Advertisement

Brain Topography

, Volume 32, Issue 2, pp 304–314 | Cite as

Brain Network Reconfiguration During Motor Imagery Revealed by a Large-Scale Network Analysis of Scalp EEG

  • Fali Li
  • Chanlin Yi
  • Limeng Song
  • Yuanling Jiang
  • Wenjing Peng
  • Yajing Si
  • Tao Zhang
  • Rui Zhang
  • Dezhong Yao
  • Yangsong Zhang
  • Peng XuEmail author
Original Paper
  • 164 Downloads

Abstract

Mentally imagining rather physically executing the motor behaviors is defined as motor imagery (MI). During MI, the mu rhythmical oscillation of cortical neurons is the event-related desynchronization (ERD) subserving the physiological basis of MI-based brain-computer interface. In our work, we investigated the specific brain network reconfiguration from rest idle to MI task states, and also probed the underlying relationship between the brain network reconfiguration and MI related ERD. Findings revealed that comparing to rest state, the MI showed the enhanced motor area related linkages and the deactivated activity of default mode network. In addition, the reconfigured network index was closely related to the ERDs, i.e., the higher the reconfigured network index was, the more obvious the ERDs were. These findings consistently implied that the reconfiguration from rest to task states underlaid the reallocation of related brain resources, and the efficient brain reconfiguration corresponded to a better MI performance, which provided the new insights into understanding the mechanism of MI as well as the potential biomarker to evaluate the rehabilitation quality for those patients with deficits of motor function.

Keywords

Motor imagery Event-related desynchronization Reconfigured network index Network reconfiguration 

Notes

Acknowledgements

This work was supported by the National Key Research and Development Plan of China (#2017YFB1002501), the National Natural Science Foundation of China (#61522105, #61603344, #81401484, and #81330032), the Open Foundation of Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology (No. HNBBL17001), and ChengDu’s HuiMin projects of science and technology in 2013.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of Institution Research Ethics Board of the University of Electronic Science and Technology of China, and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

