TY - JOUR
T1 - Resting-brain functional connectivity predicted by analytic measures of network communication
AU - Goni, Joaquin
AU - Van Den Heuvel, Martijn P.
AU - Avena-Koenigsberger, Andrea
AU - De Mendizabal, Nieves Velez
AU - Betzel, Richard F.
AU - Griffa, Alessandra
AU - Hagmann, Patric
AU - Corominas-Murtra, Bernat
AU - Thiran, Jean Philippe
AU - Sporns, Olaf
PY - 2014/1/22
Y1 - 2014/1/22
N2 - 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.
AB - 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.
KW - Brain connectivity
KW - Connectome
KW - Graph theory
KW - Network theory
UR - http://www.scopus.com/inward/record.url?scp=84892571821&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84892571821&partnerID=8YFLogxK
U2 - 10.1073/pnas.1315529111
DO - 10.1073/pnas.1315529111
M3 - Article
C2 - 24379387
AN - SCOPUS:84892571821
SN - 0027-8424
VL - 111
SP - 833
EP - 838
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 2
ER -