Increased MRI-based Brain Age in chronic migraine patients

Navarro-González, Rafael, García-Azorín, David, Guerrero-Peral, Ángel L., Planchuelo Gomez, Alvaro, Aja-Fernández, Santiago and de Luis-García, Rodrigo 2023. Increased MRI-based Brain Age in chronic migraine patients. Journal of Headache and Pain 24 (1) , 133. 10.1186/s10194-023-01670-6

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Abstract

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.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Psychology Cardiff University Brain Research Imaging Centre (CUBRIC)
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/, Type: open-access
Publisher:	BioMed Central
ISSN:	1129-2369
Date of First Compliant Deposit:	9 October 2023
Date of Acceptance:	22 September 2023
Last Modified:	11 Oct 2023 11:23
URI:	https://orca.cardiff.ac.uk/id/eprint/163067

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