Neuroimaging-based subtyping of migraine identifies clinically distinct phenotypes, 2026, Jaiashre Sridhar et al

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Abstract

Background​

Integrating brain structure and function may help characterize neurobiological heterogeneity in migraine alongside symptom presentation.

Aim​

To apply a multimodal, exploratory, data-driven approach to identify migraine subgroups using structural and functional MRI, and to describe the clinical characteristics of the resulting subgroups.

Methods​

Resting-state functional connectivity (FC) across cortical and subcortical regions, along with structural measures including cortical thickness, cortical volume, and subcortical volumes, were extracted from 111 individuals with migraine (75 chronic, 36 episodic) classified according to ICHD-3 criteria. After dimensionality reduction using principal component analysis, hierarchical agglomerative clustering was applied to identify multimodal imaging-derived subgroups. For comparison, secondary unimodal clustering models were constructed using functional-only and structural-only feature sets. The optimal number of clusters was determined using silhouette coefficients, and clustering concordance across models was quantified using the Adjusted Rand Index (ARI). Group differences in clinical characteristics, FC, and cortical and subcortical structure were assessed using covariate-adjusted statistical models with false discovery rate (FDR) correction.

Results​

Multimodal clustering identified two subgroups with distinct clinical and imaging profiles, Migraine Cluster 1 (M1f + s) and Migraine Cluster 2 (M2f + s). M2f + s showed older age, longer disease duration, greater migraine disability, widespread increases in cortical-subcortical FC (including Dorsal Attention, Somatomotor, and Visual networks), and reduced cortical volumes across frontal, parietal, temporal, and insular regions compared with M1f + s. This subgroup also exhibited increased connectivity relative to controls. In contrast, M1f + s showed preserved cortical structure and stronger Control-network–subcortical connectivity compared to M2f + s, and no significant functional or structural deviations from controls. Unimodal analyses revealed that Functional-only clustering aligned moderately with the multimodal cluster solution (ARI = 0.427), showing that FC was a primary determinant of the multimodal cluster structure, whereas structural-only clustering showed negligible overlap (ARI = 0.001), reflecting an orthogonal dimension of heterogeneity captured by structural variation.

Conclusion​

Data-driven multimodal neuroimaging-based clustering in migraine identified two subgroups with distinct clinical and imaging patterns, highlighting heterogeneity and providing a framework for further investigation of imaging-informed characterization.
Study

 

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Brain imaging reveals migraine headache subtypes, Stanford Medicine researchers find

"When they let the computer group their data into clusters, fMRI was more predictive of differences between patients than was the structural imaging. One of the two subtypes, which the scientists call cluster 1, seemed closer to the control group in their brain imaging — this group also had less severe migraine headaches overall. The other subtype, cluster 2, showed big differences in the blood flow between the cortex and subcortical regions of the brain as compared with the other subtype and the control group.

The data shows that patients with the cluster 2 subtype have a different response to sensory input than do non-migraine people and those in cluster 1, Cowan said. It makes evolutionary sense that the brain triggers pain in response to certain sensory inputs — pain makes us want to retreat, and some new-to-us sensations might prove dangerous. But for migraine sufferers, the brain seems to be going over the top, inducing pain in response to daily sensory experiences.

Patients in cluster 2 also had distinct clinical characteristics: They were older, had longer-lasting migraines and were more likely to be disabled by their condition. Overall, cluster 2 seems to have more severe migraines, Cowan said. But interestingly, there was no difference in the frequency of migraine headaches between the two groups, suggesting that the canonical classification of chronic versus episodic migraine might not fully reflect the biology of the disease.

Cowan and his colleagues are now working on migraine classifications based on blood biomarkers and detailed clinical features; they also want to determine whether these subtype classifications can predict treatment response — especially whether someone who doesn’t meet the criteria for chronic migraine might benefit from preventive, daily treatment. And because many patients won’t be able to get an fMRI due to the method’s expense, the scientists are seeking a set of clinical criteria that line up with the biological subgroups."