Extraction and Analysis of Dynamic Functional Connectome Patterns in Migraine Sufferers: A Resting-State fMRI Study

Area of Science:

• Neuroscience
• Medical Imaging
• Neurology

Background:

• Migraine significantly impacts patient health due to its recurrence and environmental hypersensitivity.
• The precise pathogenesis and pathophysiology of migraine remain incompletely understood.
• Current understanding necessitates advanced methods to explore migraine's neural underpinnings.

Purpose of the Study:

• To investigate the neural mechanisms of migraine using an advanced computational model.
• To compare dynamic functional connectome patterns (DFCPs) between migraine patients and healthy controls.
• To identify potential neuroimaging biomarkers for migraine diagnosis and monitoring.

Main Methods:

• Utilized an autodynamic functional connectome model (A-DFCM) with twice-clustering.
• Analyzed resting-state functional magnetic resonance imaging (fMRI) data from migraine patients and controls.
• Employed automatic localization of segment points for model efficiency and analyzed intergroup differences and network metrics.

Main Results:

• Identified 17 distinct DFCPs: 1 specific to migraine and 16 general between groups.
• The specific DFCP correlated with neuronal dysfunction in migraine patients.
• Network metric analysis highlighted critical regions in the specific DFCP (pain pathways, occipital lobe) and identified pain matrix alterations in general DFCPs.

Conclusions:

• The A-DFCM model successfully identified unique functional connectome patterns in migraine.
• Findings provide novel insights into the neural mechanisms underlying migraine pathophysiology.
• Identified neuroimaging biomarkers may aid in the clinical diagnosis and management of migraine.