Identifying controlling nodes in neuronal networks in different scales

Area of Science:

• Neuroscience
• Complex Systems
• Computational Biology

Background:

• Neuronal network hubs are crucial for structural and functional roles, identified through measures like degree and centrality.
• Complex network analysis across scales offers deep insights into intrinsic network properties, widely used in physics.
• Understanding controllability is vital for deciphering brain function and dysfunction.

Purpose of the Study:

• To identify controlling regions within cat cortical networks at microscopic, mesoscopic, and macroscopic scales.
• To investigate the impact of the number of driver nodes on network controllability.
• To statistically characterize the properties of controlling nodes in the feline brain.

Main Methods:

• Employed single-objective evolutionary computation methods for identifying controlling regions.
• Analyzed network controllability by considering two distinct measures separately.
• Examined the statistical properties of controlling nodes in relation to the number of driver nodes allowed.

Main Results:

• The statistical properties of controlling nodes exhibit a concave or convex trend as the number of allowed controlling nodes increases.
• A transition in driver node selection occurs, shifting from high-degree to low-degree areas.
• The auditory cortex, despite sparse inter-community connections, plays a significant role in controlling the neuronal network.

Conclusions:

• Controllability analysis in cat cortical networks reveals scale-dependent properties and driver node selection dynamics.
• The findings suggest a flexible control mechanism in the brain that adapts based on the number of available controlling nodes.
• The auditory cortex's prominent role in network control warrants further investigation into its functional significance.