Dual-attention temporal graph neural network on resting-state fMRI dynamic functional connectivity identifies...

Migraine chronification remains difficult to characterize at the individual level, and robust baseline imaging markers associated with subsequent conversion from episodic migraine (EM) to chronic migraine (CM) are lacking. Resting-state functional MRI (rs-fMRI) dynamic functional connectivity (dFC) captures transient brain network interactions, but conventional approaches provide limited interpretability for clinical translation.

We enrolled 195 participants at baseline, including 95 healthy controls (HC) and 100 patients with episodic migraine at baseline. All migraine patients underwent baseline rs-fMRI during the interictal phase and were followed for 12 months; 70 remained EM (non-converters) and 30 converted to CM. Sliding-window dFC matrices (55-TR window, 2-TR step) were constructed from 142 regions of interest and represented as temporal graph sequences. We developed an interpretable temporal graph neural network integrating a two-layer graph isomorphism network, a gated recurrent unit, and dual spatial-temporal attention mechanisms to quantify node-level ("where") and window-level ("when") importance. A multilayer perceptron performed three-class discrimination among HC, EM non-converters, and CM converters. Model performance was evaluated using stratified ten-fold cross-validation. Edge-wise ANCOVA controlling for age, sex, and mean framewise displacement with false discovery rate correction was applied to dFC matrices at key temporal-attention windows and within k-means-derived dFC states.

Four recurrent dFC states were identified. State 3 showed the highest fractional occupancy and mean dwell time and was the only state with significant between-group edge-wise differences after correction. Compared with HC, migraine groups exhibited altered connectivity involving sensory, attention, default-mode, and subcortical systems. CM converters showed additional baseline abnormalities involving sensorimotor-visual, default-mode, ventral attention, and limbic-related circuits. Spatial attention shifted from occipital-frontoparietal hubs in HC to frontal-insular prominence in EM non-converters and a frontoparietal-limbic configuration in CM converters. Temporal attention peaked at window 23, where chronification-related abnormalities were most evident. The model achieved AUCs of 0.817(HC), 0.832(EM non-converters), and 0.874(CM converters), with a macro-AUC of 0.841 (95% CI 0.77-0.91). Findings were robust across alternative window lengths and step sizes.

This rs-fMRI dFC-based temporal graph learning framework identified baseline spatiotemporal network patterns associated with subsequent migraine chronification and generated individualized time-resolved importance maps. Although the model showed promising internal discriminative performance, these findings should be interpreted as risk-related pattern identification rather than as evidence of a clinically validated prediction tool. External prospective multicenter validation is required before clinical translation.