References

  1. Blankertz B, Sannelli C, Haider S et al (2010) Neurophysiological predictor of SMR-based BCI performance. NeuroImage 51(4):1303–1309.  https://doi.org/10.1016/j.neuroimage.2010.03.022 Google Scholar
  2. Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10(3):186–198.  https://doi.org/10.1038/nrn2575 Google Scholar
  3. Burianová H, Marstaller L, Sowman P et al (2013) Multimodal functional imaging of motor imagery using a novel paradigm. NeuroImage 71:50–58.  https://doi.org/10.1016/j.neuroimage.2013.01.001 Google Scholar
  4. Chen AC, Feng W, Zhao H, Yin Y, Wang P (2008) EEG default mode network in the human brain: spectral regional field powers. NeuroImage 41(2):561–574.  https://doi.org/10.1016/j.neuroimage.2007.12.064 Google Scholar
  5. Cole MW, Bassett DS, Power JD, Braver TS, Petersen SE (2014) Intrinsic and task-evoked network architectures of the human brain. Neuron 83(1):238–251.  https://doi.org/10.1016/j.neuron.2014.05.014 Google Scholar
  6. Douw L, Schoonheim M, Landi D et al (2011) Cognition is related to resting-state small-world network topology: an magnetoencephalographic study. Neuroscience 175:169–177.  https://doi.org/10.1016/j.neuroscience.2010.11.039 Google Scholar
  7. Fransson P (2006) How default is the default mode of brain function?: further evidence from intrinsic BOLD signal fluctuations. Neuropsychologia 44(14):2836–2845.  https://doi.org/10.1016/j.neuropsychologia.2006.06.017 Google Scholar
  8. Friedrich EV, McFarland DJ, Neuper C, Vaughan TM, Brunner P, Wolpaw JR (2009) A scanning protocol for a sensorimotor rhythm-based brain-computer interface. Biol Psychol 80(2):169–175.  https://doi.org/10.1016/j.biopsycho.2008.08.004 Google Scholar
  9. Friston KJ, Harrison L, Penny W (2003) Dynamic causal modelling. NeuroImage 19(4):1273–1302.  https://doi.org/10.1016/S1053-8119(03)00202-7 Google Scholar
  10. Graimann B, Huggins J, Levine S, Pfurtscheller G (2002) Visualization of significant ERD/ERS patterns in multichannel EEG and ECoG data. Clin Neurophysiol 113(1):43–47.  https://doi.org/10.1016/S1388-2457(01)00697-6 Google Scholar
  11. Iturria-Medina Y, Sotero RC, Canales-Rodríguez EJ, Alemán-Gómez Y, Melie-García L (2008) Studying the human brain anatomical network via diffusion-weighted MRI and Graph Theory. NeuroImage 40(3):1064–1076.  https://doi.org/10.1016/j.neuroimage.2007.10.060 Google Scholar
  12. Kaufmann T, Alnaes D, Brandt CL et al (2017) Task modulations and clinical manifestations in the brain functional connectome in 1615 fMRI datasets. NeuroImage 147:243–252.  https://doi.org/10.1016/j.neuroimage.2016.11.073 Google Scholar
  13. Krienen FM, Yeo BT, Buckner RL (2014) Reconfigurable task-dependent functional coupling modes cluster around a core functional architecture. Phil Trans R Soc B 369(1653):20130526.  https://doi.org/10.1098/Rstb.2013.0526 Google Scholar
  14. Li Y, Long J, Yu T, Yu Z, Wang C, Zhang H, Guan C (2010) An EEG-based BCI system for 2-D cursor control by combining Mu/Beta rhythm and P300 potential. IEEE Trans Biomed Eng 57(10):2495–2505.  https://doi.org/10.1109/Tbme.2010.2055564 Google Scholar
  15. Li Y, Pan J, Wang F, Yu Z (2013) A hybrid BCI system combining P300 and SSVEP and its application to wheelchair control. IEEE Trans Biomed Eng 60(11):3156–3166.  https://doi.org/10.1109/Tbme.2013.2270283 Google Scholar
  16. Li F, Liu T, Wang F et al (2015) Relationships between the resting-state network and the P3: evidence from a scalp EEG study. Sci Rep 5:15129.  https://doi.org/10.1038/Srep15129 Google Scholar
  17. Li F, Chen B, Li H et al (2016a) The time-varying networks in P300: a task-evoked EEG study. IEEE Trans Neural Syst Rehab Eng 24(7):725–733.  https://doi.org/10.1109/Tnsre.2016.2523678 Google Scholar
  18. Li Y, Pan J, Long J, Yu T, Wang F, Yu Z, Wu W (2016b) Multimodal BCIs: target detection, multidimensional control, and awareness evaluation in patients with disorder of consciousness. Proc IEEE 104(2):332–352.  https://doi.org/10.1109/Jproc.2015.2469106 Google Scholar
  19. Li F, Peng W, Jiang Y et al (2018) The dynamic brain networks of motor imagery: time-varying causality analysis of scalp EEG. Int J Neural Syst:1850016.  https://doi.org/10.1142/s0129065718500168
  20. Long J, Li Y, Wang H, Yu T, Pan J, Li F (2012) A hybrid brain computer interface to control the direction and speed of a simulated or real wheelchair. IEEE Trans Neural Syst Rehab Eng 20(5):720–729.  https://doi.org/10.1109/Tnsre.2012.2197221 Google Scholar
  21. Lotze M, Halsband U (2006) Motor imagery. J Physiol-Paris 99(4):386–395.  https://doi.org/10.1016/j.jphysparis.2006.03.012
  22. Miller KJ, Schalk G, Fetz EE, den Nijs M, Ojemann JG, Rao RP (2010) Cortical activity during motor execution, motor imagery, and imagery-based online feedback. Proc Natl Acad Sci USA 107(9):4430–4435.  https://doi.org/10.1073/pnas.1002462107 Google Scholar
  23. Mulder T (2007) Motor imagery and action observation: cognitive tools for rehabilitation. J Neural Transm 114(10):1265–1278.  https://doi.org/10.1007/s00702-007-0763-z Google Scholar
  24. Pfurtscheller G (2001) Functional brain imaging based on ERD/ERS. Vision Res 41(10):1257–1260.  https://doi.org/10.1016/S0042-6989(00)00235-2 Google Scholar
  25. Pfurtscheller G, Da Silva FL (1999) Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 110(11):1842–1857.  https://doi.org/10.1016/S1388-2457(99)00141-8 Google Scholar
  26. Pfurtscheller G, Neuper C (1997) Motor imagery activates primary sensorimotor area in humans. Neurosci Lett 239(2):65–68.  https://doi.org/10.1016/S0304-3940(97)00889-6 Google Scholar
