Within node connectivity changes, not simply edge changes, influence graph theory measures in functional connectivity...

Area of Science:

• Neuroscience
• Network Science
• Computational Biology

Background:

• Graph theory is widely applied to functional connectivity studies in the brain.
• Functional brain nodes are known to reconfigure with changing brain states.
• Previous studies have predominantly used fixed nodes for graph theory analysis.

Purpose of the Study:

• To investigate the impact of different node parcellation strategies on brain functional connectivity findings.
• To determine if considering dynamic node reconfigurations influences network-level analysis outcomes.
• To highlight the importance of voxel-level changes in node definition for graph theory studies.

Main Methods:

• Utilized fixed, group, state-specific, and individualized parcellations to define graph nodes.
• Analyzed functional connectivity data across different brain states and groups.
• Compared network-level findings based on static versus dynamic node definitions.

Main Results:

• Functional connectivity changes within nodes significantly influence network-level findings.
• Some previously reported state- or group-dependent changes were not observed when considering node reconfigurations.
• Network changes were only apparent when voxel-level node reconfigurations were accounted for.

Conclusions:

• Graph theory analyses of brain connectivity should incorporate dynamic node reconfigurations.
• Voxel-level changes impacting node definition are critical for accurate network analysis.
• The choice of node parcellation significantly affects the interpretation of functional brain organization.