Neuroimaging-based subtyping of migraine identifies clinically distinct phenotypes
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 (M1 f + s ) and Migraine Cluster 2 (M2 f + s ). M2 f + s showed…
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