Small-World Brain Networks Revisited.

Area of Science:

• Neuroscience
• Network Science
• Computational Biology

Background:

• The concept of small-world networks, defined by high clustering and short path length, emerged nearly 20 years ago.
• Connectomics, utilizing complex network topology, has rapidly grown in the past decade for neuroimaging analysis.

Purpose of the Study:

• To review foundational concepts of small-world network generation and estimation.
• To assess key developments in small-world network analysis over the past decade.
• To explore the implications of high-resolution tract-tracing studies on brain network topology.

Main Methods:

• Review of graph theoretical methods for small-world network analysis.
• Analysis of recent high-resolution tract-tracing data from macaque and mouse brains.
• Comparison of topological analysis for binary (unweighted) versus weighted graphs.

Main Results:

• Distinction between binary and weighted graph analysis in brain networks is critical.
• Weighted graph topology retains more biologically relevant information than binary analysis.
• Recent studies highlight the importance of sophisticated data for network analysis.

Conclusions:

• Weighted small-worldness is more appropriate for contemporary brain connectivity data.
• Future trends point towards deeper understanding of weighted networks in mammalian cortex.
• This approach enhances the comprehension of functional significance of neural connections.