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

Мұқаба

Дәйексөз келтіру

Толық мәтін

Ашық рұқсат Ашық рұқсат
Рұқсат жабық Рұқсат берілді
Рұқсат жабық Тек жазылушылар үшін

Аннотация

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.

Толық мәтін

Рұқсат жабық

Авторлар туралы

А. Medvedeva

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

Email: kolascoco@gmail.com
Ресей, Moscow

N. Syrov

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

Хат алмасуға жауапты Автор.
Email: kolascoco@gmail.com
Ресей, Moscow; Moscow

L. Yakovlev

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

Email: kolascoco@gmail.com
Ресей, Moscow; Moscow

Y. Alieva

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

Email: kolascoco@gmail.com
Ресей, Moscow; Moscow

D. Petrova

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

Email: kolascoco@gmail.com
Ресей, Moscow

G. Ivanova

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

Email: kolascoco@gmail.com
Ресей, Moscow; Moscow

М. Lebedev

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

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

А. Kaplan

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

Email: kolascoco@gmail.com
Ресей, Moscow; Moscow

Әдебиет тізімі

  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

Қосымша файлдар

Қосымша файлдар
Әрекет
1. JATS XML
2. Fig. 1. Experimental session diagram. Rectangles represent individual blocks. “R” – blocks with right hand movements, “L” – blocks with left hand movements. Each block included the presentation of 30 visual stimuli, 15 for each button. The subject pressed only the target button in response to the flash of the corresponding light bulb. The appearance of the box with buttons and lights is shown.

Жүктеу (153KB)
3. Fig. 2. Dynamics of ERD/S in two groups of patients (LH stroke – a group of patients with left-hemisphere stroke; RH stroke – right-hemisphere stroke). (a) – ERD/S dynamics in leads C3 and C4 during movements of the right and left hands. The dynamics are presented in frequency-time coordinates. The diagrams for the conditions of movement of the affected limb are highlighted with a dark frame. (b) – averaged ERD/S values ​​in the alpha and beta ranges are presented separately for blocks with movements of the healthy and affected limbs. The graphs for ERD in leads above the affected hemisphere (C3 for the LH group and C4 for RH) are highlighted with a frame.

Жүктеу (329KB)
4. Fig. 3. Maps of the topographic distribution of alpha and beta-ERD and beta-ERS reactions for two groups of patients (LH stroke is a group of patients with left-hemisphere stroke, RH stroke is a group with right-hemisphere stroke). Maps are shown for blocks with movements of the healthy and paretic limbs. Darker values ​​on the map reflect higher values ​​of the reaction under study (more negative for ERD, more positive for ERS).

Жүктеу (212KB)
5. Fig. 4. Group dynamics of ERD/S reactions during movements of the healthy and paretic limbs in two groups of patients – with damage in the left (a) and right hemisphere (b). The values ​​for the leads above the damaged hemisphere are framed (C3 for the group with damage localized in the left hemisphere, and C4 for the group with damage in the right).

Жүктеу (137KB)
6. Fig. 5. (a) – assessment of the power of rhythmic activity in the alpha and beta ranges in two groups of patients in the leads above the affected and intact hemispheres. “LHS” – data for patients from the group with left-sided stroke, “RHS” – for patients from the group with right-sided stroke. (b) – graphs of the dependence of the alpha/beta value – ERD in the lead above the affected hemisphere and the assessment of sensorimotor skills according to the Fugl-Meyer scale. For the first two diagrams, the ERD values ​​are taken for blocks with movement of the paretic limb, for the rightmost “beta-ERD-ipsi” diagram, the values ​​are taken from the block with movement of the healthy arm.

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7. Appendix 1. Distribution of reaction speed for groups of patients and participants in the control group when moving the right and left hands.

Жүктеу (54KB)
8. Appendix 2. Results of the analysis of the desynchronization reaction in the control group of healthy volunteers. (a) – frequency-time diagrams of the desynchronization reaction associated with the movement of the right and left hands for participants in the control group. (b) – spatial distribution of alpha and beta desynchronization patterns during movements of the right and left hands for participants in the control group.

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9. Application
Жүктеу (217KB)

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