Dynamics of EEG synchronization and desynchronization when performing real and imagined hand reaching

Capa

Citar

Texto integral

Acesso aberto Acesso aberto
Acesso é fechado Acesso está concedido
Acesso é fechado Somente assinantes

Resumo

The work investigates spatial and temporal EEG patterns during real and imagined execution of hand reaching. Six independent sources of electrical activity were identified in the EEG recordings. The sources corresponded to the premotor areas, supplementary motor area, primary motor areas, and posterior parietal cortex. Their activation patterns in the alpha and beta range were studied using a continuous wavelet transform. The main differences between real and imagined movement are found in the activation of primary motor and premotor areas. Asymmetry in activation of primary motor areas was observed only during the imaginary movements. Desynchronization in premotor areas of both the alpha and beta ranges, suggesting their activation, accompanied the imaginary movements throughout their course. On the other hand, hypersynchronization was observed in premotor areas during real movement, which likely corresponds to inhibition, while desynchronization was observed in the latent period, 1.5 seconds before the start of movement. Thus, an imaginary movement bears the features of planning a real movement.

Texto integral

Acesso é fechado

Sobre autores

M. Kurgansky

Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences

Autor responsável pela correspondência
Email: m-kurg@yandex.ru
Rússia, Moscow

M. Isaev

Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences; Pirogov Russian National Research Medical University

Email: m-kurg@yandex.ru
Rússia, Moscow; Moscow

P. Bobrov

Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences; Pirogov Russian National Research Medical University

Email: m-kurg@yandex.ru
Rússia, Moscow; Moscow

Bibliografia

  1. Мокиенко О.А., Черникова Л.А., Фролов А.А., Бобров П.Д. Воображение движения и его практическое применение. Журнал высшей нервной деятельности им. И.П. Павлова. 2013. 63 (2): 195–195.
  2. Столбков Ю.К., Мошонкина Т.Р., Орлов И.В., Козловская И.Б., Герасименко Ю.П. Воображаемые движения как средство совершенствования и реабилитации моторных функций. Успехи физиологических наук. 2018. 49 (2): 45–59.
  3. Фролов А.А., Бирюкова Е.В., Бобров П.Д., Мокиенко О.А., Платонов А.К., Пряничников В.Е., Черникова Л.А. Принципы нейрореабилитации, основанные на использовании интерфейса “мозг–компьютер” и биологически адекватного управления экзоскелетоном. Физиология человека. 2013. 39: 99–113.
  4. Arts L.P., van den Broek E.L. The fast continuous wavelet transformation (fCWT) for real-time, highquality, noise-resistant time–frequency analysis. Nature Computational Science. 2022. 2: 47–58.
  5. Desmurget M., Sirigu A. A parietal-premotor network for movement intention and motor awareness. Trends in cognitive sciences. 2009. 13 (10): 411–419.
  6. Elliott D., Lyons J., Hayes S.J., Burkitt J.J., Roberts J.W., Grierson L.E. et al. The multiple process model of goaldirected reaching revisited. Neuroscience & Biobehavioral Reviews. 2017. 72: 95–110.
  7. Frank C., Kraeutner S.N., Rieger M., Boe S.G. Learning motor actions via imagery – perceptual or motor learning? Psychological Research. 2023: 3752889.
  8. Frolov A., Bobrov P., Biryukova E., Isaev M., Kerechanin Y., Bobrov D., Lekin A. Using multiple decomposition methods and cluster analysis to find and categorize typical patterns of EEG Activity in motor imagery brain–computer interface experiments. Frontiers in Robotics and AI. 2020. 7: 88.
  9. Gibson R.M., Chennu S., Owen A.M., Cruse D. Complexity and familiarity enhance single-trial detectability of imagined movements with electroencephalography. Clinical Neurophysiology. 2014. 125 (8): 1556–1567.
  10. Glover S., Bibby E., Tuomi E. Executive functions in motor imagery: support for the motor-cognitive model over the functional equivalence model. Experimental brain research. 2020. 238: 931–944.
  11. Grosse-Wentrup M., Liefhold C., Gramann K., Buss M. Beamforming in noninvasive brain-computer interfaces. IEEE Trans Biomed Eng. 2009. 56 (4): 1209–1219.
  12. Hardwick R.M., Caspers S., Eickhoff S.B., Swinnen S.P. Neural correlates of action: Comparing meta-analyses of imagery, observation, and execution. Neuroscience & Biobehavioral Reviews. 2018. 94: 31–44.
  13. Hetu S., Gregoire M., Saimpont A., Coll M.P., Eugene F., Michon P.E., Jackson P.L. The neural network of motor imagery: an ALE meta-analysis. Neurosci Biobehav Rev. 2013. 37 (5): 930–949.
  14. Hramov A.E., Maksimenko V.A., Pisarchik A.N. Physical principles of brain–computer interfaces and their applications for rehabilitation, robotics and control of human brain states. Physics Reports. 2021. 918: 1–133.
  15. Kraeutner S.N., McArthur J.L., Kraeutner P.H., Westwood D.A., Boe S.G. Leveraging the effector independent nature of motor imagery when it is paired with physical practice. Scientific reports. 2020. 10 (1): 21335.
  16. La Fougere C., Zwergal A., Rominger A., Förster S., Fesl G., Dieterich M. et al. Real versus imagined locomotion: a [18F]-FDG PET-fMRI comparison. Neuroimage. 2010. 50 (4): 1589–1598.
  17. Lee T.-W., Girolami M., Sejnowski T.J. Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural computation. 1999. 11 (2): 417–441.
  18. McFarland D.J., Miner L.A., Vaughan T.M., Wolpaw J.R. Mu and beta rhythm topographies during motor imagery and actual movements. Brain topography. 2000. 12 (3): 177–186.
  19. Metais A., Muller C.O., Boublay N., Breuil C., Guillot A., Daligault S. et al. Anodal tDCS does not enhance the learning of the sequential finger-tapping task by motor imagery practice in healthy older adults. Frontiers in Aging Neuroscience. 2022. 14: 1060791.
  20. Nakagawa K., Kawashima S., Fukuda K., Mizuguchi N., Muraoka T., Kanosue K. Constraints on hand-foot coordination associated with phase dependent modulation of corti-cospinal excitability during motor imagery. Frontiers in human neuroscience. 2023. 17: 1133279.
  21. Oostenveld R., Fries P., Maris E., Schoffelen J.-M. FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Computational Intelligence and Neuroscience. 2011. 2011: 156869.
  22. Pfurtscheller G., Linortner P., Winkler R., Korisek G., Müller-Putz G. Discrimination of motor imageryinduced EEG patterns in patients with complete spinal cord injury. Computational Intelligence and Neuroscience. 2009. 2009: 104180.
  23. Pfurtscheller G., Neuper C. Motor imagery activates primary sensorimotor area in humans. Neurosci Lett. 1997. 239 (2–3): 65–68.
  24. Rakusa M., Busan P., Battaglini P.P., Zidar J. Separating the Idea from the Action: A sLORETA Study. Brain Topogr. 2018. 31 (2): 228–241.
  25. Thrift A.G., Howard G., Cadilhac D.A., Howard V.J., Rothwell P.M., Thayabaranathan T. et al. Global stroke statistics: An update of mortality data from countries using a broad code of “cerebrovascular diseases”. Int J Stroke. 2017. 12 (8): 796–801.
  26. Villa-Berges E., Laborda Soriano A.A., Lucha-Lopez O., Tricas-Moreno J.M., Hernandez-Secorun M., GomezMartinez M., Hidalgo-Garcia C. Motor imagery and mental practice in the subacute and chronic phases in upper limb rehabilitation after stroke: a systematic review. Occupational Therapy International. 2023. 2023: 3752889.
  27. Wheaton L.A., Yakota S., Hallett M. Posterior parietal negativity preceding self-paced praxis movements. Experimental brain research. 2005. 163: 535–539.

