Identification of asymmetrical abnormalities in functional connectivity and brain network topology for migraine...

Research on the neural mechanisms underlying brain asymmetry in patients with migraine patients using fMRI is insufficient. This study proposed using lateralized algorithms for functional connectivity and brain network topology and investigated changes in their lateralization in patients with migraine. In study 1, laterality indices of functional connectivity (LFunctionCorr) and brain network topological properties (LBetweennessCentrality, LDegree, and LStrength) were defined. Differences between migraineurs and normal subjects were compared at whole-brain, half-brain, and region levels. In study 2, laterality indices were used to classify migraine and were validated using independent samples and the segment method for repeatability. In study 3, abnormal brain regions related to migraine were extracted based on the classification results and differences analysis. Study 1 found no significant differences related to in for migraine at the whole-brain level; however, significant differences were identified at the half-brain level for the hemispheric lateralization of the LFunctionCorr, while 11 significantly different brain regions were also identified at the brain region level. Furthermore, the classification accuracy in study 2 was 0.9366. With repeated validation, the accuracy reached 0.8561. Furthermore, after extending the samples according to the segmentation strategy, the classification accuracies were improved to 0.9408 and 0.8585. Study 3 identified 10 crucial brain regions with asymmetrical specificity based on laterality indices distributed across the visual network, the frontoparietal control network, the default mode network, the salience/ventral attention network and the limbic system. The results revealed novel insights and avenues for research into the mechanisms of migraine asymmetry and showed that the laterality indices could be used as a potential diagnostic imaging marker for migraine.