Small-World Brain Networks Revisited

TL;DR

This paper reviews the concept of small-world networks in brain connectivity, emphasizing recent advances in weighted graph analysis and their biological relevance, and discusses future directions in understanding brain network topology.

Contribution

It provides a comprehensive overview of small-world network analysis in neuroscience, highlighting the importance of weighted graphs over unweighted ones for biological accuracy.

Findings

Weighted graphs better capture biological brain connectivity.

Recent high-resolution tract-tracing enhances understanding of brain networks.

Future research should focus on weighted small-worldness in brain topology.

Abstract

It is nearly 20 years since the concept of a small-world network was first quantitatively defined, by a combination of high clustering and short path length; and about 10 years since this metric of complex network topology began to be widely applied to analysis of neuroimaging and other neuroscience data as part of the rapid growth of the new field of connectomics. Here we review briefly the foundational concepts of graph theoretical estimation and generation of small-world networks. We take stock of some of the key developments in the field in the past decade and we consider in some detail the implications of recent studies using high-resolution tract-tracing methods to map the anatomical networks of the macaque and the mouse. In doing so, we draw attention to the important methodological distinction between topological analysis of binary or unweighted graphs, which have provided a popular but simple approach to brain network analysis in the past, and the topology of weighted graphs, which retain more biologically relevant information and are more appropriate to the increasingly sophisticated data on brain connectivity emerging from contemporary tract-tracing and other imaging studies. We conclude by highlighting some possible future trends in the further development of weighted small-worldness as part of a deeper and broader understanding of the topology and the functional value of the strong and weak links between areas of mammalian cortex.