Graph analysis of functional brain networks: practical issues in translational neuroscience

TL;DR

This paper reviews the application of graph analysis to functional brain networks, emphasizing methodological considerations and practical issues in translational neuroscience to improve understanding of brain dysfunctions.

Contribution

It provides practical guidance on brain network analysis, highlighting methodological steps and the importance of physiological relevance in translational neuroscience.

Findings

Graph analysis helps visualize brain network topology.

Methodological rigor is crucial for meaningful results.

Understanding neural phenomena enhances analysis relevance.

Abstract

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires a know-how of all the methodological steps of the processing pipeline that manipulates the input brain signals and extract the functional network properties. On the other hand, a knowledge of the neural phenomenon under study is required to perform physiological-relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes.