Multi-channel hierarchy functional integration analysis between large-scale brain networks for migraine: An fMRI study

Abstract

Migraine is a chronic dysfunction characterized by recurrent pain, but its pathogenesis is still unclear. As a result, more and more methods have been focused on the study of migraine in recent years, including functional magnetic resonance imaging (fMRI), which is a mainstream technique for exploring the neural mechanisms of migraine. In this paper, we systematically investigated the fMRI functional connectivities (FCs) between large-scale brain networks in migraine patients from the perspective of multi-channel hierarchy, including static and dynamic FCs of group and individual levels, where the brain networks were obtained using group independent component analysis. Meanwhile, the corresponding topology properties of static and dynamic FCs networks in migraine patients were statistically compared with those in healthy controls. Furthermore, a graph metrics based method was used to detect the potential brain functional connectivity states in dynamic FCs at individual and group levels, and the corresponding topology properties and specificity of these brain functional connectivity states in migraine patients were explored compared with these in healthy controls. The results showed that the dynamic FCs and corresponding global topology properties among nine large-scale brain networks involved in this study have significant differences between migraine patients and healthy controls, while local topological properties and dynamic fluctuations were easily affected by window-widths. Moreover, the implicit dynamic functional connectivity patterns in migraine patients presented specificity and consistency under different window-widths, which suggested that the dynamic changes in FCs and topology structure between them played a key role in the brain functional activity of migraine. Therefore, it may be provided a new perspective for the clinical diagnosis of migraine.

Keywords

• fMRI
• Group independent component analysis
• Brain networks
• Dynamic functional connectivity
• Graph analysis