TY - JOUR
T1 - Using Pareto optimality to explore the topology and dynamics of the human connectome
AU - Avena-Koenigsberger, Andrea
AU - Goñi, Joaquín
AU - Betzel, Richard F.
AU - van den Heuvel, Martijn P.
AU - Griffa, Alessandra
AU - Hagmann, Patric
AU - Thiran, Jean Philippe
AU - Sporns, Olaf
PY - 2014/10/5
Y1 - 2014/10/5
N2 - Graph theory has provided a key mathematical framework to analyse the architecture of human brain networks. This architecture embodies an inherently complex relationship between connection topology, the spatial arrangement of network elements, and the resulting network cost and functional performance. An exploration of these interacting factors and driving forces may reveal salient network features that are critically important for shaping and constraining the brain's topological organization and its evolvability. Several studies have pointed to an economic balance between network cost and network efficiency with networks organized in an 'economical' small-world favouring high communication efficiency at a low wiring cost. In this study, we define and explore a network morphospace in order to characterize different aspects of communication efficiency in human brain networks. Using a multi-objective evolutionary approach that approximates a Paretooptimal set within the morphospace, we investigate the capacity of anatomical brain networks to evolve towards topologies that exhibit optimal information processing features while preserving network cost. This approach allows us to investigate network topologies that emerge under specific selection pressures, thus providing some insight into the selectional forces that may have shaped the network architecture of existing human brains.
AB - Graph theory has provided a key mathematical framework to analyse the architecture of human brain networks. This architecture embodies an inherently complex relationship between connection topology, the spatial arrangement of network elements, and the resulting network cost and functional performance. An exploration of these interacting factors and driving forces may reveal salient network features that are critically important for shaping and constraining the brain's topological organization and its evolvability. Several studies have pointed to an economic balance between network cost and network efficiency with networks organized in an 'economical' small-world favouring high communication efficiency at a low wiring cost. In this study, we define and explore a network morphospace in order to characterize different aspects of communication efficiency in human brain networks. Using a multi-objective evolutionary approach that approximates a Paretooptimal set within the morphospace, we investigate the capacity of anatomical brain networks to evolve towards topologies that exhibit optimal information processing features while preserving network cost. This approach allows us to investigate network topologies that emerge under specific selection pressures, thus providing some insight into the selectional forces that may have shaped the network architecture of existing human brains.
KW - Brain connectivity
KW - Diffusion imaging
KW - Graph theory
KW - Network science
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UR - http://www.scopus.com/inward/citedby.url?scp=84929501575&partnerID=8YFLogxK
U2 - 10.1098/rstb.2013.0530
DO - 10.1098/rstb.2013.0530
M3 - Article
C2 - 25180308
AN - SCOPUS:84929501575
SN - 0962-8436
VL - 369
JO - Philosophical Transactions of the Royal Society B: Biological Sciences
JF - Philosophical Transactions of the Royal Society B: Biological Sciences
IS - 1653
M1 - 20130530
ER -