journal article·2024-10-22Open access

Neuromodulation Effect According to Lesion Location After Dual-Mode Brain Stimulation in Patients with Subacute Stroke: A Preliminary Study

Minji Lee, Wanjoo Park, Eunhee Park, Soon-Jae Kweon, Yun-Hee Kim

Applied Sciences· Volume 14· Issue 21

Abstract

Dual-mode non-invasive brain stimulation using repetitive transcranial magnetic stimulation and transcranial direct current stimulation is known to help neurorehabilitation in patients with stroke. However, this neuromodulation effect may vary depending on the lesion location of patients with stroke, and the basis in lesion location for this is insufficient. This study aims to investigate the difference in neuromodulation effectiveness according to the lesion location after dual-mode brain stimulation using electroencephalography signals. Eight patients with ischemic subacute stroke and 11 healthy controls participated in this study. Brain stimulation was conducted in one session per day for a total of 10 days over the motor cortex, electroencephalography was measured for 5 min with eyes closed, and motor function was evaluated before and after dual-mode stimulation. The lesion location was divided into an infratentorial stroke (ITS) and a supratentorial stroke (STS) based on tentorium cerebelli. In addition, we focused on the mu and beta bands related to motor function. In terms of intrahemispheric connectivity, the mu weighted phase lag index over the contralesional primary motor cortex was significantly higher in only ITS before stimulation compared to healthy controls, and mu Granger causality over the ipsilesional primary motor cortex was significantly higher in both ITS and STS after stimulation compared to healthy controls. In contrast, from the perspective of interhemispheric connectivity, the laterality of beta Granger causality before stimulation in ITS was lower than that of healthy controls and significantly increased after stimulation. The effect of brain stimulation may vary depending on the lesion location of patients with stroke, and these findings provide indicative insights into effective dual-mode stimulation interventions for neurorehabilitation.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Transcranial Magnetic Stimulation Studies · EEG and Brain-Computer Interfaces · Functional Brain Connectivity Studies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Biomedical Software Engineering, The Catholic University of Korea, 43 Jibong-ro, Bucheon-si 14662, Republic of Korea

Engineering Division, New York University Abu Dhabi, Abu Dhabi 129188, United Arab Emirates

Department of Rehabilitation Medicine, School of Medicine, Kyungpook National University, 807 Hoguk-ro, Buk-gu, Daegu 41404, Republic of Korea

School of Information, Communications and Electronic Engineering, The Catholic University of Korea, Bucheon-si 14662, Republic of Korea

Department of Physical and Rehabilitation Medicine, Sungkyunkwan University School of Medicine, 2066 Seobu-ro, Suwon-si 16419, Republic of Korea

Funding

National Research Foundation of Korea (NRF) · IITP (Institute of Information & Communications Technology Planning & Evaluation)-ICAN (ICT Challenge and Advanced Network of HRD) · The Catholic University of Korea

References (49)

