Path-dependent connectivity, not modularity, consistently predicts controllability of structural brain networks

Area of Science:

• Neuroscience
• Network Science
• Complex Systems

Background:

• The human brain exhibits complex communication dynamics.
• Brain community structure is linked to these dynamics, but the precise relationship is unclear.
• Understanding this link is crucial for targeted manipulation of brain activity.

Purpose of the Study:

• To investigate the role of community structure in the controllability of structural brain networks.
• To explore how network properties influence the brain's ability to generate diverse communication dynamics.
• To identify reliable predictors of brain network controllability.

Main Methods:

• Analysis of regional community structure and linear controllability in structural brain networks.
• Numerical simulations using canonical graph models with varied architectures and edge weights.
• Evaluation of weighted subgraph centrality as a predictor of controllability.

Main Results:

• Community structure measures show inconsistent correlations with linear controllability.
• The relationship between community structure and controllability is dependent on edge weight distribution.
• Weighted subgraph centrality emerges as a robust predictor of controllability.

Conclusions:

• Brain community structure's influence on controllability is complex and modulated by network weights.
• Weighted subgraph centrality offers a more consistent measure for predicting brain network controllability.
• This research enhances understanding of how mesoscale brain structure supports communication dynamics.