5.0 T MRI reveals characteristic alterations in visual and somatomotor networks associated with migraine chronification: a single-center prospective cohort study.

BACKGROUND AND

OBJECTIVES

Migraine is increasingly acknowledged as a disorder of large-scale brain network hierarchy rather than a merely focal dysfunction. However, the neural substrates driving the transition from episodic migraine (EM) to chronic migraine (CM)-a key pathological process in migraine chronification, remain poorly delineated.

METHODS

Using 5.0 T MRI, we examined 122 participants (30 healthy controls [HC], 66 EM, and 16 CM). Morphometric INverse Divergence (MIND)-based structural similarity networks and resting-state functional connectivity (FC) matrices were constructed from 3D T1 and rs-fMRI data parcellated with the Schaefer-400 atlas. Diffusion map embedding was applied to derive low-dimensional gradients indexing macroscale cortical hierarchy. Group differences in the principal gradient (G1) were assessed using one-way ANOVA with FDR-corrected post hoc tests. Partial Spearman correlations were used to link regional G1 values with attack frequency, disease duration, and anxiety (HAMA), depression (HAMD), and sleep quality (PSQI) scores.

RESULTS

Both MIND- and FC-derived gradients revealed a progressive flattening of the G1 along the HC-EM-CM continuum. These alterations were most prominent in the visual and somatomotor networks. Across the top overlapping regions, lower G1 values, reflecting diminished hierarchical segregation, were consistently associated with higher HAMA and HAMD scores and poorer sleep quality on the PSQI after FDR correction, particularly within visual–somatomotor network.

CONCLUSIONS

5.0 T MRI demonstrated a consistent, cross-modal disruption of the cortical hierarchy linked to migraine chronification, emphasizing the reduced hierarchical differentiation of visual-sensorimotor integration and its coupling with affective and sleep dysfunction. Gradient-based metrics may provide promising imaging markers for tracking disease progression.