Structural Brain Network Characteristics Can Differentiate CIS from Early RRMS.

Area of Science:

• Neuroimaging
• Neurology
• Biophysics

Background:

• Multiple sclerosis (MS) progression is influenced by focal demyelination, diffuse white matter (WM) damage, and gray matter (GM) atrophy.
• Distinguishing clinically isolated syndrome (CIS) from early relapsing-remitting multiple sclerosis (RRMS) is crucial for timely treatment and management.

Purpose of the Study:

• To identify distinct structural network characteristics in GM and WM for CIS subjects compared to early RRMS patients.
• To develop an automated, investigator-independent method for discriminating between CIS and RRMS using neuroimaging data.

Main Methods:

• Utilized 3 Tesla MRI on 20 CIS patients, 33 RRMS patients, and 40 healthy controls.
• Applied diffusion tensor imaging, probabilistic tractography, fractional anisotropy (FA) mapping for WM, and cortical thickness correlation for GM.
• Employed graph theory for network topology analysis (modularity, clustering coefficient, efficiencies) and support vector machines (SVM) for classification.

Main Results:

• RRMS patients exhibited increased modular connectivity and higher local clustering in both GM and WM compared to CIS subjects.
• Both CIS and RRMS groups showed increased modularity and clustering coefficients relative to healthy controls.
• SVM achieved 97% accuracy in differentiating CIS from RRMS using GM clustering coefficients, with lower accuracy using WM metrics.

Conclusions:

• Early RRMS is characterized by significantly increased modular and local brain network connectivity compared to CIS and healthy controls.
• An automated MRI-based analysis paradigm can accurately discriminate between CIS and RRMS, potentially complementing current diagnostic criteria.