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Published on: June 2, 2014
Increased MRI-based Brain Age in chronic migraine patients
Rafael Navarro-González1, David García-Azorín2,3, Ángel L Guerrero-Peral4,5
1Laboratorio de Procesado de Imagen, Universidad de Valladolid, Valladolid, Spain.
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.
Insights
Chronic migraine patients exhibit an older brain age compared to healthy individuals, suggesting distinct brain aging patterns. This neuroimaging biomarker may help identify specific aging characteristics in migraine.
Area of Science:
- Neuroimaging
- Machine Learning
- Brain Aging
Background:
- Migraine is associated with brain structure and function alterations.
- The impact of aging on these brain changes in migraine is not well understood.
- The Brain Age framework offers a novel approach to investigate brain aging in neurological conditions.
Purpose of the Study:
- To investigate brain aging patterns in migraine patients using a machine learning-based Brain Age model.
- To determine if migraine patients exhibit an increased Brain Age Gap (predicted age minus chronological age) compared to healthy controls.
- To explore the relationship between brain age and migraine characteristics.
Main Methods:
- A machine learning model was trained to predict Brain Age using 2,771 T1-weighted MRI scans from healthy subjects.
- The model utilized 1,479 imaging features (morphological and intensity-based) within a 10-fold cross-validation.
- The trained model was applied to a cohort of 247 subjects, including healthy controls, episodic migraine (EM), and chronic migraine (CM) patients.
Main Results:
- Chronic migraine patients demonstrated a significantly increased Brain Age Gap (4.16 years) compared to healthy controls (-0.56 years).
- Episodic migraine patients showed a trend towards an increased Brain Age Gap (1.21 years), but it was not statistically significant.
- No correlation was found between the Brain Age Gap and migraine frequency, duration, or headache frequency. Key imaging features driving age differences were previously linked to migraine.
Conclusions:
- Brain-predicted age serves as a sensitive biomarker for chronic migraine patients.
- This approach reveals distinct brain aging patterns in individuals with chronic migraine.
- Neuroimaging-based brain age estimation can offer insights into the pathophysiology of migraine.
