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Characterizing the Morphological Brain Network Topology in Patients With Migraine Using Wavelet Based Morphometry: A
G D C J Prabhashana1, R L T Sirimanne1, A D I Amarasinghe1
1Department of Radiography and Radiotherapy, Faculty of Allied Health Sciences General Sir John Kotelawala Defense University Sri Lanka.
Health Science Reports
|September 19, 2025
Summary
Migraine patients show altered brain network topology with increased small worldness and global efficiency. Wavelet-based morphometry reveals enhanced information processing integration in migraineurs.
Area of Science:
- Neuroimaging
- Graph Theory
- Brain Network Topology
Background:
- Migraine is associated with gray matter changes that may affect brain network topology.
- Understanding these alterations is crucial for migraine research.
Purpose of the Study:
- To characterize the morphological network topology of brains in migraineurs versus non-migraine subjects.
- To utilize wavelet-based morphometry for this analysis.
Main Methods:
- Acquired 3D T1W brain images from 45 migraine patients and 46 controls.
- Applied wavelet-based morphometry to decompose and reconstruct gray matter volumes.
- Computed global network topological metrics from structural covariance matrices.
Main Results:
- Migraineurs exhibited significantly higher small worldness (p=0.003) and global efficiency (p=0.002).
- No significant differences were found in local efficiency and assortativity between groups.
- Synchronization characteristics were similar across network sparsities for both groups.
Conclusions:
- Migraine patients demonstrate enhanced integration of information processing.
- Wavelet-based morphometry combined with graph theory offers insights into altered brain network topology in migraine.
