Brain network similarity:Methods and applications

TL;DR

This paper reviews methods for quantitatively comparing brain networks using graph similarity metrics, highlighting their applications, challenges, and future directions in neuroscience research.

Contribution

It provides a comprehensive overview of current graph similarity metrics, discusses their strengths and limitations, and explores their applications in understanding brain network organization.

Findings

Graph similarity metrics can reveal insights into brain network organization.

Application of network similarity can aid in object categorization studies.

Future methods may enhance understanding of brain network dynamics.

Abstract

Graph theoretical approach has proved an effective tool to understand, characterize and quantify the complex brain network. However, much less attention has been paid to methods that quantitatively compare two graphs, a crucial issue in the context of brain networks. Comparing brain networks is indeed mandatory in several network neuroscience applications. Here, we discuss the current state of the art, challenges, and a collection of analysis tools that have been developed in recent years to compare brain networks. We first introduce the graph similarity problem in brain network application. We then describe the methodological background of the available metrics and algorithms of comparing graphs, their strengths and limitations. We also report results obtained in concrete applications from normal brain networks. More precisely, we show the potential use of brain network similarity to build a 'network of networks' that may give new insights into the object categorization in the human brain. Additionally, we discuss future directions in terms of network similarity methods and applications.