Show source-provided references
2. Does a Cognitive Network Contribute to Motor Recovery After Ischemic Stroke?10.1177/15459683231177604 ↗
3. Dual-mode noninvasive brain stimulation over the bilateral primary motor cortices in stroke patients
4. Immediate effects of intermittent theta burst stimulation on primary motor cortex in stroke patients: A concurrent TMS-EEG study10.1109/TNSRE.2023.3282659 ↗
5. Effects of transcranial direct current stimulation (tDCS) combined with wrist robot-assisted rehabilitation on motor recovery in subacute stroke patients: A randomized controlled trial10.1109/TNSRE.2019.2920576 ↗
6. Repetitive transcranial magnetic stimulation of contralesional primary motor cortex improves hand function after stroke10.1161/01.STR.0000189658.51972.34 ↗
7. EEG–fMRI integration for the study of human brain function10.1016/j.neuroimage.2013.05.114 ↗
8. Effects of transcranial direct current stimulation on EEG power and brain functional network in stroke patients10.1109/TNSRE.2022.3223116 ↗
9. Evidence for interhemispheric imbalance in stroke patients as revealed by combining transcranial magnetic stimulation and electroencephalography10.1002/hbm.25297 ↗
10. Longitudinal changes of resting-state functional connectivity during motor recovery after stroke10.1161/STROKEAHA.110.596155 ↗
11. Repetitive transcranial magnetic stimulation–induced corticomotor excitability and associated motor skill acquisition in chronic stroke10.1161/01.STR.0000221233.55497.51 ↗
12. Modulating brain connectivity by simultaneous dual-mode stimulation over bilateral primary motor cortices in subacute stroke patients10.1155/2018/1458061 ↗
13. Neuroplasticity and network connectivity of the motor cortex following stroke: A transcranial direct current stimulation study10.1002/hbm.24079 ↗
14. Ding, Q., Zhang, S., Chen, S., Chen, J., Li, X., Chen, J., Peng, Y., Chen, Y., Chen, K., and Cai, G. (2021). The effects of intermittent theta burst stimulation on functional brain network following stroke: An electroencephalography study. Front. Neurosci., 15.10.3389/fnins.2021.755709 ↗
15. Effects of dual-mode non-invasive brain stimulation on motor function10.1016/j.neulet.2014.03.022 ↗
16. The effects of combined repetitive transcranial magnetic stimulation and transcranial direct current stimulation on motor function in patients with stroke
17. Effect of repetitive transcranial magnetic stimulation combined with transcranial direct current stimulation on post-stroke dysmnesia: A preliminary study10.1016/j.clineuro.2023.107797 ↗
18. Lee, M., Baird, B., Gosseries, O., Nieminen, J.O., Boly, M., Tononi, G., and Lee, S.W. (2019, January 23–27). Graph theoretical analysis of cortical networks based on conscious experience. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.10.1109/EMBC.2019.8857648 ↗
19. What is the optimal anodal electrode position for inducing corticomotor excitability changes in transcranial direct current stimulation?10.1016/j.neulet.2014.10.052 ↗
20. BDNF polymorphism and differential rTMS effects on motor recovery of stroke patients10.1016/j.brs.2014.03.008 ↗
21. EEG response varies with lesion location in patients with chronic stroke10.1186/s12984-016-0120-2 ↗
22. Differences in motor network dynamics during recovery between supra-and infra-tentorial ischemic strokes10.1002/hbm.24338 ↗
23. Motor impairment in stroke patients is associated with network properties during consecutive motor imagery10.1109/TBME.2022.3151742 ↗
24. Connectivity differences between consciousness and unconsciousness in non-rapid eye movement sleep: A TMS–EEG study10.1038/s41598-019-41274-2 ↗
25. The post-stroke hemiplegic patient. 1. a method for evaluation of physical performance10.2340/1650197771331 ↗
26. Pezzotti, P., Scalmana, S., Mastromattei, A., Di Lallo, D., and Group, P.A.W. (2008). The accuracy of the MMSE in detecting cognitive impairment when administered by general practitioners: A prospective observational study. BMC Fam. Pract., 9.10.1186/1471-2296-9-29 ↗
27. Seizures as a presenting symptom of acute arterial ischemic stroke in childhood10.1016/j.jpeds.2011.02.004 ↗
28. Decoding Finger Tapping with the Affected Hand in Chronic Stroke Patients During Motor Imagery and Execution10.1109/TNSRE.2021.3087506 ↗
29. Reliability of the Fugl-Meyer assessment for testing motor performance in patients following stroke10.1093/ptj/73.7.447 ↗
30. EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis10.1016/j.jneumeth.2003.10.009 ↗
31. Neural decoding of imagined speech and visual imagery as intuitive paradigms for BCI communication10.1109/TNSRE.2020.3040289 ↗
32. Directed information transfer in scalp electroencephalographic recordings: Insights on disorders of consciousness10.1177/1550059413510703 ↗
33. Shahid, S., Sinha, R.K., and Prasad, G. (2010). Mu and beta rhythm modulations in motor imagery related post-stroke EEG: A study under BCI framework for post-stroke rehabilitation. BMC Neurosci., 11.10.1186/1471-2202-11-S1-P127 ↗
34. Effective connectivity of cortical sensorimotor networks during finger movement tasks: A simultaneous fNIRS, fMRI, EEG study10.1007/s10548-016-0507-1 ↗
35. Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG10.1016/j.compbiomed.2011.06.020 ↗
36. HERMES: Towards an integrated toolbox to characterize functional and effective brain connectivity10.1007/s12021-013-9186-1 ↗
37. An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias10.1016/j.neuroimage.2011.01.055 ↗
38. Imperatori, L.S., Betta, M., Cecchetti, L., Canales-Johnson, A., Ricciardi, E., Siclari, F., Pietrini, P., Chennu, S., and Bernardi, G. (2019). EEG functional connectivity metrics wPLI and wSMI account for distinct types of brain functional interactions. Sci. Rep., 9.10.1038/s41598-019-45289-7 ↗
39. Lee, M., Sanders, R.D., Yeom, S.K., Won, D.O., Seo, K.S., Kim, H.J., Tononi, G., and Lee, S.W. (2017). Network properties in transitions of consciousness during propofol-induced sedation. Sci. Rep., 7.10.1038/s41598-017-15082-5 ↗
40. Won, D.O., Lee, B.R., Seo, K.S., Kim, H.J., and Lee, S.W. (2019). Alteration of coupling between brain and heart induced by sedation with propofol and midazolam. PLoS ONE, 14.10.1371/journal.pone.0219238 ↗
41. A new look at the statistical model identification10.1109/TAC.1974.1100705 ↗
42. Lateralized theta wave connectivity and language performance in 2- to 5-year-old children10.1523/JNEUROSCI.2785-11.2011 ↗
43. Frontal EEG Asymmetry of Emotion for the Same Auditory Stimulus10.1109/ACCESS.2020.3000788 ↗
44. Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning10.1038/s41467-022-28451-0 ↗
45. White matter integrity of premotor–motor connections is associated with motor output in chronic stroke patients10.1016/j.nicl.2014.11.006 ↗
46. Li, Y., Wang, L., Jia, M., Guo, J., Wang, H., and Wang, M. (2017). The effects of high-frequency rTMS over the left DLPFC on cognitive control in young healthy participants. PLoS ONE, 12.10.1371/journal.pone.0179430 ↗
47. Kim, Y.K., Park, E., Lee, A., Im, C.H., and Kim, Y.H. (2018). Changes in network connectivity during motor imagery and execution. PLoS ONE, 13.10.1371/journal.pone.0190715 ↗
48. Motor planning, imagery, and execution in the distributed motor network: A time-course study with functional MRI10.1093/cercor/bhn036 ↗
49. Machine learning methods predict individual upper-limb motor impairment following therapy in chronic stroke10.1177/1545968320909796 ↗