Features of slow-wave EEG phenomena in the early recovery period of ischemic stroke

Background. One of the significant problems of early rehabilitation of acute cerebrovascular accident is the development of hemodynamic disorders caused by changes in flows in the system of basal anastomoses. As a result, actively conducted physical rehabilitation may not only fail to achieve the expected effect, but also lead to the opposite result. A solution to this problem could be a method of dynamic monitoring of the state of cerebral hemoperfusion, which could be used, including in the context of rehabilitation measures, to monitor changes in the patient's condition during the physical load imposed on him. In our study, we examined the possibility of using the clinical electroencephalography method to solve this problem, since the technique is widely used in modern healthcare, does not require significant costs for the study and does not have an adverse effect on the patient.

Objective. The purpose of the presented work is to describe the possibility of studying rhythmic slow-wave phenomena associated with the development of local hemoperfusion disorders that occur with increased physical activity in patients in the early recovery period of ischemic stroke.

Materials and methods. We examined 24 people who had suffered an ischemic stroke (atherothrombotic variant) during the year, who had a Rankin index of 3. The average age of the examined was 57.3 years, Mo – 55, Me – 58, First quartile – 55, third – 61.3. Age range – 38 years. Minimum age – 35 years, maximum – 73 years.

Results. Our study found that during the first 6 months, physical impact on the affected limbs causes significant changes in cerebral hemodynamics, which can lead to the development of hemodynamic steal in adjacent areas of the cerebral cortex, including in the unaffected hemisphere. These data should be taken into account when developing physical rehabilitation programs for patients with stroke, and the EEG method itself can be successfully used for direct monitoring of cerebral hemoperfusion, including in the context of rehabilitation activities.

1. Zhirmunskaya E. A., Koltover A. N. Atlas of electroencephalography and stroke morphology. The medicine. Moscow, 1967. 91 s. (In Russ.)

2. Grindel O. M.; Rep. ed. V. S. Rusinov; Academy of Sciences of the USSR, Institute of Higher. nerve. activity and neurophysiology, Nauch. nerve physiology advice. systems. Human electroencephalogram in traumatic brain injury. M.: Nauka, 1988. 200 p.: ill.; 22 cm; ISBN 5-02-005268-X. (In Russ.)

3. Bekhtereva N. P. Biopotentials of the cerebral hemispheres in supratentorial tumors. Leningrad: Medgiz, 1960. (In Russ.)

4. Fadeeva T. N. Neurophysiological research methods in the diagnosis and complex treatment of gliomas of the cerebral hemispheres Bulletin of the Tambov University. Series: Natural and technical sciences. 2006; 4 (11): 457-466. (In Russ.)

5. Gnezditsky V. V. Inverse problem of EEG and clinical electroencephalography. M.: MEDpress-inform, 2004. 624 p. (In Russ.)

6. Kulaichev A. P. Computer electrophysiology and functional diagnostics. Ed. 4th, revised. and additional. M.: INFRA-M, 2007. Р. 178-230. (In Russ.)

7. Penfield W., Roberts L. Speech and brain mechanisms. L.: Medicine, 1964. 264 p. UDC 612.825.249+616.89=008.434. (In Russ.)

8. https://www.egi.com/images/stories/research/documents/Sensor_Digitization_Brochure_Oct_2016_MM_482.pdf.

9. Zhirmunskaya E. A. Bioelectrical activity of a healthy and diseased human brain. Рp. 224-265. Book. Guide to Physiology. Clinical physiology. Leningrad: The science, 1972. (In Russ.)

10. Rusinov V. S., Mayorchik V. E., Grindel O. M. and others. Clinical electroencephalography. Еd. V. S. Rusinova. M.: Medicine, 1973. 339 p. (In Russ.)

11. Clinical guidelines "Epilepsy and status epilepticus in adults and children" (approved by the Ministry of Health of the Russian Federation, 2022). (In Russ.)

