Using centrality measures to extract core pattern of brain dynamics during the resting state

Area of Science:

• Neuroscience
• Computational Neuroscience
• Medical Imaging Analysis

Background:

• Resting-state brain dynamics offer insights into neurological conditions.
• Understanding macroscopic brain activity patterns is crucial for disease characterization.
• Multiple Sclerosis (MS) is a chronic neurological disease affecting the central nervous system.

Purpose of the Study:

• To investigate and compare macroscopic brain dynamics between healthy controls and MS patients during resting state.
• To identify distinct dynamical patterns characterizing MS-related brain activity.
• To develop a classification method for identifying MS based on brain dynamics.

Main Methods:

• Macroscopic brain dynamics were analyzed using successive coarse-graining techniques.
• Markov representation of brain activity was employed to identify significant patterns and transitions.
• Network centrality measures were utilized to extract core dynamical patterns.
• A classification technique was applied to differentiate MS dynamics from control dynamics.

Main Results:

• Control subjects exhibited brain dynamics organized around a single principal pattern.
• Multiple sclerosis patients displayed significantly more variable and complex dynamical patterns.
• Centrality measures effectively identified core dynamical features differentiating the groups.
• The classification technique achieved a relevant error rate in defining MS dynamics.

Conclusions:

• Macroscopic brain dynamics differ substantially between individuals with MS and healthy controls.
• The identified variability in MS brain dynamics could serve as a potential biomarker.
• Computational methods, including network analysis and classification, are valuable tools for neurological research.