Brain Network Connectivity and the Choice Motor Reaction in Combatants with Mild Traumatic Brain Injuries

We examined times (latencies) of two types of the sensorimotor (visuomotor) reactions, simple sensorimotor reaction (SSMR) and choice motor reaction (ChMR), in men (combatants) suffering from the consequences of mild traumatic brain injury (mTBI) during the Joint Forces Operation in the East of Ukraine (17 right-handed volunteers aged 27–43 years) and compared the respective values with those demonstrated by healthy control men (16 right-handed volunteers, 18–21 years). Combatants with mTBI were characterized by significantly greater values of the latency in the ChMR, mostly determined by considerably longer times necessary for decision-making processes. Results of the analysis of cerebral network connectivity based on the data of EEG recording, coherence measurements, and LORETA neurovisualization gave reasons to suppose that a large-scale synchronized frontoparietal neuronetwork functions in control subjects; this network is responsible for decision making according to the results of control, analysis of information, and coordination of various alternatives. Unlike this, an associative decision-making system mostly functions in combatants with mTBI; this system uses coding networks in the primary and secondary visual cortices; functioning of the above associative system is followed by the creation of imaginary phenomena and associations, which are then realized in motor acts.

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References

  1. 1.

    C. R. Gale, A. Harris, and I. J. Deary, “Reaction time and onset of psychological distress: the UK Health and Lifestyle Survey,” J. Epidemiol. Community Health, 70, No. 8, 813–817 (2016).

    Article  Google Scholar 

  2. 2.

    A. N. Leontiyev and Ye. P. Krinchik, “Processing of information by humans in a choice situation,” in Engineering Psychology, Moscow State University, Moscow, pp. 295–325 (1964).

  3. 3.

    O. Knyr, N. Filimonova, M. Makarchuk, et al., “Peculiarities of interregional interaction in the brain of combatants with craniocerebral traumas at the performance of a simple sensorimotor reaction,” Visn. Shevchenko Kyiv State Univ. Biol.,75, 50–54 (2018).

    Google Scholar 

  4. 4.

    A. P. Kulaichev, “On the informativity of coherence analysis,” Zh. Vyssh. Nerv. Deyat., 59, 766–775 (2009).

    Google Scholar 

  5. 5.

    R. D. Pascual-Marqui, M. Esslen, K. Kochi, and D. Lehmann, “Functional imaging with low-resolution brain electromagnetic tomography (LORETA): A review,” Methods Find Exp. Clin. Pharmacol., 24, Suppl. C, 91–95 (2002).

  6. 6.

    R. D. Pascual-Marqui, C. M. Michel, and D. Lehmann, “Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain,” Int. J. Psychophysiol., 18, No. 1, 49–65 (1994).

    CAS  Article  Google Scholar 

  7. 7.

    R. D. Pascual-Marqui, “Discrete, 3D distributed, linear imaging methods of electric neuronal activity. Part 1: exact, zero error localization,” Math. Phys. Biol. Phys. Neurons Cogn., 0710.3341, (2007).

  8. 8.

    C. E. Schroeder and P. Lakatos, “Low-frequency neuronal oscillations as instruments of sensory selection,” Trends Neurosci., 32, No. 1, 9–18 (2009).

    CAS  Article  Google Scholar 

  9. 9.

    E. M Bernat, L. D. Nelson, C. B. Holroyd, et al., “Separating cognitive processes with principal components analysis of EEG time-frequency distributions,” in Proceedings of the Society of Photo-Optical Instrumentation Engineers, 7074, 70740S (2008).

    Google Scholar 

  10. 10.

    Y. K. Kim, E. Park, A. Lee, et al.,“Changes in network connectivity during motor imagery and execution,” PLoS One,13, No. 1, e0190715 (2018).

    Article  Google Scholar 

  11. 11.

    D. Perruchoud, M. M. Murray, J. Lefebvre, and S. Ionta,“ Focal dystonia and the sensory-motor integrative loop for enacting (SMILE),” Front. Hum. Neurosci., 8, 458 (2014).

  12. 12.

    L. Avanzino, M. Tinazzi, S. Ionta, and M. Fiorio, “Sensory-motor integration in focal dystonia,” Neuropsychologia, 79, Pt. B, 288–300 (2015).

  13. 13.

    E. Shumskaya, M. A. J. van Gerven, D. G. Norris, et al., “Abnormal connectivity in the sensorimotor network predicts attention deficits in traumatic brain injury,” Exp. Brain Res., 235, No. 3, 799–807 (2017).

    Article  Google Scholar 

  14. 14.

    K. Laksari, M. Kurt, H. Babaee, et al., “Mechanistic insights into human brain impact dynamics through modal analysis,” Phys. Rev. Lett., 120, No. 13, 138101–138107 (2018).

    CAS  Article  Google Scholar 

  15. 15.

    S. Kleiven, “Predictors for traumatic brain injuries evaluated through accident reconstructions,” Stapp. Car Cras. J., 51, 81–114 (2007).

    Google Scholar 

  16. 16.

    C. Simões,O. Jensen, L. Parkkonen, and R. Hari, “Phase locking between human primary and secondary somatosensory cortices, ”Proc. Natl. Acad. Sci. USA, 100, No. 5, 2691–2694 (2003).

  17. 17.

    P. Sauseng, B. Griesmayr, R. Freunberger, and W. Klimesch, “Control mechanisms in working memory: A possible function of EEG theta oscillations,” Neurosci. Biobehav. Rev., 34, No. 7, 1015–1022 (2010).

    Article  Google Scholar 

  18. 18.

    C. Keinrath, S. Wriessnegger, G. R. Müller-Putz, and G. Pfurtscheller, “Post-movement beta synchronization after kinesthetic illusion, active and passive movements,” Int. J. Psychophysiol.,62, No. 2, 321–327 (2006).

  19. 19.

    G. Pfurtscheller and T. Solis-Escalante, “Could the beta rebound in the EEG be suitable to realize a “brain switch”?,” Clin. Neurophysiol. ,120, No. 1, 24–29 (2009).

  20. 20.

    E. T. Sciberras-Lim and A. J. Lambert,“Attentional orienting and dorsal visual stream decline: review of behavioral and EEG studies,”Front. Aging Neurosci., 9, 246 (2017).

  21. 21.

    E. Takahashi, O. Kenichi, and D. S. Kim, “Dissociation and convergence of the dorsal and ventral visual working memory streams in the human prefrontal cortex, ”Neuroimage, 65, 488–498 (2013).

    Article  Google Scholar 

  22. 22.

    J. Michely, L. J. Volz, F. Hoffstaedter, et al., “Network connectivity of motor control in the ageing brain, ” Neuroimage Clin., 18, 443–455 (2018).

    CAS  Article  Google Scholar 

  23. 23.

    H. O. Richter, P. Costello, S. R. Sponheim, et al., “Functional neuroanatomy of the human near/far response to blur cues: eye-lens accommodation/vergence to point targets varying in depth,” Eur. J. Neurosci., 20, No. 10, 2722–2732 (2004).

    Article  Google Scholar 

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Correspondence to M. Yu. Makarchuk.

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Filimonova, N.B., Makarchuk, M.Y., Zyma, I.G. et al. Brain Network Connectivity and the Choice Motor Reaction in Combatants with Mild Traumatic Brain Injuries. Neurophysiology 52, 201–211 (2020). https://doi.org/10.1007/s11062-020-09872-3

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Keywords

  • mild traumatic brain injury (mTBI)
  • simple sensorimotor reaction (SSMR)
  • choice motor reaction (ChMR)
  • concussion
  • EEG
  • coherence
  • LORETA
  • neurovisualization