Untangling the brain web: from the early days of complex functional networks to the non-linear dynamical directed functional connectivity measures

TL;DR

This paper reviews the evolution of brain network analysis using graph theory and discusses advancements in non-linear dynamical directed functional connectivity measures over the past two decades.

Contribution

It provides a perspective on the development of complex brain network characterization and introduces novel approaches to functional connectivity analysis.

Findings

Brain networks can be modeled as complex systems using graph theory.

Non-linear dynamical measures improve understanding of directed functional connectivity.

Future research directions include integrating multi-modal data.

Abstract

Already two decades passed since the first applications of graph theory to brain neuroimaging. Since that early description, the characterization of the brain as a very large interacting complex network has evolved in several directions. In this brief review we discuss our contributions to this topic and discuss some perspective for future work.