Health science reports · 2025
Characterizing the Morphological Brain Network Topology in Patients With Migraine Using Wavelet Based Morphometry: A Descriptive Cross-Sectional Retrospective Study.
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Prabhashana G D C J, Sirimanne R L T, Amarasinghe A D I, Sampath W K C, Karunasekara P P C R, Ediri Arachchi W M
Аннотация
BACKGROUND AND AIM: There is compelling evidence that gray matter changes associated with migraine, which in turn may alter morphological network topology. The aim of this study is to characterize morphological network topology of the brains of migraineurs and non-migraine subjects using wavelet-based morphometry.
METHODS: 3D, T1W brain images were obtained from 45 patients with migraine and 46 non-migraine subjects. Then, gray matter volume images were developed, and they were decomposed and reconstructed at a level of n = 3 using wavelet-based morphometry. 4D gray matter volumes were constructed for each subject and they were parcellated into 625 anatomical regions, and structural covariance matrices were developed. Each matrix was binarized by applying a series of sparsity thresholds, and global network topological metrics were computed. Finally, two sample t-tests were performed using area under curves of each metric for group-level comparisons of network topology.
RESULTS: Patients with migraine showed increased small worldness (p = 0.003) and global efficiency (p = 0.002) compared to non-migraine subjects. Local efficiency (p = 0.49) and assortativity (p = 0.70) have shown similar characteristics for both groups against network sparsity with no significant differences. Hierarchy (p = 0.41) was largely dispersed in the middle sparsity thresholds (0.15-0.35). The characteristics of synchronization (p = 0.32) between groups were almost the same from 0.05 to 0.4 of network sparsities.
CONCLUSION: Patients with migraine exhibit better integration of information processing and wavelet-based morphometry in combining with graph theory provides valuable information on altered gray matter network topologies in migraineurs.
METHODS: 3D, T1W brain images were obtained from 45 patients with migraine and 46 non-migraine subjects. Then, gray matter volume images were developed, and they were decomposed and reconstructed at a level of n = 3 using wavelet-based morphometry. 4D gray matter volumes were constructed for each subject and they were parcellated into 625 anatomical regions, and structural covariance matrices were developed. Each matrix was binarized by applying a series of sparsity thresholds, and global network topological metrics were computed. Finally, two sample t-tests were performed using area under curves of each metric for group-level comparisons of network topology.
RESULTS: Patients with migraine showed increased small worldness (p = 0.003) and global efficiency (p = 0.002) compared to non-migraine subjects. Local efficiency (p = 0.49) and assortativity (p = 0.70) have shown similar characteristics for both groups against network sparsity with no significant differences. Hierarchy (p = 0.41) was largely dispersed in the middle sparsity thresholds (0.15-0.35). The characteristics of synchronization (p = 0.32) between groups were almost the same from 0.05 to 0.4 of network sparsities.
CONCLUSION: Patients with migraine exhibit better integration of information processing and wavelet-based morphometry in combining with graph theory provides valuable information on altered gray matter network topologies in migraineurs.