title: Validation of network communicability metrics for the analysis of brain structural networks.
abstract: 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.
authors.affiliation: Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Bern, Switzerland.
authors.forename: Jennifer
authors.initials: J
authors.lastname: Andreotti
authors.affiliation: Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Bern, Switzerland; Laboratory of Functional MRI Technology, Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California Los Angeles, Los Angeles California, United States of America.
authors.forename: Kay
authors.initials: K
authors.lastname: Jann
authors.affiliation: Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Bern, Switzerland; Neuroimaging Research Laboratory (LREN), Department of Clinical Neurosciences, Vaud University Hospital Center (CHUV), Lausanne, Switzerland.
authors.forename: Lester
authors.initials: L
authors.lastname: Melie-Garcia
authors.affiliation: Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Bern, Switzerland.
authors.forename: Stéphanie
authors.initials: S
authors.lastname: Giezendanner
authors.affiliation: University Institute of Diagnostic and Interventional Neuroradiology, Inselspital and University of Bern, Bern, Switzerland.
authors.forename: Eugenio
authors.initials: E
authors.lastname: Abela
authors.affiliation: University Institute of Diagnostic and Interventional Neuroradiology, Inselspital and University of Bern, Bern, Switzerland.
authors.forename: Roland
authors.initials: R
authors.lastname: Wiest
authors.affiliation: Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Bern, Switzerland.
authors.forename: Thomas
authors.initials: T
authors.lastname: Dierks
authors.affiliation: Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Bern, Switzerland.
authors.forename: Andrea
authors.initials: A
authors.lastname: Federspiel
mesh_terms: D001931:Brain Mapping / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological; D009415:Nerve Net / Q000000981:diagnostic imaging* / Q000502:physiology*; D011859:Radiography