title: Using Pareto optimality to explore the topology and dynamics of the human connectome.
abstract: 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 Pareto-optimal 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.
authors.affiliation: Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
authors.forename: Andrea
authors.initials: A
authors.lastname: Avena-Koenigsberger
authors.affiliation: Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
authors.forename: Joaquín
authors.initials: J
authors.lastname: Goñi
authors.affiliation: Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
authors.forename: Richard F
authors.initials: RF
authors.lastname: Betzel
authors.affiliation: Rudolf Magnus Institute, Utrecht, The Netherlands.
authors.forename: Martijn P
authors.initials: MP
authors.lastname: van den Heuvel
authors.affiliation: Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.
authors.forename: Alessandra
authors.initials: A
authors.lastname: Griffa
authors.affiliation: Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.
authors.forename: Patric
authors.initials: P
authors.lastname: Hagmann
authors.affiliation: Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.
authors.forename: Jean-Philippe
authors.initials: JP
authors.lastname: Thiran
authors.affiliation: Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA osporns@indiana.edu.
authors.forename: Olaf
authors.initials: O
authors.lastname: Sporns
mesh_terms: D005075:Biological Evolution; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D003198:Computer Simulation; D063132:Connectome; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net; D038081:Organogenesis / Q000502:physiology*; D012641:Selection, Genetic
keywords: brain connectivity; diffusion imaging; graph theory; network science