Multilayer Brain Networks

TL;DR

This paper reviews the use of multilayer networks in neuroscience, emphasizing their ability to analyze complex, multi-scale brain data and uncover hidden features related to disease, structure, and function.

Contribution

It provides a comprehensive overview of multilayer network applications in neuroscience and proposes a refined definition to better describe real-world systems.

Findings

Multilayer networks reveal hidden brain network features.

Application to disease modeling and structure-function analysis.

Demonstrates evolution and multi-scale data integration in brain networks.

Abstract

The field of neuroscience is facing an unprecedented expanse in the volume and diversity of available data. Traditionally, network models have provided key insights into the structure and function of the brain. With the advent of big data in neuroscience, both more sophisticated models capable of characterizing the increasing complexity of the data and novel methods of quantitative analysis are needed. Recently multilayer networks, a mathematical extension of traditional networks, have gained increasing popularity in neuroscience due to their ability to capture the full information of multi-model, multi-scale, spatiotemporal data sets. Here, we review multilayer networks and their applications in neuroscience, showing how incorporating the multilayer framework into network neuroscience analysis has uncovered previously hidden features of brain networks. We specifically highlight the use of multilayer networks to model disease, structure-function relationships, network evolution, and link multi-scale data. Finally, we close with a discussion of promising new directions of multilayer network neuroscience research and propose a modified definition of multilayer networks designed to unite and clarify the use of the multilayer formalism in describing real-world systems.