Unsupervised learning on advanced diffusion MRI metrics for clinical characterization of migraine
Abstract
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.
Related
- Contrastive learning and advanced bundle metrics outperform fractional anisotropy for Alzheimer’s disease diffusion MRI prediction: findings from the Beyond FA challenge
- Unsupervised machine learning for clinical risk subtyping and re-fracture risk analysis in osteoporotic vertebral compression fractures.
- Quantitative MRI Characterization of Fluid-Related Phenotypes in Pediatric Epilepsy: Deep Learning-Assisted Assessment of Enlarged Perivascular Space Burden, Regional Distribution, and Clinical Associations
- Self-reported sleep quality, physical activity and anxiety associated with heart rate variability in youth tiers derived via unsupervised machine learning
- Contrastive adversarial mix-up learning for unsupervised cross-modality image segmentation
BibTeX
@article{gallo2026unsupervised,
title = {{Unsupervised learning on advanced diffusion MRI metrics for clinical characterization of migraine}},
author = {Alessandra Gallo and Antonio Tristán-Vega and Ángel L. Guerrero and David García-Azorín and Santiago Aja-Fernández and Rodrigo de Luis-García and Álvaro Planchuelo-Gómez},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-76616-4},
url = {https://www.nature.com/articles/s41598-026-76616-4},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Unsupervised learning on advanced diffusion MRI metrics for clinical characterization of migraine
AU - Alessandra Gallo
AU - Antonio Tristán-Vega
AU - Ángel L. Guerrero
AU - David García-Azorín
AU - Santiago Aja-Fernández
AU - Rodrigo de Luis-García
AU - Álvaro Planchuelo-Gómez
PY - 2026
DA - 2026/10/16
JO - Scientific Reports
DO - 10.1038/s41598-026-76616-4
UR - https://www.nature.com/articles/s41598-026-76616-4
AB - 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.
SN - 2045-2322
ER -