The economy of brain network organization

@article{Bullmore2012TheEO,
  title={The economy of brain network organization},
  author={Edward T. Bullmore and Olaf Sporns},
  journal={Nature Reviews Neuroscience},
  year={2012},
  volume={13},
  pages={336-349}
}
The brain is expensive, incurring high material and metabolic costs for its size — relative to the size of the body — and many aspects of brain network organization can be mostly explained by a parsimonious drive to minimize these costs. However, brain networks or connectomes also have high topological efficiency, robustness, modularity and a 'rich club' of connector hubs. Many of these and other advantageous topological properties will probably entail a wiring-cost premium. We propose that… Expand
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References

SHOWING 1-10 OF 192 REFERENCES
Small-World Brain Networks
  • D. Bassett, E. Bullmore
  • Computer Science, Medicine
  • The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry
  • 2006
TLDR
The authors consider the relevance of small-world models for understanding the emergence of complex behaviors and the resilience of brain systems to pathological attack by disease or aberrant development and conclude that small- world models provide a powerful and versatile approach to understanding the structure and function of human brain systems. Expand
Simple models of human brain functional networks
TLDR
This work proposes a model in which the embedded topology of brain networks emerges from two competing factors: a distance penalty based on the cost of maintaining long-range connections; and a topological term that favors links between regions sharing similar input. Expand
Networks of the Brain
Over the last decade, the study of complex networks has expanded across diverse scientific fields. Increasingly, science is concerned with the structure, behavior, and evolution of complex systemsExpand
Efficient Physical Embedding of Topologically Complex Information Processing Networks in Brains and Computer Circuits
TLDR
It is shown that human brain structural networks, and the nervous system of the nematode C. elegans, also obey Rent's rule, and exhibit some degree of hierarchical modularity, suggesting that these principles of nervous system design are highly conserved. Expand
Efficiency of Functional Brain Networks and Intellectual Performance
TLDR
Examining the overall organization of the brain network using graph analysis shows a strong negative association between the normalized characteristic path length λ of the resting-state brain network and intelligence quotient (IQ), suggesting that human intellectual performance is likely to be related to how efficiently the authors' brain integrates information between multiple brain regions. Expand
Genetic Influences on Cost-Efficient Organization of Human Cortical Functional Networks
TLDR
Evidence is reported that one such principle for functional cortical networks involves finding a balance between maximizing communication efficiency and minimizing connection cost, referred to as optimization of network cost-efficiency, which is consistent with the hypothesis that brain networks evolved to satisfy competitive selection criteria of maximizing efficiency and minimize cost. Expand
Topological Isomorphisms of Human Brain and Financial Market Networks
TLDR
The conceptual connections between brains and markets are not merely metaphorical; rather these two information processing systems can be rigorously compared in the same mathematical language and turn out often to share important topological properties in common to some degree. Expand
Exploring Brain Function from Anatomical Connectivity
TLDR
The results here exposed are mainly based on anatomical data of cats’ brain, but further observations suggest that, from worms to humans, the nervous system of all animals might share these fundamental principles of organization. Expand
Wiring cost in the organization of a biological neuronal network
TLDR
The spatial neuronal map of C. elegans is built based on geometrical positions of neurons to find out the role of the wiring cost in the organization of the neuronal network of the nematode Caenorhabditis elegans and the trade-off between the wiring costs and the performance of the network is discussed. Expand
Efficiency and Cost of Economical Brain Functional Networks
TLDR
Efficiency was reduced disproportionately to cost in older people, and the detrimental effects of age on efficiency were localised to frontal and temporal cortical and subcortical regions. Expand
...
1
2
3
4
5
...