Functional connectivity analysis using whole brain and regional network metrics in MS patients

Area of Science:

• Neuroscience
• Medical Imaging
• Graph Theory

Background:

• Multiple Sclerosis (MS) is a chronic neurological disease affecting brain connectivity.
• Understanding alterations in brain network structure is crucial for MS research.
• Functional magnetic resonance imaging (fMRI) offers insights into brain network dynamics.

Purpose of the Study:

• To investigate differences in brain network connectivity between relapsing-remitting multiple sclerosis (RRMS) patients and healthy controls (HC).
• To analyze both local and global network connectivity parameters using graph theory.
• To identify specific brain regions with altered network parameters in RRMS.

Main Methods:

• Resting-state fMRI data were acquired from 18 RRMS patients and 25 HC.
• Graph theory analysis was employed to assess brain network connectivity.
• Local and global network parameters, including modularity, clustering coefficient, and local efficiency, were calculated.

Main Results:

• RRMS patients exhibited significantly increased modularity compared to HC, indicating a more segregated network structure.
• Enhanced local information processing was observed within specific brain modules in RRMS patients.
• Increased clustering coefficient and local efficiency were noted in the insula, superior parietal gyrus, and temporal pole of RRMS patients.

Conclusions:

• Increased modularity derived from fMRI is a characteristic connectivity feature in MS patients.
• Altered brain network organization in RRMS impacts local information processing in regions associated with perception, motor function, and cognition.
• These findings contribute to understanding the neurobiological underpinnings of MS.