
  • Saeed Esmaili Sardari
  • Hadis Dashtestani
  • Amir Gandjbakhche
  • Siamak AramEmail author
Conference paper
  • 5 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1201)

Abstract

The aim of this research is to analyze brain activity using fNIRS and the Iowa Gambling Task to identify the characteristics of behavior in gaming. Previous studies showed that playing video games influences the functionality of the prefrontal cortex, suggesting that playing games affects cognition. Repeated-measures ANOVA was used to assess the pre-frontal cortex activity of the brain in the two regions of interest (left hemisphere, right hemisphere) during the experiment to determine the changes in prefrontal cortex activation levels in the regions of interest. The same statistical technique was used on the cognitive task to see if more cards from favorable decks were picked by participants as the task progressed. We found that during the completion of the cognitive task, both regions of interest were activated, specifically, the changes in concentration of oxy-hemoglobin in the left hemisphere was significantly higher than the right hemisphere, and participants tended to choose a significantly higher number of cards from the favorable decks during the end of the cognitive task.

Keywords

fNIRS Neuroimaging Iowa Gambling Task Gaming 

Notes

Acknowledgments

We acknowledge that this research was supported by funding from the Presidential Research Grant 2019 of the Harrisburg University of Science and Technology.

References

  1. 1.
    ESA: 2019 essential facts about the computer and video game industry (2019)Google Scholar
  2. 2.
    Kuss, D.J., Griffiths, M.D.: Internet gaming addiction: a systematic review of empirical research. Int. J. Ment. Health Addict. 10, 278–296 (2012)CrossRefGoogle Scholar
  3. 3.
    Petry, N.M., O’Brien, C.P.: Internet gaming disorder and the DSM-5. Addiction 108, 1186–1187 (2013)CrossRefGoogle Scholar
  4. 4.
    Kaptsis, D., King, D.L., Delfabbro, P.H., Gradisar, M.: Withdrawal symptoms in internet gaming disorder: a systematic review. Clin. Psychol. Rev. 43, 58–66 (2016)CrossRefGoogle Scholar
  5. 5.
    Diagnostic and Statistical Manual of Mental Disorders, 5th edn. DSM-5. American Psychiatric Association (2013)Google Scholar
  6. 6.
    American psychiatric association considers ‘video game addiction’ (2007)Google Scholar
  7. 7.
  8. 8.
    Gleich, T., Lorenz, R.C., Gallinat, J., Kühn, S.: Functional changes in the reward circuit in response to gaming-related cues after training with a commercial video game. NeuroImage 152, 467–475 (2017).  https://doi.org/10.1016/j.neuroimage.2017.03.032CrossRefGoogle Scholar
  9. 9.
    Wang, P., Zhu, X.-T., Qi, Z., Huang, S., Li, H.-J.: Neural basis of enhanced executive function in older video game players: an fMRI study. Frontiers Aging Neurosci. 9, 382 (2017)CrossRefGoogle Scholar
  10. 10.
    Wenger, E., Kühn, S., Verrel, J., Mårtensson, J., Bodammer, N.C., Lindenberger, U., Lövdén, M.: Repeated structural imaging reveals nonlinear progression of experience-dependent volume changes in human motor cortex. Cereb. Cortex 27, 2911–2925 (2017)Google Scholar
  11. 11.
    Ayaz, H., Onaral, B., Izzetoglu, K., Shewokis, P.A., McKendrick, R., Parasuraman, R.: Continuous monitoring of brain dynamics with functional near infrared spectroscopy as a tool for neuroergonomic research: empirical examples and a technological development. Frontiers Hum. Neurosci. 7, 871 (2013)CrossRefGoogle Scholar
