Features of components N2 and P300 of auditory evoked potential dependent on the level of internet addiction in adolescents

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Abstract

INTRODUCTION: Due to the popularization of digital technologies, people have begun to spend more time on the internet. Existing studies show mixed results about the impact of internet use on attention, cognitive control, and other cognitive functions.

AIM: To evaluate the components of the event-related potentials (ERPs) P300 and N2 in practically healthy adolescents aged 16–17 years with varying levels of internet addiction risk and a stable pattern of internet addiction (IA).

MATERIAL AND METHODS: The study involved healthy young people aged 16–17 years old who attended Simferopol city school.The Chen Internet addiction scale was used to access the level of IА in the Russian version of V.L. Malygin and K.A. Feklisov. The registration of the ERPs P300 and N2 components was carried out with the use of an electroencephalograph “Neuron-Spectrum-4/VPM” (Neurosoft, Russia). An auditory oddball paradigm was used.

RESULTS: In young men, no differences in the N2 and P300 latency were observed across the groups. There was an elongation of the N2 latency in the frontal and central regions of the brain in girls with a proclivity for IА, indicating a slower primary identification and classification of stimuli. In girls with a stable pattern of IА, there was an increase in the N2 latency in the central, left frontal, and right medial temporal regions, as well as an increase in the P300 latency in the frontal, central, and parietal right parts of the brain, compared to girls with minimal risk of IА, indicating the need for more time to identify the stimulus and make a decision.

CONCLUSIONS: An increase in the N2 latency has already been reported in girls with a proclivity for IА, which can be used to predict the development of IА and for prevention.

About the authors

Elena V. Krivonogova

N. Laverov federal center for integrated Arctic research

Email: elena200280@mail.ru
ORCID iD: 0000-0002-2323-5246
SPIN-code: 9022-9696
Scopus Author ID: 25937374900
ResearcherId: K-6121-2018

Cand. Sci. (Biol.), senior researcher

Russian Federation, 23 naberezhnaya Severnoy Dviny, 163069, Arkhangelsk

Olga V. Krivonogova

N. Laverov federal center for integrated Arctic research

Email: ja.olga1@gmail.com
ORCID iD: 0000-0002-7267-8836
SPIN-code: 1086-3008
Scopus Author ID: 57215215130
ResearcherId: AAC-3160-2022

Cand. Sci. (Biol.), researcher

Russian Federation, 23 naberezhnaya Severnoy Dviny, 163069, Arkhangelsk

Lilia V. Poskotinova

N. Laverov federal center for integrated Arctic research

Author for correspondence.
Email: liliya200572@mail.ru
ORCID iD: 0000-0002-7537-0837
SPIN-code: 3148-6180
Scopus Author ID: 24280182100
ResearcherId: K-3719-2012

