I am a
Home I AM A Search Login

Papers of the Week

Papers: 14 Sep 2019 - 20 Sep 2019

Human Studies


2020 Jan

Ann Biomed Eng



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


Frid A, Shor M, Shifrin A, Yarnitsky D, Granovsky Y
Ann Biomed Eng. 2020 Jan; 48(1):403-412.
PMID: 31520332.


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.