ORIGINAL RESEARCH

Neurophysiological assessment of speech function in individuals having a history of mild COVID-19

Gulyaev SA, Voronkova YuA, Abramova TA, Kovrazhkina EA
About authors

Federal Center of Brain Research and Neurotechnologies of the Federal Medical Biological Agency, Moscow, Russia

Correspondence should be addressed: Sergey A. Gulyaev
Ostrovitianova, 1, str. 10, Moscow, 117997, Russia; ur.xednay@ssurgres

About paper

Author contribution: Gulyaev SA — data analysis, manuscript writing, editing; Voronkova YuA, Abramova TA — data acquisition; Kovrazhkina EA — editing.

Compliance with ethical standards: the study was approved by the Ethics Committee of the Federal Center for Brain and Neurotechnologies of FMBA (protocol № 148-1 dated June 15, 2021). All the subjects took part in the experiment on a voluntary basis with no extra benefit. The experiment was studied by employees of the Federal Center for Brain and Neurotechnologies of FMBA within the limits of scientific work conducted by the institution with no third party funding.

Received: 2022-03-24 Accepted: 2022-05-04 Published online: 2022-05-25
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  1. Qi G, Zhao S, Ceder AA, Guan W, Yan X. Wielding and evaluating the removal composition of common artefacts in EEG signals for driving behaviour analysis. Accid Anal Prev. 2021; 159: 106223. DOI: 10.1016/j.aap.2021.106223. Epub 2021 Jun 10. PMID: 34119819.
  2. Dittman Z, Munia TTK, Aviyente S. Graph Theoretic Analysis of Multilayer EEG Connectivity Networks. Annu Int Conf IEEE Eng Med Biol Soc. 2021; 2021: 475–9. DOI: 10.1109/EMBC46164.2021.9629514. PMID: 34891336
  3. Pascual-Marqui RD, Michel CM, Lehmann D. Segmentation of brain electrical activity into microstates: model estimation and validation. IEEE Trans Biomed Eng. 1995; 42 (7): 658–65. Available from: https://doi.org/10.1109/10.391164.
  4. Grech R, Cassar T, Muscat J, Camilleri KP, Fabri SG, Zervakis M, et al. Review on solving the inverse problem in EEG source analysis. J Neuroeng Rehabil. 2008; 5: 25. DOI: 10.1186/1743-0003-5-25. PMID: 18990257; PMCID: PMC2605581.
  5. Hecker L, Rupprecht R, Tebartz Van Elst L, Kornmeier J. ConvDip: A Convolutional Neural Network for Better EEG Source Imaging. Front Neurosci. 2021; 15: 569918. DOI: 10.3389/ fnins.2021.569918. PMID: 34177438; PMCID: PMC8219905.
  6. Escaffre O, Borisevich V, Rockx B. Pathogenesis of Hendra and Nipah virus infection in humans. J Infect Dev Ctries. 2013; 7 (4): 308–11. Available from: https://doi.org/10.3855/jidc.3648.
  7. Wang GF, Li W, Li K. Acute encephalopathy and encephalitis caused by influenza virus infection. Curr Opin Neurol. 2010; 23 (3): 305–11. Available from: https://doi.org/10.1097/ wco.0b013e328338f6c9.
  8. Roy D, Ghosh R, Dubey S, Dubey MJ, Benito-León J, Kanti Ray B. Can Neurological and Neuropsychiatric Impacts of COVID-19 Pandemic. J Neurol Sci. 2021; 48 (1): 9–24. Available from: https://doi.org/10.1017/cjn.2020.173
  9. Song E, Zhang C, Israelow B, Lu-Culligan A, Prado AV, Skriabine S, et al. Neuroinvasion of COVID-19 in human and mouse brain. J Exp Med. 2021; 218 (3): e20202135. Available from: https://doi. org/10.1084/jem.20202135
  10. Moriguchi T, Harii N, Goto J, et al. A first case of meningetis/ encephalitis associated with SARS-Coronavirus-2. Int J Infect Dis. 2020; 94: 55–58. Available from: https://doi.org/10.1016/j. ijid.2020.03.06
  11. Ye M, Ren Y, Lv T. Encephalitis as a clinical manifestation of COVID-19. Brain Behav Immun. 2020; S0889–1591 (20): 30465– 7. Available from: https://doi.org/10.1016/j.bbi.2020.04.017.
  12. Duong L, Xu P, Liu A. Meningoencephalitis without respiratory failure in a young female patient with COVID-19 infection in Downtown Los Angeles, early April 2020. Brain Behav Immun. 2020; 87: 33. Available from: https://doi.org/10.1016/j. bbi.2020.04.024.
