Modeling the interplay between regional heterogeneity and critical dynamics underlying brain functional networks.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Human brain function relies on complex network connectivity, but how regional heterogeneity influences this is not fully understood.
• Local neural dynamics and their relationship to gene expression and large-scale network function require further investigation.

Purpose of the Study:

• To investigate how regional heterogeneity in local brain dynamics contributes to whole-brain functional connectivity and cognitive capacities.
• To develop and validate heterogeneous whole-brain network models that incorporate regional dynamics.

Main Methods:

• Analyzing functional magnetic resonance imaging (fMRI) data to measure voxelwise neural activity synchrony.
• Correlating neural synchrony with gene expression data for excitatory and inhibitory receptors.
• Constructing heterogeneous whole-brain network models with nodal excitability informed by regional synchronization measures.
• Simulating network dynamics and comparing emergent functional connectivity with empirical data.

Main Results:

• Neural activity synchrony in brain regions significantly correlates with excitatory and inhibitory receptor gene expression.
• Heterogeneous network models operating near criticality generate functional connectivity patterns similar to empirical resting-state and task-evoked data.
• These models accurately predict individual differences in functional connectivity and causal influences within brain networks.

Conclusions:

• Regional heterogeneity in local brain dynamics is crucial for generating realistic whole-brain functional connectivity.
• Heterogeneous whole-brain network models can effectively capture empirical brain network properties and individual variability.
• This approach enhances the predictive power of computational models for understanding brain function and cognition.