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

Détails

Ressource 1Télécharger: BIB_33A6423F21C5.P001.pdf (1625.71 [Ko])
Etat: Public
Version: Final published version
ID Serval
serval:BIB_33A6423F21C5
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Using Pareto optimality to explore the topology and dynamics of the human connectome.
Périodique
Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
Auteur(s)
Avena-Koenigsberger A., Goñi J., Betzel R.F., van den Heuvel M.P., Griffa A., Hagmann P., Thiran J.P., Sporns O.
ISSN
1471-2970 (Electronic)
ISSN-L
0962-8436
Statut éditorial
Publié
Date de publication
10/2014
Peer-reviewed
Oui
Volume
369
Numéro
1653
Pages
-
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
Publication Status: ppublish
Résumé
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.
Mots-clé
Biological Evolution, Brain/anatomy & histology, Brain/physiology, Computer Simulation, Connectome, Humans, Models, Neurological, Nerve Net, Organogenesis/physiology, Selection, Genetic
Pubmed
Web of science
Open Access
Oui
Création de la notice
02/10/2014 17:22
Dernière modification de la notice
20/08/2019 13:19
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