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

Avena-Koenigsberger A, Goñi J, Betzel RF, van den Heuvel MP, Griffa A, Hagmann P, Thiran JP, Sporns O - Philos. Trans. R. Soc. Lond., B, Biol. Sci. (2014)

Bottom Line: This architecture embodies an inherently complex relationship between connection topology, the spatial arrangement of network elements, and the resulting network cost and functional performance.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.

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.

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(a) Latticization and randomization of empirical brain networks. (b) Fibre length and fibre density distributions of randomized networks (blue), LAU1 network (green) and latticized networks (red). Randomized and latticized network distributions are averages over 40 repetitions of the randomization and latticization process applied to the LAU1 empirical network. (c) Adjacency matrices of latticized LAU1 network (red), LAU1 network (green) and randomized LAU1 network (blue).
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RSTB20130530F2: (a) Latticization and randomization of empirical brain networks. (b) Fibre length and fibre density distributions of randomized networks (blue), LAU1 network (green) and latticized networks (red). Randomized and latticized network distributions are averages over 40 repetitions of the randomization and latticization process applied to the LAU1 empirical network. (c) Adjacency matrices of latticized LAU1 network (red), LAU1 network (green) and randomized LAU1 network (blue).

Mentions: Figure 2a shows Ediff, Erout and CN as a function of the number of rewiring steps carried out on all three empirical networks during their randomization (blue dots) and latticization (red dots), respectively. All values are averages over 40 repetitions of the randomization and the latticization processes.Figure 2.


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

Avena-Koenigsberger A, Goñi J, Betzel RF, van den Heuvel MP, Griffa A, Hagmann P, Thiran JP, Sporns O - Philos. Trans. R. Soc. Lond., B, Biol. Sci. (2014)

(a) Latticization and randomization of empirical brain networks. (b) Fibre length and fibre density distributions of randomized networks (blue), LAU1 network (green) and latticized networks (red). Randomized and latticized network distributions are averages over 40 repetitions of the randomization and latticization process applied to the LAU1 empirical network. (c) Adjacency matrices of latticized LAU1 network (red), LAU1 network (green) and randomized LAU1 network (blue).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4150305&req=5

RSTB20130530F2: (a) Latticization and randomization of empirical brain networks. (b) Fibre length and fibre density distributions of randomized networks (blue), LAU1 network (green) and latticized networks (red). Randomized and latticized network distributions are averages over 40 repetitions of the randomization and latticization process applied to the LAU1 empirical network. (c) Adjacency matrices of latticized LAU1 network (red), LAU1 network (green) and randomized LAU1 network (blue).
Mentions: Figure 2a shows Ediff, Erout and CN as a function of the number of rewiring steps carried out on all three empirical networks during their randomization (blue dots) and latticization (red dots), respectively. All values are averages over 40 repetitions of the randomization and the latticization processes.Figure 2.

Bottom Line: This architecture embodies an inherently complex relationship between connection topology, the spatial arrangement of network elements, and the resulting network cost and functional performance.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.

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.

Show MeSH