Functional connectivity changes in multiple sclerosis patients: a graph analytical study of MEG resting state data

Area of Science:

• Neuroscience
• Medical Imaging
• Cognitive Science

Background:

• Multiple sclerosis (MS) involves significant central nervous system damage.
• Structural imaging limitations in explaining cognitive decline in MS.
• Need for advanced functional imaging in MS research.

Purpose of the Study:

• Investigate functional connectivity changes in MS using resting-state MEG.
• Correlate connectivity patterns with cognitive function and structural changes.
• Explore the diagnostic and prognostic value of graph analysis in MS.

Main Methods:

• Resting-state magnetoencephalography (MEG) to assess functional connectivity (synchronization likelihood).
• Graph analysis to characterize network topology (path length, clustering coefficient).
• Neuropsychological evaluation and MRI for cognitive and structural assessment.

Main Results:

• MS patients exhibited altered functional connectivity across theta, alpha, and beta bands.
• Graph analysis revealed a more regular network topology in the lower alpha band in MS patients.
• Increased lower alpha band clustering coefficient (γ) predicted impaired attention and working memory.

Conclusions:

• MEG reveals specific functional brain changes in MS, particularly in network topology.
• Graph analysis of functional connectivity offers insights beyond traditional measures.
• Altered network topology, especially in the lower alpha band, is linked to cognitive deficits in MS, with potential diagnostic/prognostic implications.