A robust multimodal brain MRI-based diagnostic model for migraine: validation across different migraine phases and longitudinal follow-up data

Inter-individual variability in symptoms and the dynamic nature of brain pathophysiology present significant challenges in constructing a robust diagnostic model for migraine. In this study, we aimed to integrate different types of magnetic resonance imaging (MRI), providing structural and functional information, and develop a robust machine learning model that classifies migraine patients from healthy controls by testing multiple combinations of hyperparameters to ensure stability across different migraine phases and longitudinally repeated data. Specifically, we constructed a diagnostic model to classify patients with episodic migraine from healthy controls, and validated its performance across ictal and interictal phases, as well as in a longitudinal setting. We obtained T1-weighted and resting-state functional MRI data from 50 patients with episodic migraine and 50 age- and sex-matched healthy controls, with follow-up data collected after one year. Morphological features, including cortical thickness, curvature, and sulcal depth, and functional connectivity features, such as low-dimensional representation of functional connectivity (gradient), degree centrality, and betweenness centrality, were utilized. We employed a regularization-based feature selection method combined with a random forest classifier to construct a diagnostic model. By testing the models with varying feature combinations, penalty terms, and spatial granularities within a strict cross-validation framework, we found that the combination of curvature, sulcal depth, cortical thickness, and functional gradient achieved a robust classification performance. The model performance was assessed using the test dataset and achieved 87% accuracy and 0.94 area under the curve (AUC) at distinguishing migraine patients from healthy controls, with 85%, 0.97 and 84%, 0.93 during the interictal and ictal/peri-ictal phases, respectively. When validated using follow-up data, which was not included during model training, the model achieved 91%, 94%, 89% accuracies and 0.96, 0.94, 0.98 AUC for the total, interictal, and ictal/peri-ictal phases, respectively, confirming its robustness. Feature importance and clinical association analyses exhibited that the somatomotor, limbic, and default mode regions could be reliable markers of migraine. Our findings, which demonstrate a robust diagnostic performance using multimodal MRI features and a machine-learning framework, may offer a valuable approach for clinical diagnosis across diverse cohorts and help alleviate the decision-making burden for clinicians.