Migraine classification using magnetic resonance imaging resting-state functional connectivity data.
Background This 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. Methods This 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. Results The 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 classified more accurately (>14 years; 96.7% accuracy) compared to migraineurs with shorter disease durations (≤14 years; 82.1% accuracy). Conclusions Classification of migraine using rs-fMRI provides insights into pain circuits that are altered in migraine and could potentially contribute to the development of a new, noninvasive migraine biomarker. Migraineurs with longer disease burden were classified more accurately than migraineurs with shorter disease burden, potentially indicating that disease duration leads to reorganization of brain circuitry.
1 Mayo Clinic Arizona, Department of Neurology, Phoenix, AZ, USA.
Catherine D
CD
Chong
2 Arizona State University, School of Computing, Informatics and Decision Systems Engineering, Tempe, AZ, USA.
Nathan
N
Gaw
2 Arizona State University, School of Computing, Informatics and Decision Systems Engineering, Tempe, AZ, USA.
Yinlin
Y
Fu
2 Arizona State University, School of Computing, Informatics and Decision Systems Engineering, Tempe, AZ, USA.
Jing
J
Li
2 Arizona State University, School of Computing, Informatics and Decision Systems Engineering, Tempe, AZ, USA.
Teresa
T
Wu
1 Mayo Clinic Arizona, Department of Neurology, Phoenix, AZ, USA.
Todd J
TJ
Schwedt
D000328:Adult; D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008881:Migraine Disorders / Q000145:classification / Q000000981:diagnostic imaging* / Q000503:physiopathology; D009434:Neural Pathways / Q000000981:diagnostic imaging / Q000503:physiopathology
Migraine; classification; magnetic resonance imaging; neuroimaging; principal component analysis; resting-state functional connectivity