12. Ryynänen O. R., Hyttinen J. A., Malmivuo J. A. Effect of measurement noise and electrode density on the spatial resolution of cortical potential distribution with different resistivity values for the skull. IEEE Trans Biomed Eng. 2006; 53 (9): 1851-1858. https://doi.org/10.1109/TBME.2006.873744. PMID: 16941841.

13. Lantz G., Grave de Peralta R., Spinelli L., Seeck M., Michel C. M. Epileptic source localization with high density EEG: how many electrodes are needed? Clin Neurophysiol. 2003; 114 (1): 63-9. https://doi.org/10.1016/s1388-2457(02)00337-1. PMID: 12495765.

14. Wójcik G. M. 3. Selected methods of quantitative analysis in electroencephalography. Simulations in Medicine: Computer-aided diagnostics and therapy, edited by Irena Roterman-Konieczna, Berlin, Boston: De Gruyter, 2020. Рp. 35-54. https://doi.org/10.1515/9783110667219-003.

15. Pascual-Marqui R. D., Esslen M., Kochi K., Lehmann D. Functional imaging with low-resolution brain electromagnetic tomography (LORETA): a review. Methods Find Exp Clin Pharmacol. 2002; 24 Suppl C: 91-95. PMID: 12575492.

16. Seeck M., Koessler L., Bast T., Leijten F., Michel C., Baumgartner C., et al. The standardized EEG electrode array of the IFCN. Clin Neurophysiol [Internet]. 2017; 128 (10): 2070-2077. http://dx.doi.org/10.1016/j.clinph.2017.06.254.

17. Rossini P. M., Di Iorio R., Bentivoglio M., Bertini G., Ferreri F., Gerloff C., Ilmoniemi R. J., Miraglia F., Nitsche M. A., Pestilli F., Rosanova M., Shirota Y., Tesoriero C., Ugawa Y., Vecchio F., Ziemann U., Hallett M. Methods for analysis of brain connectivity: An IFCN-sponsored review. Clin Neurophysiol. 2019; 130 (10): 1833-1858. https://doi.org/10.1016/j.clinph.2019.06.006. Epub 2019 Jul 2. PMID: 31401492.

18. Stergiadis C., Kostaridou V. D., Veloudis S., Kazis D., Klados M. A. A Personalized User Authentication System Based on EEG Signals. Sensors (Basel). 2022; 22 (18): 6929. https://doi.org/10.3390/s22186929. PMID: 36146276; PMCID: PMC9503240.

19. Romeo Z., Mantini D., Durgoni E., Passarini L., Meneghello F., Zorzi M. Electrophysiological signatures of resting state networks predict cognitive deficits in stroke. Cortex. 2021; 138: 59-71. https://doi.org/10.1016/j.cortex.2021.01.019. Epub 2021 Feb 12. PMID: 33677328.

20. Manganotti P., Furlanis G., Ajčević M., Polverino P., Caruso P., Ridolfi M., Pozzi-Mucelli R. A., Cova M. A., Naccarato M. CT perfusion and EEG patterns in patients with acute isolated aphasia in seizurerelated stroke mimics. Seizure. 2019; 71: 110-115. https://doi.org/10.1016/j.seizure.2019.07.005. Epub 2019 Jul 4. PMID: 31323445.

21. Delorme A., Sejnowski T., Makeig S. Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. NeuroImage. 2007; 4 (34). https://doi.org/10.1016/j.neuroimage.2006.11.004.

22. Tadel F., Baillet S., Mosher J. C., Pantazis D., Leahy R. M. Brainstorm: a user-friendly application for MEG/EEG analysis. Comput Intell Neurosci. 2011; 2011: 879716. https://www.doi.org/10.1155/2011/879716.

23. Poulsen C., Wakeman D. G., Atefi S. R., Luu P., Konyn A., Bonmassar G. Polymer thick film technology for improved simultaneous dEEG/MRI recording: Safety and MRI data quality. Magn Reson Med. 2017; 77 (2): 895-903. DOI: 10.1002/mrm.26116. Epub 2016 Feb 15. PMID: 26876960; PMCID: PMC4985508.

