Effects of transcranial magnetic stimulation on cortical structures during motor imagination performance in the brain-computer interface
- Authors: Savosenkov A.О.1,2, Grigorev N.A.1,2, Udoratina A.M.1, Kurkin S.A.2, Gordleeva S.Y.1,2
-
Affiliations:
- Lobachevsky State University of Nizhny Novgorod
- Immanuel Kant Baltic Federal University
- Issue: Vol 18, No 4 (2023)
- Pages: 645-648
- Section: Conference proceedings
- URL: https://genescells.ru/2313-1829/article/view/623398
- DOI: https://doi.org/10.17816/gc623398
- ID: 623398
Cite item
Open Access
Access granted
Subscription or Fee Access
Abstract
The aftermath of a stroke can frequently result in impaired motor functions causing problems performing habitual limb movements. These movement disorders stem from damage to the cerebral cortex and disruptions to neuronal connections in the central pyramidal pathways [1]. Restoring motor skills following a stroke is a time-consuming and challenging process, requiring resources from both external medical sources and the patient. Despite these challenges, it is still feasible for patients to regain control over their limb movements. The common approach for stroke neurorehabilitation consists of therapeutic physical activities and kinesiotherapy. These methods rely on afferent information during motor tasks to repair connections between intact brain areas [2]. Through exercising the impacted limbs, synaptic rearrangement happens in the cortex, awakening dormant neurons and increasing cortical areas adjacent to inactive regions. Although these techniques can partially restore movement, a significant number of stroke patients still face impairments. It is crucial to acknowledge the limitations of the current interventions and explore new approaches for better outcomes. Traditional rehabilitation methods often do not fully restore movement control, prompting researchers to explore alternative approaches. Motor-imagery-based brain-computer interfaces (BCIs) have gained attention recently [3, 4]. They allow for integrating various feedback mechanisms, which can be combined with upper and lower limb exoskeletons. The level of control a subject has over a BCI system directly affects the recovery process [5]. Introducing transcranial magnetic stimulation (TMS) into motor-imagery BCIs shows promise for developing a unified and highly efficient approach to post-stroke rehabilitation.
Twenty-nine healthy adult participants (21 females) with a mean age of 20.93±2.14 years were recruited for this experiment. All participants were right-handed and had no prior experience with brain-computer interfaces. Ethical approval was obtained from the local ethics committee of Lobachevsky State University of Nizhny Novgorod (ethical approval No. 2, dated 03/19/2021), and written informed consent was obtained from all participants. Subjects were randomly assigned to receive either a sham intervention (n=15) or active TMS (n=14). The experiment displayed tasks on a 24-inch LCD screen placed 2 meters away. Participants sat in a reclining chair with hands on adjustable armrests for EEG signal recording. Motor-imagery-based BCI training took place over two days with four daily tasks: motor performance, quasi-motor, and two motor imaginations of the dominant hand. EEG activity was recorded prior to and after completing the tasks. Each task comprised of 20 trials, each lasting 10 seconds. TMS was administered in between two motor imagination tasks, and a rest period of 2 minutes followed the stimulation. The researcher objectively monitored electromyography (EMG) in real-time during motor tasks. Certified NVX 52 amplifiers with 32 Cl/Ag electrodes positioned according to the international 10-10 system recorded electroencephalography (EEG) signals. The EEG was digitized at a sampling rate of 1000 Hz and filtered with a 50 Hz Notch filter. Disposable electrodes were used to record EMG data from musculus flexor digitorum superficialis on the right hand. EMG signals were digitized at 1000 Hz and filtered using a 50 Hz Notch filter. TMS was applied to the dorsolateral prefrontal cortex (dlPFC) using a figure-8 coil connected to a Neuro MS/D magnetic stimulator. Sham stimulation was performed with the same parameters, except the coil was tilted 90 degrees to simulate the sound of real stimulation. A nonparametric permutation test was conducted to assess the statistically significant dissimilarities between rest periods and periods subsequent to conducting motor imagery after TMS. Significant clusters of the highest event-related desynchronization (ERD) were observed. The analysis demonstrated a significant negative cluster in the theta rhythm (0–6 Hz) that was distinct from the magnetic stimulation site. Similar significant ERD clusters were identified during the first and second series of imaginary movements.
The study examined alterations in motor cortex function following targeted rTMS. The results demonstrated that TMS using particular parameters resulted in the pre-activation of brain regions comparable to those stimulated during motor imagery. The implementation of this stimulation protocol increased activity in cortical regions related to motor imagery. The outcomes propose the probable effectiveness of TMS in elevating motor cortex activation during the rehabilitation process.
Keywords
Full Text
The aftermath of a stroke can frequently result in impaired motor functions causing problems performing habitual limb movements. These movement disorders stem from damage to the cerebral cortex and disruptions to neuronal connections in the central pyramidal pathways [1]. Restoring motor skills following a stroke is a time-consuming and challenging process, requiring resources from both external medical sources and the patient. Despite these challenges, it is still feasible for patients to regain control over their limb movements. The common approach for stroke neurorehabilitation consists of therapeutic physical activities and kinesiotherapy. These methods rely on afferent information during motor tasks to repair connections between intact brain areas [2]. Through exercising the impacted limbs, synaptic rearrangement happens in the cortex, awakening dormant neurons and increasing cortical areas adjacent to inactive regions. Although these techniques can partially restore movement, a significant number of stroke patients still face impairments. It is crucial to acknowledge the limitations of the current interventions and explore new approaches for better outcomes. Traditional rehabilitation methods often do not fully restore movement control, prompting researchers to explore alternative approaches. Motor-imagery-based brain-computer interfaces (BCIs) have gained attention recently [3, 4]. They allow for integrating various feedback mechanisms, which can be combined with upper and lower limb exoskeletons. The level of control a subject has over a BCI system directly affects the recovery process [5]. Introducing transcranial magnetic stimulation (TMS) into motor-imagery BCIs shows promise for developing a unified and highly efficient approach to post-stroke rehabilitation.
