Event-related desynchronization of eeg sensorimotor rhythms in hemiparesis post-stroke patients

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

Motor impairment is one of the most prevalent consequences of a stroke, necessitating the implementation of efficacious diagnostic and rehabilitative techniques. An evaluation of alterations in sensorimotor cortical activity during the processes of movement preparation and execution can provide valuable insights into the state of motor circuits following a stroke and the potential for recovery. The objective of the present study was to evaluate the spatiotemporal characteristics of event-related desynchronization (ERD) of sensorimotor EEG rhythms in patients with hemiparesis following a stroke, during movements with the paretic and healthy hands. A total of 19 patients with hemiparesis following a stroke participated in the study. An EEG was recorded while the subject performed a visual-motor task. The analysis focused on the event-related desynchronization in the alpha (6–15 Hz) and beta (15–30 Hz) bands. An asymmetry in the ERD was observed, with a predominant response in the intact hemisphere, regardless of the hand performing the movement. The magnitude of the ERD in the affected hemisphere demonstrated a correlation with the Fugl-Meyer score. Furthermore, a notable correlation was identified between the magnitude of beta-ERD in the affected hemisphere during movements of the healthy limb and the degree of motor function recovery. The results demonstrate the utility of ERD pattern assessment for diagnosing the state of sensorimotor networks after stroke. The detection of a correlation between the magnitude of ERD during movements of the healthy arm and the assessment of sensorimotor functions of the patient expands the possibilities of using EEG to assess patients even with complete absence of movements in the paretic limb.

About the authors

А. S. Medvedeva

Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology

Email: kolascoco@gmail.com
Russian Federation, Moscow

N. V. Syrov

Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology; Lomonosov Moscow State University

Author for correspondence.
Email: kolascoco@gmail.com
Russian Federation, Moscow; Moscow

L. V. Yakovlev

Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology; Lomonosov Moscow State University

Email: kolascoco@gmail.com
Russian Federation, Moscow; Moscow

Y. А. Alieva

Federal Center of Brain Research and Neurotechnologies of the federal medical biological agency; Pirogov Russian National Research Medical University

Email: kolascoco@gmail.com
Russian Federation, Moscow; Moscow

D. А. Petrova

Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology

Email: kolascoco@gmail.com
Russian Federation, Moscow

G. Е. Ivanova

Federal Center of Brain Research and Neurotechnologies of the federal medical biological agency; Pirogov Russian National Research Medical University

Email: kolascoco@gmail.com
Russian Federation, Moscow; Moscow

М. А. Lebedev

Lomonosov Moscow State University; Sechenov Institute of Evolutionary Physiology and Biochemistry of the Russian Academy of Sciences

Email: kolascoco@gmail.com
Russian Federation, Moscow; Saint-Petersburg

А. Ya. Kaplan

Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology; Lomonosov Moscow State University

