Parcellation-dependent small-world brain functional networks: a resting-state fMRI study.

Area of Science:

• Neuroscience
• Network Science
• Medical Imaging

Background:

• Brain networks exhibit small-world properties, crucial for efficient information processing.
• The influence of different brain parcellation strategies on network topology is not well understood.

Purpose of the Study:

• To investigate how distinct brain atlases affect the topological organization of functional brain networks.
• To quantify the impact of parcellation schemes on network properties using resting-state fMRI.

Main Methods:

• Resting-state functional MRI (fMRI) data were analyzed using two different anatomical atlases, defining 90 and 70 regions of interest.
• Brain functional networks were constructed by thresholding correlation matrices and analyzed with graph theory.
• Topological parameters, including small-worldness and degree distribution, were compared between networks derived from the two atlases.

Main Results:

• Both atlas-derived networks demonstrated robust small-world properties and truncated power-law degree distributions.
• Significant differences in topological parameters were observed between networks generated using the two atlases.
• Parcellation strategy demonstrably influences the measured network architecture.

Conclusions:

• The choice of brain atlas significantly impacts the topological organization of functional brain networks.
• Quantitative evidence highlights the sensitivity of network analysis to parcellation methods.
• Understanding these differences is vital for accurate interpretation of brain network studies.