A Biomarker for Discriminating Between Migraine With and Without Aura: Machine Learning on Functional Connectivity on Resting-State EEGs

  • Alex FridEmail author
  • Meirav Shor
  • Alla Shifrin
  • David Yarnitsky
  • Yelena Granovsky
Original Article


Advanced analyses of electroencephalography (EEG) are rapidly becoming an important tool in understanding the brain’s processing of pain. To date, it appears that none have been explored as a way of distinguishing between migraine patients with aura (MWA) vs. those without aura (MWoA). In this work, we apply a mixture of predictive, e.g., classification methods and attribute-selection techniques, and traditional explanatory, e.g., statistical, analyses on functional connectivity measures extracted from EEG signal acquired from at-rest participants (N = 52) during their interictal period and tested them against the distinction between MWA and MWoA. We show that a functional connectivity metric of EEG data obtained during resting state can serve as a sole biomarker to differentiate between MWA and MWoA. Using the proposed analysis, we not only have been able to present high classification results (average classification of 84.62%) but also to discuss the underlying neurophysiological mechanisms upon which our technique is based. Additionally, a more traditional statistical analysis on the selected features reveals that MWoA patients show higher than average connectivity in the Theta band (p = 0.03) at rest than MWAs. We propose that our data-driven analysis pipeline can be used for resting-EEG analysis in any clinical context.


EEG functional connectivity analysis EEG classification Resting state EEG Migraine classification Explanatory machine learning Biomarker 



We thank the Migraine Research Foundation, USA, for supporting the research.


