An improved maximal information coefficient algorithm applied in the analysis of functional corticomuscular coupling for stroke patients_Journal of Biomedical Engineering

Authors：

LIANG Tie ^1,2 , ZHANG Qingyu ¹ , HONG Lei ¹ , LIU Xiaoguang ¹ , DONG Bin ^1,3 , WANG Hongrui ^1,2 ,  LIU Xiuling ¹

1. School of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P.R.China;
2. Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P.R.China;
3. Development Planning Ofce, Afliated Hospital of Hebei University, Baoding, Hebei 071002, P.R.China;

Corresponding author：

LIU Xiuling, Email: liuxiuling@hbu.edu.cn

Keywords：

functional corticomuscular coupling; maximal information coefficient; electroencephalogram; surface electromyography; stroke

DOI：

10.7507/1001-5515.202106062

Video：

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The functional coupling between motor cortex and effector muscles during autonomic movement can be quantified by calculating the coupling between electroencephalogram (EEG) signal and surface electromyography (sEMG) signal. The maximal information coefficient (MIC) algorithm has been proved to be effective in quantifying the coupling relationship between neural signals, but it also has the problem of time-consuming calculations in actual use. To solve this problem, an improved MIC algorithm was proposed based on the efficient clustering characteristics of K-means ++ algorithm to accurately detect the coupling strength between nonlinear time series. Simulation results showed that the improved MIC algorithm proposed in this paper can capture the coupling relationship between nonlinear time series quickly and accurately under different noise levels. The results of right dorsiflexion experiments in stroke patients showed that the improved method could accurately capture the coupling strength of EEG signal and sEMG signal in the specific frequency band. Compared with the healthy controls, the functional corticomuscular coupling (FCMC) in beta (14~30 Hz) and gamma band (31~45 Hz) were significantly weaker in stroke patients, and the beta-band MIC values were positively correlated with the Fugl-Meyers assessment (FMA) scale scores. The method proposed in this study is hopeful to be a new method for quantitative assessment of motor function for stroke patients.

Citation： LIANG Tie, ZHANG Qingyu, HONG Lei, LIU Xiaoguang, DONG Bin, WANG Hongrui, LIU Xiuling. An improved maximal information coefficient algorithm applied in the analysis of functional corticomuscular coupling for stroke patients. Journal of Biomedical Engineering, 2021, 38(6): 1154-1162. doi: 10.7507/1001-5515.202106062 Copy