Email: kolascoco@gmail.com
Russian Federation, Moscow; Moscow

References

  1. Chen R, Cohen LG, Hallett M (2002) Nervous system reorganization following injury. Neuroscience 111(4): 761–773. https://doi.org/10.1016/s0306-4522(02)00025-8
  2. Zhang H, Guo J, Liu J, Wang C, Ding H, Han T, Chen J, Yu C, Qin W (2024) Reorganization of Cortical Individualized Differential Structural Covariance Network is Associated with Regional Morphometric Changes and Functional Recovery in Chronic Subcortical Stroke. NeuroImage: Clinical. https://dx.doi.org/10.2139/ssrn.4868458
  3. Cauraugh J, Summers J (2005) Neural plasticity and bilateral movements: A rehabilitation approach for chronic stroke. Progress Neurobiol 75(5): 309–320. https://doi.org/10.1016/j.pneurobio.2005.04.001
  4. Brito R, Baltar A, Berenguer-Rocha M, Shirahige L, Rocha S, Fonseca A, Piscitelli D, Monte-Silva K (2021) Intrahemispheric EEG: A New Perspective for Quantitative EEG Assessment in Poststroke Individuals. Neural Plasticity 5664647. https://doi.org/10.1155/2021/5664647
  5. Finnigan SP, Walsh M, Rose SE, Chalk JB (2007) Quantitative EEG indices of sub-acute ischaemic stroke correlate with clinical outcomes. Clin Neurophysiol 118(11): 2525–2532. https://doi.org/10.1016/j.clinph.2007.07.021
  6. Pfurtscheller G (2000) Spatiotemporal ERD/ERS patterns during voluntary movement and motor imagery. Suppl Clin Neurophysiol 53: 196–198. https://doi.org/10.1016/s1567-424x(09)70157-6
  7. Syrov N, Vasilyev A, Solovieva А, Kaplan A (2022) Effects of the mirror box illusion on EEG sensorimotor rhythms in voluntary and involuntary finger movements. Neurosci Behav Physiol 52(6): 936–946. https://doi.org/10.1007/s11055-022-01318-z
  8. Stępień M, Conradi J, Waterstraat G, Hohlefeld FU, Curio G, Nikulin VV (2011) Event-related desynchronization of sensorimotor EEG rhythms in hemiparetic patients with acute stroke. Neurosci Lett 488(1): 17–21. https://doi.org/10.1016/j.neulet.2010.10.072
  9. Ezquerro S, Barios J, Bertomeu-Motos A, Diez J, Sanchez-Aparicio J, Donis-Barber L, Fernandez E, Garcia N (2019) Bihemispheric Beta Desynchronization During an Upper-Limb Motor Task in Chronic Stroke Survivors. Metrology: 371–379. https://doi.org/10.1007/978-3-030-19651-6_36
  10. Biryukova E, Frolov A, Kozlovskaya I, Bobrov P (2017) Robotic devices in postsroke rehabilitation. Zh Vyssh Nerv Deiat 67: 394–413. https://doi.org/10.7868/S004446771704-0017
  11. Khan MA, Das R, Iversen HK, Puthusserypady S (2020) Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation: From designing to application. Comput Biol Med 123: 103843. https://doi.org/10.1016/j.compbiomed.2020.103843
  12. Silvoni S, Ramos-Murguialday A, Cavinato M, Volpato C, Cisotto G, Turolla A, Piccione F, Birbaumer N (2011) Brain-computer interface in stroke: a review of progress. Clin EEG Neurosci 42(4): 245–252. https://doi.org/10.1177/155005941104200410
  13. Milani G, Antonioni A, Baroni A, Malerba P, Straudi S (2022) Relation Between EEG Measures and Upper Limb Motor Recovery in Stroke Patients: A Scoping Review. Brain Topogr 35(5–6): 651–666. https://doi.org/10.1007/s10548-022-00915-y
  14. Gebruers N, Truijen S, Engelborghs S, De Deyn PP (2014) Prediction of upper limb recovery, general disability, and rehabilitation status by activity measurements assessed by accelerometers or the Fugl-Meyer score in acute stroke. Am J Phys Med Rehabil 93(3): 245–252. https://doi.org/10.1097/phm.0000000000000045
  15. Lyle RC (1981) A performance test for assessment of upper limb function in physical rehabilitation treatment and research. Int J Rehabil Res 4(4): 483–492. https://doi.org/10.1097/00004356-198112000-00001
  16. Rankin J (1957) Cerebral vascular accidents in patients over the age of 60. II. Prognosis. Scott Med J 2(5): 200–215. https://doi.org/10.1177/003693305700200504
  17. Gramfort A, Luessi M, Larson E, Engemann DA, Strohmeier D, Brodbeck C, Goj R, Jas M, Brooks T, Parkkonen L, Hämäläinen M (2013) MEG and EEG data analysis with MNE-Python. Front Neurosci 7: 267. https://doi.org/10.3389/fnins.2013.00267
  18. Neuper C, Wörtz M, Pfurtscheller G (2006) ERD/ERS patterns reflecting sensorimotor activation and deactivation. Prog Brain Res 159: 211–222. https://doi.org/10.1016/S0079-6123(06)59014-4
  19. Seabold S, Perktold J (2010) Statsmodels: Econometric and Statistical Modeling with Python. Proc Python Sci Conf. https://doi.org/10.25080/Majora-92bf1922-011
