An adaptive complex network model for brain functional networks.

Area of Science:

• Neuroscience
• Complex Systems Science
• Network Science

Background:

• Brain functional networks model brain activity using graph theory, exhibiting small-world topology.
• Topological properties like degree distribution and hierarchy are not robust in brain networks.
• Understanding the emergence of complex brain network structures requires foundational models.

Purpose of the Study:

• To present an adaptive complex network model for human brain functional networks.
• To investigate the basic ingredients necessary for the emergence of complex network structures in the brain.
• To link dynamical rules to emergent topological properties.

Main Methods:

• Developed an adaptive complex network model with dynamical nodes representing brain regions.
• Employed an adaptive algorithm for link formation based on internal states of dynamical elements.
• Validated the model against topological characteristics of human brain networks from functional magnetic resonance imaging (fMRI) data.

Main Results:

• The model successfully describes topological characteristics of human brain networks.
• Integrated processing dynamics lead to scale-free, non-hierarchical networks with distinct communities.
• Local processing dynamics result in hierarchical structures with clustered communities and truncated power-law distributions.

Conclusions:

• The adaptive network model elucidates the formation of brain functional network topology.
• Dynamical rules governing information processing (integrated vs. local) are critical determinants of network structure.
• The model provides insights into the emergence of both hierarchical and non-hierarchical network architectures in the brain.