1.	谢平, 宋妍, 苏崇钦, 等. 脑卒中患者表面肌电信号与痉挛性肌张力关系分析. 生物医学工程学杂志, 2015, 32(4): 795-801.
2.	舒智林, 李思宜, 于宁波, 等. 一种脑肢融合的神经康复训练在线评价与调整方法. 自动化学报, 2021, 47: 1-11.
3.	Baker S N. Oscillatory interactions between sensorimotor cortex and the periphery. Curr Opin Neurobiol, 2007, 17(6): 649-655.
4.	Salenius S, Hari R. Synchronous cortical oscillatory activity during motor action. Curr Opin Neurobiol, 2003, 13(6): 678-684.
5.	Conway B, Halliday D M, Farmer S F, et al. Synchronization between motor cortex and spinal motoneuronal pool during the performance of a maintained motor task in man. J Physiol, 1995, 489: 917-924.
6.	Gwin J T, Ferris D P. Beta- and gamma-range human lower limb corticomuscular coherence. Front Hum Neurosci, 2012, 6: 258.
7.	Kristeva R, Patino L, Omlor W. Beta-range cortical motor spectral power and corticomuscular coherence as a mechanism for effective corticospinal interaction during steady-state motor output. Neuroimage, 2007, 36(3): 785-792.
8.	马培培, 陈迎亚, 杜义浩, 等. 中风康复运动中脑电-肌电相干性分析. 生物医学工程学杂志, 2014, 31(5): 971-977.
9.	Chen X, Xie P, Zhang Y, et al. Abnormal functional corticomuscular coupling after stroke. Neuroimage Clin, 2018, 19: 147-159.
10.	Yu H, Xu W, Zhuang Y, et al. Wavelet coherence analysis of muscle coupling during reaching movement in stroke. Comput Biol Med, 2021, 131: 104263.
11.	Mehrkanoon S, Breakspear M, Boonstra T W. The reorganization of corticomuscular coherence during a transition between sensorimotor states. NeuroImage, 2014, 100: 692-702.
12.	Mendez-Balbuena I, Huethe F, Schulte-Mönting J, et al. Corticomuscular coherence reflects interindividual differences in the state of the corticomuscular network during low-level static and dynamic forces. Cereb Cortex, 2012, 22(3): 628-638.
13.	Tuncel D, Dizibuyuk A, Kiymik M K. Time frequency based coherence analysis between EEG and EMG activities in fatigue duration. J Med Syst, 2010, 34(2): 131-138.
14.	Xu Y, Mcclelland V M, Cvetkovi Z, et al. Cortico-muscular coherence with time lag with application to delay estimation. IEEE Trans Biomed Eng, 2017, 64(3): 588-600.
15.	Ioannides A A, Mitsis G D. Do we need to consider non-linear information flow in corticomuscular interaction?. Clin Neurophysiol, 2010, 121(3): 272-273.
16.	Yang Yuan, Solis-Escalante T, Yao Jun, et al. Nonlinear connectivity in the human stretch reflex assessed by cross-frequency phase coupling. Int J Neural Syst, 2016, 26(8): 1650043.
17.	Reshef D N, Reshef Y A, Finucane H K, et al. Detecting novel associations in large data sets. Science, 2011, 334(662): 1518-1524.
18.	Su L, Wang L, Shen H, et al. Discriminative analysis of non-linear brain connectivity in schizophrenia: an fMRI study. Front Hum Neurosci, 2013, 7: 702.
19.	Tian Yin, Zhang Huiling, Li Peiyang, et al. A complementary method of PCC for the construction of scalp resting-state EEG connectome: maximum information coefficient. IEEE Access, 2019, 7: 27146-27154.
20.	Liang T, Zhang Q, Liu X, et al. Time-frequency maximal information coefficient method and its application to functional corticomuscular coupling. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(11): 2515-2524.
21.	Liang T, Zhang Q, Liu X, et al. Identifying bidirectional total and non-linear information flow in functional corticomuscular coupling during a dorsiflexion task: a pilot study. J Neuroeng Rehabil, 2021, 18(1): 74.
22.	Shao F, Li K, Xu X. Railway accidents analysis based on the improved algorithm of the maximal information coefficient. Intelligent Data Analysis, 2016, 20(3): 597-613.
23.	Hénon M. A two-dimensional mapping with a strange attractor. Commun Math Phys, 1976, 50(1): 69-77.
24.	Schiff S J, So P, Chang T, et al. Detecting dynamical interdependence and generalized synchrony through mutual prediction in a neural ensemble. Phys Rev E, 1996, 54(6): 6708-6724.
25.	Vialatte F B, Martin C, Dubois R, et al. A machine learning approach to the analysis of time-frequency maps, and its application to neural dynamics. Neural Netw, 2007, 20(2): 194-209.
26.	Albanese D, Filosi M, Visintainer R, et al. Minerva and minepy: a C engine for the MINE suite and its R, python and MATLAB wrappers. Bioinformatics, 2013, 29(3): 407-408.
27.	Arthur D, Vassilvitskii S. K-means++: the advantages of careful seeding. In Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans:SIAM, 2007: 1027-1035.
28.	Ma X F, Huang X L, Du S D, et al. Symbolic joint entropy reveals the coupling of various brain regions. Phys A, 2018, 490: 1087-1095.
29.	Gerloff C, Richard J, Hadley J, et al. Functional coupling and regional activation of human cortical motor areas during simple, internally paced and externally paced finger movements. Brain, 1998, 121(8): 1513-1531.
30.	Nunez P L, Srinivasan R, Westdorp A F, et al. EEG coherency. Clin Neurophysiol, 1997, 103(5): 499-515.
31.	Zhang Ziqing, Sun Shu, Yi Ming, et al. MIC as an appropriate method to construct the brain functional network. Biomed Res Int, 2015(1): 825136.
32.	Lu C F, Teng S, Hung C I, et al. Reorganization of functional connectivity during the motor task using EEG time-frequency cross mutual information analysis. Clin Neurophysiol, 2011, 122(8): 1569-1579.
33.	Fang Y, Daly J J, Sun J, et al. Functional corticomuscular connection during reaching is weakened following stroke. Clin Neurophysiol, 2009, 120(5): 994-1002.
34.	Meng F, Tong K Y, Chan S T, et al. Cerebral plasticity after subcortical stroke as revealed by cortico-muscular coherence. IEEE Trans Neural Syst Rehabil Eng, 2009, 17(3): 234-243.
35.	Mima T, Hallett M. Corticomuscular coherence: a review. J Clin Neurophysiol, 1999, 16(6): 501-511.
36.	Chen X L, Zhang Y Y, Cheng S C, et al. Transfer spectral entropy and application to functional corticomuscular coupling. IEEE Trans Neural Syst Rehabil Eng, 2019, 27(5): 1092-1102.

