Optimized decomposition of dynamic electroencephalography-functional near infrared spectroscopy brain functional networks for the analysis of repetitive transcranial magnetic stimulation combined with motor training_Journal of Biomedical Engineering

Authors：

LU Jiewei ^1,2,3 , LIN Jianeng ² , LIU Yinuo ^1,2,3 , ZHANG Ying ⁴ ,  WANG Chunfang ⁴ , HAN Jianda ^1,2,3 ,  YU Ningbo ^1,2,3

1. Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen, Guangdong 518083, P. R. China;
2. College of Artificial Intelligence, Nankai University, Tianjin 300350, P. R. China;
3. Engineering Research Center of Trusted Behavior Intelligence, Nankai University, Tianjin 300350, P. R. China;
4. The First Affiliated Hospital of Nankai University, Tianjin Union Medical Center, Tianjin 300121, P. R. China;

Corresponding?author：

WANG Chunfang, Email: wangchunfang@umc.net.cn; YU Ningbo, Email: nyu@nankai.edu.cn

Keywords：

Stroke; Transcranial magnetic stimulation; Optimized brain network decomposition

DOI：

10.7507/1001-5515.202510062

Video：

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Repetitive transcranial magnetic stimulation combined with motor training effectively promotes motor recovery in stroke patients. However, its underlying neurophysiological mechanisms remain unclear. Existing single-modal neuroimaging techniques are limited by the incapacity to fully characterize the neural mechanisms of combined interventions. To address the above problem, this paper proposed an optimized decomposition method for dynamic brain functional networks based on electroencephalography and functional near-infrared spectroscopy, in order to reveal the potential neural mechanisms underlying the improvement of motor function in stroke patients through combined repetitive transcranial magnetic stimulation and motor training from a multimodal perspective. Twenty-seven stroke patients were recruited to participate in a clinical trial for this study. The results showed that the real stimulation group exhibited significantly increased flexibility in the frontoparietal network after intervention, with the magnitude of change significantly correlated with clinical improvement, whereas no significant changes were observed in the sham stimulation group. In conclusion, this study reveals the brain functional rehabilitation mechanism under combined intervention, contributing to the development of personalized rehabilitation strategies.

Citation： LU Jiewei, LIN Jianeng, LIU Yinuo, ZHANG Ying, WANG Chunfang, HAN Jianda, YU Ningbo. Optimized decomposition of dynamic electroencephalography-functional near infrared spectroscopy brain functional networks for the analysis of repetitive transcranial magnetic stimulation combined with motor training. Journal of Biomedical Engineering, 2026, 43(2): 293-301. doi: 10.7507/1001-5515.202510062 Copy

