Journal of Genetics

, 98:93 | Cite as

Connection between polymorphisms in HTR2A, TPH2, BDNF, TOMM40 genes and the successful mastering of human–computer interfaces

  • Yaroslav A. Turovsky
  • Artem P. GureevEmail author
  • Inna Yu. Vitkalova
  • Vasily N. Popov
Research Article


The development of human–computer interfaces in different individuals occur with different efficiencies, this is due to the individual characteristics of the genotype determined by the single-nucleotide polymorphism (SNP) of a person. Here, we checked the connection between the success of the acquisition of the brain-computer, eye-tracking, electromyographic, and respiratory interfaces and SNP of the TOMM40, BDNF, HTR2A and TPH2 genes. Here, we show that the T-allele in rs6313 of the HTR2A gene is associated with an increase in the number of correctly submitted commands of the electromyographic and eye-tracking interfaces. This is probably due to the fact that, the T-allele carriers decrease expression of this serotonin receptor. The decreased expression of HTR2A may be a reason for an increase in the number of accurately submitted commands. It was shown that the TT genotype of rs4290270 polymorphism was associated with an increase in the accuracy of work with the myographic interface. In addition, the association of subjective interfaces work time with polymorphisms rs429358 and rs2030324 was noted. Thus, the genotypic characteristics of individuals can be a predictive sign for the degree of success of mastering human–computer interfaces, which can allow to expand the understanding of training the neural mechanisms when working with this class of devices.


electromyographic interfaces eye-tracking interfaces genotyping serotonin polymorphism 



This research was supported by Russian Fund for Basic Research 17-29-02505 ofi_m and President grant for support of leading scientific school (Agreement 14.Z57.18.3451-NSh).