1. 谢平, 宋妍, 苏崇钦, 等. 脑卒中患者表面肌电信号与痉挛性肌张力关系分析. 生物医学工程学杂志, 2015, 32(4): 795-801.
2. 舒智林, 李思宜, 于宁波, 等. 一种脑肢融合的神经康复训练在线评价与调整方法. 自动化学报, 2021, 47: 1-11.
3. Baker S N. Oscillatory interactions between sensorimotor cortex and the periphery. Curr Opin Neurobiol, 2007, 17(6): 649-655.
4. Salenius S, Hari R. Synchronous cortical oscillatory activity during motor action. Curr Opin Neurobiol, 2003, 13(6): 678-684.
5. Conway B, Halliday D M, Farmer S F, et al. Synchronization between motor cortex and spinal motoneuronal pool during the performance of a maintained motor task in man. J Physiol, 1995, 489: 917-924.
6. Gwin J T, Ferris D P. Beta- and gamma-range human lower limb corticomuscular coherence. Front Hum Neurosci, 2012, 6: 258.
7. Kristeva R, Patino L, Omlor W. Beta-range cortical motor spectral power and corticomuscular coherence as a mechanism for effective corticospinal interaction during steady-state motor output. Neuroimage, 2007, 36(3): 785-792.
8. 马培培, 陈迎亚, 杜义浩, 等. 中风康复运动中脑电-肌电相干性分析. 生物医学工程学杂志, 2014, 31(5): 971-977.
9. Chen X, Xie P, Zhang Y, et al. Abnormal functional corticomuscular coupling after stroke. Neuroimage Clin, 2018, 19: 147-159.
10. Yu H, Xu W, Zhuang Y, et al. Wavelet coherence analysis of muscle coupling during reaching movement in stroke. Comput Biol Med, 2021, 131: 104263.
11. Mehrkanoon S, Breakspear M, Boonstra T W. The reorganization of corticomuscular coherence during a transition between sensorimotor states. NeuroImage, 2014, 100: 692-702.
12. Mendez-Balbuena I, Huethe F, Schulte-Mönting J, et al. Corticomuscular coherence reflects interindividual differences in the state of the corticomuscular network during low-level static and dynamic forces. Cereb Cortex, 2012, 22(3): 628-638.
13. Tuncel D, Dizibuyuk A, Kiymik M K. Time frequency based coherence analysis between EEG and EMG activities in fatigue duration. J Med Syst, 2010, 34(2): 131-138.
14. Xu Y, Mcclelland V M, Cvetkovi Z, et al. Cortico-muscular coherence with time lag with application to delay estimation. IEEE Trans Biomed Eng, 2017, 64(3): 588-600.
15. Ioannides A A, Mitsis G D. Do we need to consider non-linear information flow in corticomuscular interaction?. Clin Neurophysiol, 2010, 121(3): 272-273.
16. Yang Yuan, Solis-Escalante T, Yao Jun, et al. Nonlinear connectivity in the human stretch reflex assessed by cross-frequency phase coupling. Int J Neural Syst, 2016, 26(8): 1650043.
17. Reshef D N, Reshef Y A, Finucane H K, et al. Detecting novel associations in large data sets. Science, 2011, 334(662): 1518-1524.
18. Su L, Wang L, Shen H, et al. Discriminative analysis of non-linear brain connectivity in schizophrenia: an fMRI study. Front Hum Neurosci, 2013, 7: 702.
19. Tian Yin, Zhang Huiling, Li Peiyang, et al. A complementary method of PCC for the construction of scalp resting-state EEG connectome: maximum information coefficient. IEEE Access, 2019, 7: 27146-27154.
