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Cost-efficiency trade-offs of the human brain network revealed by a multiobjective evolutionary algorithm.

Junji Ma1, Jinbo Zhang1, Ying Lin1

  • 1Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China.

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Human brain networks balance wiring cost and communication efficiency. This study quantitatively shows this trade-off shapes brain structure, influencing hubs and modules, but other factors also play a role.

Keywords:
ConnectomeCost-efficiency trade-offDiffusion MRIHuman brain

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Area of Science:

  • Neuroscience
  • Network Science
  • Computational Biology

Background:

  • Brain network architecture is hypothesized to balance wiring cost and communication efficiency.
  • The precise mechanisms and empirical evidence for this cost-efficiency trade-off in human brain structural networks remain unclear.

Purpose of the Study:

  • To quantitatively investigate the cost-efficiency trade-off in human brain structural networks.
  • To explore how this trade-off influences network topology and organization.

Main Methods:

  • Employed a multiobjective evolutionary algorithm to generate synthetic brain networks.
  • Compared topological features of synthetic networks with empirical human brain structural networks.

Main Results:

  • Synthetic networks reproduced key features of empirical brain networks, including connections, small-world organization, hub distribution, and modular structure.
  • Both synthetic and empirical networks exhibited high robustness against random attacks.
  • Differences were observed, with synthetic networks showing lower segregated processing capacity and weaker robustness against targeted attacks.

Conclusions:

  • Findings provide direct quantitative evidence that optimal cost-efficiency trade-offs significantly influence human brain network structure.
  • Additional factors, such as segregated processing capacity, may jointly shape brain network organization alongside cost and efficiency.