Graph theoretical analysis indicates cognitive impairment in MS stems from neural disconnection

Area of Science:

• Neuroscience
• Medical Imaging
• Computational Biology

Background:

• Cognitive impairment mechanisms in multiple sclerosis (MS) remain unclear.
• MS pathology may cause neural connectivity alterations, impacting cognition.
• Electroencephalography (EEG) offers a potential tool for assessing these changes.

Purpose of the Study:

• To compare EEG-based neural connectivity between cognitively impaired (CI) and cognitively preserved (CP) MS patients.
• To evaluate the impact of different network construction methods on connectivity measures.

Main Methods:

• Graph theoretical network analysis applied to EEG data from 308 MS patients.
• Networks constructed using various connectivity measures (correlation, coherence, partial correlation, etc.).
• Comparison of network parameters (clustering coefficient, path length, small-worldness) between CI and CP groups, with statistical correction.

Main Results:

• Theta and delta band coherence were significantly lower in CI MS patients.
• Small-world network properties differed based on connectivity measures and frequency bands.
• Modularity was associated with age, but not cognitive status.

Conclusions:

• Cognitive deterioration in MS appears to stem from neural disconnections, potentially affecting white matter tracts.
• Graph theoretical analysis of EEG data can reveal network differences associated with cognitive status in MS.
• These findings may pave the way for more accessible and objective cognitive assessments in MS.