20. Liang T, Zhang Q, Liu X, et al. Time-frequency maximal information coefficient method and its application to functional corticomuscular coupling. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(11): 2515-2524.
21. Liang T, Zhang Q, Liu X, et al. Identifying bidirectional total and non-linear information flow in functional corticomuscular coupling during a dorsiflexion task: a pilot study. J Neuroeng Rehabil, 2021, 18(1): 74.
22. Shao F, Li K, Xu X. Railway accidents analysis based on the improved algorithm of the maximal information coefficient. Intelligent Data Analysis, 2016, 20(3): 597-613.
23. Hénon M. A two-dimensional mapping with a strange attractor. Commun Math Phys, 1976, 50(1): 69-77.
24. Schiff S J, So P, Chang T, et al. Detecting dynamical interdependence and generalized synchrony through mutual prediction in a neural ensemble. Phys Rev E, 1996, 54(6): 6708-6724.
25. Vialatte F B, Martin C, Dubois R, et al. A machine learning approach to the analysis of time-frequency maps, and its application to neural dynamics. Neural Netw, 2007, 20(2): 194-209.
26. Albanese D, Filosi M, Visintainer R, et al. Minerva and minepy: a C engine for the MINE suite and its R, python and MATLAB wrappers. Bioinformatics, 2013, 29(3): 407-408.
27. Arthur D, Vassilvitskii S. K-means++: the advantages of careful seeding. In Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans:SIAM, 2007: 1027-1035.
28. Ma X F, Huang X L, Du S D, et al. Symbolic joint entropy reveals the coupling of various brain regions. Phys A, 2018, 490: 1087-1095.
29. Gerloff C, Richard J, Hadley J, et al. Functional coupling and regional activation of human cortical motor areas during simple, internally paced and externally paced finger movements. Brain, 1998, 121(8): 1513-1531.
30. Nunez P L, Srinivasan R, Westdorp A F, et al. EEG coherency. Clin Neurophysiol, 1997, 103(5): 499-515.
31. Zhang Ziqing, Sun Shu, Yi Ming, et al. MIC as an appropriate method to construct the brain functional network. Biomed Res Int, 2015(1): 825136.
32. Lu C F, Teng S, Hung C I, et al. Reorganization of functional connectivity during the motor task using EEG time-frequency cross mutual information analysis. Clin Neurophysiol, 2011, 122(8): 1569-1579.
33. Fang Y, Daly J J, Sun J, et al. Functional corticomuscular connection during reaching is weakened following stroke. Clin Neurophysiol, 2009, 120(5): 994-1002.
34. Meng F, Tong K Y, Chan S T, et al. Cerebral plasticity after subcortical stroke as revealed by cortico-muscular coherence. IEEE Trans Neural Syst Rehabil Eng, 2009, 17(3): 234-243.
35. Mima T, Hallett M. Corticomuscular coherence: a review. J Clin Neurophysiol, 1999, 16(6): 501-511.
36. Chen X L, Zhang Y Y, Cheng S C, et al. Transfer spectral entropy and application to functional corticomuscular coupling. IEEE Trans Neural Syst Rehabil Eng, 2019, 27(5): 1092-1102.

Journal of Biomedical Engineering

An improved maximal information coefficient algorithm applied in the analysis of functional corticomuscular coupling for stroke patients

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