Dynamical correlation patterns and corresponding community structure in neural spontaneous activity at criticality

Area of Science:

• Computational neuroscience
• Network science
• Complex systems

Background:

• The brain's information processing capabilities are thought to be influenced by its proximity to a critical point, a state between order and disorder.
• Understanding network characteristics like community structure is crucial for deciphering brain function.

Purpose of the Study:

• To numerically investigate how criticality affects the community structure of functional connectivity in simulated brain spontaneous activity.
• To compare dynamical correlation patterns and community structure across subcritical, critical, and supercritical states.

Main Methods:

• Simulated brain spontaneous activity using a neural field model.
• Analysis of dynamical correlations and community structure.
• Comparison of network properties across different criticality regimes (subcritical, critical, supercritical).

Main Results:

• In the critical region, distinctive properties were observed: high correlation with rapid switching, high modularity with few modules, stable functional connectivity, and adaptable community structure across timescales.
• These properties were contrasted with those in subcritical and supercritical regions.

Conclusions:

• The critical state exhibits unique network characteristics that may enhance the brain's information processing capacity.
• Findings suggest criticality plays a significant role in optimizing neural information flow and network organization.