  1. Barton B., Treister A., Humphrey M., Abedi G., Cramer S. C. and Brewer A. A. 2014 Paradoxical visuomotor adaptation to reversed visual input is predicted by BDNF Val66Met polymorphism. J. Vis. 14, 4.CrossRefGoogle Scholar
  2. Borzunov S. V., Kurgalin S. D., Maksimov A. V. and Turovsky Ya. A. 2014 Estimation of the SSVEP-based brain-computer interface performance. J. Comput. Syst. Sci. Int. 1, 116–123.CrossRefGoogle Scholar
  3. Nam C. S., Nijholt A. and Lotte F. 2018 Brain–computer interfaces handbook: technological and theoretical advances, pp. 788. CRC Press, Oxford.CrossRefGoogle Scholar
  4. Glantz S. 1998 Biomedical statistics, pp. 459. Per. from English - M., Practice.Google Scholar
  5. Gong P., Li J., Wang J., Lei X., Chen D., Zhang K. et al. 2011 Variations in 5-HT2A influence spatial cognitive abilities and working memory. Can. J. Neurol. Sci. 38, 303–308.CrossRefGoogle Scholar
  6. Jakubczyk A., Wrzosek M., Lukaszkiewicz J., Sadowska-Mazuryk J., Matsumoto H., Sliwerska E. et al. 2012 The CC genotype in HTR2A T102C polymorphism is associated with behavioral impulsivity in alcohol-dependent patients. J. Psychiatr. Res. 46, 44–49.CrossRefGoogle Scholar
  7. Liebers D. T., Pirooznia M., Seiffudin F., Musliner K. L., Zandi P. P. and Goes F. S. 2016 Polygenic risk of schizophrenia and cognition in a population-based survey of older adults. Schizophr. Bull. 42, 984–991.CrossRefGoogle Scholar
  8. Lin C. H., Lin E. and Lane H. Y. 2017 Genetic biomarkers on age-related cognitive decline. Front. Psychiatry 8, 247.CrossRefGoogle Scholar
  9. Lotte F., Congedo M., Lécuyer A., Lamarche F. and Arnaldi B. 2007 A review of classification algorithms for EEG-based brain–computer interfaces. J. Neural Eng. 4, R1–R13.CrossRefGoogle Scholar
  10. Martin W. C. 2011 Upper limb prostheses: a review of the literature with a focus on myoelectric hands WorkSafeBC, pp. 90. Evidence-Based Practice Group. (
  11. Oporto G. H., Bornhardt T., Iturriaga V. and Salazar L. A. 2016 Genetic polymorphisms in the serotonergic system are associated with circadian manifestations of bruxism. J. Oral Rehabil. 43, 805–812.CrossRefGoogle Scholar
  12. Payton A. 2006 Investigating cognitive genetics and its implications for the treatment of cognitive deficit. Genes Brain Behav. 5, 44–53.CrossRefGoogle Scholar
  13. Racine S. E., Culbert K. M., Larson C. L. and Klump K. L. 2009 The possible influence of impulsivity and dietary restraint on associations between serotonin genes and binge eating. J. Psychiatr Res. 43, 1278–1286.CrossRefGoogle Scholar
  14. Reuter M., Esslinger C., Montag C., Lis S., Gallhofer B. and Kirsch P. 2008 A functional variant of the tryptophan hydroxylase 2 gene impacts working memory: a genetic imaging study. Biol. Psychol. 79, 111–117.CrossRefGoogle Scholar
  15. Runyon R. 1982 Nonparametric statistics. A contemporary approach, pp. 198. Finansy i statistica (in Russian).Google Scholar
  16. Slavutskaia M. V., Moiseeva V. V. and Shul’govskiĭ V. V. 2008 Attention and eye movements in human: psychophysiological concepts, neurophysiological models and EEG correlates. Zh Vyssh Nerv Deiat Im I P Pavlova. 58, 131–150.PubMedGoogle Scholar
  17. Smith R. M., Banks W., Hansen E., Sadee W. and Herman G. E. 2014 Family-based clinical associations and functional characterization of the serotonin 2A receptor gene (HTR2A) in autism spectrum disorder. Autism Res. 7, 459–467.CrossRefGoogle Scholar
  18. Smith R. M., Papp A. C., Webb A., Ruble C. L., Munsie L. M., Nisenbaum L. K. et al. 2013 Multiple regulatory variants modulate expression of 5-hydroxytryptamine 2A receptors in human cortex. Biol. Psychiatry 73, 546–554.CrossRefGoogle Scholar
  19. Ursini G., Cavalleri T., Fazio L., Angrisano T., Iacovelli L., Porcelli A. et al. 2016 BDNF rs6265 methylation and genotype interact on risk for schizophrenia. Epigenetics 11, 11–23.CrossRefGoogle Scholar
  20. Wang X., Wang Z., Wu Y., Hou Z., Yuan Y. and Hou G. 2015 Tryptophan hydroxylase 2 gene is associated with cognition in late-onset depression in a Chinese Han population. Neurosci. Lett. 600, 98–103.CrossRefGoogle Scholar
  21. Wolpaw J. R., Birbaumer N., McFarland D. J., Pfurtscheller G. and Vaughan T. M. 2002 Brain–computer interfaces for communication and control. Clin. Neurophysiol. 113, 767–779.CrossRefGoogle Scholar
  22. Gao X., Xu D., Cheng M. and Gao S. 2003 A BCI-based environmental controller for the motion- disabled. IEEE Trans. Neural Syst. Rehabili. Eng. 11, 137–140.CrossRefGoogle Scholar
  23. Xie B., Liu Z., Liu W., Jiang L., Zhang R., Cui D. et al. 2017 DNA methylation and tag SNPs of the BDNF gene in conversion of amnestic mild cognitive impairment into Alzheimer’s disease: A cross-sectional cohort study. J. Alzheimers Dis. 58, 263–274.CrossRefGoogle Scholar
  24. Yildiz S. H., Akilli A., Bagcioglu E., Ozdemir Erdogan M., Coskun K. S., Alpaslan A. H. et al. 2013 Association of schizophrenia with T102C (rs6313) and 1438 A/G (rs6311) polymorphisms of HTR2A gene. Acta Neuropsychiatr. 25, 342–348.CrossRefGoogle Scholar
  25. Zhu D., Bieger J., Molina G., Ronald M. and Aarts R. M. 2010 A survey of stimulation methods used in SSVEP-based BCIs. Comput. Intel. Neurosci. 2010, 1–12.CrossRefGoogle Scholar

Copyright information

© Indian Academy of Sciences 2019

Authors and Affiliations

  1. 1.Laboratory of Medical CyberneticsVoronezh State UniversityVoronezhRussia
  2. 2.V. A. Trapeznikov Institute of Control Sciences of Russian Academy of SciencesMoscowRussia
  3. 3.Department of Genetics, Cytology and BioengineeringVoronezh State UniversityVoronezhRussia
  4. 4.Voronezh State University of Engineering TechnologiesVoronezhRussia

Personalised recommendations