Arquivos suplementares

Arquivos suplementares
Ação
1. JATS XML
Baixar
2. Fig. 1. Periods of statistically significant synchronization (light gray) and desynchronization (dark gray) of the selected EEG components during the real or imaginary movements on the go signal. The zero time mark corresponds to the go signal. Left panel refers to reaching, right panel refers to aimless movements.

Baixar (266KB)
Metadados
3. Fig. 2. Periods of statistically significant synchronization (light gray) and desynchronization (dark gray) of the selected EEG components during cued movements. The zero time mark corresponds to the beginning of the movement. Left panel refers to reaching, right panel refers to aimless movements.

Baixar (184KB)
Metadados
4. Fig. 3. Averaged patterns of synchronization and desynchronization of the selected EEG components in the course of real or imaginary movements on go signal. The zero time mark corresponds to the go signal. Left panel refers to reaching, right panel refers to aimless movements.

Baixar (291KB)
Metadados
5. Fig. 4. Results of a permutation test comparing the degree of mu rhythm suppression in the left and right hemisphere (M1L vs. M1R) during imagined movement on go signal. Light gray indicates stronger desynchronization in the right hemisphere, dark gray stronger desynchronization in the left hemisphere. The left panel refers to the imagination of movement with the left hand, the right panel to the imagination of movement with the right hand.

Baixar (37KB)
Metadados
6. Fig. 5. Results of a permutation test comparing the degree of (de)synchronization of the activity of selected components during real and imaginary movement on go signal. Light gray stripes – rhythm suppression is less during imaginary movements, dark gray – rhythm suppression is greater during imaginary movements. All panels show results for goal-directed movements only. The left panel refers to performing tasks with the left hand, the right panel refers to performing tasks with the right hand.

Baixar (55KB)
Metadados

Declaração de direitos autorais © Russian Academy of Sciences, 2024