  1. 1.
    Bellman, R. E. Adaptive Control Processes: A Guided Tour. Princeton: Princeton University Press, 1961.CrossRefGoogle Scholar
  2. 2.
    Brighina, F., G. Cosentino, and B. Fierro. Is lack of habituation a biomarker of migraine? A critical perspective. J. Headache Pain 16(S1):A13, 2015.CrossRefGoogle Scholar
  3. 3.
    Buono, V. L., et al. Functional connectivity and cognitive impairment in migraine with and without aura. J. Headache Pain 18(1):72, 2017.CrossRefGoogle Scholar
  4. 4.
    Carter, G., C. Knapp, and A. Nuttall. Estimation of the magnitude-squared coherence function via overlapped fast Fourier transform processing. IEEE Trans. Audio Electroacoust. 21(4):337–344, 1973.CrossRefGoogle Scholar
  5. 5.
    Celka, P. Statistical analysis of the phase-locking value. IEEE Signal Process. Lett. 14(9):577–580, 2007.CrossRefGoogle Scholar
  6. 6.
    Chang, C.-C., and C.-J. Lin. LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3):27, 2011.CrossRefGoogle Scholar
  7. 7.
    Charles, A., and J. M. Hansen. Migraine aura: new ideas about cause, classification, and clinical significance. Curr. Opin. Neurol. 28(3):255–260, 2015.CrossRefGoogle Scholar
  8. 8.
    Cucchiara, B., R. Datta, G. K. Aguirre, K. E. Idoko, and J. Detre. Measurement of visual sensitivity in migraine: validation of two scales and correlation with visual cortex activation. Cephalalgia 35(7):585–592, 2015.CrossRefGoogle Scholar
  9. 9.
    Damoiseaux, J. S., et al. Consistent resting-state networks across healthy subjects. PNAS 103(37):13848–13853, 2006.CrossRefGoogle Scholar
  10. 10.
    Datta, R., G. K. Aguirre, S. Hu, J. A. Detre, and B. Cucchiara. Interictal cortical hyperresponsiveness in migraine is directly related to the presence of aura. Cephalalgia 33(6):365–374, 2013.CrossRefGoogle Scholar
  11. 11.
    de Tommaso, M., S. Stramaglia, D. Marinazzo, G. Trotta, and M. Pellicoro. Functional and effective connectivity in EEG alpha and beta bands during intermittent flash stimulation in migraine with and without aura. Cephalalgia 33(11):938–947, 2013.CrossRefGoogle Scholar
  12. 12.
    de Tommaso, M., G. Trotta, E. Vecchio, K. Ricci, R. Siugzdaite, and S. Stramaglia. Brain networking analysis in migraine with and without aura. J. Headache Pain 18(1):98, 2017.CrossRefGoogle Scholar
  13. 13.
    Frid, A. Differences in phase synchrony of brain regions between regular and dyslexic readers. In: 2014 IEEE 28th Convention of Electrical Electronics Engineers in Israel (IEEEI), 2014, pp. 1–4.Google Scholar
  14. 14.
    Frid, A., and Z. Breznitz. An SVM based algorithm for analysis and discrimination of dyslexic readers from regular readers using ERPs. In: 2012 IEEE 27th Convention of Electrical Electronics Engineers in Israel (IEEEI), 2012, pp. 1–4.Google Scholar
  15. 15.
    Frid, A., and L. M. Manevitz. Analyzing Cognitive Processes from Complex Neuro-physiologically Based Data. In: AMAI, 2019.Google Scholar
  16. 16.
    Granovsky, Y., M. Shor, A. Shifrin, E. Sprecher, D. Yarnitsky, and T. Bar-Shalita. Assessment of responsiveness to everyday non-noxious stimuli in pain-free migraineurs with versus without aura. J. Pain 19(8):943–951, 2018.CrossRefGoogle Scholar
  17. 17.
    Hesse, W., E. Möller, M. Arnold, and B. Schack. The use of time-variant EEG Granger causality for inspecting directed interdependencies of neural assemblies. J. Neurosci. Methods 124(1):27–44, 2003.CrossRefGoogle Scholar
  18. 18.
    Hougaard, A., F. M. Amin, S. Magon, T. Sprenger, E. Rostrup, and M. Ashina. No abnormalities of intrinsic brain connectivity in the interictal phase of migraine with aura. Eur. J. Neurol. 22(4):702-e46, 2015.CrossRefGoogle Scholar
  19. 19.
    Kay, S. M. Modern Spectral Estimation: Theory and Application/Book and Disk. Upper Saddle River: PTR Prentice Hall, 1988.Google Scholar
  20. 20.
    Kira, K., and L. A. Rendell. A practical approach to feature selection. In: Proceedings of the Ninth International Workshop on Machine Learning, San Francisco, CA, USA, 1992, pp. 249–256.Google Scholar
  21. 21.
    Lauritzen, M. Pathophysiology of the migraine aura. The spreading depression theory. Brain 117(Pt 1):199–210, 1994.CrossRefGoogle Scholar
  22. 22.
    LeCun, Y., Y. Bengio, and G. Hinton. Deep learning. Nature 521(7553):436–444, 2015.CrossRefGoogle Scholar
  23. 23.
    Lev, R., Y. Granovsky, and D. Yarnitsky. Enhanced pain expectation in migraine: EEG-based evidence for impaired prefrontal function. Headache 53(7):1054–1070, 2013.CrossRefGoogle Scholar
  24. 24.
    Mendonça-de-Souza, M., et al. Resilience in migraine brains: decrease of coherence after photic stimulation. Front. Hum. Neurosci. 6:207, 2012.CrossRefGoogle Scholar
  25. 25.
    Nawa, N. E., and H. Ando. Classification of self-driven mental tasks from whole-brain activity patterns. PLoS ONE 9(5):e97296, 2014.CrossRefGoogle Scholar
  26. 26.
    Rabiner, L. R., and B. Gold. Theory and Application of Digital Signal Processing, F First (Edition ed.). Englewood Cliffs, NJ: Prentice Hall, 1975.Google Scholar
  27. 27.
    Raichle, M. E., A. M. MacLeod, A. Z. Snyder, W. J. Powers, D. A. Gusnard, and G. L. Shulman. A default mode of brain function. Proc. Natl. Acad. Sci. 98(2):676–682, 2001.CrossRefGoogle Scholar
  28. 28.
    Russell, M. B., and J. Olesen. A nosographic analysis of the migraine aura in a general population. Brain 119(Pt 2):355–361, 1996.CrossRefGoogle Scholar
  29. 29.
    Sand, T., N. Zhitniy, L. R. White, and L. J. Stovner. Visual evoked potential latency, amplitude and habituation in migraine: a longitudinal study. Clin. Neurophysiol. 119(5):1020–1027, 2008.CrossRefGoogle Scholar
  30. 30.
    Tfelt-Hansen, P. C. History of migraine with aura and cortical spreading depression from 1941 and onwards. Cephalalgia 30(7):780–792, 2010.CrossRefGoogle Scholar
  31. 31.
    Welch, P. The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust. 15(2):70–73, 1967.CrossRefGoogle Scholar
  32. 32.
    Wilkins, L. W. Visual cortex hyperexcitability in migraine in response to sound-induced flash illusions. Neurology 86(12):1172, 2016.CrossRefGoogle Scholar

Copyright information

© Biomedical Engineering Society 2019

Authors and Affiliations

  1. 1.The Rappaport Faculty of MedicineTechnion – Israel Institute of TechnologyHaifaIsrael
  2. 2.Department of NeurologyRambam Medical CenterHaifaIsrael

Personalised recommendations