1.	Giles D, Foulon C, Pombo G, et al. Individualized prescriptive inference in ischaemic stroke. Nat Commun, 2025, 16(1): 8968.
2.	邵謝寧, 張藝瀅, 張棟, 等. 融合多感官刺激的虛擬現實—腦機接口手功能增強康復系統. 生物醫學工程學雜志, 2024, 41(4): 656-663.
3.	Zou X, Zhu J, Hu S, et al. Application of non-invasive brain stimulation combined with functional magnetic resonance imaging in post-stroke motor function rehabilitation. J Neurosci Methods, 2025, 421: 110470.
4.	魏語佳, 王亭宇, 王春方, 等. 經顱電磁聯合刺激在腦疾病臨床應用的研究進展. 生物醫學工程學雜志, 2025, 42(4): 847-856.
5.	Huang G, Wang H, Zhao W, et al. Effects of 10-Hz rTMS over the leg motor cortex using a double-cone coil on lower limb motor recovery in subacute stroke: a randomised, double-blind, sham-controlled study. J Neuroeng Rehabil, 2025, 22(1): 201.
6.	Xie H, Li X, Wang Y, et al. Differential functional network remodeling induced by low- and high-frequency rTMS: evidence from concurrent fNIRS monitoring. IEEE Trans Neural Syst Rehabil Eng, 2025, 33: 3827-3836.
7.	Becev O, Laskov O, Bakstein E, et al. High-frequency neurostimulation of the right inferior parietal cortex alters the sense of agency: results from tACS/tRNS and rTMS-EEG studies. Neuroimage, 2025, 318: 121364.
8.	Yang D, Kang M K, Huang G, et al. Repetitive transcranial alternating current stimulation to improve working memory: an EEG-fNIRS study. IEEE Trans Neural Syst Rehabil Eng, 2024, 32: 1257-1266.
9.	Lin J, Jin S, You Y, et al. EEG-fNIRS multilayer brain network analysis revealed functional neural reorganization of rTMS with motor training in stroke. IEEE Trans Biomed Eng, 2025: 1-12.
10.	孫宏毅, 張林, 李靜, 等. 青少年抑郁癥前額葉功能與負性失匹配波特征: fNIRS-ERP多模態研究. 生物醫學工程學雜志, 2025, 42(4): 701-706.
11.	Bunterngchit C, Wang J, Su J, et al. Temporal attention fusion network with custom loss function for EEG-fNIRS classification. J Neural Eng, 2024, 21(6): 066016.
12.	Nagabhushan Kalburgi S, Kleinert T, Aryan D, et al. MICROSTATELAB: the EEGLAB toolbox for resting-state microstate analysis. Brain Topogr, 2024, 37(4): 621-645.
13.	Leske S, Dalal S S. Reducing power line noise in EEG and MEG data via spectrum interpolation. Neuroimage, 2019, 189: 763-776.
14.	Srishyla D, Webb S J, Elsabbagh M, et al. Eye-movement artifact correction in infant EEG: a systematic comparison between ICA and artifact blocking. J Neurosci Methods, 2025, 418: 110405.
15.	Vergult A, De Clercq W, Palmini A, et al. Improving the interpretation of ictal scalp EEG: BSS-CCA algorithm for muscle artifact removal. Epilepsia, 2007, 48(5): 950-958.
16.	Li R, Yang D, Fang F, et al. Concurrent fNIRS and EEG for brain function investigation: a systematic, methodology-focused review. Sensors, 2022, 22(15): 5865.
17.	Fagerland S M, L?ve A, Helliesen T K, et al. Method for using functional near-infrared spectroscopy (fNIRS) to explore music-induced brain activation in orchestral musicians in concert. Sensors, 2025, 25(6): 1807.
18.	Scholkmann F, Spichtig S, Muehlemann T, et al. How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation. Physiol Meas, 2010, 31(5): 649-662.
19.	Urquhart E L, Wanniarachchi H I, Wang X, et al. Mapping cortical network effects of fatigue during a handgrip task by functional near-infrared spectroscopy in physically active and inactive subjects. Neurophotonics, 2019, 6(4): 045011.
20.	León-Carrión J, León-Domínguez U. Functional near-infrared spectroscopy (fNIRS): principles and neuroscientific applications//Neuroimaging methods. Rijeka: InTech, 2012: 48-74.
21.	Zhu J, Chen H, Cong F, et al. The role of executive control ability in second language metaphor comprehension: evidence from ERPs and sLORETA. J Neurolinguistics, 2024, 72: 101211.
22.	Lin F H, Witzel T, Ahlfors S P, et al. Assessing and improving the spatial accuracy in MEG source localization by depth-weighted minimum-norm estimates. Neuroimage, 2006, 31(1): 160-171.
23.	?eker M, ?zerdem M S. Dementia rhythms: unveiling the EEG dynamics for MCI detection through spectral and synchrony neuromarkers. J Neurosci Methods, 2024, 409: 110216.
24.	Huang R, Hanif M F, Siddiqui M K, et al. Analyzing boron oxide networks through Shannon entropy and Pearson correlation coefficient. Sci Rep, 2024, 14(1): 26552.
25.	Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci, 2009, 10(3): 186-198.
26.	Zhu R, She Q, Li R, et al. Neurovascular coupling analysis based on multivariate variational Gaussian process convergent cross-mapping. IEEE Trans Neural Syst Rehabil Eng, 2024, 32: 1873-1883.
27.	Zamani Esfahlani F, Jo Y, Puxeddu M G, et al. Modularity maximization as a flexible and generic framework for brain network exploratory analysis. Neuroimage, 2021, 244: 118607.
28.	Yang Z, Telesford Q K, Franco A R, et al. Measurement reliability for individual differences in multilayer network dynamics: cautions and considerations. NeuroImage, 2021, 225: 117489.
29.	Haddara N, Rahnev D. Threat expectation does not improve perceptual discrimination despite causing heightened priority processing in the frontoparietal network. J Neurosci, 2024, 44(15): e1219232023.
30.	D’Cruz N, De Vleeschhauwer J, Putzolu M, et al. Sensorimotor network segregation predicts long-term learning of writing skills in Parkinson’s disease. Brain Sci, 2024, 14(4): 376.
31.	Eryurek K, Ulasoglu-Yildiz C, Matur Z, et al. Default mode and dorsal attention network involvement in visually guided motor sequence learning. Cortex, 2022, 146: 89-105.
32.	Wu K, Jelfs B, Neville K, et al. Dynamic reconfiguration of brain functional network in stroke. IEEE J Biomed Health Inform, 2024, 28(6): 3649-3659.
33.	Finc K, Bonna K, He X, et al. Dynamic reconfiguration of functional brain networks during working memory training. Nat Commun, 2020, 11(1): 2435.
34.	Sunil S, Jiang J, Shah S, et al. Neurovascular coupling is preserved in chronic stroke recovery after targeted photothrombosis. NeuroImage Clin, 2023, 38: 103377.
35.	Veldsman M, Tai X Y, Nichols T, et al. Cerebrovascular risk factors impact frontoparietal network integrity and executive function in healthy ageing. Nat Commun, 2020, 11(1): 4340.
36.	Olafson E, Russello G, Jamison K W, et al. Frontoparietal network activation is associated with motor recovery in ischemic stroke patients. Commun Biol, 2022, 5(1): 993.
37.	Wang X, Li L, Wei W, et al. Altered activation in sensorimotor network after applying rTMS over the primary motor cortex at different frequencies. Brain Behav, 2020, 10(7): e01670.
38.	Han X, Zhu Z, Luan J, et al. Effects of repetitive transcranial magnetic stimulation and their underlying neural mechanisms evaluated with magnetic resonance imaging-based brain connectivity network analyses. Eur J Radiol Open, 2023, 10: 100495.

