, Volume 9, Issue 1, pp 3–19 | Cite as

Optimizing Brain Networks Topologies Using Multi-objective Evolutionary Computation

  • Roberto Santana
  • Concha Bielza
  • Pedro Larrañaga


The analysis of brain network topological features has served to better understand these networks and reveal particular characteristics of their functional behavior. The distribution of brain network motifs is particularly useful for detecting and describing differences between brain networks and random and computationally optimized artificial networks. In this paper we use a multi-objective evolutionary optimization approach to generate optimized artificial networks that have a number of topological features resembling brain networks. The Pareto set approximation of the optimized networks is used to extract network descriptors that are compared to brain and random network descriptors. To analyze the networks, the clustering coefficient, the average path length, the modularity and the betweenness centrality are computed. We argue that the topological complexity of a brain network can be estimated using the number of evaluations needed by an optimization algorithm to output artificial networks of similar complexity. For the analyzed network examples, our results indicate that while original brain networks have a reduced structural motif number and a high functional motif number, they are not optimal with respect to these two topological features. We also investigate the correlation between the structural and functional motif numbers, the average path length and the clustering coefficient in random, optimized and brain networks.


Brain networks Evolutionary algorithm Network motifs Multi-objective optimization Network optimization 



This work has been partially supported by the Saiotek and Research Groups 2007–2012 (IT-242–07) programs (Basque Government), TIN-2008-06815-C02-02, TIN2007-62626 and Consolider Ingenio 2010 - CSD2007-00018 projects (Spanish Ministry of Science and Innovation), the CajalBlueBrain project, and the COMBIOMED network in computational biomedicine (Carlos III Health Institute).


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Roberto Santana
    • 1
  • Concha Bielza
    • 1
  • Pedro Larrañaga
    • 1
  1. 1.Universidad Politécnica de MadridBoadilla del MonteSpain

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