ОРИГИНАЛЬНОЕ ИССЛЕДОВАНИЕ

Нейрофизиологическое исследование речевой функции у лиц, перенесших легкую форму COVID-19

С. А. Гуляев, Ю. А. Воронкова, Т. А. Абрамова, Е. А. Ковражкина
Информация об авторах

Федеральный центр мозга и нейротехнологий Федерального медико-биологического агентства, Москва, Россия

Для корреспонденции: Сергей Александрович Гуляев
ул. Островитянова, д. 1, стр. 10, г. Москва, 117997, Россия; ur.xednay@ssurgres

Информация о статье

Вклад авторов: С. А. Гуляев — анализ данных, написание текста, оформление; Ю. А. Воронкова, Т. А. Абрамова — получение данных; Е. А. Ковражкина — оформление.

Соблюдение этических стандартов: исследование одобрено этическим комитетом ФЦМН ФМБА России (протокол № 148-1 от 15 июня 2021 г.). Все лица приняли участие в эксперименте на добровольных началах, без дополнительного поощрения. Исследования эксперимента проводили сотрудники ФГБУ ФЦМН ФМБА России в рамках научной работы учреждения без привлечения сторонних средств.

Статья получена: 24.03.2022 Статья принята к печати: 04.05.2022 Опубликовано online: 25.05.2022
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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.