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Using Pareto optimality to explore the topology and dynamics of the human connectome

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Publication date 2014
Author Avena-Koenigsberger A., Goni J., Betzel R. F., van den Heuvel M. P., Griffa A., Hagmann P., Thiran J.-P., Sporns O.,
Project Imaging the connectome in the early phase of psychosis
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Original article (peer-reviewed)

Journal Philosophical Transactions of the Royal Society B: Biological Sciences
Volume (Issue) 369(1653)
Page(s) 20130530 - 20130530
Title of proceedings Philosophical Transactions of the Royal Society B: Biological Sciences
DOI 10.1098/rstb.2013.0530


The complex relationship between structural and functional connectivity, as measured by noninvasive imaging of the human brain, poses many unresolved challenges and open questions. Here, we apply analytic measures of network communication to the structural connectivity of the human brain and explore the capacity of these measures to predict resting-state functional connectivity across three independently acquired datasets. We focus on the layout of shortest paths across the network and on two communication measures--search information and path transitivity--which account for how these paths are embedded in the rest of the network. Search information is an existing measure of information needed to access or trace shortest paths; we introduce path transitivity to measure the density of local detours along the shortest path. We find that both search information and path transitivity predict the strength of functional connectivity among both connected and unconnected node pairs. They do so at levels that match or significantly exceed path length measures, Euclidean distance, as well as computational models of neural dynamics. This capacity suggests that dynamic couplings due to interactions among neural elements in brain networks are substantially influenced by the broader network context adjacent to the shortest communication pathways.