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

Andreotti, Jennifer; Jann, Kay; Melie-Garcìa, Lester; Giezendanner, Stéphanie; Abela, Eugenio; Wiest, Roland; Dierks, Thomas; Federspiel, Andrea (2014). Validation of network communicability metrics for the analysis of brain structural networks. PLoS ONE, 9(12), e115503. Public Library of Science 10.1371/journal.pone.0115503

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > University Psychiatric Services > University Hospital of Psychiatry and Psychotherapy > Psychiatric Neurophysiology (discontinued)
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology

UniBE Contributor:

Andreotti, Jennifer; Jann, Kay; Melie-Garcìa, Lester; Giezendanner, Stéphanie; Abela, Eugenio; Wiest, Roland; Dierks, Thomas and Federspiel, Andrea

Subjects:

600 Technology > 610 Medicine & health

ISSN:

1932-6203

Publisher:

Public Library of Science

Language:

English

Submitter:

Jennifer Andreotti

Date Deposited:

05 Jan 2015 09:31

Last Modified:

26 Jun 2016 01:56

Publisher DOI:

10.1371/journal.pone.0115503

PubMed ID:

25549088

BORIS DOI:

10.7892/boris.61332

URI:

https://boris.unibe.ch/id/eprint/61332

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