Twenty-nine healthy adult participants (21 females) with a mean age of 20.93±2.14 years were recruited for this experiment. All participants were right-handed and had no prior experience with brain-computer interfaces. Ethical approval was obtained from the local ethics committee of Lobachevsky State University of Nizhny Novgorod (ethical approval No. 2, dated 03/19/2021), and written informed consent was obtained from all participants. Subjects were randomly assigned to receive either a sham intervention (n=15) or active TMS (n=14). The experiment displayed tasks on a 24-inch LCD screen placed 2 meters away. Participants sat in a reclining chair with hands on adjustable armrests for EEG signal recording. Motor-imagery-based BCI training took place over two days with four daily tasks: motor performance, quasi-motor, and two motor imaginations of the dominant hand. EEG activity was recorded prior to and after completing the tasks. Each task comprised of 20 trials, each lasting 10 seconds. TMS was administered in between two motor imagination tasks, and a rest period of 2 minutes followed the stimulation. The researcher objectively monitored electromyography (EMG) in real-time during motor tasks. Certified NVX 52 amplifiers with 32 Cl/Ag electrodes positioned according to the international 10-10 system recorded electroencephalography (EEG) signals. The EEG was digitized at a sampling rate of 1000 Hz and filtered with a 50 Hz Notch filter. Disposable electrodes were used to record EMG data from musculus flexor digitorum superficialis on the right hand. EMG signals were digitized at 1000 Hz and filtered using a 50 Hz Notch filter. TMS was applied to the dorsolateral prefrontal cortex (dlPFC) using a figure-8 coil connected to a Neuro MS/D magnetic stimulator. Sham stimulation was performed with the same parameters, except the coil was tilted 90 degrees to simulate the sound of real stimulation. A nonparametric permutation test was conducted to assess the statistically significant dissimilarities between rest periods and periods subsequent to conducting motor imagery after TMS. Significant clusters of the highest event-related desynchronization (ERD) were observed. The analysis demonstrated a significant negative cluster in the theta rhythm (0–6 Hz) that was distinct from the magnetic stimulation site. Similar significant ERD clusters were identified during the first and second series of imaginary movements.
The study examined alterations in motor cortex function following targeted rTMS. The results demonstrated that TMS using particular parameters resulted in the pre-activation of brain regions comparable to those stimulated during motor imagery. The implementation of this stimulation protocol increased activity in cortical regions related to motor imagery. The outcomes propose the probable effectiveness of TMS in elevating motor cortex activation during the rehabilitation process.
ADDITIONAL INFORMATION
Funding sources. The work was supported by the Russian Science Foundation under Grant No. 21-72-10121. A. Savosenkov, N. Grigorev and S. Gordleeva received support from the Federal Academic Leadership Program “Priority 2030” of the Ministry of Science and Higher Education of the Russian Federation in part of data collection.
About the authors
A. О. Savosenkov
Lobachevsky State University of Nizhny Novgorod; Immanuel Kant Baltic Federal University
Author for correspondence.
Email: Andrey.savosenkov@gmail.com
Russian Federation, Nizhny Novgorod; Kaliningrad
N. A. Grigorev
Lobachevsky State University of Nizhny Novgorod; Immanuel Kant Baltic Federal University
Email: Andrey.savosenkov@gmail.com
Russian Federation, Nizhny Novgorod; Kaliningrad
A. M. Udoratina
Lobachevsky State University of Nizhny Novgorod
Email: Andrey.savosenkov@gmail.com
Russian Federation, Nizhny Novgorod
S. A. Kurkin
Immanuel Kant Baltic Federal University
Email: Andrey.savosenkov@gmail.com
Russian Federation, Kaliningrad
S. Yu. Gordleeva
Lobachevsky State University of Nizhny Novgorod; Immanuel Kant Baltic Federal University
Email: Andrey.savosenkov@gmail.com
Russian Federation, Nizhny Novgorod; Kaliningrad
References
- Jiang L, Xu H, Yu C. Brain connectivity plasticity in the motor network after ischemic stroke. Neural plasticity. 2013;2013:924192. doi: 10.1155/2013/924192
- Gómez-Pinilla F, Ying Z, Roy RR, et al. Afferent input modulates neurotrophins and synaptic plasticity in the spinal cord. Journal of neurophysiology. 2004;92(6):3423–3432. doi: 10.1152/jn.00432.2004
- Lukoyanov MV, Gordleeva SYu, Pimashkin AS, et al. The Efficiency of the Brain-Computer Interfaces Based on Motor Imagery with Tactile and Visual Feedback. Human Physiology. 2018;44:280–288. doi: 10.1134/S0362119718030088
- Lukoyanov MV, Gordleeva SYu, Grigorev NA, et al. Investigation of Characteristics of a Motor-Imagery Brain-Computer Interface with Quick-Response Tactile Feedback. Moscow University Biological Sciences Bulletin. 2018;73:222–228. doi: 10.3103/S0096392518040053
- Grosse-Wentrup M, Mattia D, Oweiss K. Using brain-computer interfaces to induce neural plasticity and restore function. Journal of neural engineering. 2011;8(2):025004. doi: 10.1088/1741-2560/8/2/025004.
Supplementary files
