Construction of Individual Morphological Brain Networks with Multiple Morphometric Features.

Area of Science:

• Neuroscience
• Brain Imaging
• Network Science

Background:

• Morphological brain networks are typically population-based, limiting individual difference studies.
• Existing methods use single morphometric features, overlooking cerebral structure's complexity.
• Individualized brain network analysis is crucial for clinical applications.

Purpose of the Study:

• To develop a novel method for constructing individual-based morphological brain networks.
• To integrate multiple morphometric features for more robust network construction.
• To evaluate the reproducibility and topological properties of the proposed individual brain network.

Main Methods:

• Proposed a novel method combining seven morphometric features (volume, surface area, etc.) using Pearson correlation.
• Constructed individual-based morphological brain networks from these combined features.
• Evaluated test-retest reliability and analyzed topological properties (small-worldness, hubs) on a cohort of 55 healthy subjects.

Main Results:

• The method demonstrated high reproducibility with intraclass coefficients for clustering coefficient (0.83), characteristic path length (0.81), and betweenness centrality (0.78).
• Observed small-world network properties across all subjects, consistent with previous findings.
• Individual variations significantly influenced hub distribution, while averaged hubs corresponded with prior reports.

Conclusions:

• The proposed method effectively constructs reproducible, individual-based morphological brain networks using multiple morphometric features.
• This approach offers a more comprehensive and personalized representation of brain structure compared to population-based methods.
• The findings support the utility of this novel method for investigating individual brain differences and clinical applications.