1. Giles D, Foulon C, Pombo G, et al. Individualized prescriptive inference in ischaemic stroke. Nat Commun, 2025, 16(1): 8968.
2. 邵謝寧, 張藝瀅, 張棟, 等. 融合多感官刺激的虛擬現實—腦機接口手功能增強康復系統. 生物醫學工程學雜志, 2024, 41(4): 656-663.
3. Zou X, Zhu J, Hu S, et al. Application of non-invasive brain stimulation combined with functional magnetic resonance imaging in post-stroke motor function rehabilitation. J Neurosci Methods, 2025, 421: 110470.
4. 魏語佳, 王亭宇, 王春方, 等. 經顱電磁聯合刺激在腦疾病臨床應用的研究進展. 生物醫學工程學雜志, 2025, 42(4): 847-856.
5. Huang G, Wang H, Zhao W, et al. Effects of 10-Hz rTMS over the leg motor cortex using a double-cone coil on lower limb motor recovery in subacute stroke: a randomised, double-blind, sham-controlled study. J Neuroeng Rehabil, 2025, 22(1): 201.
6. Xie H, Li X, Wang Y, et al. Differential functional network remodeling induced by low- and high-frequency rTMS: evidence from concurrent fNIRS monitoring. IEEE Trans Neural Syst Rehabil Eng, 2025, 33: 3827-3836.
7. Becev O, Laskov O, Bakstein E, et al. High-frequency neurostimulation of the right inferior parietal cortex alters the sense of agency: results from tACS/tRNS and rTMS-EEG studies. Neuroimage, 2025, 318: 121364.
8. Yang D, Kang M K, Huang G, et al. Repetitive transcranial alternating current stimulation to improve working memory: an EEG-fNIRS study. IEEE Trans Neural Syst Rehabil Eng, 2024, 32: 1257-1266.
9. Lin J, Jin S, You Y, et al. EEG-fNIRS multilayer brain network analysis revealed functional neural reorganization of rTMS with motor training in stroke. IEEE Trans Biomed Eng, 2025: 1-12.
10. 孫宏毅, 張林, 李靜, 等. 青少年抑郁癥前額葉功能與負性失匹配波特征: fNIRS-ERP多模態研究. 生物醫學工程學雜志, 2025, 42(4): 701-706.
11. Bunterngchit C, Wang J, Su J, et al. Temporal attention fusion network with custom loss function for EEG-fNIRS classification. J Neural Eng, 2024, 21(6): 066016.
12. Nagabhushan Kalburgi S, Kleinert T, Aryan D, et al. MICROSTATELAB: the EEGLAB toolbox for resting-state microstate analysis. Brain Topogr, 2024, 37(4): 621-645.
13. Leske S, Dalal S S. Reducing power line noise in EEG and MEG data via spectrum interpolation. Neuroimage, 2019, 189: 763-776.
14. Srishyla D, Webb S J, Elsabbagh M, et al. Eye-movement artifact correction in infant EEG: a systematic comparison between ICA and artifact blocking. J Neurosci Methods, 2025, 418: 110405.
15. Vergult A, De Clercq W, Palmini A, et al. Improving the interpretation of ictal scalp EEG: BSS-CCA algorithm for muscle artifact removal. Epilepsia, 2007, 48(5): 950-958.
16. Li R, Yang D, Fang F, et al. Concurrent fNIRS and EEG for brain function investigation: a systematic, methodology-focused review. Sensors, 2022, 22(15): 5865.
17. Fagerland S M, L?ve A, Helliesen T K, et al. Method for using functional near-infrared spectroscopy (fNIRS) to explore music-induced brain activation in orchestral musicians in concert. Sensors, 2025, 25(6): 1807.
18. Scholkmann F, Spichtig S, Muehlemann T, et al. How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation. Physiol Meas, 2010, 31(5): 649-662.
19. Urquhart E L, Wanniarachchi H I, Wang X, et al. Mapping cortical network effects of fatigue during a handgrip task by functional near-infrared spectroscopy in physically active and inactive subjects. Neurophotonics, 2019, 6(4): 045011.
20. León-Carrión J, León-Domínguez U. Functional near-infrared spectroscopy (fNIRS): principles and neuroscientific applications//Neuroimaging methods. Rijeka: InTech, 2012: 48-74.