  20. Chatrian GE, Petersen MC, Lazarte JA (1959) The blocking of the rolandic wicket rhythm and some central changes related to movement. Electroencephalogr Clin Neurophysiol 11(3): 497–510. https://doi.org/10.1016/0013-4694(59)90048-3
  21. Pfurtscheller G, Da Silva FL (1999) Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 110(11): 1842–1857. https://doi.org/10.1016/s1388-2457(99)00141-8
  22. Pfurtscheller G, Aranibar A, Wege W (1980) Changes in central EEG activity in relation to voluntary movement. II. Hemiplegic patients. Prog Brain Res 54: 491–495. https://doi.org/10.1016/S0079-6123(08)61665-9
  23. Nunez PL, Srinivasan R (2006) Electric fields of the brain: the neurophysics of EEG. Oxford University Press. USA.
  24. Gerloff C, Bushara K, Sailer A, Wassermann EM, Chen R, Matsuoka T, Waldvogel D, Wittenberg GF, Ishii K, Cohen LG, Hallett M (2006) Multimodal imaging of brain reorganization in motor areas of the contralesional hemisphere of well recovered patients after capsular stroke. Brain 129(3): 791–808. https://doi.org/10.1093/brain/awh713
  25. Li H, Huang G, Lin Q, Zhao J, Fu Q, Li L, Mao Y, Wei X, Yang W, Wang B, Zhang Z, Huang D (2020) EEG Changes in Time and Time-Frequency Domain During Movement Preparation and Execution in Stroke Patients. Front Neurosci 14: 827. https://doi.org/10.3389/fnins.2020.00827
  26. Starkey ML, Bleul C, Zörner B, Lindau NT, Mueggler T, Rudin M, Schwab ME (2012) Back seat driving: hindlimb corticospinal neurons assume forelimb control following ischaemic stroke. Brain 135(11): 3265–3281. https://doi.org/10.1093/brain/aws270
  27. Carey JR, Kimberley TJ, Lewis SM, Auerbach EJ, Dorsey L, Rundquist P, Ugurbil K (2002) Analysis of fMRI and finger tracking training in subjects with chronic stroke. Brain 125(4): 773–788. https://doi.org/10.1093/brain/awf091
  28. Werhahn KJ, Conforto AB, Kadom N, Hallett M, Cohen LG (2003) Contribution of the ipsilateral motor cortex to recovery after chronic stroke. Ann Neurol 54(4): 464–472. https://doi.org/10.1002/ana.10686
  29. Frenkel-Toledo S, Fridberg G, Ofir S, Bartur G, Lowenthal-Raz J, Granot O, Handelzalts S, Soroker N (2019) Lesion location impact on functional recovery of the hemiparetic upper limb. PloS one 14(7): e0219738. https://doi.org/10.1371/journal.pone.0219738
  30. Park W, Kwon GH, Kim Y (2016) EEG response varies with lesion location in patients with chronic stroke. J Neuroeng Rehabil 13: 21. https://doi.org/10.1186/s12984-016-0120-2
  31. Goncharova II, McFarland DJ, Vaughan TM, Wolpaw JR (2003) EMG contamination of EEG: spectral and topographical characteristics. Clin Neurophysiol 114(9): 1580–1593. https://doi.org/10.1016/s1388-2457(03)00093-2
  32. Bartur G, Pratt H, Soroker N (2019) Changes in mu and beta amplitude of the EEG during upper limb movement correlate with motor impairment and structural damage in subacute stroke. Clin Neurophysiol 130(9): 1644–1651. https://doi.org/10.1016/j.clinph.2019.06.008
  33. Remsik AB, Williams L, Jr Gjini K, Dodd K, Thoma J, Jacobson T, Walczak M, McMillan M, Rajan S, Young BM, Nigogosyan Z, Advani H, Mohanty R, Tellapragada N, Allen J, Mazrooyisebdani M, Walton LM, van Kan PLE, Kang TJ, Sattin JA, Prabhakaran V (2019) Ipsilesional Mu Rhythm Desynchronization and Changes in Motor Behavior Following Post Stroke BCI Intervention for Motor Rehabilitation. Front Neurosci 13: 53. https://doi.org/10.3389/fnins.2019.00053
  34. Ray AM, Figueiredo TDC, López-Larraz E, Birbaumer N, Ramos-Murguialday A (2020) Brain oscillatory activity as a biomarker of motor recovery in chronic stroke. Hum Brain Mapp 41(5): 1296–1308. https://doi.org/10.1002/hbm.24876
  35. Gueugneau N, Bove M, Avanzino L, Jacquin A, Pozzo T, Papaxanthis C (2013) Interhemispheric inhibition during mental actions of different complexity. PloS One 8(2): e56973. https://doi.org/10.1371/journal.pone.0056973
  36. Armatas CA, Summers JJ, Bradshaw JL (1994) Mirror movements in normal adult subjects. J Clin Exp Neuropsychol 16(3): 405–413. https://doi.org/10.1080/01688639408402651
  37. Vasiliev A, Liburkina S, Kaplan A (2016) Lateralization of EEG patterns in humans when imagining hand movements in a brain-computer interface. Zh Vyssh Nerv Deiat 66(33): 302–302. https://doi.org/10.7868/S0044467716030126 38
  38. Mokienko O, Chernikova L, Frolov A, Bobrov P (2013) Movement imagination and its practical application. Zh Vyssh Nerv Deiat 63(2): 195–195. https://doi.org/10.7868/S0044467713020056

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2024 Russian Academy of Sciences