Hierarchical alteration of brain structural and functional networks in female migraine sufferers

Abstract

Background: Little is known about the changes of brain structural and functional connectivity networks underlying the pathophysiology in migraine. We aimed to investigate how the cortical network reorganization is altered by frequent cortical overstimulation associated with migraine.

Methodology/principal findings: Gray matter volumes and resting-state functional magnetic resonance imaging signal correlations were employed to construct structural and functional networks between brain regions in 43 female patients with migraine (PM) and 43 gender-matched healthy controls (HC) by using graph theory-based approaches. Compared with the HC group, the patients showed abnormal global topology in both structural and functional networks, characterized by higher mean clustering coefficients without significant change in the shortest absolute path length, which indicated that the PM lost optimal topological organization in their cortical networks. Brain hubs related to pain-processing revealed abnormal nodal centrality in both structural and functional networks, including the precentral gyrus, orbital part of the inferior frontal gyrus, parahippocampal gyrus, anterior cingulate gyrus, thalamus, temporal pole of the middle temporal gyrus and the inferior parietal gyrus. Negative correlations were found between migraine duration and regions with abnormal centrality. Furthermore, the dysfunctional connections in patients' cortical networks formed into a connected component and three dysregulated modules were identified involving pain-related information processing and motion-processing visual networks.

Conclusions: Our results may reflect brain alteration dynamics resulting from migraine and suggest that long-term and high-frequency headache attacks may cause both structural and functional connectivity network reorganization. The disrupted information exchange between brain areas in migraine may be reshaped into a hierarchical modular structure progressively.

Figure 1. Largest cluster size as a function of sparsity S for the healthy controls' (HC) structural networks (black line) and patients' with migraine (PM) structural networks (red line).

S = 0.17 is the lowest sparsity value that could guarantee each network was fully connected with all of the nodes.

Figure 2. Between-group differences in the mean clustering coefficient C, normalized clustering coefficient gamma, the shortest path length L, and normalized shortest path length lambda over a range of sparsity values.

(A) Differences between the HC and PM groups in subjects' structural networks. The black solid points represent the 99% confidence intervals of the between-group differences obtained from 5000 permutation tests at each sparsity value. The red open circles describe the mean values and the red solid points indicate significant between-group differences in network metrics. (B) Differences between the HC and PM groups in the subjects' functional networks. The red lines represent the network metrics in the PM. The black lines describe the network metrics in the HC. The horizontal stars indicate the significant between-group differences (p<0.01, FDR corrected).

Figure 3. Correlation between the mean clustering coefficient C/the shortest path length L and migraine duration while controlling for patients

' age. Significant correlation was found in the mean clustering coefficient (r = 0.51, p = 0.005), but not in the shortest absolute path length L.

Figure 4. Significant between-group differences of betweenness centrality.

Regions with abnormal betweenness centralities in patients' structural networks were rendered on the brain surface by visualizing it with the BrainNet viewer (red, HC>PM; blue, HC< PM). The black and grey bar graphs indicate dysregulated brain regions in patients' functional networks (nonparametric permutation test, p<0.01, corrected).

Figure 5. Significant between-group differences in the intensity of the brain connections in PM structural networks (nonparametric permutation test, p = 0.005).

For the abbreviations of the regions, see Table 2.

Figure 6. Significant between-group differences in the intensity of the brain connections in PM functional networks (NBS, p = 0.005).

These dysfunctional connections in patients' cortical networks were formed into a connected component and three dysregulated communities were identified. Besides the topological space, the brain regions were also projected onto the brain surface according to their MNI centroid stereotaxic coordinates. Different colors represent distinct modules. For the abbreviations of the regions, see Table 2. This figure was visualized with the BrainNet viewer.

Figure 7. Correlation between the mean clustering coefficient C/the shortest path length L and the mean connectivity values of dysfunctional connections in the patients

' functional networks.