Unsupervised learning on advanced diffusion MRI metrics for clinical characterization of migraine

Current migraine classification does not fully capture its clinical heterogeneity, and no objective biomarkers are available for diagnosis or progression. Neuroimaging, especially Magnetic Resonance Imaging (MRI), offers promising biomarkers; however, diffusion tensor imaging (DTI) oversimplifies diffusion properties, motivating the development of advanced approaches compatible with clinical routine. We applied unsupervised machine learning to diffusion MRI data to explore patient stratification and evaluate whether sophisticated diffusion techniques, such as AMURA, provide information complementary to DTI.

A total of 107 patients with migraine (56 chronic [CM], 51 episodic [EM]) were included. Diffusion metrics from 48 white matter regions were extracted using standard DTI parameters and advanced AMURA descriptors, alone or combined with DTI. After dimensionality reduction and feature selection, unsupervised clustering was performed using K-means, Agglomerative, and Spectral algorithms.

Two clusters consistently emerged, each characterized by distinct clinical features. DTI mainly separated patients based on chronicity-related features, particularly time since onset of CM, and showed an incremental longitudinal association with topiramate response. AMURA-based clusters were associated with migraine frequency, disease duration, and medication overuse headache.

These results position the combination of advanced diffusion descriptors and unsupervised learning as an exploratory framework for migraine characterization requiring independent validation. We thank the Laboratorio de Técnicas Instrumentales (Universidad de Valladolid). This work was supported by research grants PID2024-162453NB-I00 and PID2024-158963NB-I00, funded by MICIU/AEI/https://doi.org/10.13039/501100011033 (MICIU: Ministerio de Ciencia, Innovación y Universidades, Spain) and the European Regional Development Fund, European Union.

Further support was provided by grant GRS 1727/A/18 from Gerencia Regional de Salud CyL, and by grant VA156P24 from Junta de Castilla y León, co-financed by ERDF. Image Processing Laboratory, Universidad de Valladolid, Valladolid, 47011, Spain Alessandra Gallo, Antonio Tristán-Vega, Santiago Aja-Fernández, Rodrigo de Luis-García & Álvaro Planchuelo-Gómez LPI-BIVa, Valladolid Health Research Institute (IBioVALL), Valladolid, 47010, Spain Headache Unit, Department of Neurology, Hospital Clínico Universitario de Valladolid, Valladolid, 47003, Spain Department of Medicine, dermatology and toxicology, Faculty of Medicine, Universidad de Valladolid, Valladolid, 47005, Spain INCEVAL, Valladolid Health Research Institute (IBioVALL), Valladolid, 47003, Spain Department of Neurology, Hospital Universitario Río Hortega de Valladolid, Valladolid, 47012, Spain Correspondence to Álvaro Planchuelo-Gómez. The authors declare no competing interests.

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