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

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Abstract

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.

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Notes

  1. https://beta.ichd-3.org/.

  2. There are 262 possible connections, out of which “self-loops” need to be removed (26 possible loops) and only half of possible connections need to be selected, thus the number need to be divided by 2 (this due to the fact that the measured connectivity is undirected).

  3. For the Bonferroni correction, we assumed the most stringent case, i.e. the tested hypotheses are independent, and thus for the required confidence interval of α = 5% and the number of tested hypotheses n = 5 the corrected confidence value would be α/n resulting in 1% (i.e. the required corrected p value should be p ≤ 0.01).

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Acknowledgments

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

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Authors and Affiliations

  1. The Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Haifa, Israel

    Alex Frid, Alla Shifrin, David Yarnitsky & Yelena Granovsky

  2. Department of Neurology, Rambam Medical Center, Haifa, Israel

    Meirav Shor, Alla Shifrin, David Yarnitsky & Yelena Granovsky

Authors
  1. Alex Frid
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  2. Meirav Shor
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  3. Alla Shifrin
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  4. David Yarnitsky
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  5. Yelena Granovsky
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Correspondence to Alex Frid.

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Associate Editor Michael Gower oversaw the review of this article.

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Frid, A., Shor, M., Shifrin, A. et al. A Biomarker for Discriminating Between Migraine With and Without Aura: Machine Learning on Functional Connectivity on Resting-State EEGs. Ann Biomed Eng 48, 403–412 (2020). https://doi.org/10.1007/s10439-019-02357-3

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  • DOI: https://doi.org/10.1007/s10439-019-02357-3

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