Validation of network communicability metrics for the analysis of brain structural networks

PLoS One. 2014 Dec 30;9(12):e115503. doi: 10.1371/journal.pone.0115503. eCollection 2014.


Computational network analysis provides new methods to analyze the brain's structural organization based on diffusion imaging tractography data. Networks are characterized by global and local metrics that have recently given promising insights into diagnosis and the further understanding of psychiatric and neurologic disorders. Most of these metrics are based on the idea that information in a network flows along the shortest paths. In contrast to this notion, communicability is a broader measure of connectivity which assumes that information could flow along all possible paths between two nodes. In our work, the features of network metrics related to communicability were explored for the first time in the healthy structural brain network. In addition, the sensitivity of such metrics was analysed using simulated lesions to specific nodes and network connections. Results showed advantages of communicability over conventional metrics in detecting densely connected nodes as well as subsets of nodes vulnerable to lesions. In addition, communicability centrality was shown to be widely affected by the lesions and the changes were negatively correlated with the distance from lesion site. In summary, our analysis suggests that communicability metrics that may provide an insight into the integrative properties of the structural brain network and that these metrics may be useful for the analysis of brain networks in the presence of lesions. Nevertheless, the interpretation of communicability is not straightforward; hence these metrics should be used as a supplement to the more standard connectivity network metrics.

Publication types

  • Clinical Trial
  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Brain Mapping / methods*
  • Diffusion Tensor Imaging / methods*
  • Female
  • Humans
  • Male
  • Models, Neurological*
  • Nerve Net / diagnostic imaging*
  • Nerve Net / physiology*
  • Radiography

Grant support

KJ has a fellowship of the Swiss National Science Foundation (SNSF). Web SNSF: KJ was partially financed by the Swiss Foundation for Grants in Biology and Medicine (SFGBM)(grant no 142743). Web SFGMB: This study was partially financed by the Swiss National Science Foundation (SNSF) Grant 320000-108321/1 (healthy subjects) and Grant 3200B0-118018 (Stroke patients). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.