Skip to main content

Estimating the Asymmetry of Brain Network Organization in Stroke Patients from High-Density EEG Signals

  • Chapter
  • First Online:

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 151))

Abstract

Following a stroke, the functional brain connections are impaired, and there is some evidence that the brain tries to reorganize them to compensate the disruption, establishing novel neural connections. Electroencephalography (EEG) can be used to study the effects of stroke on the brain network organization, indirectly, through the study of brain-electrical connectivity. The main objective of this work is to study the asymmetry in the brain network organization of the two hemispheres in case of a lesion due to stroke (ischemic or hemorrhagic), starting from high-density EEG (HD-EEG) signals. The secondary objective is to show how HD-EEG can detect such asymmetry better than standard low-density EEG. A group of seven stroke patients was recruited and underwent HD-EEG recording in an eye-closed resting state condition. The permutation disalignment index (PDI) was used to quantify the coupling strength between pairs of EEG channels, and a complex network model was constructed for both the right and left hemispheres. The complex network analysis allowed to compare the small-worldness (SW) of the two hemispheres. The impaired hemisphere exhibited a larger SW (\(p < 0.05\)). The analysis conducted using traditional EEG did not allow to observe such differences. In the future, SW could be used as a biomarker to quantify longitudinal patient improvement.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Grefkes, C., Fink, G.R.: Connectivity-based approaches in stroke and recovery of function. Lancet Neurol. 13(2), 206–216 (2014)

    Article  Google Scholar 

  2. Caliandro, P., Vecchio, F., Miraglia, F., Reale, G., Della Marca, G., La Torre, G., Lacidogna, G., Iacovelli, C., Padua, L., Bramanti, P., et al.: Small-world characteristics of cortical connectivity changes in acute stroke. Neurorehabil. Neural Repair 31(1), 81–94 (2017)

    Article  Google Scholar 

  3. Ieracitano, C., Duun-Henriksen, J., Mammone, N., La Foresta, F., Morabito, F.C.: Wavelet coherence-based clustering of EEG signals to estimate the brain connectivity in absence epileptic patients. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 1297–1304. IEEE (2017)

    Google Scholar 

  4. Miraglia, F., Vecchio, F., Bramanti, P., Rossini, P.M.: EEG characteristics in “eyes-open” versus “eyes-closed” conditions: small-world network architecture in healthy aging and age-related brain degeneration. Clin Neurophysiol. 127(2), 1261–1268 (2016)

    Article  Google Scholar 

  5. Frantzidis, C.A., Vivas, A.B., Tsolaki, A., Klados, M.A., Tsolaki, M., Bamidis, P.D.: Functional disorganization of small-world brain networks in mild Alzheimer’s Disease and amnestic Mild Cognitive Impairment: an EEG study using Relative Wavelet Entropy (RWE). Front Aging Neurosci. 6, 224 (2014)

    Article  Google Scholar 

  6. Zeng, H., Dai, G., Kong, W., Chen, F., Wang, L.: A novel nonlinear dynamic method for stroke rehabilitation effect evaluation using EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 25(12), 2488–2497 (2017)

    Article  Google Scholar 

  7. Liu, S., Guo, J., Meng, J., Wang, Z., Yao, Y., Yang, J., Qi, H., Ming, D.: Abnormal EEG complexity and functional connectivity of brain in patients with acute thalamic ischemic stroke. Comput. Math. Methods Med. 2016, 9 (2016)

    MathSciNet  MATH  Google Scholar 

  8. Zappasodi, F., Olejarczyk, E., Marzetti, L., Assenza, G., Pizzella, V., Tecchio, F.: Fractal dimension of EEG activity senses neuronal impairment in acute stroke. PLoS ONE 9(6), e100199 (2014)

    Article  Google Scholar 

  9. Mammone, N., Bonanno, L., De Salvo, S., Marino, S., Bramanti, P., Bramanti, A., Morabito, F.C.: Permutation disalignment index as an indirect, EEG-based, measure of brain connectivity in MCI and ad patients. Int. J. Neural Syst. https://doi.org/10.1142/S0129065717500204

    Article  Google Scholar 

  10. Mammone, N., De Salvo, S., Ieracitano, C., Marino, S., Marra, A., Corallo, F., Morabito, F.C.: A permutation disalignment index-based complex network approach to evaluate longitudinal changes in brain-electrical connectivity. Entropy 19(10), 548 (2017)

    Article  Google Scholar 

  11. Fornito, A., Zalesky, A., Bullmore, E.: Fundamentals of Brain Network Analysis. Academic Press (2016)

    Google Scholar 

  12. Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3), 1059–1069 (2010)

    Article  Google Scholar 

  13. Watts, D.J., Strogatz, S.H.: Collective dynamics of small-world networks. Nature 393(6684), 440–442 (1998)

    Article  Google Scholar 

  14. Bandt, C., Pompe, B.: Permutation entropy: a natural complexity measure for time series. Phys. Rev. Lett. 88(17), 174102 (2002)

    Article  Google Scholar 

  15. Renyi, A.: On measures of information and entropy. In: Proceedings of the Fourth Berkeley Symposium on Mathematics, Statistics and Probability, pp. 547–561 (1961)

    Google Scholar 

  16. Shapiro, S.S., Wilk, M.B.: An analysis of variance test for normality (complete samples). Biometrika 52(3, 4), 591–611 (1965)

    Article  MathSciNet  Google Scholar 

  17. Mann, H.B., Whitney, D.R.: On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Statist. 18(1), 50–60 (1947)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The present work was funded by the Italian Ministry of Health, Project Code GR-2011-02351397.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nadia Mammone .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Mammone, N. et al. (2020). Estimating the Asymmetry of Brain Network Organization in Stroke Patients from High-Density EEG Signals. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Neural Approaches to Dynamics of Signal Exchanges. Smart Innovation, Systems and Technologies, vol 151. Springer, Singapore. https://doi.org/10.1007/978-981-13-8950-4_42

Download citation

Publish with us

Policies and ethics