Migraine classification using magnetic resonance imaging resting-state functional connectivity data

Published on Aug 1, 2017in Cephalalgia4.438
· DOI :10.1177/0333102416652091
Catherine D. Chong12
Estimated H-index: 12
(Mayo Clinic),
Nathan Gaw4
Estimated H-index: 4
(ASU: Arizona State University)
+ 3 AuthorsTodd J. Schwedt28
Estimated H-index: 28
(Mayo Clinic)
BackgroundThis study used machine-learning techniques to develop discriminative brain-connectivity biomarkers from resting-state functional magnetic resonance neuroimaging (rs-fMRI) data that distinguish between individual migraine patients and healthy controls.MethodsThis study included 58 migraine patients (mean age = 36.3 years; SD = 11.5) and 50 healthy controls (mean age = 35.9 years; SD = 11.0). The functional connections of 33 seeded pain-related regions were used as input for a brain classification algorithm that tested the accuracy of determining whether an individual brain MRI belongs to someone with migraine or to a healthy control.ResultsThe best classification accuracy using a 10-fold cross-validation method was 86.1%. Resting functional connectivity of the right middle temporal, posterior insula, middle cingulate, left ventromedial prefrontal and bilateral amygdala regions best discriminated the migraine brain from that of a healthy control. Migraineurs with longer disease durations were cla...
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