  12. 12.
    Kim, H.Y., Seo, K., Jeon, H.J., Lee, U., Lee, H.: Application of functional near-infrared spectroscopy to the study of brain function in humans and animal models. Mol. Cells 40, 523 (2017)CrossRefGoogle Scholar
  13. 13.
    Herold, F., Wiegel, P., Scholkmann, F., Mueller, N.G.: Applications of functional near-infrared spectroscopy (fNIRS) neuroimaging in exercise–cognition science: a systematic, methodology-focused review. J. Clin. Med. 7, 466 (2018)CrossRefGoogle Scholar
  14. 14.
    Bunce, S.C., Izzetoglu, M., Izzetoglu, K., Onaral, B., Pourrezaei, K.: Functional near-infrared spectroscopy. IEEE Eng. Med. Biol. Mag. 25, 54–62 (2006)CrossRefGoogle Scholar
  15. 15.
    Pinti, P., Tachtsidis, I., Hamilton, A., Hirsch, J., Aichelburg, C., Gilbert, S., Burgess, P.W.: The present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience. Ann. New York Acad. Sci. (2018)Google Scholar
  16. 16.
    Izzetoglu, M., Bunce, S.C., Izzetoglu, K., Onaral, B., Pourrezaei, K.: Functional brain imaging using near-infrared technology. IEEE Eng. Med. Biol. Mag. 26, 38 (2007)CrossRefGoogle Scholar
  17. 17.
    Ekkekakis, P.: Illuminating the black box: investigating prefrontal cortical hemodynamics during exercise with near-infrared spectroscopy. J. Sport Exerc. Psychol. 31, 505–553 (2009)CrossRefGoogle Scholar
  18. 18.
    Obrig, H., Wenzel, R., Kohl, M., Horst, S., Wobst, P., Steinbrink, J., Thomas, F., Villringer, A.: Near-infrared spectroscopy: does it function in functional activation studies of the adult brain? Int. J. Psychophysiol. 35, 125–142 (2000)CrossRefGoogle Scholar
  19. 19.
    Bechara, A., Damasio, A.R., Damasio, H., Anderson, S.W.: Insensitivity to future consequences following damage to human prefrontal cortex. Cognition 50, 7–15 (1994)CrossRefGoogle Scholar
  20. 20.
    Aram, S., Levy, L., Patel, J.B., Anderson, A.A., Zaragoza, R., Dashtestani, H., Chowdhry, F.A., Gandjbakhche, A., Tracy, J.K.: The Iowa gambling task: a review of the historical evolution, scientific basis, and use in functional neuroimaging. SAGE Open. 9, 2158244019856911 (2019)CrossRefGoogle Scholar
  21. 21.
    Psychology software tools, inc. [E-prime 3.0] (2016)Google Scholar
  22. 22.
    Ayaz, H., Izzetoglu, M., Shewokis, P.A., Onaral, B.: Sliding-window motion artifact rejection for functional near-infrared spectroscopy. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, pp. 6567–6570. IEEE (2010)Google Scholar
  23. 23.
    Delpy, D.T., Cope, M., van der Zee, P., Arridge, S., Wray, S., Wyatt, J.: Estimation of optical pathlength through tissue from direct time of flight measurement. Phys. Med. Biol. 33, 1433 (1988)CrossRefGoogle Scholar
  24. 24.
    R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2018)Google Scholar
  25. 25.
    Singmann, H., Bolker, B., Westfall, J., Aust, F., Ben-Shachar, M.S.: Afex: analysis of factorial experiments (2019)Google Scholar
  26. 26.
    Lenth, R.: Emmeans: estimated marginal means, aka least-squares means (2019)Google Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

Authors and Affiliations

  • Sagar Kora Venu
    • 1
  • Roozbeh Sadeghian
    • 1
  • Saeed Esmaili Sardari
    • 1
  • Hadis Dashtestani
    • 2
  • Amir Gandjbakhche
    • 2
  • Siamak Aram
    • 1
    Email author
  1. 1.Harrisburg University of Science and TechnologyHarrisburgUSA
  2. 2.Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of HealthBethesdaUSA

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