Multilayer Networks in Neuroimaging

TL;DR

This paper reviews how multilayer network models enhance understanding of brain structure and function by integrating multi-scale and multi-modal data, revealing hidden features and relationships in neuroscience.

Contribution

It introduces the theoretical foundation of multilayer brain networks and demonstrates their application in uncovering complex brain interactions and disease modelling.

Findings

Multilayer networks reveal hidden brain features.

Enhanced understanding of structure-function relationships.

Integration of multi-modal data improves brain modelling.

Abstract

Recent advances in network science, applied to \textit{in vivo} brain recordings, have paved the way for better understanding of the structure and function of the brain. However, despite its obvious usefulness in neuroscience, traditional network science lacks tools for -- so important -- simultaneous investigation of the inter-relationship between the two domains. In this chapter, I explore the increasing role of multilayer networks in building brain generative models and abilities of such models to uncover the full information about the brain complex spatiotemporal interactions that span across multiple scales and modalities. First, I begin with the theoretical foundation of brain networks accompanied by a brief overview of traditional networks and their role in constructing multilayer network models. Then, I delve into the applications of multilayer networks in neuroscience, particularly in deciphering structure-function relationship, modelling diseases, and integrating multi-scale and multi-modal data. Finally, I demonstrate how incorporating the multilayer framework into network neuroscience has brought to light previously hidden features of brain networks and, how multilayer networks can provide new insights and a description of the structure and function of the brain.