Eigenvector Centrality Dynamics From Resting-State fMRI: Gender and Age Differences in Healthy Subjects

Area of Science:

• Neuroimaging
• Graph Theory
• Cognitive Neuroscience

Background:

• Functional brain network properties are increasingly used as biomarkers for neurological and psychiatric disorders.
• Efficient methods for visualizing and evaluating these network properties are crucial for clinical applications.
• Eigenvector centrality mapping (ECM) provides a way to map graph-theoretical measures onto brain structures using functional MRI (fMRI) data.

Purpose of the Study:

• To investigate the utility of centrality dynamics for detecting group differences in neuroimaging studies.
• To explore gender and age-related differences in functional brain network centrality using resting-state fMRI data.
• To assess the effectiveness of eigenvector centrality mapping (ECM) in conjunction with sliding window analysis and dual regression.

Main Methods:

• Utilized resting-state fMRI data from a publicly available imaging study.
• Applied eigenvector centrality mapping (ECM) with a sliding window approach after standard space warping and cortical masking.
• Employed the dual regression method to identify dynamic centrality differences within established resting-state networks (RSNs).

Main Results:

• Identified significant gender-related differences in centrality dynamics within the medial and lateral visual, motor, default mode, and executive control RSNs, with males exhibiting more consistent variations.
• Detected age-related differences between youngest and oldest subjects in the medial visual, executive control, and left frontoparietal networks, where younger individuals showed more consistent centrality variations.
• Demonstrated that centrality dynamics can effectively differentiate between groups based on age and gender.

Conclusions:

• Centrality dynamics represent a viable method for identifying functional brain network centrality differences between groups.
• Age and gender are significant demographic factors that influence functional brain network properties and must be considered in neuroimaging research.
• The findings support the use of dynamic network measures for understanding brain function and its variations across different populations.