The role of node dynamics in shaping emergent functional connectivity patterns in the brain

Area of Science:

• Computational neuroscience
• Network neuroscience
• Brain connectomics

Background:

• The relationship between the brain's structural connections and its functional activity patterns is not well understood.
• Existing models often simplify the complex interplay between neural structure and function.

Purpose of the Study:

• To mathematically and computationally investigate the link between structural connectivity and functional brain states.
• To determine how variations in local neural dynamics affect the structure-function relationship.

Main Methods:

• Utilized a computational model of Jansen-Rit neural mass nodes with heterogeneous structural connections derived from diffusion MRI data (Human Connectome Project).
• Performed direct simulations to analyze functional connectivity (FC) derived from correlated neural activity.
• Calculated network instabilities, Hopf bifurcations, and 'false bifurcations' to understand transitions in neural dynamics.
• Employed a weakly coupled oscillator description and linear stability analysis to predict functional connectivity patterns.

Main Results:

• A non-trivial structure-function relationship was identified, particularly in regimes supporting limit cycle oscillations.
• Functional connectivity robustly inherited structural connectivity when node dynamics were near Hopf bifurcations.
• Structural influence on functional connectivity was weak near 'false bifurcations'.
• The modular structure of functional connectivity matrices could be predicted from local dynamics via linear stability analysis.

Conclusions:

• Local neural dynamics significantly shape large-scale functional brain states.
• The proximity of neural dynamics to bifurcations critically determines the extent to which structure dictates function.
• This study provides a framework for understanding how brain network structure translates into dynamic function.