  13. Helms J, Kremer S, Merdji H, Clere-Jehl R, Schenck M, Kummerlen C, et al. Neurologic Features in Severe COVID-19 Infection. N Engl J Med. 2020; 382 (23): 2268–70. Available from: https://doi.org/10.1056/NEJMc2008597.
  14. Puelles VG, Lütgehetmann M, Lindenmeyer MT, Sperhake JP, Wong MN, Allweiss L, et al. Multiorgan and Renal Tropism of COVID-19. N Engl J Med. 2020; 383 (6): 590–2. Available from: https://doi.org/10.1056/NEJMc2011400.
  15. Solomon IH, Normandin E, Bhattacharyya S, Mukerji SS, Keller K, Ali AS, et al. Neuropathological Features of Covid-19. N Engl J Med. 2020; 383 (10): 989–92. Available from: https://doi.org/10.1056/ NEJMc2019373.
  16. Troyer EA, Kohn JN, Hong S. Are we facing a crashing wave of neuropsychiatric sequelae of COVID-19? Neuropsychiatric symptoms and potential immunologic mechanisms. Brain Behav Immun. 2020; 87: 34–39. Available from: https://doi. org/10.1016/j.bbi.2020.04.027
  17. Rogers JP, Chesney E, Oliver D, et al. Psychiatric and neuropsychiatric presentations associated with severe coronavirus infections: a systematic review and meta-analysis with comparison to the COVID-19 pandemic. Lancet Psychiatry. 2020; 7 (7): 611–27. Available from: https://doi.org/10.1016/ S2215-0366(20)30203-0.
  18. Kubota T, Gajera PK, Kuroda N. Meta-analysis of EEG findings in patients with COVID-19. Epilepsy Behav. 2021; 115: 107682. Available from: https://doi.org/10.1016/j.yebeh.2020.107682.
  19. Petrescu AM, Taussig D, Bouilleret V Electroencephalogram (EEG) in COVID-19: A systematic retrospective study. Neurophysiol Clin. 2020; 50 (3): 155–65. Available from: https://doi.org/10.1016/j. neucli.2020.06.001.
  20. Pinto TCC, Machado L, Bulgacov TM, Rodrigues-Júnior AL, Costa MLG, Ximenes RCC, et al. Is the Montreal Cognitive Assessment (MoCA) screening superior to the Mini-Mental State Examination (MMSE) in the detection of mild cognitive impairment (MCI) and Alzheimer's Disease (AD) in the elderly? Int Psychogeriatr. 2019; 31 (4): 491–504. Available from: https://doi. org/10.1017/S1041610218001370.
  21. Mishkin M, Ungerleider LG. Contribution of striate inputs to the visuospatial functions of parieto-preoccipital cortex in monkeys Behav Brain Res. 1982; 6 (1): 57–77. Available from: https://doi. org/10.1016/0166-4328(82)90081-x.
  22. Lehmann D, Strik WK, Henggeler B, Koenig T, Koukkou M. Brain electric microstates and momentary conscious mind states as building blocks of spontaneous thinking: I. Visual imagery and abstract thoughts. Int J Psychophysiol. 1998; 29 (1): 1–11. Available from: https://doi.org/10.1016/s0167-8760(97)00098-6.
  23. Michel CM, Koenig T. EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review. Neuroimage. 2018; 180 (Pt B): 577–93. Available from: https:// doi.org/10.1016/j.neuroimage.2017.11.062.
  24. Van De Ville D, Britz J, Michel CM. EEG microstate sequences in healthy humans at rest reveal scale-free dynamics Proceedings of the National Academy of Sciences. 2010; 107 (42): 18179–84; Available from: https://doi.org/10.1073/pnas.1007841107.
  25. Pascual-Marqui RD, Michel CM, Lehmann D. Segmentation of brain electrical activity into microstates: model estimation and validation. IEEE Trans Biomed Eng. 1995; 42 (7): 658–65. Available from: https://doi.org/10.1109/10.391164
  26. Vitali P, et al. Integration of multimodal neuroimaging methods: a rationale for clinical applications of simultaneous EEG-fMRI Funct Neurol. 2015. PMID: 26214023.
  27. Sarter M, Fritschy JM. Reporting statistical methods and statistical results in EJN. Eur J Neurosci. 2008; 28 (12): 2363–4. Available from: https://doi.org/10.1111/j.1460-9568.2008.06581.x.