RT info:eu-repo/semantics/article
T1 Increased MRI-based Brain Age in chronic migraine patients
A1 Navarro González, Rafael
A1 García Azorín, David
A1 Guerrero Peral, Angel Luis
A1 Planchuelo Gómez, Álvaro
A1 Aja Fernández, Santiago
A1 Luis García, Rodrigo de
K1 Biomarkers
K1 Brain age
K1 Machine learning
K1 Migraine disorders
K1 Neuroimaging
AB Introduction: Neuroimaging has revealed that migraine is linked to alterations in both the structure and function of the brain. However, the relationship of these changes with aging has not been studied in detail. Here we employ the Brain Age framework to analyze migraine, by building a machine-learning model that predicts age from neuroimaging data. We hypothesize that migraine patients will exhibit an increased Brain Age Gap (the difference between the predicted age and the chronological age) compared to healthy participants.Methods: We trained a machine learning model to predict Brain Age from 2,771 T1-weighted magnetic resonance imaging scans of healthy subjects. The processing pipeline included the automatic segmentation of the images, the extraction of 1,479 imaging features (both morphological and intensity-based), harmonization, feature selection and training inside a 10-fold cross-validation scheme. Separate models based only on morphological and intensity features were also trained, and all the Brain Age models were later applied to a discovery cohort composed of 247 subjects, divided into healthy controls (HC, n=82), episodic migraine (EM, n=91), and chronic migraine patients (CM, n=74).Results: CM patients showed an increased Brain Age Gap compared to HC (4.16 vs -0.56 years, P=0.01). A smaller Brain Age Gap was found for EM patients, not reaching statistical significance (1.21 vs -0.56 years, P=0.19). No associations were found between the Brain Age Gap and headache or migraine frequency, or duration of the disease. Brain imaging features that have previously been associated with migraine were among the main drivers of the differences in the predicted age. Also, the separate analysis using only morphological or intensity-based features revealed different patterns in the Brain Age biomarker in patients with migraine.Conclusion: The brain-predicted age has shown to be a sensitive biomarker of CM patients and can help reveal distinct aging patterns in migraine.
PB Springer Nature
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/70630
UL https://uvadoc.uva.es/handle/10324/70630
LA eng
NO The Journal of Headache and Pain, vol. 24, n. 1, p. 133
NO Producción Científica
DS UVaDOC
RD 22-nov-2024