21. Zhu J, Chen H, Cong F, et al. The role of executive control ability in second language metaphor comprehension: evidence from ERPs and sLORETA. J Neurolinguistics, 2024, 72: 101211.
22. Lin F H, Witzel T, Ahlfors S P, et al. Assessing and improving the spatial accuracy in MEG source localization by depth-weighted minimum-norm estimates. Neuroimage, 2006, 31(1): 160-171.
23. ?eker M, ?zerdem M S. Dementia rhythms: unveiling the EEG dynamics for MCI detection through spectral and synchrony neuromarkers. J Neurosci Methods, 2024, 409: 110216.
24. Huang R, Hanif M F, Siddiqui M K, et al. Analyzing boron oxide networks through Shannon entropy and Pearson correlation coefficient. Sci Rep, 2024, 14(1): 26552.
25. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci, 2009, 10(3): 186-198.
26. Zhu R, She Q, Li R, et al. Neurovascular coupling analysis based on multivariate variational Gaussian process convergent cross-mapping. IEEE Trans Neural Syst Rehabil Eng, 2024, 32: 1873-1883.
27. Zamani Esfahlani F, Jo Y, Puxeddu M G, et al. Modularity maximization as a flexible and generic framework for brain network exploratory analysis. Neuroimage, 2021, 244: 118607.
28. Yang Z, Telesford Q K, Franco A R, et al. Measurement reliability for individual differences in multilayer network dynamics: cautions and considerations. NeuroImage, 2021, 225: 117489.
29. Haddara N, Rahnev D. Threat expectation does not improve perceptual discrimination despite causing heightened priority processing in the frontoparietal network. J Neurosci, 2024, 44(15): e1219232023.
30. D’Cruz N, De Vleeschhauwer J, Putzolu M, et al. Sensorimotor network segregation predicts long-term learning of writing skills in Parkinson’s disease. Brain Sci, 2024, 14(4): 376.
31. Eryurek K, Ulasoglu-Yildiz C, Matur Z, et al. Default mode and dorsal attention network involvement in visually guided motor sequence learning. Cortex, 2022, 146: 89-105.
32. Wu K, Jelfs B, Neville K, et al. Dynamic reconfiguration of brain functional network in stroke. IEEE J Biomed Health Inform, 2024, 28(6): 3649-3659.
33. Finc K, Bonna K, He X, et al. Dynamic reconfiguration of functional brain networks during working memory training. Nat Commun, 2020, 11(1): 2435.
34. Sunil S, Jiang J, Shah S, et al. Neurovascular coupling is preserved in chronic stroke recovery after targeted photothrombosis. NeuroImage Clin, 2023, 38: 103377.
35. Veldsman M, Tai X Y, Nichols T, et al. Cerebrovascular risk factors impact frontoparietal network integrity and executive function in healthy ageing. Nat Commun, 2020, 11(1): 4340.
36. Olafson E, Russello G, Jamison K W, et al. Frontoparietal network activation is associated with motor recovery in ischemic stroke patients. Commun Biol, 2022, 5(1): 993.
37. Wang X, Li L, Wei W, et al. Altered activation in sensorimotor network after applying rTMS over the primary motor cortex at different frequencies. Brain Behav, 2020, 10(7): e01670.
38. Han X, Zhu Z, Luan J, et al. Effects of repetitive transcranial magnetic stimulation and their underlying neural mechanisms evaluated with magnetic resonance imaging-based brain connectivity network analyses. Eur J Radiol Open, 2023, 10: 100495.

Journal of Biomedical Engineering

Optimized decomposition of dynamic electroencephalography-functional near infrared spectroscopy brain functional networks for the analysis of repetitive transcranial magnetic stimulation combined with motor training

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