  27. Pilgramm S, de Haas B, Helm F, Zentgraf K, Stark R, Munzert J, Krüger B (2016) Motor imagery of hand actions: Decoding the content of motor imagery from brain activity in frontal and parietal motor areas. Hum Brain Mapp 37(1):81–93.  https://doi.org/10.1002/hbm.23015 Google Scholar
  28. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL (2001) A default mode of brain function. Proc Natl Acad Sci USA 98(2):676–682.  https://doi.org/10.1073/pnas.98.2.676 Google Scholar
  29. Ramos-Loyo J, Gonzalez-Garrido AA, Amezcua C, Guevara MA (2004) Relationship between resting alpha activity and the ERPs obtained during a highly demanding selective attention task. Int J Psychophysiol 54(3):251–262.  https://doi.org/10.1016/j.ijpsycho.2004.05.008 Google Scholar
  30. Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52(3):1059–1069.  https://doi.org/10.1016/j.neuroimage.2009.10.003 Google Scholar
  31. Sakkalis V (2011) Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG. Comput Biol Med 41(12):1110–1117.  https://doi.org/10.1016/j.compbiomed.2011.06.020 Google Scholar
  32. Schultz DH, Cole MW (2016) Higher Intelligence Is Associated with Less Task-Related Brain Network Reconfiguration. J Neurosci 36(33):8551–8561.  https://doi.org/10.1523/Jneurosci.0358-16.2016 Google Scholar
  33. Sharma N, Baron J-C (2013) Does motor imagery share neural networks with executed movement: a multivariate fMRI analysis. Front Hum Neurosci 7:564.  https://doi.org/10.3389/Fnhum.2013.00564 Google Scholar
  34. Sharma N, Pomeroy VM, Baron J-C (2006) Motor imagery a backdoor to the motor system after stroke? Stroke 37(7):1941–1952.  https://doi.org/10.1161/01.Str.0000226902.43357.Fc Google Scholar
  35. Singh KD, Fawcett I (2008) Transient and linearly graded deactivation of the human default-mode network by a visual detection task. NeuroImage 41(1):100–112.  https://doi.org/10.1016/j.neuroimage.2008.01.051 Google Scholar
  36. Sporns O, Tononi G, Edelman GM (2000) Connectivity and complexity: the relationship between neuroanatomy and brain dynamics. Neural Networks 13(8):909–922.  https://doi.org/10.1016/S0893-6080(00)00053-8 Google Scholar
  37. Stam CV, Van Straaten E (2012) The organization of physiological brain networks. Clin Neurophysiol 123(6):1067–1087.  https://doi.org/10.1016/j.clinph.2012.01.011 Google Scholar
  38. Toppi J, Petti M, Mattia D, Babiloni F, Astolfi L (2015) Time-varying effective connectivity for investigating the neurophysiological basis of cognitive processes. In: Sakkalis V (ed) Modern electroencephalographic assessment techniques: theory and applications. Springer New York, New York, pp 171–204.  https://doi.org/10.1007/7657_2014_69 Google Scholar
  39. van den Heuvel MP, Hulshoff Pol HE (2010) Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol 20(8):519–534.  https://doi.org/10.1016/j.euroneuro.2010.03.008 Google Scholar
  40. van den Heuvel MP, Stam CJ, Kahn RS, Pol HEH (2009) Efficiency of functional brain networks and intellectual performance. J Neurosci 29(23):7619–7624.  https://doi.org/10.1523/Jneurosci.1443-09.2009 Google Scholar
  41. Xu P, Xiong X, Xue Q et al (2014a) Differentiating between psychogenic nonepileptic seizures and epilepsy based on common spatial pattern of weighted EEG resting networks. IEEE Trans Biomed Eng 61(6):1747–1755.  https://doi.org/10.1109/TBME.2014.2305159 Google Scholar
  42. Xu P, Xiong X, Xue Q et al (2014b) Recognizing mild cognitive impairment based on network connectivity analysis of resting EEG with zero reference. Physiol Meas 35(7):1279–1298.  https://doi.org/10.1088/0967-3334/35/7/1279 Google Scholar
  43. Yan J, Sun J, Guo X, Jin Z, Li Y, Li Z, Tong S (2013) Motor imagery cognitive network after left ischemic stroke: study of the patients during mental rotation task. PLoS ONE 8(10):e77325.  https://doi.org/10.1371/journal.pone.0077325 Google Scholar
  44. Yao Z, Zhang Y, Lin L, Zhou Y, Xu C, Jiang T, Initiative AsDN (2010) Abnormal cortical networks in mild cognitive impairment and Alzheimer’s disease. PLoS Comput Biol 6(11):e1001006.  https://doi.org/10.1371/journal.pcbi.1001006 Google Scholar
  45. Yu T, Li Y, Long J, Gu Z (2012) Surfing the internet with a BCI mouse. J Neural Eng 9(3):036012.  https://doi.org/10.1088/1741-2560/9/3/036012 Google Scholar
  46. Zhang Z, Liao W, Chen H et al (2011) Altered functional-structural coupling of large-scale brain networks in idiopathic generalized epilepsy. Brain 134(10):2912–2928.  https://doi.org/10.1093/brain/awr223 Google Scholar
  47. Zhang R, Yao D, Valdés-Sosa PA et al (2015) Efficient resting-state EEG network facilitates motor imagery performance. J Neural Eng 12(6):066024.  https://doi.org/10.1088/1741-2560/12/6/066024 Google Scholar
  48. Zhang T, Liu T, Li F et al (2016) Structural and functional correlates of motor imagery BCI performance: Insights from the patterns of fronto-parietal attention network. NeuroImage 134:475–485.  https://doi.org/10.1016/j.neuroimage.2016.04.030 Google Scholar
  49. Zhou G, Liu P, He J et al (2012) Interindividual reaction time variability is related to resting-state network topology: an electroencephalogram study. Neuroscience 202:276–282.  https://doi.org/10.1016/j.neuroscience.2011.11.048 Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.School of Electrical EngineeringZhengzhou UniversityZhengzhouChina
  3. 3.School of Life Science and Technology, Center for Information in MedicineUniversity of Electronic Science and Technology of ChinaChengduChina
  4. 4.School of Computer Science and TechnologySouthwest University of Science and TechnologyMianyangChina

Personalised recommendations