Dr. Sci. (Biol.), Cand. Sci. (Med.), associate professor, chief researcher

Russian Federation, 23 naberezhnaya Severnoy Dviny, 163069, Arkhangelsk

References

  1. Firth J, Torous J, Stubbs B, et al. The “online brain”: how the Internet may be changing our cognition. World Psychiatry. 2019;18(2):119–129. doi: 10.1002/wps.20617
  2. Green CP, Mao L, O’Sullivan V. Internet usage and the cognitive function of retirees. Journal of economic behavior & organization. 2021;190:747–767. doi: 10.1016/j.jebo.2021.08.013
  3. Vedechkina M, Borgonovi F. A review of evidence on the role of digital technology in shaping attention and cognitive control in children. Front Psychol. 2021;12:611155. doi: 10.3389/fpsyg.2021.611155
  4. Firth JA, Torous J, Firth J. Exploring the impact of internet use on memory and attention processes. Int J Environ Res Public Health. 2020;17(24):9481. doi: 10.3390/ijerph17249481
  5. Park M, Choi JS, Park SM, et al. Dysfunctional information processing during an auditory event-related potential task in individuals with Internet gaming disorder. Transl Psychiatry. 2016;6(1):e721. doi: 10.1038/tp.2015.215
  6. Klochkova OI, Gnezditskiy VV. Use of cognitive evoked potentials (p300) as an approach to assessing the frequency of possible requests towards the working memory of players during computer games. Human physiology. 2018;44(1):15–23. doi: 10.7868/S0131164618010034
  7. Gnezditskiy VV, Korepina OS, Chatskaya AV, Klochkova OI. Memory, cognition and the endogenous evoked potentials of the brain: the estimation of the disturbance of cognitive functions and capacity of working memory without the psychological testing. Usp Fiziol Nauk. 2017;48(1):3–23. (In Russ).
  8. Malygin VL, Feklisov KA, Iskandirova AB, Antonenko AA. Methodological approaches to early detection of Internet addicted behavior. Meditsinskaya psikhologiya v Rossii. 2011;6(11). Availablе from: http://medpsy.ru/mprj/archiv_global/2011_6_11/nomer/nomer03.php. Date of access: 14.11.2021. (In Russ).
  9. Yu H, Zhao X, Li N, Wang M, Zhou P. Effect of excessive Internet use on the time–frequency characteristic of EEG. Progress in Natural Science. 2009;19(10):1383–1387. doi: 10.1016/j.pnsc.2008.11.015
  10. Krokhine SN, Ewers NP, Mangold KI, et al. N2b reflects the cognitive changes in executive functioning after concussion: a scoping review. Front Hum Neurosci. 2020;14:601370. doi: 10.3389/fnhum.2020.601370
  11. Folstein JR, Van Petten C. Influence of cognitive control and mismatch on the N2 component of the ERP: a review. Psychophysiology. 2008;45(1):152–170. doi: 10.1111/j.1469-8986.2007.00602.x
  12. Buzzell GA, Fedota JR, Roberts DM, McDonald CG. The N2 ERP component as an index of impaired cognitive control in smokers. Neurosci Lett. 2014;563:61–65. doi: 10.1016/j.neulet.2014.01.030
  13. Porjesz B, Begleiter H. Event-related potentials and cognitive function in alcoholism. Alcohol Health Res World. 1995;19(2): 108–112.
  14. Lenroot RK, Giedd JN. Sex differences in the adolescent brain. Brain Cogn. 2010;72(1):46–55. doi: 10.1016/j.bandc.2009.10.008
  15. Cerniglia L, Zoratto F, Cimino S, et al. Internet Addiction in adolescence: neurobiological, psychosocial and clinical issues. Neurosci Biobehav Rev. 2017;76(Pt A):174–184. doi: 10.1016/j.neubiorev.2016.12.024
  16. Fitzroy AB, Krizman J, Tierney A, Agouridou M, Kraus N. Longitudinal maturation of auditory cortical function during adolescence. Front Hum Neurosci. 2015;9:530. doi: 10.3389/fnhum.2015.00530
  17. Peters A. Structural changes in the normally aging cerebral cortex of primates. Prog Brain Res. 2002;136:455–465. doi: 10.1016/s0079-6123(02)36038-2
  18. Tomé D, Barbosa F, Nowak K, Marques-Teixeira J. The development of the N1 and N2 components in auditory oddball paradigms: a systematic review with narrative analysis and suggested normative values. J Neural Transm (Vienna). 2015;122(3):375–391. doi: 10.1007/s00702-014-1258-3
  19. Farrar DC, Mian AZ, Budson AE, Moss MB, Killiany RJ. Functional brain networks involved in decision-making under certain and uncertain conditions. Neuroradiology. 2018;60(1):61–69. doi: 10.1007/s00234-017-1949-1
  20. Gajardo-Vidal A, Lorca-Puls DL, Hope TMH, et al. How right hemisphere damage after stroke can impair speech comprehension. Brain. 2018;141(12):3389–3404. doi: 10.1093/brain/awy270
  21. Zhu Y, Zhang H, Tian M. Molecular and functional imaging of internet addiction. Biomed Res Int. 2015;2015:378675.
  22. doi: 10.1155/2015/378675
  23. Li W, Li Y, Yang W, et al. Brain structures and functional connectivity associated with individual differences in Internet tendency in healthy young adults. Neuropsychologia. 2015;70:134–144. doi: 10.1016/j.neuropsychologia.2015.02.019
  24. Dong G, Potenza MN. Internet searching and memory processing during a recollection fMRI task: evidence from pseudo recollected trials. J Technol Behav Sci. 2016;1(1-14):32–36. doi: 10.1007/s41347-016-0002-2
  25. Sakharova VG, Shelkovskikh AI. The experience of using the questionnaire S.-Kh. Chena in the study of the image of the world in Internet-addicted young men. Lichnost’ v ekstremal’nykh usloviyakh i krizisnykh situatsiyakh zhiznedeyatel’nosti. 2015;5:539–549. (In Russ).
  26. Kuss DJ, Pontes HM, Griffiths MD. Neurobiological correlates in internet gaming disorder: a systematic literature review. Front Psychiatry. 2018;9:166. doi: 10.3389/fpsyt.2018.00166
  27. Fontes R, Ribeiro J, Gupta DS, et al. Time perception mechanisms at central nervous system. Neurol Int. 2016;8(1):5939. doi: 10.4081/ni.2016.5939
  28. Brand M, Young KS, Laier C. Prefrontal control and internet addiction: a theoretical model and review of neuropsychological and neuroimaging findings. Front Hum Neurosci. 2014;8:375. doi: 10.3389/fnhum.2014.00375
  29. Xin J, Zhang Y, Tang Y, Yang Y. Brain differences between men and women: evidence from deep learning. Front Neurosci. 2019;13:185. doi: 10.3389/fnins.2019.00185
  30. Rabinowicz T, Petetot JM, Gartside PS, et al. Structure of the cerebral cortex in men and women. J Neuropathol Exp Neurol. 2002;61(1):46–57. doi: 10.1093/jnen/61.1.46

Supplementary files

Supplementary Files
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1. JATS XML
2. Fig. 1. An example of the ERP P300 in the central region of the brain: a — subject A. from the group with minimal risk to IA (N2 latency=184 ms, P300 latency=284 ms, amplitude — 11 mkV); b — subject B. from the group with moderate risk of developing IA (N2 latency=205 ms, LV P300 latency=297 ms, amplitude — 10 mkV); c — the subject M. from the group with Internet addiction (N2 latency=210 ms, P300 latency=315 ms, amplitude — 12 mkV).

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Copyright (c) 2022 Krivonogova E.V., Krivonogova O.V., Poskotinova L.V.

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