24. Poolman P., Frank R. M., Luu P., Pederson S. M., Tucker D. M. A singletrial analytic framework for EEG analysis and its application to target detection and classification. Neuroimage. 2008; 42 (2): 787-798. https://doi.org/10.1016/j.neuroimage.2008.03.031. Epub 2008 Apr 1. PMID: 18555700.

25. Sarter M., Fritschy J. M. Reporting statistical methods and statistical results in EJN. Eur J Neurosci. 2008; 28 (12): 2363-2364. https://doi.org/10.1111/j.1460-9568.2008.06581.x. PMID: 19087166.

26. Zhirmunskaya E. A. Clinical electroencephalography M.: MAYBI, 1991. 118 s. (In Russ.)

27. Petrova E. A, Georgievskaia N. A., Kichuk I. V., Shaklunova N. V., Savina M. A., Skvortsova V. I. Bioelectrical brain activity in different affective disorders in patients with cerebral stroke. Zhurnal Nevrologii i Psikhiatrii imeni S. S. Korsakova. 2010; 110 (6): 77-85. (In Russ.)

28. Stafeeva I. V., Subbotina N. S., Dudanov I. P. The state of bioelectrical activity of the brain after carotid endarterectomy in the early recovery period of ischemic stroke. Uchenye zapiski Petrozavodskogo gosudarstvennogo universiteta. 2014; 8-1 (145): 40-43. (In Russ.)

29. Stinear C. M. Prediction of motor recovery after stroke: advances in biomarkers. Lancet Neurol. 2017; 16 (10): 826-836. https://doi.org/10.1016/S1474-4422(17)30283-1. Epub 2017 Sep 12. PMID: 28920888.

30. Abiri R., Borhani S., Sellers E. W., Jiang Y., Zhao X. A comprehensive review of EEG-based brain-computer interface paradigms. J Neural Eng. 2019; 16 (1): 011001. https://doi.org/10.1088/1741-2552/aaf12e. Epub 2018 Nov 15. PMID: 30523919.

31. Guggisberg A. G., Koch P. J., Hummel F. C., Buetefisch C. M. Brain networks and their relevance for stroke rehabilitation. Clin Neurophysiol. 2019; 130 (7): 1098-1124. https://doi.org/10.1016/j.clinph.2019.04.004. Epub 2019 Apr 15. PMID: 31082786; PMCID: PMC6603430.

32. Sun R., Wong W. W., Gao J., Wong G. F., Tong R. K. Abnormal EEG Complexity and Alpha Oscillation of Resting State in Chronic Stroke Patients. Annu Int Conf IEEE Eng Med Biol Soc. 2021; 2021: 6053-6057. https://doi.org/10.1109/EMBC46164.2021.9630549. PMID: 34892497.

33. Vasquez H. E., Murlimanju B. V., Shrivastava A., et al. Intracranial collateral circulation and its role in neurovascular pathology. Egypt J Neurosurg. 2021; 2 (36). https://doi.org/10.1186/s41984-020-00095-6.

34. Phan T. G., Hilton J., Beare R., Srikanth V., Sinnott M. Computer modeling of anterior circulation stroke: proof of concept in cerebrovascular occlusion. Front Neurol. 2014; 5: 176. https://doi.org/10.3389/fneur.2014.00176. PMID: 25285093; PMCID: PMC4168699.

35. Gubskiy I. L., Logunova T. A., Nikogosova A. K., et al. Bilateral internal carotid artery occlusion: case report. Consilium Medicum. 2016; 18 (9): 17-20. 36. Li Z., Tremble S. M., Cipolla M. J. Implications for understanding ischemic stroke as a sexually dimorphic disease: the role of pial collateral circulations. Am J Physiol Heart Circ Physiol. 2018; 315 (6): H1703-H1712. https://doi.org/10.1152/ajpheart.00402.2018. Epub 2018 Sep 21. PMID: 30239233; PMCID: PMC6336971.