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

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 the know-how of all the methodological steps of the pipeline that manipulate the input brain signals and extract the functional network properties. On the other hand, knowledge of the neural phenomenon under study is required to perform physiologically relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes.

Keywords: brain connectivity; clinical neuroscience; functional neuroimaging; network theory.

© 2014 The Author(s) Published by the Royal Society. All rights reserved.

Figures

Figure 1.

Pipeline for functional brain networks modelling and analysis. Nodes correspond to specific brain sites according to the used neuroimaging technique (§3). Links are estimated by measuring the FC between the activity of brain nodes; this information is contained in a connectivity matrix (§4). By means of filtering procedures, based on thresholds, only the most important links constitute the brain graph (§5). The topology of the brain graph is quantified by different graph metrics (or indices) that can be represented as numbers (e.g. the coloured bars) (§6). These graph indices can be input to statistical analysis to look for significant differences between populations/conditions (e.g. red points correspond to brain graph indices of diseased patients or tasks, blue points stand for healthy subjects or resting states (§7).

Figure 2.

Abstraction levels for the analysis and interpretation of brain graphs. Forward analysis (upward arrow): changes in the neural process generate modifications in the measured brain activity (univariate analysis, abstraction level 1). FC (bivariate analysis, abstraction level 2) applies to the measured brain signals, and graph modelling (multivariate analysis, abstraction level 3) applies to FC. Backward interpretation (downward arrow): results obtained with graph analysis (abstraction level 3) refer to the previous abstraction level, i.e. FC, and can be directly associated neither to changes measured in the brain activity (abstraction level 1) nor to the original neural process. Accordingly, the interpretation of the results obtained with graph analysis is mediated by the choice of the FC measure and by the used neuroimaging technique.