Resting-brain functional connectivity predicted by analytic measures of network communication

Joaquin Goni, Martijn P. Van Den Heuvel, Andrea Avena-Koenigsberger, Nieves Velez De Mendizabal, Richard F. Betzel, Alessandra Griffa, Patric Hagmann, Bernat Corominas-Murtra, Jean Philippe Thiran, Olaf Sporns*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)833-838
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume111
Issue number2
DOIs
Publication statusPublished - 22 Jan 2014

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