Altered structural brain network topology in chronic migraine

Area of Science:

• Neuroscience
• Medical Imaging
• Network Science

Background:

• Chronic migraine (CM) is prevalent and burdensome, yet its underlying pathophysiological mechanisms remain unclear.
• CM involves complex comorbidities, suggesting network-level disruptions may be key to understanding its symptoms.
• Previous studies focused on focal brain regions, potentially missing broader network alterations.

Purpose of the Study:

• To investigate structural brain network differences between patients with chronic migraine (CM) and healthy controls (HC).
• To explore how graph theoretical network analyses of MRI data can reveal insights into CM pathophysiology.
• To identify potential network-based biomarkers for tracking or predicting migraine progression.

Main Methods:

• Utilized MRI to measure cortical thickness and subcortical volume in 52 CM patients and 48 HC.
• Applied graph theoretical network analyses to assess global and local network topology (integration, efficiency, centrality, segregation).
• Compared nodal and global network properties between CM patients and HC.

Main Results:

• CM patients exhibited altered global network properties: reduced integration and efficiency, and increased segregation.
• Local network topology in CM patients was also aberrant: less integrated, less central, less efficient, and less segregated.
• These network differences were prominent in limbic, insular, frontal, temporal, and brainstem regions, independent of focal brain region changes.

Conclusions:

• Structural network analysis reveals more sensitive detection of brain alterations in CM than focal region studies.
• Altered structural connectivity in CM patients provides a network-level understanding of the disorder's symptomology.
• This network-based approach may offer novel methods for monitoring and predicting the progression of migraine disorders.